Spatial Modelling and Prediction of Soil Salinization Using Saltmod in a Gis Environment

SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

M. MADYAKA February, 2008

SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Spatial Modelling and Prediction of Soil Salinization Using SaltMod in a GIS Environment

by

Mthuthuzeli Madyaka

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: (Natural Resource Management – Soil Information Systems for Sustainable Land Management: NRM-SISLM)

Thesis Assessment Board

Prof. Dr. V.G.Jetten: Chairperson Prof. Dr.Ir. A. Veldkamp: External Examiner B. (Bas) Wesselman: Internal Examiner Dr. A. (Abbas) Farshad: First Supervisor Dr. D.B. (Dhruba) Pikha Shrestha: Second Supervisor

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.

Abstract

One of the problems commonly associated with agricultural development in semi-arid and arid lands is accumulation of soluble salts in the plant root-zone of the soil profile. The salt accumulation usually reaches toxic levels that impose growth stress to crops leading to low yields or even complete crop failure. This research utilizes integrated approach of remote sensing, modelling and geographic information systems (GIS) to monitor and track down salinization in the Nung Suang district of Nakhon Ratchasima province in Thailand. Though salinization in this region is attributed to underlying parent material and climatic conditions, it is aggravated by human activities through poor agricultural practices, deforestation, salt making, and construction of roads and reservoirs. The area was selected for this study because greater part of its population depends on agriculture and thus agricultural development is imperative for socio-economic upliftment of the area. Moreover the study area falls under one of the highly salinized regions in Thailand. The collaboration of LDD and ITC for capacity building, research and development projects in Thailand is another reason.

Two Aster images (11/2006 & 01/2007, topographic (1: 50 000), geopedologic map, EC datasets from previous studies (2003 & 2004) coupled with field observations served as the basic sources of data. These data sources were used to generate input parameters required by SaltMod model for long term prediction of salinization over 20 year period. Other parameters were logically estimated while others were estimated by a trial and error calibration of the model. Some soil related parameters were estimated from pedotransfer functions using SPAW and CropWat computer programs.

SaltMod is a one dimensional point model based on three component systems, viz. water balance (hydrological) model, salt balance model and seasonal agronomic aspects. Geostatistical analysis was used for interpolation of EC measured and simulated values. GIS was used for reclassification and mapping of salinity affected areas based on the FAO (USDA) classification systems. Regression kriging was the basic interpolation method applied with auxiliary predictors derived from the prior mentioned data sources. The auxiliary predictors included relief zones (polygon map) from the geopedologic map, relief parameters (DEM, slope in degrees, mean curvature, profile and plan curvature) derived from digitized 10 m contours (from 1:50 000 topographic map) and land-cover/use map from supervised classification of aster image, with all the processing done in Ilwis and ArcGIS.

According to the prediction output results the original saline zones of the study area will, on one hand decrease from 10% and 71% to 3% and 23% for low and moderate saline zones respectively after 20 years under present cropping patterns. On the other hand the high and severe saline soils will increase from 17% and 0% to 43% and 30% respectively. However, the lack of historical and difficulty to obtain existing salinity and groundwater data in the area has presented difficulties and uncertainty of the results. The prediction of salinity in the transition zone (60-100cm) was rather poor. Despite validation results suggesting suitability of the model for root-zone salinity prediction, concerns and uncertainties regarding the relevance and applicability of the model to the applied spatial scale remain. Nevertheless integration of the model into a GIS environment and geostatistical methods helped in upscaling from point to area scale level. The sensitivity analysis results indicated that the SaltMod model was sensitive to five out of eleven selected input parameters.

The approach presented in the study is fundamental to responding to questions related to soil salinity management thereby way of prognostic analysis to detect salinization at early stages thus providing prevention measures rather than damage control measures. However, the results presented should be taken as indicative due to uncertainties associated with large assumptions rather measured data. Besides, though accuracy of prediction may be uncertain, it is useful when the trend of prediction is clear.

i SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Acknowledgements

I’m very grateful to The Netherlands Fellowship Programme (Nuffic) for financial assistance of my studies. I’m also thankful to South African government, Department of Agriculture for allowing me the opportunity to further my studies.

I would like to express my sincere gratitude to my supervisor Dr Abbas Farshad for his guidance and invaluable comments to this work. Without his supervision and constructive criticism I would not have managed to accomplish this study. I’m also thankful to my co-supervisor Dr Druba P. Shrestha for his invaluable guidance during my fieldwork and useful suggestion towards completion of this study.

I would like to thank the LDD staff in Thailand, especially Mr Anukul Suchinai for providing all the necessary support needed for fieldwork. Many more thanks to Mr Thoi and Ms Waei for their assistance during field data collection and the driver (Pee Nai), who was so keen to take us for point to point without hesitation. I would like further extend my gratefulness to the LDD staff in Khon Kaen and laboratory staff who were so welcoming and helpful during my laboratory analysis and for finalizing the analytical analysis. Besides, their hospitality and humanity made my few days in Khon Kaen the fabulous experience in Thailand. Further I would to thank Montoon, Poo and Koi who made us feel at home and treated us like their brothers in a foreign country where very few people could understand our language.

Special thanks to all my colleagues, especially cluster mates and course mates, Edward, Yirgalem and Raju who were so courageous and helping throughout the duration of our research work. Thanks to all my friends who made my stay in Netherlands such a wonderful experience. Thanks to ITC community for all the efforts of creating a social environment with all social gatherings and activities organized.

I would like to extend my greatest appreciation to my family and friends with their kind words of encouragement and building my confidence to finish my studies. Special thanks to Thandi (my son’s mother), who never complained while leaving her to raise a three months old baby alone.

Lastly and the most all, I would like to thank the Lord for giving me strength, without His grace nothing would have been possible.

ii Table of contents

1. INTRODUCTION...... 1 1.1. General Background ...... 1 1.1.1. Soil Salinity ...... 1 1.1.2. Impacts of Soil Salinization...... 2 1.1.3. Soil Salinity Issue in Thailand...... 3 1.1.4. Soil Salinity Detection Problem...... 4 1.1.5. Modeling Salinization ...... 5 1.2. Problem Formulation and Research Justification...... 6 1.3. Research Objectives...... 8 1.3.1. Broad Research Objective...... 8 1.3.2. Specific Objectives...... 8 1.4. Research Questions...... 8 1.5. Research Hypothesis...... 9 1.6. Research Approach...... 9 2. LITERATURE REVIEW...... 11 2.1. Soil Salinity and its Effects on Crops...... 11 2.2. Models for Soil Salinization...... 13 2.2.1. Seasonal Models...... 14 2.2.2. Transient Models...... 14 2.2.3. Model Selection...... 15 2.3. SaltMod Model ...... 16 2.3.1. Brief Description and Rationale...... 16 2.3.2. Principles and Data Requirements ...... 16 2.3.3. SaltMod Application and Validation...... 19 2.4. Scope, Assumptions and Shortcomings of Saltmod ...... 19 2.4.1. Scope ...... 19 2.4.2. Assumptions ...... 19 2.4.3. Shortcomings...... 20 2.5. Geostatistics and Interpolation (GIS and Kriging) ...... 20 2.5.1. Kriging...... 22 2.5.2. GIS...... 23 3. MATERIALS AND METHODS ...... 24 3.1. The Study Area ...... 24 3.1.1. Geographic Location ...... 24 3.1.2. Climate ...... 25 3.1.3. Physiographic Description ...... 27 3.1.4. Soils and Salinity...... 28 3.2. Materials ...... 30 3.3. Research Methods...... 30 3.3.1. Data Collection...... 33 3.3.2. Data Entry and Processing...... 39 3.4. Model Assumptions/Simplifications and Calibration...... 40

iii SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

3.4.1. Assumptions ...... 40 3.4.2. Model Calibration...... 41 3.5. Exploratory Data Analysis...... 43 3.5.1. Histograms...... 43 3.5.2. Box plots...... 45 3.6. Selection of Kriging Method ...... 47 3.7. Model Validation ...... 51 4. RESULTS AND DISCUSSION...... 53 4.1. General Variation of observed EC values...... 53 4.2. Spatial Distribution of observed EC ...... 54 4.3. Model Simulation and Prediction of Salinity ...... 55 4.3.1. Soil Salinity in the Root zone...... 55 4.3.2. Soil Salinity in the Transition zone ...... 56 4.3.3. Salinity in the Aquifer ...... 57 4.3.4. Simulated Depth to water table ...... 59 4.4. Geostatistical Analysis and Mapping of Electrical Conductivity...... 60 4.4.1. Kriging and Mapping of measured EC values...... 60 4.4.2. Spatial Distribution of Soil Salinity within the Geomorphic Units ...... 66 4.5. Kriging and Mapping of Simulated EC values...... 72 4.5.1. Spatial Distribution of Simulated Salinity within the Geomorphic Units...... 80 4.5.2. The Nature and Magnitude of Change ...... 90 4.5.3. Cross Validation of Prediction Maps ...... 91 4.6. Model Validation and Sensitivity Analysis ...... 92 4.6.1. Validation ...... 93 4.6.2. Sensitivity analysis ...... 95 5. CONCLUSION AND RECOMMENDATIONS ...... 99 5.1.1. How is soil salinity distributed spatially in relation to geopedologic properties ? ...... 99 5.1.2. How does salinity change over space and time as influenced by hydro-geopedologic processes? ...... 99 5.1.3. Which areas are likely to be affected by soil salinization in future ? ...... 100 5.1.4. At what rate and extent is the development of salinity under current practices ? ...... 100 5.1.5. How accurately and reliably can SaltMod help predict salinization? ...... 100 6. REFERENCES...... 101 7. APPENDICES...... 104

iv List of figures

Figure 1.1 Categories of salt-affected soil (source:[2])...... 1 Figure 1.2 Effects of deforestation on groundwater ...... 3 Figure 1.3 The way groundwater reaches the surface (saline starts as spots then develop to larger patches)[12]...... 4 Figure 1.4 Conceptual framework of an integrated approach for assessment of salinity [8] ...... 7 Figure 1.5 General methodological approach (Adopted from Zinck)[18]...... 10 Figure 2.1Relationship between relative yield of potato and wheat versus soil salinity[23] ...... 12 Figure 2.2 Relative crop yield and salinity relationship and broad salt tolerant classes[21]...... 13 Figure 2.3 The concept of 4 reservoir with hydrological inflow and outflow components[26]...... 17 Figure 2.4 SaltMod output data frame for the root-zone salinity in the form of table and graph...... 20 Figure 3.1 Location of study area and Landsat image indicating saline areas [7]...... 24 Figure 3.2 Average monthly rainfall and evaporation (1971 – 2000)...... 25 Figure 3.3 Average monthly temperature and humidity (1971 – 2000)...... 25 Figure 3.4 Geology of Northeast Thailand ([40] ...... 26 Figure 3.5 Schematic cross section about the local geomorphology of northeast Thailand[17]...... 27 Figure 3.6 Soil (Series) map according to soil taxonomy 1999, produced by LDD[17] ...... 29 Figure 3.7 Soil salinity map produced by Environmental Science Department, Thammasat University 2001[17] ...... 29 Figure 3.8 Methodological approach before fieldwork ...... 31 Figure 3.9 Fieldwork methodological approach ...... 31 Figure 3.10 Methodological approach post fieldwork...... 32 Figure 3.11 Classified image for land cover mapping ...... 35 Figure 3.12 Location of sample points (left = auger points and right = mini pits points) in the study area ...... 36 Figure 3.13 Fieldwork picture while digging mini pits for soil classification and collecting soil core samples ...... 37 Figure 3.14 Soil samples being air dried in the barn and laboratory discussions for analysis methods ...... 38 Figure 3.15 Correlation between simulated and measured soil bulk density...... 40 Figure 3.16 Comparing of Calibrated Lr and Gn to observed soil salinity and groundwater table values ...... 42 Figure 3.17 Spatial distribution of observations points in the study area...... 43 Figure 3.18 Frequency distribution of EC and logEC values for three sampling depths...... 45 Figure 3.19 Boxplots showing EC distribution over relief units (a – topsoil, b- root-zone, c-transition zone) for primary data ...... 46 Figure 3.20 Boxplots showing EC distribution over relief units (a – topsoil, b- root-zone, c-transition zone) for secondary data...... 46 Figure 3.21 Flow diagram depicting steps followed for regression-kriging in a GIS[48] ...... 49 Figure 3.22 Comparison of experimental variogram of original data (OK) and trend residuals (UK) .51 Figure 3.23 Variogram maps for determining isotropy of the EC values for the three soil depths...... 51 Figure 4.1 Bubble plot showing spatial trend of EC distribution in the three soil depths (30, 60 & 90cm depths) ...... 54 Figure 4.2 Average predicted root-zone salinity (EC-dS/m)/landform...... 56 Figure 4.3Average predicted salinity in the transition zone (EC-dS/m)/landform ...... 57 Figure 4.4Average predicted salinity in the aquifer (dS/m)/landform...... 58 Figure 4.5 Estimated water depth for point 36 (S1=season 1, S2 = season 2)...... 59 Figure 4.6 Experimental and fitted variogram models for three soil depths...... 62 Figure 4.7 Prediction and variance maps of EC values for topsoil (0-30cm) layer ...... 63 Figure 4.8 Prediction and variance maps of EC values for subsoil (30-60cm) layer...... 64 Figure 4.9 Prediction and variance maps of EC values for transition zone (60-100cm layer) ...... 65

v SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Figure 4.10 EC distribution per landform units (a) and relief types (b) ...... 67 Figure 4.11 Maps showing salinity (EC) distribution in the relief zones for the soil depth...... 71 Figure 4.12 Experimental and fitted variogram models for simulated EC of the tenth year ...... 74 Figure 4.13 Root-zone kriging output maps of simulated EC values for the tenth year ...... 75 Figure 4.14 Transition-zone kriging output maps for simulated EC values for the tenth year...... 76 Figure 4.15 Experimental and fitted variogram models for simulated EC of the twentieth year ...... 77 Figure 4.16 Root-zone kriging maps for simulated EC values of the twentieth year ...... 78 Figure 4.17 Transition-zone kriging maps for simulated EC values of the twentieth year...... 79 Figure 4.18 Average predicted EC values per relief types for the root-zone...... 81 Figure 4.19 Average predicted EC values per relief types for the transition zone ...... 81 Figure 4.20 Percent area affected for root-zone prediction...... 83 Figure 4.21 Percent area affected for transition zone prediction ...... 83 Figure 4.22 Reclassified maps for root-zone and transition zone for the tenth year prediction ...... 84 Figure 4.23 Average predicted EC values per relief types for the root-zone...... 86 Figure 4.24 Average predicted EC values per relief types for the root-zone...... 86 Figure 4.25 Percent area affected for root-zone prediction...... 88 Figure 4.26 Percent area affected for root-zone prediction...... 88 Figure 4.27 Reclassified maps for root-zone and transition zone for the twenties year prediction..... 89 Figure 4.28 Histogram and bubble plot of residuals for the root-zone ...... 94 Figure 4.29 Histogram (a) and bubble plot (b) of residuals for the transition zone...... 94 Figure 4.30Plot of sensitivity indices as a function of % change in parameter values for selected parameters...... 96 Figure 4.31 Plot of sensitivity indices for sensitive parameter only...... 96

vi List of tables

Table 2.1 FAO (USDA) classification used for salinity assessment[22]...... 12 Table 2.2 Explanation of symbols used in the reservoir concept...... 18 Table 3.1Climatological data for the period of 1971-2000 of Nakhon Ratchasima ...... 26 Table 3.2 Data, material types used and their sources...... 32 Table 3.3 Geopedologic legend[17]...... 33 Table 3.4 Summary statistics of parameters ...... 44 Table 3.5 Summary statistics of root-zone EC (30 -60cm depth) per landforms...... 44 Table 3.6 Correlation analysis results of continuous predictors...... 47 Table 3.7 SPC coefficient and variance percentages per band ...... 47 Table 3.8 Summary results of regression for stepwise regression analysis for measured EC values....50 Table 3.9 Summary results of regression for stepwise regression analysis for simulated EC values ...50 Table 4.1 Summary statistics of EC parameters for three soil depths ...... 54 Table 4.2 Average predicted root-zone salinity (EC-dS/m)/landform...... 55 Table 4.3 Average predicted salinity in the transition zone (EC-dS/m)/landform ...... 56 Table 4.4 Average predicted salinity in the aquifer (dS/m)/landform...... 58 Table 4.5 Average predicted water table depths (m)/landform ...... 59 Table 4.6 Theoretical semi-variogram model and its parameters...... 61 Table 4.7 Numerical summary values for kriging prediction and variances (log10 EC-dS/m)...... 61 Table 4.8 Summary statistics of back transformed logEC (dS/m) prediction values ...... 61 Table 4.9 Mean measured EC (dS/m) values per landform and relief (inserted table) units...... 66 Table 4.10 EC residuals of linear modelling and ANOVA for geomorphic (relief) regions...... 67 Table 4.11 Mean interpolated EC (dS/m) values per landform and relief (inserted table) units...... 68 Table 4.12 Area percentages per severity levels for 0-30cm layer ...... 69 Table 4.13 Area percentages per severity levels for 30-60cm layer ...... 69 Table 4.14 Area percentages per severity levels for 60-90cm layer ...... 69 Table 4.15 Percent area per severity levels over entire area of interest...... 70 Table 4.16 Experimental and fitted semi-variogram model parameters ...... 72 Table 4.17 Summary statistics for kriging prediction and variance values for simulated EC...... 72 Table 4.18 Simulated mean EC (dS/m) values per landform and relief (inserted table) units for the 10 th year ...... 80 4.19 Percent area per severity levels for root zone ...... 82 4.20 Percent area per severity levels for transition zone...... 82 Table 4.21 Percent area per severity levels over entire area of interest...... 82 Table 4.22 Simulated mean EC (dS/m) values per landform and relief (inserted table) units for the 20 th year ...... 85 Table 4.23 Area percentages per severity levels for root-zone...... 87 Table 4.24 Area percentages per severity levels for transition zone ...... 87 Table 4.25 Percent area per severity levels over entire area of interest...... 87 Table 4.26 Predicted area changes of various soil salinity classes over ten year period...... 90 Table 4.27 Predicted area changes of various soil salinity classes from tenth to twentieth year ...... 90 Table 4.28 Predicted area changes of various soil salinity classes over twenty year period...... 90 Table 4.29 Validation results for kriging maps of measured EC values...... 91 Table 4.30 Validation parameters for kriging prediction of simulated EC values ...... 91 Table 4.31 Statistical parameter values for error determination...... 94 Table 4.32 Selected parameters with baseline values and percent changes used in the analysis ...... 97 Table 4.33 Sensitivity indices for all the selected parameters...... 97

vii SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

List of Appendices

Appendix 1: Input parameters for SaltMod...... 104 Appendix 2: Land cover types and water table observation points ...... 104 Appendix 3: EC, pH and GWD...... 107 Appendix 4(A): Texture (sand and clay percent), field capacity and porosity...... 109 Appendix 5: Classification Accuracy Assessment Report...... 111 Appendix 6: Histograms of pH, texture and porosity for the three soil depth...... 112 Appendix 7 : Box plots for pH, texture and porosity of the primary dataset...... 115 Appendix 8: Calibration results of root-zone leaching efficiency (Flr)...... 117 Appendix 9: Calibration results of natural drainage (Go)...... 118 Appendix 10: Simulation Results for root-zone salinity...... 119 Appendix 11: Simulation Results for the transition zone ...... 121 Appendix 12: Simulation Results for the aquifer...... 123 Appendix 13: Comparison of experimental variogram of original data (OK) and trend residuals (UK) for simulated EC values...... 125 Appendix 14: SaltMod features for data input and output display ...... 127

viii SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

1. INTRODUCTION

1.1. General Background

1.1.1. Soil Salinity

Soil salinity is considered as one of the major and widely spread environmental problems that limit crop production and lower soil productivity, particularly in arid and semi-arid environments[1-7]. In these environments the climatic conditions for agricultural production are harsh with low precipitation and high evaporation rate. Food and fibre demands are high due to rapidly increasing population, hence policies that favour agricultural intensification are promoted [8]. This, if not properly planned, results in poor land and water management practices and expansion of agricultural frontier into marginal drylands [9], and this can lead to and/or accelerate soil salinization.

Soil salinization results from accumulation of water soluble salts in the soil surface and sub-surface, mainly chlorides, carbonates and sulphates of sodium, calcium and magnesium. Several types of salinization can be distinguished (figure 1.1). Greiner [2]describes three conditions leading to soil salinization as (1) presence of salt source, (2) presence of water, and (3) mechanisation for moving the salt to the soil surface. Sources of salt can include dissolved solids in rainwater, within the soil profile, in groundwater and in water used for irrigation.

Figure 1.1 Categories of salt-affected soil (source:[2]).

1 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

There are various factors cause salinization which include natural or inherent and human induced factors and these are generally categorized into primary and secondary salinization respectively [7]. Primary salinization results from natural weathering of parent material (i.e. rock and minerals) and is influenced by factors related to climatic, topographic, hydrologic, and geologic and soil condition. Secondary salinization develops from mobilization of the stored salts in the soil profile and/or ground water due to human activities [2] and practices, which include but not limited to, cultivation of marginal lands, in appropriate irrigation practices, deforestation, and mining activities.

There are various forms of land and environmental degradation associated with salinization which affect both soil and water qualities, as well as crop production. The major soil related degradation forms include acidification, organic matter depletion, nutrient deterioration, soil biodiversity loss, soil compaction, soil crusting and erosion. In the case of crops, salinization can result in stunted plants, leaf burn, restricted root development, water stress and total death of the crop, which ultimately negatively affect crop yields. In terms of water, salinization reduces the quality and suitability of water for most uses, which may range from human consumption purposes to agricultural and industrial purposes.

All the same, there is a wide range of management options available for managing and preventing salinization, though most of these options require huge economic inputs and are influenced by technical and social circumstances. Ghassemi, et al [10] further emphasize that implementation of any of the options depends on particular conditions of salinization because one option may be effective and feasible in one case, but not at all in another. For example, salinization as a result of irrigation practices can require different management procedures from dryland salinization and/or salinization in water sources. It should also be highlighted that in other circumstances there can be no suitable control option. The various management options as described by Ghassemi, et al. [10] include engineering plans (e.g. drainage, concurrence use of surface and groundwater, irrigation efficiency), disposal of saline drainage water, biological options and policy options (e.g. water pricing, transferable water rights, catchments management). Generally a mix of these options can be more beneficial and effective as no one measure can be sufficient.

1.1.2. Impacts of Soil Salinization

Salinization is somewhat an extensively researched and fairly understood phenomenon. Despite the general awareness and knowledge of this problem, salinization has remained increasing at an alarming rate. Its continued existence has a number of negative impacts on the environment (land, water, vegetation, biodiversity), society, and economy of affected countries [1]. Environmentally, its effects are pronounced on the loss of soil productivity and yield reduction which are manifested during its early development stages. While at advanced stages it destroys vegetation resulting in loss of habitat and reduced biodiversity, and totally renders the soil barren. In terms of social side, food security levels are hampered due to reduced productive land and crop yields. It can also result in disruption and dislocation of the farm population. Economically, countries faced with this problem can spend hundreds to thousands of million dollars per year in production losses and rehabilitation of damaged land and water supply structures[1].

2 1.1.3. Soil Salinity Issue in Thailand

It is reported in the study by R.P Shrestha [7]that about one quarter of the 5.8 million hectares (Mha) of salt-affected soils in Southeast Asia occur in Thailand, which accounts for about 2.7 percent of the country’s total extent. Most of saline soils in Thailand occur in the Northeast region and accounts for approximately 2.85 million hectares (Mha) while the south coastal plain and central plain account for 0.58 Mha and 0.18 Mha respectively [10]. The Northeast region of Thailand is dominated by agriculture as the main occupation for 18 million people [10], but has relatively the lowest productivity than other regions. The erratic rainfall followed by long dry spells and poor soil conditions, which include soil salinity, texture and shallow surfaces layers are the major cause of unstable agricultural productivity [7].

The fundamental cause of salinization in this region is ascribed to the climate and extensively underlying salt-bearing rocks which include shale, siltstone and sandstone [10] . The tropical monsoon climate causes fresh water accumulation in the soil profile during the wet season reaching and pressing the saline groundwater. At the end of the dry season there will be little fresh water in the profile and rivers carry salty water flowing from groundwater layers[11]. This salt is then washed out of the rivers during the next monsoon while the saline groundwater is pushed back to the soil profile due to pressure differentials. This is however accelerated and widely spread by human activities which are associated with poor agricultural practices, deforestation, salt making, and construction of roads and reservoirs. The major effect of these activities is increased groundwater recharge which then result in deep groundwater flows to dissolve and transport salts from uplands towards lowland recharge areas (figure1.1& 1.2)[11]. Rising groundwater, mobilized salts and evaporation cause salinisation which harms crop growth, affect the ecosystems and damage water quality. Therefore management and rehabilitation measures that would improve soil productivity conditions and ensure agricultural sustainability are so indispensable for this region. Hence research studies on salinity as the major agricultural constraint in this region are being pursued in order to support informed management decisions.

Figure 1.2 Effects of deforestation on groundwater

3 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Figure 1.3 The way groundwater reaches the surface (saline starts as spots then develop to larger patches)[12].

1.1.4. Soil Salinity Detection Problem

In order to control and monitor the process of salinization for the purpose of recovering damaged land and preventing further expansion, information on its spatial distribution, its trends of expansion and severity levels is essential [13]. Various researchers have undertaken a number of studies to assess effectiveness and efficiency of various methods and techniques for acquiring information related to soil salinity. The applied methods range from ground-based to remote sensing techniques. The latter approach includes aerial and satellite sensors and the former include conventional field measurements such as soil sampling, visual inspection of the landscape, and laboratory methods[7].

In that respect a variety of remote sensing data have been examined which so far have not been able to provide both qualitative and quantitative information adequately regarding soil salinity. The inadequacy of remote sensing data to study soil salinity has been highlighted by Metternicht and Zinck [9] to be due to the complexity and dynamic nature of the salinization process, and characteristics related to spectral, spatial and temporal behaviour of salts, spectral confusion with other terrain surface features and interference by vegetation cover. Since salinization usually starts below soil surface, remote sensing lacks ability to look into the subsoil and thus cannot detect

4 salinisation early. Furthermore these techniques have limitations on quantifying salt content of the soil in terms of severity levels (low, moderate, severe), and as such cannot accurately indicate slightly and moderately affected soils. However the advantage of these techniques is that they are relatively cost-effective and efficient especially for mapping large scale areas. As a result use of the ground-based methods is somewhat less preferred as they are somehow expensive, time-consuming and laborious, and their application in large scale areas is impractical.

There are yet other recent approaches that came into the plight of soil salinity studies. These approaches include geostatistical models and electromagnetic surveys. The former method is based on spatial variability of soil properties, and was employed by Burgess and Webster around 1980s of which kriging form the basis [14]. This method has also shown some limitations, as highlighted by Heuvelink and Webster [15], related to the calibration and validation of the models as well as large amount of data requirements. Its limitations are further attributed to applicability constraints for large scale geographic areas as the models are developed for small scale areas. The latter methods have been developed based on geophysical techniques which measure soil salinity by means of electromagnetic induction and bulk soil electrical conductivity[8, 10]. Their estimation of salinity is influenced by soil solution, porosity, moisture content and type and amount of clay in the soil [9]. And as such the variations in soil texture and water content of the soil affect the accuracy and reliability of this technique. These methods however have an advantage of making realistic prediction of salinity without disturbing the soil composition and provide rapid field-wide measurement capability, especially the airborne methods.

1.1.5. Modeling Salinization

Understanding the spatial and temporal variation of soil salinity forms a crucial part for developing appropriate management strategies to control and prevent its spread. In order to understand salinization and its causes, use of rapid, efficient and reliable methods to monitor this process are essential. Soil salinity monitoring is thus described by Metternicht and Zinck [9] as identifying places where salt accumulate first, and then detect its temporal and spatial distribution to track its changes and anticipate further expansion. In that respect remote sensing technique plays an important role, but it is more useful for surface observation as it lacks capabilities to extract information from the third dimension (depth) of 3-D soil body. Then modeling becomes a fundamental technique to overcome the remote sensing constraints related to soil depth by complimentary use of these methods.

Peng Xu and Yaping Shao [16] clearly describe the process of salinization to be closely related surface-soil and groundwater hydrological processes. This stems from the fact that movement of water in the landscape is mainly responsible for the transportation of salts. From that perspective three main regions of interest can be considered in modeling salinization, namely:
The vertical exchange of salts between the groundwater system and unsaturated zone;
The accumulation of salt in the vegetation root (vadose) zone; and
The horizontal transportation of salts through groundwater movement, surface runoff and stream flow.

It is thus apparent that modelling salinization poses difficulties and challenges, due to the complexity of the hydrological processes, as well as soil properties and their variability. These modelling

5 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

difficulties are further aggravated by external forces such as the atmosphere and human activities which also influence the soil and hydrological process. Therefore interactions between the atmosphere, the land surface and groundwater system[16] including human activities need to be carefully considered to better model salinization process.

1.2. Problem Formulation and Research Justification

Northeast Thailand is one of areas adversely affected by soil salinity. The majority of the population depends on agriculture in this region, but it has relatively the lowest productivity in Thailand [10]. One of the reasons for such low productivity is soil salinization. It is thus a matter of concern that salinization be managed and controlled in the area to improve and ensure sustainable agricultural productivity. However, without understanding the process of salinization, any efforts and means to control it can be futile. Thus the current research to study spatial distribution and development of salinity is undertaken.

The majority of soil salinity studies have focused on identifying and developing plans for reclaiming already damaged land, rather than early detection of salinity to ensure preventive measures. In order to foster better management strategies in addressing the problem of soil salinity, prognostic and deterministic approaches to understand the salinization process need to be employed. In that respect, it is not only a single technique that can provide such capabilities, but an integration of diverse data and various techniques would be of significant benefit to provide better solution measures[8].

Linked to that, figure 1.4 gives a conceptual approach of various techniques to tackle the problem of salinization. In this way the various methods can compliment each other to overcome their limitations and incapabilities for detecting and assessing salinity[8]. However it must be emphasized that the focus of this particular research is mainly on predicting soil salinity using modelling technique rather than application of the whole framework. And since earlier research studies undertaken in the study area have used the other techniques, it is thus currently not essential to re-apply them. These other methods, as indicated in the conceptual framework, are fundamental for acquiring input data for the current modelling study, and for validation and comparison purposes. It is thus based on this perspective that SaltMod within a GIS environment is tested for the prediction of the soil salinization in this research.

To clarify the conceptual framework (figure 1.4) from the writer’s perspective, the following can be explained:
Hydro-geopedology: this part deals with geographic distribution of salinity over the landscape (soil-landform relation) as influenced by the parent material, topography and water movement[17]. The result of this stage would provide qualitative information on the affected areas based on soil-landform relation and further give indication of areas prone to salinization.
Remote sensing (mainly conventional) data: provide qualitative information on the present surface conditions of salinity and trends on the expansion of affected lands. A number of remote sensing studies have been conducted to study salinity but due to the limitations of various techniques more research is being pursued to improve its application.
Field and laboratory investigation: this part involves visual inspection, soil sampling and analysis of various soil solutions to obtain soil physico-chemical properties to infer soil salinity and the data will be used for validation purposes.

6
Near-surface geophysics: provide comprehensive data that highlight areas of elevated conductivity at certain depths below the soil surface where no surface expression of salt is evident.
Modelling: this is the main focus of the research and will assess salinization risk that can be caused by natural conditions and different land use practices over time. Furthermore it will simulate the salinization process thus indicate the rate of development and illustrate its impact on soil physical and chemical properties and soil productivity.

Hydro-geopedology

Prognostic/deterministic RS data (aerial/satellite) modelling

Geographic Information System (GIS)

Geophysical survey Field investigation

Laboratory analysis

Figure 1.4 Conceptual framework of an integrated approach for assessment of salinity [8]

GIS: provide geostatistical and interpolation techniques for spatial correlation between observation points and predicting values for unsampled locations. This will further enable integration and fusion of data with different spatial, spectral and temporal characteristics for analysis of trends in salinity. Another phase is production of maps.

To further substantiate the proposed conceptual approach in a scientific context, the paper on potentials and constraints of remote sensing techniques by Metternicht and Zinck[9], and scientific article by Farifteh et al[8] is referred to. The former authors have been sensibly cited in prior sections (Sec. 1.1.4) and thus not much shall be reiterated. According to the latter authors, three different non- unique techniques (remote sensing, solute modeling and near-surface geophysics) can effectively and efficiently identify, detect and monitor salt-affected areas[8]. Besides their quicker and cost-effective advantage over the traditional field measurements and analysis methods, they make more realistic prediction of the process. However they are not devoid of limitations and constraints, hence an integrated approach of these methods to assess salinity is proposed[8].

As reported by Farifteh et al., remote sensing has been used to detect and map salt-affected areas, but most of these studies focused on severely affected areas and given less attention to slightly or moderately affected areas[8]. It’s major constraint being the lack of extracting information from the

7 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

third dimension of 3-D soil body. While solute modelling is useful to predict the salt distribution in the subsoil by considering water percolation, groundwater level changes and groundwater flow. This technique provides complimentary data on dynamics of salt movement in the soil profile which can be used in combination with remote sensing data. Near-surface geophysics sensors have recently been used to map and monitor salt affected areas. These devices are designed to cover range of depths and have several applications, namely mapping saline intrusions, mapping terrain conductivity, soil and rock layers, and some general geological features such as fault and fracture zone[8]. Thus this technique has an advantage of effectiveness for cropped land and can efficiently indicate areas of elevated conductivity where no surface expression of salt is evident, while optical remote sensed imagery is effective where soil has no vegetation.

In terms of this paper, possibilities and limitations of these techniques are indicated, and which in order to overcome their limitations, an integrated methodological approach of these techniques is thus proposed. In the proposed integrated method, data are combined not only to demarcate existing salt-affected soils, but to track down the salinization/alkalinization as a hydropedogenic process[8]. Application of such an integrated methodology, in a GIS environment, involves data fusion of different natures and scales, and follows also a relevant up-scaling approach, from spot through local to regional, recognizing that both the process and data are scale dependent. Therefore soil salinization can be efficiently and effectively identified and monitored when an integrated interpolation of all available data is applied.

1.3. Research Objectives

1.3.1. Broad Research Objective

The general objective of the study is to try out the application of the model (SaltMod) to trace the spatial and temporal variability of soil salinity. To apply GIS and geostatistical techniques to indicate and map potentially salt-affected areas based on long term salinization predictions and agricultural practices currently applied in the study area. This aims at devising means that can help detect salinization at early stages to help devise appropriate mitigation and management plans to combat, control and prevent spread of soil salinity.

1.3.2. Specific Objectives

To model spatial and temporal changes of soil salinity using SaltMod
To determine and map areas that are prone to salinity development
To predict future soil salinity conditions based on current land use practices
To quantify severity levels of salt affected areas (low, moderate, severe)
To evaluate the capability and accuracy of SaltMod to predict salinization

1.4. Research Questions

How is soil salinity distributed spatially in relation to geopedology?
How does it change over space and time as influenced by hydro-geopedologic processes?
Which areas are likely to be affected by soil salinization in future?
At what rate and extent does salinization take place under current practices?
How accurately and reliably can SaltMod help predict salinization?

8 1.5. Research Hypothesis

Spatial modelling can help predict the dynamism of salinization
Modelling salinization with SaltMod can help detect soil salinity at early its stages
Spatial modelling with SaltMod can quantify soil salinity severity levels
Using SaltMod within a GIS environment can help identify and map areas potentially at risk for salinity development.

1.6. Research Approach

The general idea of the research is to implement an integrated approach including various methods (figure 1.4) towards understanding salinization process for better management of salt affected soils. However implementation of such an approach including data acquisition requires a more considerable time than the six months duration allocated for the MSc research. Therefore this present research focuses mainly on the modelling stage using SaltMod in a GIS environment. Figure 1.5 summarizes a general approach followed for the accomplishment of the study.

9 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Figure 1.5 General methodological approach (Adopted from Zinck)[18]

10 2. LITERATURE REVIEW

Though literature review forms part of the whole thesis, this chapter is important to put emphasis on some few aspects and concepts pertaining to salinity and modelling, and thus the subsequent sections give brief explanation to that effect. More so duplication and unnecessary repetition shall however be avoided as much as possible.

2.1. Soil Salinity and its Effects on Crops

Soil salinization results from accumulation of water soluble salts in the soil surface and sub-surface (i.e. the soil profile). Soluble salts are generally the product of rock and soil weathering processes. The soluble salts are defined by Peterson and Arndt[19] as salts that are more soluble than gypsum

((CaSO 4. 2H 2O), which has solubility of approximately 2 grams per litre. There are eight ions commonly associated with soluble salts which include cations of calcium (Ca 2+ ), magnesium (Mg 2+ ), sodium (Na +) + 2- - and potassium (K ) and anions of alkalinity such as carbonate (CO 3 ), bicarbonate, (HCO 3 ), and 2- - carbonic acid (H 2CO 3); sulphate (SO 4 ) and chloride (Cl )[19]. Then as such the sum of the total of these soluble salts in the root-zone is thus defined as soil salinity. Accumulation of these salts in the soil profile can result in high concentration levels that subsequently negatively affect crop yields and reduce soil productivity.

There are generally two different criteria by which degree of salinity can be measured, i.e. electrical conductivity of a saturation-paste extract (ECe) expressed in deci-Siemens per meter (dS/m) [formerly micromhos per centimetre (µmho/cm)], and total dissolved solids expressed as milligrams solute per litre (mg/L). The two measuring parameters have a kind of relationship which can be expressed as[19]:

TDS = 0.65*EC (EC in µmho/cm)………….1

In general saline soils are defined to have an electrical conductivity of more than 4dS/m at 25 oC within 25 cm of the surface, provided that the pH and ESP is less than 8 and 15 (or SAR< 13) respectively[20].

Salinity in soil or water is an environmental factor that reduces plant growth and negatively affects both yield and quality in crops[21].The effects of salinity on crops are related to growth and water stresses resulting in stunted plants, leaf burn, and restricted root development. In some other instances, particularly when salt concentrations are too high, soil salinity can lead total death of the crop. Consequently crop yields are reduced resulting in gross economic losses to the farmers and lead to food security problems. Nonetheless it should be highlighted that crops differ in their sensitivity to salt stress and as such are usually grouped into classes ranging from highly sensitive more tolerant (table 2.1. And this can be the guide to decide what kind of crops to grow at certain salinity levels. Generally, at low to moderate salinity levels, plant growth is reduced, but as salinity increases beyond some threshold tolerance, yield decline is inevitable[21]. The general relationship between relative crop yield and soil salinity for few selected crops is shown figure 2.1 and 2.2 which is obviously inversely proportional.

11 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

The impaired plant growth due to salinity has been described by Greiner[2] to result from various physiological processes and conditions which include:

Table 2.1 FAO (USDA) classification used for salinity assessment[22]

Salinity level Degree of crop sensitivity ECe of soil saturated extract at 25 oC (dS/m) Non saline Very sensitive crops 0 - 2 Low salinity Sensitive crops 2 - 4 Mid salinity Moderate sensitive crops 4 - 8 High salinity Moderate resistant crops 8 - 16 Severe salinity Resistant crops >16

Ion toxicity: occurs once the concentration of an ion or cation exceeds a toxic threshold
Osmotic effects: caused by a reversal in the osmotic potential difference between soil and plant roots, resulting in plant incapability to extract water from the soil.
Waterlogging: increasing the stress for plants through inducing an oxygen deficiency for plant roots.
Nutrient deficiency: due to denitrification, they are poor in nitrogen and saline soils tend to be deprived of plant nutrients. Resultant reduced vegetation cover leads to an increase in soil erosion. Erosion lessens the available phosphate, which in itself adversely affects plant growth.
The nutrient decline favours decrease in the soil's organic matter content, which leads to a reduction in its cation exchange capacity.

The representation of crop salt tolerance has been explained by Shannon to be based on two parameters: the threshold salinity (t) and the slope(s) of the yield decline[21]. The threshold is the level at which initial significant decline in the expected yield is experienced. The slope is the rate at which yield is expected to be reduced for each unit of salinity above the threshold value. This led to derivation of formula for calculating relative yield to salinity effects given as[21]:

YR = Y – s (ECe – t) where ECe > t……………………2

Figure 2.1Relationship between relative yield of potato and wheat versus soil salinity [23]

12

Figure 2.2 Relative crop yield and salinity relationship and broad salt tolerant classes[21]

2.2. Models for Soil Salinization

The basic reasons for development of models for unsaturated soil ecosystems area is describe by (Corwin) as to: a) increase the level of understanding the cause and effect relationship of processing occurring in the soil systems b) provide a cost-effective means of synthesizing the current the level of knowledge into a usable form for making decision in the environmental policy arena.

To understand the causes of soil salinity and devise management practices required to control its spread, rapid and reliable methods of obtaining information on the spatial distribution of salinity are required [24]. The effectiveness and efficiency of such methods depends on the understanding of the dynamism of water and solute movement in the soil, including the spatial variability of soil properties and temporal variability in climatic conditions. Thus, the selection of appropriate practices for salinity control require the quantification of movements of salts, the response of crop to soil water and salinity, and how the environment and management conditions affect these interactions.

There are several approaches for modelling soil salinization found in practice all attempting to better understand its extent and dynamics. Some of these approaches involve mathematical models which describe and quantify the basic hydrological processes and phenomena under a range of conditions[1]. The mathematical models coupled with computers and analysis techniques are useful tools to integrate these interrelated processes and their interactions to define the best management system for saline soil conditions. Various models have been developed for simulating salinisation dynamics and solute transport in the soil which are discussed in numerous publications. These models tend to vary greatly in their operation systems, ranging from simple to sophisticated, from crop specific to general, from primary crop-based to soil-based [23]. In general these models are divided into two main broad groups: seasonal and transient models.

13 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

2.2.1. Seasonal Models

The seasonal models have been described by Castrignano et al[23] to consist basically of an equation that relates yield to the amount of seasonal water of a given salinity. As cited Castrignano et al., Letely and Knapp (1985) further described that this relationship is resulting from the combination of a number of relationships such as yield and evapotranspiration; yield and average root zone salinity and leaching fraction. According to Castrignano et al[23] these kinds of models assume a steady state condition for the soil and do not include the effects of salinity variation in space and time to crop response. As a result the steady state models are considered not suitable for irrigation management in saline conditions.

The validity of these models is restricted to set of conditions assumed in the model development. The set of conditions can commonly include some kind of relationship between marketable yield and evapotranspiration, fertiliser application and drainage, and irrigation water [23]. Pertaining to that statement, the first set of factors is assumed to have linear relationship, the second set assumes adequate conditions and the third assume a constant electrical conductivity. Nevertheless, Castrignano, et al [23] cited recent reports by Royo and Aragüés (1992) that describe a sigmoidal growth response of plants to salinity using the following non-linear equation:

p Y = Y m/ [1 + (EC sw /EC 50 )] …………………3

where Y is the yield obtained per given electrical conductivity, Y m is the yield under no-saline

conditions, EC sw is the average salinity of applied water, Ec 50 is the salinity of water that reduces yield by 50% and p is the empirical constant. Estimation of model parameters is performed by nonlinear squares technique that reduces universality of the model application.

The main advantage of seasonal models is reported by Castrignano, et al[23] as their simplicity, while

the disadvantage is the ratio of EC sw /EC 50 which is not a constant value. They report that this ratio changes with each species as a function of climate, soil type, irrigation management and drainage. This implies that the results provided by this kind of models cannot be generalized.

2.2.2. Transient Models

Transient models are reported to generally use sophisticated numerical solutions to compute water and solute flow in the soil, and predict soil profile conditions with greater details[23]. However the available transient models differ in their conceptual approach, degree of complexity and in their application for research or management purpose. The transient models applied in research and management of saline conditions require a mechanistic treatment of relevant processes in the soil- water-plant atmosphere system[23]. The conclusion was drawn by Castrignano, et al[23] that water and solute flow in the soil and root water uptake are usually modeled in detailed while crop growth is simplified and does not consider interaction in the environmental variables and agronomic management.

14 2.2.3. Model Selection

The types of models are further described by Ghassemi et al[1] to include groundwater models, stream routing models, surface water quality models, root-zone salinity models, infiltration models, water balance models and solute transport models. As explained by Ghassemi et al, the groundwater models are useful for development of management strategy by considering the effects of rainfall, irrigation, cropping activity, groundwater pumping and land use behavior on groundwater levels and on land and stream salinity[1]. The surface water quality and hydrologic routing models are useful to predict downstream salinity concentrations from the upstream data to provide advance warning of salinity levels and for quantifying saline accessions within the reach of river. And the rest of the aforementioned models are useful for prediction of root zone salinity, aquifer recharge rates, crop water use and solute transportation.

However, the availability of numerous models poses challenges on the selection and deciding which model is best applicable in certain situation. The selection of applicable model and its success in simulation is described by Ghassemi et al[1] to depend upon a number of interrelated factors such as:
the objective of the modelling exercise;
the complexity of variables dominantly controlling the behavior of the system;
the level of understanding and knowledge of system structure;
the model parameter estimation problem;
the quality and quantity of data available; and
the modelling approach taken.

In the present study the interest is on predicting and detecting salinization during its early stages of development. Notwithstanding that the rate and degree of salinisation depend on many interacting process, it is useful to identify the main process and seek simplified description of these process[25]. Hence long term (decadal) prediction of root zone salinity and large scale mapping (field to regional) of vulnerable areas using a simplified modelling approach is the basis of this study. The predictions are more reliably made on seasonal (long term) than on a daily (short term) basis[26]. That is, even if the accuracy of the predictions is not very high, it may be useful when the trend of the prediction is clear. For example, it would not be a major constraint to design appropriate salinity control measures when a certain salinity level, predicted by the model to occur after 10 years, will in reality occur a few years before or a few years later.

Therefore SaltMod model (one dimensional point model) is used in the present study to predict long term spatial and temporal variation and development process of salinity in the soil. However, since the model lacks the capability of spatial analysis and mapping, its application is governed in a GIS environment to take care of up-scaling point physical and chemical processes to time and spatial scales of interest. In the ensuing sections the SaltMod model is introduced with the description of principles and data requirements, and subsequently brief discussion GIS and kriging.

15 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

2.3. SaltMod Model

2.3.1. Brief Description and Rationale

SaltMod is a computer program designed for the prediction of the salinity of the soil moisture, ground water and drainage water, the depth of water table, and the drain discharge in irrigated lands. It considers different geo-hydrologic conditions and varying water management options, and several cropping rotation schedules. In terms of water management options it also includes irrigation by ground water, subsurface drainage water from pipes drains, wells and ditches[26].

The program is designed for simplistic operation to promote use by technicians, engineers and project manager[26]. Contrary to other computer models that use short term time steps, and require complex daily data of hydrologic phenomena and soil characteristics that can vary greatly over short spatial intervals, SaltMod uses simple input data that are generally available, or can be estimated with reasonable accuracy, or can be measured with relative ease[26]. It uses long term time steps to predict salinity based on general trends rather than exact predictions. It also takes into account farmers’ responses regarding water logging, soil salinity, water scarcity and over pumping from the aquifer.

This computer program was designed and developed at the International Institute for Land Reclamation and Improvement (ILRI), Wageningen by R.J. Oosterbaan and Isabel Pedroso de Lima. The model is being improved upon by its developers and as such the present model is version 1.3 which is extension of earlier version. Further, a combination of SaltMod and a ground water flow model is being pursued which is believed to provide more flexible in the description of the depth of the water table. A provisional version of the combined model is now available under the name Sahysmod (Spatial agro- hydro-salinity model) [26].

2.3.2. Principles and Data Requirements

The SaltMod model is based on three component systems, viz. water balance (hydrological) model, salt balance model and seasonal agronomic aspects. Therefore the model would require input data that is related to agricultural aspects, hydrological data, and soils characteristics. The general principles and assumptions of the model as given by Oosterbaan[26] are discussed in the subsequent sections.

2.3.2.1. Agronomic Aspects

The computation method of SaltMod in based on seasonal input/output data of which four seasons per year can be distinguished on the basis of dry, wet, cold, hot, irrigation or fallow considerations. The duration of the seasons (Ts) is given in number of months and a combination of number of seasons (Ns) from one (minimum) to four (maximum) can be chosen. Seasonal (long term) inputs instead of daily (short term) inputs are used because the model is developed to predict long term trends. This due to the fact that future predictions are more reliably made on long terms than short terms due to high variability of short term data [26]. Moreover, daily inputs would require large amount of data resulting in immense output files which would be difficult to manage and interpret. Further, daily data may not be readily available especially for large areas.

16 The agricultural input data (irrigation, evaporation, surface runoff) are to be specified per season for three kinds of agricultural practices and their rotation over the total area, which are chosen at the discretion of the user: A: irrigated land with crops of group A B: irrigated land with crops of group B U: non-irrigated land with rainfed crops or fallow land. The A & B groups differentiate between heavily irrigated and light irrigated crops.

2.3.2.2. Water Balances

The model is built on the concept of four reservoirs namely, (1) surface reservoir, (2) upper soil reservoir or root zone, (3) intermediate reservoir or transition zone and (4) deep reservoir or aquifer, of which the first three occur within the soil profile (Figure 2.3). For each reservoir a water balance can be made with the hydrological components as input data. These are related to the surface hydrology (rainfall, evaporation, irrigation, use of drain or well water for irrigation, runoff) and the aquifer hydrology (upward seepage, natural drainage, pumping from wells). The other water balance components like downward percolation, upward capillary rise, and subsurface drainage are given as output. All quantities of the components are expressed as seasonal volumes per unit surface area. The depth of the water table is assumed to be the same for the whole area otherwise the area must be divided into separate units. The three latter reservoirs are given different thicknesses and storage coefficients. A water balance is based on the principle of the conservation of mass for boundaries defined in space and time and can be written as [26]: Inflow = Outflow + Storage. The water balance is calculated separately for each reservoir. The excess of water leaving one reservoir is considered as incoming water for the next reservoir. A schematic presentation of the four reservoirs concept is given in figure 2.3 with explanation of symbols in table 2.2.

Figure 2.3 The concept of 4 reservoir with hydrological inflow and outflow components[26]

17 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Table 2.2 Explanation of symbols used in the reservoir concept

Reservoir Symbol Explanation Surface reservoir Eo Evaporation from open water Era Total actual evapo-transpiration Ii Irrigation water supplied by the canal system Ig Gross amount of field irrigation water Io Amount of water leaving the area through the canal (by-pass) Pp Rainfall/precipitation So Amount of surface run-off or surface drain water

Root zone Lc Percolation from irrigation canal system Lr Total percolation from the root zone Rr Total capillary rise into the root zone Gu Subsurface drainage water used for irrigation λi Amount of water infiltrated through the surface into the root zone

Transition zone Gd Total amount of subsurface drainage water

VL Vertical downward drainage into the aquifer

Aquifer Gw Groundwater pumped from the wells in the aquifer Gi Horizontal incoming ground water flow through the aquifer Go Horizontal outgoing ground water flow through the aquifer

VR Vertical upward seepage from the aquifer Fw Fraction of pumped well water used for irrigation

2.3.2.3. Salt Balances

The salt balances are based on the water balances using salt concentrations of incoming and outgoing water. The salt balances are calculated separately for different reservoirs, and in addition for different types of cropping rotations. The salt concentration is expressed in terms of EC (electrical conductivity) of soil moisture when saturated under field conditions. The initial salt concentrations of water in different soil reservoirs, in irrigation water and in the incoming groundwater from deep aquifer are required as input to the model. Salt concentration of outgoing water, either from one reservoir into the other or by subsurface drainage, is computed on the basis of salt balances, using different leaching and mixing efficiencies.

With reference to figure 2.1, the salt balances are also based of principle of conservation of mass which is expressed as: incoming salt = outgoing salt + storage salt, with further consideration of salt concentration changes in terms of the following:
Incoming salt = inflow x salt concentration of the inflow
Outgoing salt = outflow x salt concentration of the outflow
Salt concentration of outflow = leaching efficiency x time average salt concentration of the water in the reservoir of outflow
Change in salt concentration of the soils = salt storage divided by amount of water in the soil

18 2.3.3. SaltMod Application and Validation

A number of articles have been published in various journals, including unpublished articles where application and evaluation of SaltMod had been undertaken. The model has been tested in just a few number of countries such as Egypt, India, Portugal, Thailand and Turkey[27-30]. In most of the publications emphasis was on determining the effect of installed subsurface drainage systems to reduce root-zone salinity of irrigated lands and to asses the effect of various irrigation management practices to soil salinity and water table depth. Pertaining to that the model was found successful to predict drainage and salinity in the Nile Delta [31]. It was also applied by Rao et al,[29] to evaluate remedial measures for waterlogged saline soil in Tungabhadra Irrigation Project, Karnataka, India, and by Vanegas Chacon(993) to predict desalinization in the Leziria Grande Polder in Portugal[32]. Shrivastava et al[30] validated the model in the Segwa minor canal command area by comparing the model predictions with field observation on soil salinity, drain discharges and depth to water table. In recent studies the model has been applied in the coastal clay soils in India[27] to predict reclamation period and design of subsurface drainage system, to analyse salt and water balances and make long term prediction of soil salinity and depth to water table in the Konanki pilot area, Andhra Pradesh, India[28]. It was also recently applied in Turkey[32] to estimate root-zone salinity of the Harran plain test area, and to simulate the effect of different drain depth on groundwater salinity. In almost all of the aforementioned articles the model has been proven to be successful in predicting and estimating the effects of soil salinity and groundwater dynamic changes under different conditions.

2.4. Scope, Assumptions and Shortcomings of Saltmod

2.4.1. Scope

The output of SaltMod is given for each season of every year for any number of years as specified in the input data. The model runs either with a fixed input data for a number of years as specified by the user, or with annually changed input values. Within a year the output of the preceding season becomes the input of the succeeding season. The output data are filled in the form of tables and graphs (figure2.4) that can be inspected directly or exported to spreadsheet programs for further analyses. The output data comprise of hydrological and salinity aspects which can be summarized as:
Salt concentration of different reservoirs at the end of each season (root-zone, transition zone and aquifer)
Seasonal average depth of water table
Seasonal average salt concentration and volume of drain water in the presence of subsurface drainage

2.4.2. Assumptions

The model assumes uniform distribution of cropping practices for various crops grown in the study area. The minimum and maximum time step of computation is one and twelve months respectively. The movement of water in the first three upper reservoirs is considered only in the vertical direction (i.e. either upwards or downwards) except for the flow to subsurface drains where they exist. The location of the subsurface drains is assumed to be anywhere in the transition zone. The deep ground water reservoir considers both horizontal and vertical movements (figure 2.3. The overall operation of the

19 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Figure 2.4 SaltMod output data frame for the root-zone salinity in the form of table and graph model is based on the principle of conservation of mass and the solute movement is thus assumed to take place as mass flow.

2.4.3. Shortcomings

Some of the input parameters required by the model are very difficult to measure, either in situ or in the laboratory. These parameters (see appendix 1) are then determined by either logical estimation or calibration using the model. The calibration can be done by trial and error runs of the model using arbitrary values for required parameters and comparing the salinity and groundwater depth outputs with the actually measured values.

The SaltMod model does not have the capability to work with data that has a spatial context. And as such simulation for multiple spatial points requires separate input file preparation for each point. This makes the application of this model for data with spatial reference so cumbersome and tedious. This is further worsened by the lack of the model to directly read or import data from other file formats. Thus inputting data into the model has to be done manually. Nonetheless, because the model is further developed and improved, a latest version of the model (SahysMod) has been designed to account for spatial variations through a network of polygons and enhance management of input and output data. This version integrates the agro-hydro-salinity model and groundwater model and therefore requires more data on groundwater and hydrological related aspects. Due to lack of groundwater related data it was not possible to apply this version of the model for the current study.

2.5. Geostatistics and Interpolation (GIS and Kriging)

Understandably soil properties of scientific nature vary continuously in space and time, and as such it is a very difficult if not impossible process to measure soil variables at every point in space[3]. Thus, in order to represent the spatial variation of soil properties in nature, sample points have to be used. However deciding on the sampling design is another challenge because of complexity, variability and dynamic processes of nature. To minimize errors, sample points need to be dispersed strategically over the study area to ensure representativity of phenomena to be measured in the area. In spatial analysis

20 sampling is often performed on regular grid or irregular set of points which however might not depict the true variation of the studied phenomena in space. Nonetheless at the present moment this is one of the only few feasible and economical methods to study soil spatial variability. In general stratified random sampling is often recommended for spatial analysis[33].

Based on the sampled data values, estimated values are assigned in all other unsampled locations to define spatial variation of the phenomena. Geostatistics is largely the application of this theory, and provides a set of stochastic techniques that account for both random and structured nature of spatial variables, the spatial distribution of sampling sites and the uniqueness of any spatial observation [3]. The most important and common tool of geostatistics is the interpolation process which relies on estimation and prediction. Interpolation process is based on the fact that objects that are nearer to each other are more related or similar in behaviour than those that are far apart. As such the output of the interpolation process is influenced by the number and distribution of sampled points, physiographic setup of the study area, and understanding of spatial variation of the phenomena.

There are a number of interpolation methods available but the most commonly used method in GIS is Kriging. Different authors have used this technique in comparing between different spatial prediction methods[9, 24, 34], as well as between different kriging methods since kriging itself has different methods (e.g. ordinary kriging, universal kriging, simple, co-kriging, kriging with external drift, etc). Nonetheless, Luan and Quang [35] classify spatial prediction (interpolation) methods into three main groups:
Local interpolation which is usually based on arithmetic average weights of nearest points.
Global interpolation, of which the common approach is trend surface analysis
Interpolation by kriging which is based on both surface analysis and average weights methods. The surface analysis finds a mathematical formula for describing the general trend without taking into account local variation. The average weights method is used to calculate deviation from global trend and considers variation due to local irregularities.

Hengl[36] has also classified spatial prediction models into two groups based on the amount of statistical analysis involved:
Mechanical/Empirical models: where arbitrary or empirical model parameters are used without estimation of model error and strict consideration of variability of a feature. The most common techniques include but not limited to, Thiessen polygon, inverse distance weighting, regression on coordinates and spline.
Statistical/Probability models: where models parameters are estimated objectively following the probability theory. The prediction outputs are accompanied by the estimate of prediction error. Four groups of statistical models are mentioned by Hengl [36] here including Kriging (plain geostatistics), environmental correlation(regression based), Bayesian-based models and mixed models (regression-kriging).

In general the mechanical prediction models are comparatively more flexible and easy to use than statistical models but are considered primitive and often sub-optimal. The statistical models follow several statistical data analysis steps making the mapping process more complicated. Moreover the input datasets need to satisfy strict statistical assumptions. Nevertheless, these models produce more reliable and objective maps, can reveal sources of errors, and depict problematic areas, and are thus more preferred in the scientific fraternity[37]. In the present study more emphasis will be given on Kriging.

21 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

2.5.1. Kriging

Kriging is a method of calculating estimates of a regionalized variable at a point, over the region of study, and uses as a criterion the minimization of an estimation variance[38]. Generally in kriging the prediction are based on the model that the unknown value Z(x) to be estimated represents both global trend m(x ) of the data and local variation é(x) [35] given by the equation:

Z(x) = m(x) + e' (x) ………………………..4,

whereby in the case of n observation points with values Z(x 1), Z( x 2), …., Z(x n) at points x1, x 2, …., x n distributed in the neighborhood of X0, the best estimator at X0 is given by: N

Z* (x 0) = Σ λi Z(x i) where i =1,…..,n. ……………….5, i=1

where λi is the vector of kriging weights and N is the number of sampled locations. For interpolation and analysis of point data, through further innovation by Matheron (1962) and Gandin (1963) as cited by Hengl [36], the derivation and plotting of semivariances was introduced. This is the difference between two neighbouring values termed as a variogram and defined as a half of mathematical expectation of random variables, and given by:

γ (h) = ½[Z(x) – Z(x+h)] 2 ……………….6, where γ (h) is the experimental variogram model, Z(x) and Z(x+h) are two known values with separation distance h. The normality around this theory is that the semivariances are smaller at shorter distances but stabilize at certain distance to levels that are more or less equal to global variance. This is known as the spatial auto-correlation effect[36]. Calculation of semivariances through this process produces an experimental variogram which necessitate transfer or fitting of such values to theoretical variogram model. There are number of variogram models that are available for choice such as linear, spherical, exponential, circular, Gaussian, Bessel, power, etc. Fitting of variogram to certain appropriate model is an iterative method and is important for deriving semivariances for all locations and solves kriging weights. The fitting of the theoretical model for the observed variogram is guided by three features of consideration[38]:

(1) presence or absence of sill (C ), which is indicated by the leveling off of the variogram once h increases beyond some distance(range); (2) behaviour (shape) of the variogram at the origin; and (3) presence of absence of nugget effect (C0), indicated by an intercept of the variogram on the y-axis of the model graph. The nugget effect implies abrupt changes in the regionalized variable over small distances, variability at spatial scales finer than sample spacing.

Basically the variogram helps in the understanding of [35]:
the extent, characteristics and structure of the variation of the parameters under study;
decision of fitting the isotropy or anisotropy of a parameter under study; and
basis for determining the kind of suitable kriging method to give good estimation results.

22 2.5.2. GIS

The expense and labour intensiveness of long term field studies has necessitated the use of computer models to understand real time and predictive changes of the environment. The ability to model environmental processes provides a means to optimize the use of the environment by sustaining its ability without detrimental consequences [39]. A GIS characteristically provides a means of representing the real world through integrated layers of constituent spatial information [39]. The use GIS for environmental problem solving is to translate the results of models into decision strategies and policies designed to sustain environment and agricultural production. Thus the integration of deterministic solute transport models with GIS is fundamental is soil and groundwater studies. The GIS based models provide diagnostic and predictive outputs that can be combined with socio-economic data for assessing local, regional and global environmental risks or natural resource management issues [39].

In soil related studies, the complexity and heterogeneity of the soil necessitates the collection of tremendous volumes of spatial data. This makes data collection for large areas prohibitively expensive due to labour cost. Consequently any attempts to model soil and groundwater processes with directly measured input and parameter data beyond a few thousand hectares is virtually impossible[39]. With the integration of GIS into simulation models of soil and water processes there is ability to dynamically described solute transport processes at scales ranging from micro to macro level. Therefore GIS in the present study provides the basic capabilities to integrate data from various sources and further analysis of outputs from both simulation models and geostatistical models.

23 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

3. MATERIALS AND METHODS

3.1. The Study Area

3.1.1. Geographic Location

The study area of the research is located in the Nong Suang district near the Ratchasima city of the Nakhon Ratchasima province in the Northeast Region of Thailand. The location of the area is shown in figure 3.1 which lies between 15 o to 15 o15’ N and 101 o45’ to 102 o E with geographic extent of around 74 000 hectares, of which a smaller watershed area (22 731ha) was selected as sampling unit for field data collection and assessment. The selection of this study area was informed by various reasons, which include firstly, the fact that Thailand is one of the countries generally affected by salinity; secondly, the reported severity of salinity occurrence in the Northeast Region of this country; thirdly, previous research studies that were conducted in the area that would provide ancillary data for the current study, and lastly, the collaboration of the Department of Land Development (LDD) of the Ministry of Agriculture in Thailand with the International Institute for Geo-information Science and Earth Observation (ITC) for capacity building, research and development projects.

A

Image: Landsat TM Band 1 A = Salt patches on the surface

Figure 3.1 Location of study area and Landsat image indicating saline areas [7]

24 3.1.2. Climate

The climate of the region is Tropical Savannah with an average annual rainfall of 1060mm, most of which occurs in May to October [7] causing moisture deficit of around six months a year. According to figure 3.2 of rainfall data (1971 -2000) from Nakhon Ratchasima meteorological station the highest rainfall is received during September (226.6 mm) while the lowest in December ( 3mm) [40]. The average annual evaporation is reported to be around 1817 mm with the highest monthly average of 183.4 mm in April and lowest of 125.6mm in October as revealed in figure 3.2. The high evaporation experienced during the major part of the year together with moisture deficit result in accumulation of salts in the upper parts of the soil profile due to capillary rise of groundwater and restricted leaching conditions, especially in the lowland areas. The average annual relative humidity is 72 per cent with a maximum of 87 % and minimum of 49% (figure 3.3). The average annual temperature is 29.2 oC with mean maximum and minimum values of 35.7 oC in April and 22.8 oC in December respectively (figure 3.2 & 3.3 and table 3.1).

Avg Monthly Rainfall & Evaporation ETo (mm/month)

250.00 180.00

160.00

200.00 140.00

120.00

150.00 100.00 Rainfall (mm) ETo (mm/month) m m m m Evaporation (mm) 80.00 100.00

60.00

40.00 50.00

20.00

0.00 0.00 Jan Feb Mar April May June July Aug Sept Oct Nov Dec Jan Feb Mar April May June July Aug Sept Oct Nov Dec Figure 3.2 Average monthly rainfall and evaporation (1971 – 2000)

Monthly Average RH Monthly Average Temperature

100.00 40.00

90.00 35.00 80.00

30.00 70.00

25.00 60.00

Tmax (oC) RHMax %

% 50.00 C 20.00 Tmin (oC) RH Min % o Tavg (oC) AvgRH % 40.00 15.00

30.00 10.00 20.00

5.00 10.00

0.00 0.00 Jan Feb Mar April May June July Aug Sept Oct Nov Dec Jan Feb Mar April May June July Aug Sept Oct Nov Dec Figure 3.3 Average monthly temperature and humidity (1971 – 2000)

25 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Table 3.1Climatological data for the period of 1971-2000 of Nakhon Ratchasima (Height of wind vane above ground 11.3 metre)

Month Rainfall Min T Max T RH Min RH Max Windspeed Dewpond Evapo Monthly (mm) oC oC (%) (%) (knots) (oC) (mm) Sunshine Hours Jan 5.9 30.8 17.7 85 40 1.4 15.9 137.3 241.10 Feb 18.1 33.5 20.4 83 38 1.5 17.7 143.9 227.60 Mar 36.1 35.8 22.7 82 37 1.6 19.4 183.2 234.90 April 66.3 36.5 24.4 84 42 1.7 21.6 183.4 249.80 May 137.2 35.1 24.7 88 50 1.9 23.0 174.8 194.70 Jun 111.8 34.3 24.7 88 52 2.3 22.9 163.4 168.60 Jul 115.3 33.8 24.3 88 53 2.4 22.7 164.3 184.60 Aug 146.2 33.2 24.1 90 56 2.3 22.8 151.0 111.10 Sept 226.6 32.2 23.7 93 61 1.4 23.3 125.8 138.90 Oct 141.2 30.9 22.8 93 60 1.8 22.0 125.6 187.60 Nov 27.0 29.7 20.5 89 53 2.1 19.1 128.6 184.10 Dec 3.0 29.1 17.5 86 44 2.0 15.9 135.9 214.90

Figure 3.4 Geology of Northeast Thailand ([40]

26 3.1.3. Physiographic Description

The geology of the Northeast region is explained by Soliman [17] as comprised of two groups, the Precambrian massif underlying the whole plateau and Mesozoic sedimentary rocks which is called the Korat group. The region is situated in Quaternary deposits in the low-lying areas, MahaSarakhan and Khok Kruat rock formations in rolling to undulating uplands [7]. Figure 3.4 gives an overview of the geological setting of area as composed of two folded basins of the Sakon Nakhon and Korat in the north and south respectively and separated by the Phu Phan Range in the middle. The common rock types include variety of sedimentary rocks like sandstone, siltstone, shale, claystone and conglomerate which are mainly from the Korat group. It is thus evident that main source of salinity in the area is associated with geological formation though it is mainly aggravated and spread by human activities.

Figure 3.5 Schematic cross section about the local geomorphology of northeast Thailand[17].

In terms of geomorphology, according to Yadav [40] the region can be divided into four units namely alluvial plain, plateau, mountainous and intra-mountainous areas. The studies by Pramojanee[41] and supported by Soliman[17] and Yadav[40] indicate that there is basically two main landscapes occurring in the area, namely the peneplain and the valley. Their development is attributed to two main formation processes, that is denudation and depositional processes. The resultant soil types are of sandy nature

27 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

(sand and sandy loam texture) in the upland ridges and alluvial clayey soils in the terraces and flood plains of the valley landscape (figure 3.5).

3.1.4. Soils and Salinity

According to LDD (2002) as cited by Soliman[17] the study area is comprised of five main soil orders in terms of USDA taxonomic system. Following a brief description of these soil orders by Soliman, these soils can be listed and explained as follows (figure 3.5)[17]:

3.1.4.1. Ultisols

These soils occur on the ridges and have resulted from high rainfall and temperatures under undisturbed favourable geomorphic conditions for soil formation. Due to dominance of the Ustic soil moisture region of these conditions these soils then fall under the suborder “Ustults”.

3.1.4.2. Alfisols

These soils are commonly found on the sloping areas adjacent to the ridges and are considered to be pedologically less developed than the previous type (Ultisols). They are grouped into two sub-orders of Ustic and Aquic soil moisture regions namely the Ustalfs and Aqualfs respectively.

3.1.4.3. Vertisols

The Vertisols are mainly occurring around rivers and channels in the northern part of the study area which result from the presence of swelling clay minerals in such places. They are generally grouped into two suborders based on soil moisture region namely Usterts and Aquerts.

3.1.4.4. Inceptisols

The Inceptisols are found mainly on the lowest part of the lateral valley in between the dissected ridges. Their development is attributed to the disturbance of soil profile development due to their geographic position in the landscape. The majority of these soils are classified into the suborder aquerts because of the poor drainage conditions of their locations.

3.1.4.5. Entisols

The occurrence of these soils is very limited and they are mainly along sloping areas. They are formed from residual materials of the sandstone. They fall under the suborder Psamments.

3.1.4.6. Soil Salinity

The fundamental cause of salinization in Northeast region of Thailand is ascribed to the climate and extensively underlying salt-bearing rocks which include shale, siltstone and sandstone [10] . The tropical monsoon climate causes fresh water accumulation in the soil profile during the wet season reaching and pressing the saline groundwater. At the end of the dry season there will be little fresh water in the profile and rivers carry salty water flowing from groundwater layers[11]. This salt is then washed out of the rivers during the next monsoon while the saline groundwater is pushed back to the soil profile due pressure differentials. This is however accelerated and widely spread by human

28

Figure 3.6 Soil (Series) map according to soil taxonomy 1999, produced by LDD[17]

Figure 3.7 Soil salinity map produced by Environmental Science Department, Thammasat University 2001 [17]

activities which are associated with poor agricultural practices, deforestation, salt making, and construction of roads and reservoirs. The major effect of these activities is increased groundwater recharge which then result in deep groundwater flows to dissolve and transport salts from uplands

29 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

towards lowland recharge areas[11]. Consequently rising groundwater, mobilised salts and evaporation cause salinisation which damages soil and water quality and affect the ecosystems.

The distribution of the salt in the study area follows the same trend as discussed above (figure 3.6), where salt affected soils are concentrated on the low laying areas (mainly lateral valley). The salt in these areas tends to accumulate as a result of water runoff from upland area that carries dissolved salts and be deposited into lowland areas. This process determines the spatial pattern and distribution of saline soils in the study area.

3.2. Materials The list of materials and data used in the study included geopedologic map, land use map, topographic map, aerial photo, and DTM, as well as some attribute data on groundwater, soils, climate and land use types. Table 2 below gives a summary of data requirements, their types, their sources and collection methods.

A number of software programs used include Erdas Imagine 9.1 for image processing and classification, ArcGIS 9.2 for spatial data management and map development, and R-Gui 2.5.1 (and Tinn-R) for geostatistical analysis and interpolation. A Garmin GPS was used during field data collection to locate and record coordinates of observation sites. SaltMod, modelling software developed at Institute for Land Reclamation and Irrigation (ILRI) in Wageningen, was used for salinity modelling. Other programs included Ms-excel for data organisation, Rcmdr and SPSS for non-spatial statistical analysis. Ilwis 3 was also used to some extent for stereo pair development and visualization and for exporting secondary data and maps to other spatial programs (GIS and ERDAS).

3.3. Research Methods In general the research method is comprised of the following main steps: a). Secondary and primary data collection through field investigation methods and previous research work undertaken in the study area (table 3.2). b). Use of the available geopedologic map (developed from previous studies) and topographic map of the area to devise stratified sampling and transect schemes for field data collection. c). Data processing and capturing which include image classification, development of attributes tables, use of anaglyph for stereo visualization (image and DTM) and interpretation to understand the physical landscape setting of the area. d). Development of input parameter file for running the SaltMod program to predict salinization and exporting of output files into spreadsheets and GIS formats for spatial analysis. e). The use of GIS and G-stat programs for spatial and statistical analysis of the model outputs and data related to: (1) salt concentration in relation to landscape; (2) distribution of salt and salinity degrees; (3) soil reaction (pH) and groundwater salinity. f). Finally these mentioned steps resulted in maps defining currently saline salt-affected areas and prediction of changes in salinity. g). More details of the processes followed in each step are discussed in the succeeding sections (also refer figure 3.9 to 3.11).

30

Figure 3.8 Methodological approach before fieldwork

Figure 3.9 Fieldwork methodological approach

31 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Figure 3.10 Methodological approach post fieldwork

Table 3.2 Data, material types used and their sources

Information / Type/Format Source and collection methods Data Geomorphology and Geopedologic map (1:50000 scale) ITC (Previous research studies), LDD pedology Topography and Topographic maps (1:50000) Digital ITC, Previous thesis and LDD terrain data terrain model Scanning, digitizing and GIS interpolation of existing contour maps Geology, soil and Maps, attributes tables (soil and water Publications, LDD, previous ITC_research work, hydrology properties) and documents field investigation and laboratory analysis Ground water quality use and management, irrigation and drainage Land cover and Maps, attributes tables and documents Previous research work, satellite image processing, utilization orthophotos, field observations and descriptions Climate Long term data of precipitation, Previous studies, meteorological stations evaporation, temperature, humidity, wind

32 3.3.1. Data Collection

This phase of the research methods included (1) the study of published documents and materials such as air photos, satellite images, maps (e.g. topographic, geopedologic, soils), (2) gathering of existing data related to climate, soil/groundwater salinity, and farming practices, (3) sampling design, and (4) field investigation and laboratory analysis.

3.3.1.1. Existing Data

This part of the study entailed collection and synthesis of available data from previous research projects by ITC and Department of Land Development (LDD) (refer table 3.2). The collected data was used to help recognize and understand the soil salinity patterns in relation to landforms, geomorphologic process and land cover and use systems. The understanding of these process and availability of geopedologic map helped to devise the designing of sampling methods taking into account the time, labour and financial constraints. The existing data of EC and land cover from observation points of previous studies was considered during this process for purposes of establishing representatively.

The scanned topographic map (1:50 000) as well as one collected from the LDD office were used as base maps for locating the observation points in the field. The existing land cover maps of 2004[17] and 2005[40] were also considered as basis for image classification. The counter map that was generated from topographic map by Soliman[17] was used for digital elevation modelling to understand the physical terrain of the study area. The geopedologic map developed in 2004 by Soliman[17] was used as basis for sampling design. According to this map two basic landscapes occur in the area, viz. peneplain and valley, with eight relief types and fourteen landform units and two lithology types ( table 3.3).

Table 3.3 Geopedologic legend[17]

LANDSCAPE RELIEF TYPE LITHOLOGY LANDFORM GP CODE

Peneplain Ridge Sedimentary rocks, Korat group Top complex Pe111 (Pe) Side complex Pe112 Slope-facet complex Pe113 Summit Pe114 Tread riser complex Pe115 Glacis Sedimentary rocks, Korat group Tread riser complex Pe211 Vale Sedimentary rocks, Korat group Slope complex Pe311 Lateral Vale Sedimentary rocks, Korat group Side complex Pe411 Bottom-Side complex Pe412 Bottom complex Pe413 Depression Sedimentary rocks, Korat group Basin Pe511

Valley Flood plain Alluvial deposits Levee–overflow complex Va111 (Va) Old Terraces Alluvial deposits Overflow–Basin complex Va211 New Terraces Alluvial deposits Overflow–Basin complex Va311

33 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

3.3.1.2. Image Processing

Satellite images (Aster) of two different dates were downloaded (http://glovi.usgs.gov) for the purpose of land use/cover classification and for generating a 3-D view. These images were 100% cloud free. The dates for these images are November 2006 and January 2007 representing the wet and dry seasons for the area respectively. The image scenes that cover the exact time (i.e. September) for fieldwork were not available and hence the two time image scenes were selected. These raw images were in the form of aster level-1A data product and thus were imported into ERDAS in one band at a time. The imported bands of the aster images included the visible and near infra-red (VNIR) band 1 to 4 (15 m resolution) and shortwave infra-red (SWIR) band 5 to 9 (30 m resolution) and were geo-coded. Then geometric correction using polynomial model followed by resample with nearest neighbour was performed [42]. The projection that was used in this process is the UTM projection, Adjusted Everest 1830 ellipsoid, and Indian 1975 datum of Zone 47 Northern hemisphere. The different bands were then stacked together starting with VNIR bands and later the SWIR bands so as to have a final image resolution of 15 m for all the bands[42]. The digitized vector road layer was used for geo-referencing the images for proper alignment. The road layer was digitized from a 1:50 000 topographical map which was used as a base map for the current study.

Digital image interpretation for land cover classification was done based on false color composite (RGB of 321 bands) and other band combinations for both unsupervised and supervised classification. Maximum likelihood classifier algorithm was applied and to counteract spectral confusion, image enhancement using 3x3 edge enhancement was applied. Finally seven classes (figure3.11) were determined and signature re-evaluation undertaken based of the 51 observation points collected during this present study as well as considering classified images from previous studies([40] and [17]). Accuracy assessment was performed by generating random points from the classified image and the 51 points were used as dereference points of which 68% accuracy was attained (see appendix 5 for the Erdas report)

3.3.1.3. Sampling design

The available geopedologic map and soil salinity map was used to understand the geomorphic characteristics and salinity distribution in the study area. This information was used to decide on the sampling (training) areas and/or transects to undertake. A smaller area (which is the same area used in previous studies) of around 28 051 ha in extent was selected as ample area for field data collection and assessment. The selection of this sample area was based on the concept of using the same area from previous studies and available geopedologic map and considering the existing observation points. In that effect a stratified random sampling based on the relief and landforms was used. The number of observation points was 51 of which were distributed proportionally to the extent of different landform units based on the available geopedologic map. The minimum distance between the observation points was set to be 1500m and the average density of points is thus estimated to be approximately five square kilometres per point. These points were generated in ArcGIS 9.2 using the Hawths’ analysis tools extension. These points were used for soil sampling at three different depths of 0-30, 30-60 and 60- 1000 cm to study the soil salinity concentration per landforms whereby an auger was used. From the same observation points the depth of water-table was also recorded as long as it was less that 3m in depth because the auger used was just about the same depth. The same points were used for land cover

34 and land use observation for the purposes of ground truth data collection and validation for digital image classification map.

Figure 3.11 Classified image for land cover mapping

35 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Furthermore, a total of 13 observation points were used for reconnaissance classification of soils using mini soil pits, as well as collection of undisturbed core soil samples for porosity determination for the different depths as explained earlier. The selection of these latter points was based on the transect kind of distribution so as to make sure that almost every landform is represented. In the latter case only few points were considered due to time and labour constraints and hence transect sampling method was used. The transect method was used to ensure representativity based on the landforms and the soils were assumed to be uniform within the landforms. Therefore the soil physical variables determined and measured from these observation points were assumed to be same within the landforms. Figure 3.12 indicate the location of the observation points overlaid over the geopedologic map as derived from stratified random sampling and transect method.

In essence this sampling scheme was designed to meet the following objectives:
Acquire reasonable data per geopedologic unit for EC and pH determination to compare their variability between the mapping units.
Determine soil EC per soil reservoirs as required by SaltMod for modelling and long term prediction of root-zone salinization.
Spatial modelling of predicted root-zone salinity using geostatistical interpolation methods (Kriging) for both horizontal and vertical directions.

Figure 3.12 Location of sample points (left = auger points and right = mini pits points ) in the study area

3.3.1.4. Field investigation The field investigation was carried out from the 4 th to the 27 th of September 2007. The main activities that were undertaken during fieldwork entailed the following steps:

Soil sampling for EC and pH measurements

A total of 153 soil samples were collected from 51 observation points. These observation points were generated through stratified random sampling based on mapping units using the Hawth’s tool extension

36 in ArcGIS 9.2 The samples were collected at three different soil depths of 0-30, 30 – 60 and 60 - 1000cm using a soil auger. Recording of other biophysical properties such as texture, soil colour, topographic position, drainage condition, ground water depth, dominant vegetation cover and land use type was also done.

Soil profile study (mini pits) and bulk density

Soil profile study was done on eleven mini pits which were distributed over the entire area to cover the maximum number of different mapping units. From each mini pit samples were also collected in three depths as above. The samples collected from these sites were for determining porosity and hence ring cores of 7.2 cm diameter and 4 cm height were used. The ring cores were capped tightly at both sides and wrapped with vinyl tape to prevent any losses of moisture.

Figure 3.13 Fieldwork picture while digging mini pits for soil classification and collecting soil core samples

Land cover points with GPS

At the same time the same points used for soil sampling were also used collecting samples for land cover and land use image classification of the study area. A Gamin GPS was used to determine the spatial location of the sample points. In this case additional points were taken to mark special feature that can be confused with salt spots on the image such as paved surfaces and salt pan evaporators.

37 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Soil analysis

The collected samples were taken to Khon Kaen regional laboratory for analyses of soil reaction (pH), and electrical conductivity (dS/m), and also for determining bulk density and soil porosity. The soil samples were air dried and later grinded with pestle and mortar and passed through a 2mm diameter sieve. The method used to measure pH is 1:1 soil water ratio while for electrical conductivity (EC) is 1:5 soil water ratio using pH meter (LT-lutron PH201) and conductivity meter (H1933000) respectively. The core samples collected for porosity determination were weighed together with rings after which were put into an oven at 105 oC over 24 hours to obtain a stable dry weight. Then the samples were removed from the oven and allowed to cool in a desiccator and weighed again. These measurements would allow determination of bulk density (Db) by using the volume of the ring core and dry mass of soil. Pycnometer method was used in this study to determine the particle density (Dp) of the soils. The total soil porosity was calculated by the following equation:

Ø = (1 – Db/Dp)[43]………….7,

where Ø is the total porosity of the soil (m 3/m), Db is the dry soil bulk density (g/cm) and Dp is the soil particle density (g/cm).

Figure 3.14 Soil samples being air dried in the barn and laboratory discussions for analysis methods

3.3.1.5. Use of Pedotransfer Methods

It is a known factor that not every data required will be available or can acquired through the above mentioned methods. So the pedotransfer functions (PDT’s) are other means available that enable derivation of data that may be required to run a model from the existing data. Considering the number of parameters (appendix 1) that are required in SaltMod it was not possible to get some of the parameters. However to run the model these parameters have be determined, hence the pedotransfer methods were applied in order to estimate these parameters based on the use of other readily available soil variables, in this case particle size distribution ( sand, silt and clay percentages).

38 The parameters determined through the use of PDT methods include effective (drainable) soil porosity ( ne), field capacity (FC), evapotranspiration (ET c), and bulk density. Basically two software programs were used for this purpose, which is SPAW (Soil-Plant-Air-Water), using the Soil Water Characteristics (SWC) function and CropWat systems. This SWC-function was used for the determination of the first two parameters while CropWat was used for the determination of evapotranspiration. Both these programs are available free on the internet and were just downloaded instantly for this purpose.

The SPAW computer program is designed for simulating hydrology of agricultural systems (farm fields and ponds) and watersheds developed at USDA[44], for the purpose of understanding and managing agricultural waters, plant production and nutrient utilization. The SWC function of the SPAW system estimates soil water tension, conductivity and water holding capability based on the soil texture, organic matter content, gravel content, soil salinity, and soil compaction. The CropWat system is also a computer program for computing reference crop evapotranspiration using the FAO (1992) Penman-Monteith methods for use in crop water requirements and irrigation [45]. Therefore the textural data of 102 samples from 34 observation points (Appendix 4A) was used to estimate the field capacity ( u) using SWC function of the SPAW program, while the long term climatic data (table 3.2) was used to estimate reference evapotranspiration from CropWat program for determining potential evapotranspiration. The estimation of potential evapotranspiration was done for the three main crops grown in the area and other land cover classes as obtained from image classification (figure3.11). The potential evapotranspiration was determined by using a generally known formula by FAO given as:

(ET c) = (k c) * (ET o)...... 8,

where ET c is potential evapotranspiration, k c is the crop factor and ET o is reference evapotranspiration.

From the estimated field capacity the effective porosity was determined using the widely known method given by the formula:

(ne) = ( nt) – ( u)………………………… 9,

where ne is the effective (drainable) porosity, nt is total porosity and u is the field water capacity.

In order to validate the estimated results from the SPAW program the laboratory measured bulk density from 39 samples was compare to the simulated bulk density from the program. The estimated and measured values did not show much differences (Appendix 3) with figure 3.10 below showing the correlation between the two bulk density values. The R 2 of the linear relationship is around 0.65 which is somehow reasonable high and hence the results of effective soil porosity derived from the SWC program were taken as reasonably acceptable for use in this research.

3.3.2. Data Entry and Processing

The data collected (both secondary and primary data) was organized and entered into spread sheets using Ms-excel. This would enable accessibility to the datasets when using spatial analysis programs such as ArcGIS and G-stat as well as any other statistical programs (e.g. SPSS). Another advantage of

39 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Ms-excel is that it allows exporting of data to other commonly used data formats like dbf, access and CSV, which are some of the formats that are normally used by most analysis programs.

The GIS (ArcGIS 9.1) and R (G-stat & Rcmdr) softwares were used for spatial analysis and interpolation of the model outputs and multi-source data, e.g. (1) salt concentration in relation to landscape; (2) distribution of salt and salinity degrees; and (3) soil reaction (pH) and electrical conductivity).

Bulk density:Measured vs Predicted

2.00

1.90

y = 0.5513x + 0.7229 2 1.80 R = 0.6497

1.70 Actual(g/m3) 1.60

1.50

1.40 1.40 1.50 1.60 1.70 1.80 1.90 2.00 Predicted (g/cm3)

Figure 3.15 Correlation between simulated and measured soil bulk density

3.4. Model Assumptions/Simplifications and Calibration

3.4.1. Assumptions

The following assumptions and simplifications were considered in the application of SaltMod for modelling salinity changes which obviously affect the output and interpretation of results to some degree. a). Seasonal Agronomic Aspects

For the purposes of modelling salinization processes in the study area agronomic practices were generalized for entire area based on the seasonal principle of the SaltMod model. Based on the climatic conditions, mainly rainfall, two seasons of six months duration each were assumed in the area, i.e. the

40 wet season (May to October) and the dry season (November to April). The climatic data (rainfall, evaporation) are considered uniform (no spatial variation) for the entire area. From the classified image and secondary data from previous studies three major land use types (forest plantation, cultivated and swampy/grass or open land) were distinguished in the area. For the cultivated lands three main crops were grown in the area, viz. rice, cassava and maize. This enabled the assumption of three kinds of agricultural practices as required by the model, namely A: Wetland crops (Paddy rice) B: Dryland crops (Cassava and maize) C: Uncultivated/fallow lands (swampy/grass and plantation)

Though the model considers heavy, light and un-irrigated (rainfed) cropping practices, this assumption was appropriate for differentiating between the three types of land uses since there is no irrigation in this area. b). System or Model Aspects

The model requires the thickness for each of the latter three soil reservoirs (root-zone, transition and aquifer) and these are assumed to be same throughout the study area. The root-zone thickness is based on the rooting depth of the maize crop since it has the deepest roots, while the latter two reservoir thicknesses were estimated and assumed logically. The other important aspect of the model is the requirement of water table depth as one input parameter. The depth to water table was noted during field observation for every point where it was reached within a depth less than 3m. From the observation points the water-table depth differed from one point to another, but for purposes of model simulation Thiessen polygons were created for every observation point. Thus within in the polygons depth to water-table was assumed to be uniform. The created polygons also allowed calculation of proportional area occupied by each crop or land use type within the polygon. The area calculation was based on the classified image produced from land use classes or crop types as noted for each observation point during field assessment. c). Soil Variables/Properties

Assumption of homogeneity was made to particle size distribution over the landform units of the study area. Since the observation points for collecting samples for texture analysis were limited to almost one per landform unit then homogeneity assumption was compelling. The same assumption applied to total porosity, effective porosity and bulk density as all these variables were derived from the same samples and depend on the particle size distribution. In essence the sampling points for texture analysis included both the current points and previous research points since the change of texture over this time difference (3 years) was assumed stable. Therefore the premise of one observation point per landform as explained prior was kind of avoided. Instead averaging of percentages of particle sizes for each soil depth was applied to assume textural uniformity within each landform unit.

3.4.2. Model Calibration

Some of the factors could not be measured, notably leaching efficiency of the root-zone (Flr) and transition zone (Flx) and the natural drainage (Gn) of the groundwater through the aquifer. However

41 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT before application of SaltMod these factors should be determined. This can be done by running trials with SaltMod using different values of Flr, Flx, and Gn, and choosing those values that produce soil salinities and depths to groundwater table that correspond with the actually measured values [27, 31, 32, 46, 47]. a). Determination of leaching efficiency

Leaching efficiency of the root (Flr) or transition zones (Flx) is defined as the ratio of the salt concentration of the water percolating from the root or transition zone to the average concentration of the soil water at saturation[26]. A range of arbitrary values of leaching efficiency for root and transition zones of 0.1, 0.2, 0.4, 0.6, 0.8 and 1.0 were given to run the model. The outputs of root-zone salinity levels from these values were obtained compared with measured values. The leaching efficiency value that best matches the measured salinity was selected for use in model simulation. The leaching efficiency of the transition was calculated the same way. This was done for each of the observation points in the study area and the data are given in appendix 9 and Figure 3.16 (a) gives an example of whereby a leaching efficiency of 0.2 was selected to run the model for observation point 21. b). Determining natural subsurface drainage of the aquifer

In SaltMod, natural subsurface drainage (Gn = Go – Gi) is defined as excess horizontally outgoing groundwater (Go, m 3/season per m 2 total area) over the horizontally incoming groundwater (Gi, m3/season per m 2 total area) in the season[26]. These values were determined by setting the natural incoming drainage (Gi) values to zero and arbitrary changing the values of outgoing groundwater (Go). The range of values used for Go were given in pairs for the first and the second season as 0.0, 0.08, 0.12, 0.16, 0.24, and 0.32 after which the corresponding depths to groundwater closest to the measured depth were selected. As the inflow Gi values were taken equal to zero, the Go values of both seasons together give Gn values [26]. This was done for each of the observation points in the study area and the data are given in appendix 10 while figure 3.16(b) gives an example (observation point 39) of graphs used for comparison. In this case a Gn value of 0.24m/year gives the closet depth to the observed groundwater depth and therefore a Go value 0.12m/season was used in the model simulation for each season.

a) Leaching Efficiency (Lr) Calibration b) Natural Drainage (Gn) Calibration

0.20 1.60 0.18 1.40 0.16 Obs Obs 1.20 0.14 0.10 0.00 1.00 0.12 0.20 0.08 0.10 0.40 0.80 0.16

0.08 0.60 (m) GWD 0.24 EC (dS/m) EC 0.60 0.06 0.80 0.32 0.40 0.04 1.00 0.40 0.20 0.02 0.00 0.00 Point 21 Point 39

Figure 3.16 Comparing of Calibrated Lr and Gn to observed soil salinity and groundwater table values

42 3.5. Exploratory Data Analysis

This part focuses on the exploratory analysis of primary data collected for present study which includes electrical conductivity (EC), pH, porosity of the soil samples, as well as the water-table level. From the previous studies only description of EC values is considered. The latter data consists of 71 observation points while the current dataset consist of 51 points. Both these datasets are considered in descriptive statistical analysis because the former is used for model simulation while the latter is used for model validation. Though the variable of interest is soil salinity but is was considered important to give some numeric statistics of other variable as well, particularly for the current dataset. The statistics of the parameter values for the concerned variables is described for three soil depths of 0-30, 30-60 and 60-90 cm.

The descriptive analysis is based on the relation between the parameter and mapping units with the objective of understanding the influence of geopedologic units to the variation of soil salinity. The G- stat and R-cmdr packages of the R-program are the basic statistical tools use in this section . Figure 3.17(a) and (b) indicate the spatial distribution of the observation points in the study area for the present study and previous studies respectively. In both instances these points were generated randomly, but for the present study stratification based on the landform units was applied as explained in section 3.3.1.3. Table 3.4 and 3.5 give a summary statistics of each parameter in terms of minimum, maximum, mean, median and standard deviation. For better description of the distribution and variation of the parameters their histograms and box-plots are subsequently discussed.

3.5.1. Histograms

Based on the fact that better statistical results are obtained from normally distributed data and that the analysis in the R-environment assumes normal data distribution [36], observation of the pattern of data distribution is necessary. Furthermore, the normality (symmetrically distributed) of the data values is important because it is a standard requirement for both regression analysis and kriging [48]. Since, if the values of the parameters are skewed around the regression line, then the model can lead to over- or under-estimation results.

a) b)

Figure 3.17 Spatial distribution of observations points in the study area

43 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

The histograms discussed and displayed in this section are only for EC values while for the rest of the variable are presented in appendix 6 to 8. As it can be observed in figure 3.18 that the EC histograms (left side) for all the three soil sampling depths the are kind of highly skewed to the right (positive), which is not a suitable condition for geostatistical analysis. To solve the problem of skewness log transformation of data values was applied, thus reducing the skewness and bringing the data close to normal and symmetrical distribution. As can be seen from the histograms on the right hand side in figure 3.10 show a better symmetry after transformation.

Table 3.4 Summary statistics of parameters Variable Mean Median Min Max S2 S Skewness Kurtosis CV % EC (0 - 30) 2.44 0.32 0.06 22.98 30.85 5.56 2.66 6.03 228 EC (30 - 60) 2.67 0.19 0.06 23.30 30.72 5.54 2.60 6.08 207 EC (60 - 100) 2.22 0.45 0.06 16.83 14.52 3.81 2.28 4.73 172 pH (0 - 30) 6.47 6.61 5.20 7.79 0.45 0.67 -0.37 -0.86 10 pH (30 – 60) 6.43 6.25 4.80 9.59 0.88 0.93 0.60 1.02 14 pH (60 – 100) 6.28 6.58 4.60 9.79 1.21 1.10 0.44 0.29 18 Por (0- 30) 0.34 0.34 0.25 0.43 0.02 0.05 -0.02 -0.95 15 Por (30 – 60) 0.34 0.34 0.03 0.40 0.01 0.03 0.40 -0.72 9 Por (60 – 100) 0.34 0.35 0.30 0.40 0.01 0.03 -0.04 -1.16 9 Sand_30 68.76 75.95 13.61 88.90 331.3 18.20 -1.43 1.65 26 Clay_30 14.42 11.50 0.00 49.19 137.85 11.75 1.54 2.18 81 Sand_60 65.02 70.39 13.79 87.97 319.03 17.86 -1.42 1.79 27 Clay_60 17.21 14.31 2.09 48.18 138.35 11.76 1.15 1.19 68 Sand_90 60.55 66.85 7.00 87.73 452.35 21.27 -1.178 0.56 35 Clay_90 19.32 16.27 0.00 48.18 153.09 12.37 0.99 0.73 64 GW_EC 1.47 2.45 0.06 16.89 10.78 3.28 3.31 11.2 223 S2 = variance; S= standard deviation; CV = coefficient of variation

Table 3.5 Summary statistics of root-zone EC (30 -60cm depth) per landforms GPU Mean Median Min Max S CV% n Pe111 0.31 0.16 0.06 1.2 0.45 145 6 Pe112 3.09 0.38 0.13 9.79 4.03 130 7 Pe113 0.89 0.19 0.06 8.13 2.29 257 12 Pe114 0.17 0.13 0.13 0.26 0.08 47 3 Pe115 0.13 0.13 0.13 0.13 NA NA 1 Pe211 3.61 0.19 0.13 23.30 8.69 241 7 Pe311 1.36 0.19 0.13 6.08 2.64 194 5 Pe411 5.82 5.82 5.82 5.82 NA NA 1 Pe412 0.26 0.26 0.26 0.26 NA NA 1 Pe413 5.10 1.22 0.58 20.10 8.43 165 5 Va111 17.70 17.70 16.06 19.33 2.31 13 2 Va211 2.69 2.69 2.69 2.69 NA NA 1

44 3.5.2. Box plots

The box plots further give understanding of the distribution and variation of data which is given on the basis of relief mapping units. This also helps to identify outliers in the data and thus help in making decision on how to better improve the data for purpose of analysis purpose, may be by excluding the outliers. The visualization of the box plots for EC distribution and variation per relief units is given in figure 3.19 & 3.20 for the three soil depths. It could be noticed that there is a contrasting situation between the current and secondary EC values with the former only indicating high variation and wide range of distribution of values only in the lateral vale while the latter also include the floodplain. In terms of highest values both datasets include the flood plain and lateral vales. High number of outliers tends to occur in the ridges for both datasets.

Figure 3.18 Frequency distribution of EC and logEC values for three sampling depths

45 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

(a (b (c

Figure 3.19 Boxplots showing EC distribution over relief units (a – topsoil, b- root-zone, c-transition zone) for primary data

(a (b (c

Figure 3.20 Boxplots showing EC distribution over relief units (a – topsoil, b- root-zone, c-transition zone) for secondary data 46 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

3.6. Selection of Kriging Method In this study both measured and simulated EC values are in point form while salinity is a spatial continuous process, and thus estimation of un-sampled locations to define spatial variation of salinity in the area is necessary. However, selection or deciding on the right spatial prediction method for mapping spatial continuous process has always been a challenge to studies of this kind. In terms of theories as proposed by geostatisticians the Best Linear Unbiased Prediction (BLUP) model is always advocated and the kriging method associated with such capabilities is the regression or universal kriging. The advantage of this method is the consideration of both deterministic and stochastic components of spatial variation and its ability to model the two aspects simultaneously. That is, it can explain both regional trend variation and small scale spatial variation of spatial continuous processes, like soil salinity in the current situation. Therefore, based on the mentioned factors, this method was selected and applied for this exercise by following the procedure (figure 3.21) as recommended by Hengl, et al [48].

Since the method involves the use of sampled data and auxiliary predictors, derivation of the latter was the first step. The auxiliary predictors used in the study include relief zones (polygon map) from a geopedologic map, relief parameters derived from digitized 10m contour map (DEM, slope in degrees, mean curvature, profile and plan curvature), and land-cover/use map from supervised classification of aster image, with all the processing done in Ilwis and ArcGIS. The geopedologic map was rasterized into relief raster format map and resampled to a 50m resolution in ArcGIS. The contour map was interpolated to produce DEM in Ilwis and exported to ArcGIS to derive the rest of the elevation parameters. All maps produce in ArcGIS were exported to Ilwis were multicolinearity analysis was performed to assess correlation between the predictors (table 3.6) using the factor analysis method. This was done to conform to the typical assumption of multi-linear regression that predictors are independent variables[37].

Table 3.6 Correlation analysis results of continuous predictors

RApect RCurv RDEM RLC_map RPLCURV RPRCURV RSlopD RApect - 1.00 -0.01 0.04 -0.03 -0.00 0.00 0.22 RCurv -0.01 1.00 0.02 -0.00 0.86 -0.81 0.00 RDEM 0.04 0.02 1.00 -0.40 0.01 -0.03 0.43 RLC_map -0.03 -0.00 -0.40 1.00 -0.00 0.01 -0.25 RPLCURV -0.00 0.86 0.01 -0.00 1.00 -0.55 0.01 RPRCURV 0.00 -0.81 -0.03 0.01 -0.55 1.00 -0.03 RSlopD 0.22 0.00 0.43 -0.25 0.01 -0.03 1.00 RApect = aspect; RCurv = mean curvature; RDEM = elevation; PRLCURV = plan curvature; RPRCURV = profile curvature; RSlopD = sloped in degrees

Table 3.7 SPC coefficient and variance percentages per band

RApect RCurv RDEM RLC_map RPLCURV RPRCURV RSlopD PC 1 0.229 0.015 0.725 -0.010 0.017 -0.025 0.648 PC 2 -0.865 0.150 0.410 -0.004 0.133 -0.129 -0.164 PC 3 -0.205 -0.603 0.124 -0.002 -0.554 0.521 -0.018 PC 4 -0.396 -0.006 -0.539 0.003 0.006 -0.003 0.743 PC 5 0.000 0.035 0.007 -0.000 0.665 0.746 0.003 PC 6 -0.000 -0.782 0.001 -0.002 0.483 -0.393 -0.011 PC 7 -0.000 -0.001 0.011 1.000 0.001 -0.000 0.003 % 39.16 22.07 20.83 13.65 3.74 0.52 0.02

47 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Since some of the parameter showed high correlation, particularly the elevation parameters, the predictors were transformed to independent components to reduce multicolinearity by running principal component analysis in Ilwis. Before application of principal analysis these were linearly stretched to a range of 0 - 255 image domain to give each map an equal contrast. After which the resultant Soil Predictive Components (SPC’s) were imported into R-program for regression analysis and variogram determination for fitting spatial prediction model. The SPC coefficients and percentage variance of each SPC band are given in table 3.7 and it can be noted that the first three explain a total of around 80 % of variation in the data.

In addition to the predictors mentioned the coordinates of the observation points were also included in regression analysis and determination of the regional trend. Step wise regression analysis was applied to select only significant predictors and eliminate any insignificant ones. This was applied for both measured (table 3.8) and simulated (table 3.9) EC values in all the three sampling depths. The number of predictors was thus reduced to around three or less significant predictors in almost all the cases. Though the percentage of variation that was explained by the model considering all predictors was somewhat low, and only two or less of the predictors were statistically significant, the correlation was significant after stepwise selection.

In the case of observed values, the full model accounted for only 16.5% (Adjusted R 2: 0.1649) variability for the topsoil layer (0-30cm), 13.3% (Adjusted R 2: 0.1332) for the second layer (30-60cm) and 20.4% (Adjusted R 2: 0.2041) for the transition zone. While in the case of simulated values the model accounted for just 27.0% (Adjusted R 2: 0.2702) for root-zone salinity and 14.3% (Adjusted R 2: 0.143) for the transition zone during the tenth year prediction. For the prediction of the twentieth year the overall model accounted for 24.1% (Adjusted R 2: 0.241) and just 7.5% (Adjusted R 2: 0.07533) variability for the root and transition zones respectively. Figure 3.23 shows comparison of the experimental variograms of original data without trend removal (OK) and of residuals after removal of the trend (UK). From the graphs it’s clear that trend has accounted for significant amount of variability because of the vast difference between the sills of the two variograms. That is, the stationary variogram has a higher sill than non stationary variogram, of which the difference has been accounted for by linear regression with the predictors in the latter case. This is a noticeable behaviour in all the three soil depths and also with the simulated EC values though their trend differences obviously vary. It was noticed that beyond the range of influence the variograms tend to mix or cross over each other which is due to the erratic behaviour exhibited by the sample values.

48

Figure 3.21 Flow diagram depicting steps followed for regression-kriging in a GIS[48]

49 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Table 3.8 Summary results of regression for stepwise regression analysis for measured EC values

Variable Predictor Coefficient Std.error t value Pr(>|t|) 0-30cm Intercept -0.080717 0.183607 -0.440 0.66163 SPC1 -0.003508 0.001223 -2.868 0.00551 ** Adj R 2 = 0.2103 SPC3 0.002617 0.001657 1.579 0.11899 Relief 0.006505 0.002288 2.844 0.00591 ** 30-60cm Intercept -0.022906 0.133714 -0.171 0.864482 Adj R 2 = 0.1658 SPC1 -0.005156 0.001335 -3.862 0.000251 *** 60-90cm Intercept 0.163911 0.129293 1.268 0.209 Adj R 2 = 0.2342 SPC1 -0.006112 0.001291 -4.734 1.13e-05 ***

Table 3.9 Summary results of regression for stepwise regression analysis for simulated EC values

Variable Predictor Coefficient Std.error t value Pr(>|t|) 10th Year Prediction Root zone Intercept 0.663332 0.248979 2.664 0.00969 ** (0-60cm) SPC1 -0.007347 0.001431 -5.136 2.69e-06 *** Adj. R 2 = 0.287 SPC2 -0.003192 0.001727 -1.848 0.06910 . SPC3 0.003336 0.002045 1.631 0.10758 SPC7 0.078415 0.054845 1.430 0.15750 Transition zone (60-90cm) Intercept 0.3052272 0.1969719 1.550 0.126020 : Adj. R 2 = 0.1641 SPC1 -0.0013118 0.0008319 -1.577 0.000270 *** SPC2 0.0041311 0.0011695 3.532 0.000757 *** SPC6 0.0033670 0.0023053 1.461 0.148881 Relief 0.0047889 0.0025221 1.899 0.061969 . 20 th Year Prediction Root zone Intercept 0.826114 0.163146 5.064 3.36e-06 *** (0-60cm) SPC2 0.005284 0.001330 3.974 0.000175 *** Adj R 2 = 0.2619 SPC4 0.006347 0.002212 2.869 0.005503 ** SPC5 0.003165 0.001850 1.711 0.091714 . Transition zone (60- Intercept 0.136300 0.153508 0.888 0.37772 90cm): Adj. R 2 = 0.1278 SPC1 -0.004450 0.001499 -2.968 0.00414 ** SPC2 -0.002951 0.00179 -1.644 5 0.10487

Visual assessment of anisotropy was performed using a variogram map (figure 3.24) of which there was no apparent or distinct direction that could be noticed in the spatial variation of EC values. Therefore the variance structure of residuals was determined with an omni-directional experimental semi-variogram.

The selected authorized semi variogram model was automatically fitted using the G-stat package in the R-environment. The best fitting and selected variogram models for all the three soil layers was the Exponential for both measured and simulated EC values except for the twentieth simulated values where a Spherical type was fitted. These two types of models are the most commonly used variogram models in soil science. The variogram parameters and resulting variograms were plotted and are shown in tables and figures of the succeeding section. The determination of pixel size or grid spacing was also undertaken which was determined by considering the minimum distance between sample points, of which a grid cell size of 50m was used for the interpolation of raster maps produced.

50

Figure 3.22 Comparison of experimental variogram of original data (OK) and trend residuals (UK)

Variogram map, log10EC (dS/m), 0-30cm layer) Variogram map, log10EC (dS/m), 30-60cm layer) Variogram map, log10EC (dS/m), 60-100cm layer)

var1 var1 var1 15000 15000 2.0 15000 2.0 2.0

10000 10000 10000

1.5 1.5 1.5

5000 5000 5000

1.0 1.0 dy 0

1.0 dy 0 dy 0

-5000 -5000 -5000

0.5 0.5 0.5

-10000 -10000 -10000

0.0 0.0 0.0 -15000 -15000 -15000

-15000 -10000 -5000 0 5000 10000 15000 -15000 -10000 -5000 0 5000 10000 15000 -15000 -10000 -5000 0 5000 10000 15000 dx dx dx

Figure 3.23 Variogram maps for determining isotropy of the EC values for the three soil depths

3.7. Model Validation

In order to evaluate the predictive quality of SaltMod, the simulated salinity concentration (EC) values of the third year were compared to the measured values. The third year prediction values from the model simulations are chosen because they timely coincide with the currently measured values since the initial input data is considered to have been collected three years back. The calibration dataset consisted of 71 observation points while the validation dataset consisted of 51 points. Each of these datasets have measurements for the root-zone (0-60cm depth) and the transition zone (60-90cm) and thus validation is performed for both soil depths. Geostatistical approach using the R-program was used to carryout the validation. The R-program was preferred because the observation points of the two datasets were generated randomly and collected at different times, so spatial overlay of the dataset points would be required. Therefore geostatistical analysis in the R-environment would provide spatial overlay capabilities for the two dataset points so as to establish prediction at the exact location of the validation points.

The first step undertaken was to create Thiessen polygons for the simulated points in ArcGIS, which were then rasterized for both the root-zone and transition zone. This was done to maintain the original simulated

51 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT values and thus assume uniform or same value within each polygonal area. This interpolation design would allow overlay of validation points such that each point could fall within closest polygon for which comparison was applied. The rasterized polygon maps were then imported into the R-environment where validation analysis was performed by computing absolute and relative mean error (ME) and root mean square error (RMSE) between the simulated and measured EC values.

52 4. RESULTS AND DISCUSSION

The main objective is to determine how salinization would change over long term basis in the study area given the present land use practices continue. In order to achieve the objective SaltMod was used to model temporal changes of salinization over two decadal (20 years) periods. The root-zone ( ≤ 60cm depth) salinity, transition zone (60-100cm depth) salinity and the level of groundwater table were the main variables of interest predicted by SaltMod. However there are other parameters that are predicted by the model but are not of concern in the present study.

For statistical analysis the F and student t-test were the basis methods used to measure salinity variation and test for significance of differences. Geostatistics was used to describe the spatial variability of salinity measurements through the use of semi-variogram models, kriging, mapping and cross validation of estimated soil salinity changes. The prediction and error outputs maps from geostatistical analysis were exported into GIS for further spatial analysis (overlay, reclassification and raster calculations). Validation of the SaltMod results as well as sensitivity analysis of the model was also performed. In the forthcoming sections of this chapter the results of these various exercises are presented.

4.1. General Variation of observed EC values

Descriptive statistical analysis was applied to characterize the target variables (soil and groundwater salinity) by means of studying the mean, median, minimum, maximum, standard deviation of the parameter (electrical conductivity) values, and by using visual graphics such as histograms and box plots. This was undertaken with the aim of understanding the distribution, dispersion and variation of these parameter values. The mean and median were used as primary measures of central tendency while standard deviation and quartile ranges are estimates of variability. The summary statistics for these parameters is given in table 4.1 and visual graphics are shown from figure 3.10 and 3.11 in the preceding chapter.

In general the EC values show great variation in all the three soil depths and the same kind of variation is evident between and within the landform units. From table 3.4 of the summary statistics it observed that there is somewhat large difference between the mean and the median, the standard deviation and the variance are also high while the range between the minimum and the maximum values are wide too. The mean EC of the three soil depths ranges from 2.2 to 2.7 dS/m while the median ranges from 0.19 to 0.45 dS/m. The minimum EC value is 0.06 dS/m in all the three depths and the maximum value ranges from 16 to 23 dS/m (table 4.1). Therefore, by considering these statistical measures it can be concluded the data is highly variable. This is further manifested in the histograms (figures 3.18 and/or appendix 8 & 9) which also indicate the positively (or rightly) skewed data. This is an indication that the data is asymmetrical and unevenly distributed and thus high variation and erratic occurrence of salinity within in the whole study area. Due to the abnormal distribution and skewness of the EC values, the data need to be transformed before applying geostatistical analysis. Thus log transformation was applied to bring data close to normal distribution and reduce skewness to enhance better spatial prediction, analysis and interpolation results.

53 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Table 4.1 Summary statistics of EC parameters for three soil depths

Variable Mean Median Min Max S2 S Skewness Kurtosis EC (0 - 30) 2.44 0.32 0.06 22.98 30.85 5.56 2.66 6.03 EC (30 - 60) 2.67 0.19 0.06 23.30 30.72 5.54 2.60 6.08 EC (60 - 100) 2.22 0.45 0.06 16.83 14.52 2.81 2.28 4.73 GW_EC 3.10 2.45 0.26 10.3 10.78 2.36 3.31 11.2

4.2. Spatial Distribution of observed EC For the purpose of the study, geostatistical methods were applied to understand the nature and spatial distribution of soil salinity (EC) over the area of interest. In this section visual analysis is the main strategy with the use of scatter and bubble plots to indicate spatial trend and variation within the study area.

From figure 4.1 it can be noticed that the electrical conductivity content tends to increase from the south western side towards the north eastern side. This is the same trend in all the three soil depths, though the high values tend to be more sparsely and few while the major part of the area is dominated by lower values. In general EC has high variation and dispersion as is indicated by large range (very low and very high values) and high standard deviation and hence coefficient of variation is quite high (table 3.4 & 3.5, p-44). This applies to all the three layers though the transition zone is somewhat relatively better than the topsoil and root-zone layers. This indicates the presence of erratic values which also led occurrence of outliers in the data. These outliers are evident from the box plots (figure 3.19 & 3.20, p-46) of the distribution of EC based of relief type . However, exclusion of outlying values on statistical basis cannot be applied because salinity is influence by physical and environmental factors which vary in space. Thus occurrence of outliers is a common tendency in soil datasets as soil properties are influenced by factors such as climate, parent material, relative position of the landscape, vegetation, ground water table depth and human activities which may vary from point to point in landscapes. Therefore there is great possibility that certain areas may have much higher values than others resulting in this kind of spatial distribution of the target variable (EC). Considering both the bubble plots and box plots it can be seen that three relief forms show high EC variation, viz. lateral vale, glacis and floodplains. This is more pronounced on the two lower depths while the in the top layer is more in the floodplains only. The rest of the relief units show little variation and low EC values.

Figure 4.1 Bubble plot showing spatial trend of EC distribution in the three soil depths (30, 60 & 90cm depths)

54

4.3. Model Simulation and Prediction of Salinity

The model has been run for a period of twenty years at each location using the input parameters as given in appendix 1. The land use and agricultural practices are assumed to remain the same throughout the simulation period and the spatial extent of the study area. The prediction outputs of salinity in terms of EC are given for each season (2 seasons) of every year. The simulated variables include root-zone, transition zone and ground water salinities and also prediction of depth to groundwater table. The results are averaged on the basis of landform units for the third, tenth and twentieth year. The time scale interval considered is decadal but the third year has been included in the table for purposes of validation as it correspond to the time of the current field measured values. The output file starts at year zero which reflects the original input values as was into fed into the model, and this ca be regarded as the spin off period of the model. Output simulation data is presented in appendix 11-13 . Separate sections hereafter are devoted to discuss the results for each the simulated output variable of concern.

4.3.1. Soil Salinity in the Root zone

The root-zone refers to the first two upper soil depth (0-30 and 30 -60cm) of which the average values have been used for input as these layers were measured separately. The results of the predicted root-zone salinity (EC_dS/m) are given in table 4.2 with the trend showing an increase in salinity from the first year through to the twentieth year. However, some of the landform units show some kind of decrease in the third year (notably Pe111, Va211 & Va311) but finally increased for the tenth and twentieth year. In general the model projects an increase in soil salinity for all the land forms provided that the current land use practices are maintained. Graphical presentation of these results based on relief units is given in figure 4.2.

Table 4.2 Average predicted root-zone salinity (EC-dS/m)/landform

GP YEAR_0 YEAR_3 YEAR_10 YEAR_20 Pe111 3.50 3.09 9.24 17.55 Pe112 0.78 0.72 0.92 1.23 Pe113 4.77 7.65 12.37 19.73 Pe114 2.52 3.78 7.60 13.05 Pe115 2.3 2.85 6.77 15.73 Pe211 1.6 2.63 5.64 8.95 Pe311 1.61 2.73 5.43 12.20 Pe412 9.6 11.24 19.16 22.00 Pe413 2.12 2.84 5.74 9.85 Va111 2.96 4.22 6.33 9.90 Va211 3.98 4.28 5.43 6.37 Va311 3.50 3.87 10.64 20.56

55 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Mean Predicted EC per landfrom

25

20

15 YEAR_0 YEAR_10 10 YEAR_20 EC (dS/m) EC

5

0

5 11 311 e412 e413 a111 Pe111 Pe112 Pe113 Pe114 Pe11 Pe2 Pe P P V Va211 Va311

Figure 4.2 Average predicted root-zone salinity (EC-dS/m)/landform

4.3.2. Soil Salinity in the Transition zone

These results of the predicted salinity in the transition zone are given in table 4.3 and figure 4.3 which basically show a different trend from the root-zone salinity. The EC values predicted by the model for this zone tend to either slightly decrease or remain almost the same for the entire period. In general there is no significantly noticeable change in the salinity in this zone except for only one landform (Va311) where it has increased from an initial value of 4.5 dS/m at the beginning to around 7.5 dS/m at the end of the second season of the twentieth year. The non-changing or slightly decreasing situation in the transition layer can be attributed to mobilization of salts from this zone and aquifer through capillary rise effects to the root-zone. Consequently salts tend to be removed from here and accumulate in the root-zone hence increase in the latter zone but no noticeable changes in the zone below it.

Table 4.3 Average predicted salinity in the transition zone (EC-dS/m)/landform

GP YEAR_0 YEAR_3 YEAR_10 YEAR_20 Pe111 0.47 0.46 0.47 0.36 Pe112 0.83 0.82 0.78 0.69 Pe113 0.59 0.59 0.57 0.52 Pe114 0.09 0.05 0.04 0.04 Pe115 2.35 2.34 2.19 2.60 Pe211 2.52 2.36 2.18 2.08 Pe311 1.77 1.74 1.39 1.19 Pe412 0.30 0.31 0.30 0.28 Pe413 2.74 2.80 2.78 2.88 Pe511 0.65 0.63 0.75 1.01 Va111 2.64 3.65 3.72 4.12 Va211 3.53 3.52 3.24 2.64 Va311 4.50 4.48 4.80 7.53

56 Mean Predicted EC per landform

4

3.5

3

2.5 YEAR_0 2 YEAR_10 YEAR_20 EC (dS/m) 1.5

1

0.5

0

1 12 12 e113 e211 e413 a211 Pe111 Pe1 P Pe114 Pe115 P Pe311 Pe4 P Pe511 Va11 V Va311

Figure 4.3Average predicted salinity in the transition zone (EC-dS/m)/landform

4.3.3. Salinity in the Aquifer

These results for the predicted salt content changes over time in the aquifer zone are given in table 4.4 and figure 4.4. The behaviour is quite similar to the transition zone whereby there is no really serious change in the salt content, i.e. the salinity tends to remain almost the same throughout the simulated 20 year period. This observed stability of the soil water salinity concentration in the aquifer (Cqf) suggests a lack salt leaching from root and transition zones into the aquifer. Another factor that can be highlighted is the horizontally incoming groundwater that was not taken into consideration due to lack data. Thus only the horizontally outgoing water was considered which was estimated through the calibration process of the natural drainage. The suggested procedure for the calibration of the natural drainage (Gn) is to set the incoming groundwater (Gi) as zero[30, 31, 46, 49]. Then arbitrary changes for outgoing ground water (Go) are made to get the best possible the value that better predicts the observed water table. In this the total natural drainage (Gn = Go – Gi) is equal to the horizontally outgoing groundwater.

57 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Table 4.4 Average predicted salinity in the aquifer (dS/m)/landform

GP YEAR_0 YEAR_3 YEAR_10 YEAR_20 Pe111 0.23 0.23 0.21 0.20 Pe112 0.52 0..38 0.35 0.43 Pe113 1.42 1.19 1.11 1.18 Pe114 0.13 0.10 0.09 0.06 Pe115 2.73 0.08 0.08 2.31 Pe211 1.15 0.58 0.55 1.05 Pe311 2.04 1.96 1.83 1.70 Pe412 0.30 0.30 0.28 0.25 Pe413 1.02 0.37 0.36 0.96 Pe511 0.20 0.20 0.19 0.17 Va111 3.56 0.61 0.59 3.37 Va211 1.93 0.29 0.27 1.68 Va311 2.60 2.57 2.45 2.31

Mean Predicted EC per landform

4

3.5

3

2.5 YEAR_0 2 YEAR_10 YEAR_20 EC (dS/m) EC 1.5

1

0.5

0

4 11 11 113 413 e e3 e a3 Pe111 Pe112 P Pe11 Pe115 Pe211 P Pe412 P Pe511 Va111 Va211 V

Figure 4.4Average predicted salinity in the aquifer (dS/m)/landform

By way of comparing the tables of the three reservoirs (root-zone, transition zone and aquifer), it can be summarized that the salt tends to move upwards due to high temperatures, particularly during the dry season. This results in accumulation of salts into the root-zone and soil surface that cannot be subsequently removed or pushed downwards in the following (wet) season. This is evident in the graphs of these three reservoirs that, unlike the root-zone reservoir which shows a general increment in the EC concentration over the specified period, the latter two reservoirs tend to fluctuate with no significant increase or decrease in the levels of their EC concentrations.

58 4.3.4. Simulated Depth to water table

The model also predicts the seasonal changes of the water table depth. The simulated depths to water table in both seasons (wet and dry season) over the 20 year period are given in table 4.2. The model tends to maintain almost the same depths for each season throughout the simulation period. Obviously the seasonal fluctuation indicated lower depth during the wet (first) season whereby the water table rises close to the surface and deeper during the dry (second) season. However, drastic change of decrease in depth is noticeable just from year zero (entry depth) to the first year which is a common tendency of the model (figure 4.5), after which the same trend as explained is maintained. During the whole simulation period none of the predicted depths recedes to the same level or deeper depth than the initial entry depth.

Table 4.5 Average predicted water table depths (m)/landform

GP YEAR_0 YEAR_3 YEAR_10 YEAR_20 Season 1 1 2 1 2 1 2 Pe111 -4.18 -1.39 -1.83 -0.85 -1.38 -0.85 -1.38 Pe112 -2.15 -1.05 -1.33 -1.10 -1.38 -1.19 -1.48 Pe113 -3.46 -0.96 -1.45 -0.81 -1.28 -0.84 -1.26 Pe114 -3.19 -0.87 -1.64 -0.83 -1.22 -1.20 -1.22 Pe115 -2.52 -0.64 -1.08 -0.64 -1.07 -0.64 -1.07 Pe211 -2.89 -0.84 -2.11 -0.80 -2.08 -0.81 -1.68 Pe311 -2.61 -0.80 -1.26 -0.76 -1.20 -0.76 -1.20 Pe412 -3.01 -0.83 -1.23 -0.75 -1.18 -0.75 -1.18 Pe413 -2.30 -0.83 -1.40 -0.82 -1.40 -0.82 -1.49 Pe511 -3.03 -0.82 -1.21 -0.71 -1.16 -0.71 -1.16 Va111 -1.79 -0.81 -1.28 -0.77 -2.19 -0.73 -1.23 Va211 -2.50 -0.78 -1.31 -0.76 -1.54 -1.44 -1.69 Va311 -2.40 -0.66 -1.13 -0.65 -1.12 -0.65 -1.12

0 -0.2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 -0.4 -0.6 -0.8 S1 -1 S2 -1.2 -1.4 -1.6 -1.8 -2

Figure 4.5 Estimated water depth for point 36 (S1=season 1, S2 = season 2)

59 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

The rising groundwater table depth during the first season can be attributed to water percolation due to high torrential rainfall received during this season. While the lowering the water-table depth in the dry season is associated with less rainfall and high evaporation and evapotranspiration rates. The consequence of this kind of cycle between wet and dry season result in capillary rise and salt mobilization to the soil surface which harms crop growth, affect the ecosystems and damage water quality

4.4. Geostatistical Analysis and Mapping of Electrical Conductivity

This section is devoted to describe the spatial distribution and mapping of salinity in relation to the geomorphic regions (relief types and landform units). The basis was to quantify spatial relationship among sample values and make prediction at unvisited locations to mimic the real situation on the ground. This was accomplished by geostatistical analysis and applying universal kriging for the interpolation of both the measured EC values and the simulated values. The universal kriging method was chosen of its advantage to model both regional trend and local spatial dependence together. Therefore both the variation due to trend and random local dependence are thus taking care of[50] . The output of interpolation consisted of both prediction and error maps of three selected soil depths (0-30, 30-60 & 60-90 cm) for measured values and two reservoirs (root and transition zones) for SaltMod simulated values. The prediction maps show the spatial distribution of soil salinity while the error map indicates associated prediction error at each defined depth or reservoir . The prediction maps were subsequently reclassified in term of salinity severity based on EC concentration levels as defined by the USDA classification system.

4.4.1. Kriging and Mapping of measured EC values

In order to determine the variance structure of field salinity measurements omni-directional experimental semi-variance values were calculated and resulting variograms were plotted (figure 4.6). The parameters of variograms used are given in table 4.6. Since the data (EC values) was not normally distributed logarithmic transformation was applied. The fitted variogram models were selected by visual inspection and interactive technique that minimizes the mean square difference between point pairs. The selected variogram model is the Exponential type for all the three depths which was fitted by the G-stat function in the R-environment.

Although the models were relatively fitted, no clear spatial structure was evident from the sample variograms. In fact, the experimental variogram showed insignificant increase with distance and generally exhibited random fluctuation and some scattering for all the three sampled soil depths. The nugget effect values were relatively large, indicating high small-scale variations and may be some experimental error. The scattering and erratic behaviour can be attributed to statistically insufficient number of observation points (71 points) which were somehow sparsely distributed. This kind of behaviour and a large nugget value suggests that spatial variation of EC values occurs at distances shorter than the sampling interval. In addition, the undulating micro-topography of the area also has an effect because salinity distribution is influenced by physical environmental factors and by human activities. Indeed in the current case areas on the ridges show lesser salinity concentration than lower lying areas. This can be associated with shallow groundwater table in the lowland areas which is relatively deeper in higher lands. This is also evident from the typical contrasting cropping systems practiced in area, where paddy rice production is practiced in the lowland areas while maize and cassava are grown in relatively upper laying grounds.

60 The fitted variogram models were used for kriging point EC values to produce spatial prediction maps. The resultant maps of the prediction and variance (estimated error of mapping) were produced in R- environment and are displayed in figure 4.7, 4.8 and 4.9. These maps were afterwards exported to ArcGIS for further spatial analysis and reclassification of which the output maps are given in figure 4.11 in the next sub-section. The numerical summary statistics of kriging is given in table 4.7 and 4.8 for logarithmic and back transformed EC values respectively.

Table 4.6 Theoretical semi-variogram model and its parameters

EC (dS/m) Model C0 C1 C C0/C a 0-30cm Exp 0.12 0.18 0.30 0.40 501

30-60cm Exp 0.02 0.37 0.38 0.05 385

60- 90cm Exp 0.01 0.35 0.36 0.03 361

Exp : exponential variogram model; C 0: nugget variance (dS/m); C1 : partial sill (dS /m); C: total sill; (dS /m); a: range of influence in meters

Table 4.7 Numerical summary values for kriging prediction and variances (log10 EC-dS/m)

Layer 0-30 cm 30-60 cm 60-90 cm Statistics Pred Var Pred Var Pred Var Minimum -2.2141 0.1747 -2.0398 1.301 -2.1642 0.0043 1st Quartile -0.7208 0.2937 -0.7340 4.480 -0.6625 -03373 Median -0.5422 0.3019 -0.4680 6.262 -0.3567 0.3509 Mean -0.5538 0.3000 -0.4827 6.621 -0.3776 0.3372 3rd Quartile -0.3677 0.3072 -0.1983 8.201 -0.0381 0,3550 Maximum 1.7809 0.6020 1.0515 28.620 1.0639 0.4606 Pred = kriging prediction ; Var = kriging variance

Table 4.8 Summary statistics of back transformed logEC (dS/m) prediction values

Layer 0-30 cm 30-60 cm 60-90 cm Statistics Pred Pred Pred Minimum 1.092 1.301 1.148 1st Quartile 4.864 4.80 5.156 Median 5.815 6.26 7.00 Mean 6.037 6.21 7.475 3rd Quartile 6.924 8.201 9.626 Maximum 59.354 28.62 28.977

61 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Figure 4.6 Experimental and fitted variogram models for three soil depths

62

Figure 4.7 Prediction and variance maps of EC values for topsoil (0-30cm) layer

63 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

Figure 4.8 Prediction and variance maps of EC values for subsoil (30-60cm) layer

64

Figure 4.9 Prediction and variance maps of EC values for transition zone (60-100cm layer)

65 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

4.4.2. Spatial Distribution of Soil Salinity within the Geomorphic Units

In order to asses the distribution and variation of soil salinity in the study area, bivariate statistical analysis of electrical conductivity between and within geomorphic the regions was applied through the use of linear modeling and analysis of variance (ANOVA). The prediction maps produced by kriging the measured EC values were used for calculation and estimation of salinity affected areas based on the geomorphic regions (relief types and landform units), while variance outputs were used for assessing uncertainty of the prediction. The estimation of affected areas was accomplished by exporting the interpolated maps from the R-environment to ArcGIS. ArcGIS enabled further spatial analysis (overlay, map reclassification and raster calculations) and improve visualization.

Table 4.9 gives numerical statistics of measured EC (observation point’s data) distribution on average basis per relief and landform units while figure 4.10 give a graphical visualization of the same units for the three sampling depths. It is observed that the highest values occur in the flood plain, lateral vale and glacis relief forms which are comprised of levee-overflow complex (Va111), bottom-side complex (Pe411& Pe413) and tread riser complex (Pe211) as landforms respectively. These areas basically form the lowlands of the study area and thus shallow water table depth can be one reason attributed to the high salinity content in the soil profile in these land units. Generally, all the three soil depths follow the same trend though the highest values are exhibited in the subsoil layer (30-60cm) in the flood plain while in the lateral vale the topsoil layer (0-30cm) has highest values.

Tables 4.10 below give outputs of the linear modelling between soil EC values of the three selected depths and the relief units. It is observed that there is some kind of significant relationship between the relief units and salinity (EC) though this is somewhat lower as indicated by the adjusted R 2 values of below 30% (varies between 22 and 29%). Nonetheless it can be concluded that some variation of the soil salinity is influenced by the geomorphic regions. This is further substantiated by the analysis of variance (ANOVA) results of mean EC values between the relief units which indicated significant different between the relief types. Due to few observation points which would not make reasonable conclusion in smaller units (e.g. landforms) for mean variance, the analysis was limited to relief types. Table 4.9 Mean measured EC (dS/m) values per landform and relief (inserted table) units

Code Area (ha) 0-30 cm 30- 60cm 60-90cm Pe111 2441.50 0.93 0.31 1.07 Depth (cm) Pe112 3311.25 2.79 3.09 2.27 Relief unit Area (ha) 0-30 30-60 60-90 Pe113 5224.00 0.24 0.89 0.89 Floodplain 74450 15.01 17.70 12.10 Pe114 1217.25 0.34 0.17 0.22 Glacis 2106.75 3.23 3.61 2.98 Pe115 2076.25 0.32 0.13 1.70 Pe211 2103.00 5.70 5.67 3.83 Lateral Vale 3254.50 5.62 4.51 3.69 Pe311 329.75 0.63 1.36 0.65 Old terraces 119.00 0.19 2.69 3.07 Pe411 390.50 12.35 5.82 3.20 Ridge 556.75 1.01 1.20 1.24 Pe412 2524.25 0.06 0.26 0.51 Pe413 657.25 1.15 5.10 3.97 Vale 12994.50 0.63 1.36 0.65 Va111 507.50 15.01 17.70 12.10 Va211 117.50 0.19 2.69 3.07

66 Average EC per Landform units a) Average EC per Relief units b) 20.00 20.00 18.00 18.00 16.00 16.00 14.00 14.00 12.00 0-30cm 12.00 0-30cm 10.00 30-60cm 10.00 30-60cm 60-100cm EC(dS/m) 8.00 8.00 60-90cm EC(dS/m ) 6.00 6.00 4.00 4.00 2.00 2.00 0.00 0.00 3 4 1 1 1 1 1 1 15 1 1 1 1 1 1 1 2 3 1 2 e e e e e a a Floodplain Glacis Lateral Vale Old terrace Ridge Vale Pe111 Pe112 P P P P P Pe411 Pe412 Pe413 V V

Figure 4.10 EC distribution per landform units (a) and relief types (b)

Table 4.10 EC residuals of linear modelling and ANOVA for geomorphic (relief) regions

EC _0-30 cm layer EC _30-60 cm layer lm(formula = ECE ~ RELIEF, data = EC_P) lm(formula = ECE ~ RELIEF, data = EC_P) Residuals: Residuals: Min 1Q Median 3Q Max Min 1Q Median 3Q Max -5.563 -0.949 -0.749 -0.234 17.953 -4.2500 -1.1960 -1.0666 -0.8466 19.6886 Coefficients: Coefficients: Estimate Std. Error t value Pr(>|t|) Estimate Std. Error t value Pr(>|t|) Intercept 15.010 3.449 4.352 7.66e-05 *** Intercept 17.695 3.305 5.354 2.80e-06 *** Glacis] -11.783 3.911 -3.013 0.004235 ** Glacis -14.084 3.747 -3.758 0.000490 *** Lateral Vale -9.387 3.911 -2.400 0.020571 * Lateral Vale] -13.185 3.747 -3.519 0.001005 ** Old terraces -14.820 5.973 -2.481 0.016906 * Old terraces] -15.005 5.724 -2.621 0.011904 * Ridge -14.001 3.566 -3.927 0.000293 *** Ridge] -16.498 3.417 -4.829 1.62e-05 *** Vale -14.382 4.081 -3.524 0.000988 *** Vale] -16.339 3.910 -4.178 0.000133 *** ------Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.877 on 45 degrees of freedom Residual standard error: 4.674 on 45 degrees of freedom Multiple R-Squared: 0.3061, Adjusted R-squared: 0.229 Multiple R-Squared: 0.36, Adjusted R-squared: 0.2889 F-statistic: 3.969 on 5 and 45 DF, p-value: 0.004564 F-statistic: 5.063 on 5 and 45 DF, p-value: 0.0009142 EC_ 60- 90 cm layer ANOVA(Type II tests) lm(formula = ECE ~ RELIEF, data = EC_P) Response: EC_0-30cm Residuals: Sum Sq Df F value Pr(>F) Min 1Q Median 3Q Max RELIEF 553.00 5 5.0633 0.0009142 *** -3.1829 -1.1055 -1.0455 -0.3648 13.8500 Residuals 982.95 45 Coefficients: Estimate Std. Error t value Pr(>|t|) Response: EC_30-60cm (Intercept) 12.095 2.287 5.289 3.49e-06 *** Sum Sq Df F value Pr(>F) Glacis -9.115 2.593 -3.515 0.001015 ** RELIEF 553.00 5 5.0633 0.0009142 *** Lateral Vale -8.402 2.593 -3.240 0.002248 ** Residuals 982.95 45 Old terraces -9.025 3.961 -2.279 0.027491 * Ridge -10.859 2.364 -4.593 3.52e-05 *** Response: EC_60-90cm Vale -11.441 2.706 -4.228 0.000114 *** Sum Sq Df F value Pr(>F) --- RELIEF 255.35 5 4.883 0.001185 ** Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residuals 470.65 45 Residual standard error: 3.234 on 45 degrees of freedom ------Multiple R-Squared: 0.3517, Adjusted R-squared: 0.2797 Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 F-statistic: 4.883 on 5 and 45 DF, p-value: 0.001185

67 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT

The interpolation mean EC values per landform and relief units are given in table 4.11. The similar trend as depicted with the measured values is exhibited by the interpolated values whereby the floodplain, glacis and lateral vales have the highest values while the ridges the least values. However due to smoothing effect by kriging, the high mean values for interpolation are somewhat lower compared to the measured values while the opposite is true for the least mean values.

The subsequent tables (4.12 - 4.14) give percentages of affected areas for the three different sampling depths (0-30cm, 30-60cm and 60-90cm) based on the relief units. In order to calculate affected areas the salinity levels were compared to the crop salt tolerance thresholds as defined by the FAO (USDA) classification (table 2.1). According to these tables the greatest area affected in the second (30-60cm) and third (60-90cm) layers falls under high salinity levels for the floodplain, glacis, lateral vale and terraces while the ridge and vale have their greater part in the moderate salinity level. For the first layer(0-30cm), only the floodplain has its greatest area under high salinity level with the rest of the relief units having major part of their areas under moderate salinity level.

Figure 4.11 show the reclassified EC maps which were produced from interpolation of measured values using universal kriging. The maps clearly indicate how salinity is distributed over the area for the three sampling depths. From these maps it’s clear that the salinity distribution follows the same pattern as depicted by the sample points of the three soil depths, whereby the low salinity levels occurred along the south western part and progressively increases towards the north eastern side. This pattern indicates the effect of physiographic condition to salinity as the major part of the south western side is dominated by ridges and the north eastern side by the flood plains and lateral vales as well as terraces. This kind of pattern can also be associated with land use types as the latter side is dominated by paddy rice while south western side mainly cassava and maize are produced.

Table 4.11 Mean interpolated EC (dS/m) values per landform and relief (inserted table) units

Code Area (ha) 0-30 cm 30- 60cm 60-90cm Pe111 2464.00 5.22 5.63 6.25 Depth (cm) Pe112 3346.75 6.68 6.11 6.32 Relief unit Area (ha) 0-30 30-60 60-90 Pe113 5303.75 5.01 4.68 4.94 Pe114 1254.25 4.49 5.09 5.35 Floodplain 74450 8.99 11.91 15.14 Pe115 625.75 8.77 8.51 8.92 Glacis 2106.75 6.06 7.56 9.75 Pe211 2106.75 6.06 7.56 9.75 Lateral Vale 3254.50 7.54 8.71 10.50 Pe311 2120.25 5.10 4.85 4.84 New terraces 119.00 7.31 10.17 10.85 Pe411 333.75 8.26 9.25 11.02 Pe412 395.75 7.84 9.50 11.57 Old terraces 556.75 6.18 8.05 7.88 Pe413 2525.00 7.40 8.51 10.27 Ridge 12994.50 5.61 5.45 5.78 Va111 744.50 8.99 11.91 15.14 Vale 2120.25 5.10 4.85 4.84 Va211 556.75 6.18 8.05 7.88 Va311 119.00 7.31 10.17 10.85

68 Table 4.12 Area percentages per severity levels for 0-30cm layer

Percentage area per salinity level Relief Unit Total area Low salinity Moderate High Salinity Severe salinity ECe (dS/m) (ha) 0 - 4 4 - 8 8 - 16 > 16 Floodplain 74450 0.57 24.24 75.18 0.57 Glacis 2106.75 5.39 78.30 16.32 5.39 Lateral Vale 3254.50 0.61 60.79 38.61 0.61 New terraces 119.00 0.42 74.58 25.00 0.42 Old terraces 556.75 5.70 87.70 6.60 5.70 Ridge 12994.50 15.10 74.47 10.44 15.10 Vale 2120.25 13.05 84.94 2.00 13.05

Table 4.13 Area percentages per severity levels for 30-60cm layer

Percentage area per salinity level Relief Unit Total area Low salinity Moderate High Salinity Severe salinity ECe (dS/m) (ha) 0 - 4 4 - 8 8 - 16 > 16 Floodplain 74450 0.00 3.09 96.34 0.57 Glacis 2106.75 0.19 67.92 31.89 0.00 Lateral Vale 3254.50 0.05 40.86 59.08 0.01 New terraces 119.00 0.00 4.41 95.59 0.00 Old terraces 556.75 0.27 47.64 52.09 0.00 Ridge 12994.50 12.78 80.33 6.89 0.00 Vale 2120.25 15.61 82.44 1.95 0.00

Table 4.14 Area percentages per severity levels for 60-90cm layer

Percentage area per salinity level Relief Unit Total area Low salinity Moderate High Salinity Severe salinity ECe (dS/m) (ha) 0 - 4 4 - 8 8 - 16 > 16 Floodplain 74450 0.00 0.03 70.95 29.01 Glacis 2106.75 0.00 16.93 83.07 0.00 Lateral Vale 3254.50 0.00 7.86 92.14 0.00 New terraces 119.00 0.00 0.00 100.00 0.00 Old terraces 556.75 0.00 53.08 46.92 0.00 Ridge 12994.50 4.85 83.73 11.42 0.00 Vale 2120.25 15.42 82.54 2.04 0.00

The estimation of affected areas in the study as determined from the reclassified kriging maps is given in table 4.15. The percentage area of soil with low salinity level <4ds/m) 10.7%, moderately saline areas (4- 8dS/m) is 72.3% while those considered as highly saline and severely salinity is 17.0% and 0% respectively for the first depth (30-60cm). The second layer (30-60cm) has 8.9%, 70.3% and 20.8% areas with low, moderate and highly saline soils respectively. The third soil depth layer (60-90 cm) is comprised of 4.2% of low saline soils, 62.9% moderately saline soils and 31.9% highly saline soils and only 0.95% severe saline soils. Thus it can be concluded that the major area in the study area is covered by moderately saline soils and followed by high saline soils which the former soils dominates the south western side while

69 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT the latter the north eastern side. Low saline area is relatively small while severely saline areas is negligible relatively low for the third layer and almost zero for the latter two depths. However the presented results should be considered with consciousness of prediction uncertainty which affect estimation of salinity affected areas. Pertaining to that cross validation was performed of which the mean error and mean square root error are basic the statistical measures used and the procedure and results are discussed later in the proceeding sections.

Table 4.15 Percent area per severity levels over entire area of interest

Total area Percentage area per salinity level Zone (ha) Low salinity Moderate High Salinity Severe salinity ECe (dS/m) 0 - 4 4 - 8 8 - 16 > 16 0-30 cm 10.72 72.28 17.00 0.0 30-60 cm 22725 8.89 70.33 20.76 0.02 60-90 cm 4.24 62.93 31.87 0.95

70

Figure 4.11 Maps showing salinity (EC) distribution in the relief zones for the soil depth

71

4.5. Kriging and Mapping of Simulated EC values

The prediction maps are produce for two decade periods as the model was simulated for twenty year period. The experimental variograms were determined for each of the two zones of concern, i.e. the root- zone and the transition zone. The root-zone covers the two upper measured soil depths (i.e. 0-30 and 30- 60cm) and the transition zone is the region between the root-zone and the aquifer. However the measured EC values were sampled up to a depth of 100cm which is assumed to represent this zone. The average EC values were used for the root-zone since the two layers were measured separately.

The nature of the point pairs of the simulated outputs display a similar trend to the measured values because the model was run for each point of the measured values. Thus the same erratic behaviour is exhibited by the measured values is also displayed with the simulated values. The erratic nature of the point pairs can be attributed to the topography changes and land use changes. Two types of semi variogram models were used in kriging process, the Exponential and the Spherical, which were automatically fitted to the experimental variogram with the G-stat package in the R-environment. The parameters of the variogram are given in table 4.15 while the summary of numerical statistics of kriging values is given in table 4.16. The EC values in the latter table (t-4.16) are back transformed from logarithmic form to normal values. The fitted variograms for both root-zone and transition zone are shown in figure 4.12 and 4.14 for the tenth and twentieth periods respectively. The fitted variogram models were used for kriging (universal kriging) point EC values to produce spatial prediction maps. The resultant maps of the prediction and variance are displayed in figure 4.13 for the tenth year and 4.15 for the twentieth year. These maps were afterwards exported to ArcGIS for further spatial analysis and other mapping calculations.

Table 4.16 Experimental and fitted semi-variogram model parameters

Variable Mode Sim_EC_10 Model Sim_EC_20 l

C0 C1 C a C0/C C0 C1 C a C0/C Root-zone Exp 0.4 0.28 0.68 5953 0.59 Sph 0.42 0.28 0.70 2350 0.60 Transition-zone Exp 0.15 0.40 0.55 1294 0.27 Sph 0.19 0.54 0.73 2014 0.26

Sim : simulated EC for year 10 & 20; C0: nugget variance; C1: partial sill value in dS/m; C: total sill; a: range of influence in meters

Table 4.17 Summary statistics for kriging prediction and variance values for simulated EC

Time TENTH YEAR TWENTIETH YEAR Layer Root zone (EC_dS/m) Transition zone(EC_dS/m) Root zone(EC_dS/m) Transition zone(EC_dS/m) Statistics Pred Var Pred Var Pred Var Pred Var Minimum 1.60 0.25 1.896 0.338 1.190 0.4727 1.599 0.007 1st Quartile 6.68 0.28 5.647 0.357 7.819 0.4962 5.770 0.476 Median 9.22 0.28 7.182 0.363 11.724 0.5028 7.263 0.484 Mean 10.30 0.29 7.444 0.369 12.589 0.5111 7.438 0.475 3rd Quartile 13.28 0.29 8.933 0.374 17.067 0.5144 9.039 0.490 Maximum 43.51 0.46 14.354 0.784 56.717 0.8787 31.604 0.778

72 The spatial dependency of electrical conductivity as observed from the fitted variograms is generally not clear for all the cases. The nugget values were relatively smaller for the transition zone compared to the root-zone but in both cases these values are positive for both the tenth and the twentieth year predictions. The high nugget values indicate spatial variation at shorter distances than the sampling interval. In the case of lower nugget effect which would indicate otherwise, is nullified by the variogram type (Exponential) which also indicates high variation at short distances [51].

The nugget to sill ratio of less than 25% indicates strong spatial dependence and 25 to 75% indicate moderate spatial dependence, otherwise weak spatial dependence[51]. Therefore the root-zone has moderate spatial dependence because its ration is around 60% for both the tenth and twentieth prediction. While for the transition zone the spatial dependence is somewhat strong as the ration is around 26%. This means that variations among all locations for the transition zone are mainly due to spatial dependencies. However the variation due to regional trend (outside spatial dependence) is thus also evident, especially for the root-zone (refer figure 3.17), and thus cannot be ignored, hence universal kriging was applied.

Form the prediction maps (figures 4.13, 4.14, 4.16 & 4.17) it is noticeable that the spatial trend of salinity increases is from the south west to the north east direction. However, this directional trend (anisotropy) was not so prominent from the variogram map (figure 3.18) and hence was never considered during the kriging process . In terms of the geopedologic map, this is where the valley starts occurring and bottom complex landforms tend to be dominant. Though the major part of the landscape where the study area occurs is the Peneplain, the micro-topography tends to be slightly undulating with common occurrence of various kinds of landforms ranging from hill summits, bottom complex and levees. The trend exhibited by salinity variability in the study area confirms the fact that low land areas are mostly affected than upland areas which can be due, in the midst of other factors, to shallow groundwater table depth, water-logging and paddy rice practices.

The error maps indicate low to moderate variance values (also refer table 4.16) around the entire area for the tenth year prediction. This is the same trend with both the root-zone and transition zone. For the twentieth year prediction the variance tend to be moderate to high with the high values more pronounced along the outer edges of the mapped area for both zones. This indicates the poor and sparsely distribution of observation samples which can be improved by using these kinds of maps as guiding tools for optimising the sampling designs. As suggested by Hengl [37] that points should be spread around extreme edges of the feature space to maximise their spreading over the area of interest. This effect is evident on these variance maps (figure 4.16and 4.17) as the outer edges indicate high variances. As a result of the smoothing effect of kriging, predicted minimum values of salinity (EC) were higher than the observed values while the opposite was true for the maximum values.

73

Fitted Variogram (Exp), log10EC(dS/m), Transition-zone

0.6 129 121 86 162 159 194 182 192 180 0.5 23

192 51 84 86

0.4

20

0.3 semivariance

0.2

0.1

5000 10000 15000

distance Separation Distance (m)

Figure 4.12 Experimental and fitted variogram models for simulated EC of the tenth year

74

Figure 4.13 Root-zone kriging output maps of simulated EC values for the tenth year

75

Figure 4.14 Transition-zone kriging output maps for simulated EC values for the tenth year

76 Fitted Variogram (Sph),EC(dS/m) Root-zone Fitted Variogram (Sph),EC(dS/m) transition-zone

249 112 166 163 109 212 251 31 263 218 0.8 25 243 100 71 162 0.6 169 157 112 148 158 27 240 129 0.6 132 168 85 59 60

0.4 14 41

0.4 semivariance semivariance

0.2 0.2

5000 10000 15000 5000 10000 15000 distance distance Separation Distance (m) Separation Distance (m)

Figure 4.15 Experimental and fitted variogram models for simulated EC of the twentieth year

77

Figure 4.16 Root-zone kriging maps for simulated EC values of the twentieth year

78

Figure 4.17 Transition-zone kriging maps for simulated EC values of the twentieth year

79

4.5.1. Spatial Distribution of Simulated Salinity within the Geomorphic Units

The same procedure as explained for measured values (section 4.4.3) was followed to determine potentially affected areas as predicted SaltMod through interpolation of simulated EC values. Then the temporal increase and spatial expansion of potential affected area was estimated in terms of percentage of the total area concerned, within the geomorphic regions and the entire study area. a). Simulation for the first decade (10 th year)

Table 4.17 gives numerical statistics of simulated EC distribution on average basis per relief and landform units while figure 4.18 and 4.19 give a graphical visualization of the values for the root-zone and transition zone respectively. It is observed that the highest values occur in the flood plain, lateral vale, terraces and glacis relief forms which are comprised of levee-overflow complex (Va111), bottom-side complex (Pe411& Pe413) and tread riser complex (Pe211) as landforms respectively. These areas basically form the lowlands of the study area and thus high salinity content in the soil profile occurs in geomorphic land units. Generally, though the trend is the same for both zones, the highest values are exhibited in the root-zone. This can be ascribed to the simulation effect of the model (SaltMod) whereby a general increase overtime is depicted in the root-zone while some kind of fluctuation is exhibited in the transition zone.

Table 4.18 Simulated mean EC (dS/m) values per landform and relief (inserted table) units for the 10 th year

Code Area (ha) Root-zone Transition zone Pe111 2441.50 8.15 6.54 Root Transition Pe112 3311.25 10.54 7.71 Relief unit Area (ha) zone zone Pe113 5224.00 6.95 5.70 Floodplain 744.50 19.33 11.98 Pe114 1217.25 5.81 5.18 Glacis 2106.75 13.37 8.17 Pe115 2076.25 18.20 10.81 Pe211 2103.00 13.37 8.17 Lateral Vale 3254.50 15.60 9.34 Pe311 329.75 8.34 6.36 New terraces 119.00 17.61 10.78 Pe411 390.50 14.68 9.00 Old terraces 556.75 13.43 9.19 Pe412 2524.25 15.39 8.74 Pe413 657.25 15.75 9.47 Ridge 12994.50 8.54 6.57 Va111 507.50 19.33 11.98 Vale 2120.25 8.34 6.36 Va211 117.50 13.43 9.19 Va311 119.00 17.61 10.78

The subsequent tables (4.18- 4.20) give percentages of affected areas for the defined zones (0-60cm & 60- 100cm) based on the relief units as well as over the entire area. According to table 4.18 (root-zone), the floodplain and new terraces have their greater area (75% & 66 % respectively) under severe salinity level in 10 years time as predicted by the model. The model predicts that about 64%, 60% and 52% area of the Glacis, Old terrace and lateral vale would be highly saline in ten years period. The model also suggest that around 45% of the ridge and vale relief units would be moderately saline and just around 40% area of the same units would highly saline. The prediction for the latter units seems to be too high as these areas occur in upper laying lands and not that much of salt accumulation is expected. Moreover, the uplands are generally

80 Mean Rootzone EC within Relief zones

25.00

20.00 19.33 17.61 15.60 15.00 13.37 13.43 EC (dS/m) EC 10.00 8.54 8.34

5.00

0.00 Floodplain Glacis Lateral New Old Ridge Vale Vale terraces terraces

Figure 4.18 Average predicted EC values per relief types for the root-zone

Mean Transition zone EC within Relief zones

14.00

11.98 12.00 10.78

10.00 9.34 9.19 8.17 8.00 6.57 6.36

EC(dS/m) 6.00

4.00

2.00

0.00 Floodplain Glacis Lateral Vale New Old Ridge Vale terraces terraces

Figure 4.19 Average predicted EC values per relief types for the transition zone

81

dominated by sandy textured soils and thus high infiltration and leaching would remove salts from the root- zone. This poor prediction can be attributed to the kriging smoothing effect which result in higher values than the observed for minimum values.

4.19 Percent area per severity levels for root zone

Total area Percentage area per salinity level Relief Unit (ha) Low salinity Moderate High Salinity Severe salinity ECe (dS/m) 0 - 4 4 - 8 8 - 16 > 16 Floodplain 74450 0.10 0.67 24.71 74.51 Glacis 2106.75 1.27 9.61 63.96 25.16 Lateral Vale 3254.50 0.14 3.56 52.36 43.94 New terraces 119.00 0.00 1.26 32.56 66.18 Old terraces 556.75 1.21 10.91 60.44 27.44 Ridge 12994.50 10.24 45.47 37.04 7.26 Vale 2120.25 7.49 45.71 43.47 3.33

4.20 Percent area per severity levels for transition zone

Total area Percentage (%) area per salinity level Relief Unit (ha) Low salinity Moderate High Salinity Severe salinity ECe (dS/m) 0 - 4 4 - 8 8 - 16 > 16 Floodplain 74450 0.00 1.71 98.29 0.00 Glacis 2106.75 1.34 53.30 45.35 0.00 Lateral Vale 3254.50 0.11 27.39 72.50 0.00 New terraces 119.00 0.00 4.41 95.59 0.00 Old terraces 556.75 0.31 22.50 77.19 0.00 Ridge 12994.50 6.13 71.86 22.02 0.00 Vale 2120.25 5.86 84.46 9.68 0.00

A different situation is predicted in the transition zone (table 4.19) where no area would be under severe salinity but the majority of the relief units would be under high salinity levels, except for the ridge, vale and glacis which would be moderately saline after ten years time. However the glacis would be almost close to 50/50 basis as around 45% of its area would be highly saline and just above 50% would be moderately saline. Quite very small areas are predicted to be under low salinity levels for both zones. Table 4.20 indicate salinity severity levels in terms of the entire area, where the root-zone would have 43%, 32% and 17% of the land highly, moderate and severely saline respectively. For the transition zone the major areas are under moderate (60%) and high (36%) saline conditions with only 4% under low salinity and none under severe salinity (also refer figure 4.20 and 4.21). The prediction maps for both zones are given in figure 4.22.

Table 4.21 Percent area per severity levels over entire area of interest

Total area Percentage area per salinity level Zone (ha) Low salinity Moderate High Salinity Severe salinity ECe (dS/m) 0 - 4 4 - 8 8 - 16 > 16 Root-zone 6.74 32.36 43.37 17.53 22725 Transition zone 4.21 59.58 36.21 0.00

82

Area (%) per salinity levels (Root-zone)

18% 7%

Low 32% Moderate

High

43% Severe

Figure 4.20 Percent area affected for root-zone prediction

Area (%) per salinity levels (Transition zone)

0% 4%

36% Low Moderate High Severe 60%

Figure 4.21 Percent area affected for transition zone prediction

83

Figure 4.22 Reclassified maps for root-zone and transition zone for the tenth year prediction

84 b). Simulation for the second decade

The interpolated mean EC values per landform and relief units are given in table 4.21. A similar trend as depicted in the measured values and the tenth year prediction is exhibited, where the highest values occur in the floodplain and new terraces and the lowest in the ridges in both zones (figure 4.23 & 4.24). The only difference is that the values have now increased particularly for the root-zone, but not much for the transition zone. Instead some relief units have shown an insignificant decrease in the transition zone as compared to the previous decade. Table 4.22 and 4.23 give percentage area affected in salinity levels per relief units for the root-zone and transition zone respectively.

The estimated areas that would be affected in the twentieth year in totality of the area are given in table 4.24 with graphical presentation (pie charts) in figure 4.25 and 4.26. Figure 4.27 show the reclassified EC maps which were produced from interpolation of simulated values using universal kriging. The maps clearly indicate how potentially affected areas are distributed over the study area. From these maps it’s clear that the salinity distribution follows the same pattern as with the previous cases, with low salinity occurring along the south western part and progressively increases in the opposite direction resulting in highly saline areas occurring in the north eastern side. This pattern indicates the effect of physiographic condition to salinity as the major part of the south western side is dominated by ridges and the north eastern side by the flood plains and lateral vales as well as terraces. This kind of pattern can also be associated with land use types as the latter side is dominated by paddy rice while south western side mainly cassava and maize are produced.

Table 4.22 Simulated mean EC (dS/m) values per landform and relief (inserted table) units for the 20 th year

Code Area (ha) Root-zone Transition zone Pe111 2441.50 10.22 6.86 Root Transition Pe112 3311.25 12.23 7.28 Relief unit Area (ha) zone zone Pe113 5224.00 8.18 5.73 Floodplain 744.50 20.60 10.17 Pe114 1217.25 7.34 6.23 Glacis 2106.75 16.03 8.42 Pe115 2076.25 20.41 9.98 Pe211 2103.00 16.03 8.42 Lateral Vale 3254.50 18.37 9.39 Pe311 329.75 10.10 6.59 New terraces 119.00 19.32 11.51 Pe411 390.50 18.91 9.80 Old terraces 556.75 14.79 7.72 Pe412 2524.25 18.99 9.62 Pe413 657.25 18.20 9.29 Ridge 12994.50 10.12 6.60 Va111 507.50 20.60 10.17 Vale 2120.25 10.10 6.59 Va211 117.50 14.79 7.72 Va311 119.00 19.32 11.51

85

Mean Rootzone EC within Relief zones

25.00

20.60 19.32 20.00 18.37 16.03 14.79 15.00

10.12 10.10 EC(dS/m) 10.00

5.00

0.00 Floodplain Glacis Lateral Vale New Old Ridge Vale terraces terraces

Figure 4.23 Average predicted EC values per relief types for the root-zone

Mean transition zone EC within Relief zones

14.00

12.00 11.51 10.17 10.00 9.39 8.42 7.72 8.00 6.60 6.59

EC(dS/m) 6.00

4.00

2.00

0.00 Floodplain Glacis Lateral Vale New Old Ridge Vale terraces terraces

Figure 4.24 Average predicted EC values per relief types for the root-zone

86 Table 4.23 Area percentages per severity levels for root-zone

Percentage area per salinity level Relief Unit Total area Low salinity Moderate High Salinity Severe salinity ECe (dS/m) (ha) 0 - 4 4 - 8 8 - 16 > 16 Floodplain 74450 0.00 0.07 9.94 89.99 Glacis 2106.75 1.01 3.86 47.64 47.49 Lateral Vale 3254.50 0.00 0.32 28.02 71.66 New terraces 119.00 0.00 0.00 17.23 82.77 Old terraces 556.75 0.31 5.88 56.58 37.22 Ridge 12994.50 4.85 34.85 45.35 14.95 Vale 2120.25 4.00 28.40 60.70 6.90

Table 4.24 Area percentages per severity levels for transition zone

Percentage area per salinity level Relief Unit Total area Low salinity Moderate High Salinity Severe salinity ECe (dS/m) (ha) 0 - 4 4 - 8 8 - 16 > 16 Floodplain 74450 0.00 8.19 88.68 3.12 Glacis 2106.75 1.66 41.51 56.62 0.21 Lateral Vale 3254.50 0.02 11.45 88.04 0.49 New terraces 119.00 0.00 3.78 87.61 8.61 Old terraces 556.75 0.85 62.24 36.24 0.67 Ridge 12994.50 6.04 72.56 21.35 0.05 Vale 2120.25 5.99 75.62 17.98 0.41

Table 4.25 Percent area per severity levels over entire area of interest

Total area Percentage area per salinity level (ha) Low salinity Moderate High Salinity Severe salinity ECe (dS/m) 0 - 4 4 - 8 8 - 16 > 16 Root-zone 3.25 23.25 43.47 30.03 22725 Transition zone 4.19 56.56 38.88 0.37

The percentage area of soil with low salinity (<4ds/m) in the root-zone (0-60 cm) is 3.25, moderately saline areas (4-8dS/m) is 23.4% while those considered as highly saline and severely salinity is 43.5% and 30.0% respectively. The transition zone (60-100cm) has been predicted to have 4.2%, 56.6% and 38.9% of low, moderate and high saline soils respectively. The percentage area predicted for severe saline soils is quite small for the transition zone, just about 0.4%. Therefore it can be concluded that the major area in the study area is anticipated to have high and severely saline soils should the same practices and conditions persist for a period of twenty years. The high saline soils tend to dominate the south western half while the severe soils the north eastern half of the investigated area. However, the reliability of these predictions depends on the validity of the model and accuracy of geostatistical maps. Hence the subsequent sections concern uncertainty assessment of the prediction results, in terms of validation and cross validation of the model (SaltMod) and predicted maps respectively.

87

Area (%) per salinity levels (Root-zone)

3% 30% 23% Low Moderate High Severe

44%

Figure 4.25 Percent area affected for root-zone prediction

Area (%) per salinity levels (transistion-zone)

0% 4%

39% Low Moderate

High 57% Severe

Figure 4.26 Percent area affected for root-zone prediction

88

Figure 4.27 Reclassified maps for root-zone and transition zone for the twenties year prediction

89

4.5.2. The Nature and Magnitude of Change

Salinization is a slow and continuous process and thus requires monitoring to prevent it from reaching levels that impair plant growth and damage the soil environment. Consequently in the current study simulation of the salinization over a twenty year period was performed and maps predicting future salinity development were produced. The produced maps were used to determine the spatial and temporal changes of salinity over the simulation period. In order to realize that the field measured EC values were compared to the simulated EC values in terms of extent of area changed and the results are given in table 4.26 to 4.28.

Table 4.26 Predicted area changes of various soil salinity classes over ten year period

Soil Area in hectares ( total area = 22 725 ha ) salinity Root-zone Transition zone class Current Tenth year Changes Percent Current Tenth year Changes Percent (dS/m) (+/-) (%) (ha) (%) 0-4 2020.25 1532.25 -488.00 2.15 964.00 956.50 -7.50 0.03 4-8 15983.25 7354.00 -8629.25 37.97 14301.75 1359.25 -762.50 3.36 8-16 4717.00 9855.50 +5138.50 22.61 7243.25 8229.25 +986.00 4.34 >16 4.50 3983.25 +3978.75 17.51 216.00 0.00 -216.00 0.95 +: indicate increase; -: indicate decrease

Table 4.27 Predicted area changes of various soil salinity classes from tenth to twentieth year

Soil Area in hectares ( total area = 22 725 ha ) salinity Root-zone Transition zone class Tenth year Twentieth Changes Percent Tenth year Twentieth Changes Percent (dS/m) year (ha) (%) year (ha) (%) 0-4 1532.25 738.50 -793.75 3.49 956.50 953.00 -3.50 0.02 4-8 7354.00 5283.00 -2071.00 9.11 13539.25 12852.50 -686.75 3.02 8-16 98.55.50 9878.25 +22.75 0.10 8229.25 8836.50 +607.25 2.67 >16 3983.25 6825.25 +2842.00 12.51 0.00 83.00 +83.00 0.37

Table 4.28 Predicted area changes of various soil salinity classes over twenty year period

Soil Area in hectares ( total area = 22 725 ha ) salinity Root-zone Transition zone class Current Twentieth Changes Percent Current Twentieth Changes Percent (dS/m) year (ha) (%) year (ha) (%) 0-4 2020.25 738.50 -1281.75 5.64 964.00 953.00 -11.00 0.05 4-8 15983.25 5283.00 -10700.25 47.09 14301.75 12852.50 -1449.25 6.38 8-16 4717.00 9878.25 +5161.25 22.71 7243.25 8836.50 +1593.25 7.01 >16 4.50 68.20 +6820.75 30.01 216.00 83.00 -133.00 0.59

As reflected in table 4.28 that after 20 years about 6% and 47% area of low and moderately saline soils respectively decreased while 23% and 30% of highly and severely saline soils increased in the root-zone. In the transition zone the situation is slightly better with 0.1% and 6.4% of low and moderate saline area decreased and only and increment of 7% high saline area increased, while 0.6% of severe saline area has decreased.

90 4.5.3. Cross Validation of Prediction Maps

In this section validation of prediction maps was performed. Due to limited number of the observation points the data could not be separated into two sets, so a leave-one-out cross validation (LOOCV) method as suggested many geostatisticians [37, 50-52] was applied to estimate the precision/accuracy of prediction of unknown values in the area of interest. The statistical measures used for validation are absolute mean prediction error (ME), absolute root mean square prediction error (RMSE), mean square deviation ratio (MSDR), and relative mean error (RME) and relative root mean square error (RMSSE). The latter two refers to the relative mean of the predicted to the mean of the observed values which measure biasness, and precision which is measured by relative root mean square error to the standard deviation and inter-quartile of the observed values. The MSDR is a measure of the variability of the cross-validation versus the variability of the sample set, which is given by the equation [50]:

……………………10, where σ2(Xi) is the kriging variance at cross-validation point Xi, obtained during the kriging procedure (not the cross-validation). The ratio of the two should be equal to one otherwise the predictor does not capture the variability well. If this ratio is higher than one, then the kriging prediction is too optimistic about the variability. The validation results of the interpolated maps are given in table 4.25 for the measured EC values and 4.26 for the simulated values.

Table 4.29 Validation results for kriging maps of measured EC values

Statistics 0-30 cm 30 – 60 cm 60 – 90 cm ME 0.0001 0.003 0.002 RME 8.883e-05 0.002 0.001 RMSE 0.624 0.638 0.629 RMSE/SD 0.148 0.215 0.196 RMSE/IQR 0.469 0.689 0.635 MSDR 1.240 1.273 1.224

Table 4.30 Validation parameters for kriging prediction of simulated EC values

Variable Sim_EC_10 Sim_EC_20 Statistics Root-zone Transition-zone Root-zone Transition-zone ME 0.0007 0.0060 -0.0020 0.0007 RME 0.0002 0.0016 -0.0002 0.0001 RMSE 0.761 0.6959 0.8402 0.7808 RMSE/SD 0.091 0.1070 0.0534 0.0763 RMSE/IQR 0.3186 0.1819 0.1301 0.1301 MSDR 1.008 1.0416 1.0255 1.0273 Sim : Simulated EC for year 10 & 20; ME : Mean error; RME : Relative mean error; RMSE : Root mean square error; SD : standard deviation; IQR : Inter-quartile; MSDR : Mean square deviation ratio

91

For the measured values the mean error and relative mean error show quite very low values for all the three soil depths which suggest that the predicted values were close to the observed values and therefore biasness was insignificant. The relative root mean square error to sample standard deviation indicate 14.8% for the first top layer, 21.5% for the second layer and 19.6% for the third layer which indicate good and reasonable precision. However when this is compared to the inter-quartile of observed values higher percentages of 46.9%, 68.9% and 63.5% are obtained for the top to the lower layer respectively. From latter results the precision can be considered somewhat poor, but when considering both measures it ca be conclude that the precision of kriging was reasonably good and acceptable. In terms of variability the values are more than one which indicate that the actual data is a bit more variable than as predicted by kriging. However it can be concluded that variability was fairly defined because the difference between predicted and actual variability is just around 0.2 for all the three soil depths which is reasonably small value.

As far as the simulated EC values are concerned (table 4.26), by considering the absolute and relative mean error values it could be established that both parameters are low for all variables. The relative mean error to the mean of the observed values is thus less than 0.2% which means that the biasness is almost eliminated. This means that the prediction values were closer to the observed values. Generally it can be concluded that the means of the kriged estimates were in full agreement with observed salinity mean values. The root mean square error varies between 0.69 to 0.85 dS/m for all the variables and these values are less than 10% and 19% of the sample standard deviation and inter-quartile respectively, except for root-zone of the tenth year which gives a percentage of around 31% for RMSE/IQT ratio. This indicates that the precision of the model is fairly high. Considering the mean deviation ratio which explains variability, it can be noted that it is greater than one for all the variables, which means that actual data is a bit more variable than as predicted by kriging. However, none of these variables has a value greater than 1.2, suggesting a very low difference between predicted and actual variability. Therefore, the model can be regarded as captured the variability fairly well. Moreover, this suggests that the nugget values used were somehow realistic as to be able to capture small scale variability. Therefore the results from the universal kriging procedure are somewhat reliable.

4.6. Model Validation and Sensitivity Analysis

The model was calibrated using the data on climatic and cropping patterns, water table depth, salinity content (EC) of soil and groundwater, leaching efficiencies, and soil properties (effective and total porosity) of the study area. The input parameters used in the model calibration are shown in appendix 1 (page 104). However not all the parameters required by the model were measurable and/or no data was available for some parameters, (e.g. natural subsurface drainage and leaching efficiency), In that case a trial and error calibration of the model was performed with the arbitrary values of these parameters. The parameter value giving an EC output closer or corresponding to the measured EC value (and/or water table depth) was chosen to be used in the running the model. Details on this exercise are given in sub-section 3.4.2.

92 4.6.1. Validation

As described by Greiner[2] that the value of the model is determined by the reliability of its results, thus validation forms an important part of the study. In principle, validation of the model was performed in the present study although there was lack of long term historical salinity and groundwater data for the concerned area. The dataset of measured EC values from the previous research studies undertaken (by ITC MSc students) in 2003 and 2004 were used for calibrating the model while the presently collected (2006) data was used for validation. This is thus considered reasonably satisfactory for validation of this model as this is based on the best available and accessible data. To substantiate this reasoning it can be highlighted that the concerned model has been applied in quite a few other areas by some researchers [26, 27, 31, 46, 49], and validation results were also reported. The majority of them have concluded that the model is somehow reliable in predicting root-zone salinity though not so satisfactory in other soil water salinity predictions such as the transition zone and aquifer and for the ground water depth prediction.

To evaluate the predictive quality of SaltMod, the simulated salinity concentration (EC) values of the third year were compared to the measured values. The third year prediction values from the model simulations are chosen because they timely coincide with the currently measured values since the initial input data is considered to have been collected three years back. The calibration dataset consisted of 71 observation points while the validation dataset consisted of 51 points. Each of these datasets have measurements for the root-zone (0-60cm depth) and the transition zone (60-90cm) and thus validation is performed for both soil depths. Geostatistical approach using the R-program was used to carryout the validation. The R-program was preferred because the observation points of the two datasets were generated randomly and collected at different times, so spatial overlay of the dataset points would be required. Therefore geostatistical analysis in the R-environment would provide spatial overlay capabilities for the two dataset points so as to establish prediction at the exact location of the validation points.

Normally for validation, the predicted values are subtracted from the measured values, and the measures of validity (reliability) used are the root mean square error (RMSE), and the mean error (ME), while coefficient of determination (R 2) was used to measure the degree (goodness-of-fit) of success of the calibration. The validation the results are given in table 4.30 with graphical presentation in figure 4.28 and 4.29 which show the residual histograms and bubble plots of the root-zone (a) and transition zone (b). Considering the histogram of the root-zone it is evident that highest frequency of residual is within the range of -5 to 0 though high variation is depicted as the highest values are -13 and 14 dS/m. This is the also displayed in the bubble plot where the highest residuals are distributed more towards the north and north east of the study area, and this is generally the same trend with measured values. Form the bubble plot it can be concluded that high variability amongst the high residual values. This is almost the same situation with the residuals of the transition zone except that the highest frequency tends to have a wider range of -5 to 5 dS/m.

Taking a closer look at table 4.30, considering the absolute and relative mean error values, it could be established that both parameters are sensibly low for both the root-zone and transition zone. The relative mean error to the mean of the observed values is 13% for the root-zone and 18% for the transition zone, which means that the biasness is somewhat reasonably low. That is, the predicted EC values were not that

93

b) a)

Figure 4.28 Histogram and bubble plot of residuals for the root-zone

a) b)

Figure 4.29 Histogram (a) and bubble plot (b) of residuals for the transition zone

Table 4.31 Statistical parameter values for error determination

Validation Statistics ME RME RMSE RMSE/SD R2 Sample (n=51) Units Mean SD (dS/m) (dS/m) Root-zone 2.67 5.54 -0.34 -0.13 5.17 0.93 0.81 Transition zone 2.22 3.81 0.39 0.18 3.20 0.84 0.44 ME : Mean error; RME : Relative mean error; RMSE : Root mean square error; SD : standard deviation; IQR : Inter-quartile

94 much different to the observed values and therefore there was good agreement between the mean simulated and observed mean salinity values. The root mean square error is about 5.17dS/m for the root-zone and 3.20 dS/m for transition one. The relative RMSE to sample standard deviation gives very high percentages of 93% and 84% for the root-zone and transition zone respectively. These values indicate vary high variation of the residuals and thus suggest that the precision of the model was very low. The high percentages of the relative RMSE can be attributed to the high coefficient of variation of the sample data. In contrast, the coefficient of determination (R 2) of the root-zone is surprisingly high with a value of 0.81 which indicate that the model has been well fitted. However, in the case of the transition zone the R 2 is 0.44 which indicate somewhat less desirable fitting of the model.

In view of the results presented above, the calibrated SaltMod can be considered to be fairly good for estimating soil salinity in the root zone. However, the validity of the SaltMod appears to be doubtful for estimating soil salinity in the transition zone.

4.6.2. Sensitivity analysis

The main idea behind sensitivity analysis is describe by [53] as to assess the input influence using the output variance quota attributed to each input obtained by some variance decomposition. This will enable determination of parameters that require additional research to strengthen the knowledge base, thus reducing output uncertainty [54]. Sensitivity analysis was carried out to some few input parameters (limited by time constraint) to check how the model behaves to varied values of certain parameters. The parameters of concern included water table depth, evapotranspiration, electrical conductivity (EC) of the aquifer, transition zone and root-zone, leaching efficiency and natural drainage.

The procedure followed for testing the effect of the selected parameters as mentioned above to the output of modelling simulation is the local one-at-a time sensitivity analysis [55] which is a Variance based method [56]. The variation of parameter values was determined by the percentage values of -20%, -10%, +10% and +20% (table 4.31) of the baseline parameter value [57]. The baseline value is taken as the initial input parameter value used for running the model over the simulation period, except for ground water depth where the critical depth (1.2 m) for capillary rise was considered. The model give output for each season of every year but for the purpose of this exercise output value of the twentieth year was considered and was average for the two seasons. The electrical conductivity of the root-zone was considered as the output of interest.

It should however be highlighted that this sensitivity analysis method, because of local one-at-a-time procedure: a) does not consider interaction and influences between parameters; b) does not include model output uncertainty (this has been taken care in the preceding section); and c) only investigated four changes in each parameter values out of many possible values.

The local sensitivity analysis used in the study adopted a dimensionless sensitivity index (S) defined as derivative of [57]: S = ∂Y/ ∂X………………Equation 11,

95

Sensitivity Indices

2 S 1.5 Area 1 PET Rain 0.5 Go 0 Flr -20 -10 0 10 20 -0.5 RZ _EC TZ_EC

Sensitivity index (S) index Sensitivity -1 AQ_EC -1.5 GWD TPor -2 EPor Relative Variation (%)

Figure 4.30Plot of sensitivity indices as a function of % change in parameter values for selected parameters

Parameter Sensivity Indices

2

1.5

1 S

0.5 PET

0 Rain -20 -10 0 10 20 -0.5 Flr

Sensitvity index (S) index Sensitvity -1 TPor

-1.5 EPor -2 Relative Variation (%)

Figure 4.31 Plot of sensitivity indices for sensitive parameter only

S = baseline; PET = potential evapotranspiration; Go = natural drainage; Flr = leaching efficiency; RZ_EC = EC of the root-zone; TZ_EC = EC of the transition zone; AQ_EC= EC of the aquifer, GWD = ground water depth; TPor =total porosity; EPor= effective porosity

96 Table 4.32 Selected parameters with baseline values and percent changes used in the analysis

Parameter Unit -20 -10 Baseline +10 +20 Area ha 273 307 341 375 409 Evapotranspiration m S1 A 0.46 0.51 0.57 0.63 0.68 B 0.84 0.95 1.05 1.16 1.26 U 1.18 1.33 1.48 1.63 1.78 S2 A 0.08 0.09 0.10 0.11 0.12 B 0.34 0.39 0.43 0.47 0.52 U 1.06 1.19 1.32 1.45 1.58 Precipitation m S1 0.70 0.79 0.88 0.97 1.06 S2 0.13 0.14 0.16 0.18 0.19 Natural Drainage m S1 0.10 0.11 0.12 0.13 0.14 S2 0.12 0.12 0.12 0.12 0.12 Leaching Efficiency RZ 0.64 0.72 0.80 0.88 0.96 TZ 0.64 0.72 0.80 0.88 0.96 Initial EC dS/m RZ 0.080 0.090 0.100 0.110 0.120 TZ 0.080 0.090 0.100 0.110 0.120 AZ 0.080 0.090 0.100 0.110 0.120 Water table depth m 0.96 1.08 1.20 1.32 1.44 Total porosity RZ 0.33 0.37 0.41 0.45 0.49 TZ 0.32 0.36 0.40 0.44 0.48 Effective porosity RZ 0.06 0.07 0.08 0.09 0.10 TZ 0.03 0.04 0.04 0.04 0.05

S1 = first season; S2 = second season; A = paddy rice, B = cassava/ maize; U = uncultivated

land; RZ =root-zone; TZ = transition zone; AZ = aquifer

Table 4.33 Sensitivity indices for all the selected parameters

Parameter Output (EC ) -20 -10 0 10 20 S 0 0 0 0 0 Area 0.24 0.00 0.00 0.00 0.00 0.00 PET 0.24 -0.44 0.06 0.00 -0.10 0.90 Rain 0.24 0.20 0.10 0.00 0.03 1.61 Go 0.24 0.00 0.01 0.00 -0.04 -0.10 Flr 0.24 -0.71 -0.42 0.00 -0.04 -0.09 RZ _EC 0.24 0.02 0.00 0.00 0.00 0.01 TZ_EC 0.24 0.02 0.00 0.00 0.00 0.01 AQ_EC 0.24 0.02 0.00 0.00 0.00 0.01 GWD 0.24 -1.09 -0.53 0.00 0.50 0.96 TPor 0.24 -1.48 -0.62 0.00 0.44 0.76 EPor 0.24 -1.09 -0.52 0.00 0.52 0.96

where ∂X is relative change in parameter from the baseline value and ∂Y is the corresponding relative change in output interest. Table 4.31 and 4.32 show parameter variation ranges and the respective sensitivity indices for each of the assessed model parameters respectively. The interpretation of sensitivity index as given in equation 1 is as follows [57]):
A values of zero indicate that the model is not sensitive to changes in input parameter value

97

A negative value indicates that the model output decreases as the input parameter increases
A positive indicates that the model output increases as the input parameter increases
The model is most sensitive to input parameters with high absolute value sensitivity index. The sensitivity indices are plotted as function percentage change in input parameter values as indicated in figure 4.3 and figure 4.31. The former figure shows all the parameters assessed while the latter only shows those that the model is sensitive to. All parameters that are not sensitive have zero or close to zero sensitivity index with their corresponding lines overlapping with S=0. These parameters were subsequently removed from the graph (figure 4.31) to improve readability. The parameters that were not sensitive included extent of area (polygon), electrical conductivity (root-zone, transition & aquifer), natural drainage (Go) and groundwater depth (GWD). For the rest of the parameters the model was sensitive to their parameter value variations but with different degrees. The most sensitive parameters are total porosity (TPor), effective porosity (EPor), potential evapotranspiration (PET) and precipitation (Rain). Though precipitation starts to be effective at around 10% increment above the baseline value while was almost insensitive below that percentage. The evapotranspiration has shown sensitivity from -20% to -10% and then remain nearly insensitive form -10 to +10% after which is become strongly sensitive. The latter two parameters have showed some slight decrease in the range where are said to be insensitive. The leaching efficiency (Flr) has shown sensitivity from -20% to around 0% after which it was less sensitive to insensitive as it show slight gradual decrease from 0 to +20%.

Despite limitations mentioned above, the results of this simple local sensitivity analysis have been useful to identify parameters to which SaltMod model salinity output was most sensitive and those that have negligible or no influence at all. This information is vital for structural improvement of the model by focusing on those parameters to which the model is most sensitive. However, any suggestion to structural changes on the model would require application of more robust sensitivity analysis and validation methods with reliable data and better acquisition methods.

98 5. CONCLUSION AND RECOMMENDATIONS

Determination of salinization in terms of when, where and how salinity may occur is vital for sustainable production and use of soils. Thus keeping track of changes of salinity and predict further salinization plays important role to timely detect salinization before causing detrimental effects to the environment. In reaction to that the present study applied long term prediction of salinity changes by means of deterministic modeling using SaltMod in a GIS environment. At the most basic level, the work undertaken in the study area has helped in mapping and characterizing the spatio-temporal salinity changes and identifying potentially affected areas within the study area under present conditions.

The lack of historical and difficulty to obtain existing data on salinity and groundwater in the area has presented difficulties and uncertainty of the results obtained. This led to preference and application of a point model (SaltMod) instead of spatial model (SahysMod) since the available data was not suitable for the use of the latter, resulting in a tedious and time consuming exercise. This has further raised concerns and uncertainty regarding the relevance and applicability of the model to the applied spatial scale. However by integrating the model into a GIS environment and geostatistical methods help in accomplishment of the work.

In relation to the research questions formulated in the study the following can be highlighted:

5.1.1. How is soil salinity distributed spatially in relation to geopedologic properties?

General statistics such as mean, ANOVA and CV give indication that spatial variability of soil salinity is influenced by geopedologic properties. The analysis of variance between relief types has shown significant difference of electrical conductivity values for all the three depths (table 4.10). This was further substantiated by high coefficient of variation with values which where larger than 1, ranges from 1.7 to 2.3. The interpolated maps also displayed a pattern that is influenced by differences in the landscape catena. The increase of salinity towards the north eastern is related to change in the landscape from Peneplain to the Valley. That is the north eastern side dominated by lowlands of the floodplain, glacis and terraces while the south western part by ridges and vales forming upper higher laying lands.

5.1.2. How does salinity change over space and time as influenced by hydro- geopedologic processes?

Simulation of EC for a two decadal period indicated progressive increase of salinity with time in the root- zone though not so pronounced in the transition zone. The change of area extent from low and moderately saline soils to high and severely saline soils showed the influence of the micro-topography, groundwater table and present practices as the main cause of changes. However, simulation results showed no significant changes in the transition zone which indicates the unreliability and shortcomings of the model to predict other soil salinities besides root-zone. Furthermore the prediction of water table fluctuations was also doubtful as the model indicated (figure 4.5) almost he same depth for each season throughout the simulation period.

99

5.1.3. Which areas are likely to be affected by soil salinization in future ?

The use of geostatistical techniques in connection with environmental factors as predictors (regression/universal kriging), together with GIS has yielded a reasonable classification and mapping of potentially affected areas. Of course the accuracy of the predicted and mapped areas depends on the validity and reliability of the input data which was the output of model (SaltMod) simulation. Furthermore the prediction of likely affected areas is based on the assumption that the present conditions and practices are to persist.

5.1.4. At what rate and extent is the development of salinity under current practices ?

The use of SaltMod within GIS environment for long term prediction of salinization has enable the delineation and classification of saline areas, the determination of spatial and temporal changes of soil salinity, helping in the estimation of the rate and extent of expansion of saline areas.

5.1.5. How accurately and reliably can SaltMod help predict salinization?

The results of SaltMod accuracy evaluation have indicated that the model makes reasonable estimates of root-zone salinity changes but was poor for the transition zone. Though the model was not validated for he prediction of other soil water salinities such as the aquifer and the prediction of groundwater depth, it is reported as unsatisfactory by other authors[26, 27, 31, 46, 49] for the prediction of the mentioned variables. Though the sensitivity analysis performed did not consider interaction between parameters, it was useful to indicate that six out of eleven parameters assessed were sensitive to influence the simulation outputs of to the model. Therefore SaltMod can work as an effective tool to forecast salinization in the rooting zone once well calibrated and validated. It should however be noted that salinization was modeled as a single constituent that reflect electrical conductivity, and with estimates and somewhat scanty data, which is fairly realistic. Thus with more detailed field and laboratory measured data the results could slightly differ, most probably for the better.

In conclusion, the approach presented in the study is a key to a practical expert system to help respond to questions related to soil salinity management thereby way of prognostic analysis to detect salinization at early stages thus providing prevention measures rather than damage control. However, the results presented here should be taken as indicative due to uncertainties associated with large assumptions rather measured data, as is always the case with modelling in data-poor areas. Besides, though accuracy of prediction is uncertain, it is useful when trend of prediction is clear. As Oosterbaan states that[26], it would not be a disaster to design appropriate salinity control measures when a certain salinity level, predicted by the model to occur in 10 years time, will in reality occur a few years before or few years later.

100 6. REFERENCES

1. Ghassemi, F., A.J. Jakeman, and H.A. Nix, Human induced salinisation and the use of quantitative methods. Environment International, 1991. 17 (6): p. 581-594. 2. Greiner, R., Optimal farm management responses to emerging soil salinisation in a dryland catchment in eastern Australia. Land Degradation & Development, 1997. 8(4): p. 281-303. 3. J. Navarro-Pedreño, et al., Estimation of soil salinity in semi-arid land using a geostatistical model. Land Degradation & Development, 2007. 18 (3): p. 339-353. 4. Jorenush, M.H. and A.R. Sepaskhah, Modelling capillary rise and soil salinity for shallow saline water table under irrigated and non-irrigated conditions. Agricultural water management : an international journal, 2003. Vol. 61, No. 2 (2003), p. 125-142 . 5. M Qadir, A. Ghafoor, and G. Murtaza, Amelioration strategies for saline soils: a review. Land Degradation & Development, 2000. 11 (6): p. 501-521. 6. Shrestha, D.P., A.S. Soliman, and A. Farshad, Salinity mapping using geopedologic and soil line approach. In: ACRS 2005 : proceedings of the 26th Asian conference on remote sensing, ACRS 2005, 7-11 November 2005, Hanoi, Vietnam. Hanoi : Asian Association on Remote Sensing (AARS), Geoinformatics Center, Asian Institute of Technology, 2005. 6 p., 2005. 7. Shrestha, R.P., Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand. Land Degradation & Development, 2006. 17 (6): p. 677-689. 8. Farifteh, J., A. Farshad, and R.J. George, Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma, 2006. 130 (3-4): p. 191-206. 9. Metternicht, G.I. and J.A. Zinck, Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment, 2003. 85 (1): p. 1-20. 10. Ghassemi, F., A.J. Jakeman, and H.A. Nix, Salinisation of land and water resources : human causes, extent, management and case studies . 1995, Canberra ; Wallingford Oxon: The Australian National University ; CAB International. 526. 11. Last, R., et al. Bio-Economic Modelling towards Optimising Land Use and Salinity Management in South-Eastern NSW and North-Eastern Thailand . in Salinity under the sun - investing in prevention and rehabilitation of salinity in Australia . 2003. Queensland, Australia: Queensland Department of Natural Resources and Mine. 12. Farshad, A., Udomsri, S., Yadav R D., Shrestha D.P., Sukchan S. , Understanding geopedologic setting is a clue for improving the management of salt-affected soils in Non Suang district, Nakhon Ratchasima, Thailand. 2005. 13. Metternicht, G., Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system , in Ecological Modelling . 2001. p. 163-179. 14. McBratney, A.B., et al., An overview of pedometric techniques for use in soil survey. Geoderma, 2000. 97 (3-4): p. 293-327. 15. Heuvelink, G.B.M. and R. Webster, Modelling soil variation: past, present, and future. Geoderma, 2001. 100 (3-4): p. 269-301. 16. Xu, P. and Y. Shao, A salt-transport model within a land-surface scheme for studies of salinisation in irrigated areas. Environmental Modelling & Software, 2002. 17 (1): p. 39-49. 17. Soliman, A.S., Detecting salinity in early stages using electromagnetic survey and multivariate geostatistical techniques : a case study of Nong Suang district, Nakhon Ratchasima, Thailand . 2004, Unpublished MSc thesis, ITC: Enschede. p. 90. 18. Zinck, J.A., Soil survey : epistemology of a vital discipline. In: ITC Journal, (1990)4, pp. 335-351 also published as inaugural address, 1990. 19. Ronald P. Peterson, J.D. and James L. Arndt, Modeling Soil Salinization Processes in Wetlands of the Upper Basin of Devils Lake and in Floodplain Soils along the Sheyenne River, with an Emphasis on the Effects of Alternatives Proposed to Reduce Devils Lake Flooding . 2002, UNITED STATES ARMY CORPS OF ENGINEERS: ST. Pault District

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20. ILACO B.V., Soil and Land classification , in Agricultural Compodium for Rural Development in the Tropics and Subtrpoics , I.L.d.C. ILACO B.V., Arnhem, Editor. 1981, Elsevier Scientific Publishing Company: Amsterdam. 21. Shannon Michael C. and CervinkaVashek, Drainage water re-use . Management of agricultural drainage water quality, ed. Madramootoo Chandra A., Johnston William R., and W.L. S. 1997, Rome: FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS 22. Shirokova, Y., I. Forkutsa, and N. Sharafutdinova, Use of electrical conducitivity instead of soluble salts for soil salinity monitoring in Central Asia. Irrigation and Drianage Systems, 2000. 14 : p. 199-205. 23. Castrignano, A., N. Katerji, and M. Mastrorilli, Modelling crop response to soil salinity: review and proposal of a new approach , in Mediterranean crop responses to water and soil salinity : eco- physiological and agronomic analysis (Options méditerranéennes Série B Number 36) 2002, Lavoisier 2000-2008. 24. Triantafilis, J., I.O.A. Odeh, and A.B. McBratney, Five Geostatistical Models to Predict Soil Salinity from Electromagnetic Induction Data Across Irrigated Cotton. Soil Sci Soc Am J, 2001. 65 (3): p. 869-878. 25. Schoups, G., J.W. Hopmans, and K.K. Tanji, Evaluation of model complexity and space-time resolution on the prediction of long-term soil salinity dynamics, western San Joaquin Valley, California. Hydrological Processes, 2006. 20 (13): p. 2647-2668. 26. Oosterbaan, R.J., SALTMOD: Description of Principles, User Manual, and Examples of Application . 2002: Wageningen, The Netherlands. 27. Man Sing, et al., Application of SALTMOD in coastal clay soil in India. Irrigation and Drainage Systems 2002. 16 : p. 213 - 231. 28. Srinivasulu, A., et al., Model studies on salt and water balances at Konanki pilot area, Andhra Pradesh, India. Irrigation and Drainage Systems 2004. 18 : p. 1 - 17. 29. Rao K.V.G.K., et al., Salt and Water Balace Modelling: Tungabhadra 'Irrigation Project (UASD): Joint Completion Report on IDNP, . ”Computer Modeling in Irrigation and Drainage”, 1992. 30. Shrivastava, P.K., A.M. Pate, and R.J. Oosterbaan, Saltmod Model Validation and application in Segwa Minor Canal Command Area. Unpublished, 2001. 31. Oosterbaan, R.J. and M. Abu Senna, Drainage and salinity predictions in the Nile Delta, Using SALTMOD . 1990, Drainage Research Institute for Land Reclamation and Improvement, : Wageningen, Netherlands. 32. Idris Bacheci and A. Suat Nacara, Estimation of root zone salinity, using SaltMod, in the arid region of Turkey. Irrigation and Drainage, 2007. 56 (5): p. 601-614. 33. Siska Peter P. and H. I-Kuai, Assessment of Kriging Accuracy in the GIS Environment , College of Forestry, Stephen F. Austin University. 34. Utset, A., et al., A geostatistical method for soil salinity sample site spacing. Geoderma, 1998. 86 (1-2): p. 143-151. 35. Luan Truong Xuan and Q.T. Xuan. Geostatistics combined with the function of interpolation in GIS . in International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences . 2004: Hanoi University of Mining and Geology, Dong Ngac, Tu Liem, Hanoi. 36. Hengl, T., A Practical Guide to Geostatistical Mapping of Environmental Variables . Vol. 2007. 2001, Italy: Luxembourg: Office for Official Publications of the European Communities. 37. Hengl, T., A Practical Guide to Geostatistical Mapping of Environmental Variables . 2007, Italy: Luxembourg: Office for Official Publications of the European Communities. 38. Divi, R.S., GIS and Geostatistical Modeling of Surface Fractures and their Subsurface Extension : A Case Study in The Arabian Shield . 2004, Dept of Earth & Environment, Kuwait University, Kuwait 39. Corwin, D.L. GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone . in Joint AGU Chapman/SSSA Outreach Conference on Application of GIS, Remote Sensing, Geostatistics, and Solute Transport Modeling . 1997. Riverside, California: SSSA Spe-cial Publication 48.

102 40. Yadav, R.D., Modeling salinity affects in relation to soil fertility and crop yield : a case study of Nakhon Ratchasima, Nong Suang district, Thailand . 2005, Unpublished MSc thesis, ITC: Enschede. p. 150. 41. Paiboon, P., Study of the relationship between salt affected soils and landforms in Amphoe Kam Sakae Saeng area, Nakorn Ratchasima province, Thailand , in Natural Resource Management . 1982, ITC: Enschede. p. 155. 42. NRM - ITC, Remote Sensing and GIS exercise instruction book- Natural Resource Management 2006, International Institute for Geo-Information Science and Earth Observations. 43. Aimrun, W., M.S.M. Amin, and S.M. Eltaib, Effective porosity of paddy soils as an estimation of its saturated hydraulic conductivity. Geoderma, 2003. 121 : p. 197 - 203. 44. Keith E. Saxton and P.H. Willey, The SPAW Mdoel for agricultural field and hydrologic simulation 2007, Washington State University: Washington. 45. Derek Clarke, Martin Smith, and K. El-Askari, CropWat for Windows : User Guide . 1998, University of Southampton: Southampton. 46. Oosterbaan, R.J. (1992) Drainage Research in Farmers fields: Analysis of Data On website Volume , DOI: www.waterlog.info 47. Silberstein, R.P., et al., Modelling the effects of soil moisture and solute conditions on long-term tree growth and water use: a case study from the Shepparton irrigation area, Australia. Agricultural Water Management, 1999. 39 (2-3): p. 283-315. 48. Hengl Tomislav , HeuvelinkGerard B.M., and S. Alfred, A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 2004. 120 p. 75-93. 49. Oosterbaan, R.J., SALTMOD: A tool for the interweaving oof irrigation and drianage for salinity control International Institute for Land Reclamation and Improvement (ILRI): Wageningen, The Netherlands. 50. Rossiter, D.G., Intorduction to the R Project for Statistical Computing for use in ITC . 2007, International Institute for Geo-Information Science & Earth Observation: Enschede (NL). 51. Panagopoulos, T., et al., Analysis of spatial interpolation for optimising management of a salinized field cultivated with lettuce. European Journal of Agronomy, 2006. 24 (1): p. 1-10. 52. Iglesias, R.R., Geostatistics for Environmental Scientists: R. Webster, M.A. Oliver (Eds.), Wiley, Statistics in Practice Series, 2001, 271 pp., US$ 105, hardcover, ISBN 0-471-96553-7. Ecological Engineering, 2004. 22 (3): p. 214-216. 53. Alessandro, F., Sensitivity Analysis for Environmental Models and Monitoring Networks . 2006, Dept. IGI, University of Bergamo, Italy. 54. Karen, C., S. Andrea, and T. Stefano. Sensitiy analysis of model output: Variance-based methods make the difference in Proceedings of the 1997 Winter Simulation Conference . 1997. ITALY: Environment Institute European Commission Joint Research Centre TP 272, 21020 Ispra (VA), ITALY. 55. Kleijnen, J.P.C. Validation of models: Statistical techniques and data availability in Proceedings of the 1999 Winter Simulation Conference . 1999. 5000 LE Tilburg, The Netherlands Department of Information Systems (BIK)/Center for Economic Research (CentER) School of Economics and Management (FEW) Tilburg University. 56. Khan, N.M., et al., Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agricultural Water Management, 2005. 77 (1-3): p. 96-109. 57. Fentie, B., N. Marsh, and A. Steven, Sensitivity Analysis Of A Catchment Scale Sediment Generation And Transport Model . 2006, Natural Resources and Mines, QLD, Environmental Protection Agency, QLD, CSIRO, Brisbane.

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7. APPENDICES

Appendix 1: Input parameters for SaltMod

Season-wise input parameter for use in SALT MOD

No. Parameters Unit Season 1 Season 2 Period of season May to Oct Nov to March 1 Duration of season mont 6 6 hs Crops grown Cassava Rice 2 Maize Cassava Rice Maize Fallow Fallow 3 Water sources Rainfall Rainfall 4 Amount of rainfall m 0.88 0.16 5 Amount of water used for irrigation m 0 0 6 Fraction of area occupied rice (paddy) 0.40 0.40 7 Fraction of area occupied other crops 0.52 0.52 8 Fraction of area fallowed / barren / non cultivated 0.08 0.08 9 Potential evapotranspiration of rice crops m 0.57 0.10 10 Potential evapotranspiration of other crops m 1.05 0.43 11 Potential evapotranspiration from non cultivated land m 1.48 1.32 12 Surface runoff (assumed) m 0 0

Soil and system input parameters for use in SALTMOD

No Parameter Unit Pe111 Pe112 Pe113 Pe114 Pe115 Pe211 Pe311 Pe411 Pe412 Pe413 Va111 Va211 Va311 1 Storage efficiency 2 Depth of root zone m 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 3 Depth of transition m 4 4 4 4 4 4 4 4 4 4 4 4 4 zone (estimated) 4 Depth of aquifer m 15 15 15 15 15 15 1 5 15 15 15 15 15 (estimated) 5 Total porosity of the (i) root zone 0.31 0.33 0.34 0.34 0.34 0.34 0.39 0.30 0.34 0.39 0.42 0.37 0.32 (ii) transition zone 0.31 0.36 0.35 0.36 0.37 0.36 0.37 0.35 0.37 0.37 0.40 0.36 0.33 (iii) aquifer 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 5 Effective porosity of the (i) root zone 0.14 0.19 0.15 0.14 0.13 0.18 0.18 0.19 0.22 0.20 0.16 0.14 0.14 (ii) transition zone 0.12 0.17 0.18 0.18 0.19 0.18 0.17 0.19 0.15 0.15 0.13 0.13 0.15 (iii) aquifer 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 6 Initial salt concentration of the soil moisture in the (i) Root zone dS/m 6.94 3.74 3.71 0.23 0.18 4.69 0.18 2.24 0.25 0.43 0.47 0.16 10.5 (ii) Transition zone dS/m 5.63 4.67 2.84 0.33 1.60 2.50 0.19 1.36 0.31 0.44 0.70 0.24 7.4 (iii) Aquifer dS/m 4..46 3.40 2.55 1.80 1.59 2.11 1.14 6.08 1.02 1.66 2.64 1.83 4.6 12 Depth of water table m 2.20 1.83 2.20 2.25 2.50 2.03 2.64 1.14 2.28 2.39 2.83 2.27 2.05 14 Critical depth for m 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 capillary rise

Appendix 2: Land cover types and water table observation points

104

Date Id GP X_Coord Y_Coord Land_Cover WT_Observed GWD_(cm)

9/15/2007 1 Pe111 808402 1659985 Maize N 9/15/2007 2 Pe111 807251 1663913 Maize N 9/7/2007 3 Pe111 818561 1663115 Cassava Y 100 9/9/2007 4 Pe111 809789 1672642 Cassava N 9/10/2007 5 Pe111 806458 1671839 Fallow/Cassava N 9/7/2007 6 Pe111 814723 1668090 Cassava N 9/8/2007 7 Pe112 812644 1673638 Paddy Rice Y 107 9/10/2007 8 Pe112 802311 1672472 Cassava N 9/7/2007 9 Pe112 812516 1669640 Cassava N 9/10/2007 10 Pe112 803138 1674158 Maize N 9/7/2007 11 Pe112 816657 1665475 Grass (Kikuyu) Y 60 9/9/2007 12 Pe112 810672 1671315 Marsh (trees/grass) Y 110 9/8/2007 13 Pe112 810877 1673618 Paddy rice/Grass Y 175 9/13/2007 14 Pe113 818263 1673303 Paddy rice Y 100 9/9/2007 15 Pe113 806456 1670389 Cassava N 9/6/2007 16 Pe113 810899 1664062 Maize/Cassava N 9/14/2007 17 Pe113 809441 1661439 Cassava N 9/15/2007 18 Pe113 809434 1664970 Cassava N 9/7/2007 19 Pe113 817664 1661045 Maize Y 80 9/6/2007 20 Pe113 808799 1664197 Cassava N 9/14/2007 21 Pe113 808420 1662252 Paddy rice/Fallow Y 100 9/16/2007 22 Pe113 812502 1663542 Cassava N 9/14/2007 23 Pe113 806034 1660127 Bushes/shrubs N 9/8/2007 24 Pe113 807705 1666017 Cassava Y 120 9/14/2007 25 Pe113 805239 1662476 Cassava N 9/15/2007 27 Pe114 812440 1660059 Maize N 9/14/2007 29 Pe114 813790 1659966 Cassava N 9/14/2007 33 Pe114 815303 1660688 Plantation-Eucalyptus N 9/14/2007 34 Pe211 816920 1668104 Paddy rice Y 85 9/8/2007 35 Pe211 811156 1667521 Cassava N 9/6/2007 36 Pe211 812558 1665464 Cassava Y 120 9/16/2007 37 Pe211 817157 1671259 Cassava Y 170 9/14/2007 38 Pe211 804109 1660311 Grass/Trees N 9/6/2007 39 Pe211 811344 1669609 Fallow/Paddy rice Y 65 9/7/2007 40 Pe211 817634 1669457 Cassava/Paddy rice Y 100 9/15/2007 41 Pe311 804898 1663795 Cassava N 9/14/2007 42 Pe311 808034 1670052 Paddy rice N 9/9/2007 43 Pe311 805523 1671888 Paddy rice N 9/15/2007 44 Pe311 810387 1660380 Cassava N 9/15/2007 45 Pe311 811341 1660440 Cassava/Fallow N 9/7/2007 46 Pe411 816593 1666831 Marsh/swampy/grass Y 70 9/7/2007 47 Pe412 816014 1663150 Cassava/plantation N 9/6/2007 48 Pe413 814586 1670533 Cassava N 9/7/2007 49 Pe413 812748 1667465 Grass N 9/8/2007 50 Pe413 812593 1671835 Paddy rice/Fallow Y 70 9/13/2007 51 Pe413 813503 1673304 Paddy rice Y 115 9/8/2007 52 Pe413 817586 1674203 Maize N 9/8/2007 57 Va111 809709 1674771 Paddy/grass Y 170 9/17/2007 58 Va111 813640 1675646 Paddy rice Y 80 9/10/2007 59 Va211 803776 1675096 Paddy rice N 9/16/2007 60 Pe115 815773 1671484 Paddy rice Y 80

105

Appendix 3: EC, pH and GWD

ID GP X_CORD Y_CORD PH_SR EC_SR ECE_SR PH_RZ EC_RZ ECE_RZ PH_TZ EC_TZ ECE_TZ WTD (m) 1 Pe111 808574 1660302 6.80 0.22 1.41 7.49 0.19 1.22 7.59 0.14 0.90 3.00 2 Pe111 807251 1663913 6.58 0.02 0.13 6.44 0.03 0.19 5.79 0.03 0.19 3.00 3 Pe111 818430 1663152 5.67 0.02 0.13 5.29 0.01 0.06 5.29 0.02 0.13 1.00 4 Pe112 809789 1673963 5.79 0.05 0.32 5.60 0.02 0.13 5.79 0.03 0.19 3.00 5 Pe111 806458 1671839 6.79 0.03 0.19 5.50 0.01 0.06 5.78 0.02 0.13 3.00 6 Pe111 814723 1668090 5.53 0.03 0.19 4.80 0.03 0.19 6.41 0.04 0.26 3.00 7 Pe112 812644 1673638 7.10 0.26 1.66 6.06 1.16 7.42 6.99 1.50 9.60 1.07 8 Pe112 803710 1672238 7.00 0.01 0.06 6.21 0.02 0.13 4.96 0.02 0.13 3.00 9 Pe112 812516 1669640 6.50 0.07 0.45 6.23 0.02 0.13 5.13 0.09 0.58 3.00 10 Pe112 803714 1673777 6.70 0.03 0.19 5.71 0.02 0.13 7.04 0.01 0.06 3.00 11 Pe112 816657 1665755 6.61 2.39 15.30 6.23 0.06 0.38 6.97 0.05 0.32 0.60 12 Pe111 809555 1671047 7.79 0.55 3.52 6.64 1.53 9.79 7.23 0.75 4.80 1.10 13 Pe112 811009 1673618 7.21 0.24 1.54 7.37 0.57 3.65 8.28 0.78 4.99 1.75 14 Pe113 818263 1673271 5.24 0.02 0.13 7.66 1.27 8.13 7.86 0.93 5.95 1.00 15 Pe113 806456 1670389 6.25 0.05 0.32 5.79 0.04 0.26 5.51 0.03 0.19 3.00 16 Pe113 810899 1664062 5.34 0.02 0.13 4.84 0.05 0.32 4.67 0.03 0.19 3.00 17 Pe113 809441 1661439 6.93 0.05 0.32 6.78 0.09 0.58 5.99 0.31 1.98 3.00 18 Pe113 809434 1664970 5.36 0.01 0.06 5.39 0.03 0.19 6.60 0.02 0.13 3.00 19 Pe113 817664 1661045 5.83 0.06 0.38 7.80 0.02 0.13 7.90 0.03 0.19 0.80 20 Pe113 808799 1664197 6.49 0.04 0.26 7.06 0.01 0.06 5.07 0.01 0.06 3.00 21 Pe113 808420 1662252 6.93 0.02 0.13 5.50 0.02 0.13 5.80 0.02 0.13 1.00 22 Pe114 813200 1663756 6.46 0.02 0.13 6.01 0.03 0.19 6.01 0.03 0.19 3.00 23 Pe113 806034 1660127 6.39 0.06 0.38 6.38 0.04 0.26 6.80 0.05 0.32 3.00 24 Pe113 807705 1666017 5.39 0.02 0.13 5.26 0.03 0.19 5.57 0.03 0.19 1.20 25 Pe113 805239 1662476 6.04 0.07 0.45 5.46 0.02 0.13 6.79 0.07 0.45 3.00 27 Pe114 812455 1660232 5.60 0.04 0.26 5.60 0.02 0.13 4.65 0.03 0.19 3.00 29 Pe114 814060 1660267 6.53 0.14 0.90 6.00 0.04 0.26 5.66 0.05 0.32 3.00 33 Pe114 815303 1660688 7.02 0.01 0.06 6.01 0.02 0.13 5.00 0.03 0.19 3.00 34 Pe211 816920 1668104 7.19 0.08 0.51 7.11 0.04 0.26 5.00 0.05 0.32 0.85 35 Pe211 811156 1667521 6.20 0.02 0.13 5.59 0.03 0.19 6.58 0.08 0.51 3.00 36 Pe211 812558 1665464 6.70 0.02 0.13 5.56 0.03 0.19 6.70 0.07 0.45 1.20 37 Pe211 817157 1671259 5.20 0.02 0.13 6.50 0.02 0.13 7.74 0.15 0.96 1.70 38 Pe211 804109 1660311 6.79 0.07 0.45 7.19 0.16 1.02 5.21 0.17 1.09 3.00 107

ID GP X_CORD Y_CORD PH_SR EC_SR ECE_SR PH_RZ EC_RZ ECE_RZ PH_TZ EC_TZ ECE_TZ WTD (m) 39 Pe211 811344 1669609 6.59 3.31 21.18 7.66 3.64 23.30 7.49 2.63 16.83 0.65 40 Pe211 817634 1669457 6.84 0.01 0.06 6.01 0.03 0.19 5.96 0.11 0.70 1.00 41 Pe311 804898 1663795 5.62 0.02 0.13 4.91 0.03 0.19 4.60 0.02 0.13 3.00 42 Pe311 808034 1670052 7.39 0.02 0.13 7.11 0.03 0.19 5.00 0.04 0.26 3.00 43 Pe311 805523 1671967 7.29 0.38 2.43 9.59 0.95 6.08 9.79 0.40 2.56 3.00 44 Pe311 810387 1660380 6.80 0.05 0.32 5.81 0.02 0.13 5.32 0.03 0.19 3.00 45 Pe311 811341 1660440 7.39 0.02 0.13 7.11 0.03 0.19 6.60 0.02 0.13 3.00 46 Pe411 816593 1666831 6.80 1.93 12.35 7.08 0.91 5.82 7.10 0.50 3.20 0.70 47 Pe412 817828 1664168 5.47 0.01 0.06 6.25 0.04 0.26 5.73 0.08 0.51 3.00 48 Pe115 814586 1670952 6.80 0.08 0.51 7.39 0.19 1.22 6.85 0.08 0.51 3.00 49 Pe211 812408 1667465 7.33 3.59 22.98 7.80 3.14 20.10 7.59 1.52 9.73 3.00 50 Pe413 812593 1671835 5.57 0.15 0.96 6.16 0.14 0.90 6.52 0.28 1.79 0.70 51 Pe413 813503 1673304 6.66 0.25 1.60 7.07 0.42 2.69 7.35 1.41 9.02 1.15 52 Pe413 817586 1674203 6.80 0.14 0.90 7.06 0.09 0.58 7.68 0.17 1.09 3.00 57 Va111 809709 1674771 7.15 2.32 14.85 6.79 3.02 19.33 6.79 1.81 11.58 1.70 58 Va111 813650 1674919 7.34 2.37 15.17 7.05 2.51 16.06 7.15 1.97 12.61 0.80 59 Va211 803779 1674998 6.09 0.03 0.19 7.20 0.42 2.69 6.89 0.48 3.07 3.00 60 Pe115 815773 1671484 6.21 0.02 0.13 7.02 0.02 0.13 6.69 0.45 2.88 0.80

108 Appendix 4(A): Texture (sand and clay percent), field capacity and porosity

0- 30 cm 30-60 cm 60-90 cm ID Sand % Clay % Class FC Tot_Por Eff_Por Sand% Clay% Class FC Tot_Por Eff_Por Sand% Clay% Class FC Tot_Por Eff_Por 1 13.60 49.19 C 0.43 0.44 0.01 13.79 48.18 C 0.42 0.44 0.02 13.79 48.18 C 0.42 0.44 0.02 2 27.72 43.48 C 0.39 0.44 0.05 21.37 46.73 C 0.41 0.44 0.03 23.09 45.58 C 0.4 0.44 0.04 3 43.09 13.92 L 0.21 0.34 0.13 64.80 10.81 SL 0.15 0.3 0.15 64.80 10.81 SL 0.15 0.19 0.04 4 50.00 29.05 SCL 0.28 0.43 0.15 38.60 24.46 L 0.29 0.36 0.07 26.20 24.46 SiL 0.3 0.4 0.1 5 57.36 20.13 SL 0.22 0.39 0.17 65.08 16.10 SL 0.18 0.34 0.16 65.08 16.10 SL 0.18 0.34 0.16 6 58.71 19.24 SL 0.21 0.39 0.18 54.22 22.65 SCL 0.24 0.39 0.15 54.22 22.65 SCL 0.24 0.36 0.12 7 61.58 17.14 SL 0.2 0.39 0.19 57.28 24.82 SCL 0.25 0.39 0.14 56.33 23.56 SL 0.19 0.34 0.15 8 72.39 6.71 SL 0.11 0.39 0.28 80.39 3.88 LS 0.15 0.34 0.19 34.22 14.33 SiL 0.24 0.36 0.12 9 75.21 9.84 SL 0.12 0.39 0.27 70.39 14.31 SL 0.16 0.34 0.18 70.39 14.31 SCL 0.21 0.39 0.18 10 75.88 7.82 SL 0.11 0.39 0.28 0.00 0.00 SL 0.11 0.38 0.27 0.00 0.00 SL 0.11 0.17 0.06 11 75.95 9.44 SL 0.12 0.39 0.27 66.50 19.17 SL 0.19 0.38 0.19 63.20 23.39 SCL 0.28 0.36 0.08 12 76.57 12.95 SL 0.14 0.39 0.25 69.38 19.85 SL 0.2 0.38 0.18 0.00 0.00 SL 0.2 0.34 0.14 13 77.61 11.91 SL 0.13 0.39 0.26 77.61 11.91 SL 0.13 0.34 0.21 0.00 0.00 SL 0.13 0.34 0.21 14 77.83 7.18 LS 0.1 0.17 0.07 75.58 6.48 LS 0.1 0.34 0.24 0.00 0.00 LS 0.1 0.17 0.07 15 78.86 13.04 LS 0.13 0.17 0.04 73.11 8.89 SL 0.12 0.2 0.08 73.11 8.89 SL 0.12 0.34 0.22 16 79.43 0.00 LS 0.13 0.17 0.04 72.46 0.00 SL 0.13 0.2 0.07 0.00 0.00 SL 0.13 0.34 0.21 17 81.50 6.33 LS 0.12 0.17 0.05 77.10 8.89 SL 0.12 0.2 0.08 77.10 8.89 SL 0.12 0.34 0.22 18 82.03 4.74 LS 0.12 0.17 0.05 76.89 10.88 SL 0.13 0.2 0.07 71.90 16.27 SL 0.17 0.34 0.17 19 82.20 11.50 LS 0.12 0.17 0.05 66.85 26.95 SCL 0.24 0.39 0.15 66.85 26.95 SCL 0.24 0.36 0.12 20 82.72 7.05 LS 0.12 0.17 0.05 71.77 13.04 SL 0.15 0.3 0.15 71.77 13.04 SL 0.15 0.34 0.19 21 87.73 2.09 S 0.1 0.38 0.28 87.73 6.00 LS 0.12 0.34 0.22 84.68 6.00 LS 0.12 0.17 0.05 22 83.00 5.00 LS 0.11 0.39 0.28 66.00 10.00 SL 0.18 0.39 0.21 65.00 10.00 SL 0.18 0.4 0.22 23 83.00 5.00 LS 0.11 0.34 0.23 66.00 10.00 SL 0.18 0.3 0.12 60.00 28.00 SCL 0.28 0.36 0.08 24 65.00 10.00 SL 0.18 0.41 0.23 60.00 27.00 SCL 0.29 0.44 0.15 60.00 28.00 SCL 0.28 0.36 0.08 25 65.00 10.00 SL 0.18 0.44 0.26 60.00 27.00 SCL 0.29 0.43 0.14 52.00 42.00 SC 0.37 0.43 0.06 26 65.00 10.00 SL 0.18 0.38 0.2 66.00 10.00 SL 0.18 0.37 0.19 60.00 28.00 SCL 0.28 0.41 0.13 27 83.00 6.00 LS 0.12 0.4 0.28 66.00 10.00 SL 0.18 0.42 0.24 65.00 10.00 SL 0.18 0.34 0.16 28 91.00 5.00 S 0.1 0.38 0.28 91.00 5.00 S 0.1 0.18 0.08 92.00 5.00 S 0.09 0.34 0.25 29 50.00 41.00 SC 0.37 0.48 0.11 43.00 28.00 CL 0.31 0.35 0.04 60.00 28.00 C 0.28 0.31 0.03 30 65.00 10.00 SL 0.18 0.38 0.2 61.00 27.00 SCL 0.28 0.39 0.11 11.00 33.00 SiCL 0.38 0.4 0.02

109

0- 30 cm 30-60 cm 60-90 cm ID Sand % Clay % Class FC Tot_Por Eff_Por Sand% Clay% Class FC Tot_Por Eff_Por Sand% Clay% Class FC Tot_Por Eff_Por 31 65.00 10.00 SL 0.18 0.49 0.31 61.00 27.00 SCL 0.28 0.41 0.13 60.00 27.00 SCL 0.28 0.41 0.13 32 82.00 6.00 LS 0.12 0.17 0.05 66.00 10.00 SL 0.18 0.2 0.02 60.00 27.00 SCL 0.28 0.39 0.11 33 72.39 6.71 SL 0.11 0.37 0.26 77.61 11.91 LS 0.13 0.31 0.18 63.20 23.39 LS 0.1 0.35 0.25 34 82.03 4.74 LS 0.19 0.43 0.24 76.89 10.88 SCL 0.27 0.38 0.11 71.90 16.27 CL 0.26 0.41 0.15 FC = Field capacity; Tot_Por = Total porosity; Eff_Por = Effective porosity

Appendix 4 (B): Measured and predicted bulk density, particle density and porosity

0-30 cm 30-60 cm 60-90 cm Point X Y GPU BDs SBD Error PDs Por BDs SBD Error PDs Por BDs SBD Error PDs Por 4 809789 1673963 Pe112 1.68 1.60 0.08 2.76 0.39 1.59 1.62 -0.03 2.61 0.39 1.68 1.68 0.00 2.82 0.40 12 809555 1671047 Pe111 1.93 1.77 0.16 2.76 0.30 1.73 1.69 0.04 2.64 0.34 1.69 1.73 -0.04 2.73 0.33 21 808420 1662252 Pe311 1.55 1.56 -0.01 2.78 0.44 1.66 1.68 -0.02 2.79 0.41 1.79 1.73 0.06 2.80 0.36 23 806034 1660127 Pe113 1.48 1.56 -0.08 2.60 0.43 1.56 1.68 -0.12 2.79 0.44 1.67 1.62 0.05 2.92 0.43 29 816010 1674772 Va111 1.73 1.56 0.17 2.76 0.37 1.73 1.69 0.04 2.77 0.38 1.62 1.65 -0.03 2.74 0.41 39 811344 1669609 Pe413 1.60 1.59 0.01 2.76 0.42 1.64 1.65 -0.01 2.73 0.40 1.88 1.70 0.18 2.84 0.34 46 816593 1666831 Pe411 1.69 1.65 0.04 2.07 0.18 1.74 1.72 0.02 2.81 0.38 1.80 1.75 0.05 2.74 0.34 47 817828 1664168 Pe412 1.51 1.44 0.07 2.59 0.37 1.50 1.47 0.03 2.65 0.37 1.62 1.64 -0.02 2.19 0.32 48 814586 1670952 Pe115 1.33 1.67 -0.34 2.06 0.35 1.45 1.68 -0.23 2.78 0.48 1.44 1.46 -0.02 2.09 0.31 49 812408 1667465 Pe211 1.69 1.67 0.02 2.76 0.39 1.73 1.68 0.05 2.81 0.38 1.72 1.67 0.05 2.85 0.40 57 808984 1674692 Va311 1.67 1.63 0.04 2.73 0.38 1.66 1.70 -0.04 2.76 0.38 1.71 1.67 0.04 2.80 0.41 58 813650 1674919 Va111 1.66 1.73 -0.07 2.18 0.24 1.45 1.75 -0.30 2.82 0.49 1.69 1.62 0.07 2.85 0.41 59 806734 1674439 Va211 1.65 1.71 -0.06 2.06 0.20 1.74 1.63 0.11 2.11 0.17 1.74 1.44 0.30 2.02 0.14 BDs = Measure bulk density; SBD = simulated bulk density; PDs = Particle density; Por = Total porosity

110 Appendix 5: Classification Accuracy Assessment Report

------Image File : g:/research/mapa_images/justnow/fina.img User Name : madyaka13957 Date : Fri Nov 30 19:01:55 2007

ACCURACY TOTALS ------

Class Reference Classified Number Producers Users Name Totals Totals Correct Accuracy Accuracy ------Unclassified 0 0 0 ------Paddy rice 18 28 16 88.89% 57.14% Water 7 5 4 4 80.00% 100.00% Saltt2 1 0 0 ------Cassava 28 24 19 67.86% 79.17% Bareland 0 1 0 ------Plantation 3 2 0 0 ------Paved4 3 0 0 ------

Totals 57 57 39

Overall Classification Accuracy = 68.42%

----- End of Accuracy Totals -----

KAPPA (K^) STATISTICS ------

Overall Kappa Statistics = 0.5002

Conditional Kappa for each Category. ------

Class Name Kappa ------Unclassified 0.0000 Paddy rice 0.3736 Water 7 1.0000 Saltt2 0.0000 Cassava 0.5905 Bareland 0.0000 Plantation 3 0.0000 Paved4 0.0000 ------End of Kappa Statistics------

111

Appendix 6: Histograms of pH, texture and porosity for the three soil depth a) pH

112 b) Texture

113

c) Porosity

114 Appendix 7 : Box plots for pH, texture and porosity of the primary dataset

a). pH

115

b). Texture

116 Appendix 8: Calibration results of root-zone leaching efficiency (Flr) Point Observed EC 0.10 0.20 0.40 0.60 0.80 1.00 3 0.13 0.22 0.17 0.11 0.08 0.06 0.05 7 4.54 3.83 3.83 3.82 3.83 3.83 3.83 11 7.84 9.20 8.70 7.80 7.04 6.42 5.88 12 0.25 0.21 0.21 0.20 0.20 0.18 0.17 12 0.67 0.58 0.55 0.52 0.48 0.45 0.42 14 2.60 1.89 1.02 1.02 0.71 0.62 0.55 19 4.13 4.14 3.89 3.50 3.07 2.75 2.48 21 0.26 0.35 0.33 0.23 0.33 0.35 0.38 24 0.13 0.19 0.14 0.08 0.05 0.04 0.03 34 0.16 0.25 0.24 0.22 0.20 0.18 0.18 36 0.13 0.47 0.45 0.41 0.37 0.34 0.31 37 0.16 0.20 0.14 0.08 0.05 0.04 0.04 39 1.30 2.71 1.62 0.60 0.24 0.11 0.06 40 22.24 10.20 16.90 29.90 42.20 53.90 65.10 46 0.13 1.45 0.98 0.38 0.16 0.07 0.03 50 9.09 9.20 8.70 7.80 7.04 6.42 5.88 51 0.16 0.22 0.16 0.10 0.07 0.05 0.04 57 0.93 0.51 0.69 1.06 1.42 1.79 2.14 58 2.69 2.43 1.57 0.66 0.29 0.14 0.07 60 17.09 9.01 14.50 25.90 37.90 50.60 64.20 47 15.62 16.00 4.55 1.73 0.77 0.30 0.14 1 0.13 1.06 0.64 0.24 0.10 0.05 0.03 2 0.29 0.30 0.41 0.65 0.90 1.16 1.44 4 0.58 0.16 0.23 0.36 0.50 0.63 7.63 5 1.32 0.02 0.31 0.52 0.73 0.94 1.36 6 0.16 0.14 0.18 0.25 0.39 0.46 0.52 8 21.54 8.15 12.90 22.30 31.70 41.00 50.10 9 0.13 0.78 1.07 1.69 2.32 2.95 3.58 10 0.19 0.15 0.20 0.31 0.43 0.54 0.66 15 0.10 0.18 0.26 0.43 0.73 0.78 0.96 16 0.29 0.41 0.62 1.04 1.46 1.88 2.28 17 0.16 0.40 0.62 1.05 1.50 1.96 2.41 18 0.29 0.42 0.64 1.08 1.53 1.98 2.42 20 0.23 0.15 0.21 0.34 0.47 0.60 0.74 22 0.45 0.16 0.22 0.31 0.47 0.59 0.72 23 0.13 0.20 0.30 0.50 0.71 0.92 1.14 27 0.16 0.15 0.20 0.31 0.42 0.53 0.65 33 0.16 0.17 0.25 0.40 0.55 0.71 0.86 35 0.32 0.12 0.14 0.19 0.24 0.29 0.35 38 0.20 0.16 0.23 0.36 0.50 0.63 0.76 41 0.10 0.15 0.20 0.30 0.41 0.53 0.64 42 0.16 1.31 2.82 5.24 7.61 9.93 12.20 43 0.74 0.12 0.14 0.19 0.24 0.29 0.35 44 0.16 0.15 0.21 0.32 0.44 0.56 0.68 45 0.16 0.42 0.64 1.08 1.53 1.98 2.42 48 4.26 5.24 8.78 15.80 22.60 29.30 35.70 49 0.23 0.61 0.82 1.25 1.69 2.14 2.60 52 0.16 0.16 0.23 0.36 0.50 0.63 0.76 59 0.87 0.78 1.07 1.69 2.32 2.95 3.58 25 0.23 1.23 2.05 3.71 5.34 6.96 8.55 29 0.74 2.23 3.27 5.34 7.34 9.29 11.10 59 1.44 1.37 3.35 6.65 9.71 12.80 15.90

117

Appendix 9: Calibration results of natural drainage (Go)

Point Calibrated_Flr Obs_GWD 0.00 0.08 0.16 0.24 0.32 1 1.00 3.00 0.74 0.59 0.93 2.15 5.83 2 0.10 3.00 -0.02 0.47 0.63 0.87 1.11 3 0.40 1.00 0.67 0.94 1.34 2.34 3.34 4 0.40 3.00 0.08 0.67 0.94 2.33 4.00 5 0.10 3.00 0.32 0.71 1.05 2.42 4.08 6 0.20 3.00 0.59 0.84 0.92 2.36 4.03 7 0.20 1.07 -0.07 0.06 0.41 0.61 0.91 8 0.10 3.00 0.38 0.76 1.07 2.68 4.24 9 0.10 3.00 0.46 0.75 0.98 2.74 5.01 10 0.10 3.00 0.04 0.67 1.05 2.69 4.26 11 0.40 0.60 0.64 0.64 0.71 0.80 1.02 12 0.10 1.10 0.45 0.93 1.06 1.21 1.70 12 0.10 1.75 0.45 0.93 1.07 1.27 1.82 14 0.10 1.00 0.45 0.56 0.68 0.81 1.01 15 0.10 3.00 0.53 0.78 0.99 1.58 3.66 16 0.20 3.00 0.55 0.79 0.88 2.56 5.06 17 0.60 3.00 0.58 0.79 0.87 1.19 3.79 18 0.10 3.00 0.59 0.82 0.94 2.77 5.54 19 0.40 0.80 0.39 0.50 0.61 0.72 0.87 20 0.10 3.00 0.59 0.81 0.93 2.09 4.87 21 0.20 1.00 0.51 0.58 0.64 0.70 0.85 22 0.10 3.00 0.51 0.76 0.89 1.73 3.65 23 0.80 3.00 0.72 0.92 0.92 3.80 5.95 24 0.80 1.20 0.55 0.67 0.83 1.05 2.32 25 0.10 3.00 0.43 0.65 0.86 1.50 4.00 27 0.20 3.00 0.53 0.77 0.90 1.73 4.23 29 0.60 3.00 0.53 0.77 0.99 1.73 4.23 33 0.10 3.00 0.56 0.84 0.98 2.48 3.95 34 0.20 1.00 0.41 0.61 0.87 1.76 4.11 35 0.10 3.00 0.12 0.35 0.63 2.74 5.52 36 0.20 1.20 0.32 0.60 1.08 3.22 5.57 37 0.20 1.70 0.40 0.61 0.96 3.14 5.92 38 1.00 3.00 0.72 0.92 1.04 3.80 5.95 39 0.40 0.65 0.31 0.42 0.53 0.66 0.90 40 0.60 1.00 -0.05 0.34 0.82 5.91 7.21 41 0.10 3.00 0.54 0.80 0.94 2.60 4.68 42 0.10 3.00 0.53 0.78 0.90 1.58 3.66 43 0.10 3.00 0.06 0.54 0.74 1.72 3.95 44 0.10 3.00 0.57 0.79 0.92 2.09 4.87 45 0.10 3.00 0.53 0.77 0.90 1.73 4.23 46 0.10 0.70 0.39 0.62 0.91 1.72 3.87 47 0.20 1.00 1.11 1.12 1.14 1.17 1.29 48 0.10 3.00 0.32 0.75 0.90 2.42 4.08 49 0.10 3.00 0.08 0.67 1.02 2.33 4.00 50 0.40 0.70 0.34 0.57 9.00 2.62 5.65 51 0.10 1.15 -0.12 0.37 0.88 2.46 5.17 52 0.10 3.00 0.42 0.71 0.90 2.34 4.62 57 0.20 1.70 -0.05 0.50 0.86 2.06 4.72 58 0.10 0.80 0.19 0.52 0.81 1.32 3.82 59 0.10 3.00 -0.05 0.61 0.84 1.73 3.30 60 0.60 0.80 -0.03 0.45 0.83 1.93 4.80

118

Appendix 10: Simulation Results for root-zone salinity

POINT GP X Y YEAR_0 YEAR_3 YEAR_10 YEAR_20 3 Pe111 816288 1663192 0.13 0.09 0.25 0.32 4 Pe111 816156 1663209 0.40 0.28 0.20 0.13 10 Pe111 814473 1668455 0.10 0.07 0.10 0.13 31 Pe111 808599 1672631 0.10 0.07 0.07 0.10 44 Pe111 804981 1661113 0.10 0.04 0.09 0.11 46 Pe111 804504 1666705 0.40 0.28 0.28 0.34 13 Pe112 812517 1668792 0.50 0.17 0.48 0.55 18 Pe112 810523 1672141 0.96 0.76 0.78 0.82 33 Pe112 809836 1670828 0.06 0.24 0.70 1.46 52 Pe112 804137 1673100 0.20 0.17 0.36 0.61 54 Pe112 817479 1674121 1.20 1.12 1.20 1.31 63 Pe112 807294 1670605 0.20 0.17 0.13 0.10 16 Pe113 808548 1665289 0.10 0.09 0.09 0.09 30 Pe113 805033 1671892 0.50 0.43 0.62 0.87 35 Pe113 806961 1669083 0.10 0.07 0.12 0.20 37 Pe113 804174 1665801 0.10 0.07 0.08 0.10 38 Pe113 805724 1663079 0.20 0.19 0.22 0.26 40 Pe113 812557 1663596 0.10 0.09 0.17 0.26 43 Pe113 804179 1669859 0.20 0.14 0.11 0.08 45 Pe113 804366 1663596 0.20 0.16 0.16 0.17 51 Pe113 818540 1672340 4.20 2.87 9.63 20.05 58 Pe113 808038 1667174 13.00 17.80 30.90 52.65 59 Pe113 807584 1661415 0.10 0.14 0.19 0.24 61 Pe113 810951 1662722 0.40 0.35 0.37 0.39 65 Pe113 818218 1672585 0.20 0.15 0.19 0.24 66 Pe113 809717 1664909 0.30 0.26 0.27 0.29 1 Pe114 815316 1662192 0.10 0.21 0.27 0.01 41 Pe114 813258 1660897 0.10 0.08 0.10 0.11 26 Pe115 817083 1671497 1.30 1.48 2.11 2.84 32 Pe115 812841 1672527 0.19 0.61 3.53 23.30 6 Pe211 817153 1668228 0.50 0.69 2.08 5.86 7 Pe211 817155 1668230 2.56 7.76 10.73 17.15 8 Pe211 815948 1668815 0.10 0.08 0.14 0.21 14 Pe211 813219 1666240 0.13 0.49 2.00 5.98 17 Pe211 809656 1667782 0.20 0.14 0.73 1.70 60 Pe211 811662 1667236 0.40 0.76 1.95 3.44 70 Pe211 810755 1668581 3.33 9.17 20.35 51.05 9 Pe311 816032 1660808 0.10 0.08 0.09 0.10 15 Pe311 810957 1664599 0.20 0.18 0.40 0.65 34 Pe311 804896 1668820 0.10 0.08 0.13 0.17 36 Pe311 806870 1665471 3.50 3.06 13.15 21.00 39 Pe311 808271 1662537 0.10 0.10 0.15 0.20 50 Pe311 816791 1661316 0.22 0.41 2.02 9.28 64 Pe311 810931 1664520 0.10 0.08 0.13 0.15 67 Pe311 812622 1663496 0.10 0.09 0.11 0.12

119

POINT GP X Y YEAR_0 YEAR_3 YEAR_10 YEAR_20 68 Pe311 816764 1661399 14.40 8.14 40.30 69.80 5 Pe412 816516 1665723 9.60 3.34 55.35 56.25 2 Pe413 815945 1662545 0.10 0.07 0.03 0.01 22 Pe413 817954 1673236 4.40 5.55 13.50 28.50 25 Pe413 815747 1671574 1.70 2.05 3.86 6.46 69 Pe413 813281 1673331 3.70 5.83 9.78 14.80 71 Pe413 810090 1669166 2.70 5.39 10.85 15.50 11 Va111 814183 1674780 0.20 4.05 5.07 5.32 12 Va111 814132 1675041 13.00 17.80 30.90 52.65 24 Va111 816010 1674772 1.10 1.11 1.15 1.21 29 Va211 806793 1673956 0.10 0.10 0.26 0.47 53 Va211 807338 1674695 0.10 0.17 0.40 0.65 28 Va311 809196 1674661 3.50 3.09 9.24 17.55

120 Appendix 11: Simulation Results for the transition zone

Point GP X Y Year_0 Year_3 Year_10 Year_20 3 Pe111 816288 1663192 0.13 0.15 4.15 19.27 4 Pe111 816156 1663209 0.40 0.37 0.73 1.38 10 Pe111 814473 1668455 0.20 0.17 0.18 0.28 31 Pe111 808599 1672631 0.10 0.08 0.06 0.09 44 Pe111 804981 1661113 0.40 0.37 0.36 0.38 46 Pe111 804504 1666705 0.40 0.34 0.26 0.32 13 Pe112 812517 1668792 1.00 0.77 1.03 1.50 18 Pe112 810523 1672141 1.00 0.87 1.00 1.33 33 Pe112 809836 1670828 0.06 0.13 0.32 0.49 52 Pe112 804137 1673100 1.20 1.17 1.37 1.67 54 Pe112 817479 1674121 1.20 1.17 1.37 1.67 63 Pe112 807294 1670605 0.20 0.24 0.43 0.72 16 Pe113 808548 1665289 0.10 0.08 0.10 0.13 30 Pe113 805033 1671892 0.50 0.51 0.80 1.28 35 Pe113 806961 1669083 0.06 0.13 0.32 0.49 37 Pe113 804174 1665801 0.10 0.09 0.08 0.09 38 Pe113 805724 1663079 0.06 0.13 0.32 0.49 40 Pe113 812557 1663596 0.10 0.09 0.13 0.22 43 Pe113 804179 1669859 0.20 0.17 0.12 0.09 45 Pe113 804366 1663596 0.10 0.09 0.09 0.10 51 Pe113 818540 1672340 4.20 3.50 6.29 15.48 58 Pe113 808038 1667174 0.01 0.10 0.41 0.65 59 Pe113 807584 1661415 0.20 0.18 0.23 0.33 61 Pe113 810951 1662722 0.40 0.37 0.36 0.38 65 Pe113 818218 1672585 0.20 0.17 0.17 0.22 66 Pe113 809717 1664909 1.00 0.87 1.00 1.33 1 Pe114 815316 1662192 0.10 0.15 0.27 0.22 41 Pe114 813258 1660897 0.20 0.18 0.16 0.17 49 Pe114 818557 1656243 0.20 0.17 0.23 0.37 57 Pe114 815025 1658844 0.20 0.18 0.16 0.15 26 Pe115 817083 1671497 4.40 5.37 11.50 19.91 32 Pe115 812841 1672527 0.19 0.33 2.04 11.56 6 Pe211 817153 1668228 0.10 0.08 0.07 0.07 7 Pe211 817155 1668230 2.56 6.09 9.35 14.01 8 Pe211 815948 1668815 0.10 0.09 0.11 0.18 14 Pe211 813219 1666240 0.10 0.15 0.86 1.88 17 Pe211 809656 1667782 0.20 0.17 0.40 1.29 60 Pe211 811662 1667236 1.70 5.04 15.26 24.17 70 Pe211 810755 1668581 3.30 6.53 12.02 12.87 27 Pe211 818665 1670203 8.30 9.70 23.09 41.85 55 Pe211 810965 1657742 0.50 0.46 0.50 0.57 56 Pe211 805838 1659510 0.50 0.43 0.24 0.09 62 Pe211 810669 1657965 0.20 0.17 0.17 1.49 9 Pe311 816032 1660808 0.50 0.45 0.47 0.52 15 Pe311 810957 1664599 0.20 0.18 0.31 0.54 34 Pe311 804896 1668820 0.10 0.08 0.09 0.16 36 Pe311 806870 1665471 0.13 0.27 1.38 7.27 39 Pe311 808271 1662537 0.13 0.19 0.43 0.77

121

Point GP X Y Year_0 Year_3 Year_10 Year_20 50 Pe311 816791 1661316 0.22 0.26 1.24 5.18 64 Pe311 810931 1664520 0.10 0.13 0.24 0.39 67 Pe311 812622 1663496 0.10 0.09 0.11 0.14 68 Pe311 816764 1661399 13.00 15.29 25.22 42.42 5 Pe412 816516 1665723 9.60 11.24 19.16 22.00 2 Pe413 815945 1662545 0.10 0.09 0.08 0.07 22 Pe413 817954 1673236 4.40 4.59 10.02 21.23 25 Pe413 815747 1671574 1.70 2.84 3.65 3.96 69 Pe413 813281 1673331 3.70 4.80 8.15 12.53 71 Pe413 810090 1669166 2.70 4.54 11.60 19.28 23 Pe413 819877 1673907 0.10 0.19 0.95 2.03 47 Pe511 807495 1657703 0.60 0.53 0.76 1.32 48 Pe511 809347 1657703 1.70 1.51 1.67 2.21 11 Va111 814183 1674780 0.20 4.21 4.72 5.29 12 Va111 814132 1675041 13.00 15.29 25.22 42.42 24 Va111 816010 1674772 1.10 1.11 1.14 1.19 21 Va111 819670 1678104 0.30 0.33 0.44 0.51 42 Va111 817627 1675366 0.20 0.18 0.14 0.10 29 Va211 806793 1673956 0.10 0.09 0.19 0.38 53 Va211 807338 1674695 0.10 0.13 0.31 0.54 19 Va211 803665 1678242 6.10 6.22 7.27 8.11 20 Va211 801618 1678078 9.60 10.66 13.96 16.45 28 Va311 809196 1674661 3.50 3.87 10.64 20.65

122 Appendix 12: Simulation Results for the aquifer

Point GP X Y Year_0 Year_3 Year_10 Year_20 3 Pe111 816288 1663192 0.17 0.17 0.18 0.19 4 Pe111 816156 1663209 0.30 0.29 0.27 0.24 10 Pe111 814473 1668455 0.10 0.10 0.09 0.08 31 Pe111 808599 1672631 0.10 0.10 0.09 0.08 44 Pe111 804981 1661113 0.40 0.39 0.37 0.33 46 Pe111 804504 1666705 0.30 0.30 0.28 0.25 13 Pe112 812517 1668792 0.60 0.00 0.00 0.52 18 Pe112 810523 1672141 0.59 0.58 0.53 0.47 33 Pe112 809836 1670828 0.10 0.10 0.09 0.09 52 Pe112 804137 1673100 0.80 0.79 0.74 0.67 54 Pe112 817479 1674121 0.80 0.79 0.74 0.67 63 Pe112 807294 1670605 0.20 0.00 0.00 0.17 16 Pe113 808548 1665289 0.10 0.00 0.00 0.08 30 Pe113 805033 1671892 0.13 0.00 0.00 0.22 35 Pe113 806961 1669083 0.10 0.10 0.09 0.08 37 Pe113 804174 1665801 0.10 0.10 0.09 0.08 38 Pe113 805724 1663079 0.10 0.10 0.09 0.08 40 Pe113 812557 1663596 0.10 0.10 0.09 0.08 43 Pe113 804179 1669859 0.20 0.20 0.18 0.16 45 Pe113 804366 1663596 0.10 0.10 0.09 0.08 51 Pe113 818540 1672340 15.00 14.74 13.71 12.27 58 Pe113 808038 1667174 0.10 0.10 0.10 0.09 59 Pe113 807584 1661415 2.64 0.00 0.00 2.37 61 Pe113 810951 1662722 0.40 0.39 0.37 0.33 65 Pe113 818218 1672585 0.20 0.20 0.18 0.17 66 Pe113 809717 1664909 0.59 0.58 0.53 0.47 1 Pe114 815316 1662192 0.10 0.10 0.10 0.09 41 Pe114 813258 1660897 0.20 0.20 0.18 0.16 49 Pe114 818557 1656243 0.10 0.10 0.09 0.00 57 Pe114 815025 1658844 0.10 0.00 0.00 0.00 26 Pe115 817083 1671497 5.30 0.00 0.00 4.45 32 Pe115 812841 1672527 0.16 0.16 0.16 0.18 6 Pe211 817153 1668228 0.20 0.20 0.18 0.16 7 Pe211 817155 1668230 1.54 0.00 0.00 1.83 8 Pe211 815948 1668815 0.20 0.20 0.18 0.16 14 Pe211 813219 1666240 0.10 0.00 0.00 0.09 17 Pe211 809656 1667782 0.40 0.40 0.39 0.36 60 Pe211 811662 1667236 1.30 0.00 0.00 1.21 70 Pe211 810755 1668581 3.30 0.00 0.00 2.84 27 Pe211 818665 1670203 5.20 5.14 4.91 4.54 55 Pe211 810965 1657742 0.10 0.10 0.09 0.09 56 Pe211 805838 1659510 0.10 0.10 0.11 0.11 62 Pe211 810669 1657965 0.20 0.20 0.18 0.10 9 Pe311 816032 1660808 0.50 0.49 0.46 0.41 15 Pe311 810957 1664599 0.20 0.20 0.19 0.17 34 Pe311 804896 1668820 0.10 0.10 0.09 0.09 36 Pe311 806870 1665471 0.13 0.00 0.00 0.22 39 Pe311 808271 1662537 0.13 0.00 0.00 0.11

123

Point GP X Y Year_0 Year_3 Year_10 Year_20 50 Pe311 816791 1661316 0.17 0.00 0.00 0.17 64 Pe311 810931 1664520 0.10 0.10 0.09 0.08 67 Pe311 812622 1663496 0.10 0.10 0.09 0.08 68 Pe311 816764 1661399 16.90 16.64 15.55 13.96 5 Pe412 816516 1665723 0.30 0.30 0.28 0.25 2 Pe413 815945 1662545 0.10 0.10 0.09 0.08 22 Pe413 817954 1673236 0.10 0.11 0.16 0.21 25 Pe413 815747 1671574 1.30 0.00 0.00 1.33 69 Pe413 813281 1673331 2.64 0.00 0.00 2.37 71 Pe413 810090 1669166 1.90 1.88 1.81 1.67 23 Pe413 819877 1673907 0.10 0.10 0.10 0.10 47 Pe511 807495 1657703 0.10 0.10 0.09 0.09 48 Pe511 809347 1657703 0.30 0.30 0.28 0.25 11 Va111 814183 1674780 0.20 2.57 2.47 2.32 12 Va111 814132 1675041 16.90 0.00 0.00 13.96 24 Va111 816010 1674772 0.20 0.20 0.18 0.16 21 Va111 819670 1678104 0.20 0.00 0.00 0.17 42 Va111 817627 1675366 0.30 0.30 0.28 0.25 29 Va211 806793 1673956 1.10 1.08 1.00 0.90 53 Va211 807338 1674695 0.10 0.10 0.09 0.08 19 Va211 803665 1678242 2.30 0.00 0.00 2.01 20 Va211 801618 1678078 4.20 0.00 0.00 3.73 28 Va311 809196 1674661 2.60 2.57 2.45 2.31

124 Appendix 13: Comparison of experimental variogram of original data (OK) and trend residuals (UK) for simulated EC values

125

126

Appendix 14: SaltMod features for data input and output display

127

128