Modeling Reference Evapotranspiration in a Semiarid Area ...
Modelling Vegetation Patterns in Semiarid Environments
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Transcript of Modelling Vegetation Patterns in Semiarid Environments
1/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Salvatore Manfreda1, Teresa Pizzolla1, Kelly K. Caylor2
1) University of Basilicata, Italy. 2) Princeton University, USA.
Modelling Vegetation Patterns in Semiarid Environments
e-mail: [email protected]
2/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Spatial Pattern of Vegetation Landscape ecology emphasizes the interaction between spatial pattern and ecological process (movement of plants & animals; edge/interior effects, isolation) that is the causes and consequences of spatial heterogeneity across a range of scales.
“Two fundamental and interconnected themes in ecology are the development and maintenance of spatial and temporal pattern, and the consequences of that pattern for the dynamics of populations and ecosystems.” – Simon A. Levin, 1992
(Photo by Yann Arthus-Bertrand)
3/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Motivation
Global precipitation projections for December, January, and February (top map) and June, July, and August (bottom map.) Blue and green areas are projected to experience increases in precipitation by the end of the century, while yellow and pink areas are projected to experience decreases.
Source: Christensen et al. (2007)
How climate change will impact on vegetation patterns?
How this will modify water resources?
4/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
The Study Case
Sevilleta LTER
(Caylor et al., AWR 2005)
Upper Rio Salado Catron County, NM Cibola National Forest Basin Area: 681 km2 Mean Annual Rainfall: 218±84 mm
5/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
! Couple patterns of vegetation, soil, and climate to generate patterns of steady state water balance and soil moisture distribution within the basin
! Use existing stochastic model of soil moisture:
( ) ( )stsdtdsnZr χϕ −= ,
Input is a poisson process of rainfall events with a characteristic
distribution of storm depths
(Rodriguez-Iturbe et al., 1999; Laio et al., 2001; Manfreda et al, 2010)
Losses are determined according to a loss function that includes
evaporation, transpiration, and leakage
0
0.5
1
1.5
2
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0 shsw s* sfc 1
χ (s)
cm
/d
Emax
Evap
Soil Water Balance
6/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Basin Water Stress
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90
0.2
0.4
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1
x
θʹ′
1.0 0.0
⎪⎩
⎪⎨
⎧<⎟
⎟⎠
⎞⎜⎜⎝
⎛=
otherwise
kTTifkTT
seass
n
seas
ss
1
''
*
*
*
/1
ζζ
θ
t
s(t)
Duration of the growing season, Tseas
ξ
Duration of an excursion below ξ
(Porporato et al., AWR – 2001)
Frequency of crossing Number of crossing Mean time of crossing )()(
)()(ξξρ
ξνξ
ξ
ξp
PPT ==
Dynamic water stress defined as a function of frequency of crossing, number of crossing, mean time of crossing, etc.
7/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Potential evapotranspiration
Rn = Ra 1−α( )−εsσTs4 +εaσTa4
Rdir
Rdif Rem
αRdir Rrif
Ra atmosphere
target
Ra =GSCd2
cos θ( )dωω1
ω2
∫
Net solar radiation
8/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
DWS Tree
km
km
1 10 20 30 40 50 60 70 80 90 100
1
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80 0
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DWS Shrub
km
km
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1
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80 0
0.1
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DWS Grass
km
km
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DWS Tree
km
km
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1
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1
DWS Shrub
km
km
1 10 20 30 40 50 60 70 80 90 100
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80 0
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DWS Grass
km
km
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1
T ! s! = T!! s! − T ! s + 1ν s − 1
ν s! + γ 1ν u !− T! u
!!
!du
θ′ = T!"#! − T ! s!T!"#! θ
Dynamic Water Stress Dynamic water stress computed including initial conditions
The mean first passage time (in days) of the stochastic process between s0 (initial condition) and <s>
Basin morphology modifies dynamic water stress allowing the existence of some species.
9/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Dynamics of Organization within River Networks Initial Condition
Neighbor model: Interactions can occur between all 8 neighbors
Network model: Interactions constrained by flow path – only
downstream neighbors can be replaced
2
1
Cells replace neighbor pixels if it lowers the local amount of water stress with a probability p
How well do each of these interactions represent the observed distribution of water
stress? (Caylor et al., GRL 2004)
⎟⎟⎠
⎞⎜⎜⎝
⎛
+−=
21
11θθ
θp
Cell becomes bare when θ is 1 for all vegetation types
10/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Neighbor Model
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90
0.2
0.4
0.6
0.8
1
xθʹ′
Network modelActual
Steady-State Condition
Model calibration
11/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Vegetation Pattern obtained including the Effects of Morphology on Solar Radiation
The hypothesis of feasible optimality is explored using four cellular automata approaches. The initial random vegetation mosaic is modified through the iteration of local interactions that occur between adjacent locations. These interactions are defined such that the replacement probabilities (P ) adopted combine both the dynamic water stress (𝜃′ ) and the plant transpiration (T ). The schemes proposed are the following:
12/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
(Manfreda et al., Procedia Environ. Sci. 2013)
Vegetation Pattern obtained including the Effects of Morphology on Solar Radiation
Among all considered cases, the second and third schemes (see Fig. 5 B and C) provide spatial patterns that replicate more closely the actual distribution of vegetation in the Rio Salado basin.
13/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Vegetation Pattern obtained including the Effects of Morphology on Solar Radiation Initial Condition
Cells replace neighbor pixels if it lowers the local amount of water stress with a probability p
⎟⎟⎠
⎞⎜⎜⎝
⎛
+⎟⎟⎠
⎞⎜⎜⎝
⎛
+−=
21
1
21
11TT
Tpθθ
θ
Vegetation strategy is: • to minimize of stress • and maximize transpiration
14/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Ecohydrological Model: Simulation Results (Hurvitz, 2002)
The proposed model has been used to predict 256 scenario defined changing both the mean rainfall rate (λ) and the mean rainfall depth (α).
Increasing mean annual rainfall
200 400 600 800 1000 1200
100
200
300
400
500
600
700
800
900
1000
(A) (B) (C) Bare soil
Grass
Shrub
Tree
Maps are obtained using the measured rainfall rate (λ = 0.284 day−1) and changing the parameter α that assumes the following values: 0.474cm (A), 0.517cm (B), 0.631cm (C).
15/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Shannon’s Entropy – Diversity Index
0.4
0.5
0.6
0.70.2
0.250.3
0.350.4
0.450.5
0
0.5
1
1.5
λ
α
Shan
non'
s en
tropy
SHDI = − pi *ln pi( )i=1
m
∑
SHDI increases as the number of different patch types increases and/or the proportional distribution of area among patch types becomes more comparable
pi = proportion of landscape occupied by the class i.
The Shannon’s evenness index (SHDI) represents a well-known landscape metric that accounts for both abundance and evenness of species in the landscape. This index has the same expression of the informational entropy and is defined by
(Manfreda and Caylor, Water 2013)
16/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Same Rainfall with Different Rate or Mean Depth
…changes in α provides sharper modifications of landscape.
17/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Same Rainfall with Different Rate or Mean Depth
Land
scap
e D
iver
sity
Annual rainfall
Changing α
Changing λ
18/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Results on a Mediterranean Area
Aridity Index (De Martonne) Basin Subasins
(Manfreda, Ann Arid Zone 2013)
19/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Conclusions The main outcomes of the present work can be summarized in following points:
i) The algorithm that seems to explain the actual structure of vegetation observed in the Upper Rio Salado basin is the one that tend to minimize dynamic water stress and maximize vegetation water use;
ii) The landscape analyses, based on the modeling applications, show that reduction of landscape diversity (described by the Shannon’s Index) may occur rapidly for small changes in the rainfall characteristics;
iii) These changes are exacerbated when rainfall modifications are due to reduction in the mean rainfall depth;
iv) The impact of climate change on the vegetation pattern depends on the vulnerability of a system with respect to the expected changes.
20/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
References ! Manfreda, S., K.K. Caylor, On The Vulnerability of Water Limited Ecosystems to
Climate Change, Water, 5, 819-833; doi:10.3390/w5020819, 2013. ! Manfreda, S., T. Pizzolla, K.K. Caylor, Modeling Vegetation Patterns in Semiarid
Environment, Procedia Environmental Science, 2013. ! Pizzolla, T., S. Manfreda, K.K. Caylor, M. Fiorentino, Il ruolo dell’esposizione e della
pendenza dei versanti sullo stress idrico della vegetazione, Atti del Convegno di idraulica e Costruzioni Idrauliche - IDRA2012, 9-14 settembre 2012, Brescia, 2012.
! Acampora, A., T. Pizzolla, S. Manfreda, Effects of Morphology on Solar Radiation and Evapotranspiration, 3rd International Meeting on Meteorology and Climatology of the Mediterranean - IMCM 2011.
! Acampora, A., A. Sole, M. T. Carone, T. Simoniello, S. Manfreda, Le Metriche del Paesaggio come Strumento di Analisi del Territorio, in Informatica e Pianificazione Urbana e Territoriale a cura di Las Casas G., Pontrandolfi P., Murgante B., Atti della Sesta Conferenza Nazionale INPUT 2010, Libria, pp 221-231, vol.1, 2010.
! Manfreda, S., Ecohydrology: a New Interdisciplinary Approach to Investigate on Climate-Soil-Vegetation Interactions, Annals of Arid Zones, 48 (3 & 4), 219-228, 2009.
21/21 FOUR DECADES OF PROGRESS IN MONITORING AND MODELING OF PROCESSES IN THE SOIL-PLANT-ATMOSPHERE SYSTEM: APPLICATIONS AND CHALLENGES – 19-20 June 2013 Napoli
Thanks for your attention…