The study of spatial and temporal aspects of ...

The study of spatial and temporal aspects of denitrification processes in Roxo catchment, Portugal.

Fransiska Gamises February, 2009

The study of spatial and temporal aspects’ of denitrification processes in Roxo catchment, Portugal.

By

Fransiska Gamises Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation: Integrated Watershed Modelling and Management-Environmental Hydrology Thesis Assessment Board Chairman: Prof. Dr. Z. Su WRS, ITC External Examiner: Dr. Ir. D. C. M. Augustijn, Twente University First Supervisor: Dr. Ir. C. M. M. Mannaerts WRS, ITC Second Supervisor: Dr. Ir. Mhd S. Salama WRS, ITC Observer: M. Yevenes (PhD) WRS, ITC

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

ENSCHEDE, THE NETHERLANDS

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

DEDICATION The fruit of this hard work is dedicated to;

My beloved, late brother, Reheul Simson //Guruseb, who taught me to smile in days of rain and sunshine,

My loving family, who sustained, strengthened and encouraged me with prayers in my quest for knowledge.

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Abstract

In this study, spatial and temporal soil denitrification of the Roxo catchment in southern Portugal was investigated based on field samples, laboratory techniques and satellite data. Further, a simple prototype model is proposed and implemented to obtain actual spatial and temporal denitrification rates (Da) at the catchment level. Undisturbed soil samples were incubated under anaerobic condition with addition of 3 mg potassium nitrate (KNO3) for 45 days, and potential denitrification rates were measured at regular intervals with the nitrate reduction method. Potential denitrification rates obtained for the current study area ranged from 0.0 to 0.7 g N m-2 d-1 with average NO3-N loss of 3.7 to 23.4 mg N kg-1 soil. The Nemis denitrification model concept was used and embedded in a spatial and temporal modelling framework. To obtain the denitrification model reduction functions for soil pH, soil nitrate, land surface temperature and soil moisture, all respective data of the model variables, where normalised to [0, 1] to be used in the prototype model to estimate Da

. rates. To evaluate the soil moisture effect on the catchment level, we tested a Topographic Wetness Index (TWI). Dynamic and spatial soil moisture influences on denitrification for the catchment was tested using soil moisture outputs from the SWAT model. Land surface temperatures were derived from the Meteosat geostationary satellite and used in spatial temporal modelling. The annual denitrification rate for the current study area catchment was estimated at 0.29 g N m-2 d-1 when using SWAT derived soil moisture estimates. The Da for TWI derived soil moisture was estimated at 0.35 g N m-2 d-1. The proposed methodology is promising, but more detailed process descriptions and modelling of spatially and temporally varying soil moisture and temperature seems required to obtain more reliable estimates of this important environmental process, also in relationship to climate change.

Keywords: Denitrification rate, nitrate reduction m ethod, Nemis, soil moisture, land surface temperature, Topographic Wetness Index, remote sensing, GIS

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Acknowledgements

All honour and glory goes to the Heavenly Father.

This study would not have been possible without the financial assistance of the World Bank through the Joint Japan/World Bank Graduate Scholarship Program (JJ/WBGSP) and the enabling environment created by International Institute for Geo-information Science and Earth Observation (ITC). I therefore acknowledge Dr C. Braham, the secretariat of the World bank, Mrs Grossa, for their support and coordination in times when I was in need. I would like to express my heartfelt thanks to my supervisors Dr. Ir. C.M.M. Mannaerts and Dr. Ir. Mhd S. Salama that through their cool, calm and collected way has turned me in a critical and capable researcher. I am forever grateful to Dr. Ir. C.M.M. Mannaerts who graciously and promptly refined the ideas to this study by constantly tapping into his wealth of experience and sharing informative discussions concerning the field least known to me. Special thanks to Drs. J.B. De Smeth for the laboratory sessions and being actively involved in analyses of the soil samples. Thank you also for allowing me to make the laboratory my “second home”. The WRS course Coordinator, Arno Lieshout, who was always helpful in improving the course content, available to listen to issues affecting students. I want to appreciate the impartation of knowledge from Dr. A.S.M Gieske, Dr. B.H.P Maathuis, MSc Ir G.N. Parodi and Dr. T.H.M Rientjies who sailed me through the most challenging yet interesting courses outside my domain. I also thank Dr G. G Rossiter who has increased my interest in the field of Geo- Statistics. I would like to acknowledge the staff of Centro Operativo de Technological de Regadio (Cotr) in Beja city, Portugal for their warm welcome and spontaneous assistance during the field work campaign in October 2008. In particular, the Director, Dr I Oliveira, the technical team, Mr J Maia, M Fabiao, M. Varela etc who availed data, assistance and advised on Portuguese soils and sampling. Their effort also played a role in producing this document. Special thanks go to the Roxo team; Ing Murrat Ucer, Mariela and family, Imesh, Daphny and Priscy. Great teamwork and support, without you, there won’t be soil samples and thank you for being a great sport. I appreciate the exchange with my colleagues from taught courses to finalising of thesis stage. The discussions especially with Michael, Janaka, Setimela, Shadreck, Pilar, Imesh, Alebachew were fruitful and beneficial. Mariela thank you once again for improving the document with valuable criticism. I acknowledge ITC staff: Benno and Job, who tirelessly assisted in sorting field work items; ITC Christian Fellowship for providing me a home far away from home; Reception desk- Ivo and Roelof for making ITC closing times fun with their announcement; Andre and Roland for the chit chats over cup of hot chocolate; ITC Dish Hotel, Saskia, Ruben and Patrick, for their warm welcome and making my stay hospitable. I cannot end without acknowledging my family. The inspiration, support and encouragement during challenging times were always met by the warriors, my sister and mother. I also extend appreciation to Teo, who tirelessly commented on the drafts to improve the research, advice and encouragement when I needed it. I love you all, and God’s blessing I pray for you.

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Table of contents

Abstract ................................................................................................................................................... i 1. Introduction ................................................................................................................................... 1

1.1. Background ............................................................................................................................. 1 1.1.1. Nitrogen cycle......................................................................................................... 1

1.2. Justification ............................................................................................................................. 2 1.3. Research problem.................................................................................................................... 3 1.4. Research objective................................................................................................................... 3

1.4.1. General research objective...................................................................................... 3 1.5. Research plan .......................................................................................................................... 3 1.6. Thesis outline .......................................................................................................................... 5 1.7. Thesis approach....................................................................................................................... 5

2. Literature review........................................................................................................................... 7 2.1. Determination of denitrification rate ....................................................................................... 7

2.1.1. Direct method ......................................................................................................... 7 2.1.2. Indirect methods ..................................................................................................... 8 2.1.3. Potential denitrification rates (Dp) found in literature ............................................ 9

2.2. Factors that limit denitrification rate ....................................................................................... 9 2.3. Other processes...................................................................................................................... 10 2.4. Dentrification simulation models .......................................................................................... 11 2.5. Simple denitrification model: Nemis..................................................................................... 11

2.5.1. Model description ................................................................................................. 11 3. Materials and Methods ............................................................................................................... 15

3.1. Study area.............................................................................................................................. 15 3.1.1. Selection of study area.......................................................................................... 15 3.1.2. General study area description.............................................................................. 15 3.1.3. Climate.................................................................................................................. 16 3.1.4. Soil types .............................................................................................................. 16 3.1.5. Software used........................................................................................................ 17

3.2. Auxiliary information............................................................................................................ 17 3.2.1. Digital Elevation Model (DEM)........................................................................... 17 3.2.2. Hydro-processing- Catchment delineation ........................................................... 18 3.2.3. Soil map................................................................................................................ 18 3.2.4. Land cover/use...................................................................................................... 19

3.3. Field campaign ...................................................................................................................... 21 3.3.1. Soil sampling ........................................................................................................ 21 3.3.2. Field data .............................................................................................................. 22

3.4. Laboratory measurement....................................................................................................... 23 3.4.1. Potential denitrification rate: Experiment design ................................................. 23 3.4.2. Soil physico- chemical analysis............................................................................ 25

3.5. Model input preparation ........................................................................................................ 26 3.5.1. Modelling data...................................................................................................... 26 3.5.2. Potential denitrification rate (Dp) and map ........................................................... 27 3.5.3. pH & Nitrate map ................................................................................................. 28

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3.5.4. Land surface temperature and map ....................................................................... 28 3.5.5. Soil moisture and map .......................................................................................... 29

4. Results and Analysis.................................................................................................................... 31 4.1. Exploratory analysis .............................................................................................................. 31

4.1.1. Soil Information.................................................................................................... 31 4.1.2. Soil properties ....................................................................................................... 32

4.2. Laboratory experiments......................................................................................................... 33 4.2.1. NO3-N loss............................................................................................................ 33 4.2.2. Potential denitrification rate (Dp).......................................................................... 35 4.2.3. Trends analysis ..................................................................................................... 36

4.3. Distribution of temperature ................................................................................................... 39 4.4. Prototype modelling .............................................................................................................. 42

4.4.1. Reduction functions .............................................................................................. 42 4.4.2. Actual denitrification rate (Da).............................................................................. 45 4.4.3. Temporal Da rate ................................................................................................... 45

5. Discussion &conclusions, and recommendations...................................................................... 49 5.1. Discussion & conclusion ....................................................................................................... 49

5.1.1. Field data collection.............................................................................................. 49 5.1.2. Potential denitrification rates ................................................................................ 49 5.1.3. Electron donors and acceptors with reference to trend analysis ........................... 51 5.1.4. Prototype model development .............................................................................. 52 5.1.5. Limitations............................................................................................................ 52

5.2. Recommendations ................................................................................................................. 54 6. References .................................................................................................................................... 56 7. Appendix ...................................................................................................................................... 59

Annex A_1: Pictures- soil sampling and measurement......................................................... 59 Annex A_2: Soil Moisture measurements............................................................................. 59 Annex B: Denitrification rate laboratory experiment............................................................ 60 Annex C: Soil sample preparation for particle size analysis ................................................. 60 Annex D : Physical and chemical characteristics of Roxo catchment................................... 61 Annex E: Summarised Dp rates and Nitrate loss of Roxo catchment.................................... 62 Annex F: Histograms of some soil properties ....................................................................... 63 Annex G: Nitrate reduction (mg NO3-N kg-1) as a function of time per soil location........... 64 AnnexH: Denitrification rate (g N m-2 d-1) as a function of time per soil type (location) ..... 65 Annex I: Meteosat product (LST °C) images........................................................................ 66 Annex J: Diurnal cycle of gauged stations in Roxo reservoir/catchment.............................. 68 Annex K: Maximum daily Relva temp. (°C) per gauged station for 2008 of 10 -21days

of each month ....................................................................................................... 69 AnnexL: Monthly weather parameters of Beja gauged station from 2006-2008 .................. 71 AnnexM: ILWIS scrip for prototype modelling of actual denitrification rates..................... 72 AnnexN: Monthly soil water derived from Soil and Water Assessment Tool Model........... 75

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List of figures

Figure 1-1: Schematisation of the transformation of nitrogen................................................................ 2

Figure 1-2: Research layout.................................................................................................................... 4

Figure 1-3: Research approach .............................................................................................................. 6

Figure 2-1: Famous pipes and holes model. Shows how the output of nitrification (aerobic condition), NO3

-, is an input to denitrification (anaerobic). Adopted from Lajtha and Michener (1994).................8

Figure 3-1: Location, meteorological stations and DEM of the study area ......................................... 15 Figure 3-2: Hyetograph, monthly average temperature and rainfall from Beja Station (2006-2008)

http: //www.cotr.pt/ ............................................................................................................................... 16

Figure 3-3 : Distribution of soil types................................................................................................... 17

Figure 3-4: Reclassified soil map ......................................................................................................... 19

Figure 3-5: Land cover/use map of the study area showing the sampling locations............................ 20

Figure 3-6: Schematisation of the experiment. Soil samples are represented by a circle and soil code. Analysis was carried out per row for Specified day.............................................................................. 24 Figure 3-7: Setup the experiment in laboratory using a water bath for incubation of soils (A). Soil samples per location after incubation (B) ............................................................................................. 24

Figure 3-8: Proposed implementation of the Nemis model................................................................... 27 Figure 3-9 : Input maps of DEM (A) and flow accumulation map (D) was required to generate TWI

map. Steps A-D indicate how flow accumulation map.......................................................................... 29 Figure 4-1: Assessment of the Walkley- Black method with in- house laboratory soil samples ........... 31

Figure 4-2: Comparisons of organic matter composition in soils of Roxo catchment......................... 32 Figure 4-3: Catchment spatial distribution of pH, NO3-N (mg kg-1) and Potential denitrification rate (DP) in g m-2 d-1. Note: See chapter 3, section 3.5.2 to 3.5.3 for the preparation of these maps. ......... 33

Figure 4-4: Variation of NO3-N loss in the studied soils/location. The error bars are represented with standard deviation................................................................................................................................. 34

Figure 4-5: Overall average NO3-N loss of soils as a function of time ................................................ 34

Figure 4-6: Mean variation of denitrification rate in soil types. The mean is represented with standard deviation. ...............................................................................................................................................35

Figure 4-7: Mean temporal variation of denitrification rate using the nitrate reduction method

(Yeomans et al., 1992)........................................................................................................................... 35

Figure 4-8: Spatial distribution of NO3-N, NH4-N, Dp, pH_H2O and organic carbon with respect to soil

location, overlaid on texture map of the study are. Note: NO3-N and NH4-N are converted to mapping unit of g m-2............................................................................................................................................ 36

Figure 4-9: Nutrient variation .............................................................................................................. 37

Figure 4-10: Organic carbon, Dp, nutrients and NO3-N loss............................................................... 38 Figure 4-11: Sulfate concentration per soil location ............................................................................ 38 Figure 4-12: Diurnal cycle indicating time in UCT +1 of maximum ................................................... 39

Figure 4-13: Correlation between gauged soil temperature and MSG product (LST)......................... 39

Figure 4-14: Monthly Land Surface Temperature (LST °C) derived from MSG product..................... 40

Figure 4-15: Modelling parameters; Potential denitrification rate (top left), Nitrate reduction function (top right), pH reduction function (bottom left) and Topographic Wetness Index (bottom right) ........ 42

Figure 4-16: MSG product (LST °C) – left column used to derive soil temperature reduction function (right column)........................................................................................................................................ 43

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Figure 4-17: Soil water output from SWAT, integrated in ILWIS, showing soil water % per sub catchment of the study area. Temporal Soil water ( left column ) and reduction function (right column)

............................................................................................................................................................... 44 Figure 4-18: Actual Da rate in g m-2 d for the month of January. Map on left shows TWI derived Da and the map on the right shows water balanced derived Da ................................................................. 45 Figure 4-19: Temporal Da rates in g m-2 d-1, derived from water balance............................................ 46

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List of tables

Table 2-1: Dp from literature ................................................................................................................... 9 Table 2-2: Parameters that were obtained from Heinen (2006b) for the current study......................... 13 Table 3-1: Elevation and location of the meteorological stations located within the catchment. ......... 18

Table 3-2: Brief description of the soil codes ....................................................................................... 19

Table 3-3: Description of Corine Land cover classes used in the study................................................ 20 Table 3-4: Spatial information of soil samples collected during field campaign in Roxo catchment... 21

Table 3-5: Summary of materials and data used in the study................................................................ 22 Table 3-6: Summary of soil characteristics investigated in the ITC soil and water laboratory............. 26

Table 3-7: Summary of input data for the denitrification rate model.................................................... 27 Table 4-1: Means and ranges of physical-chemical properties of Roxo catchment. The mean is represented with the ± standard deviation ............................................................................................ 32

STUDY OF SPATIAL AND TEMPORAL ASPECTS OF DENITRIFICATION PROCESSES IN ROXO CATCHMENT, PORTUGL

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1. Introduction

This chapter introduces the research problem by providing background information on denitrification process. Research objectives, questions and hypothesis are also provided, followed

by a brief outline of the thesis chapters.

1.1. Background

Denitrification is a complex, microbial process that is known to be the major transformation route in the nitrogen cycle (Korom, 1992; 2007). This complex process is responsible for permanent removal of nitrate (NO3

-) or nitrite (NO-2) in the soil to non-reactive nitrogen (N2) gas that is

released into the atmosphere through sequence of conversions depicted in equation 1-1 (Korom, 1992)

NO3- → NO2

- → NO → N2O → N2

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Microorganisms and plants make up part of the conversions and are regarded as role players in partially reducing nitrogen by means of uptake (Hernandez and Mitsch, 2007). In anaerobic denitrification process, different groups of denitrifying bacteria reduce the organic matter by using NO3

- or NO2- as an electron acceptor. Denitrification is therefore not only the major

transformation route but is recognized as the major sink for N (Korom, 1992). Besides the harmless N2 end product of denitrification conversion, the process also produces nitrous oxide (N2O), a green house gas as a by-product. The gas has negative effect on the environment and is associated with global warming and ozone layer damage as is the case with CO2. The warming potential of N2O, approximately 300, exceeds that of carbon dioxide and this drive studies in N2O and its effects on the environment to be crucial to research on (Hefting et al., 2003).

Apart from denitrification, anaerobic process that remove nitrate was recently established (Laverman et al., 2007; Yu et al., 2008). Anammox, (ANaerobic AMMnomiun Oxidation), oxidises ammonia and nitrite to N2 without the formation of the intermediate product. A study in Moscow, applied this process on waste water to convert ammonia and nitrite to N2 (Kalyuzhnyi et al., 2006). However, this process could be excluded when ammonia levels are not present in the soils.

1.1.1. Nitrogen cycle

Nitrogen is the most abundant and essential element for living organisms (Chapman, 1996). Before it is absorbed by plants and animals in the soil, nitrogen (N) is converted to inorganic forms of N, nitrate (NO3

-), nitrite (NO2-), ammonium (NH4

+), and molecular nitrogen (N2), with varying oxidation states (Figure 1-1). This conversion of nitrogen takes place through five major pathways; nitrogen fixation, nitrification, denitrification, and ammonification (anammox) and volatilization as depicted in Figure 1-1. Nitrogen fixation process takes place through biological and non biological process. In biological process, bacteria convert inert N2 from the atmosphere into ammonia, nitrate and to nitrogen dioxide. In non biological process, lightning and combustion converts the inert N2 gas. Nitrification is a microbial process in which reduced form

STUDY OF SPATIAL AND TEMPORAL ASPECTS OF DENITRIFICATION PROCESSES IN ROXO CATCHMENTR, PORTUGL

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of nitrogen, ammonia, is oxidised to nitrite and nitrate (Figure 1-1). The amount of nitrate that is oxidised depends on factors like the availability of organic matter, pH, temperature etc. Denitrification on the other hand is described as the key process in the nitrogen cycle and involves the reduction of nitrogen oxides to non reactive N2 gas in the absence of oxygen (Davidson and Seitzinger, 2006). Similar to denitrification process, ammonification, oxidises NH4

+ to N2 under anaerobic conditions (Kalyuzhnyi et al., 2006). In the last pathway, volatilisation, NH4

+ is transformed into NH3 and the pathway is well suited when soil condition is greater or equal to pH 7.3

Figure 1-1: Schematisation of the transformation of nitrogen Source: Gardner, 2003

1.2. Justification

Attempts are made in Europe to address the increase in nitrate contribution through EU directive that regulate nitrate through the adoption of the Nitrate directive approved in 1991 (Goodchild, 1998). Human activities including agriculture (overuse of N fertilizers in agricultural food production), waste from livestock and sludge, conversion of virgin land to cultivation land, etc. are among the contributing sources of nitrate that consequently lead to the increase in the concentration level of nutrients in most receiving water bodies (Korom, 1992).

In soils, excess nitrate that cannot be absorbed by plants is not strongly held in the soil. As a result of the charged environment (particles) in the soil, the mobility and the soluble nature of nitrate, it is leached to the surface or ground water (Richards and Webster, 1999). Pollution of ground and surface water from nitrate contamination is therefore of global concern to both public health and the environment. Ecological significance of nitrogen/denitrification is thus important as a primary nutrient in freshwater systems and as control on eutrophication in lakes and reservoirs. Quantifying the attenuation potential of nitrate in the sub-surface could thus be helpful. Denitrification rates have been found to vary significantly both spatially and temporally depending on the characteristics of the soil. A clear understanding of the ranges of denitrification rates that can be expected in soils with different characteristics would be very useful for determining the probability of adverse effects from NO3

- contamination of the water supply.


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1.3. Research problem

Roxo catchment is situated in Beja region of southern Portugal and is the source of domestic and irrigation water supply. This catchment is however surrounded by agricultural fields, and producing 75% of the country’s total wheat, increased nutrient inflow of polluted runoff is experienced (Paralta and Oliveira, 2005)(Paralta et al., 2002). Scientist as far back in 1987 raised concern on the increase of nitrogen trend and advised that 40% of nitrogen entering the system be removed (Fields, 2004). The adverse effect of reactive nitrogen (from polluted runoff) in aquatic environment is the increase in nitrate concentration, thus providing constant nutrient supply for algae bloom. In addition, cereal crop production is the major land use practice near Beja city with annual fertilizer usage of 100-150 kg N-1 ha-1yr. Findings by Paralta (2002) confirm that irrigation fluxes of 45-62 kg N-1 ha-1 yr are represented by average nitrate content of 50 mg NO3

- l-1. In this context, a significant part of the total nutrient load from agricultural activities could be present in the soils and therefore is a concern over the impact of denitrification in the catchment.

Despite various studies carried out in Roxo catchment, nitrate loss, the interaction of environmental factors and denitrification rate have not been evaluated nor quantified. The current study therefore aims to investigate and quantify denitrification process in the catchment by using laboratory experimental method and spatiotemporal modelling approaches.

1.4. Research objective

1.4.1. General research objective

To study spatiotemporal process of denitrification in Roxo catchment.

1.4.1.1. Specific objective

• Determine potential denitrification rate using field samples, laboratory experiment and data analysis;

• Explore spatial and temporal datasets from satellite and ground points for reduction factors; and

• propose a space-time denitrification model

1.4.1.2. Research questions

• How are denitrification rates distributed spatially and temporally with respect to soils?

• How can model parameters be calibrated?

1.4.1.3. Research hypothesis

• Calibration of the model parameters can be achieved using laboratory technique for the potential denitrification rate.

• Local hydrological, meteorological, soil and satellite data for the Nemis model reduction sub-factors can be used to quantify denitrification rates of soils in space and time.

1.5. Research plan

The research plan is composed of five major parts which include the 1) conceptualisation of the topic to realise and incorporate the research problem and design; 2) set up of laboratory based


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experiment; 3) obtain remote sensing data; 4)proposed how a prototype denitrification model can be implemented; 5) finally, writing of the report. The breakdown of the research plan and the implementation of the four parts through pre-field work, field work and post filed work as illustrated in Figure 1-2 and Figure 1-3.

Figure 1-2: Research layout

L I T

E R

A T

U R

E

R E

V I

EW

Pre

-fie

ld w

ork

Pos

t- fi

eld

wor

k F

ield

wor

k

Task 1

Task2 Task3

Task 4

Task 5


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1.6. Thesis outline

The content of the thesis is arranged in 5 chapters. These chapters contain structured ideas to address the objective of the research and to answer the set research questions. The chapters are as follows: Chapter 1 Includes the introduction of the thesis. Background information together with

the problem statement, objective of the thesis, research questions and proposed the hypothesis is outlined.

Chapter 2 Reviews the literature studies on various methods of denitrification rate

determination, limitation associated with some denitrification rate methods and factors that affect denitrification rate. Common denitrification models are also revised but emphasis is drawn on the model proposed for the current study

Chapter 3 This chapter deals with material and methods. The selection and description of

the study area with respect to location, topography, climate, soil type and land cover is addressed. A brief description of source data types used in the thesis is also covered. Methods applied to achieve the aim of the study are highlighted in this chapter as well.

Chapter 4 This chapter is dedicated to the analyses of results from laboratory experiments

and the prototype model. Chapter 5 Discussion will be presented in this chapter. Conclusions and recommendations

for follow up research are also part of this chapter.

1.7. Thesis approach

The general concept behind the current study is to know and understand the complex process of denitrification for management of nitrate balance in Roxo catchment. This is implemented in two phases for the present study; 1) to quantify the denitrification rate at catchment scale and 2) use prototype model to spatialise the denitrification rate, obtained in phase1 .Though it has not been done before in the study area, comprehensive information for example on soils, land use activity, remote sensing (RS) data, is crucial. The emphasis therefore is to realise phase 1 through laboratory experiments. Due to time needed to fully implement the prototype model, coupled with the short duration of the MSc research, the current study in phase 2 thus investigate on how a prototype denitrification model would look. A summary of the general research approach is given in Figure 1-3.


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Figure 1-3: Research approach


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2. Literature review

This chapter gives a review on the direct and indirect methods of determining denitrification rate as well as modelling approaches that are available. The aim of the chapter is to provide basic concepts of denitrification rate, to strengthen better understanding on the overall objectives set for the research. The chapter focuses on widely used methods which are the acetylene inhibition, isotope tracer, nitrate disappearance and mass balance methods and their limitations. Processes within the soil that in a way affect or compliment denitrification is addressed in sections 2.3 and 2.4 briefly discuss models used in determining denitrification rate. The last part of this chapter focuses on related prototype, denitrification model.

2.1. Determination of denitrification rate

Various direct, indirect and modelling methods are applied in nitrogen transformation methods to determine denitrification rate. In most of these methods, denitrification rates are evaluated by measuring the reduction of the substrates (NO3

- and NO2-) or alternatively the end products ( N2O

or N2) (Sánchez et al., 2000). Frequently used methods have been revised by Groffman (2006) and are listed below,

• Acetylene inhibition method

• 15 N tracer methods

• Direct N2 determination methods

• Mass balance methods

• Stochiometry methods

• Molecular approaches

• Ratio method (e.g. N2 / Argon or nitrate/ chloride)

• In situ gradient environnemental tracer method

2.1.1. Direct method

Acetylene inhibition, also called acetylene blocking technique, is among the first discovered, the most common and widely applied method in denitrification rate determination (Laverman et al., 2007; van Beek et al., 2004; Well et al., 2003; Well and Myrold, 2002). When acetylene is applied, the denitrification reaction (Chapter 1, equation 1-1) is changed so that N2O become the end product. Thus, the role of C2H2 is to inhibit the reduction of N2O to N2. This method proved to be simple, cost effective and sensitive (Revsbech, 1991), hence it’s wide applicability (Korom, 1992; Mengis et al., 1997; Piña-Ochoa and Álvarez-Cobelas, 2006; Revsbech, 1991) In recent studies, acetylene method was found to underestimate the denitrification rate in sediments where nitrate concentrations were low (Groffman et al., 2006). This underestimation was due to the inhibition of nitrate by acetylene in nitrification process that is linked to denitrification pathway by means of supplying NO3

- and NO2- as depicted in the famous pipes and

holes model Figure 2-1.

STUDY OF SPATIAL AND TEMPORAL ASPECTS OF DENITRIFICATION PROCESSES IN ROXO CATCHMENT/RESERVOIR, PORTUGL

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Figure 2-1: Famous pipes and holes model. Shows how the output of nitrification (aerobic

condition), NO3-, is an input to denitrification (anaerobic). Adopted from

Lajtha and Michener (1994).

In the review by Korom (1992), reduction of nitrate did not occur in the presence of acetylene, where the concentration of ammonium was reported as < 0.1 mg NH4

+ -N L-1. This explained why low denitrification rate of 0.58 mg N l-1 d-1 from aquifer over 10 day incubation was obtained. Steingruber (2001) also documented similar reasons, as Korom (1992), for the underestimation of denitrification by acetylene inhibition method; 1) incomplete blocking of N2O reductase1 in environments with low nitrate, 2) presence of sulphide inhibit complete blockage of N2O reductase, 3) oxidation of NO to NO2, 4) N2O diffuses to deeper sediment layers where it is reduced to N2. Another commonly recognised setback associated with acetylene inhibition is the overestimation of the denitrification rate (Piña-Ochoa and Álvarez-Cobelas, 2006). 15N tracer methods have successfully been used with minor amendments to trace the labelled N in gaseous formation and consumption of nitrogen. The most common 15N tracer method, 15N labelled NO3

-, is based on the amendment of field or incubated soils with labelled 15NO3- (tracer).

The method is implemented on assumptions that denitrifying bacteria don’t differentiate between the added isotopes and the natural background isotopes (14NO3

-) in the soil as well as homogenous distribution of the 15N labelled nitrogen with the soil nitrogen (Tan, 1996). Comparison between 15N tracer methods and acetylene method showed that 20% less denitrification rate estimation was obtained with 15N tracer methods (Groffman et al., 2006).

2.1.2. Indirect methods

The common indirect methods of denitrification rate are the nitrate disappearance (reduction/loss) measurement (Yeomans et al., 1992), nitrate/chloride ratio and nitrogen mass

balance methods (Steingruber et al., 2001; Well et al., 2003). Yeomans (1992) investigated the denitrification activity in soils by assessing the capacity and the potential of the denitrifying bacteria. The method of choice by afore mentioned author, and also considered for the current study, was nitrate disappearance method which is similar to the

acetylene inhibition method in that incubations were carried out in anaerobic conditions though

no inhibitor is added. This method however measures the denitrification rate through the substrates.

1 Enzyme found in soil that enhance the conversion of N2O to N2

Nitrification process Denitrification process


9

The application of this method does not require bulky, sophisticated equipment. Another advantage to this method is that it is fast, and easy to use and required very limited space to execute the method. Mass balance method has been used as early as 1908 in marine environments. It was used to determine nitrogen fluxes and in recent cases, to estimate denitrification rates from literature studies (Groffman et al., 2006). Nielson (1995) found the annual denitrification rate (8 t N yr -1) of the estuarine obtained using mass balance method was in agreement with the rate obtained using the isotope pairing technique. As with most denitrification rate methods, mass balance methods are associated with cumulative errors when applied on large scale (Steingruber et al., 2001).

2.1.3. Potential denitrification rates (Dp) found in literature

The table below indicate the variability found in the various methods for determination of denitrification rate. Table 2-1: Dp from literature

Note: unit is represented in g N m-2d-1

2.2. Factors that limit denitrification rate

Denitrification is the major pathway of nitrate removal from soil and water, with organic matter or minerals, primarily pyrite, serving as electron donor.

The availability of organic matter in the form of carbon is found to be a limiting factor to denitrification process (Hefting et al., 2006). The soil bacteria utilise the organic matter as source of energy supply, growth and maintenance. Study by Yeomans (1992) established that the rate of denitrification was influenced by the presence of organic carbon that is needed by the microorganisms. The environmental conditions such as soil temperature, nitrogen (in the form of nitrate), soil pH, and oxygen (soil moisture) are also considered as factors that affect denitrification (Kjellin et al., 2007; Laverman et al., 2007).

Method Dp Surface Reference Acetylene inhibition Not Specified Acetylene inhibition Acetylene inhibition Mass balance Acetylene inhibition Nitrate Disappearance Database Acetylene inhibition Acetylene inhibition

0.6-1.2 0.48 0-0.4 0.01-0.28 0.007 (Da) 0.002-0.5 (Da) 0.0006-0.003 0.56±0.5 0-0.4 g m-2 d-1 0.03 g m-2 d-1

crops Sandy loam Riparian Loam Estuarine Pasture Not Specified Database Riparian Pasture

(Sánchez et al., 2000) Simmone and Mogant, 2005 Oehler, 2007 Simmone and Mogant, 2005 (Nielsen et al., 1995) (Machefert and Dise, 2004) Simmone and Mogant, 2005 (Henault and Germon, 2000) (Oehler et al., 2008) (van der Salm et al., 2007)


10

Though soil temperature has shown varying results among the conducted studies, findings by Hernandez (2007) pointed towards significant positive correlation between temperature and the rate denitrification in wetlands. Laverman (2007) reported that temperature variations (10 °C difference) during sampling partly accounted for half the difference in potential of the reduction rates in nitrate. Sánchez (2001) found that changes in temperature during the day is not significant towards the role it plays in denitrification process. Other studies however related that increase in temperature, result in a increase in bacteria which ultimately increases denitrification rate (Aulakh et al., 2000).

Denitrification rate can occur between 4-9.0 pH range, with most favourable values found between pH 7-8. Recently, Heinen (2006b) in his findings stated that denitrification rates more or less stops at pH < 4 or pH greater than 10 to 11.5. Commonly, the denitrification rate decreases with decrease in pH. Study by Aulakh (2000) illustrated that when pH decreases below 7, NO and N2O are by products, whereas N2O and N2 are by products above pH 7. Some studies have found that combination of limiting factors also limit denitrification rate. For example, combination of low pH and organic matter in Puerto Rico soils slowed denitrification rate down (Tate, 1995).

Denitrification rate decreases with decrease in soil moisture. Thus, in dry environments, less organic matter is decomposed compared to areas under irrigation. Soil moisture has cascading effect on denitrification as it affects the earlier mentioned factors as well. This is due to movement of oxygen molecules, linked to soil moisture, which has aeration effect on denitrification rates, thus reducing denitrification rates in some soils. (Tate, 1995)

2.3. Other processes

There exist other soil processes that are partially involved in the nitrogen cycle to aid the return of N2 gas to the atmosphere. There have however been questions on whether some processes play a role in the return of N2 gas. Chemodenitrification, also known as chemical denitrification or anaerobic denitrification, is an aerobic process where abiological reactions give NO, NO2 and N2O as end products in low pH (below 5) and high ferrous iron condition (Tan, 1996). Studies so far on the function of Chemodenitrification in returning N2 gas to the atmosphere has shown little significance as products (equation 2-1 and 2-2) are retained in the soil thus, reacting further with water and available oxygen. The latter, posed problems in some agricultural soils where it resulted in acidic soils.

3222

22

2

22

HNOHNOOHNO

NOONO

+→+

→+

−

−

2-1

2-2

Denitrification studies are carried out either in heterotrophic or autotrophic conditions. Review by (Korom, 1992) documented that difference between the earlier mentioned 2 conditions is the requirement of organic carbon by denitrifiers. Hence, in heterotrophic denitrification, organic carbon is required as the main electron donor. In autotrophic denitrification, reduced electron donors (of manganese, sulphide and iron) are used in the absence of organic carbon, while reducing nitrate.


11

2.4. Dentrification simulation models

Currently, there are challenges to directly measure the rate of denitrification at catchment or larger scale due to draw backs associated with measurement techniques. Additionally, lack or unreliable data, soil heterogeneity and variability of the factors that affect denitrification rate in space and time are common challenges to overcome (Boyer et al., 2006). Thus, the use of models have been proposed to overcome the above mentioned challenge as well as to consider the storage of nitrogen in soils and emission of gasses through denitrification in space and time (Boyer et al., 2006; Cinnirella et al., 2005; Heinen, 2006a; Heinen, 2006b; Henault and Germon, 2000; Well et al., 2005).

There are generally three (3) common approaches for developing denitrification sub models, i.e. on microbial growth, soil structure and simplified process models. Microbial growth models considered the dynamics in organisms that are responsible for N cycling process whereas the soil structural models considered gaseous exchange in and out of the soil layers (Heinen, 2006b). Nonetheless, simplified models consider neither the gaseous exchange nor microbial growth. Instead, they consider denitrification rate via easily measurable parameters like temperature, N, soil moisture (Heinen, 2006b; Parton et al., 1996). Simplified process models utilize response functions. Most studies vary in response functions which indicated the extent of complexity and difficulty associated with modeling denitrification (Heinen, 2006b; Parton et al., 1996). Currently, more models are applicable in agricultural fields due to the incorporation of the denitrification component to consider nitrate fluxes. For example, Heinen (2006b) reviewed 50 models that are based on simple denitrification approach and AMINO, NCSOIL, NEMIS, CERES, LEACHMN, MELEF, are just few of them. A study by Boyer (2006) also revised models based on perspectives of hydrology, biochemistry, ecology, agronomics agriculture managers. Though, spatially physically based models, like SHETRAN, account for spatial variability in parameters, variables and the process, applying and parameterization of the model still remain a challenge (Boyer et al., 2006)

2.5. Simple denitrification model: Nemis

Interest in the use of simplified models to estimate denitrification has increased and various combinations of models are integrated to simulate denitrification at temporal and spatial scales. Most simple denitrification models are based on potential denitrification rate, that can be measured through soil property or calculated from organic carbon, coupled with modelling approaches that are simple (Heinen, 2006b; Henault and Germon, 2000; Johnsson et al., 1987). Nemis model consider dimensionless reduction functions to account for water content, the effects of temperature, pH and nitrate concentration in the soil. Since the model considered mathematical functions, it relates bacterial activity in the soil to nitrate concentration based on Michealis- Menten relation (Heinen, 2006b; Johnsson et al., 1987; van der Salm et al., 2007).

2.5.1. Model description

Nemis model is a simple predictive soil denitrification model. Henault and Germon (2000) indicated that simple predictive models account denitrification as a multiplicative functions of

rate coefficients or soil potential denitrification rate and dimension functions Thus, Heinen


12

(2006b) proposed a simple model form as shown in equation 2-3 to predict the denitrification rate.

pHSTNpa ffffDD =

2-3

is the actual denitrification rate (g N m-2 d-1) at 0.3 m soil layer thickness and is the

potential denitrification rate (same units as )

NK

NfN +

= 2-4

Nf is a dimensionless reduction function for nitrate content that is in the range [0,1]. N is the

nitrate N content (mg N kg-1) and K is the Michaelis –Menten half – saturation constant (mg N kg-1) that control nitrate N content.

( )

=−

1

01.0

10Q rs TT

Tf

Sr

rS

s

TT

TT

T

≤<<

≤0

0

2-5

Tf is a dimensionless reduction function for soil temperature T (oC). Tr is a reference

temperature (° C) at which is determined, is the soil temperature (℃) and is an

increase factor for a 10 ℃ increase in T. Nemis model is among the few models that incorporate

two ranges for i.e. 2.1 and 89, thus indicating the presence and operation of denitrification

bacteria at different temperature level (Heinen, 2006b; Oehler et al., 2008).

−=1

35.3

0

pHf pH

5.6

5.65.3

5.3

≥<<

≤

pH

pH

pH

2-6

is the dimensionless reduction function for soil pH.

In order to represent the water content reduction function ( ) in models, power reduction

functions can be used (Heinen, 2006b; Oehler et al., 2008; Wang et al., 2008).


13

−−

=

1

1

0w

fc

fcs SW

SWSWf

SWSW

SWSWSW

SSW

fc

sfc

fc

<

≤≤

<

2-7

Where; is the dimensionless reduction function for water reduction function which is in the range of [0,

1]. SW is the soil water content (cm-3 cm-3), fcSW is the soil water at field capacity (cm-3 cm-

3). sSW is the saturated soil water content (cm-3 cm-3). Parameter w is the measure for the

steepness of curvefc

fc

SW

SWSW

−−

1and for the current study current study w=1 was used.

Table 2-2: Parameters that were obtained from Heinen (2006b) for the current study.

Parameters

Value Unit

mK 22 mg kg-1

10Q 2.1 unit less

rT 21 °C

Soil moisture in many studies has been represented at catchment level by areal distribution of wetness from Digital Elevation Model (DEM). For example, among the indices, topographic wetness index (TWI) based on equation 2-8, has been the earliest formulation to be applied to consider topography and hydrological processes. This index uses upslope contributing areas and slope to determine the spatial patterns of soil moisture at pixel level (Burt, 2004; Kim and Jung, 2008; Sørensen and Seibert, 2007). This index has also been integrated in models, for example TOPMODEL, STICS and TNT 2, to characterise the heterogeneity of topography in conjunction with hydrologic and biogeochemical properties of the catchment (Boyer et al., 2006).

( )βtanln a

2-8 Where,

tanβ is the slope gradient, evaluated from the DEM and a is the upslope area of the catchment.


14


15

3. Materials and Methods

3.1. Study area

3.1.1. Selection of study area

In order to answer the research questions and propose a spatio-temporal prototype model for denitrification, an area with sufficient data coverage was considered. The research field campaign was conducted in Roxo catchment in Southern Portugal due to 1) the availability of spatial data; 2) temporal data availability; 3) Portugal, Roxo catchment, is among the European countries generally affected by high levels of nutrients produced through human activities, mainly agricultural practices; 4) ongoing research at MSc and PhD level with the International Institute of Geo-Information Science and Earth Observation (ITC) which made ancillary and soil characteristic data already available and lastly 5) the need to access or quantify denitrification rate in the selected study area was identified crucial as increased polluted runoff is experienced in the study area.

3.1.2. General study area description

Roxo catchment is located in the district of Beja, province of Alentejo in Southern Portugal (Figure 3-1). It has an area coverage of 353 km2 with geographical bounding coordinates of 37° 46’ 44” N to 38° 02’ 39” N latitude and 7° 51’ 47” W to 8° 12’ 24” W longitude (Sen and Gieske, 2005). This catchment lies in Universal Transverse Mercator (UTM), zone 29 and is known for agricultural land use activity where cereal and sunflower are the dominant crops with corn as alternative cereal in dry periods (Paralta et al., 2002). Other main crops include grapes, olives, rice, potatoes and tomatoes.

Map produced by Fransiska Gamises 10/01/2009

Figure 3-1: Location, meteorological stations and DEM of the study area

Source of map: http://www.theodora.com [accessed date: 10 December 2008]

Elevation (m)

Meteo stations


16

3.1.3. Climate

Portugal is well known for its Mediterranean climate with Atlantic Ocean influencing the climate. The study area is drier and warmer than the rest of the country (Sen, 2005). The long term climate records (1961-1990) according to Alain Pascal (2008) show a converging precipitation trend with an average of 587mm year-1. The mean maximum and minimum air temperature of 33 and 6 °C were also recorded, occurring in July and January respectively (Pascal, 2008). The current study considered climatic records from 2006-2008. The maximum air temperature ranged between 15.4 to 33.8 °C and minimum temperature from 4.8 to 15.1 °C (Figure 3- 2). This explains the mild winters experienced in the southern part of Portugal. The average recorded minimum and maximum air temperature were 4.8 and 33.8 °C for the months of January and July, respectively. With maximum air temperatures of up to 40 °C recorded, the driest period according to the current records were between June and August. This coincided with the mean maximum soil temperature (Relva_max) of approximately 37°C, recorded in July. Roxo catchment has a very distinct rainfall pattern. The wet season starts from October to April, with the highest rainfall occurring in November (80.6 mm) and the dry season is from June to September (Kiama, 2008; Pascal, 2008). The month of July is the driest of all months, recording an average of 0.7 mm. The average precipitation for Roxo catchment was about 500 mm year-1 for the period of 2006 and 2008 (figure 3.2).

Figure 3-2: Hyetograph, monthly average temperature and rainfall from Beja Station (2006-2008)

http: //www.cotr.pt/

3.1.4. Soil types

Tagus river divides Portugal in two parts; the northern and southern parts. The southern part is characterised mostly by limestone (Woldie, 2003). However, four distinct soil orders, base on FAO-UNESCO soil classification are found in the catchment; Litossols, Luvissols, Planosols and


17

vertissols (Sen and Gieske, 2005). Luvissols make up 64% of the study area’s soils which makes them the dominant soil type. A brief description of the soils is given below; Litossols These are the least common soil types

in the catchment. Lithossols are commonly found in steep areas or in flood plains where rich alluvial soils accumulate frequently.

Vertisols These soils are found in north- north eastern and north western part of the catchment. Vertissols are poorly drained clay soils with high expanding clay minerals (http://www.fao.org).

Luvissols Being the dominant soil type in the catchment, it spreads from the north- eastern to the southern part with loam to clay loam texture (Sen and Gieske, 2005).

Planossols This soil types are found in the northern part of the catchment and Spread to the centre.

3.1.5. Software used

Statistical package, SPSS and Excel spreadsheet, were applied to obtain descriptive information and to store data, respectively. None of the data required transformation as descriptive statistics was sufficient to describe the data. Histogram plots were used to visualise the variation and distribution of soils (Annex F). Microsoft office 2007 package was to prepare and store data as well as writing documents (reports) ILWIS 3.3 and ARC GIS 9.1 software packages were used for creation, projection and calculations of raster the maps. Soil Water Characteristics software was utilised to obtain soil characteristics (bulk density, field capacity and saturation) that could not be obtained in the laboratory due to minimal amount of soil material and the priority allocated to the denitrification experiment that required undisturbed soil samples.

3.2. Auxiliary information

3.2.1. Digital Elevation Model (DEM)

The digital elevation model for the study was accessed from the Shuttle Radar Topography Mission (SRTM) website (http://Srtm.csi.cgiar.org/SELECTION/inputCoord.aSp). DEM information was available at spatial horizontal resolution of 90m and in 1m vertical increment (Maathuis and Wang, 2006). Bilinear resampling algorithm was applied to map SRTM to 15 m resolution. Skidmore (2002) reported that each pixel in the model has an elevation value similar to the real value on the surface of the earth. Thus, the reliability of the DEM was assessed by comparing the agreement of elevation points of the ground stations located within the study area

Figure 3-3: Distribution of soil types


18

(obtained from the Portuguese Water Resources Information System (SNIRH) and Centro Operativo de Technologia (COTR) offices) with the elevation values generated from the DEM.

Aljustrel, Beja, Barragen do Roxo, Herdade, Santa Victoria, and Trindad stations were selected for the elevation comparisons as they were located within close proximity of the study area (Figure 3- 1). The elevation of the study area obtained from the DEM varied from 128 to 244 m. Good agreement was observed between DEM derived elevation and ground station elevation with an R2 value of 0.99 which suggested that the DEM derived elevation is reliable, acceptable and fit for use in catchment delineation and compound extraction.

Table 3-1: Elevation and location of the meteorological stations located within the catchment.

Station X-Coord Y-Coord ground truth DEMHerdade do Outeiro 38.045 -8.266 74 72Aljustrel 37.971 -8.19 104 103Baragen do Roxo 37.934 -8.083 127 132Santa Victoria 37.964 -8.023 150 154Trindade 37.886 -7.893 172 168Beja 38.038 -7.885 206 210

Coordinate Elevation (m)

3.2.2. Hydro-processing- Catchment delineation

Integrated Land and Water Information System (ILWIS 3.3) package was selected as choice of software to delineate the drainage and study area because; 1) the software was freely available, 2) DEM hydro-process in ILWIS is fast and easy to use and 3) the delineated output compared well to actual size of the catchment. Roxo catchment was delineated based on DEM hydro processing as outlined by Maathuis (2006). The delineated area (352 km2) was acceptable, though it was

slightly bigger by 0.28 % when compared to area size obtained by Sen and Gieske (2005).

3.2.3. Soil map

Concalves, 2004 gave a brief breakdown of the soil survey history of Portugal where soils in Southern Portugal were mapped based on FAO- UNESCO legend of 1988 at 1:1 000 000 scale. The first soil map was published by Estação Agronómica Nacianal (EAN) in 1949 which was reprinted in 1973 as contribution to the European Soil Map (Concalves, 2004). The secondary soil map data used in the study was obtained from COTR at a scale of 1:50 000. This soil map was reclassified in ARC GIS 9.1 based on existing soil attributes (soil polygons) and background soil information from Cardosa (1965) as shown in Figure 3-4 and Table 1.3 gives a general and short description of the soil types found in the catchment.


19

Table 3-2: Brief description of the soil codes

Source:Cardoso, 1965

Soil Class Description

Bpc Vertisols – calcareous black, strongly decarbonated Pb Ps

Hydromorphic soils – without alluvial horizon – para not strongly unsaturated Hydromorphic soils – with eluvial horizon – planosols

Px Vc

Brown Mediterranean Soils from Non-calcareous rocks –normals Red calcareous soils – red calcareous soils of semi arid climate -normals

Pxd Sp Sr

Brown Mediterranean Soils from Non-calcareous rocks – normals Hydromorphic soils – hydromorphic organic soils Red-yellow Mediterranean soils from non-calcareous rocks – normals Red-yellow Mediterranean soils from non-calcareous rocks – normals Vx

3.2.4. Land cover/use

Southern Portugal is known extensively as an agricultural region. According to the land cover/use map (Figure 3-5) obtained from Coordination of Information on Environment (CORINE) data base. Landsat 7- Enhanced Thematic Mapper (ETM) was used to characterise the land surfaces of European member states at 1:100 000 thus generating a land cover/use as one of its products. Corine data base product, created by European Environment Agency (EEA) was chosen because

Figure 3-4: Reclassified soil map Source: http://www.cotr.pt/


20

of it high thematic accuracy of 85% agreement in land classes. Moreover, the dataset is available without financial cost and compatible with diverse software (ILWIS 3.3, ARC GIS 9.1 and Erdas Imagine 9.1). Arable land made up 71 % coverage of the study area, followed by heterogeneous agricultural areas with 18% coverage. Forest cover 6% of the total area, with permanent crops and water each contributing 2%. The lowest coverage of 0.2 % is for water and the remaining 0.8% of the total study area is covered by shrubs.

Table 3-3: Description of Corine Land cover classes used in the study

Figure 3-5: Land cover/use map of the study area showing the sampling locations Source: http://dataservice.eea.europa.eu

Corine land cover classes

% coverage

Description

Arable land (non irrigated)

71 Refer to non irrigated land that includes all cereal, fodder crops as well as flowers and trees.

Forests

6

Consist of broad leaved trees like Eucalyptus trees, coniferous forest with Pinus trees and forests with combination of both

Heterogeneous agricultural areas

17.9 Composed of annual crops where land use is principally agriculture, mixed with patches of natural vegetation

Permanent crops 2 Include irrigated crops e.g. vineyards, fruit trees, rice etc.


21

Adopted from: http://dataservice.eea.europa.eu

3.3. Field campaign

Field campaign was set out during the period of 1-12 October 2008 in Beja region, Southern Portugal. The aim for the campaign was to collect soil samples for laboratory analysis and to update climate database of the Roxo catchment.

3.3.1. Soil sampling

Soil sampling was based on the scheme by Gökmen (2006). Since it was impossible to sample every polygon in the soil map due to time and financial constrains, polygons with area size of >1% of the catchment where considered. Minor modifications were made to the existing scheme with respect to considering points in close proximity to the road (accessibility) and incorporating land cover as well as the soil type. Sampling was set to compliment earlier research by Gökmen (2006) to try and cover the gaps of unsampled soil polygons thus, providing basic soil information. We therefore considered 12 sampling locations within the study area (refer to Figure 3-5). In this study, additional sampling points where considered to cover more sites in the catchment and to have the best spatially represented distribution of the catchment. Also, it was important to consider the land use in the catchment. During the campaign, 2 kinds of soil samples were collected at each location; undisturbed also referred to as intact and disturbed soil samples. Since it is the first study of its kind in the study area, we decided to consider and investigate soil processes (i.e. denitrification) in the top soil layer (0-30 cm). Therefore, undisturbed soil samples were taken with 100cm3 sample ring, using an Edelman auger (Annex A_1). These soil samples were used for chemical soil analysis and for the denitrification experiment (Gökmen, 2006; Yeomans et al., 1992). Homogenised soil samples taken also by auguring were placed in sampling bags and stored away for later use. (Annex A_1). The disturbed samples were used for physical soil analysis and where taken in duplicate. Undisturbed soil samples were kept at 4°C after sampling to prevent any further bacterial activity from taking place and thus were stored away until analysis was to be carried out Table 3-4: Spatial information of soil samples collected during field campaign in Roxo catchment.

Sample Id

Soil Code/Location

Soil Colour Latitude Longitude

S 1 Bpc_1 very dark gray 596375.84 4206651.18 S 2 Bpc dark brown 592861.00 4207941.50 S 4 Vx dark yellowish

brown 581347.00 4190707.00

S 5 Sr strong brown 588893.00 4202028.00

0.2 Consist of natural grasslands, moors and heath land Scrubs Urban build up water

0.8 2.1

Consist of construction in a form of buildings and road networks. Refers to inland water bodies


22

S 6 Vx_1 yellow 595479.00 4200864.00 S 7 Ps light olive brown 589359.45 4205681.76 S 8 Vc dark yellowish

brown 582813.69 4201678.18

S 9 Sp light olive brown 584867.10 4205007.20 S 10 Pxd yellowish brown 583733.00 4207685.00 S 11 Sr_1 olive brown 584923.80 4196977.00 S 12 Px dark olive brown 582229.23 4197736.79 S 13 Pb pale yellow 578969.00 4187164.00

Coordinate system: UTM, zone 29 with WGS 1984 Datum

3.3.2. Field data

The soil colour information on field moist samples were determined Munsell soil colour chart that is based on hue, light reflected by the soils; value, the darkness and lightness of the colour with respect to the neutral gray scale; and chroma, which defines the brightness or richness of the colour (http://www.water-research.net). This test was performed to have a rough indication of the organic matter and mineral content of the soil as dark surface soils are associated with high organic matter and manganese whereas orange brown soils are associated with presence of oxidized Iron oxide. Soil moisture measurements in the field were determined by Theta Probe moisture meter with HH2 readout (type ML2x) and Stevenson Hydra Probe Soil Sensor near locations where soil samples were taken (Annex A_2). The moisture meter convert the voltage signal from the probe into moisture content by built in soil parameters and linearization table (www.delta-t.co.uk). It was however not possible to calibrate the probe measurements against gravimetric method due to priority in terms of undisturbed soil samples that were allocated to the denitrification experiment as well as financial constraints that limited additional samples to be considered. Therefore, only inter comparisons of probe measurements were carried out that gave a good correlation (R2= 0.88). Spatial point locations were market with the Etrex GPS. Though most of the soil data for the current study was generated in the laboratory, materials and background data for the research is outlined in Table 3-5.

Table 3-5: Summary of materials and data used in the study

Data Scale/resolution Source Digital soil map 1:100 000 COTR

Land cover/use map 1:100 000 Corine

Climate (2006-2008) Precipitation Temperature LST ** (2008)

Monthly Monthly

ITC (previous research studies), COTR and INAG Land Surface Analysis Satellite Applications Facility (LSA SAF) http://landsaf.meteo.pt

Topographic sheet 1:50 000 ITC (previous research studies)


23

SRTM DEM 15 m

http://Srtm.csi.cgiar.org/SELECTION/inputCoord.aSp

Soil data Chemical: pH, temp, carbon, nitrate, organic matter, ammonia, total N#, and total P*

n/a COTR and ITC (previous research studies)

Physical: Soil texture, bulk density, soil moisture, and water content ETREX GPS

n/a 10 m

ITC (previous research studies) ITC

# Total Nitrogen * Total Phosphates ** Land Surface Temperature All maps and images used for the current research were re-projected to the following coordinate system: Projection Universal Transverse Mercator (UTM) Datum World Geographic System, 1984 (WGS 1984) Hemisphere Northern, Zone 29

3.4. Laboratory measurement

This section gives a breakdown of all the laboratory methods and analysis applied for the study. The overall aim of this section is to realise the second task of the thesis which involves potential denitrification rate as well as the chemical and physical soil characteristics quantification (chapter 1, section 1.5)

3.4.1. Potential denitrification rate: Experiment design

In denitrification studies, potential denitrification rate is defined as denitrification taking place in excess amount of nitrate at reference temperature under anaerobic conditions (Heinen, 2006b). Laboratory experiment was thus carried out to measure the potential denitrification rate (Dp), also referred to as denitrification capacity, on field moist undisturbed soil samples. We followed the indirect approach of anaerobic incubation of soils amended with potassium nitrate (KNO3) without addition of organic carbon (Wang et al., 2008; Well et al., 2005; Yeomans et al., 1992; Zhou and Hosomi, 2008). This approach was chosen because 1) the instruments and materials for the experiment were readily available in the ITC soil laboratory and 2) very minimal sophisticated instrumentation was required. Each ringed soil sample collected during field campaign was mixed thoroughly and passed through 2mm sieve to ensure that unwanted pebbles and plant material were removed.


24

Approximately, 0.1 mg NO3-N g-1 soil of nitrate as KNO3 was prepared in solution and mixed with 5g soil sample in 25ml test tube (van Beek et al., 2004; Yeomans et al., 1992). To completely maintain anaerobic environment, the test tubes were flushed with Argon gas, sealed tightly with rubber stoppers and incubated at 30 °C. In order to maintain constant temperature, we used the water bath as an incubator instead of the oven as preliminary testing on the water bath gave acceptable steady temperature values (Figure 3-7). Based on preliminary results, it was decided to centrifuge and filter the sub samples (supernatant). This process was found to remove possible interferences and clarify the sample. The supernatant was centrifuged at 8000 rpm for 30 minutes and filtered through 0.45 µm millex filter. Nitrate loss was determined by measurement of nitrate concentration with portable HACH Spectrometer. Sub samples (supernatant) per soil sample location were measured at 0, 4, 8, 12, 25, 32, 39 and 42 day interval (Figure 3-6). Potential denitrification rate per soils were calculated by determining the difference in nitrate loss concentration over incubation time. This was further converted to express the rate relative to the surface area and was finally reported as g N m-2 d-1. However, to compare with other studies, inter conversion units of mg kg-1 d-1 or mg ha-1 d-1 were also applied.

A B Figure 3-7: Setup the experiment in laboratory using a water bath for incubation of soils (A). Soil samples per

location after incubation (B)

Figure 3-6: Schematisation of the experiment. Soil samples are represented by a

circle and soil code. Analysis was carried out per row for Specified day.

Soil code

Incu

batio

n tim

e


25

3.4.2. Soil physico- chemical analysis

Most of the analytical methods were carried out at the ITC laboratory and standard methods were used to measure the soil characteristics. Inorganic form of NO3-N and NH3-N was determined by extraction of undisturbed sieved soil samples with Potassium Chloride (20 ml of 2M KCl per 5g of dry soil) solution according to Richard and Webster (1999). The vials containing the soil mixture from each sampling location were continuously shaken for 2 hrs. The suspension was filtered through 0.45 µm millex filter to remove suspended plant material and analysed for NO3

- and NH4+ using portable HACH

Spectrometer. Cadmium reduction and Nessler methods where applied to analyse NO3- and NH4

+ at 400 and 655 nm wavelengths, respectively (Hefting et al., 2003; Pattinson et al., 1998; Richards and Webster, 1999; Tan, 1996). The determined values were reported as average soil values for each soil in mg NO3-N kg-1 Analytical methods from ISRIC (International Soil reference and Information Centre) were the bases of determining the pH, organic matter and soil texture (van Reeuwijk, 1992). Determination of pH-H2O and pH-KCl according to earlier mentioned author were obtained by shaking the suspension with 1:2.5 soil/water and 2M KCL, respectively for 2 hrs and analysed potentiometrically with glass electrode (HACH sensION pH meter). Modified Walkley-Black method was used to determine the portion of soil’s organic matter that decomposed. This method was chosen because it required minimal sophisticated equipment, was fast and simple. Also, it considered interfering agents present in soils. The method is based on wet combustion of organic matter (0.5g of sieved soil) with known amount of potassium dichromate (K2Cr2O7) in the presence of sulphuric acid (H2SO4). Interferences from ferric ion (Fe3+) that can be found in soils were illuminated by addition of phosphoric acid (H3PO4) (Tan, 1996; van Reeuwijk, 1992). After the extraction phase, the quantity of organic carbon in soils were determined through manual titration where excess dichromate (Cr2O7

2-) as titrated against ferrous sulphate (FeSO4) in the presence of an indicator until color change was observed (van Reeuwijk, 1992). Total nitrogen and phosphate were also analysed for each soil location on disturbed soil samples by Waterschap Regge en Dinkel external laboratory. Digestion methods according to van Reeuwijk (1992) were applied and reported in mg NO3-N kg-1 dry soil. Extraction of sulfate was according to Prietzel and Hirsch, (2000).

Walkey- Black method in progress


26

Disturbed soil samples where dried at 105°C for prolonged time (72 hours) to ensure that heavy black clay soils where completely dry. Samples where sieved through a 2mm mesh and coarse fraction (Coarse) was determined. Soils were pre treated with soil hydrogen peroxide (H2O2) (to destroy organic matter) and dispersed with a mixture of hexa- phosphate and soda. Method based on the separation of mineral part of the soils according to size fractions size was followed (van Reeuwijk 1992). These size proportions were later used in Soil Water Characteristic Software to classify soils in terms of texture (sand, silt and clay). Bulk density (kg m-3) measurement was obtained by using texture % (Sand and Clay), organic matter and coarse fraction values as input in the SWC software. The software which is based on Pedo Transfer Functions (PDF) available from the United States Department of Agriculture (USDA) was used to estimate wilting point, field capacity, saturation, available water content and soil saturation of the soils. Table 3-6: Summary of soil characteristics investigated in the ITC soil and water laboratory

Note: TN is Total Nitrogen

TP is Total Phosphorus

3.5. Model input preparation

This section describes the data preparation processes involved to setting up the proposed model. Thus, the data generated from previous section 3.4 was utilised in combination with remote sensing data. Figure 3-8 gives the general outline of the section and also steps involved in the implementation of the prototype model.

3.5.1. Modelling data

Actual denitrification rate simply represents the reduction of the potential denitrification rate (Dp) by the soil conditions. Data required for modelling actual denitrification rate (Da) as indicated in

3 &4 Samples sent to Waterschap Regge en Dinkel external laboratory for analysis

Analysis Method Reference Soil characteristic NO3-N NH3-N TN2 TP3 pH Organic matter Bulk density Particle size Sulfate Dp

Cadmium Reduction Nessler

Total Kjeldahl Murphy-Riley Potentiometric Walkley & Black SWC Pipette Method Sodium hydroxide Nitrate Reduction

Richards & Webster,1999 Hefting et al., 2003 Pattinson al., 1998 van Reeuwijk, 1992 van Reeuwijk, 1992 van Reeuwijk, 1992 van Reeuwijk, 1992 Kaur et al., 2002 van Reeuwijk, 1992 Prietzel&Hirsch, 2000 Yeomans et al., 1992

Chemical Chemical Chemical Chemical Chemical Chemical Physical Physical Physical Physical Chemical


27

chapter 2 (section 2.5.1) is in the form of raster maps. Preparations of these maps are highlighted in sections 3.5.2 to 3.5.5 with Table 3-7 summarising the main input into the maps. Table 3-7: Summary of input data for the denitrification rate model

Note: refer to section Chapter 2, section 2.5.1 for output map formulae

.

3.5.2. Potential denitrification rate (Dp) and map

Potential denitrification rate can be obtained from laboratory experiment (as explained in section 3.4.1.). In order to realise the potential denitrification rate map, attributes from the soil map served to be vital. Potential NO3-N loss per area (1m2) of soil was quantified, taking into account the bulk density, thickness (soil depth in meters) and the potential denitrification rate at each

Primary Input

Temporal scale

Unit Output map

Dp rate daily g N m-2 d-1 Dp NO3-N static mg N kg-1 fN

pH Temperature4 Soil moisture

static monthly monthly

n/a ° C mm

fpH fT fW

Figure 3-8: Proposed implementation of the Nemis model


28

sampling location. The quantified potential NO3-N loss value was incorporated as an attribute in each corresponding polygon of the soil map using ARC GIS 9.1 software.

3.5.3. pH & Nitrate map

Both the soil pH, (pH-H2O), and nitrate concentration values obtained in section 3.4.2., were used to generate the maps based on soil attributes. The measured values were extended to the soil map attributes, thus spatialising the measured attributes. The reduction functions of these maps are generated according to equations 2-6 and 2-4.

3.5.4. Land surface temperature and map

The uses of remote sensing techniques enhance data collection, therefore providing information to areas where no gauged stations are located or inaccessible areas are crucial. Also, by employing remote sensing techniques, the frequency and scale at which data is ordinarily obtained can be improved. Land Surface Analysis Satellite Applications facility Product (Land SAF) from Meteosat Second Generation (MSG) was downloaded from the website (http://landsaf.meteo.pt/) without any cost to utilise land surface temperature (LST) in °C. This approach was chosen because of the availability of various codes for pre-processing MSG product in ILWIS 3.3 software, which was considered to be fast. LST diurnal cycle was constructed from maximum hourly temperatures for the reference day of 15 August 2008 to determine the time at which maximum temperature occurred. We decided to represent monthly temperatures from Land SAF product by generating decadal composite of maximum temperature images, taken at 14:00 (Universal Central Time) and using ILWIS 3.3 software. This approach was followed to minimise time Spend on processing high temporal resolution images (every 15 minute). Ideally, at least 13 images (considering day time hourly images from 08:00 – 20:00 hrs) are required to obtain 1 day maximum temperature image which is further processed to obtain a monthly temperature (map) by compositing all daily maximum temperatures (maps) obtained for the month (± 30 images) This approach however is time demanding and due to lack of automated procedure, could not be applied for the current study. Bilinear resampling algorithm was applied to re-prpjrct the monthly LST maps into common projection system used in the study (UTM, WGS 1984). The bilinear algorithm approach works on 2 principles, namely; establishing coordinates for each output pixels and output values are interpolated around 4 nearest pixel values in the input map. Nearest neighbour algorithm is ideal as it assigns the value of input pixel nearest to the determined coordinate as output value. Due to technical hitches in the nearest neighbour algorithm in ILWIS 3.3 software, bilinear algorithm was chosen for re-mapping of LST maps. The temperature reduction function used in the modelling of actual denitrification rate can be generated according to equation 2-5 In parallel, random images were selected and dates matched with gauged station data, in order to determine the degree of agreement. Thus, daily maximum LST composite pixels were compared with daily maximum soil temperature (Relva) from meteorological stations obtained from CORT in order to determine fitness of use of remote sensing data (LST) for the current study. Good


29

correlation (R2 = 0.8) was observed between the LST and the gauge station data. LST was therefore used to prepare input temperature maps for the model. In order to model the actual denitrification rate, due to time constraints, only four months based on season were selected to represent the temporal scale. The chosen months were January, March, June, September.

3.5.5. Soil moisture and map

Soil moisture is one of the most important parameters used in denitrification studies. Various methods including soil water balance, in situ measurements or remote sensing techniques are available for soil moisture determination (Heinen, 2006a; Heinen, 2006b; Henault and Germon, 2000; Johnsson et al., 1987; Sørensen and Seibert, 2007). In recent years, some of these techniques have been incorporated in models like STIC, Top model and SWAT as a tool to simulate soil moisture and other parameters at spatial scales. However, the study experimented with topographic indices and water balance method as initial step to obtain soil moisture which was essential for setting up the denitrification prototype model. Approach by Kim and Jung (2008) was implemented, where spatial patterns of soil moisture was described by Topographic Wetness Index (TWI) which considers topography of an area. Compound Index calculation tool in ILWIS 3.3 software was used to compute the index according to equation 2-8. The required data of elevation and accumulative number of pixels that drain into the outlet are derived from DEM and flow accumulation map respectively (Figure 3-9). Reduction function of the TWI is achieved by rescaling the index, so as it get [0, 1] as final outcome.

A B C D

Figure 3-9 : Input maps of DEM (A) and flow accumulation map (D) was

required to generate TWI map. Steps A-D indicate how flow accumulation map was generated from DEM using ILWIS 3.3. Source: ILWIS, 2003

Soil moisture measurement derived from water balance study of Roxo catchment through the use of Soil and Water Assessment Tool (SWAT) was also tested in denitrification prototype model. Monthly output per 13 sub catchments from the SWAT model was used (Annex N). The water


30

balance methodology and complete dataset is outlined and available from Igbinosun (2009). The

SWAT soil water data was georeferenced and integrated in ILWIS as SWraster map. Soil Water

Characteristics software was used to transform the volumetric soil moisture point measurements

to Soil Water at field capacity ( fcSW ) and Soil Water at saturation ( sSW ) points. Point

interpolation technique using trend surface operation was applied to obtain both fcSW and

sSW raster maps in ILWIS 3.3 environment.

Application of equation 2-5 is the basis of obtaining the reduction function of soil moisture form SWAT model to be incorporated in the prototype model.


31

4. Results and Analysis

4.1. Exploratory analysis

This section on exploratory data analysis focuses on the data generated in the laboratory as well as comparative data analysis from previous study by Gökmen (2006) and Cardoso (1965). Bulk density data from past studies area were also considered for verification purpose as the bulk density measurements for the current study was obtained through the Soil Water Characteristics software (SWC software). Soil name and soil location will be used interchangeably from this section onward.

4.1.1. Soil Information

4.1.1.1. Walkley- Black method and previous study comparison

Since organic matter is crucial and is one of the limiting factors in the denitrification pathway, Walkley- Black method was tested first on known soil material. We accepted the method based on the R2 it yield (0.99), Figure 4-1.

Figure 4-1: Assessment of the Walkley- Black method with in- house laboratory soil

samples

The organic matter results from the current study were compared with the Gökmen (2006) and Cardoso (1965) dataset as shown in Figure 4-2. There is a trend in the compared soil samples over the years. The samples (Px, Sr, Pb, Ps, Bpc, Vc, Vx, Pxd, and Sp) were chosen on the basis of matching locations, especially for 2006 and 2008 dataset, and respective soil information was available for the earlier mentioned years. There is however an increase in organic matter percentage in some soils (Vx, Pxd, Vx and Sp) in comparison with 1965 dataset. Red calcareous soils (Vc) have the highest organic carbon (2.3 %) for 2008 dataset whereas 18.4% was observed for hydromophic organic soils (Sp for the year 2006). Non calcareous soils, derived from rock Mediterranean soils (Pxd) contained the highest organic matter of 1.7 % for 1965 dataset.


32

4.1.2. Soil properties

Descriptive statistics was conducted, on the data generated in the laboratory to describe the variation of the parameters in the study area. Soil nitrate concentration ranged from 1.3 to 16.3 mg kg-1 measured in Pb and Bpc soils respectively, with mean nitrate of 5.9± 4.8 mg kg-1 (Table 4- 1). Generally, NO3-N was present in higher concentrations than NH4-N in most soils.

Maximum NH3-N levels of 8.2 mg kg-1 were found in Bpc whereas Px recorded a minimum of

2.8 mg kg-1. Dp measurements ranged from none to 0.7 g m-2 d-1 in Px and Ps soils respectively

and the Dp mean was 0.3± 0.2 g m-2 d-1. The highest sand, silk and clay content of 47.2, 23.1 and 60.1 % were measured in the northern tip of the study area in Vx_1, Bpc and Bpc_1 soil locations, respectively. The lowest sand, silt and clay percentages of 20.3, 2.4 and 12.8 were found in Sr_1, Pxd and Sr_1 soils correspondingly. They are located in central (Sr_1) and the far north western part of the study area. The texture classes of the study area were found to be silty loam, clay loam, clay and loam. Complete summary of the physical and chemical characteristics are given in Table 4-1 and Annex D. Table 4-1: Means and ranges of physical-chemical properties of Roxo catchment. The mean is

represented with the ± standard deviation

Figure 4-2: Comparisons of organic matter composition in soils of Roxo catchment

Variable Unit Mean (± std. deviation)

Minimum Maximum

NO3-N NH3-N TN TP pH-H2O pH-KCl Dp

5 Sand (>50µm) Silt (<2-50µm) Clay (<2µm)

mg kg-1 mg kg-1

mg kg-1

mg kg-1

g m-2d-1 % % %

5.9 ± 4.8 5.0 ± 1.8 1875 ± 514 327 ± 151 6.3 ± 1.0 5.0 ± 1.1 0.3± 0.2 28.5± 8.3 10.7 ± 5.7 31.5 ± 17.1

1.3 2.8 1300 130 5.1 3.6 0 20.3 2.4 12.8

16.3 8.2 3200 570 8.1 7.0 0.7 47.2 23.1 60.1


33

In addition, spatial patterns of NO3-N, pH and Dp rate maps represented in Figure 4-3 show variability in the central and far southern part of the catchment. These depicted parts have overall higher to highest levels of NO3-N, Dp rates and pH than the rest of the study area.

4.2. Laboratory experiments

This section is made up 2 main parts that provide results on potential denitrification experiment and the prototype modelling.

4.2.1. NO3-N loss

Mean NO3-N loss among soils vary between 3.7± 2.3 to 23.4 ± 13.2 mg N kg-1 as observed from Figure 4-4. The mean maximum NO3-N loss (23.4 ± 13.2 mg N kg-1) was found in Sr_1 soils with silty loam texture which correspond to sampling point S11. This point is located near the catchment’s largest reservoir. The highest NO3-N loss could be as a result of abundant initial

Organic matter Bulk density

% kg m-3

1.8 ± 0.5 1418 ± 92

0.9 1300

2.7 1500

Figure 4-3: Catchment spatial distribution of pH, NO3-N (mg kg-1) and Potential

denitrification rate (DP) in g m-2 d-1. Note: See chapter 3, section 3.5.2 to 3.5.3 for the preparation of these maps.


34

nitrate content (40 mg kg-1) that was available (Annex G). The mean minimum NO3-N loss of 3.7 mg N kg-1 was observed in Bpc_1 clay soils, which corresponded to sampling location S1.The trend in soil code Px does not resemble the general downward trend observed in other soils, instead the trend goes up for the first 5 days from which it then decreases. The initial NO3-N loss for all soils (day 0) is variable with a rapid decrease over the first 4 days for most soils (Annex G).

Temporal average variation of NO3-N loss followed an exponential decrease as expected (Figure 4-5). This can be explained from individual soils in (Annex G), where almost half of the soils had fluctuating yet decreasing trends due to reduction of nitrate in the soils. Overall, the trend in Figure 4-5 indicated the presence of NO3-N loss activity with respect to time, although slight increase was observed on day 15 and last day of the experiment. The mean maximum NO3-N loss over the 45 days was 21± 9.8 with a minimum value of 6.6 ± 5.3 mg NO3-N kg-1 which happen to be on day 0 and day 40 of the experiment duration.

Figure 4-4: Variation of NO3-N loss in the studied soils/location. The error bars are represented

with standard deviation

Figure 4-5: Overall average NO3-N loss of soils as a function of time


35

4.2.2. Potential denitrification rate (Dp)

Potential denitrification rates were measured after addition of nitrate to undisturbed soil samples, incubated at 30 °C. Bulk density and standard soil depth of 0.3 m was used to convert NO3-N loss mg kg-1 unit to Dp rate unit of g N m-2 d-1. The totalled mean denitrification rate (Figure 4-6) of the study area was found to be 4.1 ± 3.1 g N m-2d-1. The measured mean potential denitrification rate of the soils varied from 0-0.7 g N m-2d-1. No minimum and maximum values could be reported for Px soil location as zero denitrification activity was reported throughout the experiment. Ps soil type was responsible for 17% of the overall potential denitrification rate which made it the maximum contributor to the overall denitrification rate of the catchment. Generally, the rates were highly variable with higher rates found near moist areas e.g. near forests (Vx and Vx_1) or streams (Ps and Sr). Thus, from the spatial distribution in (Figure 4.8), the central part of the study area has relatively high rates (0.4-0.7 g N m-2 d-1) compared to the southern and northern part of the catchment with 0-0.3 g N m-2 d-1.

Potential denitrification decreased with time. For example, a mean potential rate decreased from 0.98 to 0.13 g N m-2 d-1 is observed on day 4 and day 45, respectively (Figure 4-7). As expected, maximum potential denitrification rate of 33% took place on day 4. The most active period of nitrate reduction (day 4-day12) was followed by a steep drop in the rate which is a possible indication of the rapid consumption of large amounts of nitrate by bacteria. This is not the case after day 12, as gradual reductions in the Dp rates continue to be observed until day 45, which resulted in a gentle trend (Figure 4-7).

Figure 4-6: Mean variation of denitrification rate in soil types. The mean is represented with

standard deviation.

Figure 4-7: Mean temporal variation of denitrification rate using the nitrate reduction method

(Yeomans et al., 1992)


36

4.2.3. Trends analysis

Spatial trend in nutrients (NH4-N and NO3-N) was variable across the study area with highest nutrients contents found in Bpc_1 clay soils with 16.3 mg NO3-N kg-1 and 8.2mg NH4-N kg-1 concentrations (Figure4-9). The earlier mentioned soil code is located in the northern part of the study area (Figure 4-8). Gradual decrease in nutrient level from north to south-west (towards the reservoir) was observed. For example the decrease in NO3-N kg-1 and NH4-N kg-1 for the studied soil types of Bpc_1, Ps and Sp were found to be; 16.3 and 8.2, 12.5 and 5.6, 3.8 and 3.6 respectively. A sudden increase was observed in nitrate concentration, close to the outlet of the reservoir, where 7.5 and 8.8 mg NO3-N kg-1 was measured in Px and Sr_1 soils, respectively. Spatially, high soil pH values of 7.4 and 8.1 were found near the reservoir in Px and Vc soils, accordingly as well as in the northern clay textured part of the study area (Bpc and Vc with 6.9 and 8.1 for pH_H2O respectively), Figure 4-8 and Figure 4-9.

Figure 4-8: Spatial distribution of NO3-N, NH4-N, Dp, pH_H2O and organic carbon with respect

to soil location, overlaid on texture map of the study are. Note: NO3-N and NH4-N are

converted to mapping unit of g m-2.


37

Some soil types have been found to be low in both NO3-N and NH4-N concentration, yet high in NO3-N loss. For example, Sr, Vx_1, Sp and Pb soils were all high in NO3-N loss and low nutrient concentration (Figure 4-9).Contrary to the low nutrient concentration, the earlier mentioned soils measured 0.6± 0.4, 0.4± 0.4, 0.1± 0.1 and 0.1± 0.1 g m-2 d-1 Dp rates, respectively (Figure4-10)

A general trend was observed in Figure 4-10, where high NO3-N subsequently resulted in an increase in Dp rates for most soils. Dp rates were highest (Ps-0.7± 0.1 and Sr-0.6 ± 0.4 g N m-2 d-1 soil) located in the centre of the study area. Decreasing trend in rates were (Figure 4-8, Figure 4-9 and Annex D) observed from northern to south western part of the catchment, where the reservoir is located. An isolated high nutrient (8.8 and 5.2 mg kg-1 for NO3-N and NH4-N, respectively) and high Dp rate (0.6 ± 0.6 g N m-2 d-1) was also observed in the far north western part of the study area in Pxd silty loam soils (Figure 4-8 and Annex D). Though large amount (16.3, 8.8 and 7.5 mg kg-1) of nitrate concentration in the form of NO3-N is observed in Bpc_1, Sr_1 and Px respectively, less quantity of Dp

(0.2, 0.4 and 0 mg kg-1 d-1 g m-2 d-1, respectively) was obtained in clay, silty loam and clay loam textured soils (Figure 4-10, Figure 4-8 and Annex D). Two of these soils are located in the centre of the study area, near the reservoir (Px and Sr_1) and Bpc_1 is found in the northern part of the catchment. Also, the NO3-N loss was high in Sr_1 (23.4 mg N kg-1) and Px (10.3 mg N kg-1) soil location compared to Bpc_1 (3.7 mg N kg-1), Figure 4-10. It can also be observed that when both NO3-N and NH4-N were high, high Dp rates were obtained (Pxd: 0.6 ± 0.6, Sr_1: 0.4± 0.1 and Ps: 0.7 ± 0.1 g N m-2 d-1)

Figure 4-9: Nutrient variation


38

No apparent trend can be observed spatially for organic carbon as variability is inconsistent with location (Figure 4-8). It is observed that organic carbon and Dp rates have similar trends where with high organic carbon (Sr: 1%), high Dp rates (Sr-0.6 g m-2 d-) occurred, except for the last four soils (Pxd, Sr_1, Px, Pb), Figure 4-10. Also, highest Dp rate corresponded with rather low organic carbon content of 0.6%, occurring in Ps silty loam textured soil, located in northern part of the study area (Figure 4-8, Figure 4-10 and AnnexD)

Figure 4-10: Organic carbon, Dp, nutrients and NO3-N loss

Figure 4-11: Sulfate concentration per soil location

Sulfate in the soil locations are extremely high with highest measurement recorded in loamy textured, red yellow Mediterranean soils. Vx_1 recorded 195±0.71 mg kg-1. Bpc and Ps also recorded high sulphate concentrations of 185 ±0.71 and 180, respectively. All these locations are located in north eastern part of the catchment where soil texture is clay to loam. Low sulphate concentration ranged from 20±0.71-35±0.71 mg kg-1 and found in Pxd, Sr, Bpc_1 and Vc with clay to loamy textures (Figure 4-8). The location of the low concentration soils were in the


39

northern part of the study area. The mean sulphate measurement obtained for the study area is 97±0.47 mg kg-1 (Figure 4-11)

4.3. Distribution of temperature

Maximum soil temperatures on 25 August 2008 were found between 13:00-14:00 hrs for almost all stations except for Odemira gauged station where maximum temperature was recorded at 12:00. While the distribution of all stations where around the study area, Odemira was located at the southern coast of Portugal where temperatures are cooler compared to the interior located stations. The soil temperatures ranged from 39 °C (Odemira) to 50 °C (Castro Verde), Figure 4-12).

MSG product and Beja gauge station data showed good correlation in 11-14 °C and 45 -55 °C

temperature ranges, Figure 4-13. Though, the aim of the study was not to validate the product, but to explore remote sensing products to derive reduction function from soil temperature obtained from MSG product. Acceptable correlation was the basis on which MSG product was used for the current study.

Figure 4-12: Diurnal cycle indicating time in UCT +1 of maximum

hourly temperature

Figure 4-13: Correlation between gauged soil temperature and MSG product (LST)


40

The LST monthly maps showed extreme variation which can be related to seasons. From all these maps, the minimum LST temperature was 18.1 °C and maximum of 54.6 °C for January and July months respectively (Figure 4-14). The prior mentioned temperature values coincided with winter and summer seasons of the study area, Figure 3-2.

Figure 4-14: Monthly Land Surface Temperature (LST °C) derived from MSG

product


41

Figure 4-14 continued


42

4.4. Prototype modelling

This section describes the input maps required by the model. The output obtained from applying TWI and the water balance methods for soil water together with other reduction functions are also shown.

4.4.1. Reduction functions

The reduction functions of pH, Dp, and NO3-N, are similar to the respective maps shown in Figure 4-3. The difference is that all reduction functions, including TWI and water balance derived soil water, were normalised to 0 and 1, (Figure 4-15) The highest water accumulation per pixel was found in the riparian zones perfectly outlining the drainage pattern of the catchment. The index ranged from 0.5 to 0.98, where the maximum value was found at the reservoir and the minimum found away from the streams, (Figure 4-15). The temporal temperature variations showed an increase in temperature from January, March to June and a slow drop in temperature was found for September. Temperature reduction functions were observed to be between 0 and 1 for 3 months and a zero for the month of June, (Figure 4-16).

Figure 4-15: Modelling parameters; Potential denitrification rate (top left), Nitrate reduction function (top right), pH reduction function (bottom left) and Topographic Wetness Index function (bottom right)


43

Figure 4-16: MSG product (LST °C) – left column used to derive soil temperature reduction function (right column)


44

The monthly reduction functions shown in Figure 4-17, derived from water balance study by Igbinosun (2009) indicated no soil water during dry season month of June for the whole study area. Variation in soil moisture was observed for the months of January, March, June (zero soil water) and September. Some sub catchments also showed no presence of soil water. Highest soil water of 13.9 % was measured for January and September recorded the least (7.6%).

Figure 4-17: Soil water output from SWAT, integrated in ILWIS, showing soil water %

per sub catchment of the study area. Temporal Soil water ( left column ) and reduction function (right column)


45

4.4.2. Actual denitrification rate (D a)

Actual denitrification rate derived from TWI varied from denitrification rates derived from SWAT model water balance (Figure 4-18). Comparing the two rates, derived from different methods, higher rates (0-0.35 g m-2 d-1) were found with TWI opposed to water balance derived rate (0-0.3 g m-2 d-1) for the month of January. Generally, both maps indicated that there were pixels (in terms of TWI derived Da), or sub catchments (for water balance derived Da) where no denitrification activity was observed. Once again, highest denaturisation rates were found in the central, northern tip and the southern tip of the catchment for both TWI and water balance derived Da. Hot spot zones can clearly be seen in water balance derived Da TWI derived Da

showed clearly zones where nitrates were reduced, which normally is along streams.

4.4.3. Temporal Da rate

A pattern can be observed from the maximum rates for the 4 months representing the seasons of the study area. Winter season was represented by the month of January where maximum rate of 0.26 g m-2 d-1 was estimated. Da rate increased in summer compared to the earlier obtained rate. The model estimated 0.30 g m-2 d-1 rate most variable rates occurring in the central part of the study area. No denitrification rate was observed for the month of June which corresponded to the summer season. Finally, denitrification rate increased to a maximum of 0.30 g m-2 d-1 in September. Since the water balance calculation was base on sub catchment scale, sub catchments with no denitrification activity were observed. Clay soils found at the tip of the northern study area showed 0.12 g m-2 d-1 in January have shown 0.10 rate for both March and September rates. Central part of the study area was mostly variable.

Figure 4-18: Actual Da rate in g m-2 d for the month of January. Map on left shows TWI derived Da and the

map on the right shows water balanced derived Da


46

Figure 4-19: Temporal Da rates in g m-2 d-1, derived from water balance


47


49

5. Discussion &conclusions, and recommendations

The chapter discusses briefly the findings of this research and contains a number of conclusions with respect to the research questions. Recommendations are given for further study and research in this important and hydrological field with its direct relationship to climate change. Denitrification is a biogeochemical process which besides inert N2 gas also usually produces N2O, as an important fraction of the total N loss from the earth to the atmosphere.

5.1. Discussion & conclusion

5.1.1. Field data collection

Field data collection phase was carried out as an initial step to achieve the objectives set out in chapter 1, section 1.4.1.1. In order to investigate and quantify the denitrification rates of the study area, soil denitrification rates of the study area, characteristics were highly important to consider. The study therefore sampled an important number of soil units in order to capture the spatial distribution of soils in the study area, also taking into account the land use. For this reason, the samples where organised in a way to consider the most important aspects of denitrification process within the given limited time so as to deduce information that will compliment the temporal as well as the spatial aspects of the denitrification process in conjunction with other datasets. Most of data needed for the current study were produced in the laboratory; hence a limited field dataset was further collected. These included soil moisture at time of sampling, detailed weather data, 1:50,000 soil maps using Portuguese classification system and land cover land use mapping, based on European Corine 2000 LULC database. All these data were essential and crucial for modelling the denitrification rates in space and time.

5.1.2. Potential denitrification rates

The laboratory experiment carried out at the ITC lab, to determine the potential soil denitrification rates using the nitrate reduction method, showed occurrence of potential denitrification in most soils of the study area. Reporting unit for Dp rates for the current study was g m-2 d-1, though mg N kg -1 d-1 unit was used in cases were current and previous studies were compared. Our calculated potential denitrification rates from the nitrate reduction method were highly variable, spatially and temporally (Annex E). After converting the current Dp rates to mg N kg -1 d-1, findings were compared to Well et al (2005) Though it was variable, agreement was observed in soils with high clay content. The current study found 0.4-1.1 mg N kg-1 d-1 rates for Bpc_1, Bpc, Sr and Vc clay soils which agreed well with rates from Well et al (2005) who also found medium (0.1-1 mg N kg-1 d-1) to high (>1 mg N kg-1 d-1) potential denitrification rate using 15N tracer technique. The overall potential denitrification rate for the current study (0 - 0.7 g m-2 d-1) over estimated slightly the findings by Oehler et al (2008) in riparian zones where 0-0.4 g m-2 d-1 was observed. Our Dp findings of 0-0.7


50

g m-2 d-1 were in agreement with the Dp values assessed from the database (5613±5096 g N ha-1 d-

1 or 0.56 ±0.5 g N m-2 d-1) by Henault and Germon (2000).Soil core measurements reported by van der Salm (2007) found 101 kg N ha-1 yr-1 (0.03 g m-2 d-1) yearly loss as denitrification in the top 20 cm of the soil. This however does not compare with our current findings in which the mean rate is ten times more (0.3 g m-2 d-1). The over estimation both on the current rates could be as a result of the high nitrogen fertilization status (typical in this region), of the soils that were just prepared for the winter wheat and/or barley planting season, which coincided with our field campaign sampling. No potential denitrification rate was observed in Px soils despite the presence of denitrifying bacteria which could be responsible for high nitrate loss of 10.3±2.1 mg kg-1. The probable explanation is given below;

1) The possibility of nitrate being the only limiting factor to denitrification was ruled out for the current study due to the high observed extractable NO3-N content in Px soils of 7.5 mg N kg-1. Similar findings where cited by Korom (1992) where no denitrification was reported even with nitrate loss. Korom (1992) concluded that organic carbon limited the potential denitrification rates .This could be also the case for the Px soils where the lowest organic carbon content of 0.4% was observed (Figure 4- 8 and Annex D).

2) The results are further supported by evidence provided by van Beek (2004) who concluded that when NO3

- supply exceeded organic carbon, no denitrification would occur unless other electron donors are present. Therefore, the current study also assumed that no other electron donor occurred under these field conditions that resulted in denitrification activity not taking place in Px soil type.

3) The location of the Px soil location was at the edge of the reservoir. According to Machefert and Dise (2004), higher denitrification rates are expected under high soil moisture condition but our findings for the Px soils did not agree with those findings. We therefore concluded that other processes e.g. nitrification could have occurred hence no detection of denitrification activity in the Px clay loam soils, located near the reservoir was observed.

4) A general downward trend in Dp rates was observed for all soil locations for the first few days of the nitrate reduction measurements, with exceptions of Px soils (Annex G) where a different trend was observed. This however can be an indication of a different activity or process occurring in this soil mentioned earlier.

The study area is heterogonous with 71% land covered and dominated by arable agricultural land (annual crop, like winter wheat, barley, maize, sunflower, etc.). Though the soil locations are mostly distributed within arable land coverage, variations with respect to Dp rates and NO3-N loss are experienced, with high rates and losses common in silty loam textured soils found almost in the central and southern part of the catchment (Figure 4- 8). The spatial distribution of texture as obtained from combination of Soil Water Characteristics software (Madyaka, 2008)and Arc GIS could together with elevation be used to identify hotspots of denitrification rates. Observations can be made those areas closer to the river drainage (riparian zones) had higher rates provided that the streams did not dry out completely. For example, soil locations further away from the riparian zone (Pb, Vx, Sp Bpc_1 and Bpc) had lowest denitrification rates ranging from 0.1-0.3 g m-2 d-1 compared to locations within the riparian zone (Sr, Vc, Ps, Pxd) that ranged from 0.4-07 g


51

m-2 d-1 (Annex D and Annex E). In the current study, hotspots are prevalent in low elevated areas (128-160 m above mean sea level) with silty loam and clay textures (Figure 4-8). Sr, Sr_1 and Vc are located within these low elevations where the denitrification rate ranged from 0.4 -0.6. g m-2 d-1.

5.1.3. Electron donors and acceptors with reference to trend analysis

With several factors that influence potential denitrification, it was difficult to directly highlight or pinpoint a single factor since the scope of the study was not designed to look at the factors that affect denitrification but determine potential denitrification rates based on field samples and laboratory techniques. Though, some conclusions can be drawn from inorganic nitrate and ammonia data obtained from the study. The spatial trends of nutrients (NH4-N and NO3-N) were highest in the clay soils found in the northern part of the study area. There could be a contribution from the waste water treatment plant that is situated in the vicinity of Bpc_1 soil location as concluded also by Chisha (2005). This contribution could be responsible for high nutrients especially in bcp_1 and the surrounding area. Also the characteristics of clay soils that are small in size (<2µm) with binding and swelling nature contributed to the high values. More measurements are required to investigate the electron donor effect on denitrification. For example, the study area is known for calcareous soils as well as its red Mediterranean soils of which the presence of iron, manganese and other minerals could be present which could be potential electron donors. The study however investigated the sulfate content in the soils and found large amounts of sulfate with maximum reported concentration at 195 mg kg-1 in loamy Vx_1 soil location. These high amounts of gypsum type minerals (CaSO4.2H2O) can also be due partly to mineral ore presence and mining in the Roxo area. The region and Roxo catchment lies on the Mesejana fault line, being the most important ore deposit zone in southern Portugal, with especially pyrite (FeS2) and associated mineral ore occurrences. The Aljustrel mine in the close vicinity of the southern part of the Roxo catchment, and the old abandoned mine (Mina de Juliana), in the catchment and close to the Roxo reservoir. All these are clear examples of potential strong iron, manganese and sulphide mineral presence. When pyrite is oxidised, by products of sulfate and Fe2+ are released. Alternatively, a good source of sulfate could be from the fertilizer (in a form of ammonium sulfate), applied to agricultural lands to increase crop yield. 5 FeS2 + 14 NO3

- + 4H+ ↔ 7 N2 + 10 SO42-+ 5Fe3++H2O

5-1

It could be that denitrification was also based on the oxidation of pyrite (ferrous disulfide) as shown in equation 5-1. In literature, similar findings were drawn by Korom (1992) on autotrophic denitrification study. Due to abundance of SO4

2- in the soils, it is possible that when nitrate concentration became limiting, SO4

2 could have been the potential electron donor to denitrification. Comparisons of Dp rate and sulfate show an opposite trend where when Dp rate is high (Sr-0.6 ± 0.4 g m-2 d-1), a lower sulfate is observed (Sr-20 ± 0.71 mg kg-1). Due to the nature of FeS2 and the end products which can be potential donor, more measurements are required including manganese and Fe 3+ determinations to distinguish between the type of denitrification ( autotrophic or heterotrophic) taking place. In conclusion, we believe that especially in this area, soil biogeochemical processes mentioned above can interact above can interact strongly with the denitrification process in soils and sediment.


52

5.1.4. Prototype model development

With the implementation of a prototype model, actual denitrification (Da) at catchment level can be obtained. This simplified proposed model incorporated reference the variable (potential denitrification rate), adjusted using the reduction functions (for soil pH, soil temperature, soil nitrogen and soil moistures) in a form of raster maps, derived from laboratory field and remotely sensed data. Spatial and temporal soil moisture, we used a catchment water balance SWAT modelling output (Igbinosun, 2009). Spatially and time in varying land surface temperatures were derived from Meteosat MSG satellite data, validated with soil temperature data from meteorological stations. Variability was observed in actual denitrification rate calculated using the SWAT derived soil water content information. For this initial study, non time varying static functions that were applied to obtain the reduction function for nitrate concentration and pH. We considered this feasible as nitrate was not a limiting factor for denitrification and soil pH were in normal range (5.5<pH<8), therefore not affecting the denitrifying process. Dp rates were kept constant with respect to rates obtained at each soil location, thus representing spatial trend. Temporal scale was represented by using dynamic soil water and soil temperature reduction functions for the months of January, March, June and September, together with the nitrate and pH reduction function and Dp. The fact that temperature and soil water where aggregated to a monthly time step, made the respective reduction functions dynamic. Actual denitrification rate calculated using the soil moisture reduction function based on a Topographic wetness index (TWI), yielded similar result with the prototype model estimating a rate of 0.35 g Nm-2 d-1. Due to time constraints, it was not possible to test the topographic wetness index approach in dynamic mode. Temporal Da was therefore tested with the SWAT model derived monthly soil water balance evaluations. Temporally, actual denitrification rate obtained using SWAT model derived soil moisture information for the months of January, March, June and September ranged from 0 – 0.26; 0-30; 0 and 0-0.30 g N m-2 d-1. The chosen months represents denitrification rate in the four seasons. The wet winter season is represented by January, Spring was represented by March, June is represented by summer and Autumn is represented by September. No denitrification activity was observed for the month of June as almost no plant available soil water could be found in the study area the dry and hot season. This was perhaps reason for no rate detection. The current study found high temporal denitrification rates in March and September. Our study underestimated actual denitrification findings by Henault and Germon (2000) with 0.52 %. Henault and Germon (2000) observed D a rates of 5613± 5096 g N ha-1 d-1 (or 0.56 g m-2 d-1) compared to our D a rates findings of 0.29 g m-2 d-1. The current study however agreed with findings by Simonne and Morgant (2005) that found actual denitrification rates of 0.01-0.28 g m-2 d-1.

5.1.5. Limitations

The nitrate reduction method for determining potential denitrification has drawbacks in the indirect measurement of nitrate loss. The risk of over or underestimation of nitrate is possible due to the interferences that are associated with the detection method used for the current study.


53

The need to rescale the soil water contents derived by the SWAT model or TWI – index to the soil moisture reduction function is in the prototype model requires in fact also validation as soil water is an important driving force in denitrification studies. The SWAT hydrology model has been successfully calibrated on the Roxo catchment level, using the Roxo reservoir data but the derived soil water content for the sub catchment areas, soils and hydrological response units could not be verified by field measurements. So, we used these simulated soil moisture balance data to derive the soil water reduction function. These combinations can have overestimated or underestimated the soil moisture effect on the final Da rate as too limited field measurements were used to obtain a full spatial and temporal distribution of soil moisture. The topographic wetness index (TWI) was also used and tested as a proxy for soil moisture. An arbitrary direct linear scaling to soil moisture was applied without field validation of TWI. Therefore, in order to obtain the soil moisture reduction function based on this index, further investigation is needed to link the TWI to soil moisture. This can be achieved using model approaches such as the topmodel (Quinn et al., 1995), but that was not part of the objectives due to the limit on time and such an approach requires more than 6 months. Thus we used the SWAT model approach to derive soil moisture content information for this purpose. Soil moisture derived from the SWAT model was based on sub catchment scale. The weakness therefore in the SWAT model is that it does not accommodate riparian zones, thus the potential hotspots of denitrification rates along the streams could therefore not be observed in the model output. It is highly important to research more and come up with better model representation of riparian zones in hydrological models to represent this information The objectives of determining potential denitrification rate for field samples of Roxo catchment were achieved through nitrate reduction laboratory experiment and subsequent data analysis. This method was chosen based the applicability in terms of available instruments in the ITC soil laboratory and the fact that it is simple and fast to implement. Additionally, an exploratory study was conducted on temporal and spatial datasets to determine typical reduction functions and to make them space and time varying in a prototype simple denitrification model based on the modified Nemis model of temporal and spatial dataset to determine reduction functions for the proposed space and time prototype denitrification model.

How are denitrification rates distributed spatially and temporally? Spatial distribution of denitrification rate is wide spread and variable within the catchment. Highest rate (0.7 g N m-2d-1) is found in the northern part of the catchment, whereas the lowest rate (zero g N m-2d-1) is found near the reservoir. Low rates are common in away from the reservoir as well as south of the catchment. Moderate denitrification rates (0.4-0.6 g N m-

2d-1) were observed in central parts of the study area. How can model parameters be calibrated?

Model parameters can be calibrated through the application of laboratory experiments


54

5.2. Recommendations

This research was a first and initial attempt to gather information on denitrification rates in the Roxo catchment. The following recommendations can be made related to this exploratory and original research:

• The determination of potential denitrification rate using the nitrate reduction method is a cheap and rapid technique. In order to control other processes, it can be recommended to also monitor other chemical state variables like pH, Eh, and other chemical compounds (electron donors) in the test tubes. This could shed more light on differences and some anomalies observed among the soil types tested, and permit also a possible full geochemical simulation of the experiment (using e.g. PHREEQC software or similar).

• The determination of the space and time variations in the reduction functions (e.g. soil moisture, soil temperature, soil nitrate levels and soil pH) should ultimately be modelled to sufficient spatial and temporal detail, in order to be valid.

• The use of land surface temperatures using geostationary satellite data is promising and feasible. A higher spatial resolution should ideally be attempted, to differentiate between local temperature differences in the landscape,

• The soil moisture status in time and space could be approached using a topographic wetness index based mode (e.g. topmodel) or any other model applicable to the local landscape and soil conditions. The derivation of soil moisture from earth observation data (e.g. from microwave sensors, or other), could be attempted, but downscaling methods to appropriate spatial scale would be needed.

• In highly fertilized agricultural land and regions, nitrate is usually not a limiting nutrient or factor in the denitrification process. That’s why the reduction function was kept initially time invariant, and only spatially variable (e.g. different soil units). However, ideally, a soil nitrogen model could be developed to analyse the spatial and temporal behaviour of this model parameter.

• Lastly, we also suggest to monitor in the field, denitrification losses from soils, using experiments, which capture gas exchanges with the soil. As such the contributions of the greenhouse gas N2O to the total N-loss (N2+N2O) by denitrification can be quantified in the area, and has not to be assumed from experimental evidence, gathered elsewhere.


55


56

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7. Appendix

Annex A_1: Pictures- soil sampling and measurement (October 2008)

Annex A_2: Soil moisture measurement

Soil moisture measurements with Theta and Stevenson’s Hydra probes.

Undisturbed soil sample in a sampling ring 100 cm3 ring

Disturbed soil sample collected with an auger and places in sampling bag

Stevenson’s probe

Theta probe


60

Annex B: Denitrification rate laboratory experiment

Annex C: Soil sample preparation for particle size analysis

Anaerobic incubation of soil samples in water bath

Incubated soil samples in test-tune with clear,coloured or turbid supernatent

A B

.

C D

Soil sample preparation showing soils of <2mm (top row”s) and >2 mm (bottom row’s) in A-C. One of the test (Particle size analysis) in progress after soil preparation (D)


61

Annex D: Physical and chemical characteristics of Roxo catchment

S_C

ode

Nitr

ate

Amm

onia

TNTP

N L

oss

Sulfa

teD

pSa

ndSi

lt C

layO

rgan

ic

mat

ter

Org

anic

C

arbo

nBu

lk

dens

ityPH

_H2O

pH_K

Cl

soil

text

ure

g N

m-2 d

-1kg

m-3

bpc

16.3

8.2

1600

190

3.7

200.

222

.823

.151

.42

113

208

6.6

clay

bpc_

12.

56.

818

0013

05.

818

50.

325

.710

.260

.12.

11

1300

6.9

5.3

clay

vx1.

33.

817

0043

014

.511

50.

333

.77.

924

.52.

71.

414

005.

44.

2lo

amsr

3.8

2.9

2100

460

11.3

200.

623

.410

.544

.12

113

006.

34.

8cl

ay

vx_1

2.5

3.6

1600

150

1919

50.

447

.210

.316

.81.

50.

814

005.

43

.9lo

amps

12.5

5.6

1500

420

10.6

180

0.7

27.8

8.3

14.9

1.3

0.6

1500

6.3

4.8

silty

loam

vc2.

57

2000

570

4.8

350.

421

.213

.255

.22.

31.

213

008.

17

clay

sp3.

83.

613

0052

010

.810

00.

125

.511

.919

.71.

50.

715

005.

74

.3si

lty lo

ampx

d8.

85.

224

0027

011

.820

0.6

21.8

2.4

15.4

2.5

0.7

1500

5.5

4.5

silty

loam

sr_1

8.8

418

0018

023

.412

00.

420

.35.

312

.81.

50.

815

005.

84.

5si

lty lo

ampx

7.5

2.8

1500

320

10.3

950

35.7

5.8

33.1

0.9

0.4

1500

7.4

6.1

clay

loam

pb1.

36

3200

280

9.5

750.

137

.318

.830

.21.

40.

715

005.

13.

6cl

aylo

am

ave

5.9

518

7532

711

.397

0.3

28.5

10.6

31.5

1.8

0.9

1418

6.3

5st

d4.

81.

851

415

15.

766

0.2

8.3

5.7

17.1

0.5

0.3

921

1.1

min

1.3

2.8

1300

130

23.4

200

20.3

2.4

12.8

0.9

0.4

1300

5.1

3.6

max

16.3

8.2

3200

570

3.7

195

0.7

47.2

23.1

60.1

2.7

1.4

1500

8.1

7

% m

g kg

-1


62

Annex E: Summarised Dp rates and Nitrate loss of Roxo catchment


63

Annex F: Histograms of some soil properties


64

Annex G: Nitrate reduction (mg NO3-N kg-1) as a function of time per soil location


65

Annex H: Denitrification rate (g N m-2 d-1) as a function of time per soil type (location)


66

Annex I: Meteosat product (LST °C) images


67

Continue


68

Annex J: Diurnal cycle comparison on August 15 for 4 gauged stations with Roxo catchment

time

Castro

verde Aljustrel Serpa Odemira

00:00 15.88 15.88 16.79 14.08

01:00 16.12 14.01 16.11 13.9

02:00 15.12 13.42 15.44 12.72

03:00 13.09 14.22 14.45 13.42

04:00 13.41 12.46 12.79 12.54

05:00 13.29 11.51 12.09 11.58

06:00 12.79 13.12 13.67 13.42

07:00 17.83 17.17 18.8 15.57

08:00 25.15 23.95 24.51 21.91

09:00 31.97 30.29 30.11 28.2

10:00 37.8 35.41 35.83 33.25

11:00 43.85 40.54 41.54 38.29

12:00 47.61 44.42 45.24 39.25

13:00 49.78 46.25 47.81 38.01

14:00 49.49 45.98 47.3 35.73

15:00 47.26 43.9 46.03 33.49

16:00 43.27 40.7 42.26 30

17:00 37.84 36.34 37.78 26.38

18:00 31.41 31.34 31.28 23.24

19:00 25.24 25.85 25.81 19.55

20:00 22.67 23.11 22.32 17.26

21:00 19.91 21.48 21.18 16.71

22:00 19.03 20.26 21.56 15.71

23:00 17.84 19.19 18.75 14.21

23:30 17.71 17.67 18.23 14.07

Diurnal cycle for 15 August 2008


69

Annex K: Maximum daily Relva temp. (°C) per gauged station for 2008 of 10 -21days of each month

Vidigueira castro verde aljustrel H. Outerei beja viana serpa odemira h. lameiroes r.vigia

jan 16.11 14.39 17.26 15.74 15.81 18.44 20.44 19.58 17.97 17.15

15.53 13.54 15.65 15.31 15.26 16.77 17.88 19.92 17.15 16.7

15.04 11.51 16.24 13.64 13.57 17.79 16.33 21.3 14.46 18.57

10.89 11.33 12.89 13.09 11.99 11.8 12.44 18.01 15.08 12.96

18.06 12.33 17.34 15.56 14.3 19.32 19 23.01 18.94 20.39

14.2 11.93 15.33 13.74 14.86 16.5 16.88 21.73 16.56 15.68

15.9 13.5 16.24 15.71 15.44 16.9 16.68 20.05 17.78 18.24

17.43 16.2 19.33 15.35 23.71 19.65 17.95 25.05 18.74 18.64

18.14 14.42 18.77 15.87 21.75 18.85 17.36 22.21 18.14 20.47

17.81 13.64 19.22 15.55 22.17 18.56 20.34 23.74 18.08 20.39

18.01 14.79 18.6 16.01 23.78 19.16 22.55 22.73 19.2 21.54

feb

16.43 17.27 21.48 17.12 23.31 16.84 26.24 23.69 21.04 24.64

15.46 17.56 19.95 15.97 21.81 16.35 26.56 24.47 24.16 25.33

16.44 17.9 20.17 16.47 21.92 16.03 24.62 22.23 22.11 22.87

24.73 18.44 20.93 16.79 23.03 19.05 26.47 23.49 26.41 24.7

23.77 20.22 22.18 17.66 23.25 25.9 28.44 23.17 24 26.33

23.6 21.47 22.17 17.81 24.64 27.11 28.07 23.8 23.86 28.41

24.43 19 21.73 17.31 20.58 27.69 27.55 20.65 24.82 25.36

12.58 13.87 13.47 13.17 14.47 12.8 16.36 19.46 13.68 12.79

18.53 19.29 17.83 17.75 17.88 18.77 23.23 19.36 27.28 15.54

21.74 23.42 21.92 20.65 24.77 18.9 28.98 22.17 26.15 23.26

23.89 21.68 22.53 20.81 21.84 25.96 24.53 24.02 19.38 21.65

march

17.62 18.41 14.99 14.84 17.98 17.45 17.78 15.73 16.43 21.64

27.6 28.75 20.78 23.1 23.69 26.23 30.88 23.53 27.91 30.44

29.24 28.29 23.16 31.07 25.69 30.04 32.69 28.54 29.23 35.01

30.64 34.4 26.26 32.77 29.59 32.93 34.04 30.45 30.33 33.74

27.76 30.05 24.99 27.97 27.14 31.44 30.67 25.69 29.72 30.72

27.46 29.88 24.56 27.94 26.12 31.33 26.81 26.24 28.61 31.37

29.17 31.9 23.92 29.45 26.19 30.74 31.52 25.11 27.57 34.77

26.9 30.6 22.92 29.57 24.8 31.19 27.7 23.7 29.49 32.76

26.47 29.67 20.18 29.5 21.87 30.93 30.16 23.44 29.24 26.06

19.13 24.92 19.62 24.75 19.62 24.94 22 19.27 24.84 20.4

18.69 18.83 18.3 19.81 18.33 24.31 23.39 18.68 24.9 25.02

april

29.37 17.22 20.05 20.9 22.13 22.08 20.61 17.83 24.24 20.78

34.66 16.1 19.63 22.82 24.36 29.42 24.66 23.67 30.01 26.48

37.17 18.65 23.53 23.47 24.96 29.99 32.28 25.28 31.4 29.12

31.08 19.46 24 21.91 23.4 29.22 24.83 26.3 30.43 28.24

39.73 20.29 26.95 24.44 25.75 32.82 34.1 27.67 32.59 30.72

38.96 23.78 26.74 23.79 27.93 33.36 33.19 28.67 33.32 31.18

36.67 25.1 27.17 26.25 26.16 34.63 31.36 27.33 31.52 33.51

21.94 22.64 23.01 23.61 26.81 25.11 25.5 24.84 28.47 24.9

19.97 17.97 20.18 21.66 22.9 22.76 19.67 22.27 27.16 26.51

19.67 18.51 18.6 20.69 23.25 20.93 20.2 19.71 27.99 20.5

19.99 17.72 17.92 19.79 21.07 21.82 19.13 23.04 29.34 21.89

may

26.74 23.59 27.46 23.93 25.75 27.25 27.45 29.43 33.33 35.71

26.45 23.88 29.91 24.28 26.86 33.18 27.32 21.57 28.94 37.58

27.66 25.23 27.4 21.66 25.98 31.51 26.37 28.32 33.53 39.75

26.49 25.89 30.59 26.16 26.89 34.28 25.86 29.2 27.7 39.33

23.74 25.95 31.45 23.11 26.94 33.19 29.18 26.54 28.47 32.89

26.43 24.25 29.08 21.54 24.96 21.53 29.98 24.55 32.39 24.92

27.35 24.88 30.77 25.26 37.37 31.36 31.39 26.1 27.38 29.7

28.69 24.81 31.45 24.49 34.89 34.29 32.99 26.36 35.56 37.9

25.48 23.36 31.47 23.51 29.93 28.61 27.91 24.48 29.63 30.53

27.4 25.52 32.95 23.59 35.9 32 29.72 25.89 31.17 35.05

22.33 24.59 29.35 25.1 31.9 29.63 33.83 25.77 35.76 30.99

june

37.9 35.7 42.9 29.1 39.1 44.7 43.3 32.5 35.3 42.5

39.8 36.1 45.6 30.2 39.5 45.8 45.9 33.9 35.9 45.4

39.8 36.2 46.4 31 41 41.2 46.5 34.7 36.6 43.5

42.3 37.6 47.1 31.4 42.1 46.3 47.8 36.3 37.4 45.2

41.4 36.6 46 31.3 39.5 46 45.7 31.5 36.5 29.7

38.7 33.3 44.8 30.7 37.8 40.4 38.4 27.8 35.6 27.4

36.5 30.9 40.1 29.1 35 38 32.8 26.6 30.9 25.7

37.9 32.5 44.9 29.6 40.2 42.8 45 27.4 35 36

42.9 37.1 46.9 30.9 41.2 44.5 44.6 27.3 35.8 30.6

43.1 38.3 48.2 31.5 41.7 46.3 49.3 27.7 36.4 30

44.9 40.1 49.4 31.9 41.3 48.3 47.4 29.1 36.5 30.6


70

Continue july Vidigueira castro verde aljustrel H. Outerei beja viana serpa odemira h. lameiroes r.vigia

39.7 39.4 47.1 29.4 44.9 44.4 46.1 29.8 50.4 29.9

38.6 38.5 46.2 28.4 41.1 41.3 39.7 30.4 49 35.5

34.3 33.4 43.5 27.4 41.3 41.2 38.2 27.7 46.7 35.9

38.6 38.6 47 29.6 42.1 44.8 46.6 28.5 48.4 40.2

42.6 44.5 48.9 30.6 45.6 47.2 46.4 30.2 49.7 42

43.7 44.1 49.8 30.9 41.8 47.5 47.7 30.2 51.8 31.2

43.1 43.3 49.7 31.3 41.7 49.5 46.2 30.2 49.7 32.3

43.3 43.2 52.2 31.9 41.3 51.3 50.8 29.6 49.8 35.9

46.6 46 53.3 32.6 45.1 51.8 53.9 29.9 51.7 38.8

44.8 42.6 48.9 31.1 38.4 44.3 42.4 29.3 50.4 39.6

41.8 43.8 48.3 31.4 37.8 42.9 45.8 28.9 49.4 40.9

aug

42.3 42.5 47.9 32.5 42.7 46.7 45 32.2 40.8 45.4

39.1 39.8 44.9 32.1 35.9 40.7 38.7 32.1 39.6 41

35.2 33.7 38.2 27.9 36.6 40.8 38.6 31.8 38.3 31

35.2 38.4 43.3 27.8 36.5 42.2 42.7 30.8 37.2 31.1

36.1 37.4 42.1 28.7 36.8 39.9 39.6 28.9 35.1 38.9

36.1 40 45.1 28.9 36.2 43.6 44.4 30.9 36.5 39.5

37 38.2 40.2 28.4 33.9 37.5 37.6 30.3 32.8 39.2

36.7 40.3 45 29.2 38.5 41.8 43.1 29.7 35.5 41.7

38.78 39.86 44.74 30.5 35.09 42.72 46.02 30.42 35.72 28.4

36.47 38.71 42.87 28.18 35.08 41.01 41.87 25.7 33.41 27.98

39.13 41.95 44.62 28.91 35.28 44.33 46.41 25.65 34.48 36.52

sept

39.8 40.61 39.18 30.99 42.86 40.98 43.65 31.1 34.81 37.71

33.83 33.85 34.53 27.33 37.38 34.97 40.06 31.27 32.51 31.65

44.35 32.94 35.75 24.41 33.37 34.25 38.14 27.07 28.39 35.29

51.95 38.62 39.28 26.51 40.18 38.7 44.77 29.08 29.81 23.31

57.21 43.25 40.56 28.88 44.95 43.42 45.59 30.02 30.94 26.9

59.55 43.61 40.18 28.9 38.87 42.75 41.86 28.86 31.07 32.95

52.87 39.7 36.78 27.48 34.9 36.78 37.56 28.49 30.05 32.49

48.83 39.98 36.95 28.34 34.47 40.54 38.32 27.99 29.56 31.72

48.39 38.93 36.74 28.86 34.37 40.19 38.97 28.04 27.42 33.6

47.99 41.86 38.78 29.44 36.35 41.78 45.35 29.54 30.29 35.92

48.26 39.95 38.09 28.2 35.05 40.27 38.79 29.4 29.45 35.82

oct

27.17 28.22 29.31 23.02 26.38 33.7 31.9 28.5 22.08 18.68

22.41 22.65 23.63 22.48 23.05 24.97 25.26 26.18 22.71 22.56

24.95 23.06 24.46 22.94 24.01 28.38 24.85 27.98 23.87 24.6

25.68 26.56 25.94 24.32 23.65 30.82 28.44 28.59 24.1 27.29

25.07 25.24 27.44 24.68 24.35 33.01 29 27.79 24.13 26.06

28.19 27.69 27.93 25.49 24.1 36.89 32.91 28.02 24.67 27.23

25.65 26.4 28.17 24.45 21.66 32.64 30.37 27.08 23.69 25.6

26.35 24.47 27.92 25.3 22.63 31.47 27.76 27.22 20.98 26.96

27.24 26.9 30.79 24.81 23.28 34.07 33.3 28.4 24.63 27.14

27.47 27.48 26.94 24.55 23.86 35.99 31.61 27.67 23.58 27.04

15.25 26.97 28.16 23.89 23.11 33.85 30.92 26.77 22.18 29.34

nov

19.25 17.62 20.26 17.34 18.41 26.44 25.59 34.06 15.86 20.1

18.68 16.64 19.05 17.82 17.42 25.43 22.12 31.76 16.35 20.17

17.11 15.41 19.53 15.04 15.72 21.25 25.68 27.74 18.44 16.52

17.67 15.41 20.94 16.1 29.39 23.61 26.22 30.52 19.54 17.99

17.24 15.44 20.65 15.93 27.41 24.79 26.3 31.58 19.53 17.12

17.28 14.59 21.68 15.49 28.2 23.84 26.35 29.82 19.61 16.97

18.65 15.25 21.93 15.8 29.91 26.38 29.36 31.86 20 18.79

19.33 16.01 21.3 15.92 30.71 26.61 28.15 33.28 19.94 18.54

18.1 16.61 19.33 15.27 28.31 23.74 27.39 28.84 20.25 17.82

17.49 15.75 21.46 14.7 25.62 22.31 24.54 28.97 19.56 17.39

18.25 15.64 22.61 15.12 24.58 24.37 24.59 30.25 19.68 18.84

dec

11.36 9.31 13.95 11.11 20.44 14.35 19.19 22.72 15.42 12.89

12.02 10.82 13.97 11.75 19.73 16.07 19.09 25.15 14.83 13.9

12.93 11.41 13.33 12.03 17.03 14.36 15.14 19.41 12.94 13.63

12.14 11.92 12.38 11.59 12.44 12.47 12.24 13.68 11.55 13.05

15.3 10.68 13.02 11.73 15.63 13.39 12.7 18.48 14.47 11.37

16.83 8.42 11.31 10.07 18.16 12.18 13.37 19.24 13.54 10.82

19.68 9.64 12.06 10.59 19.32 14.04 15.96 21.59 15.53 11.56

19.28 11.03 12.13 11.17 21 15.33 18.23 20.01 16.82 14.89

18.13 9.32 10.38 10.07 18.8 16.62 8.96 24.92 9.78 15

22.04 11.24 12.81 11.19 22.9 15.96 18.99 28.85 15.66 13.1

21.46 11.25 14.11 11.62 24.73 17.01 22.31 29.94 17.11 13.44


71

Annex L: Monthly weather parameters of Beja gauged station from 2006-2008

Est

açã

o M

eteo

roló

gic

a d

e Q

uin

ta d

a S

aúd

e

Bej

a ga

uged

sta

tion

.: C

ara

cter

ístic

as

da

Est

açã

o (

Da

tum

73

) -

Latit

ude

: 38

º 0

2' 1

5''

N-

Long

itud

e: 0

7º

53

' 06

'' W

- A

ltitu

de:

20

6 m

Dat

aT

me

dT

max

Tm

inH

Rm

edH

Rm

ax

HR

min

DV

VV

me

dV

Vm

axP

Tm

ed

Re

lva

Tm

ax

Rel

vaT

min

Rel

vaE

T0

(ºC

)(º

C)

(ºC

)(%

)(%

)(%

)(g

raus

)(m

/s)

(m/s

)m

on

thly

(ºC

)(º

C)

(ºC

)(m

m)

jan

_2

00

68

.01

3.9

3.5

82

.19

5.1

59

.81

43

.91

.85

.64

1.8

8.1

13

.2

4.8

1.1

feb

9.1

15

.64

.28

0.5

94

.85

4.8

20

6.2

2.0

6.6

34

.69

.31

5.5

5.6

1.6

mar

12

.31

8.7

7.5

80

.89

4.7

55

.32

21

.22

.16

.59

2.4

12

.72

1.6

8.

22

.3ap

ril1

5.3

22

.49

.17

5.5

95

.04

6.6

21

0.9

1.9

6.2

26

.91

6.8

28

.41

0.3

3.5

may

19

.32

8.3

11

.05

8.0

87

.72

8.1

25

6.5

1.9

6.7

0.0

20

.43

4.4

11

.45

.4ju

ne2

2.1

30

.81

4.5

59

.28

7.5

30

.22

36

.62

.17

.03

2.3

22

.63

3.7

15

.86

.1ju

l2

5.3

35

.01

6.4

51

.58

2.3

23

.02

64

.92

.37

.62

.02

4.0

35

.41

6.6

7.0

aug

25

.53

5.9

16

.35

1.4

83

.02

2.1

25

4.2

2.2

7.2

7.9

24

.94

0.7

15

.66

.7se

p2

2.5

31

.11

5.4

60

.08

5.5

30

.92

67

.52

.16

.83

5.0

20

.62

9.9

15

.54

.7o

ct1

9.0

25

.51

4.0

75

.09

3.6

47

.91

85

.52

.47

.21

59

.71

8.2

26

.81

4.2

2.8

nov

15

.22

0.6

11

.38

2.8

94

.85

9.2

15

5.8

2.0

6.2

16

8.8

14

.82

0.2

11

.51

.5d

ec9

.31

5.6

4.8

79

.79

2.2

56

.21

46

.01

.96

.04

4.0

8.7

15

.84

.71

.2ja

n_

20

07

8.9

15

.54

.48

4.6

96

.35

9.9

14

5.4

1.6

5.8

18

.88

.31

7.

23

.41

.2fe

b1

2.0

18

.17

.58

7.4

98

.16

3.9

21

6.8

2.2

6.6

67

.11

1.4

19

.57

.3

1.8

mar

12

.51

9.9

6.1

73

.09

3.1

46

.72

03

.42

.27

.15

.91

2.6

31

.14

.72

.9ap

ril1

5.0

22

.58

.67

7.6

95

.94

9.8

19

2.0

2.0

7.0

55

.11

4.9

26

.78

.43

.5m

ay1

7.8

26

.01

0.8

71

.79

3.0

44

.32

17

.92

.16

.85

2.7

18

.73

3.9

10

.54

.5ju

ne2

0.9

29

.31

3.0

68

.19

3.7

41

.22

58

.62

.16

.83

3.9

21

.33

5.0

13

.25

.4ju

l2

4.5

34

.51

4.8

56

.88

7.1

30

.42

82

.32

.47

.20

.02

2.9

35

.21

4.9

6.9

aug

23

.43

2.6

15

.05

7.2

86

.42

8.9

24

3.0

2.1

7.1

12

.02

1.3

31

.31

5.2

6.0

sep

t2

1.3

29

.81

4.9

67

.99

5.1

35

.92

19

.11

.76

.14

3.2

20

.32

9.6

15

.64

.0o

ct1

7.7

25

.31

1.9

67

.79

0.8

38

.11

63

.91

.45

.41

8.7

16

.82

6.8

11

.62

.8no

v1

2.9

20

.86

.96

1.5

83

.93

7.1

14

6.7

1.7

5.5

30

.81

1.2

20

.76

.0

2.0

dec

9.4

15

.74

.88

3.1

97

.65

5.2

16

5.0

1.7

5.4

18

.48

.71

4.7

4.8

1.1

jan

_2

00

81

1.0

16

.86

.58

7.0

99

.25

8.7

18

4.9

1.6

5.2

62

.31

0.4

18

.36

.01

.1fe

b1

1.9

18

.07

.48

4.5

98

.75

8.6

14

0.8

2.0

5.9

85

.31

1.6

21

.47

.0

1.7

mar

ch1

1.8

18

.75

.97

7.3

97

.24

5.3

23

6.3

2.2

7.2

17

.11

2.2

24

.05

.82

.6ap

ril1

5.0

21

.99

.27

1.3

93

.34

3.0

20

5.9

2.5

7.5

83

.21

5.5

28

.28

.33

.8m

ay1

5.5

22

.01

0.1

77

.89

8.8

46

.02

68

.42

.16

.96

4.6

17

.53

0.7

10

.83

.6ju

ne2

2.3

31

.31

3.2

56

.48

9.6

27

.12

25

.52

.07

.00

.12

3.2

41

.61

2.4

6.3

jul

22

.83

1.9

14

.25

7.9

92

.42

7.6

28

0.2

2.4

7.4

0.0

24

.24

0.0

13

.86

.6au

g2

2.8

32

.41

4.0

57

.99

3.4

26

.02

94

.22

.37

.10

.02

2.9

37

.71

4.2

6.1

sep

t2

0.2

28

.41

3.9

67

.89

4.8

36

.21

98

.92

.06

.83

8.1

20

.63

4.6

13

.44

.2o

ct1

6.7

23

.81

0.8

70

.19

2.7

41

.61

88

.22

.17

.03

6.4

15

.62

2.4

11

.02

.8no

v1

0.7

11

7.6

95

.26

73

.15

94

.03

42

.85

17

4.6

01

.94

6.6

54

2.2

10

.06

22

.05

4.2

41

.80

dec

8.8

11

4.0

64

.99

90

.22

99

.52

67

.90

21

1.2

02

.25

6.5

76

9.6

8.

93

18

.81

4.7

00

.84


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Annex M: ILWIS scrip for prototype modelling of actual denitrification rates

/POTENTIAL DENITRIFICATION DETERMINATION //Dp:=final_soilmap.pot_denit /fN DETERMINATION //nitrate_map:= final_soilmap.nitrate //k_nitratemap:= 22 + nitrate_map //fN:= nitrate_map/k_nitratemap /SOIL pH DETERMINATION //pH_map:=final_soilmap.pH_H2O //pH_35:=IFF(pH_map<=3.5,0,pH_map) //pH_65:=iff(pH_35>=6.5,1,pH_35) //f_pH:=iff(pH_65>3.5, (pH_65-3.5)/3,pH_65) // SOIL TEMP //Monthly temperature maps with bilinear algorithm //T_jan_14:= ifnotundef(roxo_cat, jan_14_max_re) //T_feb_14:= ifnotundef(roxo_cat, feb_14_max_re) //T_mar_14:= ifnotundef(roxo_cat, mar_14_max_re) //T_apr_14:= ifnotundef(roxo_cat, apr_14_max_re) //T_may_14:= ifnotundef(roxo_cat, may_14_max_re) //T_june_14:= ifnotundef(roxo_cat,june_14_max_re) //T_july_14:= ifnotundef(roxo_cat, jul_14_max_re) //T_aug_14:= ifnotundef(roxo_cat, aug_14_max_re) //T_sept_14:= ifnotundef(roxo_cat, sept_14_max_re) //T_oct_14:= ifnotundef(roxo_cat, oct_14_max_re) //T_nov_14:= ifnotundef(roxo_cat,nov_14_max_re) //T_dec_14:= ifnotundef(roxo_cat, dec_14_max_re) /calculating monthly reduction temp, assuming the reference temperature to be 20 //f_T_jan_14:=iff(T_jan_14<=0,0,iff(T_jan_14>=%3,1,(%2^(0.1*(T_jan_14-%3))))) //f_T_feb_14:=iff(T_feb_14<=0,0,iff(T_feb_14>=%3,1,(%2^(0.1*(T_feb_14-%3))))) //f_T_mar_14:=iff(T_mar_14<=0,0,iff(T_mar_14>=%3,1,(%2^(0.1*(T_mar_14-%3))))) //f_T_apr_14:=iff(T_apr_14<=0,0,iff(T_apr_14>=%3,1,(%2^(0.1*(T_apr_14-%3))))) //f_T_may_14:=iff(T_may_14<=0,0,iff(T_may_14>=%3,1,(%2^(0.1*(T_may_14-%3))))) //f_T_june_14:=iff(T_june_14<=0,0,iff(T_june_14>=%3,1,(%2^(0.1*(T_june_14-%3))))) //f_T_july_14:=iff(T_july_14<=0,0,iff(T_july_14>=%3,1,(%2^(0.1*(T_july_14-%3))))) //f_T_aug_14:=iff(T_aug_14<=0,0,iff(T_aug_14>=%3,1,(%2^(0.1*(T_aug_14-%3))))) //f_T_sept_14:=iff(T_sept_14<=0,0,iff(T_sept_14>=%3,1,(%2^(0.1*(T_sept_14-%3))))) //f_T_oct_14:=iff(T_oct_14<=0,0,iff(T_oct_14>=%3,1,(%2^(0.1*(T_oct_14-%3))))) //f_T_nov_14:=iff(T_nov_14<=0,0,iff(T_nov_14>=%3,1,(%2^(0.1*(T_nov_14-%3))))) //f_T_dec_14:=iff(T_dec_14<=0,0,iff(T_dec_14>=%3,1,(%2^(0.1*(T_dec_14-%3))))) //resample temperature reduction maps to soil_map georef and creating undef areas around the study area //f_t_jan_14_re:=MapResample(f_T_jan_14.mpr,final_soilmap.grf,BiLinear) //f_t_jan_14_re2:=iff(f_t_jan_14_re>0,f_t_jan_14_re,?)


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//f_t_feb_14_re:=MapResample(f_T_feb_14.mpr,final_soilmap.grf,BiLinear) //f_t_feb_14_re2:=iff(f_t_feb_14_re>0,f_t_feb_14_re,?) //f_t_mar_14_re:=MapResample(f_T_mar_14.mpr,final_soilmap.grf,BiLinear) //f_t_mar_14_re2:=iff(f_t_mar_14_re>0,f_t_mar_14_re,?) //f_t_apr_14_re:=MapResample(f_T_apr_14.mpr,final_soilmap.grf,BiLinear) //f_t_apr_14_re2:=iff(f_t_apr_14_re>0,f_t_apr_14_re,?) //f_t_may_14_re:=MapResample(f_T_may_14.mpr,final_soilmap.grf,BiLinear) //f_t_may_14_re2:=iff(f_t_may_14_re>0,f_t_may_14_re,?) //f_t_june_14_re:=MapResample(f_T_june_14.mpr,final_soilmap.grf,BiLinear) //f_t_june_14_re2:=iff(f_t_june_14_re>0,f_t_june_14_re,?) //f_t_july_14_re:=MapResample(f_T_july_14.mpr,final_soilmap.grf,BiLinear) //f_t_july_14_re2:=iff(f_t_july_14_re>0,f_t_july_14_re,?) //f_t_aug_14_re:=MapResample(f_T_aug_14.mpr,final_soilmap.grf,BiLinear) //f_t_aug_14_re2:=iff(f_t_aug_14_re>0,f_t_aug_14_re,?) //f_t_sept_14_re:=MapResample(f_T_sept_14.mpr,final_soilmap.grf,BiLinear) //f_t_sept_14_re2:=iff(f_t_sept_14_re>0,f_t_sept_14_re,?) //f_t_oct_14_re:=MapResample(f_T_oct_14.mpr,final_soilmap.grf,BiLinear) //f_t_oct_14_re2:=iff(f_t_oct_14_re>0,f_t_oct_14_re,?) //f_t_nov_14_re:=MapResample(f_T_nov_14.mpr,final_soilmap.grf,BiLinear) //f_t_nov_14_re2:=iff(f_t_nov_14_re>0,f_t_nov_14_re,?) //f_t_dec_14_re:=MapResample(f_T_dec_14.mpr,final_soilmap.grf,BiLinear) //f_t_dec_14_re2:=iff(f_t_dec_14_re>0,f_t_dec_14_re,?) /ACTUAL DENITRIFICATION DETERMINATION with Wetness I ndex //Da_january:=(Dp)*(fN)*(f_pH)*(f_T_jan_14_re)*(wetness_f_re) //Da_feb:=(Dp)*(fN)*(f_pH)*(f_T_feb_14_re)*(wetness_f_re) //Da_mar:=(Dp)*(fN)*(f_pH)*(f_T_mar_14_re)*(wetness_f_re) //Da_apr:=(Dp)*(fN)*(f_pH)*(f_T_apr_14_re)*(wetness_f_re) //Da_may:=(Dp)*(fN)*(f_pH)*(f_T_may_14_re)*(wetness_f_re) //Da_june:=(Dp)*(fN)*(f_pH)*(f_T_june_14_re)*(wetness_f_re) //Da_july=(Dp)*(fN)*(f_pH)*(f_T_july_14_re)*(wetness_f_re) //Da_aug:=(Dp)*(fN)*(f_pH)*(f_T_aug_14_re)*(wetness_f_re) //Da_sept:=(Dp)*(fN)*(f_pH)*(f_T_sept_14_re)*(wetness_f_re) //Da_oct:=(Dp)*(fN)*(f_pH)*(f_T_oct_14_re)*(wetness_f_re) //Da_nov:=(Dp)*(fN)*(f_pH)*(f_T_nov_14_re)*(wetness_f_re) //Da_dec:=(Dp)*(fN)*(f_pH)*(f_T_dec_14_re2)*(wetness_f_re)


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WATER BALANCE FROM SWAT //sw_fc:=ifnotundef(roxo_re, fc_trend_plane) //sw_sat:=ifnotundef(roxo_re, saturation_trend_plane) /Monthly soil moisture from SWATwater balance ///sw_jan:=sub.sw_perc //sw_feb:=sub.sw_feb_perc //sw_mar:=sub.sw_mar_perc //sw_apr:=sub.sw_apr_per //sw_aug:=sub.sw_aug_perc //sw_sept:=sub.sw_sept_perc //sw_oct:=sub.sw_oct_perc //sw_nov:=sub.sw_nov_perc //sw_dec:=sub.sw_dec_perc /soil moisture function //F_S_jan:=IFF(sw_jan<sw_fc,0,iff(sw_jan>sw_fc,1,((sw_jan-sw_fc)/(1-sw_fc)))) //F_S_feb:=IFF(sw_feb<sw_fc,0,iff(sw_feb>sw_fc,1,((sw_feb-sw_fc)/(1-sw_fc)))) //F_S_mar:=IFF(sw_mar<sw_fc,0,iff(sw_mar>sw_fc,1,((sw_mar-sw_fc)/(1-sw_fc)))) //F_S_apr:=IFF(sw_apr<sw_fc,0,iff(sw_apr>sw_fc,1,((sw_apr-sw_fc)/(1-sw_fc)))) //F_S_sept:=IFF(sw_sept<sw_fc,0,iff(sw_sept>sw_fc,1,((sw_sept-sw_fc)/(1-sw_fc)))) //F_S_aug:=IFF(sw_oct<sw_fc,0,iff(sw_oct>sw_fc,1,((sw_oct-sw_fc)/(1-sw_fc)))) //F_S_nov:=IFF(sw_nov<sw_fc,0,iff(sw_nov>sw_fc,1,((sw_nov-sw_fc)/(1-sw_fc)))) //F_S_dec:=IFF(sw_dec<sw_fc,0,iff(sw_dec>sw_fc,1,((sw_dec-sw_fc)/(1-sw_fc))))

ACTUAL DENITRIFICATION RATE WITH SWAT output //Da_jan_sw:=(Dp)*(fN)*(f_pH)*(f_T_jan_14_re)*(F_S_jan) //Da_feb_sw:=(Dp)*(fN)*(f_pH)*(f_T_feb_14_re)*(F_S_feb) //Da_mar_sw:=(Dp)*(fN)*(f_pH)*(f_T_mar_14_re)*(F_S_mar) //Da_apr_sw:=(Dp)*(fN)*(f_pH)*(f_T_apr_14_re)*(F_S_apr) //Da_may_sw:=(Dp)*(fN)*(f_pH)*(f_T_may_14_re)*(0) //Da_june_sw:=(Dp)*(fN)*(f_pH)*(f_T_june_14_re)*(0) //Da_jul_sw:=(Dp)*(fN)*(f_pH)*(f_T_july_14_re)*(0) //Da_aug_sw:=(Dp)*(fN)*(f_pH)*(f_T_aug_14_re)*(F_S_aug) //Da_sept_sw:=(Dp)*(fN)*(f_pH)*(f_T_sept_14_re)*(F_S_sept) //Da_aug_sw:=(Dp)*(fN)*(f_pH)*(f_T_oct_14_re)*(F_S_aug) //Da_nov_sw:=(Dp)*(fN)*(f_pH)*(f_T_nov_14_re)*(F_S_nov) //Da_dec_sw:=(Dp)*(fN)*(f_pH)*(f_T_dec_14_re)*(F_S_dec)


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Annex N: Monthly soil water derived from Soil and Water Assessment Tool Model for 13 sub catchments of Roxo catchment. (Igbinosun, 2009)


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