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UNIVERSIDAD DE CANTABRIA
DEPARTAMENTO DE CIENCIAS Y TÉCNICAS
DEL AGUA Y DEL MEDIO AMBIENTE
TESIS DOCTORAL
DESARROLLO E IMPLEMENTACIÓN DE
HERRAMIENTAS MATEMÁTICAS PARA EL MODELADO
DE LA EUTROFIZACIÓN EN SISTEMAS COSTEROS
SEMI-ENCERRADOS EUTRÓFICOS E
HIPEREUTRÓFICOS.
Ph.D. THESIS
DEVELOPMENT AND IMPLEMENTATION OF
EUTROPHICATION MODELING TOOLS FOR
EUTROPHIC AND HYPERTROHIC SEMI-ENCLOSED
COASTAL SYSTEMS.
AUTORA
Pilar del Barrio Fernández
DIRECTORES
Andrés García Gómez
José Antonio Revilla Cortezón
Santander, 2015
UNIVERSIDAD DE CANTABRIA
E.T.S INGENIEROS DE CAMINOS, CANALES Y PUERTOS
DPTO. DE CIENCIAS Y TÉCNICAS DEL AGUA Y DEL MEDIO AMBIENTE
TESIS DOCTORAL
DESARROLLO E IMPLEMENTACIÓN DE HERRAMIENTAS
MATEMÁTICAS PARA EL MODELADO DE LA EUTROFIZACIÓN EN
SISTEMAS COSTEROS SEMI-ENCERRADOS EUTRÓFICOS E
HIPEREUTRÓFICOS.
Ph.D. THESIS
DEVELOPMENT AND IMPLEMENTATION OF EUTROPHICATION
MODELING TOOLS FOR EUTROPHIC AND HYPERTROPHIC SEMI-
ENCLOSED COASTAL SYSTEMS.
Autora: Pilar del Bario Fernández
Dirigida por: Andrés García Gómez
José Antonio Revilla Cortezón
Santander, 2015
A David
A mis padres
A toda mi familia
Si buscas resultados distintos, no hagas siempre lo mismo.
Albert Einstein.
AGRADECIMIENTOS
Quiero agradecer en primer lugar a la Universidad de Cantabria y al Gobierno de
Cantabria por haberme dado la oportunidad de realizar la Tesis doctoral disfrutando de
una beca predoctoral.
En segundo lugar a mis directores de Tesis, Andrés García Gómez y José Antonio Revilla
Cortezón por la confianza depositada en mí y por permitirme realizar este trabajo. Sin su
ayuda y apoyo este estudio no podría haberse realizado.
Me gustaría agradecer en concreto a Andrés García Gómez por su dedicación, ayuda,
paciencia y esfuerzo. Por sus revisiones y comentarios, con los que siempre he aprendido,
y por inculcar en mí el sentido del rigor académico, sin el cual no podría tener una
formación completa como investigadora.
Quiero agradecer especialmente a Neil K. Ganju por su ayuda y por darme la oportunidad
de realizar una estancia de investigación en el United States Geological Survey (USGS),
Woods Hole (Massachusetts, USA). También me gustaría darle las gracias por todo lo
que me ha enseñado y por las contribuciones realizadas a este trabajo.
Agradezco también a Sonia Castanedo, Raúl Medina, Giovanni Coco y a José Antonio
Juanes por su apoyo, respaldo y motivación para ayudarme a terminar este proyecto.
Quiero agradecer también a todos los compañeros de trabajo que me han ayudado, en
especial a Javier García Alba, a Alfredo Aretxbaleta y a Javier Barcena por su ayuda, en
especial en todas las dudas que he tenido sobre la implementación de los códigos en los
distintos lenguajes de programación, pero más especialmente por sus ánimos y su sentido
del humor. Me habéis enseñado los entresijos de la programación, y de la creación de
muchísimos tipos de figuras con distintos programas y la verdad es que sin vosotros esta
Tesis no hubiera sido posible.
Gracias también a todos los coautores de los artículos que forman parte de esta Tesis,
además de los ya mencionados, y a todos los investigadores que han aportado
conocimientos y datos imprescindibles a este estudio, especialmente a César Álvarez,
Melanie Hayn, Robert W. Howarth, y Aina García.
Agradecer también a Elvira, Tamara, Lara, Mar, Miriam, Alba, Andrea, Fernando y Diego
por su amistad. Gracias por apoyarme, ayudarme, escucharme y por estar a mi lado
siempre que lo he necesitado. ¡Muchas gracias a todos!
También agradecer al resto de mis compañeros Bea, Zhen, Rafa, Javi, Iñaki, Maitane,
Pablo A., Pablo H., María, Marisa, Sheila, Bárbara, Carolina, Imen, Nabil y muchos más.
Gracias por vuestra ayuda cuando lo he necesitado.
También agradecer a mis amigas y amigos por estar siempre ahí, entendiendo que me
encerrara a escribir la Tesis, y siempre haciendo planes para pasar un buen rato todos
juntos. Por esos momentos inolvidables que hemos compartido durante todo este tiempo,
gracias.
Finalmente, quiero dar las gracias a toda mi familia, por su apoyo, paciencia y ayuda.
Ellos son las principales personas en mi vida y sin las cuales esta Tesis nunca hubiera
sido posible. Agradecer en especial a mis padres por todas las oportunidades que me han
dado en la vida, por invertir en mi educación, por su apoyo incondicional y por haberme
enseñado a ser la persona que soy. A mi hermana y a Steven, por su ayuda y apoyo siendo
al tiempo familia y amigos. Y a Ingrid, por toda la alegría y felicidad que me ha dado
durante estos últimos años. También quiero agradecer a Carmen y a Daniel por todos los
ánimos y el apoyo en todo el proceso, por su comprensión y cariño en todo momento.
Por último, quiero agradecer especialmente a David, mi marido, compañero y mejor
amigo. Gracias por todo, por tu apoyo, ayuda, paciencia, por animarme siempre, y por
creer en mí. Gracias por tu ayuda incondicional y por estar siempre a mi lado, en los
buenos y malos momentos. Por eso y por mucho más quiero dedicarte a tí especialmente
este trabajo.
1
Contents
CONTENTS
List of figures 7
List of tables 13
Resumen en español 17
Capítulo I: Introducción y antecedentes de la investigación 19
1.1 Motivación de la investigación 19
1.2 Objetivos 24
Capítulo II: Descripción y análisis de las zonas de estudio. 26
Capítulo III. Desarrollo e implementación de un modelo simplificado para
sistemas costeros semi-encerrados hipereutróficos. Aplicación a una laguna
costera fuertemente regulada. 30
3.1 Descripción abreviada del modelo 31
3.2 Datos de campo y establecimiento del modelo 34
3.3 Calibración y validación. 35
3.3.1 Análisis de sensibilidad 35
3.3.2 Calibración del modelo 37
3.3.3 Validación del modelo 41
3.4 Resultados y discusión. 42
3.5 Conclusiones 46
Capítulo IV. Desarrollo e implementación de un sistema de modelado
ecológico para sistemas costeros eutróficos semi-encerrados. Aplicación a un
estuario alimentado por aguas subterráneas con vegetación acuática
sumergida. 48
4.1 Métodos y resultados del muestreo 50
4.2 Descripción resumida del modelo 52
4.3 Calibración del modelo 55
4.3.1 Calibración de los modelos biogeoquímicos y de irradiancia. 55
4.3.2 Calibración del modelo bio-óptico de zostera marina. 58
4.4 Escenarios de carga de nitratos y subida del nivel del mar 61
4.5 Discusión 66
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4.6 Conclusiones 68
Capítulo V. Conclusiones y futuras líneas de investigación 70
5.1 Conclusiones generales 70
5.2 Conclusiones del análisis de sensibilidad 71
5.3 Conclusiones del modelado de luz 72
5.4 Conclusiones de calibración y resultados 73
5.5 Futuras líneas de investigación 75
Chapter I. Introduction and research background 79
1.1 Motivation of the research 81
1.2 History of coupled-linked models in estuaries and SECS 88
1.3 Review of hydrodynamic, water quality, ecosystem and bio-optical
irradiance models with coupled-linking capabilities: description and
applications. 91
1.3.1 Hydrodynamic models 92
1.3.1.1 Regional Ocean Modelling System (ROMS) 93
1.3.1.2 Estuarine and Coastal Ocean Model (ECOM) 94
1.3.1.3 Finite Volume Coastal Ocean Model (FVCOM) 94
1.3.1.4 MIKE 3 95
1.3.1.5 Mohid Water Modelling System (MWMS) 96
1.3.1.6 HAMSOM 97
1.3.1.7 Delft3D-Flow 98
1.3.1.8 Environmental Fluid Dynamics Code (EFDC) 99
1.3.1.9 Estuary and Lake Computer Model (ELCOM) 100
1.3.1.10 TELEMAC 100
1.3.1.11 H2D/H3D 101
1.3.1.12 Discussion 102
1.3.2 Water quality and ecosystem models 103
1.3.2.1 Water Quality Analysis Simulation Program (WASP) 106
1.3.2.2 CE-QUAL-ICM 107
1.3.2.3 DELWAQ 107
1.3.2.4 WQ 108
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Contents
1.3.2.5 MIKE3-WQ (ECO LAB) 109
1.3.2.6 European Regional Seas Ecosystem Model (ERSEM) 110
1.3.2.7 Computational Aquatic Ecosystem Dynamics Model (CAEDYM) 110
1.3.2.8 Pelagic Interaction Scheme for Carbon and Ecosystem Studies
(PISCES) 111
1.3.2.9 ECOPATH with ECOSIM (EwE) 112
1.3.2.10 Integrated Generic Bay Ecosystem Model (IGBEM) 113
1.3.2.11 Ecological North Sea Model, Hamburg (ECOHAM) 114
1.3.2.12 Flexible Biological Module (FBM) 115
1.3.2.13 Mohid Water Quality Module 116
1.3.2.14 CE-QUAL-W2 117
1.3.2.15 NEUTRO 117
1.3.2.16 EnvHydrEM 118
1.3.2.17 Intermittently Closed and Open Lakes or Lagoons (ICOLLS) model
119
1.3.2.18 SEACOM 120
1.3.2.19 Phytoplankton-Zooplankton (P-Z) Models 120
1.3.2.20 Nutrient-Phytoplankton-Zooplankton (NPZ) Model 121
1.3.2.21 North Pacific Ecosystem Model for Understanding Regional
Oceanography (NEMURO) 123
1.3.2.22 Port Phillip Bay Model (PPBM) 124
1.3.2.23 Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) Model 124
1.3.2.24 Fasham 125
1.3.2.25 Fennel 125
1.3.2.26 System Wide Eutrophication Model (SWEM) 126
1.3.2.27 Discussion 127
1.3.3 Bio-optical irradiance models with coupling or linking capabilities 129
1.3.3.1 Hydrolight-Ecolight v5 (HE5) 130
1.3.3.2 Fuji et al.’s model 131
1.3.3.3 Gallegos et al.’s model 132
1.3.3.4 Zimmerman model 133
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1.3.3.5 Discussion 133
1.4 Balancing spatial and temporal resolution 134
1.4.1 Low spatial, low temporal 135
1.4.2 High spatial, low temporal 136
1.4.3 Low spatial, high temporal 137
1.4.4 High spatial, high temporal 137
1.5 Modelling Tradeoffs 138
1.6 Discussion 140
1.7 Objectives 142
1.8 Layout of Thesis 143
Chapter II. Study sites 145
2.1 Introduction 147
2.2 Albufera of Valencia 148
2.3 West Falmouth Harbor 154
2.4 Study sites comparison and modeling strategies 160
Chapter III. Development and implementation of a simplified model for
semi-enclosed hypertrophic coastal systems. Application to a heavily
regulated coastal lagoon. 163
3.1 Introduction 167
3.2 Materials and methods 169
3.2.1 The hydrodynamic model 169
3.2.1.1 The long wave model 170
3.2.1.2 The wind model 172
3.2.2 Eutrophication model 173
3.2.2.1 Transport equation 176
3.2.2.2 Chemical and biological interactions 176
3.2.2.3 Phytoplankton growth 177
3.2.2.4 Phytoplankton death 179
3.2.2.5 Chlorophyll-a concentration 180
3.3 Numerical techniques 181
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Contents
3.3.1 The numerical grid 181
3.3.2 Field data and model set up 182
3.4 Calibration and validation 185
3.4.1 Sensitivity analysis 186
3.4.2 Model calibration 188
3.4.3 Model validation 194
3.5 Results and discussion 197
3.6 Conclusions 202
Chapter IV. Development and implementation of a coupled ecological
modelling system for semi-enclosed eutrophic coastal systems. Application
to a groundwater-fed estuary with submerged aquatic vegetation. 205
4.1 Introduction 210
4.2 Observational methods 212
4.3 Observational results 214
4.4 Model description 217
4.4.1 Physical model 219
4.4.2 Biogeochemical model 221
4.4.3 Irradiance model 223
4.4.4 Bio-optical seagrass model 224
4.5 Model skill assessment 227
4.5.1 Biogeochemical and irradiance model assessment 227
4.5.2 Seagrass bio-optical model assessment 232
4.6 Nitrate loading and sea-level rise scenarios 235
4.7 Discussion 243
4.8 Conclusions 246
Chapter V. Conclusions and future research 247
5.1 Introduction 249
5.2 Conclusions 250
5.1.1 General conclusions 250
5.1.2 Sensitivity analysis conclusions 251
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Contents
5.1.3 Light modelling conclusions 251
5.1.4 Conclusions of calibration and results 252
5.2 Future research 254
5.3 Thesis impact and dissemination 257
5.3.1 Research articles 257
5.3.2 Communications in conferences and workshops 258
References 259
7
Lists of figures and tables
LIST OF FIGURES
Figura 1. Albufera de Valencia, canales de regadío y localización de las estaciones
de muestreo. 26
Figura 2. West Falmouth Harbor, estaciones de muestreo y cargas contaminantes. 27
Figura 3. Resumen gráfico del capítulo III 31
Figura 4. Diagrama de flujo del modelo 33
Figura 5. Concentración de clorofila-a media calculada en el análisis de sensibilidad
en la Albufera para los valores mínimos y máximos de los principales parámetros del
modelo. 37
Figura 6. Distribución del flujo de fósforo soluble reactivo (SRP) desde el sedimento
a la columna de agua en la Albufera de Valencia. 38
Figura 7. Comparación entre los resultados obtenidos por el modelo (líneas) y los
datos de campo (puntos) en las estaciones de muestreo y en toda la laguna para cada
periodo de calibración. 40
Figura 8. Evolución de la concentración de clorofila-a simulada (línea continua) y
los datos observados (puntos negros) en cada estación de muestreo, para los periodos
de calibración del año hidrológico 2005/2006. 41
Figura 9. Evolución de la concentración de clorofila-a promedio para la laguna
calculada por el modelo (línea) para todo año hidrológico 2005/2006 y los datos
observados (puntos) para los periodos de validación. 42
Figura 10. Distribución espacial de clorofila-a en la Albufera de Valencia en el año
hidrológico 2005/2006. 44
Figura 11. Balance de masas del fósforo soluble reactivo (SRP) que entra y sale de
la Albufera. 46
Figura 12. Resumen gráfico del capítulo IV 49
Figura 13. Histogramas de los datos de campo de clorofila y Kd en Outer y Snug
Harbors. 52
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Figura 14. Diagrama de flujo del sistema de modelado y sus interacciones. En el
panel inferior se muestra que el ratio P/R>1 indica hábitat potencial de zostera
marina, mientras que el P/R<1 indica perdida potencial de hábitat de zostera marina.
U y V son las velocidades, h la profundidad de la columna de agua, T la temperatura
del agua y η la variación de la superficie libre. 54
Figura 15. Análisis de sensibilidad del modelo biogeoquímico de Fennel et al. (2006)
con un nuevo módulo integrado de irradiancia espectral. 55
Figura 16. a) Variación de la concentración vertical de clorofila-a en tres capas del
modelo; b) Promedio temporal y vertical de clorofila-a en el estuario completo. 58
Figura 17. a) Distribución del ratio Producción/Respiración; b) Distribución del ratio
Producción/Respiración aplicando el criterio de P/R>1, y comparación con los datos
del campo (línea negra continua); c) Detalle de la distribución de P/R en Snug y Outer
para P/R>1 y comparación con datos de campo (línea negra continua); d)
Distribución de zostera marina obtenida con la ecuación de limitación de
profundidad (Duarte et al., 2007). El área blanca representa las zonas donde se
descarta la presencia de zostera marina (P/R<1), el área verde es el hábitat potencial
de zostera marina (P/R>1) y la línea negra continua delimita el área de presencia de
zostera marina medida en la campaña de campo. 60
Figura 18. Variación espacial de P/R debido a la reducción de nutrientes (NR), al
aumento del nivel del mar (SLR) y a los escenarios combinados (CS). 62
Figura 19. Variación de P/R debida a la reducción de nutrientes y aumento del nivel
del mar. 63
Figura 20. Variación de clorofila-a debido a la reducción de nitratos y al SLR. 64
Figura 21. Variación espacial de clorofila-a debido a la reducción de nutrientes
(NR), aumento del nivel del mar (SLR) y escenarios combinados (CS). 65
Figura 22. Clorofila-a, Kd, P/R y variación del área de z. marina para los escenarios
combinados. 66
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Lists of figures and tables
Figure 1.1. Examples of different kind of SECS: a) Open (Bay of Wismar,
Germany); b) Leaky (Venice Lagoon, Italy); c) Restricted (Quanzhou Bay, China);
d) Choked (Étang de Thau, France). 82
Figure 1.2. Trophic status. 85
Figure 1.3. Seagrass loss and pressures 86
Figure 1.4. Seagrass changes due to sea-level rise (SLR) 88
Figure 1.5. Water quality and ecosystem models coupling and/or linkage
possibilities. 104
Figure 1.6. Model tradeoff between generality, precision, and realism, adapted from
Levins (1966). 140
Figure 2.1. a) Aerial view of the Albufera of Valencia Natural Park (abc.es) and b)
West Falmouth Harbor (fineartamerica.com). 147
Figure 2.2. Albufera of Valencia, irrigation channels and sampling stations location.
148
Figure 2.3. Some of the pollutant pressures that surround the Albufera of Valencia. 149
Figure 2.4. Rice fields and irrigation channels surrounding the Albufera of Valencia.
150
Figure 2.5. Albufera of Valencia connection with the sea (“golas”), a) Gola Pujol, b)
Gola Perellonet, c) Gola Perelló. 151
Figure 2.6. a) Submerged aquatic vegetation that used to be at the Albufera of
Valencia (potamogetun pectinatus); b,c,d) water column and bottom of the Albufera
nowadays at different areas of the lagoon. 153
Figure 2.7. West Falmouth Harbor, site locations and input loads. 154
Figure 2.8. West Falmouth Harbor connection with the sea (a) and view from the
marked point (b) 155
Figure 2.9. West Falmouth Harbor closed shellfish activity due to pollution. 156
Figure 2.10. West Falmouth Harbor sailing activity. 156
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Lists of figures and tables
Figure 2.11. West Falmouth Harbor seagrass (area delimited by red line)
disappearance. Adapted from http://buzzardsbay.org/historical-eelgrass-west-
falmouth.htm. 157
Figure 2.12. Outer and Snug Harbors seagrass presence in 2010 (green area) and
2012 (blue area). Adapted from Hayn (2012). 158
Figure 2.13. West Falmouth Harbor seagrass meadows at outer harbor (a and b),
seabed covered by macroalgae at south cove (c) and with some Ulva lactuca at Snug
Harbor (d) in 2012. 159
Figure 3.1. Graphical Abstract 166
Figure 3.2. Model flow chart 175
Figure 3.3. Variation of the light extinction coefficient (Ke) with the chlorophyll-a
concentration. 183
Figure 3.4. Average cloudiness variation during the hydrological year 2005/2006. 184
Figure 3.5. Comparison between calculated and observed lagoon water surface
during the period October 2005-September 2006 in a point of the lagoon located in
front of gola Pujol. 185
Figure 3.6. Mean chlorophyll-a (Chl-a) concentration calculated for the period of the
sensitivity analysis in the Albufera of Valencia for the minimum and maximum
calibration parameter values. 187
Figure 3.7. Distribution of soluble reactive phosphorus (SRP) flux from the sediment
to the water column into the lagoon. 191
Figure 3.8. Comparison between results obtained by the model (solid lines) and the
observed data (black dots) in the sampling stations and the whole lagoon for each
calibration period. 194
Figure 3.9. Evolution of the simulated chlorophyll-a concentration (“full line”,
calculated) and the observed data (“black dots”, observed) in each sampling station,
for the validation periods of the hydrological year 2005/2006. 196
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Figure 3.10. Evolution of the lagoon-averaged chlorophyll-a concentration
calculated by the model (“full line”, calculated) for the hydrological year 2005/2006
and the observed values (“black dot”, observed) for the validation periods. 197
Figure 3.11. Chlorophyll-a spatial distribution in the Albufera of Valencia in the
hydrological year 2005/2006. 199
Figure 3.12. Mass balance of the soluble reactive phosphorus loads that comes in
and out of the Albufera of Valencia. 201
Figure 3.13. Comparison between calculated and observed chlorophyll-a calibration
and validation data 202
Figure 4.1. Graphical Abstract 209
Figure 4.2. Field survey stations and equipment 212
Figure 4.3. Groundwater fluxes and nitrate concentrations at West Falmouth Harbor.
Arrows indicate main contributions from Falmouth Wastewater Treatment Plant
(FWTP). 214
Figure 4.4. Chlorophyll-a and Kd field data histograms in Outer and Snug Harbors.
Data collected from sensors deployed during summer 2012 with a 5 minutes
sampling interval. 216
Figure 4.5. Modeling system flowchart and interactions. In the bottom panel, P/R
ratio > 1 indicates potential seagrass habitat; P/R < 1 indicates potential loss of
seagrass habitat. U and V are the velocities, h the water depth, T the water
temperature and η the water surface variation. 218
Figure 4.6. Comparison of hourly model and field data values of chlorophyll-a at the
sampling stations. 227
Figure 4.7. Comparison of hourly model and field data values of chlorophyll-a at the
sampling stations. 229
Figure 4.8. Comparison of mean model and field data values of Chlorophyll-a and
Kd at the sampling stations. 230
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Lists of figures and tables
Figure 4.9. Spectral analysis of chlorophyll-a based on model results at the sampling
stations 231
Figure 4.10. a) Vertical chlorophyll-a variation in three layers of the model; b) Time-
averaged and mean vertical chlorophyll-a concentration for the whole estuary. 232
Figure 4.11. a) Photosynthesis/Respiration ratio distribution; b)
Photosynthesis/Respiration ratio distribution applying the P/R>1 criterion and
comparison with field data (black solid line); c) Detail of Snug and Outer P/R
distribution with P/R> 1, and comparison with field data (black solid line) ; d)
Seagrass distribution obtained with the depth-limited equation (Duarte et al., 2007).
The white area represents where seagrass presence is discouraged (P/R<1), the light
green area the potential seagrass habitat (P/R >1), and the black solid line delimits
the seagrass presence area measured in the field survey. 234
Figure 4.12. P/R spatial variation under nitrate reduction (NR), sea-level rise (SLR)
and combined (CS) scenarios. See Table 4.9 for an explanation of the scenarios
nomenclature. 237
Figure 4.13. P/R variation due to nitrate reduction and sea-level rise 238
Figure 4.14. Chlorophyll-a variation due to nitrate reduction and sea-level rise 240
Figure 4.15. Time-averaged and mean vertical chlorophyll-a spatial variation under
nitrate reduction (NR), sea-level rise (SLR) and combined (CS) scenarios. See Table
4.9 for an explanation of the scenarios nomenclature. 241
Figure 4.16. Chlorophyll-a, Kd, P/R and seagrass area variation for the combined
scenarios (CS). See Table 4.9 for an explanation of the scenarios nomenclature. 243
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Lists of figures and tables
LIST OF TABLES
Tabla 1. Características principales de la Albufera de Valencia y West Falmouth
Harbor. 28
Tabla 2. Rango de variación de los principales parámetros y valores asignados en la
calibración. 36
Tabla 3. Errores obtenidos para todas las estaciones de muestreo en cada periodo de
calibración. 39
Tabla 4. Errores globales obtenidos con la concentración de clorofila-a media para
cada periodo de calibración en toda la laguna. 40
Tabla 5. Parámetros principales del modelo biogeoquímico y de irradiancia y valor
asignado. 56
Tabla 6. Valores medios de clorofila-a y Kd, desviación estándar (Std), y BIAS para
Outer, Snug y South. Los datos de campo de clorofila-a y Kd fueron obtenidos
procesando los datos de los sensores desplegados durante el verano de 2012. Los
resultados del modelo fueron obtenidos para el mismo periodo de tiempo. 57
Table 1.1. Characteristics of physical models with linked or coupled ecological
models. 103
Table 1.2. Some relevant characteristics of the main water quality and ecosystem
models. 129
Table 1.3. Some relevant characteristics of the main bio-optical models. 134
Table 2.1. Albufera of Valencia and West Falmouth Harbor main characteristics
comparison. 161
Table 3.1. Range of variation and assigned calibration value of the main
eutrophication parameters of the model. 186
Table 3.2. Error formulations applied in the calibration process. Фi is the calculated
concentration in cell i, Фiobs is the observed concentration in cell i and N is the number
of cells analyzed. 189
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Lists of figures and tables
Table 3.3. Errors obtained with the chlorophyll-a values of all the sampling stations
in each calibration period. 192
Table 3.4. Global errors obtained with the mean chlorophyll-a concentration of each
calibration period in the whole lagoon. 193
Table 3.5. Errors of the different sampling stations obtained with the validation
periods. 195
Table 4.1. Mean values and standard deviation (Std) of measurements. 215
Table 4.2. Mean values, standard deviation (Std) and percentile 84 of measured
optical data during daylight hours. 215
Table 4.3. Physical model main equations and parameters 220
Table 4.4. Irradiance model main equations and parameters 222
Table 4.5. Irradiance model main equations and parameters 224
Table 4.6. Bio-optical seagrass model main equations and parameters 226
Table 4.7. Main biogeochemical and irradiance model parameters and chosen value.
228
Table 4.8. Mean values of chlorophyll-a and Kd, standard deviation (Std), and BIAS
for Outer, Snug and South Harbors. Field values of chlorophyll-a and Kd were
obtained processing data from sensors deployed during summer 2012. Model results
were obtained for the same time-period. 230
Table 4.9. Nitrate reduction and sea level rise scenarios, being CS_0/ NR_0/
SLR_2012 the initial scenario. 235
15
Lists of figures and tables
16
Lists of figures and tables
17
Resumen en español
Resumen en español
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Resumen en español
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De acuerdo a la normativa de estudios de doctorado de la Universidad de Cantabria en
relación a los requerimientos exigidos para aquellas Tesis redactadas en un idioma
diferente al español, aprobada por la Junta de Gobierno de 12 de marzo de 1999 y
actualizada a 17 de diciembre de 2012, a continuación se presenta un resumen de la Tesis
redactada en inglés.
Capítulo I: Introducción y antecedentes de la investigación
1.1 Motivación de la investigación
Los sistemas costeros semi-encerrados (SECS, semi-enclosed coastal systems) engloban
lagunas costeras y aguas de transición (Newton et al., 2013), siendo importantes sistemas
ecológicos con un considerable valor socio-económico (Lassere, 1979). Sin embargo, la
geomorfología de los SECS los hace especialmente vulnerables a cambios globales, tales
como el aumento del nivel del mar, variaciones de temperatura, tormentas, sequías,
inundaciones y cambios en la dinámica del sedimento (Newton et al., 2013). Además, las
actividades humanas, el desarrollo agrícola e industrial y la navegación provocan cambios
que afectan a la estructura y función de estos ecosistemas costeros. Por otra parte, se
caracterizan por sus propiedades de intercambio hidrodinámico con el sistema acuático
adyacente y pueden clasificarse como abiertos, permeables, restringidos o estrangulados
(Newton et al., 2013). Además, los SECS son ecosistemas complejos con una alta
productividad. Soportan una flora y fauna autóctona rica y variada por lo que
normalmente son áreas protegidas, siendo habitualmente lugares de especial importancia
para la alimentación y anidación de una multitud de especies de aves. La gama de
servicios ecosistémicos proporcionados por los SECS es extensa e incluye la provisión
de alimento y protección a larvas, moluscos y peces, así como la producción de oxígeno
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que realiza la vegetación acuática sumergida. Además proporcionan servicios culturales,
como la recreación y el ecoturismo entre otros (Millenium Ecosystem Assessment, 2005),
y ayudan a la regulación de los flujos de nutrientes, partículas y organismos entre los
sistemas de agua dulce y el océano. Sin embargo, estos valiosos ecosistemas están siendo
sometidos a fuertes presiones antropogénicas como el marisqueo, la piscicultura,
descargas de aguas residuales, el turismo, o la urbanización. Una de las principales
consecuencias de las presiones antropogénicas sobre estos sistemas es la pérdida de
hábitats como las praderas marinas, que pueden actuar como viveros y atrapar partículas
en suspensión. Además, estas presiones también han provocado el problema de la
eutrofización cultural, suponiendo una grave amenaza para la conservación de dichos
hábitats.
La eutrofización es el proceso mediante el cual un cuerpo de agua adquiere una alta
concentración de nutrientes, especialmente fosfatos y nitratos, generando un crecimiento
de algas excesivo. Este enriquecimiento de nutrientes puede ocurrir de forma natural o
puede ser el resultado de la actividad humana, produciéndose en este caso "eutrofización
cultural", que generalmente es provocada por la descarga de fertilizantes y aguas
residuales al sistema (Lawrence et al., 1998). Debido a los efectos indeseables que la
eutrofización tiene sobre el agua, se considera una forma de contaminación que perjudica
gravemente la calidad del agua, ya que incrementa el crecimiento de floraciones algales,
el agotamiento de oxígeno, y provoca la pérdida de vida en el fondo del sistema. En
consecuencia, el estudio y la determinación del estado trófico es un tema de suma
importancia debido a los efectos que este proceso tiene en ecosistemas costeros semi-
encerrados. Los términos que se utilizan para su determinación son (OCDE, 1982):
oligotrófico, mesotrófico, eutrófico, e hipereutrófico.
Cabe destacar que los estados que causan mayor preocupación son los eutróficos e
hipereutróficos, por lo que en los sistemas que presentan estos niveles de eutrofización
conviene realizar un análisis profundo de las causas que han provocado dicha situación,
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y de los procesos que tienen lugar en el mismo. De hecho, los procesos de los sistemas
que presentan estos dos tipos de estados tienen similitudes y diferencias, siendo una de
las principales diferencias entre ellos la penetración de la luz en el agua. En sistemas
eutróficos puede llegar a existir vegetación acuática sumergida, ya que la luz podría
penetrar a través de la columna de agua tanto como permita la concentración de
fitoplancton, expresada en función de clorofila-a, entre otros factores; por lo que podrían
vivir distintas especies de vegetación, aunque su crecimiento estaría limitado por la luz
de manera diferente en función de los requisitos lumínicos de cada especie. Sin embargo,
en un sistema hipereutrófico la presencia de fitoplancton, y por tanto, la concentración de
clorofila-a en la superficie del agua es tan alta, que la penetración de la luz a través de la
columna de agua está muy limitada. En consecuencia, en sistemas hipereutróficos la
supervivencia de vegetación sumergida es poco probable, ya que en general, tiene altos
requisitos de luz, siendo habitualmente reemplazada por macroalgas oportunistas con
menores necesidades lumínicas. Además, el exceso de nutrientes en sistemas
hipereutróficos también produce un mayor crecimiento de epífitos que contribuyen a una
mayor atenuación de la luz.
Por otra parte, para determinar el crecimiento y la productividad de la vegetación acuática
sumergida, es importante tener en cuenta la atenuación espectral de la luz en el agua para
poder reproducir con exactitud el proceso de fotosíntesis llevado a cabo por la
misma. Esto es debido a que de la radiación que llega a la superficie, sólo la de longitudes
de onda entre 400 y 700 nm puede ser utilizada para la fotosíntesis, denominándose
radiación fotosintéticamente activa (PAR, Photosynthetically Active Radiation). La luz
llega a la superficie del agua previamente atenuada por los componentes atmosféricos, el
ozono, el vapor de agua, y los aerosoles marinos entre otros. Una vez en la columna de
agua, se atenúa por diversos factores, como por ejemplo la profundidad, la clorofila-a, los
pigmentos del fitoplancton, la turbidez y la materia orgánica disuelta coloreada (CDOM,
Colored Dissolved Organic Matter). Estas sustancias producen absorción, dispersión y
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retrodispersión de la luz en el agua, variando la cantidad de luz que llega a la planta; por
lo que, es importante determinar con precisión el ambiente lumínico en sistemas con
vegetación acuática sumergida, donde el hábitat puede existir, pero es extremadamente
vulnerable, como es el caso de los SECS eutróficos. Mientras que en el caso de los SECS
hipereutróficos, estos procesos no son tan relevantes ya que hay una menor disponibilidad
de luz por los altos niveles de concentración de clorofila-a que atenúan la misma, lo que
origina una escasez de vegetación acuática sumergida. Por consiguiente, el carácter
tridimensional que presentan los sistemas eutróficos no es significativo en sistemas
hipereutróficos, ya que en estos últimos, la penetración de la luz en el agua es muy baja.
Sin embargo, ambos tipos de sistemas presentan una alta variabilidad horizontal ya que
los gradientes horizontales de la distribución del fitoplancton son generalmente muy altos.
Otro aspecto que influye en la atenuación de la luz en sistemas eutróficos es la subida del
nivel del mar (SLR, Sea Level Rise) debido al cambio climático, que podría modificar el
hábitat de las praderas marinas a largo plazo. Cuando el nivel del mar aumenta, se produce
una variación de las condiciones lumínicas a medida que la profundidad se incrementa,
pudiendo cambiar la distribución de las praderas marinas. De hecho, las partes más
profundas de los sistemas presentan menor disponibilidad de luz, por lo que las praderas
marinas podrían desaparecer de estas zonas, mientras que en las zonas someras podría
crearse nuevo hábitat y algunas plantas migrarían a esas áreas. Sin embargo, la creación
de nuevo hábitat vendrá limitado en cada sistema por factores tales como barreras físicas,
la inadecuación del sustrato, construcciones litorales, aumento de la competencia entre
especies, la salinidad, y la temperatura entre otros. De manera que, aunque las condiciones
de luz fueran adecuadas para el crecimiento de praderas marinas, existirían otros
parámetros que podrían influir en la distribución de los hábitats.
La complejidad y variabilidad espacial de estos sistemas, hacen que el estudio de las
relaciones causa-efecto entre las distintas acciones humanas, hidrográficas,
hidrodinámicas y los procesos ecológicos sea una tarea difícil. De hecho, para estudiar
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estas relaciones se suelen utilizar modelos que ayudan al análisis del comportamiento de
los SECS, y que son útiles para la determinación y predicción del comportamiento de los
mismos ante diversas perturbaciones. Los modelos complejos, con un gran número de
formulaciones y de parámetros son ampliamente utilizados para describir SECS,
independientemente del nivel de eutrofización de los mismos. No obstante, muy pocos
presentan una formulación espectral de la atenuación de la luz en la columna del agua, y
casi ninguno tiene formulaciones para calcular el hábitat de vegetación acuática
sumergida. Además, a la hora de utilizar estos modelos suele hacer falta una gran cantidad
de datos de entrada que dificultan el proceso, así como amplios conocimientos específicos
del modelo. Por todo ello, existe la necesidad de desarrollar herramientas
computacionales simplificadas para la gestión de SECS eutróficos e hipereutróficos,
teniendo en cuenta la compleja hidrodinámica de estos sistemas, y los procesos
específicos que rigen cada tipo. En los últimos años, modelos acoplados y/o conectados
han demostrado ser una buena solución para integrar de manera flexible y simplificada
los procesos más importantes para cada problemática. Por otra parte, el compromiso entre
simplificación, realismo y precisión, ha dado buenos resultados en la descripción y
predicción del comportamiento de sistemas mediante modelos, por lo que serán factores
a tener en cuenta en el presente estudio.
A su vez, cabe resaltar que en el Chapter I de la Tesis se ha realizado un análisis sobre la
historia de los modelos acoplados y conectados, una profunda revisión bibliográfica de
modelos hidrodinámicos, ecológicos, de irradiancia y bio-ópticos; y un análisis sobre el
equilibrio entre resolución espacial y temporal, y el compromiso que debe cumplirse entre
generalidad, realismo y precisión en la definición de la complejidad de un modelo.
El análisis del estado del arte revela que la complejidad de la mayoría de los modelos
ecológicos existentes aplicados a SECS hace difícil su utilización y comprensión, siendo
además necesario un equilibrio entre generalidad, realismo y precisión. Por otra parte, se
ha puesto de manifiesto la necesidad de ahondar en el modelado de la interacción entre la
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eutrofización y la vegetación acuática sumergida. De hecho, el fitoplancton producido
por un incremento en la disponibilidad de nutrientes, la turbidez, las concentraciones de
CDOM y el aumento de la profundidad del agua debido a la subida del nivel del mar son
cuestiones fundamentales que afectan a la atenuación de la luz en la columna de agua y
que limitan el proceso de fotosíntesis y crecimiento de la vegetación acuática
sumergida. Con todo, el estudio de este proceso y el modelado espectral de la luz de forma
tridimensional con alta resolución espacial apenas se ha llevado a cabo para estrategias
de gestión, ya que los modelos existentes que podrían simular estas características son
muy complejos y difíciles de utilizar.
El grado de simplificación y la selección de los procesos que deben tenerse en cuenta en
un modelo es una tarea compleja que depende del sistema y que requiere una comprensión
completa de los procesos que lo controlan. De hecho, los modelos complejos de
eutrofización son generalmente utilizados tanto para sistemas hipereutróficos como
eutróficos, aunque la problemática y los procesos que rigen cada uno de ellos son
diferentes. Asimismo, el gran número de ecuaciones y parámetros de estos modelos
conducen a que normalmente la obtención de datos para su calibración y validación sea
un proceso exigente y caro, y su ajuste difícil y tedioso, por lo que no serían apropiados
para analizar estrategias de gestión que deben ser evaluadas en un corto periodo de
tiempo.
De lo antes dicho, se desprende la necesidad de desarrollar nuevas herramientas de
modelado ecológico, surgiendo los objetivos de la presente Tesis.
1.2 Objetivos
El objetivo general de esta Tesis es: desarrollar nuevas herramientas de modelado
ecológico para evaluar y describir el comportamiento de sistemas costeros semi-
encerrados eutróficos e hipereutróficos. Además, esta Tesis permite ahondar en el
conocimiento del funcionamiento de dos sistemas costeros semi-encerrados, la Albufera
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de Valencia, que es un sistema hipereutrófico, y West Falmouth Harbour, que es un
sistema eutrófico.
Los objetivos específicos de esta Tesis son:
Analizar diferencias entre sistemas eutróficos e hipereutróficos y las limitaciones
de los modelos existentes.
Diseñar, conectar e implementar un modelo de calidad del agua simplificado para
un sistema hipereutrófico costero semi-encerrado fuertemente regulado.
Diseñar, acoplar e implementar un sistema de modelado para describir el
comportamiento de un sistema eutrófico costero semi-encerrado y las
implicaciones de la eutrofización en la atenuación de la luz y en la vegetación
acuática sumergida.
Evaluar la sensibilidad de los modelos, calibrarlos a partir de datos de campo, y
aplicarlos a ecosistemas costeros semi-encerrados con problemas de eutrofización
cultural y con importancia socio-económica y ambiental.
Analizar diferentes factores, tales como los efectos de la reducción de nutrientes
y el aumento del nivel del mar en un sistema eutrófico semi-encerrado, y las cargas
de entrada y salida en un sistema hipereutrófico mediante un balance de masas.
Analizar las limitaciones de los modelos desarrollados y proponer futuras líneas
de investigación.
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Capítulo II: Descripción y análisis de las zonas de estudio.
La Albufera de Valencia (ver Figura 1) y West Falmouth Harbor (ver Figura 2) son dos
SECS con similitudes y diferencias. Por un lado, ambos son SECS con un problema de
eutrofización cultural, están cerca de una zona poblada, y tienen una gran importancia
económica y ambiental. Por otro lado, sus características específicas son diferentes, como
puede observarse en la Tabla 1.
Figura 1. Albufera de Valencia, canales de regadío y localización de las estaciones de muestreo.
La Albufera de Valencia está deteriorada en un mayor grado que West Falmouth Harbor
debido en parte a que está muy cerca de una gran ciudad (Valencia, España). Es un SECS
muy cerrado y constreñido, cuya conexión con el mar solamente se efectúa unas pocas
veces al año mediante tres canales artificiales (golas) regulados por compuertas. A su vez,
está rodeado de campos de arroz lo que hace que la carga de nutrientes de entrada sea
excesiva. Estos factores han provocado el estado hipereutrófico del sistema, que ha
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llevado a la desaparición de muchas especies de fauna y flora. Una de las principales
razones de esta desaparición es la limitada penetración de la luz a través de la columna
de agua.
Por el contrario, West Falmouth Harbor es un SECS eutrófico cerca de la ciudad de
Falmouth (Massachusetts, EEUU), con mucha menor población alrededor que la
Albufera. Una de las mayores fuentes contaminantes de este SECS son las cargas de
nutrientes procedentes de una Estación Depuradora de Aguas Residuales (EDAR) que
llegan al SECS a través de aguas subterráneas con largos tiempos de viaje (hasta 10
años). Esta bahía es un SECS restringido, pero esta restricción está provocada por dos
diques situados en la bocana (ver Figura 2), por lo que la conexión con el mar es
constante.
Figura 2. West Falmouth Harbor, estaciones de muestreo y cargas contaminantes.
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En este estuario, la vegetación acuática sumergida ha ido desapareciendo durante años
debido principalmente a la limitación de la penetración de la luz a través de la columna
de agua y a la eutrofización. Todos estos factores explican la desaparición y disminución
del área cubierta por zostera marina, existiendo también una preocupación por las
consecuencias de la subida del nivel del mar en esta zona debido al cambio climático y a
la constante aunque restringida conexión con el mar.
Tabla 1. Características principales de la Albufera de Valencia y West Falmouth Harbor.
CARACTERÍSTICAS ALBUFERA DE VALENCIA WEST FALMOUTH HARBOR
SUPERFICIE (Km2) 23.2 0.7
PROFUNDIDAD (m) 0.9 1
FUENTES DE CONTAMINACIÓN
- Vertidos de aguas residuales - Fertilizantes y pesticidas
- Vertidos de aguas residuales
ESTADO TRÓFICO Hipereutrófico Eutrófico
NUTRIENTE LIMITANTE P N
TIPO DE SECS Estrangulado Restringido
CONEXIÓN CON EL MAR Regulado por 3 canales con compuertas Limitado por 2 diques
PRADERAS MARINAS No Sí
Las diferencias entre los sistemas estudiados hacen que la estrategia de modelado pueda
ser diferente, aunque ambos son SECS con serios problemas de eutrofización. Para un
sistema hipereutrófico podría utilizarse un modelo simple tipo NPZ, ya que el crecimiento
del fitoplancton debido a la sobreabundancia de nutrientes, y su mortalidad por el
herbivorismo del zooplancton son los principales procesos que rigen el sistema. Sin
embargo, en un sistema eutrófico la penetración de la luz a través de la columna de agua
podría permitir la supervivencia de praderas marinas, por lo que no sería suficiente
solamente con un modelo biogeoquímico simple, sino que además sería necesario el uso
de un modelo bio-óptico. Sumado a esto, las condiciones hidrodinámicas son bastante
diferentes en los dos SECS, siendo necesario tener en cuenta en la Albufera la regulación
antropogénica con el mar, mientras que en West Falmouth Harbor el efecto de la marea
y el aumento del nivel del mar podrían tener consecuencias en los hábitats de praderas
marinas. Finalmente, West Falmouth Harbor presenta un carácter tridimensional debido
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a su hidrodinámica, a las diferencias de concentración de clorofila-a en diferentes capas
de la columna de agua, y a la distribución de la luz que penetra en el agua. Mientras que,
la Albufera presenta un carácter bidimensional, ya que la comunicación con el mar es
muy limitada y la luz apenas penetra en la columna de agua debido a la alta concentración
superficial de fitoplancton, y por tanto de clorofila-a.
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Capítulo III. Desarrollo e implementación de un modelo
simplificado para sistemas costeros semi-encerrados
hipereutróficos. Aplicación a una laguna costera fuertemente
regulada.
Se desarrolló un modelo simplificado de eutrofización bidimensional para simular las
variaciones temporales y espaciales de clorofila-a en sistemas costeros hipereutróficos
semi-encerrados. Este modelo considera una conexión con el mar muy limitada y regulada
antropogénicamente. También tiene en cuenta la entrada y salida de cargas variables de
nutrientes, el flujo de los sedimentos a la columna de agua, y la cinética de crecimiento y
mortalidad del fitoplancton. El modelo fue calibrado y validado aplicándolo a la Albufera
de Valencia, un SECS hipereutrófico cuya conexión con el mar está fuertemente regulada
por un sistema de compuertas. Los resultados de calibración y validación presentan un
acuerdo significativo entre el modelo y los datos obtenidos. La exactitud se evaluó
mediante un análisis cuantitativo, en el cual la incertidumbre promedio de la predicción
del modelo fue menos del 6%. Los resultados confirmaron un bloom de fitoplancton en
abril y octubre, alcanzando unos valores máximos alrededor de 250 µg L-1 de clorofila-
a. Un balance de masas reveló que el proceso de eutrofización está magnificado por la
limitada conexión de la laguna con el mar, y por el flujo de sedimentos existente a la
columna de agua. Este estudio ha demostrado que el modelo desarrollado es una
herramienta eficaz para describir el problema de la eutrofización en sistemas costeros
hipereutróficos semi-encerrados.
En este capítulo, se presenta de manera abreviada la descripción del modelo desarrollado
(ver Figura 3), el análisis de sensibilidad de los principales parámetros del mismo, y la
calibración, validación y aplicación del modelo a un SECS hipereutrófico, la Albufera de
Valencia. La información completa sobre el desarrollo, evaluación y aplicación de este
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modelo puede encontrarse en el Chapter III de la presente Tesis. También puede
encontrarse una descripción más detallada de la zona de estudio en el Chapter II.
Figura 3. Resumen gráfico del capítulo III
3.1 Descripción abreviada del modelo
El comportamiento hidrodinámico de la laguna está controlado por varios factores, como
las entradas de agua dulce de los canales de riego, el equilibrio entre la precipitación y
evaporación, el viento y las salidas a través de las golas. Este último factor es fuertemente
dependiente del régimen de apertura de las compuertas y de la diferencia del nivel de agua
entre la laguna y el mar. Con el fin de evaluar estos procesos se utilizaron dos modelos
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hidrodinámicos, un modelo de onda larga y un modelo de viento. El primero de ellos, un
modelo bidimensional promediado en profundidad, fue utilizado para caracterizar la
circulación del agua en la Albufera, los flujos de agua entrando en la laguna a través de
los canales de regadío, y los de salida y entrada que se producen a través de las
tres golas al mar. El segundo, un modelo cuasi-tridimensional, fue aplicado para calcular
las corrientes de viento en el sistema. Ambos modelos consideran todo el dominio
(canales de riego, laguna, golas, mar), estando el efecto de la regulación de las salidas de
agua a través de las golas específicamente incluido en el modelo de onda larga. Este
modelo, resuelve las ecuaciones de Navier-Stokes con la aproximación promediada de
Reynolds (RANS, Reynolds Averaged Navier Stokes). Esta aproximación, propuesta por
Reynolds en 1895, está basada en la descomposición de las variables de flujo en un valor
medio y otro fluctuante. Aunque en ingeniería los flujos son turbulentos en su mayoría,
muchos de estos flujos pueden ser considerados como muy poco dependientes del tiempo,
con unas fluctuaciones superpuestas a la corriente principal estacionaria. En dicho caso,
solo interesarán las magnitudes promediadas en el tiempo, en lugar de los detalles de
variación con el tiempo. Por ello, las ecuaciones de Navier Stokes con la aproximación
de Reynolds se resuelven para los valores medios, que son los más interesantes en muchas
aplicaciones, como es el caso de la Albufera de Valencia.
En cuanto al modelo de eutrofización desarrollado, es un modelo numérico simplificado
bidimensional que resuelve la ecuación de advección-dispersión para cada variable de
calidad de agua seleccionada. La poca profundidad que generalmente caracteriza los
SECS hipereutróficos junto con su baja variación vertical justifica la simplificación de
promediar en vertical. El modelo de eutrofización desarrollado simula la calidad del agua
con respecto a la concentración de fitoplancton y fósforo soluble reactivo (SRP, Soluble
Reactive Phosphorus) en la columna de agua. En este sentido, el fitoplancton es un
indicador de la clorofila-a presente en el lago, mientras que el fósforo soluble reactivo es
el nutriente limitante del sistema. Por ello, el crecimiento de fitoplancton se calculó como
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una función del fósforo soluble reactivo, de la temperatura, y de la intensidad de luz en la
columna de agua; y su consumo se centró principalmente en la respiración endógena y en
el herbivorismo del zooplancton. La Figura 4 describe el diagrama de flujo del modelo
desarrollado y los principales procesos considerados.
Figura 4. Diagrama de flujo del modelo
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3.2 Datos de campo y establecimiento del modelo
Este estudio se llevó a cabo para el año hidrológico 2005/2006, para el cual se tenían
datos tanto de aportes de flujos y contaminantes al lago, como de las principales variables
de calidad de agua dentro del mismo, de la apertura y cierre de las golas y de las
condiciones ambientales y mareales. Los principales datos de aportes y de calidad del
agua del modelo fueron medidos por la Entidad Pública de Saneamiento de Aguas de
Valencia (EPSAR) entre Octubre de 2005 y Septiembre de 2006, en siete estaciones
distribuidas a lo largo del lago. Los datos recolectados en dichas estaciones fueron
clorofila-a, temperatura, SRP y la profundidad del disco de Secchi. Estos datos se
recogieron todos los meses de Octubre 2005 a Septiembre de 2006, obteniéndose 12
muestras para cada variable y cada estación a lo largo del año hidrológico, sumando un
total de 336 muestras. La localización de las estaciones de muestreo, de los canales de
regadío, y de las golas puede observarse en la Figura 1. Además, el SRP de los canales
de regadío principales fue medido durante cada mes del periodo de estudio para describir
las concentraciones de nutrientes que se descargan al lago, obteniéndose un total de 156
muestras de SRP. Los valores máximos de SRP se encontraron en las acequias del norte,
debido a que el origen de este tipo de nutriente es urbano e industrial.
La concentración media de clorofila-a obtenida fue de 115.7 µg L-1, lo que significa que
la Albufera de Valencia es un sistema hipereutrófico. El promedio de profundidad del
disco de Secchi varía entre 0.12 y 0.36 metros, lo que significa que la penetración de la
luz está altamente atenuada en la columna de agua. Con los datos observados de clorofila-
a y de profundidad del disco de Secchi en las siete estaciones de muestreo distribuidas
alrededor del lago, se obtuvo una expresión para describir la variación del coeficiente de
extinción luz en el agua (Ke) con la concentración de clorofila-a (ver Eq. 30 Chapter III)
que fue implementada en el modelo.
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Resumen en español
Los datos climatológicos para el año hidrológico 2005/2006, tales como nubosidad y
viento, fueron obtenidos de la estación meteorológica Valencia Viveros (a 10 Km de la
Albufera). La nubosidad fue medida por la Agencia Estatal de Meteorología en una escala
de 0 a 8 oktas, siendo 0 oktas la mínima cobertura de nubosidad y 8 oktas la máxima. El
viento más frecuente fue del sureste, siendo en general de baja intensidad. Sin embargo,
en algunos casos, el viento alcanzó intensidades por encima de 3 m s-1.
Otro parámetro importante de entrada que ha sido tenido en cuenta en este trabajo es el
flujo de fósforo soluble reactivo (SRP) del sedimento a la columna de agua. Este
parámetro fue obtenido a partir de un estudio específico con el fin de caracterizar la
influencia del flujo de SRP desde el sedimento a la columna de agua en la eutrofización
del sistema, para lo cual se muestrearon datos en 17 estaciones.
3.3 Calibración y validación.
La calibración del modelo de eutrofización implica ajustar los parámetros de éste, de
manera que sus resultados se ajusten a los datos medidos en los periodos de calibración.
Previamente, se llevó a cabo un análisis de sensibilidad con el objeto de cuantificar el
efecto de la variación de los parámetros del modelo en los resultados del mismo. Una vez
calibrado, el modelo fue validado para un año hidrológico (año 2005/2006), de cara a
confirmar el valor asignado a los parámetros del modelo durante la calibración.
3.3.1 Análisis de sensibilidad
Un análisis de sensibilidad fue utilizado para identificar los parámetros más influyentes
en la variación de los resultados del modelo. Los rangos de variación de cada parámetro
se muestran en la Tabla 2.
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Resumen en español
Tabla 2. Rango de variación de los principales parámetros y valores asignados en la calibración.
PARAMETRO DESCRIPCION UNIDADES RANGO DE
VARIACIÓN
VALOR
ASIGNADO
apc Ratio fosforo-carbono gP gC-1 0.011-0.025a,d 0.011***, d
Kr Tasa de respiración endógena del fitoplancton
día -1 0.05-0.5b,a,d 0.12***, g, h, d
Kmp Constante de semi-saturación del fósforo
mgP L-1 0.001-0.005c 0.0027***, d
aC/CHL-a Ratio carbono/clorofila-a mgC mgChl-a-1 50 – 133d 88***, d
Gmax Tasa de crecimiento máximo del fitoplancton
día-1 1.5-2.5c 1.5***, i
Is Intensidad de saturación de la luz en el agua del fitoplancton
Ly día-1 100-400c *(ver Eq. 33 Chapter III)
Fs Flujo de fósforo soluble reactivo del sedimento a la columna de
agua.
mgP m-2 día-1 5-50e 20.72 **
Cg Tasa de herbivorismo (y filtracion) del zooplancton
LmgC-1día-1 0.05-0.3f 0.3 ***, f
Source: a(Ambrose, 1988), b(Di Toro and Matystik, 1980), c(Thomann and Mueller, 1987), d(Martín, 1998), e field data, f (Chau and Haisheng, 1998); g (Lindenschmidt, 2006); h (Ambrose et al., 1993); i (Parslow et al., 1999).
*The values were assigned by empiricism;**The values were field data; ***The values were verified by calibration and literature.
Para realizar el análisis de sensibilidad, se fijaron ocho parámetros en sus valores mínimos
y máximos, definidos en los rangos marcados por la bibliografía (ver Tabla 2). Cada
simulación fue llevada a cabo fijando uno de los parámetros en su valor mínimo o
máximo, y dejando el resto de parámetros en el valor medio de su rango de variación
correspondiente. Este proceso se repitió para cada parámetro, por lo que la variación
media de la concentración de la clorofila-a sobre toda la laguna fue utilizada para evaluar
la sensibilidad del modelo a la variación de cada parámetro.
El histograma de la Figura 5 revela que los parámetros para los cuales los resultados de
clorofila-a del modelo presentan mayor sensibilidad son Kr, Cg, Gmax, Fs, y apc. Es
importante mencionar que Kr, Cg y Gmax alteran directamente las tasas de crecimiento del
37
Resumen en español
fitoplancton, mientras que Fs y apc afectan a la posible utilización o captamiento de fósforo
por parte del fitoplancton. Estos resultados están de acuerdo con los obtenidos en otros
estudios (Schladow and Hamilton, 1997; Wu et al., 2009), en los que Kr era el parámetro
de sensibilidad que más afectaba al crecimiento del fitoplancton, y por tanto a la
concentración de clorofila-a.
Figura 5. Concentración de clorofila-a media calculada en el análisis de sensibilidad en la Albufera para
los valores mínimos y máximos de los principales parámetros del modelo.
3.3.2 Calibración del modelo
Los periodos seleccionados para la calibración fueron Octubre, Febrero, Mayo y Julio.
Dado que en Octubre y Mayo suele ocurrir un bloom de fitoplancton, y durante Febrero
y Julio la concentración de clorofila-a suele ser más baja en comparación con el resto del
año. La comparación entre las medidas de campo y las predicciones del modelo se llevó
a cabo mediante el cálculo de diferentes tipos de errores. Los errores calculados fueron el
error absoluto (AE), el error relativo (RE), el error relativo medio (MRE), el error
cuadrático medio (RMSE), el error normalizado cuadrático medio (PRMSE), el error
absoluto medio (MAE), el error absoluto medio normalizado (NMAE), y el BIAS.
La calibración del modelo fue llevada a cabo teniendo en cuenta los parámetros más
influyentes resultantes del análisis de sensibilidad y las variables estacionales. A pesar de
38
Resumen en español
la compleja naturaleza y la alta variabilidad de la Albufera, el valor asignado a los
parámetros utilizados en el modelo se mantuvo constante en todos los periodos. Sin
embargo, la intensidad de saturación de luz del fitoplancton (Is), varía dependiendo de la
estación y la temperatura, siendo mínima en invierno y máxima en verano (Macedo et al.,
2001) (ver Eq.33 Chapter III).
Otro parámetro importante que ha sido calibrado en este trabajo es el flujo de SRP desde
el sedimento a la columna de agua. Este parámetro fue obtenido mediante una campaña
de campo específica en la que, como ya se ha indicado, se muestrearon datos en 17
estaciones para representar la distribución espacial del flujo de SRP en el lago. Los datos
medidos fueron interpolados utilizando el método geoestadístico Kriging (Kitanidis,
1997). Los resultados obtenidos, los datos medidos, y la localización de las estaciones de
muestreo del flujo de SRP están representadas en la Figura 6. Como puede verse en dicha
figura, la zona norte del lago presenta el flujo máximo difusivo, debido a las altas
descargas de SRP que durante años llevan depositándose en esa zona, siendo el valor
medio de flujo de SRP para todo el lago de 20.72 mgPm-2día-1.
Figura 6. Distribución del flujo de fósforo soluble reactivo (SRP) desde el sedimento a la columna de
agua en la Albufera de Valencia.
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Resumen en español
Además, se realizaron diversas simulaciones para asignar un valor a los parámetros más
significativos del modelo, los cuales fueron ajustados para obtener el menor error entre
los datos observados y los resultados del modelo. Los valores asignados se pueden
observar en la Tabla 2, encontrándose en los rangos recogidos en la literatura. Los errores
entre los resultados del modelo y los datos observados en las campañas de campo se
resumen en la Tabla 3. Estos errores se calcularon en las siete estaciones de muestreo para
cada mes, siendo el mes con el menor error relativo medio Febrero, mientras que el de
mayor error relativo medio fue Mayo. Se ha calculado también el error cuadrático medio
porcentual (PRMSE) para cada periodo de calibración, y este se ha comparado con el
error absoluto medio normalizado (NMAE). Como resultado, se obtiene que los valores
más bajos de estos errores se han encontrado en el mes de Mayo, siendo el PRMSE 7.30%
y el NMAE de 8.50%.
Tabla 3. Errores obtenidos para todas las estaciones de muestreo en cada periodo de calibración.
Periodo MRE (%) RMSE(μg L-1) PRMSE (%) MAE(μg L-1) NMAE (%) BIAS(μg L-1)
Octubre 2.52 26.94 11.95 23.10 10.70 2.41
Febrero 1.75 10.46 19.26 8.52 16.13 -1.22
Mayo -6.56 16.12 7.30 12.45 8.50 8.36
Julio -3.62 6.23 14.76 4.84 14.01 -0.28
El BIAS también se ha calculado, siendo Julio el mes en el que se ha obtenido un valor
más cercano a cero. De hecho, los valores calculados por el modelo fueron en torno a 0.28
μg L-1 más bajos que los valores observados. Después se calcularon los errores globales
utilizando los valores medios observados y calculados. Las simulaciones mostraron que
para Octubre, Febrero, Mayo y Julio, los errores relativos obtenidos comparando la media
de los datos observados con la de los resultados del modelo están entre un 3 y un 5 %.
Los errores absolutos se encuentran entre 1.86 y 7.15 µg L-1, como puede verse en la
Tabla 4.
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Resumen en español
Tabla 4. Errores globales obtenidos con la concentración de clorofila-a media para cada periodo de
calibración en toda la laguna.
Periodo EA (μg L-1) ER (%)
Octubre 7.15 3.17
Febrero 1.86 -3.42
Mayo 7.13 4.27
Julio 2.23 -5.28
Como puede verse en la Figura 7, la comparación entre los resultados del modelo
numérico y los datos medidos dan un ajuste adecuado. Además, la Figura 7 muestra la
evolución de la concentración de la clorofila-a en las siete estaciones de muestreo y la
concentración media para toda la laguna en los periodos de calibración. Respecto a la
concentración de clorofila-a, el mejor ajuste se ha obtenido en la estación A2.
Figura 7. Comparación entre los resultados obtenidos por el modelo (líneas) y los datos de campo
(puntos) en las estaciones de muestreo y en toda la laguna para cada periodo de calibración.
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Resumen en español
3.3.3 Validación del modelo
Una vez calibrado, el modelo numérico fue validado en los meses que no se habían tenido
en cuenta en la calibración (Noviembre, Diciembre, Enero, Marzo, Abril, Junio, Agosto
y Septiembre), calculándose una serie de errores en cada estación. Como puede
observarse en la Figura 8, la estación C2 es la que presenta un mejor ajuste, con un error
relativo medio de 1.02%, mientras que el peor ajuste ocurre en A1, con un error relativo
medio de 47.74%. Además, todas las estaciones excepto A1, A2 y C1 tienen errores
relativos medios por debajo de 16 %, que es un valor aceptable para la validación espacial.
Figura 8. Evolución de la concentración de clorofila-a simulada (línea continua) y los datos observados
(puntos negros) en cada estación de muestreo, para los periodos de calibración del año hidrológico
2005/2006.
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Resumen en español
En lo que concierne a la validación, la Figura 9 muestra la comparación entre los datos
promediados espacialmente, calculados y observados, para el año hidrológico 2005/2006,
en los meses donde se ha llevado a cabo la validación. El error relativo global obtenido
tiene un valor de 5.81%, lo cual indica que el modelo es capaz de reproducir
adecuadamente el comportamiento del sistema.
Figura 9. Evolución de la concentración de clorofila-a promedio para la laguna calculada por el modelo
(línea) para todo año hidrológico 2005/2006 y los datos observados (puntos) para los periodos de
validación.
3.4 Resultados y discusión.
Los resultados evidencian que el modelo propuesto es una herramienta efectiva para
describir la distribución de la clorofila-a en un SECS hipereutrófico con alta resolución
espacial (ver Figura 10). Además, la distribución espacial está de acuerdo con la
evolución temporal como se muestra en las Figuras 9 y 10, donde Octubre y Abril son los
meses que presentan unas concentraciones de clorofila-a mayores, mientras que Febrero
y Marzo son los que presentan las menores concentraciones. Esto es coherente con Romo
et al. (2008), que encontraron los menores niveles de clorofila-a en la Albufera de
Valencia en Febrero y Marzo y los mayores en Octubre y Abril. Además, los valores de
43
Resumen en español
concentración de clorofila-a calculados en este estudio son consistentes con los resultados
obtenidos por Romo et al. (2005) con valores medios máximos comprendidos entre 200
y 250 µg L-1.
El aumento de la concentración de clorofila-a en Octubre es producido por la cálida
temperatura del agua (23 ºC), el hidrodinamismo, y las altas cargas de nutrientes durante
ese periodo. La cosecha del arroz se produce entre Septiembre y Octubre, por lo tanto una
gran cantidad de nutrientes entran al lago durante esa época (Onandia et al., 2014),
favoreciendo el proceso de eutrofización. El mismo comportamiento fue observado por
Menéndez et al. (2002) en la laguna Buda (España). En abril y Mayo un gran número de
nutrientes llegan a la Albufera procedentes de fertilizantes y pesticidas utilizados en la
preparación de los campos de arroz que rodean al sistema. Febrero y Marzo por otra parte
presentan una menor concentración de clorofila-a. En Febrero, la mayoría de las
compuertas están abiertas, por lo que la hidrodinámica del SECS aumenta
considerablemente. En Marzo también se renueva el agua, y la entrada de cargas de
nutrientes es menor que en Febrero.
Además, esta reducción de nutrientes produce un aumento de la especie zooplanctonica
Daphnia magna, que es un crustáceo Cladocero responsable de una gran parte de la
consumición del fitoplancton durante ese periodo (Romo et al., 2005; Onandia et al.
2015). Como consecuencia de esto, el efecto del herbivorismo del zooplancton aumenta,
y se produce una “fase clara” en ese periodo, dando lugar a la menor concentración de
clorofila-a de todo el año.
En vista a esos resultados, la conexión de la laguna con el mar, el herbivorismo del
zooplancton y las cargas de entrada de nutrientes afectan directamente a la eutrofización
de esta laguna costera altamente regulada, dando lugar a un SECS hipereutrófico. Para
evaluar la influencia del SRP en la evolución de la clorofila-a, se ha llevado a cabo un
44
Resumen en español
balance de masas, que determina las cargas de SRP que entran y salen, y la cantidad neta
que se queda en el sistema cada mes.
Figura 10. Distribución espacial de clorofila-a en la Albufera de Valencia en el año hidrológico
2005/2006.
45
Resumen en español
El análisis del balance neto de masas de los contaminantes que entran en el sistema menos
los que salen del mismo dan una medida de como el SECS está acoplado con los sistemas
adyacentes, como en este caso con el Mar Mediterráneo. El balance de masas revela que
la mayor parte del SRP que entra en el sistema permanece en él. La carga total de SRP
que entra en la Albufera es de 26.4 t año-1, y la de salida es de 5.8 t año-1. Estos resultados
son similares a los de Burger et al (2008) en el Lago Rotorua, que es un lago altamente
eutrófico que tiene una carga de SRP de entrada de 27.5 t año-1.
Utilizando el método descrito anteriormente, se obtuvo que Abril es uno de los meses con
mayor carga de entrada de SRP (ver Figura 11), aumentando por tanto su concentración
de clorofila-a debido tanto a la alta carga de entrada de SRP, como a la cálida temperatura
del agua. Durante el otoño, la carga de SRP que entra en el lago es también elevada (ver
Figura 11). En Octubre tanto la concentración de SRP como el caudal de entrada de las
acequias de regadío aumenta, haciendo que sea uno de los meses con mayores cargas de
entrada de SRP. Como puede verse en la Figura 11, en todos los meses del año
hidrológico, la carga de entrada de SRP ha sido considerablemente mayor que la de salida,
especialmente en Octubre y Abril, donde no había flujo de salida al estar las golas casi
completamente cerradas. Además, en estos meses, la entrada de carga de SRP fue
considerablemente mayor que en otros meses, y es igual a la carga neta acumulada en la
Albufera en dichos periodos, lo cual produce serios problemas de eutrofización.
Por otra parte, es importante resaltar que en la Albufera de Valencia la carga de entrada
de SRP que viene de las acequias de regadío es aproximadamente el 35 % de la carga
total de SRP que entra en la columna de agua, mientras que el flujo de fósforo desde el
sedimento a la columna de agua constituye el 65 % (IHCantabria, 2009). Por tanto, se
puede concluir que los blooms de fitoplancton están directamente afectados no solo por
la temperatura, sino también por la carga de nutrientes de entrada, el flujo de fósforo
soluble reactivo desde el sedimento y la conexión del sistema con el mar.
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Resumen en español
Figura 11. Balance de masas del fósforo soluble reactivo (SRP) que entra y sale de la Albufera.
Finalmente, los valores simulados por el modelo están correlacionados positivamente con
los valores medidos, con un coeficiente de correlación de Pearson de 0.93 para los
periodos de calibración y de 0.92 para los periodos de validación. Además, también se
calculó el coeficiente de eficiencia de Nash-Sutcliffe (Moriasi et al., 2007), que arrojó un
valor de 0.96, que es considerado excelente de acuerdo a Usaquen et al. (2012).
3.5 Conclusiones
En este capítulo se ha desarrollado un modelo simplificado bidimensional para SECS
hipereutróficos. Este modelo se ha aplicado satisfactoriamente a la Albufera de Valencia,
un sistema hipereutrófico cuya conexión con el mar está altamente regulada. La
concentración de clorofila-a en la Albufera de Valencia fue utilizada para calibrar y
validar el modelo propuesto en distintos periodos, así como para evaluar la sensibilidad
del mismo. Tras el análisis de sensibilidad, se puede concluir que los parámetros para los
cuales la concentración de clorofila-a en el modelo es más sensible son: Kr, Cg, Gmax, Fs
y acp. Siendo la tasa de respiración endógena del fitoplancton, Kr, el parámetro dominante
que más afecta a la concentración de clorofila-a, ya que afecta directamente al crecimiento
del fitoplancton.
0
1
2
3
4
O N D J F M A M J J A S
SRP
(t/m
on
th)
IN
OUT
NET
47
Resumen en español
El modelo nos proporciona una mayor comprensión del comportamiento del sistema. De
hecho, los resultados del modelo concluyen que los blooms de fitoplancton en Abril y
Octubre, no se deben solamente a la temperatura, sino también a las cargas de nutrientes
y a la conexión entre la laguna y el mar en dichos periodos. Además, el modelo es capaz
de reproducir la existencia de una “fase clara” en torno al mes de Marzo, que se debe
principalmente a la reducción de nutrientes, a los cambios hidrodinámicos y al efecto del
herbivorismo del zooplancton.
Como se demostró en el balance de masas, las cargas de entrada en el sistema son mayores
que las de salida, por lo que la limitada conexión con el mar magnifica la eutrofización
del sistema. Además, el flujo de SRP desde el sedimento a la columna de agua contribuye
a mantener la alta concentración de clorofila-a.
Por otra parte, un análisis estadístico cuantitativo fue aplicado para calcular la
incertidumbre y eficiencia del modelo, obteniéndose valores excelentes que demuestran
que un modelo simplificado puede caracterizar la eutrofización en un SECS
hipereutrófico.
Los resultados confirman que el modelo es una herramienta válida para la gestión de la
eutrofización en SECS hipereutróficos altamente regulados como la Albufera de
Valencia, siendo capaz de describir, con alta resolución temporal y espacial, y bajos
requerimientos computacionales, la evolución de la concentración de clorofila-a durante
un año hidrológico completo.
48
Resumen en español
Capítulo IV. Desarrollo e implementación de un sistema de
modelado ecológico para sistemas costeros eutróficos semi-
encerrados. Aplicación a un estuario alimentado por aguas
subterráneas con vegetación acuática sumergida.
La eutrofización en SECS ha provocado numerosos cambios ecológicos, entre los que se
incluye la desaparición de praderas marinas. Una causa potencial de esta pérdida es la
reducción de la disponibilidad de luz debido a la creciente atenuación de la luz en el agua
por el fitoplancton. El cambio climático y el consecuente aumento del nivel del mar
también tenderán a reducir la penetración de la luz en el agua y a modificar el hábitat de
zostera marina.
Por todo ello, se ha integrado un modelo de irradiancia espectral dentro de un modelo
biogeoquímico acoplado al Sistema de Modelado Regional Oceanográfico (ROMS), que
se conectaron a su vez a un modelo bio-óptico capaz de calcular la producción y
respiración de la zostera marina en el agua, para poder evaluar y predecir el hábitat
potencial de zostera marina en SECS hipereutróficos.
El sistema de modelado se aplicó a West Falmouth Harbor, que es un estuario poco
profundo semi-encerrado, localizado en Cape Cod (Massachusetts) donde los nitratos que
llegan al estuario a través de aguas subterráneas y su limitada pero permanente conexión
con el mar ha causado la eutrofización del sistema y la consecuente pérdida de praderas
de zostera marina. Para la calibración y aplicación del sistema de modelado se realizaron
medidas de campo de clorofila-a, turbidez, atenuación de la luz, y cobertura de zostera
marina durante un verano completo. La concentración media de clorofila-a medida varió
desde 28 µg L-1 en las zonas más interiores del estuario a 6.5 µg L-1 en las zonas más
exteriores, mientras que la atenuación de la luz se movió en un rango de 0.86 a 0.45 m-1.
El modelo reproduce la variabilidad espacial de la clorofila-a y de la atenuación de la luz
en el agua con un error cuadrático medio de 3.72 µg L-1 y 0.07 m-1 respectivamente. Se
49
Resumen en español
simularon también distintos escenarios para estudiar el efecto de una futura reducción de
nutrientes y del aumento del nivel del mar en el estuario, y los resultados revelaron que
con una reducción de un 75% de la carga de nitratos se conseguiría una mejoría
considerable de las condiciones lumínicas en el agua. Este sistema de modelado puede
ser útil para evaluar la variación de clorofila-a y del hábitat potencial de zostera marina
desde la perspectiva de las condiciones lumínicas y de la atenuación de la luz en el agua,
así como para describir la variabilidad temporal y espacial del sistema. En la Figura 12
se puede ver un resumen gráfico de los temas abordados en este apartado. Una mayor
descripción del sistema de modelado, así como de la problemática bajo estudio, de la
evaluación del modelo y de sus resultados puede ser encontrada en el Chapter IV de la
presente Tesis.
Figura 12. Resumen gráfico del capítulo IV
50
Resumen en español
4.1 Métodos y resultados del muestreo
Se tomaron datos de campo en West Falmouth Harbor para medir datos meteorológicos,
hidrodinámicos, de calidad del agua y de condiciones lumínicas durante el verano de 2012
en tres puntos de control (Outer, Snug y South) (ver Figura 2). Los datos meteorológicos
fueron medidos a intervalos de 1 minuto por una estación meteorológica Onset desde el
28 de Junio de 2012 hasta el 11 de Septiembre de 2012. Los parámetros incluidos fueron
la dirección del viento, la velocidad del viento, la presión atmosférica, la humedad
relativa, la radiación de onda corta, el PAR y la temperatura del aire. En cuanto a los datos
hidrodinámicos, de luz y de calidad del agua dentro de la bahía, se instaló una plataforma
subacuática que consistía en un Nortek Aquadopp ADCP (para medir velocidad del agua),
un SeaBird SeaCat (para medir presión), una YSI 6600 multisonda (para la salinidad,
temperatura, clorofila-a, turbidez, oxígeno disuelto), y dos sensores WetLabs ECO-
PARSB (para medir el PAR). Todos los sensores se colocaron a 0.3 m de profundidad
medidos desde el fondo (mab, meters above bed) excepto el de el PAR superior que está
colocado a 0.8 mab. Los valores de clorofila-a fueron obtenidos por un sensor YSI 6025
localizado en la multisonda YSI 6600. Las medidas se tomaron en intervalos de 5 minutos
del 3 al 19 de julio 2012 en Outer Harbor, del 19 de julio 2012 al 9 de Agosto de 2012 en
South Cove, y del 9 al 27 de agosto de 2012 en Snug Harbor. Debido al hecho de que no
se observaron cambios intra-estacionales entre julio y agosto durante una campaña de
campo previa en 2010 (Ganju et al., 2011), los datos tomados en cada localización fueron
considerados representativos de la estación de verano en cada punto de muestreo. Por otra
parte, se calculó el coeficiente de atenuación difusa de la luz en el agua, Kd, siguiendo las
formulaciones de Gallegos et al. (2011) (ver Eq. 34, Chapter IV).
Para determinar la extensión superficial de las praderas de zostera marina en West
Falmouth Harbor se realizaron campañas de campo durante Junio de 2012 utilizando un
escáner sonar. Se remolcó un EdgeTech, Inc. 4125 y se utilizó un EdgeTech Discover
software para tomar datos acústicos a 900 y 1250 kHz a lo largo de transectos espaciados.
51
Resumen en español
Las posiciones fueron proporcionadas por un Trimble AgGPS 132 con correcciones
diferenciales por radio de la U.S. Coast Guard. Los datos se preprocesaron con un
Chesapeake Technology Inc. SonarWiz5, se georeferenciaron y se exportaron las
imágenes georeferenciadas a ArcGIS 10.1 para su clasificación. Se delinearon las
praderas marinas manualmente en ArcGis tras examinar y verificar las zonas. Finalmente
se validó la interpretación de las imágenes y se verificó la extensión final de praderas de
zostera marina mediante un muestreo adicional en una serie de localizaciones aleatorias
elegidas al azar. Los flujos de agua subterránea y sus concentraciones asociadas de
nitratos fueron obtenidas en estudios previos (Kroeger et al., 2006; Ganju et al., 2012;
Hayn et al., 2014).
Finalmente, el análisis de los datos de campo reveló que todos los valores medios de las
principales variables de calidad de agua, tales como temperatura, salinidad, pH, oxígeno
disuelto y turbidez, fueron similares espacialmente, sin encontrarse grandes variaciones
a lo largo del estuario, excepto para la clorofila-a, que fue más alta en Snug Harbor. Las
medidas de clorofila-a sugirieron una mayor eutrofización en las zonas interiores de la
bahía, con un valor medio de 28 µg L-1, mientras que en la bahía exterior la concentración
media de clorofila-a fue de 6.5 µg L-1. En consecuencia, las medidas de irradiancia han
demostrado que Snug presentaba valores de PAR considerablemente menores que en
Outer. Hay que tener en cuenta además, que los datos de PAR de South Harbor no se
pudieron obtener debido a un mal funcionamiento del instrumento medidor. El coeficiente
de atenuación difuso de la luz en el agua, Kd, presentó un valor medio de 0.45 m-1 en
Outer y 0.86 m-1 en Snug. La distribución estadística de las medidas de clorofila-a y de
Kd en Outer y Snug confirma que el mayor coeficiente de atenuación de la luz en el agua
en Snug es consistente con la elevada concentración de clorofila-a (ver Figura 13).
Además, medidas anteriores confirman que el CDOM es espacialmente uniforme y
relativamente bajo en West Falmouth Harbor (M. Hayn, no publicado). Estas medidas
también indican que la turbidez es relativamente baja con diferencias espaciales mínimas.
52
Resumen en español
Por lo tanto, existe una gran relación entre la eutrofización y la atenuación de la luz en el
agua en la zona de estudio.
Figura 13. Histogramas de los datos de campo de clorofila y Kd en Outer y Snug Harbors.
4.2 Descripción resumida del modelo
Se ha integrado un modelo de irradiancia espectral (Gallegos et al., 2011) en un modelo
existente NPZD-biogeoquímico (Fennel et al., 2006) para calcular la penetración
espectral del PAR a través de la columna de agua. El modelo de irradiancia-
biogeoquímico resultante utiliza el PAR para calcular el crecimiento del fitoplancton.
Este modelo ha sido acoplado al Sistema de Modelado de Circulación Oceanográfica
Regional ROMS 3D (Haidvogel et al., 2008). Esto permite calcular la hidrodinámica, la
luz y las variables de calidad del agua tridimensionalmente en el mismo paso de tiempo.
El PAR y el Kd que se computan sirven de dato a un modelo bio-óptico (Zimmerman,
2003) que se ha conectado a los anteriores para calcular el balance de carbono de la
zostera marina en función de las condiciones lumínicas (Figura 14), el cual permite la
predicción de la presencia/ausencia de zostera marina y su potencial supervivencia.
Además, se definió con éxito un criterio para evaluar la futura evolución de las praderas
53
Resumen en español
de zostera marina en SECS eutróficos, basado únicamente en las condiciones de luz. Se
seleccionó como criterio el umbral de P/R=1 (P/R, Producción/Respiración), dado que
tanto los ecosistemas autótrofos como los heterótrofos tienden a aproximarse este valor a
lo largo del tiempo (Giddings and Eddlemon, 1978). Además, como el ecosistema bajo
estudio es autótrofo, se ha asumido que P/R>1 está asociado al éxito, supervivencia y
crecimiento de la zostera marina, mientras que P/R<1 da lugar a la desaparición de la
misma. Las capacidades fruto del enlace de estos modelos aporta una descripción integral
de las dinámicas físicas, ópticas y biológicas en la columna de agua.
54
Resumen en español
Figura 14. Diagrama de flujo del sistema de modelado y sus interacciones. En el panel inferior se
muestra que el ratio P/R>1 indica hábitat potencial de zostera marina, mientras que el P/R<1 indica
perdida potencial de hábitat de zostera marina. U y V son las velocidades, h la profundidad de la columna
de agua, T la temperatura del agua y η la variación de la superficie libre.
55
Resumen en español
4.3 Calibración del modelo
En esta sección se describe la calibración de los modelos biogeoquímico, de irradiancia
y bio-óptico, teniendo en cuenta que el modelo hidrodinámico y los flujos de agua dulce
se calibraron en un estudio previo (Ganju et al., 2012). Durante el proceso de calibración,
los parámetros del modelo se variaron para ajustarse a los valores medidos. Los
indicadores utilizados para calibrar los modelos biogeoquímico, de irradiancia y bio-
óptico fueron la concentración de clorofila-a, el coeficiente de atenuación de la luz en el
agua y la presencia/ausencia de zostera marina, respectivamente.
4.3.1 Calibración de los modelos biogeoquímicos y de irradiancia.
Se realizó un análisis de sensibilidad para evaluar la influencia de los parámetros
principales del modelo en los resultados de clorofila-a. Estos parámetros se fijaron a los
valores máximos y mínimos encontrados en la bibliografía (ver Tabla 5) obteniendo el
comportamiento observado en la Figura 15. Los parámetros que tienen mayor efecto en
los resultados de clorofila-a del modelo fueron μ0, α, gmax, mp, τ, y mz.
Figura 15. Análisis de sensibilidad del modelo biogeoquímico de Fennel et al. (2006) con un nuevo
módulo integrado de irradiancia espectral.
La calibración de la integración del modelo biogeoquímico con el de irradiancia se centró
en los tres puntos donde se obtuvieron las medidas de campo, cada uno representativo de
56
Resumen en español
Outer, Snug y South respectivamente (ver Figura 2). Por otra parte, los valores finales de
simulación de cada parámetro pueden verse en la Tabla 5, encontrándose todos ellos
dentro de los rangos de variación de la bibliografía.
Tabla 5. Parámetros principales del modelo biogeoquímico y de irradiancia y valor asignado.
SIMBOLO DEFINICIÓN VALOR
CALIBRADO
UNIDADES RANGO
µ0 Rango de crecimiento del
fitoplancton
3 d-1 0.62a-3.0b
KNO3 Concentración de semi-
saturación para el consumo de
NO3
0.1 Mmol N m-3 0.007-1.5c
KNH4 Concentración de semi-
saturación para el consumo de
NH4
1.5 Mmol N m-3 0.007-1.5 c
α Pendiente inicial de la curva P-I 0.13 Mol C gChl-1(Wm-2)-
1d-1
0.007-0.13d
gmax Ratio máximo de herbivorismo 0.6 (mmol N m-3)-1 d-1 0.5e-1.0f
Kp Concentración de semi-
saturación de ingestión del
fitoplancton
2 (mmol N m-3)2 0.56-3.5c
mp Mortalidad del fitoplancton 0.05 d-1 0.05-0.2g
Parámetro de agregación 0.005 (mmol N m-3)-1d-1 0.005-0.1c
Θmax Ratio máximo de clorofila-a a
fitoplancton
0.068 mgChl mg C-1 0.005-0.072d
mz Mortalidad del zooplancton 0.025 (mmol N m-3)-1 d-1 0.025-0.25c
RSD Ratio de re-mineralización de
detritos suspendidos
0.03 d-1 0.01-0.25h
RLD Ratio de re-mineralización de
grandes detritos
0.01 d-1 0.01-0.25h
Nmax Ratio máximo de nitrificación 0.05 d-1 0.05-0.1c
a(Taylor, 1988) b(Andersen et al., 1987) c(Lima and Doney, 2004) d(Geider et al., 1997) e(Wroblewski, 1989) f(Fasham, 1995) g(Taylor et al., 1991) h(Leonard et al., 1999).
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Resumen en español
La comparación entre los promedios de los resultados del modelo y los datos de campo
en las estaciones de muestreo puede verse en la Tabla 6. En lo que respecta a la
concentración de clorofila-a y al coeficiente de atenuación de la luz en el agua se obtuvo
un BIAS cercano a cero en Snug y Outer. La similitud entre las desviaciones estándar
sugiere que el modelo describe apropiadamente la variabilidad del sistema en estas áreas.
Por el contrario, en South Cove, la diferencia entre los datos modelados y los resultados
de campo de clorofila-a es mayor que en los otros puntos de muestreo.
Tabla 6. Valores medios de clorofila-a y Kd, desviación estándar (Std), y BIAS para Outer, Snug y South.
Los datos de campo de clorofila-a y Kd fueron obtenidos procesando los datos de los sensores desplegados
durante el verano de 2012. Los resultados del modelo fueron obtenidos para el mismo periodo de tiempo.
CLOROFILA-A KD
Punto Media ± Std Modelo (µg/L)
Media ± Std Campo (µg/L)
BIAS (µg/L)
Media ± Std Modelo (1/m)
Media ± Std Campo (1/m)
BIAS (1/m)
Outer 6.9 ± 3.7 6.5 ± 2.8 0.41 0.45 ± 0.07 0.45 ± 0.30 -0.001
Snug 28 ± 12 28 ± 9.9 0.33 0.79 ± 0.19 0.86 ± 0.16 -0.077
South 6.3 ± 3.9 10 ± 9.3 -3.9 - - -
Finalmente, la media vertical promediada temporalmente de clorofila-a para el estuario
completo puede observarse en la Figura 16. Este cálculo se obtuvo con el procesado de
los resultados de concentración de clorofila-a tridimensionales durante todo el periodo de
estudio (Figura 16 a), para posteriormente calcular la concentración vertical promedio
(Figura 16 b). Los resultados de la simulación presentan mayores concentraciones de
eutrofización en las capas superiores (ver Figura 16 a), y también mayores niveles de
clorofila-a en Snug que en Outer y South (ver Figuras 16 a y b). Además, estos resultados
concuerdan con los datos de campo. Por lo tanto, el modelo es capaz de describir con
exactitud la concentración de clorofila-a con una alta resolución espacial.
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Resumen en español
Figura 16. a) Variación de la concentración vertical de clorofila-a en tres capas del modelo; b) Promedio
temporal y vertical de clorofila-a en el estuario completo.
4.3.2 Calibración del modelo bio-óptico de zostera marina.
Los parámetros calibrados para el modelo bio-óptico fueron el ángulo de inclinación, la
máxima altura del manto de vegetación y la densidad de brotes. El ángulo de inclinación
seleccionado fue de 45º, representando el ángulo medio durante un ciclo mareal, y la
máxima altura de manto vegetal fue de 1 metro (Ackerman, 2002). La densidad elegida
fue de 525 brotes/m2, dado que la densidad observada de plantas varió entre 250-800
brotes/m2 en Outer (McGlathery, Marino, Hayn, y Howarth no publicado). El PAR
espectral proviene del modelo de irradiancia, y es propagado a través del manto de
vegetación hasta el fondo. Las propiedades de absorbancia y reflectancia del fondo
también fueron consideradas, siendo la composición de este una mezcla entre fango y
(a) (b)
59
Resumen en español
arena. La luz reflejada también se propagó hacia arriba a través del manto vegetal, por lo
que la producción primaria fue calculada con el total de luz absorbida. El hábitat potencial
fue evaluado en función de la distribución del ratio Producción/Respiración (P/R) (Figura
17 a) calculado a partir de los datos medios del verano completo. Se ha considerado el
ratio P/R obtenido como representativo de la estación del año bajo estudio. Además, se
ha asumido que para P/R>1 hay condiciones favorables para el crecimiento de zostera
marina, delimitando este umbral el hábitat potencial de z. marina en el estuario. Por el
contrario, para P/R≤1 se asumen condiciones desfavorables para la presencia de zostera
marina (Figura 17 b) ya que el crecimiento estaría limitado, siendo la respiración mayor
que la producción fotosintética en estas áreas. Basándose en este criterio, se ha obtenido
una correspondencia del 73.39 % entre los resultados del modelo y los datos de campo de
ausencia/presencia, teniendo en cuenta Outer y Snug (Figura 17 c), ya que en South se
estima que la zostera ha desaparecido debido a razones hidrodinámicas y perturbaciones
ambientales, y no debido a las condiciones de luz, como puede ser observado en la Figura
17 b. Del mismo modo en la Figura 17 se puede observar que la zostera no está presente
en zonas poco profundas del estuario. Esto se debe al efecto de inundación-secado
simulado por el modelo y el comportamiento sub-mareal de la zostera marina impuesto
en el modelo. Finalmente, destacar que la distribución de zostera obtenida por el criterio
P/R (Figura 17 d) está en correspondencia con la distribución de la profundidad crítica
obtenida cuando se aplica la ecuación propuesta por Duarte et al. (2007).
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Resumen en español
Figura 17. a) Distribución del ratio Producción/Respiración; b) Distribución del ratio
Producción/Respiración aplicando el criterio de P/R>1, y comparación con los datos del campo (línea
negra continua); c) Detalle de la distribución de P/R en Snug y Outer para P/R>1 y comparación con
datos de campo (línea negra continua); d) Distribución de zostera marina obtenida con la ecuación de
limitación de profundidad (Duarte et al., 2007). El área blanca representa las zonas donde se descarta la
presencia de zostera marina (P/R<1), el área verde es el hábitat potencial de zostera marina (P/R>1) y la
línea negra continua delimita el área de presencia de zostera marina medida en la campaña de campo.
61
Resumen en español
4.4 Escenarios de carga de nitratos y subida del nivel del mar
El sistema de modelado propuesto se aplicó a West Falmouth Harbor para evaluar los
efectos de reducción de nitratos y de subida del nivel del mar en la concentración de
clorofila-a y en el hábitat potencial de zostera marina. El periodo de simulación fue de
dos meses, correspondientes a Julio y Agosto de 2012. Se simuló una disminución gradual
prevista de la carga de entrada de nitratos y una subida en el nivel del mar basada en las
predicciones del IPCC (IPCC, 2007) para los próximos cien años.
Primero, se analizaron los efectos de la reducción de nutrientes (NR) y de la subida del
nivel del mar (SLR) por separado, después se configuraron escenarios combinados (CS)
para evaluar el efecto simultáneo de ambos parámetros (Figura 18). Los resultados
apuntan a una potencial recuperación de la zostera marina en Snug basada en las
condiciones lumínicas, cuando la carga de nitratos se haya reducido un 50% (Figura 18;
NR 50). El ratio P/R mejora considerablemente en Snug con una reducción de nitratos
del 75% (NR 75). Por el contrario, la subida del nivel del mar provoca una reducción del
ratio P/R en áreas donde hay presencia de zostera marina, como puede verse en SLR
2112. No obstante, cuando ambos efectos (SLR y NR) se estudian en conjunto, se observa
una clara relación entre su comportamiento combinado (CS) y la respuesta del sistema a
la reducción de nitratos (NR). Por lo tanto, aunque la subida del nivel del mar incremente
el nivel de agua y reduzca la penetración de la luz, este factor no es tan significativo como
el de las cargas de nitrógeno. Esto es evidente comparando la variación temporal de P/R
debido al aumento del SLR y a la reducción de la carga de nitratos (Figura 19). En Snug,
la Figura 19 muestra una variación de P/R desde 0.97 hasta 1.14 debido a esta reducción,
mientras que el efecto de la atenuación de la luz debido al SLR hace decrecer P/R desde
0.97 hasta 0.94. Un efecto similar, debido al SLR, puede observarse en Outer, con una
variación de P/R desde 1.05 hasta 1.01 (Figura 19). Sin embargo, el efecto de la reducción
de nitratos es más bajo en Outer, con un P/R entre 1.05 y 1.10, debido a los menores
niveles de clorofila-a en este punto.
62
Resumen en español
Figura 18. Variación espacial de P/R debido a la reducción de nutrientes (NR), al aumento del nivel del
mar (SLR) y a los escenarios combinados (CS).
63
Resumen en español
Figura 19. Variación de P/R debida a la reducción de nutrientes y aumento del nivel del mar.
Un comportamiento similar se obtuvo en la concentración de clorofila-a, ya que la
reducción de nutrientes es en general el factor principal que provoca una disminución en
los niveles de clorofila-a en sistemas eutróficos, mientras que el aumento del nivel del
mar afecta a la eutrofización pero de una manera menos relevante (ver Figura 20). De
hecho, el descenso de clorofila-a promedio debido a la reducción de nutrientes en el
escenario final de NR es del 80%, mientras que esta disminución debida al aumento del
nivel del mar, en el escenario SLR final es solo del 24% (ver Figura 20). Este
comportamiento puede también observarse en la Figura 21, donde se presenta la
distribución espacial de clorofila-a para los diferentes escenarios (ver Table 4.5 Chapter
IV para una explicación de la nomenclatura de los escenarios). La figura presenta una
influencia dominante de la reducción de nutrientes en los escenarios combinados, y
también una relación inversamente proporcional entre la distribución de clorofila-a y la
presencia de zostera marina, que puede ser observada comparando las Figuras 18 y 20,
ya que la clorofila-a es uno de los principales factores que limitan la disponibilidad de
luz.
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Resumen en español
Figura 20. Variación de clorofila-a debido a la reducción de nitratos y al SLR.
Adicionalmente, el efecto combinado del aumento del nivel del mar ha contribuido a una
disminución significativa de concentración de clorofila-a y Kd, especialmente en Snug,
con una reducción desde 27.77 µg L-1 hasta 2.79 µg L-1 y desde 0.79 m-1 hasta 0.36 m-1
respectivamente (Figura 22). En consecuencia, P/R en Snug crece desde 0.97 hasta 1.09,
proporcionando las condiciones adecuadas para el crecimiento de zostera marina a partir
de CS_3. Por otra parte, P/R en Outer incrementa ligeramente hasta CS_4, donde alcanza
un valor máximo de 1.07, y decrece hasta 1.05 en CS_5 debido a la influencia del aumento
del nivel del mar. También se obtuvo una evolución del área potencial de zostera marina
en el estuario para diferentes escenarios combinados (Figura 22). Se obtuvo un
incremento del 8% en CS_1, teniendo un crecimiento acumulado del 21% y del 34% en
CS_2 y CS_3 respectivamente. En el caso de CS_4 y CS_5 la influencia del aumento del
nivel del mar hace que la evolución sea más lenta, obteniéndose un incremento del área
desde CS_4 hasta CS_5 de solamente un 3%, teniendo en CS_5 un crecimiento
acumulado de área del 45% con respecto al escenario original (CS_0). Por lo tanto, los
resultados muestran que en este sistema las reducciones de carga de nitratos tendrán más
influencia que el aumento del nivel del mar. No obstante, en otros sistemas con baja carga
de nitratos el aumento del nivel del mar puede ser más relevante.
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Resumen en español
Figura 21. Variación espacial de clorofila-a debido a la reducción de nutrientes (NR), aumento del nivel
del mar (SLR) y escenarios combinados (CS).
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Resumen en español
Figura 22. Clorofila-a, Kd, P/R y variación del área de z. marina para los escenarios combinados.
4.5 Discusión
Los resultados de este estudio soportan la idea de que cuando llega una cantidad de luz
insuficiente a la superficie de la pradera marina la presencia y/o extensión de esta pradera
disminuye, lo cual está de acuerdo con otros estudios previos, por ejemplo: Dennison,
1987; Orth and Moore, 1988; Duarte, 1991; Duarte et al., 2007. Además, aunque hay
relativamente pocos ejemplos de praderas de zostera marina que se hayan recuperado
siguiendo una política únicamente de reducción de nitratos (Burkholder et al. 2007), los
resultados obtenidos en este estudio sugieren que con una reducción progresiva de
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
CS_0 CS_1 CS_2 CS_3 CS_4 CS_5
Kd
(m-1
)
Scenarios
Outer Snug
0.00
5.00
10.00
15.00
20.00
25.00
30.00
CS_0 CS_1 CS_2 CS_3 CS_4 CS_5
Chlo
rophy
ll-a
(µ
gL
-1)
Scenarios
Outer Snug
0.90
0.93
0.95
0.98
1.00
1.03
1.05
1.08
1.10
1.13
CS_0 CS_1 CS_2 CS_3 CS_4 CS_5
P/R
Scenarios
Outer Snug
0.00
10.00
20.00
30.00
40.00
50.00
60.00
CS_0 CS_1 CS_2 CS_3 CS_4 CS_5
Sea
gra
ss A
rea
Var
iati
on (
%)
Scenarios
Acumulated
Variation per scenario
67
Resumen en español
nitratos, Snug Harbor podría recuperarse desde una perspectiva estrictamente de mejora
de sus condiciones lumínicas, ya que la concentración de clorofila-a, debida
principalmente a la presencia de fitoplancton en sistemas con problemas de eutrofización,
es uno de los principales factores que atenúan la luz en el sistema. Otros factores como la
competición de las macroalgas y los cambios morfodinámicos también podrían tenerse
en cuenta. Por ejemplo, en South Cove, podrían estudiarse efectos como la competición
con macroalgas oportunistas, o la muerte de la zostera marina por perturbaciones
naturales.
La recuperación potencial de zostera marina en Snug Harbor podría ser posible debido al
hecho de que las presiones antropogénicas que afectan a esa zona principalmente se basan
en aportes de nitratos que podrían reducirse, y a que la cobertura de macroalgas es mínima
en esa zona. Además, también se obtuvo que la concentración de nutrientes y por lo tanto
la eutrofización son procesos que controlan la distribución de la zostera marina en el
estuario estudiado, debido a la atenuación de la luz producida por esta eutrofización, lo
cual está de acuerdo con Burkholder et al. (2007) y con Costa (1988). Sin embargo,
aunque la distribución de la zostera marina es altamente sensible a la eutrofización y a la
atenuación de la luz, también está afectada por otros factores, tales como la hipoxia, el
crecimiento de epifitos, el herbivorismo, y la interacción entre la hidrodinámica y la
vegetación, que no están incluidos en este modelo ya que tiene ciertas limitaciones.
La aproximación de modelado propuesta resuelve el patrón espacial del hábitat potencial
de la zostera marina desde el punto de vista de las condiciones lumínicas, y de la
influencia que la eutrofización tiene en las mismas. En contraste a la mayoría de los
modelos ecológicos existentes, esta implementación acoplada computa la atenuación
espectral de la luz como una función de diferentes sustancias atenuantes. Además, como
el modelo irradiancia-biogeoquímico que se ha acoplado está integrado en ROMS, que es
un sistema de modelado flexible de código abierto, esta técnica puede ser utilizada en un
amplio rango de aplicaciones. Por ejemplo, podría servir para estudiar la influencia de la
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resuspensión y del transporte de sedimentos en la disponibilidad de luz. Otra posible
aplicación sería analizar las variaciones de las condiciones lumínicas debidas al CDOM
producido por riadas y escorrentías. Todo esto es posible debido a que el ROMS está
acoplado al Sistema de Modelado de la Comunidad de Transporte de Sedimentos
(CSTMS) por lo que la interacción dinámica entre sedimentos y disponibilidad de luz
podrían ser modelados con esta implementación.
4.6 Conclusiones
En el presente estudio se ha desarrollado un nuevo sistema de modelado para SECS
eutróficos, que es capaz de describir la concentración de clorofila-a, la disponibilidad de
luz y la recuperación potencial de comunidades de zostera marina bajo escenarios futuros
de cargas de nutrientes y de subida del nivel del mar desde el punto de vista de las
condiciones lumínicas. Se ha evaluado el modelo en un SECS eutrófico, y se ha descrito
la variabilidad espacial de la clorofila-a, la atenuación de la luz, y la potencial
presencia/ausencia de zostera marina.
La implementación acoplada computa la atenuación espectral de la luz en el agua como
una función de diferentes sustancias atenuantes con alta resolución vertical y horizontal,
lo que permite la determinación con exactitud del ambiente lumínico en las praderas de
zostera marina. Se encontró que, en general, el aumento del nivel del mar reducirá la
disponibilidad de la luz en el agua, y se espera que esto tenga un impacto negativo en las
praderas de zostera marina; presentándose una reducción del 11.4% en el área de
presencia de zostera marina con un aumento del nivel del mar de 0.35 m. Sin embargo,
en el SECS estudiado, la reducción de la carga de nutrientes es el factor que mayor efecto
tiene en la mejora de la disponibilidad de luz, ya que la eutrofización debida a la carga de
nutrientes es uno de los principales problemas del sistema. De hecho, los resultados
obtenidos muestran que el hábitat de zostera marina se expande en un 42.3 % con un 94
% de reducción de carga de nitratos. Este estudio contribuye a los esfuerzos de modelado
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de eutrofización existentes aportando una nueva implementación que es capaz de evaluar
el hábitat potencial de zostera marina en términos de disponibilidad de luz con
formulaciones sencillas, computando resultados fácilmente interpretables con un alto
nivel de exactitud.
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Capítulo V. Conclusiones y futuras líneas de investigación
Según el objetivo general y los objetivos específicos establecidos, se desarrollaron dos
modelos para predecir el comportamiento de SECS hipereutróficos y eutróficos
respectivamente. Para evaluar las habilidades de las herramientas desarrolladas fueron
utilizados datos de campo procedentes de campañas de las zonas bajo estudio. Los
resultados obtenidos permiten la extracción de las siguientes conclusiones con respecto a
las características de los modelos, al análisis de sensibilidad, a las formulaciones de luz,
a la calibración y a los resultados obtenidos.
5.1 Conclusiones generales
Los modelos ecosistémicos simplificados con alta resolución espacial pueden
caracterizar la eutrofización en sistemas costeros semi-encerrados, debido a
la alta variabilidad que presenta la distribución del fitoplancton en estos
sistemas. Por lo tanto, la resolución espacial es uno de los factores principales
a tener en cuenta en el diseño de un modelo de eutrofización para SECS.
Los modelos ecosistémicos requieren un mayor nivel de complejidad para
describir SECS eutróficos que hipereutróficos, dado que en el primer caso
deben tenerse en cuenta un mayor número de procesos al tener estos una más
difícil simplificación. Por ello, los SECS hipereutróficos pueden ser descritos
por modelos con un nivel inferior de parametrización que en el caso de los
eutróficos.
Los modelos ecosistémicos simplificados con una demanda de datos no tan
alta como los tradicionales pueden ser herramientas eficaces para calcular
fácilmente resultados interpretables con un alto nivel de exactitud si las
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ecuaciones principales y consiguientes variables y parámetros están bien
seleccionados y definidos.
El nuevo modelo simplificado desarrollado en la presente Tesis fue capaz de
caracterizar la eutrofización en un SECS hipereutrófico fuertemente regulado,
que es uno de los casos existentes más complejos, describiendo con alta
resolución espacial y temporal la evolución de la concentración de clorofila-
a durante un año.
El acoplamiento de modelos es una herramienta eficaz y flexible para
describir procesos ecológicos específicos en sistemas complejos. De hecho,
este estudio contribuye a los esfuerzos de modelado existentes
proporcionando una nueva implementación, acoplamiento y conexión de
modelos que no sólo describe la concentración de clorofila-a, sino también el
hábitat potencial de zostera marina en términos de la disponibilidad de luz en
SECS eutróficos.
El modelo hidrodinámico es crítico para el correcto desempeño de los
modelos ecológicos y debe describir con precisión el comportamiento del
sistema. Una adecuada representación de la hidrodinámica es fundamental
para el análisis de SECS, debido a su singular conexión con los sistemas
adyacentes. Por lo tanto, los modelos y condiciones hidrodinámicas han sido
cuidadosamente seleccionadas en el presente estudio.
5.2 Conclusiones del análisis de sensibilidad
Los parámetros para los cuales la clorofila-a es altamente sensible en el
sistema hipereutrófico estudiado son la tasa de respiración endógena del
fitoplancton, la tasa de herbivorismo del zooplancton, la tasa de crecimiento
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de fitoplancton, el flujo de fósforo soluble reactivo del sedimento a la columna
de agua y la proporción de carbono-clorofila-a. Mientras que en el sistema
eutrófico fueron la tasa de crecimiento del fitoplancton, la pendiente inicial de
la curva que relaciona la fotosíntesis con la intensidad de luz para el
fitoplancton (curva PI, Photosynthesis-Light Intensity curve), la tasa de
herbivorismo del zooplancton, la muerte de fitoplancton, el parámetro de
agregación, y la tasa de mortalidad del zooplancton. Se puede concluir por
tanto, que en ambos casos los parámetros más influyentes son los relacionados
con el crecimiento y muerte del fitoplancton y del zooplancton, menos en el
estuario eutrófico, donde la pendiente inicial de la curva PI parece tener mayor
relevancia ya que los efectos de la luz siguen siendo importantes en los
sistemas eutróficos.
El análisis de sensibilidad ha demostrado a su vez ser una herramienta valiosa
para reducir el número de parámetros que deben ser ajustados en los modelos.
5.3 Conclusiones del modelado de luz
La disponibilidad de luz en SECS eutróficos e hipereutróficos está
influenciada principalmente por la concentración de clorofila-a.
Las condiciones lumínicas podrían mejorarse reduciendo la carga de
nutrientes en SECS eutróficos con eutrofización cultural, mientras que la
restauración de los sistemas hipereutróficos requiere un análisis más complejo
que involucra también las condiciones hidrodinámicas y los flujos de
sedimentos.
La implementación de la atenuación espectral de la luz en el agua en modelos
ecosistémicos en función de diversas sustancias, con alta resolución horizontal
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y vertical, permite una determinación de las condiciones lumínicas con mayor
exactitud que las formulaciones tradicionales. En este estudio, se han
implementado las formulaciones de atenuación espectral de la luz de Gallegos
et al. (2011) en el modelo de Fennel et al. (2006) que está acoplado al
ROMS. Esta configuración permite ser conectada con un modelo bio-óptico
de praderas marinas, como por ejemplo el modelo de Zimmerman (2003) ya
que este tipo de modelos necesita el PAR espectral como dato de entrada.
El presente estudio revela que un modelo tridimensional con formulaciones
de atenuación espectral de la luz es necesario en un SECS eutrófico para
describir con exactitud la heterogeneidad y el ambiente lumínico del sistema,
mientras que un modelo bidimensional con atenuación no-espectral fue
utilizado con éxito en un SECS hipereutrófico debido a la baja penetración de
la luz a través de la columna de agua en estos sistemas.
5.4 Conclusiones de calibración y resultados
La incertidumbre promedio de la predicción del modelo hipereutrófico fue de
menos del 6%, con dos coeficientes de correlación de Pearson de 0.93 y 0.92
para la calibración y validación respectivamente, y un coeficiente de eficacia
de Nash-Sutcliffe de 0.96, que son valores excelentes.
El sistema de modelado eutrófico reproduce con precisión la variabilidad
espacial de la clorofila-a y la atenuación de la luz con errores RMS de 3.72 µg
L-1 y de 0,07 m-1 respectivamente.
Se ha propuesto un criterio de hábitat potencial de zostera marina basado en
el ratio entre Producción/Respiración (P/R). Se ha asumido que para áreas
donde P/R>1 existe crecimiento de vegetación acuática sumergida y por lo
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tanto presencia potencial de la misma, delimitando este umbral el hábitat
potencial de zostera marina en el estuario. Sin embargo, para zonas donde
P/R≤1 asumimos que las condiciones son desfavorables para la presencia de
praderas zostera marina ya que el crecimiento sería limitado, siendo la
respiración más grande que la producción en esas áreas. Los resultados
obtenidos con el criterio de P/R seleccionado para evaluar el hábitat potencial
de zostera marina presentan una coincidencia de un 73.39% entre los
resultados modelados y los datos de campo.
Se ha contribuido a una mejor comprensión de los impactos del cambio
climático en SECS eutróficos. Se ha encontrado que, en general, el aumento
del nivel del mar reducirá la disponibilidad de luz y se espera que esto tenga
un impacto negativo en las praderas de zostera marina. En West Falmouth
Harbor el modelo muestra una reducción de 11.4% de hábitat potencial de
zostera marina con un aumento en el nivel del mar de 0.35 m.
Se ha concluido que la reducción de nutrientes puede ser el factor principal
necesario en la restauración de sistemas eutróficos, mientras que en los
hipereutróficos la solución es más compleja. En los SECS eutróficos
estudiados en esta Tesis, la reducción de la carga de nitratos es un factor
principal en la mejora de la disponibilidad de luz. La concentración de
clorofila-a se reduce un 89.3% mientras que el hábitat zostera marina se
expande en un 42.3%, con una reducción de un 94% de la carga de nitratos.
Según lo demostrado por el balance de masas calculado, en un SECS
hipereutrófico las cargas de entrada pueden ser superiores a las cargas de
salida, y la limitada conexión con el mar magnifica la eutrofización del
sistema. Además, el flujo SRP del sedimento a la columna de agua contribuye
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al mantenimiento de concentraciones elevadas de clorofila-a en el área
estudiada. Por lo tanto, en SECS hipereutróficos, la reducción de nutrientes
podría no tener un impacto significativo en la restauración del sistema si no
cambian las condiciones hidrodinámicas y las características de los
sedimentos del mismo.
Un modelo bio-óptico se ha conectado con éxito al modelo de eutrofización y
al ROMS, demostrando la flexibilidad de este sistema para describir procesos
específicos, como la relación entre la producción y la respiración de la
vegetación acuática sumergida.
5.5 Futuras líneas de investigación
Esta Tesis ha puesto de manifiesto algunas limitaciones en los modelos desarrollados que
abren nuevas líneas de estudio. A continuación se mencionan los aspectos más relevantes
de la Tesis que necesitan una futura investigación.
Los modelos desarrollados podrían aplicarse a otras zonas de estudio con
datos de campo disponibles. Esto sería útil para consolidar la eficacia y la
utilidad de las herramientas de modelado desarrolladas.
Del mismo modo, se plantea la conveniencia de diseñar y evaluar estrategias
de gestión factibles que puedan mejorar el estado de la Albufera de Valencia,
teniendo en cuenta tanto las características del sistema como los factores
económicos que indicen en la zona de estudio, tales como el cultivo del arroz,
las actividades industriales y el turismo entre otros.
También sería interesante implementar el modelo simplificado hipereutrófico
en el ROMS para expandir la técnica desarrollada y hacer el código abierto
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para la comunidad científica como se está haciendo con el sistema de
modelización eutrófico.
Además se podría continuar investigando la influencia de la concentración de
nitratos en el crecimiento del fitoplancton. Dicha influencia también podría
incluirse en el modelo simplificado para SECS hipereutróficos, para poder
extender su aplicación a sistemas donde el nitrógeno sea el nutriente limitante.
Convendría que futuros trabajos sobre el sistema de modelización eutrófico
incorporaran otras comunidades ecológicas como macroalgas y epifitos, para
ser capaces de estudiar la competición de las praderas marinas con macroalgas
oportunistas, y la influencia de los epifitos en la atenuación de la luz.
Otro factor que también podría ser de interés investigar en el sistema de
modelado eutrófico es la anoxia debida a la eutrofización. El contenido de
oxígeno en las praderas marinas es fuertemente dependiente de la fotosíntesis
y la respiración, que han sido computadas en el modelo como una función de
luz y temperatura. La concentración de oxígeno también podría ser calculada
por el modelo de eutrofización, y ser usada por el modelo bio-óptico, ya que
niveles bajos de oxígeno podrían limitar el crecimiento de la vegetación
acuática sumergida y su producción primaria. Por lo tanto, un término de
oxígeno debería ser incluido en el modelo bio-óptico para modificar la
formulación de la producción y respiración de las plantas basándose en la
concentración de oxígeno.
Se podría profundizar también en el estudio de la captura de partículas por
parte de la vegetación acuática sumergida y su efecto sobre las condiciones
lumínicas. Las praderas marinas disminuyen la velocidad del flujo y reducen
la turbulencia. Esto provoca la sedimentación de las partículas suspendidas en
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el agua en la zona de las praderas marinas, consiguiendo una mejora de las
condiciones lumínicas. Además, dado que en la presente Tesis se ha integrado
un modelo de irradiancia espectral en ROMS, que es un modelo de código
abierto flexible, podría evaluarse la influencia de la resuspensión y del
transporte horizontal del sedimento en la disponibilidad de luz. Esto es posible
debido a que el ROMS está acoplado al Sistema de Modelado de Transporte
de Sedimentos de la Comunidad Científica (CSTMS) por lo que la interacción
dinámica entre los sedimentos y la disponibilidad de luz podría ser modelada
con esta implementación.
Otra posible aplicación del sistema de modelado acoplado para SECS
eutróficos sería analizar la variación de las condiciones lumínicas debida a
cambios espaciales en el CDOM causados por aumentos en los flujos de los
ríos, escorrentía terrestre y/o procesos microbianos. Esto es de nuevo posible
debido al hecho de que ROMS está acoplado al CSTMS.
Otra mejora en el sistema de modelado podría ser el acoplamiento del modelo
de Zimmerman al resto del sistema para poder computar el hábitat potencial
de zostera marina al mismo tiempo que el PAR espectral y la clorofila-a, ya
que en el presente estudio el modelo de Zimmerman se ha conectado al resto
de los modelos acoplados, pero no ha llegado a acoplarse.
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Chapter I Introduction and research background
Chapter I
1 Chapter I. Introduction and research background
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Chapter I Introduction and research background
Chapter I. Introduction and research background
1.1 Motivation of the research
Semi-enclosed coastal systems (SECS) include coastal lagoons and transitional waters
(Newton et al., 2013) and are not only important ecological systems, but also have
considerable socio economic value (Lassere, 1979). In recent years, there has been
increasing recognition of the economic importance of SECS through their provision of
ecosystem services. However, these services are increasingly threatened as SECS are
among the most vulnerable coastal systems to both natural and human pressure
(Eisenreich, 2005). Moreover, the geomorphology of SECS makes them particularly
vulnerable to global changes, such as sea-level rise, increased temperatures, storminess,
droughts, floods and changes in sediment dynamics (Newton et al., 2013). Additionally,
human activities cause changes in demographics, urbanization, agriculture and land-use,
as well as industrial development and shipping that affect the structure and function of
these vulnerable and valuable coastal ecosystems. These systems are characterized by
their hydrodynamic exchange properties with the adjacent open sea, and can be classified
as open, leaky, restricted or choked (Kjerfve, 1994; Newton et al., 2013). Some examples
of different kind of SECS can be seen at Figure 1.1.
SECS are complex ecosystems characterized by a natural high spatial variability and high
productivity. They support a rich indigenous fauna and flora because they are sheltered
and, in most cases, shallow water systems of high productivity. Moreover, SECS are
important feeding and nesting sites for a multitude of bird species, as well as important
stop-over sites for bird migration (European Commission, 2009). The range of ecosystem
services provided by SECS is extensive and includes provisioning services, also known
as ecosystem goods, such as fish and shellfish; supporting services, such as oxygen
production from photosynthesis; and cultural services, such as recreation and ecotourism
(Millenium Ecosystem Assessment, 2005). The ecosystem functions of SECS provide
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Chapter I Introduction and research background
essential services including decomposition, nutrient cycling, and nutrient production.
They also function in the regulation of fluxes of nutrients, water, particles, and organisms
to and from land, rivers, and the ocean. SECS serve as buffers, sinks and transformers,
for example due to processes like sedimentation, transformation or de-nitrification.
Figure 1.1. Examples of different kind of SECS: a) Open (Bay of Wismar, Germany); b) Leaky (Venice
Lagoon, Italy); c) Restricted (Quanzhou Bay, China); d) Choked (Étang de Thau, France).
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Chapter I Introduction and research background
It is also important to remark that SECS help to protect the adjacent sea from
eutrophication and pollution, retaining riverine nutrients. However, these valuable
ecosystems are being subject to strong anthropogenic pressures due to tourism, heavy
shellfish or fish farming, wastewater discharges, pollution and urbanization. Moreover,
one of the consequences of the anthropogenic pressures on these systems has been the
loss of habitats such as seagrasses that can act as nurseries and trap particles. Therefore,
the ongoing eutrophication produced in these systems by the anthropogenic pressures and
climate change are a threat to future structure and function of SECS.
Eutrophication is the process by which a body of water acquires a high concentration
of nutrients, especially phosphates and nitrates. These typically promote excessive growth
of algae. As the algae die and decompose, the oxidation of this organic matter and the
respiration by the decomposing organisms can deplete the water of available dissolved
oxygen, causing the death of other organisms, such as fish (Art, 1993). This nutrient
enrichment may occur naturally or can be the result of human activity, then the process it
is called “cultural eutrophication”, and it is usually provoked by fertilizers runoff and
sewage discharge (Lawrence et al., 1998). Eutrophication is a natural slow-aging process
for a water body, but human activity sometimes greatly speeds up the process.
Many human activities result in water pollutants, with the main sources of eutrophication
being discharged from urban wastewater treatment and agricultural pollution. During the
last century, population growth, wastewater production and discharge from urban areas
(point sources) resulted in a marked increase in water pollution. On the one hand, in spite
of the improvements on wastewater treatment in recent years, and the optimization of
industrial production processes, pollution discharges are today sadly still related to
population growth and economic growth. On the other hand, agriculture is a key source
of diffuse pollution, as agricultural production is becoming increasingly intensive, with
high input of fertilizers and pesticides, in turn resulting in significant loads of pollutants
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Chapter I Introduction and research background
to the water environment through diffuse pollution and/or irrigation channels. All these
human activities can provoke different trophic status in an aquatic system.
Consequently, the study and determination of the trophic status of a coastal ecosystem is
an important issue due to the effects that this process has in semi-enclosed coastal
ecosystems, and because aquatic plants and animals react to changes in their environment
caused by changes in water quality. For example, it changes the function of the semi-
enclosed lagoons and embayments as buffer zones, and contribute to the proliferation of
macroalgal mats that outcompete with perennial benthic producers, such as seagrasses.
Therefore, in order to characterize the stage at which this process is at any given time in
a particular water body we have the ‘trophic status’ (see Figure 1.2). For this purpose, the
following terms are used (OCDE, 1982; Walmsley, 2000):
Oligotrophic: low nutrient levels and not productive in terms of aquatic animal
and plant life.
Mesotrophic: intermediate levels of nutrients, fairly productive in terms of
aquatic animal and plant life and showing emerging signs of water quality
problems.
Eutrophic: rich in nutrients, very productive in terms of aquatic animal and plant
life and showing increasing signs of water quality problems.
Hypertrophic: very high nutrient concentrations where plant growth is
determined and limited by physical factors. Water quality problems are serious
and almost continuous.
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Figure 1.2. Trophic status.
Not surprisingly, the status that cause more concern are eutrophic and hypertrophic,
which are the ones that produce more severe consequences on the ecosystems.
Additionally, the processes that rule these systems have similarities and differences, and
a well understanding of these processes is important for the management and restoration
of their ecosystem services. In fact, one of the main differences between these systems is
the rule of light on the system’s productivity. In eutrophic systems the light can still
penetrate through the water column as the chlorophyll-a concentration still allows it, and
submerged aquatic vegetation such as seagrasses can live, although its growth could be
limited by light. However, in a hypertrophic system the phytoplankton and consequently
the chlorophyll-a concentration in the water surface is so high, and the system is so
impaired that the penetration of light through the water column is very limited. Therefore,
in hypertrophic systems the survival of seagrasses is unlikely as they have high light
requirements, and they are usually replaced by opportunistic macroalgae with lower light
requirements. Moreover, the overabundance of nutrients also produces a higher epiphytes
growth that also cause light attenuation (Figure 1.3).
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Chapter I Introduction and research background
Figure 1.3. Seagrass loss and pressures
Therefore, regarding seagrass growth it is important to take into account the spectral
character of light in order to accurately reproduce the photosynthesis process and the
biomass production. This is due to the fact that from the radiation that reaches the water
surface, only the ones of wavelengths between 400 and 700 nm can be used for
photosynthesis, and are called the Photosynthetic Active Radiation (PAR). However, in
the water column this light is limited first due to atmospheric processes such as ozone and
cloudiness that limit the amount of light that reaches the water surface, and then by the
water depth, chlorophyll-a and phytoplankton pigments, turbidity, and Coloured
Dissolved Organic Matter (CDOM) between others. Moreover these substances produce
light absorption, scattering and backscattering, changing the light field that reaches the
plant, and limiting their growth. Therefore, it is important to accurately determine the
light climate in systems with submerged aquatic vegetation where the habitat is
vulnerable to disappear as is the case of eutrophic systems, whereas in the case of
hypertrophic systems this processes are not so relevant due to the scarce influence of light
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in the water column as most of it is absorbed at the water surface by the present high
levels of chlorophyll-a concentration and the lack of life in the bottom of these systems.
Moreover, the three-dimensional character that eutrophic systems, as they are usually
very shallow and the life on the bottom is very impaired. In these systems, the differences
between bottom and water column could be simplified assuming a well-mixed water
column, due to the fact that there is not usually stratification in the vertical direction in
these systems. However, in eutrophic systems there is still life in the bottom and complex
processes happened there, therefore it is important in this kind of system to accurately
describe the three-dimensional character of the system. However, both kind of systems
present a high horizontal variability as the horizontal gradients of the phytoplankton
distribution are usually very high as they are strongly dependent on the nutrient loading.
Another aspect that produces light attenuation in eutrophic systems is the sea-level rise
(SLR), which could modify the seagrass habitat in the long term (Duarte, 2002). When
the sea-level rises the light climate changes as the water depth increases. Therefore,
seagrass meadows distribution can change. In fact, the deeper parts of the system present
less light availability and seagrass will disappear from this areas, whereas in the shallower
areas new habitat could be created and some seagrass will migrate to those areas as can
be seen in Figure1.4. However, the new habitat creation will be limited in each system by
other factors such as physical barriers, unsuitability of the substrate, shoreline
constructions, increase of salinity, temperature and species competition (Hauxwell et al.,
2003). Therefore, although the light conditions would be good for seagrass growth, there
could be other parameters that could change the species distribution. As a conclusion,
seagrass will disappear due to sea-level rise as overall effect, although some migration is
possible.
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Figure 1.4. Seagrass changes due to sea-level rise (SLR)
The complexity and spatial variability of these systems makes the study of the cause-
effect relationships between the different human actions and hydrographical,
hydrodynamical and ecological processes a difficult task, and complex models are widely
used for SECS independently of the level of eutrophication presented by the system (e.g:
Fulton et al., 2004; Zouiten, 2012). Therefore, there is a need of developing specific
simplified mathematical tools for the management of eutrophic and hypertrophic SECS,
taking into account the system hydrodynamics, the specific processes governing each
system and a proper space and time resolution. In recent years, coupled-linked models
have been demonstrated to be a good solution to integrate the main system processes,
simplifying the modelling approach, and optimizing computational resources (Mainardi
et al., 2015).
1.2 History of coupled-linked models in estuaries and SECS
Ecological models of aquatic systems can be traced back to the pioneering work of
Gordon A. Riley (1946, 1947). Riley’s major advancement was to formulate models in
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Chapter I Introduction and research background
which changes in daily growth and loss processes were functions of biomass, abundance
of prey and predators, and environmental variables such as temperature, light, and
nutrients. These independent models of phytoplankton (P) and zooplankton (Z) were then
combined along with a state variable for nutrients (N) to produce the first coupled NPZ
model of an aquatic system (Riley et al., 1949).
The next major phase in the application of ecological models to aquatic systems came
during the 1960s and 1970s with the development of models primarily for estuaries, shelf
ecosystems, and lakes. Early examples included Di Toro et al. (1971) model of the
Sacramento-San Joaquin Delta, Steele’s (1974) model of the North Sea, and Kremer and
Nixon’s (1978) model of Narragansett Bay. The use and complexity of models continued
to expand in the 1980s to systems such as Chesapeake Bay (HydroQual, 1987), the Baltic
Sea (Stigebrand and Wulff, 1987), and the Ems-Dollard Estuary (Baretta and Ruardij,
1988). These models added complexity, with dissolved oxygen as a state variable and
addition of dissolved organic and particulate forms of nutrients and implementation in a
relatively high resolution, three-dimensional domain. The application of models to
management began in this period, particularly related to anthropogenic nutrient
enrichment and the cultural eutrophication of estuaries (HydroQual, 1987).
Models continued to be developed in the 1990s with an increasing focus on management.
During this period, the developed ecological models included more mechanistic
relationships, a greater number of state variables, and frequently finer spatial scales, and
started to be coupled to other models (Ambrose et al., 1993; Doney et al., 1996; Bissett
et al., 1999a, b). Models also began to include benthic primary producers such as
macroalgae, seagrasses and their epiphytes, and tidal marshes (Bach et al., 1992;
Malmgren-Hansen and Warren, 1992; Vested et al., 1992; Madden and Kemp, 1996;
Solidoro et al., 1997; Buzzelli et al., 1999).
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Chapter I Introduction and research background
After 2000, models became essential management tools, and coupled models
considerably increased in order to better understand the interaction between
hydrodynamic and ecological processes in estuaries. These models included greater
complexity and processes (Fulton et al., 2004b), taking into account toxic chemical
materials (Tetra Tech, 1999), heavy metals (Hipsey et al., 2007), and submerged aquatic
vegetation (SAV) (Burd and Dunton, 2001).
As estuarine ecosystem models incorporated greater complexity and more numerous state
variables, modellers delved into the detail associated with various model components.
For example, the interaction between SAV and drag was investigated using a variety of
numerical approaches (Bouma et al., 2005; Bal et al., 2011), and is still an active area of
research. Another increasing field of interest nowadays are the benthic–pelagic coupled
ecosystem models to estimate hypoxia in estuaries, one of the most relevant recent
examples of these models is the model ECOHYM (Somha et al., 2008), which is a
coupled hydrodynamic-pelagic and benthic ecosystem model that succeeds on describing
the vertical DO profiles during hypoxic season in estuaries. For shellfish modelling,
particle uptake is a rate processes that is often a component of larger models (e.g. Cerco
and Noel, 2005, 2007; and Fulford et al., 2007). In such models, particle availability often
does not vary, although real hydrodynamic processes create varying concentrations.
Published models (Simpson et al., 2007; Petersen et al., 2013; Forsyth, 2014) have
incorporated particle variations into mussels and oyster uptake rates. Other shellfish
models that couple biology with physical processes include those simulating larval
transport (North et al., 2008; Bidegain et al., 2012), where simple rules describing larval
behaviour were shown to have a large effect on the spatial patterns of larval settlement.
All these coupled models have improved our knowledge in different processes in estuaries
in the last decades and continue under constant study and research. This is the case for
example of harmful algal blooms, although we may roughly attribute its occurrence to
excessive nutrients, there could be other mechanism that are under study as life cycle
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Chapter I Introduction and research background
(Hense, 2010), physiology (Hood et al., 2006) residence time and toxins models
(Ramanathan, 2010).
Despite the trend towards increasing model complexity and spatial resolution, there has
been a growing recognition of trade-offs among complexity, resolution, over
parameterization, and model uncertainty. Numerous investigators have addressed the role
of model complexity (Fulton et al., 2003, 2004a; Friedrichs et al., 2006; Ménesguen et al.
2007) and others have embraced the use of simpler, reduced and intermediate complexity,
and alternative modeling approaches (Rigler and Peters, 1995; NRC, 2000; Pace, 2001;
Duarte et al., 2003; Scavia et al., 2006; Swaney et al., 2008), going back to the initial
NPZD models (Fennel et al., 2006) due to its precision.
In order to have a better knowledge about the capabilities, limitations and complexity of
the most widely used models nowadays, a review with the description and main
applications of the most relevant existing models with coupled-linked proved capabilities
that can be used in SECS, estuaries, coastal areas and coastal lagoons, is presented in the
following section. In this section we have focused on hydrodynamic models, on water
quality and ecosystem models able to assess eutrophication and phytoplankton dynamics,
and on bio-optical irradiance models.
1.3 Review of hydrodynamic, water quality, ecosystem and bio-optical
irradiance models with coupled-linking capabilities: description
and applications.
Eutrophication in SECS is a complex environmental problem that can be produced by
different factors (hydrodynamic conditions, anthropogenic pressures, input loads etc.).
This problem can cause severe changes in the ecosystem such as submerged aquatic
vegetation disappearance between others, so there is a clear need for solutions for dealing
with this environmental problem. To achieve this goal, the use of models becomes
indispensable. Models are useful to provide a way to synthesize observations, theoretical
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Chapter I Introduction and research background
knowledge, and information about stocks, flows, rates, and interconnections of many
parts of the system. They are also a useful way to test scientific hypotheses, as each model
equation itself is a hypothesis about the way the system functions can be compared with
observations. Additionally, they allow for the generation of scenarios to be developed and
investigated, and reveal indirect pathways and connections between system components.
Finally, they are also useful tools for decision makers.
Due to the fact that primary producers play a relevant role in eutrophic and hypertrophic
systems, we give particular insight into the models that take into account phytoplankton
dynamics, and the interaction between light and primary producers. Due to the importance
of hydrodynamics in the transport and dispersion of pollutants a common type of model
used in coastal waters is a coupled physical hydrodynamic - water quality or ecological
model. Such models can be used to predict future conditions in a specific region to try to
fully understand the changes that have occurred or will occur, and the main human factors
and natural processes that can explain such changes. Therefore, in the next sections the
main characteristics of the physical hydrodynamic models which have coupled or linked
ecological models and the most important ecological models that could be applied to
eutrophic and hypertrophic semi-enclosed coastal systems are presented.
1.3.1 Hydrodynamic models
In aquatic systems, biological and chemical processes are, to a large degree, controlled
by physical processes. For example, rates of phytoplankton growth, zooplankton grazing,
and organic matter re-mineralization are temperature dependent, and species composition
of the biological community are affected by salinity and temperature. Recent open ocean
modeling studies have emphasized the importance of improving the representation of
physical processes and variability in order to improve the performance of biogeochemical
models (Oschlies and Garcon, 1999; Hood et al., 2003; Friedrichs et al., 2006). In fact, a
high spatial resolution and the capability of nesting or downscaling are important
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Chapter I Introduction and research background
characteristics in the selection of a physical model for its application to SECS. Due to the
importance of reproducing the correct physical conditions in a coupled physical-
biological model for modeling biogeochemical variability, the selection of the physical
model is fundamental issue in eutrophication modeling. Thus, we analyze the main
characteristics of the most widely used physical models with coupled or linked ecological
models in order to determine their suitability for its application to eutrophic and
hypertrophic SECS. The physical models we describe are ROMS, ECOM, FVCOM,
MIKE 3, MOHID, HAMSOM, DELFT3D FLOW, EFDC, ELCOM, TELEMAC, and
H2D/H3D.
1.3.1.1 Regional Ocean Modelling System (ROMS)
Description:
ROMS is a free-surface, three-dimensional, terrain-following, primitive equations model.
It includes accurate and efficient physical and numerical algorithms and several coupled
models for biogeochemical, sediment, and sea ice (Budgell, 2005) applications. The wet
and dry capabilities of ROMS have also been tested by Warner et al (2013), who proved
them to be successful. It also includes several vertical mixing schemes (Warner et al.,
2005a), and multiple levels of nesting and composed grids. ROMS is coupled to series of
biogeochemical models of increasing complexity, from simple 5-component or 7-
component NPZD models (Franks et al., 1986; Fennel et al., 2006; Gruber et al., 2006;
Powel et al., 2006;) to NEMURO, which is a lower trophic level model with 11 state
variables, PISCES with 24 components (Aumont, 2005), or ECOSIM (Bissett et al.,
1999a, 1999b) with 21 state equations and 115 parameters. It has also been successfully
linked to CE-QUAL-ICM (Kim et al., 2011), which is also a complex biogeochemical
model. Moreover, as ROMS is also coupled to the Community Sediment Transport Model
(Warner et al., 2005b; Blaas et al., 2007; Warner et al., 2008) it has also the capability to
take into account the effect of sediment dynamics on water quality .
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Chapter I Introduction and research background
Applications:
The wide range of applications of ROMS coastal and ocean waters (e.g., Haidvogel et al.,
2000; Di Lorenzo, 2003; Dinniman et al., 2003; Marchesiello et al., 2003; Peliz et al.,
2003; Budgell, 2005; Warner et al., 2005a, b; Wilkin et al., 2005) and the number of
coupled systems (Franks et al., 1986; Fennel et al., 2006; Gruber et al., 2006; Powel et
al., 2006; Blaas et al., 2007; Warner et al., 2008; Kim et al., 2011) makes it to be a
pioneering force in ocean and coastal research modelling due to the continued innovative
improvements and new developments that are continuously made by its users.
1.3.1.2 Estuarine and Coastal Ocean Model (ECOM)
Description:
The ECOM is the 3-D finite-difference, primitive equation coastal ocean model
developed based on the original code of the Princeton Ocean Model (POM) (Blumberg
and Mellor, 1987). It was developed principally by Alan Blumberg of HydroQual. It has
incorporated a wet/dry technique. It can be used to simulate tides, tide-induced current,
and salinity distribution due to mixing between freshwater and oceanic water in estuaries.
Applications:
It is good for estuaries and bays, but it is inappropriate to be used for real-time simulation
and coastal management. In particular, ECOM fails to resolve areas with barriers, and
islands. It has been applied by HydroQual to Boston Harbor, New York Harbor and
Onondaga Lake (New York) between others, and it is not generally available as open
source.
1.3.1.3 Finite Volume Coastal Ocean Model (FVCOM)
Description:
FVCOM is an unstructured-grid, finite-volume, free-surface, 3-D primitive equation
coastal ocean circulation model developed by UMASSD-WHOI joint efforts (Chen et al.,
2006). The model consists of momentum, continuity, temperature, salinity and density
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Chapter I Introduction and research background
equations and is closed physically and mathematically using turbulence closure
submodels. The horizontal grid is comprised of unstructured triangular cells and the
irregular bottom is presented using generalized terrain-following coordinates. FVCOM is
solved numerically by a second-order accurate discrete flux calculation in the integral
form of the governing equations over an unstructured triangular grid. This approach
combines the best features of finite-element methods (grid flexibility) and finite-
difference methods (numerical efficiency and code simplicity) and provides a much better
numerical representation of both local and global momentum, mass, salt, heat, and tracer
conservation.
Applications:
The ability of FVCOM to accurately solve scalar conservation equations in addition to
the topological flexibility provided by unstructured meshes and the simplicity of the
coding structure has make FVCOM ideally suited for many coastal and interdisciplinary
scientific applications. The present version of FVCOM includes a water quality module
to simulate dissolved oxygen and other environmental indicators, a 3-D sediment
transport module for estuarine and near-shore applications, a flexible biological module
for the study of food web dynamics. FVCOM is only permitted for use in non-commercial
academic research and education. Moreover it has recently been applied to Tamar Estuary
(Uncles and Torres, 2011), and to the south-western coast of Korea (Lee et al., 2013).
1.3.1.4 MIKE 3
Description:
MIKE 3 is a 3D free surface model that is able to compute associated sediment and water
quality processes. The hydrodynamic module of MIKE 3 (MIKE HD) solves the
equations for the conservation of mass and momentum as well as for salinity and
temperature in response to a variety of forcing functions. MIKE 3 HD simulates the water
level variation and current velocities in response to a variety of forcing functions in lakes,
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Chapter I Introduction and research background
estuaries, bays and coastal areas. The water levels and currents are solved on either a
rectilinear grid, a curvilinear grid, a triangular element mesh or any combination covering
the area of interest. MIKE 3 HD includes formulations for the effects of: convective and
cross momentum, bottom shear stress, wind shear stress at the surface, barometric
pressure gradients, Coriolis forces, density effects, sources and sinks, evaporation and
precipitation, flooding and drying, and hydraulic structures.
Applications:
It is useful for a number of applications: assessment of hydrographic conditions for
design , coastal and oceanographic circulation studies, including fine sediment dynamics,
optimization of coastal outfalls, environmental impact assessment of marine
infrastructures, ecological modelling including optimization of aquaculture systems, lake
hydrodynamics and ecology, coastal and marine restoration projects, analysis and
optimization of cooling water recirculation and desalination. However, MIKE 3 is not
open source, and requires a license for its use.
1.3.1.5 Mohid Water Modelling System (MWMS)
Description:
MOHID is a three-dimensional water modelling system,
developed by MARETEC (Marine and Environmental Technology Research
Center) at Instituto Superior Técnico (IST) which belongs to Technical University of
Lisbon. Its development was started by Neves in 1985, and by that time the model was
bi-dimensional (Mohid 2D) and was used to study estuaries and coastal areas using a
classical finite difference approach. However, in the following years a eulerian and
lagrangian transport model were included in it, and also a turbulence model, a water
quality model, an ecosystem module and an oils spill model. The MOHID modelling
system allows the adoption of an integrated modelling, not only of processes (physical
and biogeochemical), but also of different scales (allowing the use of nested models) and
systems (estuaries and watersheds). MOHID has different tools integrated in tis modelling
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Chapter I Introduction and research background
system, such as MOHID Water, MOHID Land and MOHID Soil, which can be used to
study the water cycle in an integrated approach. It has also coupled MOHID WQ and CE-
QUAL-W2 for water quality and ecological modelling, and it also has and Oil module for
oil spills tracking as mentioned before.
Applications:
Several different coastal and estuarine areas have been modelled with MOHID in the
framework of research and consulting projects. Along the Portuguese coast, different
environments have been studied, from river mouths, and estuaries (Martins et al., 2001;
Saraiva et al., 2007; Oliveira et al., 2015), to coastal lagoons (Vaz et al., 2007). The model
has also been adapted to simulate Galician Rías hydrodynamics, such as Ría de Vigo
(Montero et al., 1999; Montero, 1999) and Ría de Pontevedra (Taboada et al., 2000). Far
from the Atlantic coast of the Iberian Peninsula, some European estuaries have been
modelled, Western Scheldt (Holland), Gironde (France) (Cancino and Neves, 1999) and
Hellingford (Leitão, 1996). Regarding to open sea, MOHID has been applied to the
North-East Atlantic region where some processes including the Portuguese coastal
current (Coelho et al., 1994), the slope current along the shelf (Neves et al., 1998) and the
generation of internal tides (Neves et al., 1998) have been studied, and also to the
Mediterranean Sea to simulate the seasonal cycle (Taboada, 1999) or the circulation in
the Alboran Sea (Santos, 1995). Mohid has also been applied to the Portuguese Monte
Novo, Roxo and Alqueva reservoirs (Braunschweig, 2001).
1.3.1.6 HAMSOM
Description:
The development of the HAMSOM coding goes back to the mid-eighties where it
emerged from a co-operation between Backhaus and Maier-Reimer. From the very
beginning HAMSOM was designed with the intention to allow simulations of both
oceanic and coastal and shelf sea dynamics. It is a primitive equation model and it is
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Chapter I Introduction and research background
defined in Z co-ordinates on the Arakawa C-grid. It has been coupled with two eco-system
models (ECOHAM, ERSEM), an atmospheric model (REMO), and both Lagrangian and
Eulerian models for sediment transport. For polar applications HAMSOM was coupled
with a viscous-plastic thermo-hydrodynamic ice model of Hibler type. Since about 15
years in Hamburg, and overseas in more than 30 laboratories, HAMSOM is already being
in use as a community model.
Applications:
It has been applied to the Kara Sea (Harms, 1997) and to estuaries and bays, as it is the
case of the estuary Rio de la Plata (Meccia et al., unpublished) and to La ría de Vigo
(Souto et al., 2001).
1.3.1.7 Delft3D-Flow
Description:
Delft3D-FLOW is a multi-dimensional (2D/3D) hydrodynamic and transport simulation
program which calculates non-steady flow and transport phenomena that result from tidal
and meteorological forcing on a rectilinear or a curvilinear, boundary fitted grid. In 3D
simulations, the vertical grid is defined following the sigma coordinate approach.
The hydrodynamic conditions (velocities, water elevations, density, salinity, vertical eddy
viscosity and vertical eddy diffusivity) calculated in the Delft3D-FLOW module are used
as input to the other modules of Delft3D, which are: Delft3D-WAVE for short wave
propagation, Delft3D-SED for cohesive and non-cohesive sediment transport, Delft3D-WAQ
for far-field water quality, Delft3D-PART for mid-field water quality and particle tracking,
and Delft3D-ECO for ecological modeling. The hydrodynamics of Delft3D-FLOW are not
coupled to the other sub-models, being externally supplied to them.
Applications:
There are applications of Delft3D to SECS, lakes, rivers, estuaries and coastal
environments. Some examples of its application are to the Wadden Sea (Van Leeuwen et
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Chapter I Introduction and research background
al., 2010), to San Diego Bight (Van Dongeren, 2010), to Swansea Bay (Bent et al., 1991),
to the Gulf of Uraba (Escobar, 2011), to Nam Theun Reservoir (Chanudet et al., 2012)
and to the Bay of Cadiz (Zarzuelo, 2014).
1.3.1.8 Environmental Fluid Dynamics Code (EFDC)
Description:
The Environmental Fluid Dynamics Code (EFDC Hydro) is a hydrodynamic model that can
be used to simulate aquatic systems in one, two, and three dimensions. EFDC uses stretched
or sigma vertical coordinates and Cartesian or curvilinear, orthogonal horizontal coordinates
to represent the physical characteristics of a waterbody. It solves three-dimensional, vertically
hydrostatic, free surface, turbulent averaged equations of motion for a variable-density fluid.
Dynamically-coupled transport equations for turbulent kinetic energy, turbulent length scale,
salinity and temperature are also solved. The EFDC model allows for drying and wetting in
shallow areas by a mass conservation scheme. The physics of the EFDC model and many
aspects of the computational scheme are equivalent to the widely used Blumberg-Mellor
model (Blumberg and Mellor, 1987) and U. S. Army Corps of Engineers' Chesapeake Bay
model. The output of this model will provide all the necessary hydrodynamic inputs to the
Water Quality Analysis Simulation Program (WASP) (Di Toro et al., 1983; Connolly and
Winfield, 1984; Ambrose et al., 1988), QUAL2K from QUAL2E (Brown and Barnwell,
1987), AQUATOX (Park, 1990) and EPD-RIV1 (Martin and Wool, 2002).
Applications:
It has been applied to estuaries, coastal and ocean waters. It is remarkable its application to
St. Lucie Estuary, located on the east coast of south Florida by Ji et al. (2007) and also to
Morro Bay, California (Ji et al., 2001), both are semi-enclosed coastal systems.
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Chapter I Introduction and research background
1.3.1.9 Estuary and Lake Computer Model (ELCOM)
Description:
ELCOM (Estuary and Lake Computer Model) is a numerical modelling tool developed
at Centre for Water Research (CWR) of the University of Western Australia in 1996. It
is a 3D hydrodynamics, thermodynamics and transport model. It applies hydrodynamic
and thermodynamic models to simulate the temporal behaviour of stratified water bodies
with environmental forcing. ELCOM is designed to facilitate modelling studies of aquatic
systems over time scales extending to a few weeks, though the limit of computational
feasibility depends on the size and resolution requirements of an application and
computational resources. ELCOM is suited for comparative studies of the summer and
winter circulation patterns, spring versus neap tidal cycles, or dispersal conditions under
different flow regimes. ELCOM can be run either in isolation for hydrodynamic studies,
or coupled with the Computational Aquatic Ecosystem Dynamics (CAEDYM) for
simulation of biological and chemical processes.
Application:
It can be applied to semienclosed ecosystems (estuaries and coastal lagoons) but the most
important applications are to lakes, such as Lake Kinneret, Israel (Hodges et al., 2000).
However, as the code is not open source, it needs a licence to be used.
1.3.1.10 TELEMAC
Description:
TELEMAC-3D is a three-dimensional (3D) model that uses a horizontally unstructured
mesh of triangular elements with a finite-element or finite-volume computational method.
It can take into account the propagation of long waves with non-linear effects, bed
friction, Coriolis force, the influence of meteorological factors such as atmospheric
pressure and wind, turbulence, torrent and river flows, influence of horizontal
temperature or salinity gradients on density, Cartesian or spherical coordinates for large
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Chapter I Introduction and research background
domains, dry areas in the computational domain and diffusion of a tracer, with source and
sink terms, monitoring of floats and lagrangian drifts and singular point sources such as
pipes.
Applications:
It is an open source model that can be applied to bays, estuaries, coastal waters, seas and
oceans. Some examples of its application are to Atlanta Bay, to the English Channel, to
the beaches of Anglet and the Adour River (Brière et al., 2007), and wind induced
response of the Irish Sea (Jones and Davies, 2006). It is open-source since July 2010.
1.3.1.11 H2D/H3D
Description:
The H2D/H3D are two finite differences hydrodynamic models developed by the
Environmental Hydraulics Institute of the University of Cantabria (Castanedo, 2000).
They solve the two-dimensional/ three-dimensional hydrodynamic equations based on
the Reynolds Averaged Navier Stokes equations (RANS) for incompressible and
unsteady turbulent flows, including the effects of the earth’s rotation, bottom friction and
wind shear. These models involve the solution of the continuity, momentum and transport
equations for salinity and temperature dividing the study area into square cells. The
numerical computation should be carried out on a spatial domain that represents the entire
estuary through a finite-difference grid. The cell dimension is a function of the size of the
study area, and its resolution depends on the desired level of detail (García et al., 2010b).
H2D model has been linked to a medium complexity biogeochemical model (García et
al., 2010a) and to a very complex water quality model (EnvHydrEM) (Zouiten et al.,
2013).
Applications:
H2D has been successfully applied to coastal waters, estuaries and coastal lagoons. They
have been applied to estuaries such as Suances Estuary (Barcena et al., 2012), Urdaibai
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Chapter I Introduction and research background
(García et al., 2010a), and coastal lagoons such as Victoria (Spain) (Zouiten et al., 2013).
H3D has only been applied to Santander’s Bay (Castanedo, 2000).
1.3.1.12 Discussion
In Table 1.1 some of the most important physical models with application to SECS and
with coupled or linked ecological modeling capabilities are shown. All of them can be
used in both three-dimensional and two-dimensional configurations allowing the high
spatial and temporal resolution needed to accurately reproduce the hydrodynamic
processes, substances transport and biogeochemical and ecological processes in SECS.
However, some of them are not open-source, like MIKE 3, HAMSON, ELCOM, ECOM,
EFDC and H2D/H3D. Other models like the FVCOM have the code open only for
research or educational purposes, but not for commercial or management strategies. The
three-dimensional open source existing models are ROMS, TELEMAC, MOHID and
Delft3D, which are widely used models that have been successfully applied to many
different areas and problematics as could be seen in the previous section. These models
have therefore great future and their users are growing every day, and all of them could
be recommended to be used in many kinds of applications. Specifically, ROMS, is an
open source model since its creation, and it has been successfully coupled to several open-
source ecological models of different complexities. The great number of users of these
coupled configurations, the existing interest on improving them, and the flexibility of its
code makes it a good option for developing and testing new coupled or linked modeling
strategies. Moreover, ROMS is coupled to an open-source sediment transport model that
increase its capabilities. Additionally, at the IHCantabria we have been adding processes
and improving the H2D code, in order to have a simpler management tool to solve
different 2D situations. The knowledge of the code and its flexibility and accuracy in its
applications made it also a good tool for management strategies. In fact, its results are
easily linked to ecological models.
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Chapter I Introduction and research background
Therefore, at the present Thesis we have selected ROMS for the development of a three-
dimensional new hydrodynamical-ecological coupled system, and H2D for a two-
dimensional study where the third dimension could be omitted due to the characteristics
of the system under study. Although it is important to remark that nowadays there are
many different models that could be successfully applied in any of the cases mentioned
before.
Table 1.1. Characteristics of physical models with linked or coupled ecological models.
*Only for research or educational purposes
1.3.2 Water quality and ecosystem models
Water quality and ecosystem models are important tools for the study of semi-enclosed
coastal systems. On the one hand, water quality models deal with the basic aspects of
water quality of coastal areas influenced by human activities, namely, the oxygen
depletion as a result of nutrients, organic matter loadings, sediment interaction and fate
of pollutants. On the other hand, ecosystem models are essential tools for the better
knowledge of the coastal waters ecosystems and prediction of their evolution as well as
for their effective management (Jorgensen, 2011). Ecosystem models describe in general
phytoplankton and zooplankton dynamics, nutrient cycling, growth and distribution of
MODEL
CODE
LINKAGED OR COUPLED WATER QUALITY AND ECOSYSTEM MODELS
SEDIMENT TRANSPORT
MODULE
DOWNSCALING
ROMS
FREE EcoSim, PISCES, NEMURO, NPZD Fennel, NPZD Franks, NPZD Powell, NPZD Gruber, CE-QUAL-ICM
YES YES
FVCOM FREE* FBM(NPZ, NPZD), WQ YES YES
ECOM NO WQ YES YES
MIKE 3 NO WQ (ECO LAB) YES YES
MOHID FREE MOHID WATER QUALITY, CEQUALW2 YES YES
HAMSOM NO EcoHam1, EcoHam2, ERSEM NO NO
EFDC NO WASP, QUAL2K, AQUATOX,EPD-RIV1 YES YES
ELCOM NO CAEDYM NO NO
Delft3D FREE DELWAQ YES YES
TELEMAC-3D FREE DELWAQ YES YES
H2D/H3D NO T3DCOLI, T2D8, ENVYDREM NO YES
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Chapter I Introduction and research background
rooted vegetation, and macroalgae. In addition, they allow us to focus on phytoplankton
related processes: the primary production and the oxygen budget within the water column.
Water quality models can be effective tools to simulate and predict pollutant transport in
the water environment. Therefore, water quality models become an important tool to
identify water environmental pollution and the final fate and behaviours of pollutants in
the water environment. However, nowadays water quality models are usually coupled to
ecosystem models in order to take into account more processes. Therefore, their
capabilities have increased in the last decades and sometimes it is very difficult to
distinguish them from ecosystem models. Most of them started being only water quality
models and were afterwards coupled to ecological modules as mentioned before. A
simplified scheme of the main coupling and/or linking possibilities between the different
models can be seen in Figure 1.5. Additionally, some of the most important and widely
applied water quality models are: WASP, CE-QUAL-ICM, DELWAQ, WQ, MIKE3-
WQ.
Figure 1.5. Water quality and ecosystem models coupling and/or linkage possibilities.
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Chapter I Introduction and research background
Ecosystem models capture processes known to be physically and biologically important
determinants of phytoplankton dynamics, which are the driving process of eutrophication.
They also represent some of the most complex models available and so there is a lot of
scope for simplification. Ecosystem models explicitly include complex trophic webs,
nutrient dynamics and recycling. They can also be spatially resolved and include highly
detailed process formulations. The degree of detail employed in the formulation of any
one of these features and the data available for assessing them may have an important
impact upon model behaviour and performance. There are models that try to represent the
entire ecosystem by including all processes in the system, from physics to chemistry, and
plankton to fish. To achieve this, three types of models are usually coupled:
hydrodynamic models, lower trophic level (bacteria, phytoplankton and zooplankton)
models and complex higher trophic level (mainly fish species) models. Some examples
of higher trophic level models are: ERSEM, CAEDYM, PISCES, ECOPATH with
ECOSIM, and IGBEM, which are described in the next section. The complexity of higher
trophic level models due to the great number of state variables and parameters made them
difficult to use and interpret, so nowadays there is a need to build simpler models.
One example of simpler ecosystem models are biogeochemical models, which are
essentially lower trophic level models focusing in single species or a low number of
functional groups, such as NPZ and NPZD models. These models take into account only
the main processes of phytoplankton dynamics and are easier to calibrate and use, having
less parametrization errors. However, they have been equally used to eutrophic systems
without taking into account the level of eutrophication, and they usually only work with
nitrogen as the limitant nutrient of the system. Examples of lower trophic level models
are: ECO LAB, ECOHAM, FBM, MOHID Water Quality Module, CE-QUAL-W2,
NEUTRO, EnvHydrEM, NEMURO, Delft3D-ECO, PPBM, ICOLLS model, SEACOM,
P-Z, NPZ, NPZD, Fasham, Fennel, and SWEM.
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Moreover, many more models have been published over the past few decades, all of
which cannot be described here. The ones below are a representative sample of the most
popular models, which have been applied equally to eutrophic and hypertrophic coastal
systems, and have been coupled or linked to hydrodynamic models.
1.3.2.1 Water Quality Analysis Simulation Program (WASP)
Description:
The Water Quality Analysis Simulation Program (WASP7), is an enhancement of the
original WASP (Di Toro et al., 1983; Connolly and Winfield, 1984; Ambrose et al.,
1988). This model helps users to interpret and predict water quality responses to natural
phenomena and man-made pollution for various pollution management decisions. WASP
is a dynamic compartment-modelling program for aquatic systems, including both the
water column and the underlying benthos. WASP allows the user to investigate 1, 2, and
3 dimensional systems, and a variety of pollutant types. The time varying processes of
advection, dispersion, point and diffuse mass loading and boundary exchange are
represented in the model. WASP also can be linked with hydrodynamic and sediment
transport models that can provide flows, depths velocities, temperature, salinity and
sediment fluxes. WASP has an ecosystem module called EUTRO that was specifically
designed for the assessment of processes impacting eutrophication and dissolved oxygen
dynamics. Five state variables are modelled in EUTRO: phytoplankton carbon, ammonia,
nitrate, carbonaceous biochemical oxygen demand, and dissolved oxygen.
Application:
WASP has been used to examine eutrophication of Tampa Bay (Sheng and Yassuda,
1995), phosphorus loading to Lake Okeechobee, eutrophication of the Neuse River
Estuary, eutrophication of Coosa River and its Reservoirs, PCB pollution of the Great
Lakes, eutrophication of the Potomac Estuary, volatile organic pollution of the Delaware
Estuary, heavy metal pollution of the Deep River, and mercury in the Savannah River.
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1.3.2.2 CE-QUAL-ICM
Description:
CE-QUAL-ICM (Cerco and Cole, 1993) is a 3-D finite-volume water quality model
whose processes are based on WASP (Ambrose, et al., 1988) and was developed by the
Water Quality and Contaminant Modelling Branch at Waterways Experiment Station for
application to Chesapeake Bay. The model can link to hydrodynamic models of any
dimension or combination of dimensions using either structured or unstructured grids.
However, certain numerical solution techniques in the model complicate the linkage to
unstructured grid hydrodynamic models.
Application:
The model has been used by the U.S. Army Engineer Waterways Experiment Station
(WES) to evaluate eutrophication in Chesapeake Bay (Linker et al., 2001; Cerco and
Noel, 2013). It has also been used to address management issues in the New York Bight
(Hall and Dortch, 1994), Florida Bay (Cerco et al., 2000), San Juan Bay/Estuary (Bunch
et al., 2000), and many others.
1.3.2.3 DELWAQ
Description:
DELWAQ (Postma, 1988) is the engine of the D-Water Quality and D-Ecology programs
of the Delft3D suite (e.g: Delft3D-WAQ, Delft3D-ECO). It is based on a rich library from
which users and developers can pick relevant substances and processes to quickly put
water and sediment quality models together. It computes from basic tracers, dissolved
oxygen, nutrients, organic matter, inorganic suspended matter, heavy metals, bacteria and
organic micro-pollutants, to complex algae and macrophyte dynamics. High performance
solvers enable the simulation of long periods, often required to capture the full cycles of
the processes being modelled. The finite volume approach underlying DELWAQ allows
it to be coupled to both structured and flexible unstructured grid hydrodynamics.
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Advanced features allow for increased flexibility in model configuration: hydrodynamics
can be aggregated in space and time, water and sediment layers may have a different
resolution and coarser sub-grids can be used to improve performance or facilitate input
specification.
The ecological module (Delft3D-ECO) includes processes related to algae growth and
mortality, mineralization of organic matter, nutrient uptake and release and oxygen
production and consumption. The Delft3D-ECO modelling instrument considers three
nutrient cycles: nitrogen, phosphorus and silica. The carbon cycle is partially modelled,
with a mass balance of all components containing organic carbon. Algal diversity are
represented in three species groups: diatoms, flagellates and green algae, and three genera
of cyanobacteria: Microcystis, Aphanizomenon and Planktothrix. To model variable
stoichiometry, each group is represented by three types defined by the physiological state
of the phytoplankton: phosphorus, nitrogen, or light limited. Different formulations are
available for the characterization of grazers, microphytobenthos, bottom sediment and
sediment–water exchange. Extinction of visible light is not spectrally computed, but is a
function of: inorganic suspended matter, yellow substances, detritus and phytoplankton
suspended particulate matter (SPM).
Applications:
It can be applied to marine, coastal waters and lakes (Los, 2009), estuaries (Blauw et al.,
2009), and SECS like the Berre Lagoon in France (Martin et al., 2010), and the Venice
lagoon (Runca et al., 1996) between many other applications.
1.3.2.4 WQ
Description:
The unstructured grid finite-volume water quality model (WQ), coupled to ECOM and
FVCOM, was developed based on the framework of the water quality analysis simulation
program (called WASP5) created by Ambrose et al. (1993). A Modification is made to
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include the nutrient fluxes due to sediment resuspension via sedimentation process at the
bottom. In the water quality, the water quality variables include dissolved oxygen,
phytoplankton as carbon, carbonaceous biochemical oxygen demand, ammonium
nitrogen, nitrate and nitrite nitrogen, ortho-phosphorus or inorganic
phosphorus, organic nitrogen, and organic phosphorus.
Applications:
It has been used for estuarine, coastal and ocean applications. For example, it was applied
to Jiaozhou Bay, China (Zhang et al., 2012), and to industrial dumping and pollutant
dispersion (Wang et al., 2011), between other applications.
1.3.2.5 MIKE3-WQ (ECO LAB)
Description:
MIKE3 WQ is a water quality model that computes advection-dispersion, sediment
transport, eutrophication and heavy metals by using different templates. This templates
integrate the main biogeochemical processes associated to the phytoplankton
photosynthesis and respiration, oxidation due to the biochemical oxygen demand (BOD),
nitrification, sediment oxygen demand, phytoplankton, and bacterial respiration, and deal
with the basic aspects of water quality associated to oxygen depletion and the
transforming processes of the main biochemical compounds, namely, BOD and ammonia
levels resulting from the organic matter loadings. It is usually used with an ecosystem
template called ECO LAB that computes nutrient cycling, phytoplankton and
zooplankton growth, growth and distribution of rooted vegetation, macroalgae and
oxygen conditions; and also with a template called MIKE-EU for simulating algae growth
and primary production.
Application:
It has been widely applied by DHI (Danish Hydraulic Institute), for example to rivers
such as Yamuna River (India), to coral reefs, and to restore eelgrass.
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1.3.2.6 European Regional Seas Ecosystem Model (ERSEM)
Description:
ERSEM (the European Regional Seas Ecosystem Model) is a plankton functional type
box model which was developed by Baretta et al. (1995) and improved by Baretta-Bekker
et al. (1997). It is related to NPZD type models but includes several refinements
necessaries to represent more accurately some processes of temperate shelf ecosystems:
plankton community complexity, the microbial loop, variable nutrient stoichiometry,
variable carbon, chlorophyll-a ratios and a comprehensive description of benthic
biochemical and ecological processes. It has 36 state variables, 20 pelagics and 16
benthics. The units of ERSEM are Carbon, Nitrogen, Phosphorus, Silica and Oxygen.
Applications:
It has been applied to the North Sea by Radach and Lenhart (1995) for assessing the
nutrient cycling. Additionally Ebenhoh et al. (1997) applied ERSEM to assess microbial
dynamics, and more studies showed that ERSEM was capable of continuously simulating
complex food webs (Baretta-Bekker et al., 1997). There were many applications to this
respect during the following years, and more recently ERSEM has been applied to test
scenarios of fisheries management strategies by Petihakis et al. (2007) for example.
1.3.2.7 Computational Aquatic Ecosystem Dynamics Model (CAEDYM)
Description:
CAEDYM (Hipsey et al., 2007) is a complex water quality model which can run in 1D
(coupled to DYRESM) or 3D (coupled to ELCOM) configurations, and it has 112 state
variables in total, taking into account inorganic particles, light, metals, phytoplankton
dynamics, zooplankton, bacteria, pathogens and microbial indicator organisms, higher
biology, carbon, nitrogen, phosphorus and silica, dissolved oxygen, sediments and
resuspension. However, for a simple water quality simulation there is a minimum
configuration of dissolved oxygen, extinction coefficient, Photosynthetically Active
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Radiation (PAR), Particulate Organic Phosphorus, Particulate Organic Nitrogen,
Dissolved Organic Phosphorus, Dissolved Organic Nitrogen, Particulate Organic Carbon,
Dissolved Organic Carbon, Filterable Reactive Phosphorus, Ammonium, and Nitrate.
CAEDYM allows simulation of up to seven phytoplankton groups, five zooplankton
groups, three fish groups, four macroalgae groups, three invertebrate groups, three
clam/mussel groups, one seagrass/macrophyte group, one jellyfish group and three
pathogen/microbial indicator organism groups. However, although it computes PAR, it
does not compute the spectral character of PAR or the spectral character of the light
attenuation coefficient, which is fundamental for phytoplankton and seagrass growth and
primary production.
Applications:
It can be applied to SECS, lakes, estuaries, rivers and coastal waters but the 3D
configuration of ELCOM-CAEDYM is not open source. Some examples of its
application are to Swan River (Chan et al., 2002) and to Marmiom Marine Park coastal
lagoon in Western Australia (Machado and Imberger, 2012).
1.3.2.8 Pelagic Interaction Scheme for Carbon and Ecosystem Studies (PISCES)
Description:
PISCES (Pelagic Interaction Scheme for Carbon and Ecosystem Studies) was created by
Aumont and Bopp (2006) and is a medium complexity ecosystem model that has been
implemented in ROMS for its suitability for a wide range of applications. PISCES currently
has five modeled limiting nutrients for phytoplankton growth: Nitrate and Ammonium,
Phosphate, Silicate and Iron. It also has two phytoplankton size-classes/groups corresponding
to nanophytoplankton and diatoms, and two zooplankton size classes which are micro-
zooplankton and mesozooplankton. For phytoplankton, prognostic variables are total
biomass, the iron, chlorophyll-a and silicon contents. In addition to the ecosystem model,
PISCES also simulates dissolved inorganic carbon, total alkalinity and dissolved oxygen. The
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dissolved oxygen formulation is also used to define the regions where oxic or anoxic
remineralization takes place.
Applications:
Some oceanic applications of PISCES can be seen at Tagliabue et al. (2014) for assessing
the impact of external sources of iron on the global carbon cycle, and some applications to
estuaries were made by Laws et al. (2000) and by Schlitzer (2000).
1.3.2.9 ECOPATH with ECOSIM (EwE)
Description:
ECOPATH (Christensen and Pauly, 1991) is an ecosystem mass balance model for food
webs, where functional groups are represented as biomasses, linked through their trophic
interactions. The model establishes mass balances by solving sets of linear equations that
describe the production and consumption of each group. ECOPATH has reasonably low
data requirements, and single mass balances give valuable insights into how energy is
transferred through the food web. Multiple balances are used for temporal or spatial
comparisons of system functioning. The time-dynamic module ECOSIM (Bissett et al.,
1999a, 1999b) applies differential equations to describe temporal variations of the flows
identified by ECOPATH mass balances and is mostly used to study the effects of
fisheries’ management policies in both marine and freshwater systems. In the EwE
software there is also a module called Ecospace, which is a spatial and temporal dynamic
module primarily designed for exploring impact and placement of protected areas. This
modelling system takes into account the spectral attenuation of light and spectral PAR for
phytoplankton growth kinetics, but has a great input parameter uncertainty.
Applications:
It is usually used to study management strategies of fisheries’ policies, and it can be
applied different to marine and coastal waters, lakes, lagoons, estuaries and SECS. One
example of its application to SECS is to Hudson Bay (Hoover et al., 2013), to estuarine
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and coastal waters we can remark the case of Pearl River Delta (Duan et al., 2009), and
to lakes the application to Lake Victoria (Downing et al., 2012) and to Baoan Lake (Guo
et al., 2013). It is important to note that EcoSim model itself has also been applied to
SECS, as it is the case of Port Phillip Bay (Fulton and Smith, 2004), and also to the
Sargasso Sea (Bissett et al., 1999a, 1999b).
1.3.2.10 Integrated Generic Bay Ecosystem Model (IGBEM)
Description:
It is a box model developed by Fulton et al. (2004b). It is a coupled physical transport-
biogeochemical process box model constructed as a basis to explore the effects of model
structure and complexity. The formulations of the model are based on two existing
models, the European Regional Seas Ecosystem Model II (ERSEM II) and the Port Phillip
Bay Integrated Model (PPBIM). IGBEM provides a spatially and temporally resolved
model of nutrient cycles and population biomasses for enclosed temperate bays. The
model has 24 living components (groups), two dead, five nutrient, six physical and two
gaseous components. These components are linked through both biological and physical
interactions. The main processes taken into account in the water column are
Phytoplankton, Bacteria, Zooplankton, Fish, Nutrients and Detritus. In the epibenthic the
main processes are the Epifauna and the Phytobenthos, and in the sediment the Infauna,
Bacteria, Nutrients and Detritus. A daily time-step is utilized for standard runs. This is a
limitation as within the biological modules, a daily time-step may make the variables with
fast dynamics become unstable.
Application:
It was designed for semi-enclosed coastal systems, such as Port Phillip Bay (Fulton et al.,
2004b) where it was applied and tested.
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1.3.2.11 Ecological North Sea Model, Hamburg (ECOHAM)
Description:
ECOHAM is a three-dimensional model for estimating the annual primary production in
the North Sea (Moll, 1998). The plankton system was represented by one phytoplankton
bulk variable, triggered by one nutrient, namely phosphate or dissolved inorganic
nitrogen. The mechanism for nutrient regeneration was represented by immediate
regeneration of part of the dead organic matter in the water and by a linear process for
regenerating the detritus at the bottom. Coupling of the benthic nutrient reservoir to the
water column was achieved by regeneration of inorganic nutrients from detritus and
transfer into the water column by bottom boundary conditions. There are two versions of
ECOHAM:
ECOHAM1
The ECOlogical North Sea Model, HAMburg, Version 1 (ECOHAM1) is a three-
dimensional model system that study nutrient and phytoplankton dynamics. It is the
German ecosystem model of the North Sea that has been developed at the Institute fur
Meereskunde, Hamburg, first, for the simple phosphorus cycle, and later extended for an
additional simple nitrogen cycle. It simulates the annual primary production under actual
circulation and solar radiation forcing (Moll, 1998) and describes concentrations and
fluxes of biologically important elements in space and time. Phytoplankton is represented
by one state variable and the model formulations are based on phosphorus and nitrogen
to limit the phytoplankton production. Grazing of phytoplankton by zooplankton is
treated dynamically due to a formulation according to Michaelis–Menten including a
threshold value below phytoplankton grazing ceases. The model is conceptualized for a
shelf sea including the shallow sea characteristic for the replenishment of the water
column with nutrients from the bottom. Two ordinary differential equations describe the
benthic detritus in terms of phosphorus and nitrogen pools. Underwater light is calculated
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by a diagnostic ordinary differential equation that includes shelf shading due to
phytoplankton but it is not computed spectrally.
ECOHAM2
The “Ecological North Sea Model, Hamburg, Version 2” (ECOHAM2) predicts the
carbon and nitrogen cycle of the North Sea, the zooplankton total biomass dynamics and
the population dynamics of one single key copepod species (Pseudocalanus elongatus).
In contrast to the earlier version ECOHAM1, ECOHAM2 includes the carbon cycle in a
shelf sea and resolves the nitrogen cycle completely. It is a biogeochemical model with a
medium set of 13 “bulk” state variables to simulate the nutrient phytoplankton dynamics
in a three-dimensional physical frame for estimating bulk derived trophic state variables
for the functional units of phytoplankton, zooplankton, bacteria and additional detritus,
organic dissolved matter and nutrient concentrations including all fluxes between state
variables, especially the primary production, in shelf seas.
Applications:
Most remarkable applications of ECOHAM1 and ECOHAM2 were to the Bohai Sea (Wei
et al., 2004), and to the North Sea (Moll, 1998; Moll and Radach, 2003).
1.3.2.12 Flexible Biological Module (FBM)
Description:
The “Flexible Biological Module (FBM)” provides a platform that allows users to build
their own parallelized biological model from a discrete set of functions that is independent
of the physical model. This module can be run simultaneously coupled to FVCOM with
an unstructured-grid or an structured-grid for 3-D applications. Advection and diffusion
variables can be run separately by itself in 1-D applications. In the FBM code, the
biological module is an independent 1-D system that is self-maintained and upgraded
without linking to a physical model. It can be also converted to a Lagrangrian-based
biological model by linking it with the 3-D Lagrangian particle-tracking module.
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FBM includes seven groups: nutrients, autotrophy, heterotrophy, detritus, dissolved
organic matter, and bacteria.
Application:
The most remarkable application of FBM was to build a NPZ model for the Gulf of Maine
(Tian and Chen, 2006).
1.3.2.13 Mohid Water Quality Module
Description:
The Water Quality Mohid model development started in 1985 by MARETEC, and
computes sinks and sources terms associate with the Carbon, Nitrogen and Phosphorous
cycle. The main dynamic processes that are computed by this model are: phytoplankton,
zooplankton and Nitrogen (Ammonia, Nitrate Nitrite, particulate organic nitrogen,
dissolved refractory and non-refractory organic nitrogen), Phosphorus inorganic and
organic, Dissolved Oxygen and BOD (Biochemical Demand of Oxygen). It can also solve
nutrients-dissolved oxygen-organic matter interactions, larvaes transport, selective
withdrawal from stratified reservoir outlets, hypolimnetic aeration, multiple algae,
epiphyton/periphyton, zooplankton, macrophyte, CBOD, and vegetative cover. Finally,
light penetration is not computed spectrally, but depends on suspended particulate matter
and phytoplankton.
Application:
It has been used for a wide range of applications such as for bathing water quality
management (Viegas and Nunes, 2009; Viegas et al., 2012), for assessing the quality of
the water of Madeira island (Portugal) (Campuzano et al., 2010).
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1.3.2.14 CE-QUAL-W2
Description:
CE-QUAL-W2 (Cole and Wells, 2008) is an ecosystem model in 2D (longitudinal-
vertical). The model can simulate suspended solids, nutrient and organic matter groups,
residence time, derived variables such as TN, TKN, TOC, chlorophyll-a, as well as pH,
total dissolved gases and optional biotic groups, including multiple periphyton, multiple
phytoplankton, multiple zooplankton and multiple macrophyte groups interacting with
hydrodynamics (Berger and Wells, 2008). The model includes various vertical turbulence
closure, weirs/spillways, gates, pipes and pumps and re-aeration schemes for engineered
systems, which can be simulated depending on the nature of the water body. The model
is an open-source code written in FORTRAN.
Applications:
It has been used extensively throughout the United States for rivers, estuaries, lakes,
reservoirs and river basin systems (Deliman et al., 2002; Bowen and Hieronymous, 2003;
Debele et al., 2006) and elsewhere in the world (Chung and Oh, 2006; Kuo et al., 2006)
as a management and research tool. The model has also been used to drive models of food
web dynamics (Saito et al., 2001) and to support the studies of fish habitat (Sullivan et
al., 2003).
1.3.2.15 NEUTRO
Description:
NEUTRO is a 3-D eutrophication model, which is under development in the Physical
Oceanography Research Laboratory of Singapour (PORL) since 1997. Detailed
NEUTRO model description and eutrophication kinetics are given in Tkalich and
Sundarambal, 2003. It consists of a 3-D transport formulation, WASP eutrophication
kinetics and Silica cycles. NEUTRO takes into consideration tidal forcing, advection,
diffusion and settling of suspended particles, as well as chemical and physical kinetic
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reactions of the diluted and suspended substances. It has been applied to coastal waters
within several commercial and research projects for environment impact assessment as
well as water quality management. The Eutrophication model (NEUTRO)
predicts water quality with respect to nutrients, plankton and dissolved oxygen,
suspended solids as well as bacteria decay. Seven interacting Systems (Cycles) are
included with benthic coupling for dissolved oxygen and nutrients: Nitrogen, Phosphorus,
Carbon and Silica cycles, Phytoplankton and Zooplankton Dynamics, and Dissolved
Oxygen Balance. The total of 13 state variables are considered: Ammonia Nitrogen,
Nitrate Nitrogen, Phosphate, Phytoplankton, Carbonaceous Biochemical Oxygen
Demand, Dissolved Oxygen, Organic Nitrogen, Organic Phosphorus, Zooplankton,
Bacteria, Total Solids, Available Dissolved Silica and Particulate Biogenic Silica.
Applications:
It has been used for Oceanic and Coastal waters of Southeast Asia, at Singapure area
(Sundarambal et al., 2010, 2012).
1.3.2.16 EnvHydrEM
Description:
EnvHydrEM (Zouiten et al., 2013) is a very complex computational time demanding 2D
model. The model considers a total of nineteen variables and 110 parameters. The
processes included are phytoplankton dynamics, total inorganic carbon, sediment carbon,
organic phosphorus, inorganic phosphorus, organic nitrogen, ammonia, nitrate, available
dissolved silica, particulate biogenic silica, dissolved oxygen, organic matter or
carbonaceous biochemical oxygen demand CBOD, zooplankton, bacterioplankton,
detritus, total iron and ferrous iron, total manganese and manganous ion. This model also
analyses a total of seventy-two interactions between the considered variables, including
the following sixteen processes: respiration, uptake, excretion, sedimentation, oxidation,
mineralization, nitrification, denitrification, photosynthesis, resuspension, grazing,
remineralization, predation, reaeration, sediment oxygen demand (SOD) and death. The
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main limitations of the model are that it has a great input parameter uncertainty and it has
been explored to a limited extent.
Applications:
It has been applied to the Albufera of Valencia (Zouiten, 2012) and to Victoria coastal
lagoon, Spain (Zouiten et al., 2013).
1.3.2.17 Intermittently Closed and Open Lakes or Lagoons (ICOLLS) model
Description:
It is a spatially resolved, eleven-box ecological model for Australian Intermittently
Closed and Open Lakes or Lagoons (ICOLLs) (Everett et al., 2007). ICOLLs are
characterized by low flow from the catchment and a dynamic sand bar blocking oceanic
exchange, which creates two distinct phases, open and closed. The processes of the
ecological model are based on a combination of physical and physiological limits to the
processes of nutrient uptake, light capture by phytoplankton and predator-prey
interactions. An inverse model is used to calculate mixing coefficients from salinity
observations. The model is characterized by strong oscillations in phytoplankton and
zooplankton abundance, typical of predator-prey cycles. The model contains 17 state
variables (dissolved inorganic nitrogen, dissolved inorganic phosphorus, small
phytoplankton, large phytoplankton, small zooplankton, large zooplankton, epiphytic and
benthic microalgae, seagrass, refractory detritus, sediment dissolved inorganic nitrogen,
unflocculated phosphorus, flocculated phosphorus, sediment dissolved inorganic
phosphorus, unflocculated sediment phosphorus, flocculated sediment phosphorus,
unflocculated total suspended solids, flocculated total suspended solids). Moles of
nitrogen is the currency of the state variables, being one of them seagrasses. It is
remarkable that it takes into account seagrass nutrient uptake and constant light
attenuation. In this model, the photosynthetically active radiation in the water column is
assumed to be a constant value, the 43% of the surface irradiance, but it is attenuated not
spectrally by epiphytes and seagrasses between others.
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Applications:
It was configured for Smiths Lake, NSW Australia and it has been explored to a very
limited extent (Everett et al., 2007).
1.3.2.18 SEACOM
Description:
It is a six-compartment ecological model designed to investigate the effects of
eutrophication on submerged vascular plants in coastal areas and bays. It was created by
Madden and Kemp (1996). The state variables include plant leaf biomass, leaves, plant
roots and rhizomes or roots, epiphytic algae attached to plant leaves, phytoplankton
(diatom group), phytoplankton (flagellate group), and labile sediment organic material.
Grazing by zooplankton is also taken into account but not as a state variable. In the
improved version of 2009 (Madden and McDonal, 2009) two seagrass species were taken
into account, Thalassia and Halodule. The attenuation of PAR is taken into account but
not spectrally. However, the attenuation constants of epiphytes, chlorophyll, water and
suspended particle matter are taken into account. The light attenuated by seagrasses is not
taken into account, the production is computed as a function of light reaching the top of
the canopy but attenuated by epiphytes.
Application:
It has been applied to coastal waters and bays, such as Chesapeake Bay (Madden and
Kemp, 1996), and Florida Keys (Madden and McDonald, 2009), but is potentially
applicable to SECS.
1.3.2.19 Phytoplankton-Zooplankton (P-Z) Models
Description:
These models are based on Lotka-Voltera predator-prey model. In 1926, the Italian
mathematician Vito Volterra proposed a differential equation model to explain the
observed increase in predator fish (and corresponding decrease in prey fish) in the
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Adriatic Sea during World War I. At the same time in the United States, the equations
studied by Volterra were derived independently by Alfred Lotka (1925) to describe a
hypothetical chemical reaction in which the chemical concentrations oscillate. The Lotka-
Volterra model is the simplest model of predator-prey interactions. It is based on linear
per capita growth rates. Then, the equations were applied to phytoplankton and
zooplankton dynamics complicating them with a phytoplankton mortality term. In this
models production is limited by the value of P (Phytoplankton), which depends on the
values of Z (zooplankton).
Applications:
This kind of model was designed to be applied to semi-enclosed systems and bays, as it
is the case of Bay of Villefranche (Ross and Nival, 1976).
1.3.2.20 Nutrient-Phytoplankton-Zooplankton (NPZ) Model
Description:
This kind of model incorporates one of the simplest sets of dynamics that usefully
describe plankton dynamics. An NPZ model has, by definition, three state variables:
nutrients, phytoplankton and zooplankton. These are usually modelled in terms of
nitrogen content, since nitrogen is often limiting to primary production in both ocean and
coastal waters. In an NPZ model there are 5 transfer functions to consider: phytoplankton
response to light, phytoplankton nutrient uptake, zooplankton grazing, and phytoplankton
and zooplankton loss terms due to death, excretion and predation. The phytoplankton
response to irradiance in these kind of models can be formulated in different ways as
described by Franks (2002), but as far as we know, it has never been formulated
spectrally.
Some authors such as Denman and Gargett (1995) and McClain et al. (1996) started to
use NPZ models after the boom of complex models in the 70’s, and they have been using
since them, although there is always discussion between the degree of complexity needed
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to accurately describe the eutrophication in the system. One of the most used NPZ models
is NEMURO (Kishi, 1994; Kishi et al., 2011), which is coupled to ROMS, and has been
described in a separate section due to its importance. Another relevant NPZ model is Port
Phillip Bay Model (PPBM), which is also separately described. Depending on the model
they present different architectures from 1D (Denman and Gargett, 1995), 2D (Franks
and Walstad, 1997), to 3D (Franks and Chen, 2001; Kishi et al., 2011). NPZ models tend
to be very good at reproducing biomasses of nutrients and phytoplankton (Franks, 2002).
The level of detail that can be reproduced by the model depends on the physical model
employed. However, they have been usually used with low resolution physical models to
oceanic waters.
The NPZ model is a simplification of an extremely complex system, and it must be used
and applied carefully and appropriately. It is essential to make a clear statement about the
question being asked, so it could be extremely good for management purposes.
Applications:
Nowadays, one of the most common uses of NPZ models is for theoretical investigations.
There are many authors that have studied how does the model behaves if different transfer
functions are used (Sjoberg, 1977; Steele and Henderson, 1981, 1992; Franks et al., 1986;
Murray and Parslow, 1999; Ruan, 2001), if different parameters are used (Jeringan and
Tsokos, 1979, 1980; Hastings and Powell, 1991; Ruan, 1993; Abrams and Roth, 1994;
McCann and Yodzis, 1994; Truscott and Brindley, 1994; Edwards and Bridley, 1996,
1999; Edwards et al., 2000 a, b), or if different physical models are used (Franks, 1997).
Most of these studies showed a good behaviour under the use of different transfer
functions and parameterizations, as the little data requirement for its calibration made the
model easily obtain good and realistic results.
Other theoretical investigations have concentrated less on the model structure and
parameterization and more on the biological implications of the model. Evans (1978) and
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Evans et al., (1977) coupled the simple NPZ model to a kinematic physical model of
vertical shear to explore how the interaction of vertical migration with vertical shear could
lead to patchiness of plankton. Steele and Frost (1977) used an elaboration of an NPZ
model to investigate the factors controlling the size structure of the phytoplankton. Kiefer
and Atkinson (1984) used an NPZ model to study nitrogen cycling efficiency in the
plankton. Evans and Parslow (1985) used their model to understand the factors
controlling the very different annual plankton cycles in the Atlantic and Pacific oceans.
In the 90’s, Marra and Ho (1993), Wroblewski et al. (1988) and McGillicuddy et al.
(1995a, b) between others used NPZ models to explore the spring phytoplankton bloom.
The NPZ model was usually the Franks et al. (1986) formulation, coupled to a range of
physical frameworks. Therefore, coupling the NPZ ecosystem model to a variety of
physical models has allowed exploration of a range of physical-biological interactions in
the ocean.
1.3.2.21 North Pacific Ecosystem Model for Understanding Regional
Oceanography (NEMURO)
It is known as the North Pacific Ecosystem Model for Understanding Regional
Oceanography. The model is a lower trophic ecosystem model that as most NPZ models
takes into account the nitrogen cycle. Nemuro was initially developed for the North
Pacific ecosystem and process formulations. Phytoplankton is represented by two
functional groups (small (flagellates) and large (diatoms)) and zooplankton by three
functional groups (small, large, and predatory). The unit of the model is nitrogen, and
many of the process equations are similar to other existing NPZ models.
Applications:
Nemuro model has been widely applied to the North Pacific (Kishi, 1994; Kishi et al.,
2007; Kishi et al., 2011).
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1.3.2.22 Port Phillip Bay Model (PPBM)
Description:
It is a simple biogeochemical model created by Murray and Parslow in 1997 and detailed
by Murray and Parslow (1999). It is based on the biogeochemistry of only the lower
trophic levels, so it can no consider fisheries and eutrophication simultaneously. It is
locally designed for Port Phillip Bay (South Eastern Australia) biogeochemical processes.
It is a NPZ model with N as limitant nutrient; therefore, it is based on the nitrogen cycle.
The water column and the sediment are computed in different compartments, which are
linked by sedimentary flux of detritus from the water column to the sediment, and the net
flux of dissolved inorganic nitrogen from the sediment to the water column.
Application:
Semienclosed coastal systems such as Port Phillip Bay (Murray and Parslow, 1999;
Murray et al., 2001).
1.3.2.23 Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) Model
Description:
NPZD models simulate the interactions of the four variables nutrients (N), phytoplankton
(P), zooplankton (Z) and detritus (D). The mathematical formulation of the internal fluxes
varies in kind and complexity. For example, the growth of phytoplankton can be modelled
to be limited by nutrients only, to be limited additionally by light or even by more factors
(Denman and Peña, 1999; Oschlies and Garcon, 1999; Fennel et al., 2001; McCreary et
al., 2001; Kawamyko, 2002; Spitz et al., 2003; Losa et al., 2006; Pahlow et al., 2008).
There are well known NPZD models such as Fennel’s biological model (Fennel et al.,
2006), Fasham’s model (Fasham et al., 1990), Powell et al.’s model (2006), Gruber et
al.’s (2006) model, all of them coupled to ROMS. An others such as Doney et al. (1996),
Oschlies and Garcon (1999), Hood et al. (2001, 2003), and Hinckley and Dobbins (2004).
Fennel’s and Fasham models have been described in separate sections due to their
importance.
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Applications:
NPZD models have been used for a wide range of study areas such as Middle Atlantic
Bight (Fennel et al., 2006), and Lake Hamana and Lake Sanaru (Ohno and Nakata, 2008)
between others.
1.3.2.24 Fasham
Description:
Biogeochemical model developed by Fasham et al. (1990) with seven compartments
(Phytoplankton, Zooplankton, Bacteria, Nitrate, Ammonium, Dissolved organic nitrogen
and Detritus), it has 7 state variables and 27 parameters. In this model ecosystem
seasonality was assumed to be driven by seasonal changes in incident Photosynthetically
Active Radiation (PAR) and mixed layer depth (Evans and Parslow, 1985). Nitrogen is
regarded as the limiting nutrient of primary production, therefore, the model is based on
the nitrogen cycle. The use of nitrogen as a model currency has the additional advantage
that the primary production can be partitioned into "new" production, fuelled by nitrate,
and "regenerated" production, fuelled mainly by ammonium (Dugdale and Goering,
1967; Eppley and Peterson, 1979). It is comprised of seven compartments:
Phytoplankton, Zooplankton, Bacteria, Nitrate nitrogen, Ammonium nitrogen, Labile
dissolved organic nitrogen and Detritus.
Applications:
It has been applied to oceanic waters. Some examples are its application to Bermuda area
(Fasham et al., 1990) and to the North Pacific (Fasham, 1995).
1.3.2.25 Fennel
Description:
It is a biogeochemical nutrient, phytoplankton, zooplankton, and detritus (NPZD) model
(Fennel et al., 2006) that is implemented into ROMS, and assumes nitrogen as the
controlling nutrient for primary production. Therefore, it is based on the nitrogen cycle,
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and includes the source, sink, and biogeochemical transformation terms of seven state
variables: nitrate, ammonium, small and large detritus, phytoplankton, zooplankton, and
chlorophyll. The main biogeochemical model equations are described by Fennel et al.
(2006), who adapted them from the plankton dynamics model of Fasham et al. (1990). In
the Fennel implementation, phytoplankton growth is a function of temperature, nutrient
concentration, and the homogenously integrated PAR distribution.
Applications:
It has been applied for example to the Middle Atlantic Bight (MAB) (Fennel et al., 2006)
and to the Northwest North Atlantic (Fennel et al., 2008).
1.3.2.26 System Wide Eutrophication Model (SWEM)
Description:
An improved model, the System Wide Eutrophication Model (SWEM), has been
developed and tested by Hydroqual Inc. (1987) to simulate the biogeochemistry and
circulation in Long Island Sound (LIS) and adjacent waters. SWEM is a complex model
with many parameters that represent short-term variability in the rates of the processes
that influence the dissolved oxygen concentration. SWEM is coupled to a hydrodynamic
module that represents winds and the state of the ocean. The products of this module
(velocities and vertical eddy coefficients) are passed to the water quality module to
compute the evolution of nutrients, plankton, dissolved oxygen etc.
Application:
It has been applied to western Long Island Sound (LIS) (O’Donnell et al., 2009), and the
States of New York and Connecticut have developed a Comprehensive Conservation and
Management Plan (CCMP) using a computer model to assess the likely impact of
reductions in nitrogen discharged from water treatment plants and non-point sources.
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1.3.2.27 Discussion
There are a high number of water quality and ecosystems models that vary from very high
complex ecosystem models such as ECOSIM (Bisset et al., 1999a, 1999b), ERSEM
(Baretta et al., 1995), IGBEM (Fulton et al., 2004b), CAEDYM (Hipsey et al., 2007),
CE-QUAL-ICM (Cerco and Cole, 1993), ICOLLs model (Everett et al., 2007) and
EnvHydrEM (Zouiten et al., 2013) to medium complexity models such as PPBM and
PPBIM (Murray and Parslow, 1997), ECOHAM (Moll, 1998), NEUTRO
(Tkalich and Sundarambal, 2003), WASP (Di Toro et al., 1983; Connolly and Winfield,
1984; Ambrose et al., 1988), to simple models such as P-Z (Ross and Nival, 1976), NPZ
(Denman and Gargett, 1995; McClain et al., 1996), NPDZ (Fasham et al., 1990; Fennel
et al., 2006) (see Table 1.2). All these models solve the eutrophication taking into account
a different number of processes and are used equally for systems with different trophic
levels (oligotrophic, mesotrophic, eutrophic, hypertrophic). The trophic level and the
main governing processes of each system should be taken into account when deciding the
model to use. In fact, complex water quality and ecosystem models are many times used
without taking into account the level of eutrophication and therefore the main processes
that rule the ecosystem. Moreover, the large parametrization of complex models with
numerous equations, the complexity of getting data for calibrating them, and the
indiscriminate use that the scientific community makes of them justifies the increasing
development of simpler models with less data requirements, especially for decision
makers and management strategies. Besides, these models could be coupled to others if
necessary, depending on the processes needed to accurately define the system under
study.
One of the most important simple models are PZ, NPZ and NPDZ. Although they have
been traditionally used for testing hypothesis, after our analysis we support that they can
be used for management strategies, due to their accurate results and its design. As
mentioned in their corresponding section, they are focused on the most important
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variables and parameters of eutrophic systems and give a quick and certain answer to
relevant questions. We support the use of these kind of models to evaluate eutrophication
in SECS due to the fact that they take into account the phytoplankton, the limitant nutrient
cycle, and they generally have the possibility of taking into account sediment interaction
which could be very relevant in these kind of systems. However, most of the
biogeochemical models suitable for SECS are codified with the nitrogen cycle, especially
those that are open source; whereas in some SECS phosphorus is the limitant nutrient.
There is another important aspect to remark about the analyzed models, which is the light
formulation. Only one of the analyzed models take into account the spectral behavior of
irradiance (see Table 1.2) and its influence on phytoplankton growth. Additionally, only
few of them have the possibility to model submerged aquatic vegetation (SAV), for which
light formulation is essential. In fact, SAV growth is based on the spectral irradiance that
reaches the canopy, and on the photosynthesis and respiration carbon balance. This could
be solved by coupling or linking the analyzed models to a bio-optical irradiance model.
Therefore, we conclude that there is a need of using different simplified biogeochemical
models for studying eutrophication in SECS, if possible NPZ or NPDZ type, with
different parameters and formulations depending on the trophic level of the studied area,
as in the case of eutrophic and hypertrophic SECS. Additionally, for studying the
submerged aquatic vegetation dynamics the coupling or linkage of specific irradiance and
bio-optical models is recommended.
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Table 1.2. Some relevant characteristics of the main water quality and ecosystem models.
MODEL COMPLEXITY SAV MODULE LIGHT ATTENUATION
SPECTRAL LIGHT ATTENUATION
SPATIAL RESOLUTION
WASP MEDIUM NO YES NO HIGH
CE-QUAL-ICM COMPLEX NO NO NO LOW
DELWAQ COMPLEX NO YES NO HIGH
WQ MEDIUM NO YES NO HIGH
MIKE WQ (ECO LAB) MEDIUM YES YES NO HIGH
ERSEM COMPLEX NO *Added in 2008
NO *Added in 2008
NO LOW
CAEDYM COMPLEX YES YES NO LOW & HIGH
PISCES MEDIUM NO YES NO HIGH
ECOPATH with ECOSIM COMPLEX NO YES YES HIGH
IGBEM COMPLEX YES YES NO LOW
ECOHAM MEDIUM NO YES NO LOW
FBM SIMPLE NO YES NO LOW
MOHID WQ MEDIUM YES YES NO HIGH
CE-QUAL-W2 COMPLEX NO YES NO MEDIUM
NEUTRO MEDIUM NO NO NO HIGH
EnvHydrEM COMPLEX NO YES NO HIGH
ICOLLS COMPLEX YES YES NO LOW
SEACOM MEDIUM YES YES NO LOW
P-Z models SIMPLE NO YES NO LOW
NPZ models SIMPLE NO NO NO LOW
NEMURO SIMPLE NO NO NO LOW
PPBM MEDIUM YES YES NO LOW
NPZD models SIMPLE NO NO NO LOW
FASHAM MEDIUM NO YES NO LOW
FENNEL SIMPLE NO YES NO LOW
SWEM COMPLEX NO YES NO HIGH
1.3.3 Bio-optical irradiance models with coupling or linking
capabilities
Light is essential for photosynthetic plants and algae. However, due to the rapid
attenuation in water, light is often a limiting factor in primary production in the aquatic
environment (Fisher et al., 1999). The degree of light attenuation also varies
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tremendously in SECS due to the variable presence of “chromophoric” organic matter,
such as phytoplankton, CDOM and detritus. Therefore, reproducing the correct
underwater light field is a key problem in modelling the biogeochemical processes in
these systems.
Because the incoming Photosynthetically Active Radiation (PAR) at the air-water
interface can be measured or calculated quite accurately (Fisher et al., 2006), the main
issue in calculating the underwater light field is to have correct estimate of the vertical
diffuse attenuation coefficient (Kd). The diffuse attenuation coefficient is referred to as
an Apparent Optical Property (AOP) because its value depends on the ambient
underwater light field. For monochromatic light the vertical light attenuation can be
decomposed as a set of partial attenuation coefficients, each characterizing absorption
and scattering by a different waterborne material. Strictly speaking, a complete spectrum
of Kd (Kd (λ)) is needed to obtain the average Kd for the whole photosynthetic waveband
and for each narrow band it is necessary to know the wavelength-specific absorption and
scattering coefficients for each waterborne material. Spectral bio-optical models have
been developed and applied to different kinds of water bodies (Platt and Sathyendranath,
1988; Gallegos et al., 1990; Arrigo and Sullivan, 1994). However, due to the optical
complexity of estuarine waters, and specifically of eutrophic SECS, and the complexity
of obtaining good quality light data most of the ecological models do not take into account
the spectral character of light (see Table 1.2), which is one of the functions of bio-optical
models. The main bio-optical models that take into account the spectral underwater light
climate are described below.
1.3.3.1 Hydrolight-Ecolight v5 (HE5)
Description:
Hydrolight was developed in 1992 and Ecolight in 2008. They were coupled by Mobley
and Sundman in 2008. It is a modelling system composed by Inherent Optical Properties
(IOP) models with different formulations for: particle absorption, CDOM absorption, and
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scattering. In fact, it has specific absorption and scattering models, bottom reflectance
models, sky radiance and irradiance models, and inelastic-scattering models. It is a
complex modelling system that allows obtaining all the inherent and apparent optical
properties of the water and present substances, and the light climate. It is not open source
but its free use is allowed by the developers for scientific research with a license.
However, Ecolight was coupled to EcoSim (a complex ecosystem model detailed in
section 1.3.2.2). The complexity of the coupling of the two models makes them difficult
to use and interpret.
Applications:
It can be applied to all kind of water bodies and ecosystems, and especially by NASA to
obtain information from satellite data. It has been recently applied to assess the light
climate of different ocean ecosystems (Mobley, 2011).
1.3.3.2 Fuji et al.’s model
Description:
Fuji et al. (2007) developed an optical and radiative transfer model that explicitly
represented and spectrally resolved the Inherent Optical Properties (IOPs, e.g. absorption,
scattering, and attenuation) from the ecosystem, and a radiative-transfer model to obtain
the Apparent Optical Properties (AOPs), such as diffuse attenuation, and radiometric
quantities, Photosynthetically Active Radiation (PAR) and remotely sensed reflectance
(ocean colour). The absorption coefficient is determined from the sum of the absorption
coefficients of seawater, picoplankton (plankton composed by cells between 0.2 and 2
μm), and diatoms (based on their chlorophyll-a content), non-algal particles (NAP), and
coloured dissolved organic matter (CDOM). The absorption spectra of different
phytoplankton functional groups are also taken into account. This model was coupled to
a modelling system developed by Fuji et al., (2007) that consists of three individual
models: a physical-ecosystem model (simulating the dynamics of different ecosystem
components in time and space), a photo-acclimation model (specifying the chlorophyll-a
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to carbon ratio of phytoplankton), an optical (converting ecosystem state variables into
inherent optical properties) and a radiative transfer (calculating the underwater light field
and the ocean colour) model. It is a complex model that is not open source.
Application:
It can be applied to all kind of aquatic systems but as far as we know it has only been
calibrated and validated by Fuji et al. (2007).
1.3.3.3 Gallegos et al.’s model
Description:
It is a simplified model design for management purposes and decision makers (Gallegos
et al., 2011). It includes the spectral attenuation effects of water, CDOM, phytoplankton,
and non-algal particulates (e.g., detritus, minerals, bacteria). Because the model is
spectrally based, it can be used to calculate the attenuation of either Photosynthetically
Active Radiation (PAR, equally weighted quanta from 400 nm to 700 nm) or
Photosynthetically Usable Radiation (PUR, the integral of the quantum spectrum
weighted by the pigment absorption spectrum of SAV). PUR is a more accurate
measurement of light that can be absorbed by SAV and it is more strongly affected by
phytoplankton chlorophyll-a in the water column than is PAR. In this model the empirical
descriptor of the light available at a depth in terms of that available at the surface is the
diffuse attenuation coefficient of downward propagating irradiance, Kd.
Application:
It has been applied on its actual and early form to the Rhode River (Gallegos et al., 1990),
Chesapeake Bay (Gallegos et al., 1990; Gallegos et al., 2011), the Indian River Lagoon
(Gallegos and Kenworthy, 1996; Gallegos et al., 2009), Carrie Bow Cay, Belize (Gallegos
et al., 2009), and in Bahia Almirante, Panama (Gallegos et al., 2009).
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1.3.3.4 Zimmerman model
Description:
The model developed by Zimmerman (2003) consists of three different modules: a
module that simulates the seagrass relative biomass and architecture including leaf
geometry, an irradiance module that calculates the light absorption and scattering through
the canopy, and a photosynthesis module that calculates the carbon balance (production
and respiration) of the submerged plant canopy. The model simulates the light
environment of a submerged canopy at a fixed horizontal point (1D model). It allows the
description of the light environment by dividing the seagrass canopy volume, including
the leaves and the water column, into a series of horizontal sections of finite thickness.
The optical properties of each section are based on the architecture of the canopy, the
orientation and optical properties of the leaves, and the optical properties of the dissolved
materials and suspended particles in the water column. Given the spectral PAR at canopy
height from an irradiance model, it is able to compute the seagrass Photosynthetically
Usable Radiation (PUR) by computing the spectral absorption, reflection, and diffraction
of the downwelling and upwelling photosynthetically active irradiance through the
seagrass canopy. Finally, the model calculates the canopy production and respiration.
Application:
It has been applied to populations near Lee Stocking Island, Bahamas (Zimmerman,
2003) and to eelgrass from California, USA (Zimmerman, 2003).
1.3.3.5 Discussion
There are different characteristics to take into account when selecting a Bio-optical
model. Hydrolight-Ecolight is a complex model used mainly to obtain the apparent
optical properties of different substances in the water column by means of two coupled
models. Another complex bio-optical model is Fuji’s et al. (2007) model, which has only
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been calibrated in one area in 1D. On the other hand Gallegos et al. (2011) is a simple
bio-optical model designed for decision makers and management strategies, which in
spite of its detailed formulations it is easy to use and interpret, it can be used for eutrophic
systems and SAV (although it does not take into account the canopy architecture), and
gives us the spectral light climate and the Kd (λ). Finally Zimmerman’s model is a simple
bio-optical model focused on SAV. It takes into account the architecture of the canopy,
but needs the spectral PAR at canopy height to start computing. Therefore, a combination
of Zimmerman’s model with another model (see Table 1.3) could be a good solution for
assessing not only the spectral underwater light climate in a eutrophic SEC, but also the
SAV potential habitat and photosynthesis-respiration carbon balance.
Therefore, in terms of simplicity and accuracy, a combination between Gallegos’ et al.
(2011) and Zimmerman’s models can be a good way to predict light climate in both water
column and canopy. Gallegos’ et al. (2011) could be used to obtain the spectral PAR at
canopy height and Zimmerman to propagate light through the canopy, and calculate the
production-respiration ratio and potential SAV habitat based on light climate.
Table 1.3. Some relevant characteristics of the main bio-optical models.
MODEL COMPLEXITY SPECTRAL PAR SAV FORMULATION
HYDROLIGHT-ECOLIGHT COMPLEX YES NO
FUJI ET AL. (2007) COMPLEX YES NO
GALLEGOS ET AL. (2011) SIMPLE YES NO
ZIMMERMAN (2003) SIMPLE NO YES
1.4 Balancing spatial and temporal resolution
Because computer resources are limited, one inevitable problem faced in all numerical
modelling studies is resolution. Even with today’s powerful computers, it is still
impossible to resolve all processes at all relevant scales in a model.
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In this section, the trade-offs involved in the balance between spatial and temporal
resolution in coupled-linked ecological models are highlighted by presenting a series of
model case studies. It is clear that a variety of modelling frameworks have developed over
time that operate over rather different time and space scales. Each of these model types
are guided by different questions, the answer to which influences the chosen spatial and
temporal aggregation used in the model. Below, a series of examples of model space-time
complexity is discussed.
1.4.1 Low spatial, low temporal
As the sophistication of modelling has increased over time, few models currently exist
that include both low spatial and low temporal resolution, although they were historically
developed (e.g., Riley, 1946). The remaining models that include both low spatial and
temporal resolution are, in general, empirical models that describe the relationship
between a variable of interest (chlorophyll-a, hypoxic volume) and one or more
controlling variables (e.g., Cole and Cloern, 1987; Lee et. al., 2013), or semi-empirical
approaches based on first principles (Scavia et al., 2006; Testa and Kemp, 2008). In
general, these models predict variables at a single place or over a defined area, and are
primarily used to understand the dominant controls on a variable of interest. Such models
do not include mechanisms explicitly, but mechanisms are often inferred based on our
knowledge of the system derived from experimental and observational effort. The fact
that mechanisms are often inferred from empirical models allows these tools to be
excellent hypothesis generators; that is, when significant statistical relationships emerge
from an empirical model, the speculated underlying mechanisms that drive these
relationships can be tested with more resolved mechanistic models. For example, such
statistical models have been used to predict the spatial extent of hypoxic water in many
coastal ecosystems (e.g., Greene et al., 2009; Lee et al., 2013). These models have
generally included multiple linear regressions, which suggest that multiple controlling
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Chapter I Introduction and research background
variables, operating during specific seasons, ultimately control the spatial extent of
hypoxic volume in temperate ecosystems. In reality, however, the controlling variables
influence the biology and physics of the system at time and space scales that are much
finer than the seasonal or regional mean values used in the statistical model, so in many
cases, the hypotheses generated from statistics can be readily tested using hydrodynamic-
ecosystem models.
1.4.2 High spatial, low temporal
Some ecological models focus on the description of ecological processes that does not
change in a small period of time, but are spatially variable. One example of these
processes are those that involve vegetation presence/absence which are seasonally
variable and could be predicted by modelling strategies. In that line models with high
spatial, low temporal resolution try to answer questions like the influence of different
pressures on the seagrass presence absence, the effects of eutrophication, pollutants and
macroalgae competition on seagrass biomass distribution (Giusti et al., 2010), the
influence of phytoplankton and epiphytes on the underwater light environment
(Cunningham, 2002), seagrass shoot densities (Bearling et al., 1999), and the conditions
necessary for the restoration of submerged vegetation (Duarte et al., 2013).
High spatial resolution has increased in modelling techniques as shoot density and roots
of seagrasses are very variable with sediment type and light availability. In fact, depth
and type of sediments are quite variable in estuaries, and physiological and morphological
responses of seagrass are quite sensible to these parameters. The role of time and the
importance of temporal scale have received considerable less attention than issues on
spatial scale in recent years, although understanding complex ecological systems requires
linking space with time over the appropriate spatial and temporal scales (O’Neill et al.,
1986). Time resolution could be low (days, months, seasons, years) regarding some
ecological processes such as submerged aquatic vegetation dynamics due to low growth
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Chapter I Introduction and research background
kinetics and the biological time scales and seasonal variability of the different species
(Alexandre et al., 2008).
1.4.3 Low spatial, high temporal
Some examples of models with low spatial, but high temporal resolution include simple
sediment biogeochemical models, 1-D water-column process models, and box models.
These models can be useful for single point phenomena or processes which are
homogeneous over large areas. However, there is a considerable risk of missing adjacent
factors with high spatial variability that could affect the description of the main processes
of the system. In this respect, SWEM is a complex model that has been applied to the
western Long Island Sound with low spatial and high temporal resolution. However, the
low resolution of the model diminishes its value to the management of water quality in
embayments like Hempstead Harbour, Smithtown Bay, etc, but due to the complexity of
SWEM model higher spatial resolution will need many computational resources, data,
time and costs. Anyway, the high spatial, low temporal resolution can be many times
determined based on field data spatial and temporal variations.
1.4.4 High spatial, high temporal
Individual-based models (IBMs) represent another class of models developed in estuarine
systems that combine biological and physical processes, in this case using high resolution
spatial scales and temporal resolution that varies with the relevant life history
characteristic being modelled. They cannot be classified as in a lower or higher trophic
level group in general as it depends on the individual been studied in each case, for
example a type of larvae, as larvae transport should be simulated at a very high temporal
resolution. Miller (2007) provides a review of IBMs describing larval recruitment using
2-D and 3-D hydrodynamic simulation platforms and a variety of approaches to describe
particle transport and larval behaviour.
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Other examples of models with both high spatial and temporal resolution include some
of the coupled hydrodynamic water quality and ecosystem models explained in section
1.3.2, such as particle transport models, and multi-layer ecosystem models used to assess
eutrophication and phytoplankton and nutrients dynamics. However, these models
(especially ecosystems models) usually have a relatively high complexity (as can be seen
in Table 1.2), so they are not commonly used with high temporal and spatial resolution
because of the computational requirements.
1.5 Modelling Tradeoffs
Due to their complexity, many coupled or linked hydrodynamic-ecosystem or even bio-
optical models are not used with high spatial or temporal resolution because of the
computational times and resources, and data requirements. This makes more difficult to
obtain quality and reliable modelling results, and limits the application of models to
research activities as they are slow and hard to understand and are not useful for decision
makers who need a quick answer to their questions.
Due to biological complexity, Levins (1966) supported that when building a model a
trade-off between generality, precision or realism should be chosen (Figure 1.6). There is
no best all-purpose model and there is no perfect strategy associated simultaneously for
maximizing the three aspects. In fact, in some cases models should prioritize precision
and realism at the expenses of generality. Whereas, in some other cases models misprize
realism for generality and precision. There could be also useful models that build on
generality and realism at the expense of precision in some other cases. In the water quality
and ecological modelling community, there was a tendency to make more and more
complex models to ensure that all relevant processes were included in the model.
However, nowadays there is a need in reducing model complexity due to the difficulties
in expressing all the relevant processes by a mathematical equation and finding the values
for all the induced parameters, which can end in more parametrization errors. Although
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Chapter I Introduction and research background
ecological modelling is a branch of science that has been strongly developed in the last
40 years, there is not yet a conclusion about the most appropriate trade-off in each case
(Fath et al., 2011), and all approaches are still implemented and applied for any ecological
problem or study site.
Therefore, the increasing complexity of ecological models is a growing concern in the
modelling community. Ecological models are used to integrate process knowledge from
different parts of the system, and in doing so allow us to test system understanding and
generate hypotheses about how the system will respond to particular actions via virtual
experiments. However, as we strive to make our models more ‘realistic’, the more
parameters and processes we include. With increased model complexity we are less able
to manage and understand model behaviour. We need enough complexity to realistically
model a process, but not so much that we ourselves cannot handle. In fact, a recent
biogeochemical model intercomparison study has shown that increasing model
complexity may not lead to increased skill or predictive ability (Friedrichs et al., 2006).
It is though that the ecosystem, the problematic, the available data and the objective of
the study (management or research) should determine the complexity and the balance
between generality, precision and realism of the model (Figure 1.6). In fact, a simpler
integrated approach, ease of use can give the reliable results needed to meet management
goals to protect marine resources and support their sustainable exploitation, and to help
decision makers in their strategies. Therefore it is essential to develop models for
addressing the complex impact of drivers and ecosystem responses, being essential
numerical models which can simulate and predict changes in the state of marine
ecosystem in response to different drivers and management scenarios, and who can
support the decision-making processes. Simplifications can be made depending on the
characteristics of the area under study and its problematic. Therefore, a detailed
knowledge of the study area is a fundamental issue in building or choosing the model.
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Figure 1.6. Model tradeoff between generality, precision, and realism, adapted from Levins (1966).
1.6 Discussion
The need to develop models for ecological and environmental management has been
increasing since the late 1970s. Indeed, models provide both a keener understanding of
causal relationships driving ecological functioning, and the quantitative knowledge which
is required for evaluation, at ecological and economic levels, of consequences of the
implementation of possible alternative scenarios of environmental policy options in
SECS. Many mathematical models have been developed with the main aim of gaining
insights into given biogeochemical and ecological processes, regardless of the relevance
of the models for management purposes. In most cases, high spatial variability has not
been taken into consideration, the proper reproduction of the hydrodynamics that rule
these complex systems has not had a fundamental role, and the complexity of these
models made them hard to use for decision makers while a tradeoff between generality,
realism and precision should have been done. Besides, most of the models that have been
applied to SECS and lead with eutrophication in recent years are complex (e.g. Zouiten
et al., 2013) and difficult to apply and interpret. Additionally, despite the complexity of
the existing models there is a lack of models leading processes like the effects of
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eutrophication on underwater light climate and submerged aquatic vegetation between
others. The phytoplankton blooms produced by nutrient overloading, turbidity, CDOM
concentrations and increase in water depth due to sea-level rise are fundamental issues
that produce light attenuation in the water column and limit the photosynthesis process
and submerged aquatic vegetation growth. The study of this process from a spectral light
modelling perspective with high spatial resolution has scarcely been done for
management strategies, as the existing models that could lead with these characteristics
are complex and difficult to use. In fact, the degree of simplification and the selection of
the processes to be taken into account in a model is a difficult task that depends on the
system and requires a complete understanding of the processes that control it.
Additionally, we support that coupling and linking simple models could be an adequate
solution for many problematics when defining the model complexity and processes
needed. In fact, complex eutrophication models are usually been used equally for
hypertrophic and eutrophic systems, although the problematic and processes governing
each one are different. Moreover, the great number of equations and parameters of these
models make their calibration difficult and data demanding, and therefore expensive for
management purposes.
Consequently, there is a need of developing new ecological modelling tools, arising the
objectives of the present Thesis.
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1.7 Objectives
As described above, the main objective of this Thesis is to develop novel ecological
modelling tools for assessing and describing the behaviour of semi-enclosed eutrophic
and hypertrophic coastal systems. Thus, the present Thesis provides a deeper knowledge
about the behaviour of semi-enclosed coastal systems such as the Albufera of Valencia,
and West Falmouth Harbour which are complex hypertrophic and eutrophic systems
difficult to study. The specific objectives of this Thesis are:
To analyse the differences between eutrophic and hypertrophic systems and the
limitations of the existing models.
To design, link and implement a simplified water quality model for a hypertrophic
heavily regulated semi-enclosed coastal system.
To design, couple and implement a system of models to describe the behaviour of
a semi-enclosed coastal eutrophic system and the influence of eutrophication on
light attenuation and on submerged aquatic vegetation.
To assess the models sensitivity, calibrate them with field data and apply them to
semi-enclosed coastal systems with cultural eutrophication problems and socio-
economic and environmental importance.
To analyse different factors, such as the effects of future nutrient reduction and
sea level rise in a eutrophic semi-enclosed coastal system, and the input and output
loads in a hypertrophic system performing a mass balance.
To carry out an analysis of the developed models limitations and the future
research needed.
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1.8 Layout of Thesis
The Thesis is organized in five chapters that address the objectives of the study. Chapter
I includes an introduction and the research background, Chapter II the description and
problematic of the study sites, and Chapters III and IV are edited versions of published
articles in SCI journals that describe the models development and its applications. Finally,
Chapter V reveals the conclusions and proposes future research lines. The structure of
the Thesis and a brief description of the contents of each chapter can be seen as follows:
Chapter I: Introduction and research background
In the present chapter are explained: the motivations of the research, the socio-economic
importance of SECS, the different behaviour and characteristics of eutrophic and
hypertrophic systems, and the state of the art with the history, classification, description
and applications of the main existing models. Other important aspects such as an
evaluation of cases with different temporal and spatial resolution, and the modelling
trade-off between generality, precision and realism needed when building a model have
also been analysed. At the end of this chapter the specific objectives designed to solve
the needs raised, and the structure of the present Thesis are also described.
Chapter II: Study sites.
In Chapter II, a detailed description of the study sites is presented. The problematic and
importance of the Albufera of Valencia (Spain) and West Falmouth Harbour (USA),
which are hypertrophic and a eutrophic SECs respectively, is explained. This chapter also
includes a comparison between the two sites, assessing their differences, similarities and
modelling strategies.
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Chapter III - Development and implementation of a simplified model for semi-enclosed
hypertrophic coastal systems. Application to a heavily regulated coastal lagoon.
A simplified ecological model was linked to a hydrodynamic model to assess the
eutrophication in hypertrophic heavily regulated coastal lagoons. This chapter presents
the model description, the numerical techniques, the calibration and validation, the
results, discussion and conclusions.
Chapter IV - Development and implementation of a coupled ecological modelling system
for semi-enclosed eutrophic coastal systems. Application to a groundwater-fed estuary
with submerged aquatic vegetation.
We have developed a modelling system to assess eutrophication and potential seagrass
habitat in eutrophic semi-enclosed coastal systems. We have coupled a hydrodynamic
model, a simple biogeochemical model, and a spectral irradiance model to describe the
eutrophication and its effect on the light climate of SECS. We have linked a bio-optical
model to the system coupled before, in order to have a reliable tool to assess the potential
habitat of submerged aquatic vegetation in SECS based on a light perspective.
The chapter describes the observational methods and results, the description and
assessment of the modelling system, the results, discussion and conclusions.
Chapter V - Conclusions and future research
In this chapter, the general and specific conclusions that arise from this study are
described. Future research lines and the impact and dissemination of the present Thesis
are also summarized.
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Chapter II
2 Chapter II. Study sites
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Chapter II Study sites
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Chapter II. Study sites
2.1 Introduction
The presented study has been done in two SECS with different characteristics and some
similarities, the Albufera of Valencia (Spain) and West Falmouth Harbor (USA) (see
Figure 2.1). Both were selected for being SECS with the adequate characteristics for
assessing the developed models, and for having environmental problems for which
modeling strategies can be efficient tools to help solving them. The Albufera of Valencia
is a hypertrophic SECS with regulated connection with the sea, whereas West Falmouth
Harbor is a eutrophic SECS whose connection with the sea is limited but not regulated.
The singularity and problematic of each site, a comparison between them and the
modelling strategy are presented in the next sections.
Figure 2.1. a) Aerial view of the Albufera of Valencia Natural Park (abc.es) and b) West Falmouth
Harbor (fineartamerica.com).
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2.2 Albufera of Valencia
The Albufera of Valencia is an oligohaline coastal lagoon situated on the Mediterranean
coast 12 km south of the city of Valencia, Spain (see Figure 2.2).
Figure 2.2. Albufera of Valencia, irrigation channels and sampling stations location.
Its average depth is approximately 0.90 m (TYPSA, 2005; Soria, 2006) and it is located
within the Natural Park of the Albufera of Valencia, which is mainly formed by rice fields.
The Natural Park of the Albufera of Valencia was included in the List of Wetlands of
International Importance of the Ramsar Conference in 1990 (Soria, 2006). It was
designated as SPAS (Special Protection Area for Birds) based on Directive 94/24/EC on
the 8th of June 1994 for the conservation of Wild Birds. Furthermore the Albufera of
Valencia has also been declared a Site of Community Importance (SCI).
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Despite its environmental interest, the future of the lagoon is threaten by contamination
and silting (Canet et al., 2003). Currently the Albufera of Valencia can be considered a
highly modified water body with “bad” water quality according to the Water Framework
Directive (WFD) (Romo et al., 2008). This situation is produced by the contributions of
nutrients from the watershed and by its hydrological regulation.
Although in 1991 a partial removal of wastewaters in the lagoon commenced, after which,
as described by Romo et al. (2005), a reduction of 77% of phosphorus load was achieved,
the Albufera of Valencia has been hypertrophic since the 1970s, as it is subjected to many
different environmental pressures such as wastewaters coming from wastewater treatment
plants, chemical and manufacturing industries, scrapyards, and agriculture activities
among others (see Figure 2.3).
Figure 2.3. Some of the pollutant pressures that surround the Albufera of Valencia.
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One of the most important pressures are the irrigation waters of the 14000 ha. of rice
fields that surround the Albufera, which are fed into the lagoon through irrigation
channels and gullies (see Figure 2.2 and Figure 2.4). Additionally, the communication of
the Albufera with the sea is carried out through three artificial channels called golas
(Pujol, Perellonet and Perelló) (see Figure 2.2 and Figure 2.5) whose water flow is
regulated by sluice gates that keep the lake level at the appropriate values for rice
cultivation (Roselló, 1979) and limit the hydrodynamic flow (García Alba et al., 2014).
Figure 2.4. Rice fields and irrigation channels surrounding the Albufera of Valencia.
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Figure 2.5. Albufera of Valencia connection with the sea (“golas”), a) Gola Pujol, b) Gola Perellonet, c)
Gola Perelló.
In addition to this, waste water laden with phosphorus and ammonia come into the lagoon
through numerous ditches. In fact, combined sewer overflows (CSOs) as well as water
from sewage treatment plants from a population of 300,000 inhabitants and industrial
waste water are dumped into the Albufera. Between 1980-1988, the stress suffered by the
lagoon, due to the contributions of P and N, reached an average value of chlorophyll-a of
300 µg L-1 and a density of phytoplankton of 10 5 -10 6 ind mL-1, corresponding to a
biomass of 30-300mg L-1 (fresh weight) (Vicente and Miracle, 1992). Nine years after
the partial remove of wastewaters which started in 1991, the annual mean chlorophyll-a
composition decreased to 180 µg L-1 (Romo et al., 2005). However, despite these actions
the lagoon still presents serious hypertrophic conditions, as the situation has not
significantly improved since then (Usaquen et al., 2012). The evolution of water quality
in the lagoon over the years causes great concern and makes it necessary to take measures
for the management and recovery of the system due to its ecological and environmental
relevance.
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An aspect that should not be forgotten is that in the Albufera the phytoplankton is
dominated by filamentous cyanobacteria (over 80%) (Miracle et al., 1984). These are
characterized by their ability to fix nitrogen and to produce cyanotoxins. There are other
groups of phytoplankton in the lagoon, such as diatoms and chlorophyta, but to a much
lesser extent. As far as the zooplankton is concerned, during most of the year the dominant
species is Brachionus angularis, a detritivore filter feeder that belongs to the rotifers and
their predator Acantocyclops belonging to the vernalis copepods (Vicente and Miracle,
1992). However, around February and March a “clear-water phase”, usually takes place
in the Albufera because of the substitution of the cyanobacteria plankton by micro algae
and the increase of the zooplankton grazing activity. As described by Romo et al. (2005),
during the clear-water phase, phytoplankton is dominated by chlorophytes and diatoms,
which replace the filamentous cyanobacteria. These authors support the idea that during
this period zooplankton grazing increases and Secchi depth reaches the lagoon bottom.
Moreover, the development of the zooplanktonic specie Daphnia magna occurs, which is
a Cladocera planktonic crustacean that disappears in late March and is responsible for
much of the consumption of phytoplankton in this period (Romo et al., 2008; Sahuquillo
et al., 2007). Furthermore, clear-water phases were observed in the lake with values under
10 µg L-1 of chlorophyll-a (Romo et al., 2008), which are characterized by the increase
of the zooplankton grazing (Romo et al., 2005) and the communication of the system with
the sea. However, this phase is produced only once a year during one month, and the rest
of the time the lagoon is considerably impaired. The economic consequences of its
contamination is also important due to the activities that are disappearing such as fishing,
which has dropped considerably in the area. Moreover, some fauna and flora species have
disappeared also due to high levels of pollutants. In fact, there used to be submerged
aquatic vegetation (potamogetun pectinatus) in the bottom that have disappeared (Blanco
and Romo, 2006). Figure 2.6 shows the kind of submerged aquatic vegetation that the
Albufera used to have and the aspect of the water column and bottom nowadays at
different areas of the lagoon. Nevertheless, as rice fields give to Valencia high economic
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profits, and they need so many fertilizers and pesticides, the solution to this problem is
complex, and before taking any action having a model able to properly describe the
complex behaviour of the system in a simple but accurate way is the first step.
Figure 2.6. a) Submerged aquatic vegetation that used to be at the Albufera of Valencia (potamogetun
pectinatus); b,c,d) water column and bottom of the Albufera nowadays at different areas of the lagoon.
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2.3 West Falmouth Harbor
West Falmouth Harbor is a eutrophic groundwater-fed estuary situated on the western
shore of upper Cape Cod, Massachusetts, USA (Figure 2.7). Tidal range at the harbor
entrance is 1.9 m during spring tides and 0.7 m during neap tides (Ganju et al., 2012). The
average depth is approximately 1 m, and the surface area is 0.7 km2. The Harbor is
connected to Buzzards Bay and ultimately the Atlantic Ocean through a 3 m deep, 150 m
wide channel constrained by rock jetties on both sides (Ganju et al., 2012) (see Figure
2.8). The Harbor is comprised of different sub-embayments (Outer Harbor, South Cove,
and Snug Harbor).
Figure 2.7. West Falmouth Harbor, site locations and input loads.
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(a) (b)
Figure 2.8. West Falmouth Harbor connection with the sea (a) and view from the marked point (b)
Regarding the socio-economic importance of West Falmouth Harbor, it used to be a
shellfish recreational area. However, this activity has been forbidden due to bacterial
pollution (see Figure 2.9). This pollution is especially important in the harbor during the
summer, due to the increasing number of boats (see Figure 2.10) and population in the
area as it is located in a very touristic area due to their beaches and its short distance to
Boston. Moreover, due to this pollution a number of species have also disappeared, and
not only shellfish but also fishing activity and submerged aquatic vegetation has been
affected. Moreover, the eutrophication of the area makes the light penetration through the
water column difficult, so submerged aquatic vegetation dies. This has provoked the
accelerated disappearance of eelgrass meadows in the last decades. This has caused great
concern as seagrasses are considered one of the major primary producers in shallow
waters, they provide habitat and food for a variety of organisms, act as nursery for many
species, stabilize bottom sediments and baffle currents, and improve water quality by
filtering and trapping suspended matter.
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Figure 2.9. West Falmouth Harbor closed shellfish activity due to pollution.
Figure 2.10. West Falmouth Harbor sailing activity.
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Therefore, the presence of eelgrass (Zostera marina), fish, and shellfish communities in
the Harbor is particularly important from a habitat perspective. However, Costello and
Kenworthy (2011) showed that there has been an ecologically significant alteration of
eelgrass distribution in West Falmouth Harbor within the past decades. In 1981, eelgrass
meadows were found throughout the Harbor, with beds in Outer Harbor, South Cove, and
Snug Harbor (Costa, 1988) (see Figure 2.11).
Figure 2.11. West Falmouth Harbor seagrass (area delimited by red line) disappearance. Adapted from
http://buzzardsbay.org/historical-eelgrass-west-falmouth.htm.
Seagrasses disappeared first from South Cove in very little time. It is thought that this
disappearance was mainly due to a big storm that happened at the late 80’s, which
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devastated that area. Then seagrasses started to disappear from Snug Harbor (see Figure
2.11) and although this disappearance was slower it has not stopped yet. At present,
seagrass meadows have been lost from the landward basins, and eelgrass beds are only
found in the Outer Harbor (see Figure 2.12 and Figure 2.13). In Snug Harbor, eelgrass
beds died off as of mid-summer of 2010 remaining only patches (Hayn, 2012). Moreover,
grasses present during the previous several seasons had very high epiphyte loads on their
blades and showed signs of considerable physiological stress (Hayn, 2012; Howarth et al.
2014).
Figure 2.12. Outer and Snug Harbors seagrass presence in 2010 (green area) and 2012 (blue area).
Adapted from Hayn (2012).
As of 2012, no eelgrass beds were present in Snug Harbor (see Figure 2.12). This pattern
of eelgrass loss from the landward portions of the Harbor expanding toward the seaward
regions is due to the excess nitrate loading to the Harbor, which has contributed to
eutrophication (Hayn et al. 2014; Howarth et al. 2014). This high nitrate load comes
mainly from groundwater which naturally flows from the Sagamore lens of the Cape Cod
aquifer; nitrate loads are high due to input from the Town of Falmouth Wastewater
Treatment Plant (FWTP). The FWTP was constructed in the mid 1980's and is located
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landward of the Harbor, at a distance of 1 km east, with an average elevation of 30 m
above sea level (Howes et al., 2006). Since 2005, nitrate input to groundwater from the
FWTP has been substantially reduced due to an improvement in the sewage treatment.
Nevertheless, given the groundwater travel time between the FWTP and West Falmouth
Harbor (up to 10 years) (Kroeger et al., 2006), the effects of the nitrate loading reduction
were still not apparent as of 2012 (Hayn et al., 2014). Moreover, surveys indicate that
both the inner and outer basins would be capable of supporting eelgrass when the
watershed nitrogen loading rates reached the levels of 1979-1985 (Howes et al., 2006).
Therefore, it is thought that lowering nitrogen inputs to this system should provide the
possibility of recovering seagrass communities and benthic habitats.
Figure 2.13. West Falmouth Harbor seagrass meadows at outer harbor (a and b), seabed covered by
macroalgae at south cove (c) and with some Ulva lactuca at Snug Harbor (d) in 2012.
However, due to the complexity of the system, and its environmental and economic
relevance, a model able to describe the behaviour of this eutrophic SEC and to predict
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future scenarios of seagrass presence-absence, and its relationship with eutrophication
and sea-level rise is needed.
2.4 Study sites comparison and modeling strategies
The Albufera of Valencia and West Falmouth Harbor are two SECS with both similarities
and differences. On the one hand, both are SECS with a cultural eutrophication problem,
the systems mean depth are approximately the same, and both are close to a population
area, having a big economic and environmental importance. On the other hand, their
specific characteristics are different as can be seen in Table 2.1.The Albufera of Valencia
is damaged in a bigger extent than West Falmouth Harbor, as it is close to a big city
(Valencia, Spain), it is a SECS whose connection with the sea is only a few times a year
by three artificial channels, and it is also surrounded by rice fields what makes the nutrient
input load excessive and uncontrolled. These factors have provoked the hypertrophic
status of the system, which has led to the disappearance of many species of fauna and
flora such as submerged aquatic vegetation. One of the main reasons of this disappearance
is the limited light penetration through the water column, which has led into a
disappearance of submerged aquatic vegetation and therefore in a decrease of oxygen
levels in the lagoon that have provoked also the disappearance of many kinds of fishes.
On the contrary, West Falmouth Harbor is a eutrophic SECS close to the Town of
Falmouth (Massachusetts, USA), with input loads coming from a wastewater treatment
plant through underground waters with long travel times (up to 10 years). This harbor is
a restricted SECS, but this restriction is provoked by two jetties so the connection with
the sea is constant. In this harbor, the submerged aquatic vegetation has been disappearing
during the years due to the limitation of the light penetration through the water column.
In fact, recent studies support the idea that the less light penetrates the water column in
this area, the more light requirements seagrasses develop (Kenworthy et al., 2014). All
these factors have been demonstrated to provoke the disappearance of seagrasses, and
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there is also concern with the consequences of sea-level rise in this area due to the constant
although restricted connection with the sea.
Table 2.1. Albufera of Valencia and West Falmouth Harbor main characteristics comparison.
CHARACTERISTICS ALBUFERA OF VALENCIA WEST FALMOUTH HARBOR
SURFACE (Km2) 23.2 0.7
DEPTH (m) 0.9 1
POLLUTION SOURCES -WASTEWATER TREATMENT PLANTS AND SEPTIC SYSTEMS
- FERTILIZERS AND PESTICIDES FROM RICE FIELDS
-INDUSTRIAL WASTEWATER
-WASTEWATER TREATMENT PLANT
-SEPTIC SYSTEMS
TROPHIC STATUS HYPERTROPHIC EUTROPHIC
LIMITANT NUTRIENT P N
TYPE OF SEC CHOKED RESTRICTED
CONNECTION WITH THE SEA REGULATED WITH 3 CHANNELS AND GATES LIMITED BY 2 JETTIES
SEAGRASS PRESENCE NO YES
Focusing on the use of simple and accurate reliable models, the differences between them
make the modeling strategy to be different although both are eutrophic SECS. For a
hypertrophic model a simple NPZ model could be used, as phytoplankton blooms due to
the overabundance of nutrients, and zooplankton grazing are the main processes
governing the system. However, in a eutrophic system were light penetration through
water column could be still enough for seagrass survival, not only a simple
biogeochemical model would be needed, but also the use of bio-optical models, especially
if one of the main points of concern is seagrass disappearance.
Additionally, the hydrodynamic conditions are quite different in both SECS, being
necessary for the Albufera to take into account the anthropogenic regulation with the sea,
whereas in West Falmouth Harbor the tidal forcing could determine in a bigger extent the
system behavior. In fact, as West Falmouth Harbor is a shallow SEC whose connection
with the sea is not regulated, sea-level rise could have consequences in light attenuation
and therefore in seagrass habitats in future years. Finally, the three-dimensional character
of West Falmouth Harbor due to the hydrodynamics, chlorophyll-a concentration,
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seagrass presence and light climate, versus the two-dimensionality of the Albufera of
Valencia where the communication with the sea is very limited and light does not
penetrate in the water column provoking no changes through it contributes to justify the
use of different modeling strategies, and consequently of developing two different models
applicable to SECS. The selection of each model depends on the system characteristics,
such as the connection with the sea, the level of eutrophication and the main important
physical and biological processes.
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coastal systems. Application to a heavily regulated coastal lagoon.
Chapter III
3 Chapter III. Development and implementation of a
simplified model for semi-enclosed
hypertrophic coastal systems. Application
to a heavily regulated coastal lagoon.
164
Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
Chapter III. Development and implementation of a simplified
model for semi-enclosed hypertrophic coastal systems.
Application to a heavily regulated coastal lagoon.
This chapter is an edited version of two published Articles:
“A model for describing the eutrophication in a heavily regulated coastal lagoon.
Application to the Albufera of Valencia (Spain).” by Pilar Del Barrio Fernández, Andrés
García Gómez, Javier García Alba, César Álvarez Díaz, José Antonio Revilla Cortezón.
Journal of Enviromental Management. 2012.
DOI: 10.1016/j.jenvman.2012.08.019
“Hydrodynamic modelling of a regulated Mediterranean coastal lagoon, the Albufera of
Valencia (Spain)” by Javier García Alba, Aina G. Gómez, Pilar del Barrio Fernández,
Andrés García Gómez, César Álvarez Díaz, Journal of Hydroinformatics. 2014
DOI: 10.2166/hydro.2014.071
Abstract
A simplified two-dimensional eutrophication model was developed to simulate temporal
and spatial variations of chlorophyll-a in semi-enclosed hypertrophic coastal systems.
This model considers the hydrodynamics of a coastal system with a heavily regulated
connection with the sea, taking into account the whole study area, the variability of the
input and output nutrient loads, the flux from the sediments to the water column, the
phytoplankton growth and mortality kinetics, and the zooplankton grazing (see Figure
3.1). The model was calibrated and validated by applying it to the Albufera of Valencia,
a hypertrophic SECS whose connection to the sea is strongly regulated by a system of
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
sluice-gates. The calibration and validation results presented a significant agreement
between the model and the data obtained in several surveys. The accuracy was evaluated
using a quantitative analysis, in which the average uncertainty of the model prediction
was less than 6%. The results confirmed an expected phytoplankton bloom in April and
October, achieving mean maximum values around 250 µg L-1 of chlorophyll-a. A mass
balance revealed that the eutrophication process is magnified by the input loads of
nutrients, mainly from the sediments, as well as by the limited connection of the lagoon
with the sea. This study has shown that the developed model is an efficient tool to manage
the eutrophication problem in semi-enclosed hypertrophic coastal systems.
Figure 3.1. Graphical Abstract
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
3.1 Introduction
Eutrophication is a widespread phenomenon in inhabited areas of the planet, being
considered one of the major threats to the health of marine and coastal ecosystems (Nixon,
1995). This phenomenon produces a very large increase of biomass in the system, a
serious impoverishment of the diversity, and a decline in the quality of the affected water
body (Chau and Haisheng, 1998). Due to the importance, complexity, and variability of
eutrophicated systems, mathematical models are essential tools to represent the degree of
eutrophication of natural water bodies (Fan et al., 2009; Chao et al., 2010). The
complexity of the models that describe eutrophication in aquatic systems ranges from
simple NPZ (Denman and Gargett, 1995; McClain et al., 1996), or NPDZ (Oschlies and
Garcon, 1999; Hood et al., 2003), to multi-nutrient, multi-species and size-structured
ecosystem models (Lima and Doney, 2004; Lopes et al., 2009; Sundarambal et al., 2010).
In fact, most of the available models assessing water quality in variable and high
productive environments like coastal lagoons (Ferrarin and Umgiesser, 2005; Everett et
al., 2007; Ohno and Nakata, 2008) are complex. Moreover, large data requirements and
high computational costs make them time consuming and expensive to develop (Lawrie
and Hearne, 2007). In addition, more complexity in an ecosystem model does not
necessarily improve model performance (Hood et al., 2003; Friedrichs et al., 2006). On
the contrary, models that use simpler formulations have lower computational demands
and can be easier to parameterize and interpret (Fulton, 2001). These mathematical tools
are usually coupled to physical models that range from 1-box models (Li et al., 1999;
Usaquen et al., 2012), which do not represent the heterogeneity of the entire system, to
full models (Skogen et al., 1995; Lima and Doney, 2004), which usually have fine
resolution grids and sophisticated numerical schemes to describe the system
hydrodynamics. However, in spite of the fact that spatial resolution and heterogeneity are
crucial characteristics in model performance (Fulton, 2001) the significant increase of the
use of low spatial resolution models (Baird et al., 2003; Everett et al., 2007) is common
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
in order to avoid high time consumption and computational demands. Hence, the
combination of a fine resolution grid, a sophisticated numerical scheme, and a simple
ecological model, could give a reasonable description of the observed phenomena in
complex aquatic systems.
Due to the complexity of describing eutrophication processes in semienclosed
hypertrophic coastal systems the use of mathematical models is needed. These numerical
tools are based on different assumptions and formulations to characterize the system. On
the one hand, there are models that present complex formulations (Fulton et al., 2004b ;
Everett et al., 2007;), which use a large number of parameters (ranging from 41 to 775).
These models focus on giving a detailed description of the biogeochemical processes and
interactions of the system. However, these complex models are usually applied with low
spatial resolution grids in order to reduce their computational costs (Martin, 1998; Fulton
et al., 2004a,b; Everett et al., 2007). On the other hand, there are models with simple
formulations that assume the system is a well-mixed box (Murray and Parslow, 1999;
Baird et al., 2003; Bastón, 2008;). This reduces computational demands but can only
provide a rough picture of the transport and distribution of the chlorophyll-a in the system.
Additionally, some models with complex formulations and high resolution grids (Lonin
and Tuchkovenko, 2001) do not have an expression to describe variable connections of
the system with the sea, which is a critical factor that must be implemented to successfully
describe the behaviour of the system. In fact, regulated connections are one of the most
complex scenarios in SECS and are usually strongly linked to economic and social
interests and high levels of eutrophication. There are also three-dimensional models such
as NEUTRO (Sundarambal et al., 2010), CE-QUAL-ICM (Cerco and Cole, 1993),
DELWAQ (Postma, 1988), and MOHID (Neves, 1985) which can be applied to these
systems. However, the shallowness of hypertrophic SECS justifies the deep-averaged
well-mixed assumption in order to simplify the model. In fact, most of the existing models
that operate with high resolution or three-dimensional formulations have a long execution
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
time, require a large amount of data for calibration and validation, and involve many
parameters that are usually difficult to measure and whose choice can affect model
outputs. This situation can improve substantially when simplifying the model equations.
In addition, modelling the regulated connection with the sea would help on the
understanding of these systems, as they are one of the most complex and less studied
cases. Therefore, in order to manage and predict the chlorophyll-a concentration in
semienclosed hypertrophic coastal systems, a simplified, high resolution model, which
takes into account regulated hydrodynamics, is needed.
The aim of this chapter is to develop a simple two-dimensional depth-averaged
eutrophication model, for semienclosed hypertrophic coastal systems. The model presents
high spatial and temporal resolution, takes into account the regulated connection with the
sea, considers the hydrodynamics of the whole study area, the variability of the input and
output loads of the system, the flux of nutrients from the sediment to the water column,
the phytoplankton growth and mortality, and the zooplankton grazing. Moreover, it is
able to accurately describe the chlorophyll-a distribution in these systems, with simple
formulations, 3 state variables, 14 parameters, and low computational demands. In this
study, we present the sensitivity analysis of the key model parameters, the calibration and
validation, and the application of the model to a hypertrophic SECS, the Albufera of
Valencia, a heavily regulated coastal lagoon, which was described in Chapter II.
3.2 Materials and methods
3.2.1 The hydrodynamic model
The definition and implementation of a hydrodynamic model for a hypertrophic SECS
shall be based on a deep knowledge of the system under study. The hydrodynamic
behaviour of the lagoon is controlled by several factors, such as the fresh water inputs
from the irrigation channels, the balance between precipitation and evaporation, the wind,
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
and the outflows through the golas. The last one is strongly dependent of the opening
regime of the sluice gates and the water level difference between the lagoon and the sea.
In order to assess these processes two different finite hydrodynamic models (a long wave
model and a wind model) were used in this work. The first one, a two-dimensional depth-
averaged model, was used to characterize the water circulation in the Albufera, the water
flows entering the lagoon through the irrigation channels, and those occurring through
the three golas to the Mediterranean Sea. The second one, a quasi three-dimensional
model, was applied to calculate the wind induced currents in the system. Both models
consider the whole domain (irrigation channels, lagoon, golas, sea), being the effect on
the water circulation of the outflows through the golas specifically included in the long
wave model.
3.2.1.1 The long wave model
The complex relations between the irrigation channels, the lagoon, the golas and the
Mediterranean Sea were analyzed using a long wave model which has been proved to
provide good results in shallow coastal and estuarine areas (García et al., 2010a; Barcena
et al., 2012). The model solves the depth-averaged three-dimensional Reynolds Averaged
Navier-Stokes equations, dividing the study area into rectangular cells to calculate
velocity and water surface elevation. Governing motion equations are expressed as
follows:
St
H
x
VH
x
UH
(Eq. 1)
bxsxyx
y
UHN
x
UHN
x
gH
xgHfVH
y
UVH
x
HU
t
UH
0
22
0
0
22
1
2 (Eq. 2)
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
bysy
o
yx
o
o
y
VHN
x
VHN
y
gH
ygHfUH
y
HV
x
UVH
t
VH
1)()(
.2
)()(
2
2
2
2
22
(Eq. 3)
Where U and V are the components of depth-averaged velocities in the x and y directions,
H is the water depth, g is gravity, S is the balance between precipitation and evaporation
(P-E), is the water surface elevation from mean sea level, f is the Coriolis parameter,
Nx and Ny are the horizontal eddy viscosity coefficients, 0 is the averaged density, sx,sy
are the friction terms in the water surface, and bx,by are the friction terms in the bed.
Water surface friction terms are expressed as a function of wind as:
22)(yxx
o
aa
o
sz WWWC
(Eq. 4)
22)(yxy
o
aa
o
sz WWWC
(Eq. 5)
Where Ca is a drag coefficient, a is the air density, and Wx and Wy are the wind velocities
in the x and y directions.
The bed friction terms are given by the following expressions:
HC
VUgUh
o
bx
2
22)(
(Eq. 6)
HC
VUgVh
o
by
2
22)(
(Eq. 7)
Where C is the Chezy loss friction coefficient which can adopt variable values depending
on water depth as follows:
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
K
HC
12log18 (Eq. 8)
where K is the Nikuradse roughness. Besides this formulation, the model also allows
specifying a constant Chezy friction coefficient.
As far as the water circulation is concerned, one important aspect should be mentioned,
which is how the connection between the lagoon and the sea through the golas was
modelled, as this connection is controlled by several gates. The effect of these hydraulic
structures on the lagoon discharge was included in the long wave model, using the
following weir discharge equation:
2/32
11
2/1
2/3
23
2
g
UhgbCQ g
(Eq. 9)
Where Q is the discharged flow, Cg is a discharge coefficient which was calibrated, b is
the weir width, g is the gravity constant, U1 is the upstream velocity, and h1 is the free
surface height over the weir. The discharged flow is correlated with the number of open
sluice gates through the discharge coefficient Cg. This coefficient determines the effect
of the flow through the golas on the lagoon velocity field calculated in the long wave
model.
3.2.1.2 The wind model
A quasi three-dimensional wind model, which takes into account the different structure
over the depth of horizontal velocities due to wind action, was used. This model provided
good results in shallow coastal areas (García et al., 2010b). Its governing equations are
the following:
0
t
H
x
VH
x
UH (Eq. 10)
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
HH
U
H
fVx
gy
UaV
x
UaU
y
UV
x
UU
t
U
sxsxsx
yx
000
5.018.0
402.0
402.0
(Eq. 11)
HH
V
H
fUx
gy
VaV
x
VaU
y
VV
x
VU
t
V
sysysy
yx
000
5.018.0
402.0
402.0
(Eq. 12)
Where
0
6.16
sxxa (Eq. 13)
0
6.16
sy
ya (Eq. 14)
Where sx,sy are the friction terms on the water surface.
3.2.2 Eutrophication model
The mathematical model proposed is a two-dimensional simplified numerical model
which solves the depth-averaged advection-dispersion equation for each water quality
variable selected. The shallowness that usually characterizes hypertrophic SECS along
with their low vertical variation justifies the depth averaged simplification (Calero et al.,
2003), so complete vertical mixing has been assumed. The developed eutrophication
model simulates water quality with respect to phytoplankton and soluble reactive
phosphorus in the water column. In this regard, phytoplankton is an indicator of primary
biomass producers and of chlorophyll-a present in the lagoon, and soluble reactive
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
phosphorus is the limiting nutrient in the system that usually controls the phytoplankton
growth when the main input flows come from fresh water sources, as is the case of many
hypertrophic SECS like the Albufera (Soria et al., 1987; Martín, 1998) being its N/P ratio
19.6. Based on this approach, phytoplankton growth was calculated as a function of the
soluble reactive phosphorus, temperature, and light intensity in the water column;
whereas its consumption was mainly focused on the endogenous respiration and the
zooplankton grazing activity.
As far as the phosphorus cycle is concerned, soluble reactive phosphorus (SRP) is utilized
by phytoplankton for growth, and is incorporated into phytoplankton biomass. As
observed by Chao et al. (2006), the various forms of organic phosphorus undergo settling,
hydrolysis, and mineralization, and are converted to inorganic phosphorus at temperature
dependent rates. Furthermore, phosphorus may interact with sediments through the
processes of adsorption, desorption, and bed release. It is important to mention that only
soluble reactive phosphorus (SRP) has been defined in the current simplified model
because it is considered the limiting nutrient of the system. Moreover, the initial
conditions of the model are the phytoplankton concentration (Cf), soluble reactive
phosphorus concentration (Cp), water temperature (T), solar radiation (Iom), and the light
extinction coefficient (Ke), all of which were initially assigned the mean measured values
for the first simulation period. Figure 3.2 describes the flow chart of the developed model,
and the main biological processes considered.
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
Figure 3.2. Model flow chart
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
3.2.2.1 Transport equation
The water quality model is coupled to the hydrodynamic model through the depth-
averaged transport equation Eq. 15, which integrates the advection and the diffusion
properties of the flow, as well as the main processes occurring in the water column.
i
i
y
i
x
iii Ry
CHD
yx
CHD
xy
vHC
x
uHC
t
HC
(Eq. 15)
Where Ci is the depth-averaged concentration of the substance i (soluble reactive
phosphorus and phytoplankton); H is the depth of the water column that is given by
, η being the free surface elevation and h the mean water depth; Dx and Dy are
the diffusion coefficients; u and v are the current velocity in the x and y directions; Ri
represents the chemical and biological transformations of the substance i.
3.2.2.2 Chemical and biological interactions
A system of two differential equations describes the main chemical and biological
transformations for soluble reactive phosphorus (Cp) and phytoplankton (Cf):
(Eq. 16)
f
fCDG
dt
dC (Eq. 17)
where Cp is the concentration of soluble reactive phosphorus (SRP) (g m-3); Cf is the
concentration of phytoplankton in (g m-3); Fs is soluble reactive phosphorus released from
the sediment; G is the growth rate of phytoplankton; D is the death rate of phytoplankton,
and apc is the phosphorus to carbon ratio in phytoplankton.
hH
fpc
spCGa
H
F
dt
dC
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
3.2.2.3 Phytoplankton growth
Phytoplankton growth is described by a first-order kinetic expression where the net
growth rate is defined as the difference between the growth (G) and the death (D) rates.
The proposed model considers the population as a whole, using the total biomass of the
phytoplankton present.
The growth rate of phytoplankton (G) in a natural environment is a complex function of
the phytoplankton present and its differing reactions to solar radiation, temperature, and
the balance between nutrient availability and phytoplankton requirements. The growth
rate of phytoplankton (G) depends on four basic components: the maximum growth rate
at 20 º C (Gmax), the temperature correction factor (GT), the light limitation factor, and the
nutrient effect (GN), as shown in the following expression:
(Eq. 18)
One of the factors that affect phytoplankton growth is temperature. The variation and
relationship between growth rate and temperature is described by Eppley (1972). This
relationship is determined by the temperature correction factor (GT). In order to define
this factor, the reference temperature has been fixed at 20 º C. This factor is expressed as:
(Eq. 19)
Where θ is a temperature coefficient.
Moreover, the degree of penetration of sunlight into the water column has a significant
effect on phytoplankton growth, as phytoplankton needs sunlight to carry out its
photosynthetic function. The light limitation factor, GI, allows for photosynthesis to
increase with light levels up to a maximum, after which further increases in light result in
photo-inhibition (Tkalich and Sundarambal, 2003). The highest productivity occurs under
NITmáxGGGGG
20 T
TG
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
conditions of constant temperature and nutrients for a given light intensity known as
optimal intensity. Additionally, in hypertrophic systems, the overabundance of
phytoplankton, the big light attenuation, and the consequent absence of submerged
vegetation justifies the simplification of using non-spectral radiation.
The penetration of incoming solar radiation is described by the Lambert-Beer equation:
𝐼𝑧 = 𝐼0 𝑒𝑥𝑝 (−𝐾𝑒𝑍) (Eq. 20)
Where Iz is the light intensity at depth z (ly day-1) calculated from the light surface
intensity (I0) and the light extinction coefficient Ke (m-1).
The light limitation function of Steele and Baird (Thomann and Mueller, 1987) is used in
the model. The vertically averaged light limitation factor (GI) over a given water depth is
integrated as:
01
7182 expexp
HK
.G
e
I (Eq. 21)
Where
HKI
Ie
S
T exp1 (Eq. 22)
S
T
I
I0
(Eq. 23)
IT being the photosynthetically active solar radiation at the water surface (ly day-1), and
IS the saturating light intensity of phytoplankton (ly day-1). The photosynthetically
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
active solar radiation at the water surface (IT) is calculated using the equation given by
Kremer and Nixon (1978):
bmT C.II 71010 (Eq. 24)
Where Iom (ly day-1) is half the incident solar radiation, and Cb is the cloudiness (oktas).
The final effect on the growth that must be evaluated is the impact of varying nutrient
levels on the growth rate of the phytoplankton. The nutrient limitation factor (GN) which
describes the nutrient effect on the growth of the phytoplankton is expressed as a function
of dissolved inorganic phosphorus (Cp) in the form (Chapra, 1997):
pmp
p
NCK
CG (Eq. 25)
Where the constant, Kmp, is the Michaelis or half-saturation constant of phosphorus.
3.2.2.4 Phytoplankton death
Phytoplankton mortality is described as the sum of the phytoplankton endogenous
respiration and the zooplankton grazing. Therefore the mortality rate of phytoplankton
(D) can be expressed as the sum of two components (Thomann and Mueller, 1987):
zr DTKD (Eq. 26)
Where Kr (T) is the endogenous respiration of phytoplankton as a function of temperature
and Dz is the death rate due to zooplankton grazing.
The endogenous respiration of phytoplankton represents the processes by which the
phytoplankton oxidizes its organic carbon into CO2. The endogenous respiration rate of
phytoplankton (Kr) varies with temperature as follows (Di Toro and Matystik, 1980):
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
20
T
rrr TK (Eq. 27)
Where μr is the endogenous respiration rate of phytoplankton at 20 °C, and θr is a
temperature coefficient for phytoplankton respiration.
The loss of phytoplankton due to zooplankton grazing by herbivorous zooplankton is
proportional to the concentration of zooplankton present in the environment. Therefore
the mortality rate due to grazing (DZ) can be expressed as (Thomann and Mueller, 1987):
(Eq. 28)
where Cg is the grazing (filtering) rate of zooplankton (L mgC-1 day-1), which is the rate
at which the zooplankton feed on the phytoplankton, and Z is the zooplankton
concentration in equivalent carbon units (mgC L-1).
3.2.2.5 Chlorophyll-a concentration
The concentration of chlorophyll-a (CCHL-a) expressed in μgChl-a L-1 is a good indicator
of the eutrophication level of an aquatic system and can be obtained from the
concentration of phytoplankton by the following expression (Thomann and Mueller,
1987):
(Eq. 29)
where Cf is the concentration of phytoplanktonic carbon (mgC L-1) and aC / CHL-a is the
carbon to chlorophyll-a ratio (mgC / mgChl-a).
ZCDgz
f
aCHLC
aCHL Ca
C
/
1000
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
3.3 Numerical techniques
The transport equation Eq. 15 was solved using the eulerian technique, with an explicit
finite-difference discretization scheme based on the split operator approach, in which
advection and diffusion processes are computed independently for each time-step (García
et al., 2010b). Hence, different numerical methods were used to solve each process.
Advective transport was computed using an upwind scheme, whereas diffusion was
described through a centred scheme. Moreover, the system of differential equations that
describes chemical and biological interactions in the transport equation was solved by the
4th order Runge-Kutta integrator, with a relative and absolute tolerance of 10-8. The model
also includes an algorithm which adjusts the time-step based on two numerical stability
criteria, Courant (Courant and Hilbert, 1962) and Peclet (Fletcher, 1991). The time-step
for the model was 20 seconds, in order to ensure stable conditions, and a constant
horizontal diffusion coefficient of 0.4 m2 s-1 was used. The model was coded in Fortran
90 (non-parallel codification), and it is able to run a 12-month simulation with high spatial
(345x300 cells) and temporal resolution (time-step 20 s) in 4.46 hours using an Intel Core
i7 2.3 GHz with 8 Gb RAM.
3.3.1 The numerical grid
Both hydrodynamic and eutrophication models were applied over the same numerical
grid. This grid consists of 345x300 square cells with a cell dimension of 50.0 m, to
provide the high spatial resolution of the model. The bathymetric data was taken from
detailed topographic works developed for the entire Albufera Park by the Polytechnic
University of Valencia and TYPSA (2005), and the Navigation Charts of the State Naval
Hydrographical Institute (numbers 47, 48, 474, 481, 482 and 791). This numerical grid
includes the irrigation channels that discharge into the lagoon, the lagoon itself, its
connection to the sea through the three golas, and the coastal area. Due to the complexity
of the system and to the high number of discharge points in the Albufera, a simplification
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
was introduced in the numerical grid in order to limit the complexity of the model. The
sewage and irrigation water has been distributed into thirteen main irrigation channels.
3.3.2 Field data and model set up
This study was carried out for the hydrological year 2005/2006, where the available data
was enough for calibrating and validating the model. The main input data of the model
were measured by the Entidad Pública de Saneamiento de Aguas de Valencia (EPSAR)
between October 2005 and September 2006, at seven sampling stations distributed within
the lagoon. The collected data included chlorophyll-a, temperature, SRP and Secchi
depth, which were sampled monthly, taking 12 samples for each variable and station
throughout the hydrological year, making a total of 336 samples. The location of the
sampling stations, the irrigation channels, and the golas, can be observed in Figure 2.1.
Furthermore, SRP measurements from the main irrigation ditches were sampled during
each month of the study period, in order to describe the nutrient concentrations flowing
into the lagoon, making a total of 156 SRP samples. The maximum values of SRP were
found in the north irrigation channels (Alfafar to Beniparrell), due to the origin of this
kind of nutrient, which is mainly urban and industrial.
Chlorophyll-a levels were not measured for each individual phytoplankton species, since
in the Albufera the phytoplankton is strongly dominated by filamentous cyanobacteria
(over 80%) (Miracle et al., 1984). Instead, the total chlorophyll-a level was measured.
This simplification has been used in recent studies (He et al., 2011). The mean
concentration of chlorophyll-a is 115.7 μg l-1, meaning that the Albufera of Valencia is a
hypertrophic system. The mean Secchi depth varies between 0.12 and 0.36 metres, which
means that the light penetration is blocked in the water column. Consequently, the light
extinction coefficient values are considerable, in agreement with Martin (1998). With the
observed data of chlorophyll-a and Secchi disc depth in seven sampling stations
distributed around the lagoon, an expression to describe the variation of the light
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
extinction coefficient (Ke) with the chlorophyll-a concentration was obtained as can be
seen in Eq. 30 and Figure 3.3.
74.0
0457.04245.3
2
r
CK aCHLe (Eq. 30)
where CCHL-a is the chlorophyll-a concentration in µg L-1.
Figure 3.3. Variation of the light extinction coefficient (Ke) with the chlorophyll-a concentration.
The calculated regression coefficient (r2) in Eq. 30 shows a positive correlation between
the concentration data and the obtained light extinction coefficient (Ke) values. The good
obtained value of r2 enables us to use this expression to describe the relationship between
the chlorophyll-a concentration (CCHL-a) and the light extinction coefficient (Ke).
Therefore the light extinction coefficient (Ke) was calculated as a function of the
chlorophyll-a concentration (see Eq. 30).
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
Climatological data for the hydrological year 2005/2006, such as cloudiness and wind
parameters, were obtained for each hour from the Valencia Viveros Meteorological
Station (10 km from the Albufera). The cloudiness was measured by The Meteorological
State Agency (Agencia Estatal de Meteorología) on a scale of 0-8 oktas, 0 oktas being
the minimum cloud coverage and 8 oktas the maximum. The cloudiness evolution at the
Albufera of Valencia during the hydrological year 2005/2006 can be seen in Figure 3.4.
Figure 3.4. Average cloudiness variation during the hydrological year 2005/2006.
The most frequent wind was from the southeast, being in general of low intensity.
However, in some cases, wind events reached intensities above 3 m s-1, thus being able
to transport the pollutants from the banks of the lagoon to its centre, due to the
shallowness of the lagoon. Meteorological data, water temperature, and nutrient loads
were introduced into the model as input data per hour, showing the temporal resolution
of the model.
Another important input parameter that has been taken into account in this work is the
flux of soluble reactive phosphorus from the sediment to the water column. This
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
parameter was obtained from a specific one day field survey, in order to characterize the
influence of the SRP flux from sediments. Data were sampled once at 17 stations in order
to describe the spatial distribution of the SRP flux in the lagoon.
3.4 Calibration and validation
In the present study the calibration and validation of the water quality model is described.
Moreover, the hydrodynamic model was also calibrated taking into account the discharge
flow through the golas, which is characterised by a weir discharge coefficient (Cg) (see
Eq. 9). The hydrodynamic calibration was conducted through the comparison of the
predicted model results with water lagoon levels measured at the Pujol gola during the
simulation period (see Figure 3.5). The values of the discharge coefficient Cg varied from
0.2 to 0.5 as a function of the number of opened sluice gates.
Figure 3.5. Comparison between calculated and observed lagoon water surface during the period October
2005-September 2006 in a point of the lagoon located in front of gola Pujol.
As far as the eutrophication model is concerned, the calibration of this model involves
adjusting the parameter rates so that the model output fits the measured values in some
periods. In addition, a sensitivity analysis which describes the effect of model parameters
on the model output (Van Griensven et al., 2006) was carried out. Once calibrated, the
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
model was validated for a hydrological year, in order to confirm the values of the
parameters previously set.
3.4.1 Sensitivity analysis
Global sensitivity analysis is used as an initial screening tool to identify the most
influential model parameters (Arhonditsis and Brett, 2005). The ranges shown in Table
3.1 for each of the major model parameters were used to set acceptable parameter limits
for the model calibration. All of the parameter ranges were assigned based on published
literature values (Di Toro and Matystik, 1980; Thomann and Mueller, 1987; Ambrose,
1988; Chau and Haisheng, 1998; Martín, 1998), or sampling surveys.
Table 3.1. Range of variation and assigned calibration value of the main eutrophication parameters of the
model.
PARAMETER DESCRIPTION UNITS ASSIGNED RANGE
ASSIGNED VALUE
apc Phosphorus to carbon ratio gP gC-1 0.011-0.025a,d 0.011***, d
Kr Endogenous phytoplankton respiration rate
day -1 0.05-0.5b,a,d 0.12***, g, h, d
Kmp Half-saturation constant of phosphorus
mgP L-1 0.001-0.005c 0.0027***, d
aC/CHL-a Carbon to chlorophyll-a ratio mgC mgChl-a-1 50 – 133d 88***, d
Gmax Maximum phytoplankton growth rate
day-1 1.5-2.5c 1.5***, i
Is Saturating light intensity of phytoplankton
Ly day-1 100-400c * (see Eq.33)
Fs Factor of soluble reactive phosphorus from the sediment
mgP m-2 day-1 5-50e 20.72 **
Cg Grazing (filtering) rate of zooplankton
LmgC-1day-1 0.05-0.3f 0.3 ***, f
Source: a(Ambrose, 1988), b(Di Toro and Matystik, 1980), c(Thomann and Mueller, 1987), d(Martín, 1998), e field data, f (Chau and Haisheng, 1998); g (Lindenschmidt, 2006); h (Ambrose et al., 1993); i (Parslow et al., 1999).
*The values were assigned by empiricism;**The values were field data; ***The values were verified by calibration and literature.
For the sensitivity analysis, eight adjustable parameters were fixed at the mean of their
defined range, given in Table 3.1. Each simulation was performed with one of the
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
parameters fixed to its minimum or maximum value, and the remaining free parameters
set to the medium value of their assigned range. This process was repeated for each
parameter. The variation of mean concentration of chlorophyll-a over the whole lagoon
was used for the evaluation of the sensitivity.
The histogram in Figure 3.6 reveals that the parameters for which chlorophyll-a was
highly sensitive are Kr, Cg, Gmax, Fs, and acp. It is important to mention that Kr, Cg and
Gmax directly alter growth rates, whereas Fs and acp affect the possibility to take in or
utilise phosphorus. This is in good agreement with the results obtained in other studies
(Fasham et al., 1990; Schladow and Hamilton, 1997; Wu et al., 2009), in which Kr was
the sensitivity parameter that most affects chlorophyll-a concentration and phytoplankton
growth.
Figure 3.6. Mean chlorophyll-a (Chl-a) concentration calculated for the period of the sensitivity analysis
in the Albufera of Valencia for the minimum and maximum calibration parameter values.
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
3.4.2 Model calibration
The model free parameters were calibrated by trial-and-error adjustment to give the best
match with trends in the measured field data over four calibration periods, which were
selected because they usually present the minimum or maximum values of chlorophyll-a.
The selected periods were October, February, May, and July. In October and May a
phytoplankton bloom usually occurs, and during February and July the concentration of
chlorophyll-a is usually low in comparison with the rest of the year. The parameters can
be varied within a certain range (see Table 3.1) to match the model results with the
measurements of the most critical variable regarding water quality in the system (Fasham
et al., 1990; García et al., 2010b); being, in this case, chlorophyll-a concentration. This
methodology is in agreement with Lonin and Tuchkovenko (2001), Martin (1998) and
Garcia et al. (2010b). The comparison between measurements and model predictions was
performed with the help of different types of errors calculated between the observed and
predicted values for a given variable. The errors calculated were absolute error (AE),
relative error (RE), mean relative error (MRE), root mean square error (RMSE),
normalised root mean squared error (PRMSE), mean absolute error (MAE), normalised
mean absolute error (NMAE), and BIAS. The formulation of these errors can be seen in
Table 3.2.
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Table 3.2. Error formulations applied in the calibration process. Фi is the calculated concentration in cell
i, Фiobs is the observed concentration in cell i and N is the number of cells analyzed.
DESCRIPTION FORMULATION
Absolute error
Relative error
Mean relative error
Root mean square error
Normalised root mean squared error
Mean absolute error
Normalised mean absolute error
BIAS
The model calibration was carried out taking into account the most influential parameters
from the sensitivity analysis, and the seasonal parameters. Despite the complex nature
and high variability of the Albufera, the assigned value of most of the parameters used in
the model were kept constant over all the periods. However, different values of the light
intensity saturation (Is) have been used for distinct simulation periods. Light intensity
saturation (Is) changes depending on the temperature and the season, being minimal in
winter and maximal in summer (Macedo et al., 2001). Moreover, Is is positively correlated
with temperature and can be described as a function of temperature, using an Arrhenius
iobsiAE
100(%)1
N
i iobs
iobsiRE
1001
(%)1
N
i iobs
iobsi
NMRE
N
i
iobsi
NRMSE
1
2
100obs
RMSEPRMSE
N
i
iobsi
NMAE
1
N
i
iobs
iobsi
NNMAE
1
N
i
iobsi
NBIAS
1
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
equation (Falkowski and Raven, 1997). A linear equation describing Is as a function of
temperature was obtained (see Eq. 31), based on the maximum and minimum
temperatures measured at the lagoon and the Is range described by Thoman and Mueller,
1987.
(Eq. 31)
Where Is is the saturation light intensity (ly day-1) and T is the water temperature (º C).
Equation 31 was applied to obtain a value of Is for the rest of the periods, using the mean
water temperature of the lagoon for each month. As Is changes with temperature following
an Arrhenius equation, the data obtained was approximated to Eq.32.
ln(𝐼𝑠) = 6.557 − 18.7741
𝑇 (Eq. 32)
Finally, this logarithmical equation was solved, and was included in the model as follows
(see Eq. 33):
𝐼𝑠 = 704.156𝑒−18.774
𝑇 (Eq. 33)
Another important input parameter that has been calibrated in this work is the flux of
soluble reactive phosphorus from the sediment to the water column. This parameter data
was obtained through a specific field survey developed to characterise the influence of
the SRP flux from sediments. Data were sampled at 17 stations in order to represent the
spatial distribution of the SRP flux in the lagoon. Sampling data were interpolated
following the Kriging Gridding Interpolation Method (Kitanidis, 1997). The results,
measured data, and location of the sampling stations, are presented in Figure 3.7. As can
be seen in Figure 3.7, the maximum diffusive flux is found in the north of the lagoon, due
561.47516.15 TI s
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
to the high discharges of SRP coming from the northern ditches. In addition, a mean SRP
flux value of 20.72 mgPm-2day-1 was found in the lagoon.
Figure 3.7. Distribution of soluble reactive phosphorus (SRP) flux from the sediment to the water column
into the lagoon.
Additionally, a simulation process was carried out in order to fix some of the most
significant free parameters of the model, including Kr, acp, Kmp, ac/chl-a, Gmax, and Cg, which
were adjusted to achieve the lowest error between observed data and model results. The
assigned values of these parameters corresponding to the best fit are described in Table
3.1, and are in good agreement with those of the published literature (Ambrose et al.,
1993; Chau and Haisheng, 1998; Martín, 1998; Parslow et al., 1999; Lindenschmidt,
2006). Moreover, due to the model sensitivity to the endogenous phytoplankton
respiration rate, special care has been taken in order to set the assigned value, which is
that commonly recommended by published literature (Ambrose et al., 1993;
Lindenschmidt, 2006).
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
As can be observed in Table 3.3, there are differences between the values of the errors
calculated at the selected calibration periods where the light intensity saturation was
adjusted. The errors summarised in Table 3.3 were calculated using the observed data at
the seven sampling stations for each month compared with the model output obtained at
the same points. In fact, the month with the lowest mean relative error is February,
whereas the highest mean relative error occurs in May. The root-mean-square error
(RMSE) and the mean absolute error (MAE) have also been calculated. Each of these
measures is “dimensioned”, meaning that it expresses average model-prediction error in
the units of the variable of interest, in this case the chlorophyll-a concentration in μg L-1.
As each period has different levels of chlorophyll-a, depending on the inputs and the
weather conditions, the percent mean square error (PRMSE) has also been calculated for
every calibration period, and has been compared with the normalised mean absolute error
(NMAE). As a result, the lowest values of both errors have been found in the month of
May, being PRMSE 7.30% and NMAE 8.50%.
Table 3.3. Errors obtained with the chlorophyll-a values of all the sampling stations in each calibration
period.
Period MRE (%) RMSE(μg L-1) PRMSE (%) MAE(μg L-1) NMAE (%) BIAS(μg L-1)
October 2.52 26.94 11.95 23.10 10.70 2.41
February 1.75 10.46 19.26 8.52 16.13 -1.22
May -6.56 16.12 7.30 12.45 8.50 8.36
July -3.62 6.23 14.76 4.84 14.01 -0.28
The BIAS has also been calculated, and July is the month where the BIAS is closest to
zero. In fact, the values calculated by the model were about 0.28 μg L-1 lower than the
observed ones. Afterwards, the overall errors for each month were calculated comparing
the mean chlorophyll-a concentration given by the model for the whole system with the
averaged data measured at the sampling stations. This methodology is in agreement with
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
Haggard et al. (1999). Simulations showed that for October, February, May and July, the
relative errors obtained comparing the mean observed data with the mean model results
are between 3-5%. The absolute errors are between 1.86 and 7.15 µg L-1, as can be seen
in Table 3.4.
Table 3.4. Global errors obtained with the mean chlorophyll-a concentration of each calibration period in
the whole lagoon.
Period Фi (μg L-1 ) Фiobs (μg L-1) Фi - Фiobs (μg L-1) EA (μg L-1) ER (%)
October 232.59 225.44 7.15 7.15 3.17
February 52.47 54.33 -1.86 1.86 -3.42
May 174.33 167.20 7.13 7.13 4.27
July 39.99 42.23 -2.23 2.23 -5.28
As can be seen in Figure 3.8, the comparison between the results given by the numerical
model and the measured data for the best fit is adequate. In addition, Figure 3.8 shows
the evolution of chlorophyll-a concentration at the seven sampling stations and the lagoon
averaged concentration for the whole lagoon in the calibration periods. Concerning
chlorophyll-a, the best adjustment was found at station A2, and the global relative error
for the whole lagoon is less than 6%. The oscillation of the calculated values describes
the variation produced between day and night.
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
Figure 3.8. Comparison between results obtained by the model (solid lines) and the observed data (black
dots) in the sampling stations and the whole lagoon for each calibration period.
3.4.3 Model validation
Once calibrated, the numerical model was validated for the remaining months of the year
during which the calibration was not carried out. These are November, December,
January, March, April, June, August, and September. Model validation implies verifying
that the parameter values assigned in the calibration process are the best to describe the
degree of eutrophication of the Albufera of Valencia. Therefore, errors have been
calculated at each station for the validation periods (see Table 3.5). As can be observed
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
in Table 3.5 and Figure 3.9, the station that best fits the chlorophyll-a concentration is
C2, with a mean relative error of 1.02%, whereas the worst adjustment occurs at A1, with
a mean relative error of 47.74%. Additionally, all the stations except for A1, A2, and C1
have mean relative errors of under 16%, which is an acceptable value for the spatial
validation.
Table 3.5. Errors of the different sampling stations obtained with the validation periods.
MRE (%) RMSE(μg l-1) PRMSE (%) MAE(μg l-1) NMAE (%) BIAS(μg l-1)
A1 47.74 40.72 38.24 29.35 59.92 14.67
A2 29.20 31.28 28.74 21.80 30.95 18.85
A3 14.21 20.82 18.21 16.67 20.72 6.77
B1 -3.69 30.92 25.55 24.31 19.22 -13.57
B2 15.65 34.35 27.83 24.07 24.14 4.51
C1 22.70 37.76 31.01 28.05 39.11 -1.83
C2 1.02 12.63 10.63 11.41 13.56 -3.23
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
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Figure 3.9. Evolution of the simulated chlorophyll-a concentration (“full line”, calculated) and the
observed data (“black dots”, observed) in each sampling station, for the validation periods of the
hydrological year 2005/2006.
As far as the temporal validation is concerned, Figure 3.10 shows the comparison between
lagoon-averaged calculated and observed data for the hydrologic year 2005/2006, in the
months where the validation has been carried out. The overall relative error obtained is
5.81 %, indicating that the model is able to reproduce the observed data values and trends.
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
Figure 3.10. Evolution of the lagoon-averaged chlorophyll-a concentration calculated by the model (“full
line”, calculated) for the hydrological year 2005/2006 and the observed values (“black dot”, observed) for
the validation periods.
3.5 Results and discussion
Our results provide compelling evidence that the proposed model is effective in
describing the chlorophyll-a distribution in the Albufera with high spatial resolution (see
Figure 3.11). In addition, the spatial distribution is in accordance with the temporal
evolution as shown in Figures 3.10 and 3.11, where October and April are the months
that present higher chlorophyll-a concentrations, whereas February and March are the
ones that present lower concentration values of chlorophyll-a. These findings are in good
agreement with those of Romo et al. (2008), achieving the lowest levels of chlorophyll-a
in February and March and the highest levels in October and April. Additionally, the
chlorophyll-a concentration values obtained in the present study are consistent with
results obtained by Romo et al. (2005) with maximum mean values between 200 and 250
µg L-1. The increase of the chlorophyll-a concentration in the month of October is caused
by the warm temperature of the water (23 ºC), the hydrodynamism, and the large input
loads of nutrients during that period. Between September and October the harvest of the
rice fields takes place (Villena and Romo, 2003) and there is a large quantity of nutrients
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
that go into the lagoon, increasing the eutrophication process. Similar behaviour was
observed by Menendez et al. (2002) in the Buda lagoon (Spain), which is also a
Mediterranean coastal lagoon where the fresh water comes from the irrigation of rice
fields. This lagoon also shows a phytoplankton bloom in the month of October, as the
input loads are determinated by the rice cultivation. In April and May a large number of
nutrients arrive at the Albufera from the fertilizers and pesticides used in the preparation
of the surrounding rice fields. February and March, on the other hand, present
significantly lower chlorophyll-a concentrations. In February most of the gates of the
output channels are open, so the hydrodynamic of the lagoon increases considerably. In
March there is also a renewal of water, and the input loads of nutrients are lower than in
February. This nutrient reduction leads to an increase of the zooplanktonic species
Daphnia magna, which is a Cladocera planktonic crustacean that is the responsible for
much of the consumption of phytoplankton in this period (Romo et al., 2005). As a
consequence of this, zooplankton grazing effect increases, so a clear water phase is
produced in this period, and the chlorophyll-a concentration is the lowest of the year. In
view of these results, the connection of the lagoon with the sea, the zooplanktonic grazing,
and the nutrient loads directly affect the eutrophication of this heavily regulated coastal
lagoon. In order to evaluate the limiting nutrient influence on the chlorophyll-a evolution,
a mass balance has been carried out to determine the input and output SRP loads of the
lagoon.
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
Figure 3.11. Chlorophyll-a spatial distribution in the Albufera of Valencia in the hydrological year
2005/2006.
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
The net mass balance analysis of pollutant inputs to an ecosystem minus outputs from
that system provides a measure of how it is coupled with adjacent systems as, in this case,
on the coast of Valencia. On the one hand, the input loads taken into account in the mass
balance are the sewage water and nutrients that come from the surrounding townships and
from the irrigation water streams, particularly from 14,000 ha. of rice fields that surround
the Albufera. On the other hand, the output load includes the SRP flux that goes to the
sea, which depends on the sluice gates opening regime. In addition, the mass balance
reveals that most of the SRP that comes into the lagoon remains in it. The total SRP load
that enters the Albufera is 26.4 t y-1, and the output load is 5.8 t y-1. These results are
similar to those of Burger et al (2008) in Lake Rotorua, which is a eutrophic lake that has
an SRP input load of 27.5 t y-1.
Using the method described above, we found that April was one of the months with the
highest SRP input load (see Figure 3.12). In April, the chlorophyll-a concentration
increases mainly due to the high SRP input load, the warm temperature, and the light
intensity in the water column. During autumn the load of SRP that comes into the lagoon
is also high (see Figure 3.12). In October both SRP concentration and water flow in the
irrigation channels increase, making it one of the months with the highest input load of
SRP. As can be seen in Figure 3.12, in all the months of the 2005/2006 hydrological year
the SRP input load was considerably higher than the output load, especially in October
and April, where there was no outflow because the golas are almost completely closed.
In these months the input SRP load was considerably higher than that of the other months,
and is equal to the net load accumulated in the Albufera lagoon, which produces the main
eutrophication problems. Additionally, it is important to note that in the Albufera of
Valencia the SRP input load coming from the irrigation channels is only approximately
35 percent of the total SRP water column input load, whereas the sediment SRP flux to
the water column constitutes approximately 65 percent (IHCantabria, 2009). Once in the
water column, the SRP can be assimilated by phytoplankton or can become adsorbed to
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
sediment as particulate phosphorus, which can be dissolved again, returning to the water
column. Hence, we can conclude that the phytoplankton blooms are directly affected not
only by the temperature, but also by the input load of nutrients, the SRP flux to the water
column, and the connection of the lagoon with the sea.
Figure 3.12. Mass balance of the soluble reactive phosphorus loads that comes in and out of the Albufera
of Valencia.
In order to assess the accuracy of the model and to make it suitable for other similar
situations, statistical model evaluation techniques were applied. The simulated
chlorophyll-a values were positively correlated to the measured values with a Pearson
correlation coefficient of 0.933 for the calibration periods and of 0.917 for the validation
periods (see Figure 3.13). The Nash-Sutcliffe efficiency coefficient (Moriasi et al., 2007)
has also been calculated, resulting in a value of 0.96, which is considered as excellent
according to Usaquen et al. (2012). Finally, the error indices were used to quantify the
deviation between measured and calculated data. In this study, the calculated deviation
was about 5.81%. Due to the mathematical simplicity formulation, the high resolution
capacities and the accuracy of the developed model, it could be extended to other heavily
regulated aquatic systems.
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
Figure 3.13. Comparison between calculated and observed chlorophyll-a calibration and validation data
As far as the sensitivity analysis is concerned, the results obtained are in agreement with
those presented by Wu et al. (2009) applied to the Fuchunjiang Reservoir. The results of
this sensitivity analysis show that the parameters that strongly influence simulated
chlorophyll-a concentration are the endogenous respiration, the grazing rate of
zooplankton, the maximum growth rate and the sediment SRP flux.
3.6 Conclusions
In this study a simple two-dimensional eutrophication model, for hypertrophic SECS was
developed. This model was successfully tested in the Albufera of Valencia, a
hypertrophic system whose connection to the sea is strongly regulated. The chlorophyll-
a concentration in the Albufera of Valencia was used to calibrate and validate the
proposed model, as well as to assess its sensitivity.
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
Better knowledge of the influence that the main sensitivity parameters have on the model
was achieved. These parameters were proved to be the main ones for the model
calibration. Therefore the sensitivity analysis permits a reduction in the number of
parameters to be adjusted. We can conclude that the parameters for which chlorophyll-a
is highly sensitive are Kr, Cg, Gmax, Fs and acp. The endogenous respiration rate, Kr, is the
dominant parameter affecting the chlorophyll-a concentration, as it directly alters the
phytoplankton growth.
The model gave us a better understanding of the system. In fact, the results of the
modelling concluded that there were phytoplankton “blooms” in April and October, due
not only to the temperature, but also to the high nutrient loads and the lagoon-sea
connection characteristics. Nevertheless, the results confirmed that a “clear water phase”
took place around the month of March, mainly due to the nutrient reduction and the
zooplankton grazing effect. Moreover, the zooplankton species Daphnia magna is
primarily responsible for the predation on phytoplankton during the “clear water phase”.
Furthermore, a quantitative statistical analysis was applied to determine modelling
uncertainties between the measured and calculated data. The average uncertainty of the
model prediction for this study was less than 6%, which is an acceptable limit, with two
Pearson correlation coefficients of 0.933 and 0.917 for calibration and validation
respectively and a Nash-Sutcliffe efficiency coefficient of 0.96, which are excellent
values. Therefore, the modelled results demonstrated that a simplified model can
characterise eutrophication in heavily regulated SECS.
As demonstrated by the calculated mass balance, the input loads in the lagoon are higher
than the output loads, so the limited connection of the lagoon with the sea magnifies the
eutrophication of the system. Furthermore, the SRP flux from the sediment to the water
column contributes to maintaining high chlorophyll-a concentrations.
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Chapter III Development and implementation of a simplified model for semi-enclosed hypertrophic
coastal systems. Application to a heavily regulated coastal lagoon.
The results confirmed that the model constitutes a valuable tool for the eutrophication
management in heavily regulated hypertrophic SECS like the Albufera of Valencia, being
able to describe, with high temporal and spatial resolution, the chlorophyll-a
concentration evolution during a whole year.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Chapter IV
4 Chapter IV. Development and implementation of a
coupled ecological modelling system for
semi-enclosed eutrophic coastal systems.
Application to a groundwater-fed estuary
with submerged aquatic vegetation.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Chapter IV. Development and implementation of a coupled
ecological modelling system for semienclosed eutrophic coastal
systems. Application to a groundwater-fed estuary with
submerged aquatic vegetation.
This chapter is an edited version of a published Article:
“Modeling future scenarios of light attenuation and potential seagrass success in a
eutrophic estuary.” by Pilar Del Barrio, Neil K. Ganju, Alfredo L. Aretxabaleta, Melanie
Hayn, Andrés García, Robert W. Howarth. Estuarine, Coastal and Shelf Science. 2014.
DOI: 10.1016/j.ecss.2014.07.005
This work has also been successfully presented to the scientific community at the
following events:
Workshop: Linking hydrodynamic and ecological models in estuaries: a workshop to
discuss recent advances and approaches. Presentation: “A modeling approach to
assess light availability and potential seagrass success under nitrate loading and sea
level rise scenarios”. Authors: Pilar del Barrio, Neil K. Ganju, Alfredo L. Aretxabaleta,
Melanie Hayn, Andrés García, Robert W. Howarth. Woods Hole, Massachusetts, USA.
September 10-11, 2013.
MABPOM2012 Symposium. Poster presentation: "A seagrass and light attenuation
model for a eutrophic estuary: Calibration, validation and predictions under nitrogen
loading scenarios". Authors: Pilar del Barrio, Neil K. Ganju, Alfredo L. Aretxabaleta.
Groton, Connecticut, USA. Nov. 2012.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Abstract
Eutrophication in SECS has led to numerous ecological changes, including loss of
seagrass beds. One potential cause of these losses is a reduction in light availability due
to increased attenuation by phytoplankton. Future sea level rise will also tend to reduce
light penetration and modify seagrass habitat. In the present study, we integrate a spectral
irradiance model into a biogeochemical model coupled to the Regional Ocean Model
System (ROMS). It is linked to a bio-optical seagrass model to assess potential seagrass
habitat in a eutrophic SECS under future nitrate loading and sea-level rise scenarios. The
model was applied to West Falmouth Harbor, a shallow semienclosed estuary located on
Cape Cod (Massachusetts) where nitrate from groundwater has led to eutrophication and
seagrass loss in landward portions of the estuary. An schematic description of the model
and the processes can be seen in Figure 4.1. Measurements of chlorophyll-a, turbidity,
light attenuation, and seagrass coverage were used to assess the model accuracy. Mean
chlorophyll-a measurements varied from 28 µg L-1 at the landward-most site to 6.5 µg L-
1 at the seaward site, while light attenuation ranged from 0.86 to 0.45 m-1. The model
reproduced the spatial variability in chlorophyll-a and light attenuation with RMS errors
of 3.72 µg L-1 and 0.07 m-1 respectively. Scenarios of future nitrate reduction and sea-
level rise suggest an improvement in light climate in the landward basin with a 75%
reduction in nitrate loading. This coupled model can be useful to assess chlorophyll-a
variation and seagrass habitat availability changes from a light perspective. It also fully
considers spatial variability on the tidal timescale in eutrophic SECS.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed eutrophic coastal systems.
Application to a groundwater-fed estuary with submerged aquatic vegetation.
Figure 4.1. Graphical Abstract
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
4.1 Introduction
In eutrophic SECS light may penetrate through the water column and can be the limiting
factor controlling the presence of submerged aquatic vegetation (SAV) such as
seagrasses. Seagrass meadows are found in many coastal areas around the world and are
regarded as key indicators of ecosystem health (Dennison et al., 1993). They are among
the most productive plant communities, and represent one of the major sources of primary
production in shallow waters worldwide (Hemminga and Duarte, 2000). These plants
serve as a nursery for many species, providing habitat and food for a variety of marine
organisms (Orth et al., 2006). They also trap nutrients, thereby improving water
transparency and filtering substantial quantities of both N and P from estuarine waters,
serving as a buffer between land-based pollution sources and adjacent estuaries (Nixon et
al. 2001; Short and Short 2004; McGlathery et al., 2007; Hayn et al., 2014). Consequently,
the increasing loss of seagrass beds raises concern because of a potential reduction in
coastal ecosystem productivity, a decrease in water quality, and a decline in fishing
resources. Additionally, in a report prepared for the European Union, Terrados and Borum
(2004) estimate the value of ecosystem services provided by seagrasses as two orders of
magnitude higher than productive agricultural lands.
Despite the ecological and economic value of seagrass meadows, their disappearance has
accelerated in the last decades (Short and Wyllie-Echeverria, 1996; Waycotta et al.,
2009). The causes of decline range from natural disturbances (e.g., storms) to
anthropogenic pressures (e.g., nutrient loading). In temperate estuaries and coastal areas,
one of the dominant factors for seagrass loss is eutrophication (Short and Neckles, 1999;
Orth et al., 2006). In eutrophic SECS, there is an overabundance of nutrients that leads to
phytoplankton blooms, an increase in epiphytes growing on seagrass tissues, and
subsequent light reduction (Burkholder et al., 2007). This reduction can impede seagrass
growth and its ability to assimilate nitrogen, as they are vascular benthic autotrophs that
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
require clear water and high levels of Photosynthetically Active Radiation (PAR). In fact,
minimum light requirements of seagrasses (2-37% of surface irradiance, SI) are much
higher than those of macroalgae and phytoplankton (about 1-3% of SI) (Dennison et al.,
1993; Lee et al., 2007). Therefore, seagrass photosynthesis, and thereby their growth,
survival, and depth distribution, are directly linked to PAR reaching the plant surface
(Cabello-Pasini et al., 2003). The spatial variation in light availability in eutrophic SECS
can cause changes in the spatial distribution of seagrass on the order of meters. Another
aspect that should be taken into account is that the allocation and abundance of seagrasses
have changed over evolutionary time in response to sea-level rise (SLR) (Orth et al.,
2006). In areas where the tidal range increases, plants at the lower edge of the bed will
receive less light at high tide, which increases plant stress, reduces photosynthesis, and
therefore decreases the growth and survival of the vegetation (Short and Neckles, 1999;
Titus et al., 2009). The complexity and variability of eutrophic SECS with seagrass
meadows highlights the need for a spatially explicit model that can resolve spatial
distributions of chlorophyll, turbidity, colored dissolved organic matter (CDOM), and
ultimately light attenuation. There are relatively few coupled hydrodynamic-light models
that calculate light attenuation as a function of different attenuating substances apart from
chlorophyll and water (Everett et al., 2007; Hipsey and Hamilton, 2008), and even fewer
take into account spectral underwater irradiance (Bissett et al., 1999a, b).
In the present study, we develop a new tool to assess eutrophication and potential seagrass
habitat in eutrophic SECS. We have used a three-dimensional circulation model
(Regional Ocean Model System, ROMS) coupled to a Nutrient Phytoplankton
Zooplankton Detritus (NPZD) eutrophication model (Fennel et al., 2006), where we have
integrated a spectral light attenuation formulation (Gallegos et al., 2011). We describe the
model and the linkage of this tool with a benthic seagrass model (Zimmerman, 2003),
which calculates seagrass distribution. We apply the model to West Falmouth Harbor, a
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eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
eutrophic SECS where we assess the effects of future nitrate loading and sea level rise
scenarios on seagrass habitat and eutrophication. In the sections that follow we describe:
the observational methods and results, the numerical model and skill assessment, and
future scenarios of nitrate loading and sea-level rise. Finally, we discuss the utility and
limitations of the approach and future directions.
4.2 Observational methods
We deployed instrumentation in West Falmouth Harbor (described in Chapter II) to
measure meteorological, hydrodynamic, water quality, and light conditions during
summer 2012 (See Figure 4.2 ).
Figure 4.2. Field survey stations and equipment
Meteorological data were measured at 1 minute intervals by an Onset weather station
from 28 June 2012 to 11 September 2012. Parameters included wind direction, wind
speed, atmospheric pressure, relative humidity, shortwave radiation, PAR, and air
temperature. The subaqueous instrument platform consisted of a Nortek Aquadopp
ADCP (water velocity), a SeaBird SeaCat (pressure), a YSI 6600 multisonde (salinity,
temperature, chlorophyll-a, turbidity, dissolved oxygen), and a pair of WetLabs ECO-
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
PARSB sensors (PAR). All sensors were located 0.3 mab except for the upper PAR sensor
located at 0.8 mab. The PAR sensors were equipped with wipers to prevent bio-fouling.
The chlorophyll-a values were obtained by a YSI 6025 sensor located in the YSI 6600
multisonde. This sensor uses a light source with a peak wavelength of 470 nm which
provokes the chlorophyll-a emission of light between 650 – 700 nm. The output of the
sensor is automatically processed via the sonde software, which provides the chlorophyll-
a (µg/L) readings. Measurements were collected at 5 min intervals from 3 to 19 July 2012
in Outer Harbor, from 19 July 2012 to 9 August 2012 in South Cove, and from 9 to 27
August 2012 in Snug Harbor. Due to the fact that no large intra-seasonal changes were
observed between July and August during a previous field survey in 2010 (Ganju et al.,
2011), the data collected in each location were considered representative of the season.
Following Gallegos et al. (2011), we calculated the diffuse attenuation coefficient of
downward propagating irradiance, Kd, as:
upper
lowerd
PAR
PAR
zK ln
1 (Eq. 34)
where Kd is the light attenuation coefficient, z is the distance between the two sensors
(0.5 m), PAR lower and PAR upper are the PAR measurements near the bottom and just
below the water surface respectively during daylight.
To determine the areal extent of seagrass beds in West Falmouth Harbor we conducted
surveys during early June 2012, using side scan sonar. We used an EdgeTech, Inc. 4125
towfish and EdgeTech Discover software to collect acoustic data at 900 and 1250 kHz
along survey transects spaced to provide 200% bottom coverage in the survey area, and
horizontal positions were provided by a Trimble AgGPS 132 with U.S.Coast Guard
beacon differential corrections. We pre-processed the data in Chesapeake Technology
Inc. SonarWiz5 to adjust for signal attenuation through the water, georeference the data,
and export georeferenced imagery to ArcGIS 10.1 for classification. We manually
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eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
delineated the seagrass beds in ArcGIS after examining ground-truthed locations to
calibrate our image interpretation, and verified the final extent by surveying a random set
of locations using a combination of surface and underwater observational techniques.
Groundwater fluxes and associated nitrate concentrations (Figure 4.3) were obtained in
previous studies (Kroeger et al., 2006; Ganju et al., 2012; Hayn et al., 2014).
Figure 4.3. Groundwater fluxes and nitrate concentrations at West Falmouth Harbor. Arrows indicate
main contributions from Falmouth Wastewater Treatment Plant (FWTP).
4.3 Observational results
Mean values of water column properties (temperature, salinity, pH, dissolved oxygen and
turbidity) were overall spatially similar except for chlorophyll-a, which was highest at
site Snug (Table 4.1).
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eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Table 4.1. Mean values and standard deviation (Std) of measurements.
FIELD
MEASUREMENT
UNITS
MEAN ± STD
OUTER SNUG SOUTH
Chlorophyll-a µgL-1 6.46 ± 2.75 27.50 ± 9.90 10.22 ± 9.26
Turbidity NTU N/A* 4.26 ± 1.49 3.41 ± 1.39
Temperature °C 24.64 ± 0.84 25.84 ± 0.91 24.37 ± 0.81
Salinity psu 30.94 ± 0.31 29.74 ± 0.65 30.51 ± 0.56
pH - 8.01 ± 0.10 8.06 ± 0.49 7.97 ± 0.32
Dissolved oxygen mgL-1 7.17 ± 1.17 7.54 ± 1.44 6.75 ± 1.22
* Turbidity at site Outer was compromised by reflective copper tape (for anti-fouling) accidentally placed
near the optical window. The tape tarnished within two weeks and did not affect subsequent measurements
at sites Snug or South.
Chlorophyll-a measurements suggested more eutrophication at landward ends of the
harbor, with a mean value of 28 µg L-1, whereas in the outer harbor the mean
concentration was 6.5 µg L-1. Accordingly, site Snug demonstrated considerably lower
PAR values than site Outer (Table 4.2). PAR data from site South were not obtained due
to instrument malfunction.
Table 4.2. Mean values, standard deviation (Std) and percentile 84 of measured optical data during
daylight hours.
FIELD
MEASUREMENT
UNITS
MEAN ± STD PERCENTILE 84
OUTER SNUG OUTER SNUG
PARupper µE/m2s 504 ± 387 301 ± 300 945 545
PARlower µE/m2s 416 ± 327 198 ± 209 795 363
It is important to note that for the Kd calculation we only considered PAR values over the
84th percentile of the distribution, which corresponds to the hours of highest light
incidence, usually around noon. These values were selected because when a beam of light
impacts the water surface perpendicularly or with low angles measured from the vertical,
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
most of the light penetrates the water column, and the scattering on the water surface is
minimal. However, the larger the incident angle, the less light penetrates the water column
and the less accurate the PAR measurements become. We have used PAR values during
times when sunbeams impact the water surface with low incidence angles to minimize
this effect.
The mean diffuse light attenuation coefficient Kd was 0.45 m-1 at site Outer and 0.86 m-1
at site Snug. Statistical distribution of chlorophyll-a measurements and Kd between the
two sites confirms that the larger light attenuation coefficient present at site Snug is
consistent with elevated chlorophyll-a concentration (Figure 4.4). Prior measurements
showed that CDOM is spatially uniform and relatively low in West Falmouth Harbor
(absorbance at 440 nm < 0.01 m-1; M. Hayn, unpublished). These measurements also
indicate that turbidity is relatively low with minimal spatial differences. Therefore there
is a strong relationship between eutrophication (and ensuing chlorophyll-a levels) and
light attenuation in the study area.
Figure 4.4. Chlorophyll-a and Kd field data histograms in Outer and Snug Harbors. Data collected from
sensors deployed during summer 2012 with a 5 minutes sampling interval.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
4.4 Model description
We integrated a spectral irradiance model (Gallegos et al., 2011) into an existing NPZD-
biogeochemical model (Fennel et al., 2006) to compute the spectral penetration of PAR
through the water column. The coupled optical-biogeochemical model uses the PAR from
the irradiance model to calculate phytoplankton growth. This model was integrated in the
ROMS 3D circulation model (Haidvogel et al., 2008) that simulates the three-dimensional
hydrodynamics. The computed PAR and Kd are provided to a bio-optical model
(Zimmerman, 2003) that calculates the seagrass carbon balance under the estimated light
climate (Figure 4.5). The carbon balance allows the prediction of seagrass
presence/absence and its potential survival. The capabilities of the linkage of these models
provide an integral description of the physical, optical, and biological dynamics of the
water column. This allows us to define a success criterion for assessing seagrass future
evolution in a eutrophic SECS, based on light climate alone. In this section, we present a
brief description of the main processes of each different model, although further
information can be found in their respective references.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Figure 4.5. Modeling system flowchart and interactions. In the bottom panel, P/R ratio > 1 indicates
potential seagrass habitat; P/R < 1 indicates potential loss of seagrass habitat. U and V are the velocities,
h the water depth, T the water temperature and η the water surface variation.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
4.4.1 Physical model
The circulation model used is the Regional Ocean Modeling System (ROMS)
(Marchesiello et al., 2003; Shchepetkin and McWilliams, 2005; Warner et al., 2005a;
Haidvogel et al., 2008). ROMS is a three-dimensional, free-surface, terrain-following
numerical model that solves the Reynolds-averaged Navier-Stokes equations using the
hydrostatic and Boussinesq assumptions (Haidvogel et al., 2008). The main equations and
parameters of the hydrodynamical core of this model can be seen at Table 4.3.
In the physical configuration adopted, the outer domain is 2 km in the north-south
direction centered on 41.6° latitude and 1.5 km in the east-west direction centered on -
70.64° longitude (Figure 2.7) and includes the entire West Falmouth Harbor estuarine
system. The horizontal grid spacing is 10 m (150x200 grid points). The grid has 10
vertical levels using an evenly spaced vertical stretching. The spatial discretization of
such a model allows for the representation of the spatial heterogeneity of the estuary in
terms of light climate.
The model was forced at the western boundary (Figure 2.7) with tidal free surface
elevation, velocity, salinity and temperature. Additionally, the atmospheric forcing
included wind velocities, atmospheric pressure, shortwave radiation, surface air
temperature and relative humidity. These data were obtained from the weather station
located in the study area (Figure 4.2) and were applied as surface forcing in the entire
computational domain. Groundwater fluxes, nitrogen loads, and fresh water temperature
were given to the model as point sources (Figure 4.3). These fluxes were quantified from
velocity and salinity measurements and a Total Exchange Flow (TEF) methodology
(Ganju et al., 2012).
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eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Table 4.3. Physical model main equations and parameters
ROMS HYDRODYNAMIC CORE (SHCHEPETKIN AND MCWILLIAMS, 2005; WARNER ET AL., 2005a; HAIDVOGEL ET AL., 2008)
𝒖, 𝒗, and w: components of velocity in the horizontal (x and y) and vertical (scaled sigma coordinate, s) directions respectively. ζ: wave-averaged free-surface elevation. h: depth of the sea floor below mean sea level. Hz: vertical level thickness. f: Coriolis parameter. p: Pressure. ρ and ρ0: total and reference densities. g: acceleration due to gravity. 𝐯 𝐚𝐧𝐝 𝐯𝟎: Molecular viscosity and diffusivity. C: tracer quantity. Csource: tracer source/sink terms. KM: eddy viscosity for momentum. KH: eddy diffusivity for tracers. Note: An over-bar represents a time average, and a prime (‘) represents turbulent fluctuations.
Reynolds-averaged Navier Stokes equations
𝜕(𝐻𝑧𝑢)
𝜕𝑡+
𝜕(𝑢𝐻𝑧𝑢)
𝜕𝑥+
𝜕(𝑣𝐻𝑧𝑢)
𝜕𝑦+
𝜕(𝑤𝐻𝑧𝑢)
𝜕𝑠− 𝑓𝐻𝑧𝑣 = −
𝐻𝑧
𝜌0
𝜕𝑝
𝜕𝑥− 𝐻𝑧𝑔
𝜕𝜁
𝜕𝑥−
𝜕
𝜕𝑠(𝑢′𝑤′ −
v
𝐻𝑧
𝜕𝑢
𝜕𝑠)
𝜕(𝐻𝑧𝑣)
𝜕𝑡+
𝜕(𝑢𝐻𝑧𝑣)
𝜕𝑥+
𝜕(𝑣𝐻𝑧𝑣)
𝜕𝑦+
𝜕(𝑤𝐻𝑧𝑣)
𝜕𝑠− 𝑓𝐻𝑧𝑢 = −
𝐻𝑧
𝜌0
𝜕𝑝
𝜕𝑦− 𝐻𝑧𝑔
𝜕𝜁
𝜕𝑦−
𝜕
𝜕𝑠(𝑣′𝑤′ −
𝑣
𝐻𝑧
𝜕𝑣
𝜕𝑠)
0 = −1
𝜌0
𝜕𝑝
𝜕𝑠−
𝑔
𝜌0
𝐻𝑧𝜌
The continuity equation:
𝜕𝜁
𝜕𝑡+
𝜕(𝐻𝑧𝑢)
𝜕𝑥+
𝜕(𝐻𝑧𝑣)
𝜕𝑦+
𝜕(𝐻𝑧𝑤)
𝜕𝑠= 0
Scalar transport: 𝜕(𝐻𝑧𝐶)
𝜕𝑡+
𝜕(𝑢𝐻𝑧𝐶)
𝜕𝑥+
𝜕(𝑣𝐻𝑧𝐶)
𝜕𝑦+
𝜕(𝑤𝐻𝑧𝐶)
𝜕𝑠= −
𝜕
𝜕𝑠(𝑐′𝑤′ −
𝑣0
𝐻𝑧
𝜕𝐶
𝜕𝑠) + 𝐶𝑠𝑜𝑢𝑟𝑐𝑒
State equation: 𝜌 = 𝑓(𝐶, 𝑝)
Reynolds stresses and turbulent tracer fluxes
parametrization: 𝑢′𝑤′ = −𝐾𝑀
𝜕𝑢
𝜕𝑧
𝑣′𝑤′ = −𝐾𝑀
𝜕𝑣
𝜕𝑧
𝑐′𝑤′ = −𝐾𝐻
𝜕𝜌
𝜕𝑧
Variable Grid Location 𝒖 X 𝒗 C, Csource, ρ, ρ0
w, KM, KH
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
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4.4.2 Biogeochemical model
The phytoplankton dynamics are simulated using a biogeochemical nutrient,
phytoplankton, zooplankton, and detritus (NPZD) model (Fennel et al., 2006). This model
is implemented into ROMS, and assumes nitrogen as the controlling nutrient for primary
production. Therefore, it is based on the nitrogen cycle (Figure 4.5), and includes the
source, sink, and biogeochemical transformation terms of seven state variables: nitrate
(NO3), ammonium (NH4), small and large detritus (SDet and LDet), phytoplankton
(Phy), zooplankton (Zoo), and chlorophyll-a (Chl). We added the effect of seagrass
nutrient uptake in order to account for its influence in nutrient cycling. This is represented
in Figure 4.5 by the arrow that goes from NH4 to the sediment. We assumed that the
uptake decreases with depth as seagrass biomass and production are strongly related with
light availability (Cunningham, 2002). The mean nitrogen uptake by seagrasses in the
bottom layer of the model varies between 0 and 10 mmolNm-2day-1 (Hemminga et al.,
1991; Risgaard-Petersen et al., 1998; Hansen et al., 2000; Risgaard-Petersen and Ottosen,
2000), describing the nitrogen removal due to seagrass.
The main biogeochemical model equations were described by Fennel et. al (2006), who
adapted them from the plankton dynamics model of Fasham et al. (1990). In the Fennel
implementation, phytoplankton growth is a function of temperature, nutrient
concentration, and the homogenously integrated PAR distribution. The main equations
and parameters of this model can be seen at Table 4.4.
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Table 4.4. Irradiance model main equations and parameters
BIOGEOCHEMICAL MODEL (FENNEL ET AL., 2006)
mp: Phytoplankton mortality.
: Aggregation parameter. KNO3: Half-saturation concentration for uptake of NO3 KNH4: Half-saturation concentration for uptake of NH4 μr: Phytoplankton growth rate at reference temperature (Tref) T: Temperature α: Initial slope of the P-I curve I: Photosynthetically available radiation lBM: excretion rate due to basal metabolism lE: maximum rate of assimilation related excretion. β: Assimilation efficiency. mz: Zooplankton mortality. gmax: Maximum grazing rate kp: Half-saturation concentration of phytoplankton ingestion. Θmax: Maximum chlorophyll-a to phytoplankton ratio. rSD: Remineralization rate of suspended detritus. rLD: Remineralization rate of large detritus. wPhy: Sinking velocity of phytoplankton wSDet: Sinking velocity of suspended detritus wLDet: Sinking velocity of larger particles. nmax: Maximum nitrification rate. I0: Threshold for light-inhibition of nitrification KI: half-saturated light intensity of nitrification inhibition.
Phytoplankton balance: 𝜕𝑃ℎ𝑦
𝜕𝑡= 𝜇𝑃ℎ𝑦 − 𝑔𝑍𝑜𝑜 − 𝑚𝑝𝑃ℎ𝑦 − 𝜏(𝑆𝐷𝑒𝑡 + 𝑃ℎ𝑦)𝑃ℎ𝑦 − 𝑤𝑃ℎ𝑦
𝜕𝑃ℎ𝑦
𝜕𝑧
Growth rate of phytoplankton: 𝜇 = 𝜇𝑚𝑎𝑥𝑓(𝐼) (𝑁𝑂3
𝐾𝑁𝑂3+𝑁𝑂3∙
1
1+𝑁𝐻4
𝐾𝑁𝐻4
+𝑁𝐻4
𝐾𝑁𝐻4+𝑁𝐻4)
Maximum phytoplankton growth rate:
𝜇𝑚𝑎𝑥 = 𝜇𝑟1.066𝑇−𝑇𝑟𝑒𝑓
Photosynthesis-light (PI) relationship:
𝑓(𝐼) =𝛼𝐼
√𝜇𝑚𝑎𝑥2 + 𝛼2𝐼2
Photosynthesis available radiation:
𝐼 = 𝐼0 ∙ 0.43 ∙ 𝑒𝑥𝑝{−𝑧𝐾𝑑}
Light attenuation coefficient:
𝐾𝑑 = 𝐾𝑤 + 𝐾𝑐ℎ𝑙 ∫ 𝐶ℎ𝑙(𝑧)𝑑𝑧0
𝑧
Zooplankton balance: 𝜕𝑍𝑜𝑜
𝜕𝑡= 𝑔𝛽𝑍𝑜𝑜 − 𝑙𝐵𝑀𝑍𝑜𝑜 − 𝑙𝐸
𝑃ℎ𝑦2
𝐾𝑃+𝑃ℎ𝑦2 𝛽𝑍𝑜𝑜 − 𝑚𝑧𝑍𝑜𝑜2
Chlorophyll balance: 𝜕𝐶ℎ𝑙
𝜕𝑡= 𝜌𝐶ℎ𝑙𝜇𝐶ℎ𝑙 − 𝑔𝑍𝑜𝑜
𝐶ℎ𝑙
𝑃ℎ𝑦− 𝑚𝑝𝐶ℎ𝑙 − 𝜏(𝑆𝐷𝑒𝑡 + 𝑃ℎ𝑦)𝐶ℎ𝑙
Relationship between chl-a and phytoplankton:
𝜌𝐶ℎ𝑙 =𝜃𝑚𝑎𝑥𝜇𝑃ℎ𝑦
𝛼𝐼𝐶ℎ𝑙
Rate of phytoplankton grazing by zooplankton:
𝑔 = 𝑔𝑚𝑎𝑥
𝑃ℎ𝑦2
𝐾𝑝 + 𝑃ℎ𝑦2
Small Detritus balance:
𝜕𝑆𝐷𝑒𝑡
𝜕𝑡= 𝑔(1 − 𝛽)𝑍𝑜𝑜 + 𝑚𝑧𝑍𝑜𝑜2 + 𝑚𝑝𝑃ℎ𝑦 − 𝜏(𝑆𝐷𝑒𝑡 + 𝑃ℎ𝑦)𝑆𝐷𝑒𝑡 − 𝑟𝑆𝐷𝑆𝐷𝑒𝑡 − 𝑤𝑆𝐷𝑒𝑡
𝜕𝑆𝐷𝑒𝑡
𝜕𝑧
Large Detritus balance: 𝜕𝐿𝐷𝑒𝑡
𝜕𝑡= 𝜏(𝑆𝐷𝑒𝑡 + 𝑃ℎ𝑦)2 − 𝑟𝐿𝐷𝐿𝐷𝑒𝑡 − 𝑤𝐿𝐷𝑒𝑡
𝜕𝐿𝐷𝑒𝑡
𝜕𝑧
Nitrate balance 𝜕𝑁𝑂3
𝜕𝑡= −𝜇𝑚𝑎𝑥𝑓(𝐼) (
𝑁𝑂3
𝐾𝑁𝑂3+𝑁𝑂3∙
1
1+𝑁𝐻4
𝐾𝑁𝐻4
) 𝑃ℎ𝑦 + 𝑛𝑁𝐻4
Nitrification rate: 𝑛 = 𝑛𝑚𝑎𝑥 (1 − 𝑚𝑎𝑥 [0,𝐼−𝐼0
𝑘𝐼+𝐼−𝐼0])
Ammonium balance:
𝜕𝑁𝐻4
𝜕𝑡= −𝜇𝑚𝑎𝑥𝑓(𝐼) (
𝑁𝐻4
𝐾𝑁𝐻4 + 𝑁𝐻4) 𝑃ℎ𝑦 − 𝑛𝑁𝐻4 + 𝑙𝐵𝑀𝑍𝑜𝑜 + 𝑙𝐸
𝑃ℎ𝑦2
𝑘𝑝 + 𝑃ℎ𝑦2 𝛽𝑍𝑜𝑜 + 𝑟𝑆𝐷𝑆𝐷𝑒𝑡 + 𝑟𝐿𝐷𝐿𝐷𝑒𝑡
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4.4.3 Irradiance model
Phytoplankton and seagrass growth are intrinsically dependent not only on light quantity
but also on light quality. The basic irradiance formulation included in Fennel et al. (2006)
did not account for spectral effects in considering light attenuation by water and
chlorophyll-a (see Table 4.4). To better approximate light behavior, the spectral
irradiance model used by Gallegos et al. (2011) was implemented. The atmospheric
evolution of the spectral irradiance was formulated following Gregg and Carder (1990),
which included absorption and scattering by ozone, oxygen, water vapor, and marine
aerosols and also reflectance at the air-sea interface. In the current experiment, the
observed PAR at the weather station was imposed at the water surface while enforcing
the spectral shape given by the Gregg and Carder (1990) formulation for that time.
The implemented spectral attenuation in the water column from Gallegos et al. (2011)
included the effects of water, CDOM, phytoplankton, and non-algal particulates (e.g.,
detritus, minerals, bacteria). In our simulations, the attenuation due to CDOM was
assumed to be minimal because CDOM concentrations were negligible in the area under
study (<3 Quinine Sulfate Units, QSU; <0.01 m-1 absorbance at 400 nm). The water
absorption and backscattering was assumed to follow the spectral characteristics of pure
water. The light absorption and scattering by phytoplankton was represented as
proportional to the chlorophyll-a concentration given by the Fennel et al. (2006)
implementation. Meanwhile, the non-algal component of the spectral attenuation was
taken as proportional to the total suspended solids concentration, which was considered
constant in the present study. The spectral shape of the attenuation by each component
followed the description in Gallegos et al. (2011). The PAR distribution across the entire
spectrum was integrated and used for the calculation of phytoplankton growth. The
spectral PAR was used to determine seagrass growth as part of the Zimmerman (2003)
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bio-optical model. The main equations and parameters of Gallegos et al. (2011) model
can be seen at Table 4.5.
Table 4.5. Irradiance model main equations and parameters
SPECTRAL IRRADIANCE MODEL (GALLEGOS ET AL, 2011)
λ: Wavelenght z: Depth within the canopy E0(λ): Surface incident downwelling irradiance θ0: Solar incidence angle aw(λ): Absorption due to water aCDOM(λ): Absorption due to CDOM aNAP(λ): Absorption due to nonalgal particulates aChla(λ): Absorption due to chlorophyll-a bbw(λ): Backscattering due to water bbNAP(λ): Backscattering due to nonalgal particulates bbPhyto(λ): Backscattering due to phytoplankton
Spectral diffuse attenuation coefficient
𝐾𝑑(𝜆) = (1 + 0.005𝜃0)𝑎(𝜆) + 4.18{1 − 0.52𝑒𝑥𝑝[−10.8𝑎(𝜆)]}𝑏𝑏(𝜆)
Total light absorption 𝑎(𝜆) = 𝑎𝑤(𝜆) + 𝑎𝐶𝐷𝑂𝑀(𝜆) + 𝑎𝑁𝐴𝑃(𝜆) + 𝑎𝐶ℎ𝑙𝑎(𝜆)
Total light backscattering 𝑏𝑏(𝜆) = 𝑏𝑏𝑤(𝜆) + 𝑏𝑏𝑝ℎ𝑦𝑡𝑜(𝜆) + 𝑏𝑏𝑁𝐴𝑃(𝜆)
Downwelling spectral irradiance
𝐸𝑑(𝑧, 𝜆) = 𝐸0(𝜆)𝑒𝑥𝑝[−𝐾𝑑(𝜆)𝑧]
4.4.4 Bio-optical seagrass model
We linked a bio-optical model (Figure 4.5) to compute the carbon balance based on light
conditions and photosynthesis. The model developed by Zimmerman (2003) consists of
three different modules: a module that simulates the seagrass relative biomass and
architecture including leaf geometry, an irradiance module that calculates the light
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
absorption and scattering through the canopy, and a photosynthesis module that calculates
the carbon balance (primary production and respiration) of the submerged plant canopy.
The original 1D model simulates the light environment of a submerged canopy at a fixed
horizontal point. However, we have applied the model to the entire domain. We have
assumed initial seagrass presence in the entire system allowing the description of the light
environment by dividing the canopy volume, including the leaves and the water column,
into a series of horizontal sections of finite thickness. The optical properties of each
section are based on the architecture of the canopy, the orientation and optical properties
of the leaves, and the optical properties of the dissolved materials and suspended particles
in the water column. Given the spectral PAR at canopy height from the irradiance model,
it computes the seagrass Photosynthetically Usable Radiation (PUR) by computing the
spectral absorption and scattering of the downwelling and upwelling photosynthetically
active irradiance through the seagrass canopy. Finally, the model calculates the canopy
carbon balance, by computing the photosynthesis/respiration ratio. This ratio was used to
assess the seagrass presence/absence and survival under different scenarios and
conditions. The threshold of P/R=1 was chosen, as both autotrophic and heterotrophic
ecosystems tend to approach P/R=1 over time (Giddings and Eddlemon, 1978).
Moreover, as the ecosystem under study is autotrophic, we have assumed that P/R>1 is
associated with seagrass success and growth, while P/R<1 leads to seagrass
disappearance. The main equations and parameters of this model can be seen at Table 4.6.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Table 4.6. Bio-optical seagrass model main equations and parameters
BIO-OPTICAL SEAGRASS MODEL (ZIMMERMAN, 2003)
λ: Wavelength z: Depth within the canopy β: Bending angle of the seagrass canopy hm: Maximum canopy height h(z): Height above the seabed s: Shape factor for biomass distribution Rd (λ,z): Canopy reflectance of downwelling irradiance. �̅�𝒅(𝒛): Average cosine of downwelling irradiance. 𝑲𝒅(𝝀, 𝒛): Water column attenuation of downwelling irradiance. 𝒂𝑳(𝝀): Leaf absorption coefficient Rd (λ,z): Canopy reflectance of upwnwelling irradiance. 𝑲𝒖(𝝀, 𝒛): Water column attenuation of upwelling irradiance. Ap (λ): Photosynthetically absorptance DT: Daylenght ∅𝒑: Light-use efficiency
FR: Fraction of total plant biomass represented by root and rhizome. RR: Respiration rate of below-ground tissue. FL: Fraction of total plant biomass represented by leaves.
RL: Leaf respiration
Module I: vertical canopy architecture and leaf geometry
Horizontally projected leaf area at depth z 𝑙𝑝(𝑧) = 𝑙(𝑧)𝑠𝑖𝑛𝛽
Leaf area index at depth z 𝑙(𝑧) = 𝐿 × 𝐵(𝑧)
Canopy leaf area index 𝐿 = 𝑠ℎ𝑜𝑜𝑡 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 × 𝐿𝑠
Leaf area per shoot 𝐿𝑠 = 0.0063ℎ𝑐 + 0.019ℎ𝑐2
Realized canopy height ℎ𝑐 = ℎ𝑚 cos 𝛽
Biomass fraction in layer z 𝐵(𝑧) = 𝜓 (1 + [
ℎ(𝑧)
𝐼]
𝑠
)⁄
Percent of canopy biomas at the seabed 𝜓 = 2.51ℎ𝑐−0.79
Intermediate height of biomas distribution 𝐼 = 0.588[1 − 𝑒𝑥𝑝(−1.12ℎ𝑐)]
Module II: two-flow irradiance distribution
Downwelling plane irradiance transmitted through layer z
𝐸𝑑(𝜆, 𝑧) = 𝐸𝑑(𝜆, 𝑧 − 1)[1 − 𝑅𝑑(𝜆, 𝑧)] × 𝑒𝑥𝑝 [−𝑎𝐿(𝜆)𝑡𝐿𝑙𝑝(𝑧)
�̅�𝑑(𝑧)− 𝐾𝑑(𝜆, 𝑧)∆𝑧]
Upwelling plane irradiance transmitted through layer z
𝐸𝑢(𝜆, 𝑧) = {[𝐸𝑑(𝜆, 𝑧)𝑅𝑏(𝜆, 𝑧 + 1)] + 𝐸𝑢(𝜆, 𝑧 + 1)} × [1 − 𝑅𝑢(𝜆, 𝑧)] × 𝑒𝑥𝑝 [−𝑎𝐿(𝜆)𝑡𝐿𝑙𝑝(𝑧)
�̅�𝑢− 𝐾𝑢(𝜆)∆𝑧]
Module III: Canopy photosynthesis
Photosynthetically used irradiance in layer z
𝑃𝑈𝑅(𝑧) = ∑ 𝐴𝑝(𝜆)𝑙𝑝(𝑧) [𝐸𝑑(𝜆, 𝑧 − 1)
�̅�𝑑(𝑧 − 1)+
𝐸𝑢(𝜆, 𝑧 + 1)
�̅�𝑢
]
𝜆
Daily integrated biomass-specific photosynthesis 𝑃 = ∑ 𝐵(𝑧){1 − 𝑒𝑥𝑝[−0.67∅𝑝𝑃𝑈𝑅(𝑧)]}𝐷𝑇
𝑧
Daily plant respiration (adapted from Zimmerman et al. 1995)
𝑅 = 24 × 𝑅𝐿 × 𝐹𝐿 + 𝐷𝑇 × 𝑅𝑅 × 𝐹𝑅 + (24 − 𝐷𝑇)× 0.65𝑅𝑅 × 𝐹𝑅
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
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4.5 Model skill assessment
In the present section the assessments of the biogeochemical, irradiance and bio-optical
models are described. The hydrodynamics and freshwater fluxes were assessed in a
previous study (Ganju et al., 2012). During the calibration process, all model parameters
were adjusted to match measured values. The indicators used to calibrate the
biogeochemical, irradiance and bio-optical models were chlorophyll-a concentration,
light attenuation coefficient, and seagrass presence/absence, respectively.
4.5.1 Biogeochemical and irradiance model assessment
A sensitivity analysis was conducted to assess the influence of the main model parameters
on chlorophyll-a results. These parameters were fixed to their minimum and maximum
literature value (see Table 4.7) obtaining the behavior observed in Figure 4.6 The
parameters that have a major effect on the chlorophyll-a model results were μr, α, gmax,
mp, τ, and mz, which is in agreement with Fasham et al. (1990) and Fennel et al. (2011).
Figure 4.6. Comparison of hourly model and field data values of chlorophyll-a at the sampling stations.
The calibration of the biogeochemical and irradiance model was focused on the three sites
where field measurements were obtained, each one representative of Outer, Snug and
South, respectively. The values of the model parameters were chosen within the range
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
found in the literature (Andersen et al., 1987; Taylor, 1988; Wroblewski, 1989; Taylor et
al., 1991; Fasham, 1995; Geider et al., 1997; Leonard et al., 1999; Lima and Doney, 2004)
to maximize the agreement between model results and field data (Table 4.7). We achieved
the highest skill by adjusting values of some of the main influential parameters according
to the results of the sensitivity analysis (see Figure 4.6) setting the rest of the parameters
with the same value as Fennel et al. 2006. The final simulation values can be seen in Table
4.7, being all of them in agreement with the literature variation ranges.
Table 4.7. Main biogeochemical and irradiance model parameters and chosen value.
SYMBOL DEFINITION CALIBRATED VALUE
UNITS RANGE
µr Phytoplankton growth rate at
reference temperature
3 d-1 0.62a-3.0b
KNO3 Half-saturation concentration for
uptake of NO3
0.1 Mmol N m-3 0.007-1.5c
KNH4 Half-saturation concentration for
uptake of NH4
1.5 Mmol N m-3 0.007-1.5 c
α Initial slope of the P-I curve 0.13 Mol C gChl-1(Wm-2)-1d-1 0.007-0.13d
gmax Maximum grazing rate 0.6 (mmol N m-3)-1 d-1 0.5e-1.0f
Kp Half-saturation concentration of
phytoplankton ingestion
2 (mmol N m-3)2 0.56-3.5c
mp Phytoplankton mortality 0.05 d-1 0.05-0.2g
Aggregation parameter 0.005 (mmol N m-3)-1d-1 0.005-0.1c
Θmax Maximum chlorophyll-a to
phytoplankton ratio
0.068 mgChl mg C-1 0.005-0.072d
mz Zooplankton mortality 0.025 (mmol N m-3)-1 d-1 0.025-0.25c
RSD Remineralization rate of
suspended detritus
0.03 d-1 0.01-0.25h
RLD Remineralization rate of large
detritus
0.01 d-1 0.01-0.25h
Nmax Maximum nitrification rate 0.05 d-1 0.05-0.1c a(Taylor, 1988) b(Andersen et al., 1987) c(Lima and Doney, 2004) d(Geider et al., 1997) e(Wroblewski, 1989) f(Fasham, 1995) g(Taylor et al., 1991) h(Leonard et al., 1999).
Calibration results show that in general, hourly temporal series of modelled chlorophyll-
a follows the field data behaviour as can be seen in Figure 4.7, finding the biggest
deviations at South Cove.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed eutrophic coastal systems.
Application to a groundwater-fed estuary with submerged aquatic vegetation.
Figure 4.7. Comparison of hourly model and field data values of chlorophyll-a at the sampling stations.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
A comparison between mean modeled and field data at the sampling stations allows a
better understanding of the model results (see Table 4.8 and Figure 4.8). In Snug and
Outer harbors, with regards to the chlorophyll-a concentration and the light attenuation
coefficient we achieved a BIAS close to zero. The similarity between the standard
deviations suggests that the model properly describes the variability of the system in those
areas. By contrast, in South Cove the difference between modeled and observed
chlorophyll-a is larger.
Table 4.8. Mean values of chlorophyll-a and Kd, standard deviation (Std), and BIAS for Outer, Snug and
South Harbors. Field values of chlorophyll-a and Kd were obtained processing data from sensors deployed
during summer 2012. Model results were obtained for the same time-period.
CHLOROPHYLL-A KD
Site Mean ± Std
Model (µg/L)
Mean ± Std
Field (µg/L)
BIAS
(µg/L)
Mean ± Std
Model (1/m)
Mean ± Std
Field (1/m)
BIAS (1/m)
Outer 6.9 ± 3.7 6.5 ± 2.8 0.41 0.45 ± 0.07 0.45 ± 0.30 -0.001
Snug 28 ± 12 28 ± 9.9 0.33 0.79 ± 0.19 0.86 ± 0.16 -0.077
South 6.3 ± 3.9 10 ± 9.3 -3.9 - - -
Figure 4.8. Comparison of mean model and field data values of Chlorophyll-a and Kd at the sampling
stations.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Additionally, a spectral analysis was performed in order to assess the tidal and diurnal
influence in chlorophyll-a model results at the sampling stations (see Figure 4.9). The
spectral analysis shows that chlorophyll-a in the Outer and South basins is mainly
influenced by tidal transport from landward to award (12 h peak in Figure 4.9) while Snug
Harbor is strongly influenced by solar radiation (24 h peak in Figure 4.9). This is
consistent with the fact that Snug presents the highest phytoplankton concentration being
its variation strongly correlated with light availability, whereas Outer and South have a
stronger tidal influence due to the lower phytoplankton presence.
Figure 4.9. Spectral analysis of chlorophyll-a based on model results at the sampling stations
Finally, the mean vertical and time averaged chlorophyll-a concentration for the whole
estuary can be seen in Figure 4.10. This calculation was obtained by computing the
chlorophyll-a concentration three-dimensionally during the complete study period
(Figure 4.10 a) and then calculating the time-averaged and mean vertical concentration
(Figure 4.10b). The simulation results show higher eutrophication levels in the upper
layers (see Figure 4.10a), and also higher chlorophyll-a levels in Snug harbor than in
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Outer Harbor and South Cove (see Figure 4.10 a and b). Moreover, these results are in
agreement with field data as can be seen in Figure 4.8. Therefore, the model is able to
accurately reproduce chlorophyll-a concentration behavior with high spatial resolution.
Figure 4.10. a) Vertical chlorophyll-a variation in three layers of the model; b) Time-averaged and mean
vertical chlorophyll-a concentration for the whole estuary.
4.5.2 Seagrass bio-optical model assessment
The calibrated parameters for the bio-optical model were the bending angle, the maximum
canopy height, and the shoot density. The bending angle selected was 45 º representing
the average angle over a tidal cycle, and the maximum canopy height was 1 meter
(Ackerman, 2002). The chosen density was 525 shoots/m2, which is the mean observed
plant density, as it varies between 250-800 shoots/m2 in Outer Harbor (McGlathery,
Marino, Hayn, and Howarth unpublished). The spectral PAR comes from the irradiance
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
model, and is propagated through the canopy to the seafloor. The seafloor absorbance and
reflectance properties were also considered with the composition of the bottom being a
mixture between mud (representing organic detritus) and sand. The reflected light was
also propagated upward through the canopy, so the primary production was calculated
with the total light absorbed. The potential habitat was evaluated as a function of the
Photosynthesis/Respiration (P/R) ratio distribution (Figure 4.11a) obtained using the
mean data of the entire summer. We have considered the P/R ratio obtained as
representative of the season in the year under study. We assumed that for P/R >1 there is
seagrass growth, and therefore presence, delimiting this threshold as the potential
seagrass in the estuary. However, for P/R≤1 we assumed conditions are unfavorable to
seagrass presence (Figure 4.11b) as growth would be limited, being respiration larger than
photosynthesis in those areas. Based on this criterion, we have obtained an agreement of
73.39 % between modeled and field presence/absence data, taking into account Outer and
Snug Harbor (Figure 4.11c), as in South Cove seagrass is thought to have disappeared
due to hydrodynamic reasons, and not due to the light conditions as can be seen in Figure
11b. Additionally, in Figure 4.11 we can see that seagrass is not present in the shallower
areas of the estuary. This is due to the wetting and drying effect simulated by the model
and the subtidal behaviour of zostera marina imposed in the model. The seagrass
distribution obtained by the selected P/R criterion (Figure 4.11d) was in agreement with
the critical depth distribution obtained applying the depth-limitation equation proposed
by Duarte et al. (2007).
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Figure 4.11. a) Photosynthesis/Respiration ratio distribution; b) Photosynthesis/Respiration ratio
distribution applying the P/R>1 criterion and comparison with field data (black solid line); c) Detail of
Snug and Outer P/R distribution with P/R> 1, and comparison with field data (black solid line) ; d)
Seagrass distribution obtained with the depth-limited equation (Duarte et al., 2007). The white area
represents where seagrass presence is discouraged (P/R<1), the light green area the potential seagrass
habitat (P/R >1), and the black solid line delimits the seagrass presence area measured in the field survey.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
4.6 Nitrate loading and sea-level rise scenarios
The coupled modeling system was applied to West Falmouth Harbor to assess the effects
of nitrate reduction and sea-level rise on chlorophyll-a concentration and potential
seagrass habitat during the summer. The simulations time period was of two months
corresponding with July and August. Different nitrate loading and sea-level rise scenarios
were conducted corresponding to anticipated future summer scenarios (Table 4.9). We
implemented a gradual decrease of the nitrate input load and an increase in sea-level rise
based on the IPCC predictions (IPCC, 2007) for the next one-hundred years.
Table 4.9. Nitrate reduction and sea level rise scenarios, being CS_0/ NR_0/ SLR_2012 the initial
scenario.
COMBINED SCENARIO (CS)
NITRATE REDUCTION
SCENARIOS (NR)
NITRATE INPUT LOAD REDUCTION
(%)
SEA-LEVEL RISE SCENARIOS
(SLR)
SEA LEVEL RISE (m)
CS_0 CS_1 CS_2 CS_3 CS_4 CS_5
NR_0 NR_10 NR_25 NR_50 NR_75 NR_94
0 10 25 50 75 94
SLR_2012 SLR_2015 SLR_2022 SLR_2037 SLR_2062 SLR_2112
0 0.02 0.04 0.09 0.18 0.35
First, we analyzed the effects of nitrate reduction (NR) and sea-level rise (SLR)
separately, and then we have configured combined scenarios (CS) to evaluate the
simultaneous effect of both parameters (Table 4.9; Figure 4.12). Not surprisingly, our
results support the idea that improvements in light conditions for seagrass, and
consequently higher P/R ratios, are achieved with decreases in nitrate loading (Figure
4.12). The results point to a potential recovery of seagrass in Snug Harbor area when
nitrate loading is reduced by 50% (Figure 4.12; NR 50). The P/R ratio improves
considerably in Snug Harbor with a 75% nitrate reduction (NR 75). On the contrary, sea-
level rise provokes a P/R ratio decline in areas where there is currently seagrass presence,
as can be seen in SLR 2112. However, when both effects (SLR and NR) were studied
together, a clear relationship between their combined behavior (CS) and the system
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
response to nitrate reduction (NR) was observed. Hence, although sea-level rise increases
water level and reduces light penetration, the light attenuation change is not as significant
as the nitrate loading effect. This is evident from comparing the temporal variation of P/R
due to SLR vs. nitrate loading (Figure 4.13). In Snug, the Figure shows a variation of P/R
from 0.97 to 1.14 due to nitrate reduction, whereas light attenuation due to sea-level rise
decreases P/R from 0.97 to 0.94. A similar effect, due to sea-level rise, can be observed
in Outer, with a P/R variation from 1.05 to 1.01 (Figure 4.13). However, the nitrate
reduction effect is lower in Outer, ranging from 1.05 to 1.10, due to the lower chlorophyll-
a levels at this point.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Figure 4.12. P/R spatial variation under nitrate reduction (NR), sea-level rise (SLR) and combined (CS)
scenarios. See Table 4.9 for an explanation of the scenarios nomenclature.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Figure 4.13. P/R variation due to nitrate reduction and sea-level rise
A similar behavior was obtained on chlorophyll-a concentration, as nutrient reduction is
the main factor that provokes a decrease in chlorophyll-a levels, whereas sea level rise
also affects to eutrophication but in a lower extent (see Figure 4.14). In fact, the mean
chlorophyll-a decrease due to nutrient reduction at the final NR scenario is 80 %, and due
to sea level rise at the final SLR scenario is 24 % (see Figure 4.14). This behavior can
also be seen in Figure 4.15, where the spatial chlorophyll-a distribution for the different
scenarios is presented. The figure shows a dominant influence of nutrient reduction
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
effects on the combined scenarios, and also an inverse relationship between chlorophyll-
a spatial distribution and seagrass presence, which can be seen comparing Figures 4.12
and 4.15, as chlorophyll-a is one of the main factors that limit light availability. In fact,
in general, seagrass presence is strongly related with eutrophication, as its presence is
inversely related to chlorophyll-a concentration due to its light limitation effect. South
cove was not represented in Figure 4.12 as the seagrass absence in that area is not related
to light availability but to a natural disturbance. As a consequence of this, the bio-optical
model is not able to represent its absence on this area. On the contrary, eutrophication
distribution can be correctly described by the model for the whole estuary as presented in
Figure 4.15. Additionally, measured chlorophyll-a data was available at South Cove,
whereas an instrument malfunction occurred at the light sensor for that area, so the
calibration of light attenuation at South could not have been properly done anyway.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Figure 4.14. Chlorophyll-a variation due to nitrate reduction and sea-level rise
241
Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Figure 4.15. Time-averaged and mean vertical chlorophyll-a spatial variation under nitrate reduction
(NR), sea-level rise (SLR) and combined (CS) scenarios. See Table 4.9 for an explanation of the
scenarios nomenclature.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Moreover, the combined effect of sea-level rise and nutrient reduction led to a significant
decrease of chlorophyll-a concentration and Kd, especially in Snug Harbor, with a
reduction from 27.77 µg L-1 to 2.79 µg L-1 and 0.79 m-1 to 0.36 m-1 respectively (Figure
4.16). Consequently, P/R in Snug Harbor increases from 0.97 to 1.09, providing adequate
conditions for seagrass growth from CS_2. P/R in Outer Harbor slightly increased until
CS_4, where it reached a maximum value of 1.07, and decreased to 1.05 in CS_5 due to
sea-level rise influence. We have also obtained the evolution of potential seagrass area
on the estuary for the different combined scenarios (Figure 4.16). We obtained an 8%
increase at CS_1, having an accumulated growth of 21 % and 34% at CS_2 and CS_3
respectively. In the case of CS_4 and CS_5 the influence of sea level rise makes the
evolution slower, obtaining an area increase from CS_4 to CS_5 of only 3 %, having
CS_5 an accumulated area growth of 45% with respect to the original scenario (CS_0).
Therefore, our results show that in this system, potential reductions in nitrate loading will
be more important than sea-level rise. However, in other systems with low nitrate loading,
sea-level rise may be more relevant.
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
Figure 4.16. Chlorophyll-a, Kd, P/R and seagrass area variation for the combined scenarios (CS). See
Table 4.9 for an explanation of the scenarios nomenclature.
4.7 Discussion
Phytoplankton bloom intensity can significantly depress the light climate in the bottom
of the water column, and therefore, light sensitive biogeochemical processes such as
photosynthesis and photo-oxidation. This has significant effects on seagrass distribution,
as light is one of the main factors for seagrass growth and primary production. Our results
support the idea that when insufficient light reaches the canopy seagrass presence
diminishes, which is in agreement with previous studies (e.g. Dennison, 1987; Orth and
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
CS_0 CS_1 CS_2 CS_3 CS_4 CS_5
Kd
(m-1
)
Scenarios
Outer Snug
0.00
5.00
10.00
15.00
20.00
25.00
30.00
CS_0 CS_1 CS_2 CS_3 CS_4 CS_5
Ch
loro
ph
yll
-a (
µg
L-1
)
Scenarios
Outer Snug
0.90
0.93
0.95
0.98
1.00
1.03
1.05
1.08
1.10
1.13
CS_0 CS_1 CS_2 CS_3 CS_4 CS_5P
/RScenarios
Outer Snug
0.00
10.00
20.00
30.00
40.00
50.00
60.00
CS_0 CS_1 CS_2 CS_3 CS_4 CS_5
Sea
gra
ss A
rea
Var
iati
on
(%
)
Scenarios
Acumulated
Variation per scenario
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Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
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Moore, 1988; Duarte, 1991; Duarte et al., 2007). Additionally, although there are
relatively few known examples of seagrass meadow recovery following nitrate reductions
(Burkholder et al., 2007) our results suggest that with progressive nitrate removal, Snug
Harbor may recover from strictly a light perspective, as chlorophyll-a concentration,
which is one of the main light attenuation factors, will decrease in the system. Other
factors such as macroalgae competition and morphodynamic changes should also be
taken into account. For example, in South Cove, effects like competition with
opportunistic macroalgae or seagrass death due to natural disturbances should be studied.
However, the potential recovery of seagrass in Snug Harbor could be achieved due to the
fact that the anthropogenic pressures that affect that area mainly consist of nitrate loading
while macroalgal coverage is minimal. Nevertheless, the future effects of nitrate
reduction in West Falmouth Harbor are hard to predict or evaluate with conventional
techniques, as the transit time of the enriched water in the aquifer to the estuary is as much
as 10 years (Kroeger et al., 2006). Moreover, we also obtained that nutrient concentration
and therefore eutrophication are the processes that control seagrass distribution in the
studied semienclosed microtidal shallow estuary, due to the light attenuation produced by
them, which is in agreement with Burkholder et al. (2007) and Costa (1988). However,
although seagrass distribution is strongly sensitive to eutrophication and light attenuation,
it is also affected by other factors such as hypoxia, epiphyte growth, grazing, and
hydrodynamic feedback, which are not included in this model as it has some limitations.
However, further work could include these formulations. One of the factors that could
also be considered in the modeling system is anoxia due to eutrophication, as the plant
oxygen content is strongly dependent on photosynthesis and respiration (Greve et al.,
2003), which have been computed in the model as a function of light and temperature.
In fact, low oxygen levels could cause anoxia in the meristem that could also limit
seagrass growth and primary production. The maintenance of oxic conditions in
meristematic and belowground tissues is important for support seagrass growth, nutrient
245
Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
uptake by roots, and translocation of nutrients between roots and leaves (Zimmerman and
Alberte, 1996; Greve et al., 2003). We have also neglected the role of epiphytes.
Epiphytes attenuate light and expand around leaves limiting uptake of oxygen, inorganic
carbon, and nutrients (Hauxwell et al., 2001). It is also important to include the effect of
grazers: small invertebrate grazers generally have minimal negative impacts on seagrass
growth and biomass, and may have important positive functions by controlling epiphyte
growth; however, large grazers can impact seagrass meadows significantly (Stoner et al.,
1995). Moreover, although currents and wave action can play an important role in
seagrass distribution, the interactions between hydrodynamics and seagrasses were
considered negligible in the present study, as this estuary is microtidal with limited fetch.
Most of the recent modeling advances with respect to seagrasses are based on flow motion
(Maza et al., 2013), morphodynamic changes (Bouma et al., 2008), and particle trapping
(Hendriks et al., 2008). However, our modeling approach resolves the spatial pattern of
seagrass habitat quality from a light perspective. In contrast to most of the existing
ecological models, our coupled implementation computes spatially varying spectral light
attenuation as a function of different attenuating substances with high vertical and
horizontal discretization, allowing for delineation of the light climate for seagrass
meadows. Moreover, as the irradiance model has been integrated into ROMS, which is
an open-source flexible modeling system, our technique could be used in a wide range of
applications. Moreover, the simplicity of its formulation makes it a non-high data
demanding tool that is able to easily compute interpretable results. For example, the
influence of sediment re-suspension and of the horizontal sediment transport on light
availability could be assessed with this tool. Another possible application would be to
analyze light climate variations due to spatial changes in CDOM caused by rivers flows,
terrestrial runoff and/or microbial processes. This is possible due to the fact that ROMS
is coupled to the Community Sediment Transport Modeling System (CSTMS) so the
246
Chapter IV Development and implementation of a coupled ecological modelling system for semi-enclosed
eutrophic coastal systems. Application to a groundwater-fed estuary with submerged aquatic vegetation.
dynamical interaction between sediments and light availability can be modeled with this
implementation.
4.8 Conclusions
In the present study, we have developed a new ecological modelling system for eutrophic
SECS, which is able to describe chlorophyll-a concentration, light availability and the
potential recovery of seagrass communities under future nitrate loading and sea-level-rise
scenarios from a light perspective. We have assessed the model in a shallow temperate
estuary, and capture the spatial variability of chlorophyll-a, light attenuation, and seagrass
presence/absence. The coupled implementation computes spectral light attenuation as a
function of different attenuating substances with high vertical and horizontal resolution,
which allows the accurate determination of the light climate in the seagrass meadow. We
find that, in general, increased sea-level will reduce light availability and is expected to
negatively impact seagrasses, with a 11.4% reduction in presence/absence area with a
0.35 m increase in sea level. However, in the estuary studied here, reduction of nitrate
loading is a larger factor in improvement of light availability, as eutrophication due to
nutrient loading is one of the main problems of the system. In fact, our results show that
seagrass habitat is expanded by 42.3 % with a 94 % reduction in nitrate loading. This
study contributes to existing eutrophication modeling efforts by providing a new linked
implementation which is able to assess seagrass potential habitat in terms of light
availability with simple formulations. This linkage of simple ecological models is a
powerful non-high data demanding tool, which easily computes interpretable results with
a high level of accuracy. Future work should incorporate other ecological communities
(macroalgae, epiphytes, grazers) as well as the effects of oxygen stress and hydrodynamic
drag caused by vegetation.
247
Chapter V Conclusions and future research
Chapter V
5 Chapter V. Conclusions and future research
248
Chapter V Conclusions and future research
249
Chapter V Conclusions and future research
Chapter V. Conclusions and future research
5.1 Introduction
Semi-enclosed coastal systems (SECS) are complex and vulnerable areas with relevant
economical and environmental importance. The increasing impact of eutrophication and
its consequences is causing great concern, especially in systems with the most severe
trophic levels, eutrophic and hypertrophic. The complexity of these systems makes the
study of the involved processes a difficult task. Complex modelling tools are widely used
for this purpose, but the high number of parameters and variables make them difficult to
calibrate and interpret, and require important computational resources. Therefore, there
is a need of developing simplified mathematical tools that take into account the most
relevant processes for eutrophic and hypertrophic SECS. In addition, the technique of
coupling and linking models seems to be a good solution to integrate the main system
processes, simplifying the modelling approach.
In this study, the main hydrodynamic, ecological and irradiance models with coupled-
linking capabilities were reviewed. The complexity and formulations of these models
confirmed the need of developing novel simplified ecological modelling tools with
coupling and linking capabilities in order to assess eutrophic and hypertrophic SECS. As
a consequence, the general aim of this Thesis was to develop novel ecological modelling
tools for assessing and describing the behaviour of eutrophic and hypertrophic SECS.
In order to accomplish this general objective, two models were developed to predict the
behaviour of hypertrophic and eutrophic SECS respectively. The first one was applied to
the Albufera of Valencia, a hypertrophic system with regulated connection with the sea,
and the second to West Falmouth Harbor, a eutrophic system with an additional issue of
seagrass meadows disappearance. Field data was successfully used to assess the skills of
the developed tools. The results obtained permit the extraction of the conclusions
described at the following section, regarding the characteristics of the models, the
sensitivity analysis, the light formulations, calibration and results.
250
Chapter V Conclusions and future research
5.2 Conclusions
5.1.1 General conclusions
Simplified modelling tools with high spatial resolution can characterize
eutrophication in semi-enclosed coastal systems due to the high spatial variability
of phytoplankton distribution. Therefore, spatial resolution is one of the main
factors to consider when designing an ecosystem modeling tool for SECS.
Ecosystem models require one more level of complexity for describing eutrophic
SECS than for hypertrophic SECS, as more ecological processes should be taken
into account in the first case. In fact, hypertrophic SECS can be described by
models with a lower level of parametrization than eutrophic ones.
Non-high data demanding models can be powerful tools, which can easily
compute interpretable results with a high level of accuracy if the main equations
and consequent variables and parameters are well selected and defined.
Simplified tools can be very useful to accurately describe eutrophication in
hypertrophic SECS. The new developed simplified model was able to characterise
eutrophication in hypertrophic heavily regulated SECS, describing with high
temporal and spatial resolution, the chlorophyll-a concentration evolution during
a whole year.
The coupling and/or linkage of models is an efficient and flexible tool to describe
specific ecological processes in complex systems. In fact, this study contributes
to existing modeling efforts by providing a new implementation that not only
describes chlorophyll-a concentration, but also seagrass potential habitat in terms
of light availability in eutrophic SECS.
251
Chapter V Conclusions and future research
The hydrodynamic model is critical for the performance of the ecological models.
An adequate representation of the hydrodynamics is a fundamental factor for the
analysis of SECS, due to their singular connection with the adjacent systems.
Therefore, the hydrodynamic models and conditions have been carefully selected
at the present study.
5.1.2 Sensitivity analysis conclusions
Better knowledge of the most influential processes that affect eutrophication in
semi-enclosed systems was achieved. We can conclude that the parameters for
which chlorophyll-a is highly sensitive in the hypertrophic studied area are
phytoplankton respiration rate, grazing rate, phytoplankton growth rate, flux of
soluble reactive phosphorus from sediment to the water column and chlorophyll-
a phytoplankton carbon ratio. Whereas in the eutrophic studied area were
phytoplankton growth rate, initial slope of Photosynthesis-Light Intensity curve
(PI curve), grazing rate, phytoplankton death, aggregation parameter, and
zooplankton mortality rate. Therefore, we can conclude that in both cases the most
influential parameters are those related to phytoplankton and zooplankton growth,
and death, unless in the eutrophic estuary, where the initial slope of the PI curve
appears to have more relevance as light effects are still important in eutrophic
systems.
The sensitivity analysis has been also proved to be a valuable tool to reduce the
number of parameters to be adjusted in both models.
5.1.3 Light modelling conclusions
Light availability in eutrophic and hypertrophic SECS is mainly influenced by
chlorophyll-a concentration.
252
Chapter V Conclusions and future research
Light climate could be improved by reducing nutrient loading in eutrophic SECS
with cultural eutrophication, whereas restoration of hypertrophic systems require
a more complex analysis involving also hydrodynamic conditions and sediment
fluxes.
The implementation of spectral light attenuation in ecological models as a
function of different attenuating substances with high vertical and horizontal
resolution, allows an accurate determination of the light climate. In this study, we
have implemented Gallegos et al (2011) formulations into Fennel et al (2006)
model. This configuration allowed the linkage of a bio-optical seagrass model that
required spectral PAR as input data.
The present study reveals that a three dimensional model with spectral light
attenuation formulations is required in a eutrophic SECS to accurately describe
the heterogeneity and light climate of the system, whereas a two-dimensional
model with non-spectral attenuation was successfully used in hypertrophic SECS
due to the low light penetration through the water column in these systems.
5.1.4 Conclusions of calibration and results
The average uncertainty of the hypertrophic model prediction was less than 6%,
with two Pearson correlation coefficients of 0.933 and 0.917 for calibration and
validation respectively and a Nash-Sutcliffe efficiency coefficient of 0.96, which
are excellent values.
The eutrophic modeling system accurately reproduced the spatial variability in
chlorophyll-a and light attenuation with RMS errors of 3.72 µg L-1 and 0.07 m-1
respectively.
253
Chapter V Conclusions and future research
A potential seagrass habitat criteria based on Production/Respiration (P/R) ratio
was proposed. We assumed that for areas where P/R >1 there is potential seagrass
growth, and therefore presence, delimiting this threshold as the potential seagrass
habitat in the estuary. However, for P/R≤1 we assumed conditions are unfavorable
to seagrass presence as growth would be limited, being respiration larger than
photosynthesis in those areas. The results obtained with the P/R criterion selected
to assess seagrass potential habitat present an agreement of 73.39 % between
modeled and field data.
We contribute to a better understanding of the impacts of climate change on
eutrophic SECS. We find that, in general, increased sea-level will reduce light
availability and is expected to negatively impact seagrasses. In West Falmouth
Harbor the model showed an 11.4% reduction in seagrass presence area with a
0.35 m increase in sea level.
We conclude that nutrient reduction can led to system restoration in eutrophic
systems, whereas in hypertrophic ones the solution is more complex. In the
eutrophic SECS studied in this Thesis, the reduction in nitrate loading is a larger
factor in improvement of light availability. Chlorophyll-a concentration is reduced
a 89.3% whereas seagrass habitat is expanded by 42.3 %, with a 94 % reduction
in nitrate loading.
As demonstrated by the calculated mass balance, in hypertrophic SECS the input
loads can be higher than the output loads, so the limited connection with the sea
magnifies the eutrophication of the system. Furthermore, the SRP flux from the
sediment to the water column contributes to maintain high chlorophyll-a
concentrations in the studied area. Therefore, in hypertrophic SECS, nutrient
reduction could not have a significant impact on the system restoration if the
hydrodynamic and sediment conditions do not change.
254
Chapter V Conclusions and future research
A bio-optical model has been successfully linked to the eutrophication model,
demonstrating the flexibility of this approach to describe singular processes
occurring in SECS, such as the ratio between production and respiration.
5.2 Future research
This Thesis has revealed some limitations in the developed models that open new research
lines. These issues have been analyzed in detail in each chapter at their corresponding
discussion section. Here, the most relevant aspects of the Thesis needing future research
are mentioned.
The developed models should be applied to other locations with available field
data. This would be helpful in order to consolidate the effectiveness and utility of
the modeling tools.
Evaluate realistic management strategies at the Albufera of Valencia in order to
find solutions to its ecological problem, taking into account the economical factors
that have an impact on the study site, such as the rice cultivation, manufacturing
industry and tourism.
It would also be interesting to implement the hypertrophic simplified model into
ROMS in order to expand our technique and make it open source for the scientific
community as we are doing with the eutrophic modelling system.
Nitrogen influence on phytoplankton growth could also be included in the
hypertrophic simplified model, in order to allow its application to systems where
nitrogen is the limiting nutrient.
255
Chapter V Conclusions and future research
Future work on the eutrophic modelling system could incorporate other ecological
communities such as macroalgae and epiphytes. For example, effects like
competition of seagrass with opportunistic macroalgae, and epiphytes influence
on light attenuation should be studied.
One of the factors that could also be considered in the eutrophic modeling system
is anoxia due to eutrophication. The seagrass oxygen content is strongly
dependent on photosynthesis and respiration, which have been computed in the
model as a function of light and temperature. Oxygen concentration could be also
computed by the eutrophication model and be used by the seagrass model, as low
oxygen levels could limit seagrass growth and primary production. Therefore, an
oxygen term shall be included to the seagrass model formulation in order to
modify the calculation of the plant respiration and production based on the water
oxygen concentration.
Particle trapping and its effect on light climate could also be included in the
eutrophic modeling system. Seagrass canopies decrease flow velocity and reduce
turbulence. This promotes sedimentation within a seagrass meadow and reduces
resuspension of particles improving light climate. Moreover, as in the present
Thesis an spectral irradiance model has been integrated into ROMS, which is an
open-source flexible modeling system, the influence of sediment re-suspension
and of the horizontal sediment transport on light availability could be assessed.
This is possible due to the fact that ROMS is coupled to the Community Sediment
Transport Modeling System (CSTMS) so the dynamical interaction between
sediments and light availability can be modeled with this implementation.
Another possible application of the coupled modelling system for eutrophic SECS
would be to analyze light climate variations due to spatial changes in CDOM
256
Chapter V Conclusions and future research
caused by rivers flows, terrestrial runoff and/or microbial processes. This is again
possible due to the fact that ROMS is coupled to the CSTMS.
As we have linked Zimmerman’s model to Fennel’s model in which we integrated
the spectral light formulation, and Fennel’s is coupled into ROMS, the next step
would be to couple Zimmerman’s model in order to compute the seagrass
potential habitat at the same time than chlorophyll-a and spectral PAR.
257
Chapter V Conclusions and future research
5.3 Thesis impact and dissemination
5.3.1 Research articles
“A model for describing the eutrophication in a heavily regulated coastal lagoon.
Application to the Albufera of Valencia (Spain).” by Pilar Del Barrio, Andrés García
Gómez, Javier García Alba, César Álvarez Díaz, José Antonio Revilla Cortezón. Journal
of Enviromental Management. 2012.
DOI: 10.1016/j.jenvman.2012.08.019
“Modeling future scenarios of light attenuation and potential seagrass success in a
eutrophic estuary.” by Pilar Del Barrio, Neil K. Ganju, Alfredo L. Aretxabaleta,
Melanie Hayn, Andrés García, Robert W. Howarth. Estuarine, Coastal and Shelf Science.
2014.
DOI: 10.1016/j.ecss.2014.07.005
“Hydrodynamic modelling of a regulated Mediterranean coastal lagoon, the Albufera of
Valencia (Spain)” by Javier García Alba, Aina G. Gómez, Pilar del Barrio, Andrés
García Gómez, César Álvarez Díaz. Journal of Hydroinformatics. 2014
DOI: 10.2166/hydro.2014.071
“Progress and challenges in coupled hydrodynamic-ecological estuarine modeling” by
Neil K. Ganju, Mark J. Brush, Brenda Rashleigh, Alfredo L. Aretxabaleta, Pilar del
Barrio, Melinda Forsyth, Jason S. Grear, Lora A. Harris, Samuel J. Lake, Grant
McCardell, James O’Donnell, David K. Ralston, Richard P. Signell1, Jeremy M. Testa,
and Jamie M.P. Vaudrey. Estuaries and Coasts. In press.
258
Chapter V Conclusions and future research
5.3.2 Communications in conferences and workshops
This work has also been successfully presented with great acceptation to the scientific
community at the following events:
MABPOM2012 Symposium. Poster presentation: "A seagrass and light attenuation
model for a eutrophic estuary: Calibration, validation and predictions under nitrogen
loading scenarios". Authors: Pilar del Barrio, Neil K. Ganju, Alfredo L. Aretxabaleta.
Groton, Connecticut, USA. Nov. 2012.
Linking hydrodynamic and ecological models in estuaries: a workshop to discuss recent
advances and approaches. Presentation: “A modeling approach to assess light availability
and potential seagrass success under nitrate loading and sea level rise scenarios”.
Authors: Pilar del Barrio, Neil K. Ganju, Alfredo L. Aretxabaleta, Melanie Hayn,
Andrés García, Robert W. Howarth. Woods Hole, Massachusetts, USA. September 10-
11, 2013.
259
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