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:
Prof. Dr.Ir. A. Veldkamp:
B. (Bas) Wesselman:
Dr. A. (Abbas) Farshad:
Dr. D.B. (Dhruba) Pikha Shrestha:
Chairperson
External Examiner
Internal Examiner
First Supervisor
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.
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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
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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
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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 10th
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 20th
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
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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
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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]).
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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
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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
modelling
RS data (aerial/satellite)
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 nonunique 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.
1.4.
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
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
((CaSO4.2H2O), which has solubility of approximately 2 grams per litre. There are eight ions commonly
associated with soluble salts which include cations of calcium (Ca2+), magnesium (Mg2+), sodium (Na+)
and potassium (K+) and anions of alkalinity such as carbonate (CO3 2- ), bicarbonate, (HCO3-), and
carbonic acid (H2CO3); sulphate (SO42-) 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 25oC 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
Non saline
Low salinity
Mid salinity
High salinity
Severe salinity
Degree of crop sensitivity
Very sensitive crops
Sensitive crops
Moderate sensitive crops
Moderate resistant crops
Resistant crops
ECe of soil saturated extract at 25oC (dS/m)
0-2
2-4
4-8
8 - 16
>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:
Y = Ym/ [1 + (ECsw/EC50)]p…………………3
where Y is the yield obtained per given electrical conductivity, Ym is the yield under no-saline
conditions, ECsw is the average salinity of applied water, Ec50 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 ECsw/EC50 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 soilwater-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 agrohydro-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
Surface reservoir
Symbol
Eo
Era
Ii
Ig
Io
Pp
So
Explanation
Evaporation from open water
Total actual evapo-transpiration
Irrigation water supplied by the canal system
Gross amount of field irrigation water
Amount of water leaving the area through the canal (by-pass)
Rainfall/precipitation
Amount of surface run-off or surface drain water
Root zone
Lc
Lr
Rr
Gu
λi
Percolation from irrigation canal system
Total percolation from the root zone
Total capillary rise into the root zone
Subsurface drainage water used for irrigation
Amount of water infiltrated through the surface into the root zone
Transition zone
Gd
VL
Total amount of subsurface drainage water
Vertical downward drainage into the aquifer
Aquifer
Gw
Gi
Go
VR
Fw
Groundwater pumped from the wells in the aquifer
Horizontal incoming ground water flow through the aquifer
Horizontal outgoing ground water flow through the aquifer
Vertical upward seepage from the aquifer
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.
2.4.1.
Scope, Assumptions and Shortcomings of Saltmod
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(x1), Z( x2), …., Z(xn) at points x1, x2, …., xn
distributed in the neighborhood of X0, the best estimator at X0 is given by:
N
Z* (x0) = Σ λi Z(xi)
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.
3.1.1.
The Study Area
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 15o to 15o15’ N and 101o45’ to 102o 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 29.2oC with
mean maximum and minimum values of 35.7oC in April and 22.8oC in December respectively (figure
3.2 & 3.3 and table 3.1).
ETo (mm/month)
Avg Monthly Rainfall & Evaporation
180.00
250.00
160.00
200.00
140.00
120.00
150.00
Evaporation
(mm)
100.00
mm
mm
Rainfall (mm)
ETo (mm/month)
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
Jan
Dec
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
RHMax %
o
20.00
Tmin (oC)
%
C
Tmax (oC)
50.00
RH Min %
AvgRH %
Tavg (oC)
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
Jan
Feb
Mar
April
May
Jun
Jul
Aug
Sept
Oct
Nov
Dec
Rainfall
(mm)
5.9
18.1
36.1
66.3
137.2
111.8
115.3
146.2
226.6
141.2
27.0
3.0
Min T
o
C
Max T
o
C
RH Min
(%)
RH Max
(%)
Windspeed
(knots)
Dewpond
(oC)
Evapo
(mm)
30.8
33.5
35.8
36.5
35.1
34.3
33.8
33.2
32.2
30.9
29.7
29.1
17.7
20.4
22.7
24.4
24.7
24.7
24.3
24.1
23.7
22.8
20.5
17.5
85
83
82
84
88
88
88
90
93
93
89
86
40
38
37
42
50
52
53
56
61
60
53
44
1.4
1.5
1.6
1.7
1.9
2.3
2.4
2.3
1.4
1.8
2.1
2.0
15.9
17.7
19.4
21.6
23.0
22.9
22.7
22.8
23.3
22.0
19.1
15.9
137.3
143.9
183.2
183.4
174.8
163.4
164.3
151.0
125.8
125.6
128.6
135.9
Monthly
Sunshine
Hours
241.10
227.60
234.90
249.80
194.70
168.60
184.60
111.10
138.90
187.60
184.10
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 /
Data
Type/Format
Source and collection methods
Geomorphology and
pedology
Topography and
terrain data
Geopedologic map (1:50000 scale)
ITC (Previous research studies), LDD
Topographic maps (1:50000) Digital
terrain model
Geology, soil and
hydrology
Maps, attributes tables (soil and water
properties) and documents
Ground water quality use and
management, irrigation and drainage
Maps, attributes tables and documents
ITC, Previous thesis and LDD
Scanning, digitizing and GIS interpolation of existing
contour maps
Publications, LDD, previous ITC_research work,
field investigation and laboratory analysis
Land cover and
utilization
Climate
Long term data of precipitation,
evaporation, temperature, humidity,
wind
Previous research work, satellite image processing,
orthophotos, field observations and descriptions
Previous studies, meteorological stations
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
(Pe)
Ridge
Sedimentary rocks, Korat group
Glacis
Vale
Lateral Vale
Sedimentary rocks, Korat group
Sedimentary rocks, Korat group
Sedimentary rocks, Korat group
Depression
Sedimentary rocks, Korat group
Top complex
Side complex
Slope-facet complex
Summit
Tread riser complex
Tread riser complex
Slope complex
Side complex
Bottom-Side complex
Bottom complex
Basin
Pe111
Pe112
Pe113
Pe114
Pe115
Pe211
Pe311
Pe411
Pe412
Pe413
Pe511
Flood plain
Old Terraces
New Terraces
Alluvial deposits
Alluvial deposits
Alluvial deposits
Levee–overflow complex
Overflow–Basin complex
Overflow–Basin complex
Va111
Va211
Va311
Valley
(Va)
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 601000 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 4th to the 27th 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 105oC 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 (m3/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 (ETc), 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:
(ETc) = (kc) * (ETo).....................................8,
where ETc is potential evapotranspiration, kc is the crop factor and ETo 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 R2 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
R = 0.6497
Actual(g/m3)
1.80
1.70
1.60
1.50
1.40
1.40
1.50
1.60
1.70
1.80
1.90
2.00
Pre dicte d (g/cm3)
Figure 3.15 Correlation between simulated and measured soil bulk density
3.4.
3.4.1.
Model Assumptions/Simplifications and Calibration
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, m3/season per m2 total area) over the horizontally incoming groundwater (Gi,
m3/season per m2 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)
b)
Leaching Efficiency (Lr) Calibration
1.60
0.20
0.18
1.40
0.16
Obs
0.14
0.10
0.12
0.20
0.10
0.40
0.08
0.60
0.06
0.80
0.04
1.00
Obs
1.20
0.00
GWD (m)
EC (dS/m)
Natural Drainage (Gn) Calibration
1.00
0.08
0.80
0.16
0.60
0.24
0.32
0.40
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 Gstat 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
Sand_60
Clay_60
Sand_90
Clay_90
GW_EC
65.02
17.21
60.55
19.32
1.47
70.39
14.31
66.85
16.27
2.45
13.79
2.09
7.00
0.00
0.06
87.97
48.18
87.73
48.18
16.89
319.03
138.35
452.35
153.09
10.78
17.86
11.76
21.27
12.37
3.28
-1.42
1.15
-1.178
0.99
3.31
1.79
1.19
0.56
0.73
11.2
81
27
68
35
64
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
Pe111
Pe112
Pe113
Pe114
Pe115
Pe211
Pe311
Pe411
Pe412
Pe413
Va111
Va211
0.31
3.09
0.89
0.17
0.13
3.61
1.36
5.82
0.26
5.10
17.70
2.69
0.16
0.38
0.19
0.13
0.13
0.19
0.19
5.82
0.26
1.22
17.70
2.69
0.06
0.13
0.06
0.13
0.13
0.13
0.13
5.82
0.26
0.58
16.06
2.69
1.2
9.79
8.13
0.26
0.13
23.30
6.08
5.82
0.26
20.10
19.33
2.69
0.45
4.03
2.29
0.08
NA
8.69
2.64
NA
NA
8.43
2.31
NA
CV%
145
130
257
47
NA
241
194
NA
NA
165
13
NA
n
6
7
12
3
1
7
5
1
1
5
2
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
PC 1
PC 2
PC 3
PC 4
PC 5
PC 6
PC 7
%
RApect
0.229
-0.865
-0.205
-0.396
0.000
-0.000
-0.000
RCurv
0.015
0.150
-0.603
-0.006
0.035
-0.782
-0.001
RDEM
0.725
0.410
0.124
-0.539
0.007
0.001
0.011
39.16
22.07
20.83
RLC_map
-0.010
-0.004
-0.002
0.003
-0.000
-0.002
1.000
RPLCURV
0.017
0.133
-0.554
0.006
0.665
0.483
0.001
RPRCURV
-0.025
-0.129
0.521
-0.003
0.746
-0.393
-0.000
RSlopD
0.648
-0.164
-0.018
0.743
0.003
-0.011
0.003
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 R2: 0.1649) variability
for the topsoil layer (0-30cm), 13.3% (Adjusted R2: 0.1332) for the second layer (30-60cm) and 20.4%
(Adjusted R2: 0.2041) for the transition zone. While in the case of simulated values the model accounted
for just 27.0% (Adjusted R2: 0.2702) for root-zone salinity and 14.3% (Adjusted R2: 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 R2: 0.241) and just 7.5% (Adjusted R2: 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
0-30cm
Adj R2 = 0.2103
30-60cm
Adj R2 = 0.1658
60-90cm
Adj R2 = 0.2342
Predictor
Coefficient
Std.error
t value
Pr(>|t|)
Intercept
SPC1
SPC3
Relief
Intercept
SPC1
-0.080717
-0.003508
0.002617
0.006505
-0.022906
-0.005156
0.183607
0.001223
0.001657
0.002288
0.133714
0.001335
-0.440
-2.868
1.579
2.844
-0.171
-3.862
0.66163
0.00551 **
0.11899
0.00591 **
0.864482
0.000251 ***
Intercept
SPC1
0.163911
-0.006112
0.129293
0.001291
1.268
-4.734
0.209
1.13e-05 ***
Table 3.9 Summary results of regression for stepwise regression analysis for simulated EC values
Variable
10th Year Prediction
Root zone
(0-60cm)
Adj. R2 = 0.287
Transition zone (60-90cm)
: Adj. R2 = 0.1641
20th Year Prediction
Root zone
(0-60cm)
Adj R2 = 0.2619
Transition zone (6090cm): Adj. R2 = 0.1278
Predictor
Coefficient
Std.error
t value
Pr(>|t|)
Intercept
SPC1
SPC2
SPC3
SPC7
Intercept
SPC1
SPC2
SPC6
Relief
0.663332
-0.007347
-0.003192
0.003336
0.078415
0.3052272
-0.0013118
0.0041311
0.0033670
0.0047889
0.248979
0.001431
0.001727
0.002045
0.054845
0.1969719
0.0008319
0.0011695
0.0023053
0.0025221
2.664
-5.136
-1.848
1.631
1.430
1.550
-1.577
3.532
1.461
1.899
0.00969 **
2.69e-06 ***
0.06910 .
0.10758
0.15750
0.126020
0.000270 ***
0.000757 ***
0.148881
0.061969 .
Intercept
SPC2
SPC4
SPC5
Intercept
SPC1
SPC2
0.826114
0.005284
0.006347
0.003165
0.136300
-0.004450
-0.002951
0.163146
0.001330
0.002212
0.001850
0.153508
0.001499
0.00179
5.064
3.974
2.869
1.711
0.888
-2.968
-1.644 5
3.36e-06 ***
0.000175 ***
0.005503 **
0.091714 .
0.37772
0.00414 **
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)
var1
Variogram map, log10EC (dS/m), 60-100cm layer)
var1
15000
var1
15000
2.0
15000
2.0
2.0
10000
10000
10000
5000
0
1.0
-5000
5000
1.0
0
-5000
-5000
-10000
0.0
-15000
-15000
-10000
-5000
0
5000
10000
15000
dx
0.5
-10000
0.0
-15000
1.0
0
0.5
0.5
-10000
dy
5000
dy
dy
1.5
1.5
1.5
0.0
-15000
-15000
-10000
-5000
0
dx
5000
10000
15000
-15000
-10000
-5000
0
5000
10000
15000
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
Pe111
Pe112
Pe113
Pe114
Pe115
Pe211
Pe311
Pe412
Pe413
Va111
Va211
Va311
YEAR_0
YEAR_3
YEAR_10
3.50
3.09
9.24
YEAR_20
17.55
0.78
0.72
0.92
1.23
4.77
7.65
12.37
19.73
2.52
3.78
7.60
13.05
2.3
2.85
6.77
15.73
1.6
2.63
5.64
8.95
1.61
2.73
5.43
12.20
9.6
11.24
19.16
22.00
2.12
2.84
5.74
9.85
2.96
4.22
6.33
9.90
3.98
4.28
5.43
6.37
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
EC (dS/m)
20
15
YEAR_0
YEAR_10
YEAR_20
10
5
V
a3
11
V
a2
11
V
a1
11
P
e4
13
P
e4
12
P
e3
11
P
e2
11
P
e1
15
P
e1
14
P
e1
13
P
e1
12
P
e1
11
0
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
Pe111
Pe112
Pe113
Pe114
Pe115
Pe211
Pe311
Pe412
Pe413
Pe511
Va111
Va211
Va311
YEAR_0
YEAR_3
0.47
0.83
0.59
0.09
2.35
2.52
1.77
0.30
2.74
0.65
2.64
3.53
4.50
YEAR_10
0.46
0.82
0.59
0.05
2.34
2.36
1.74
0.31
2.80
0.63
3.65
3.52
4.48
0.47
0.78
0.57
0.04
2.19
2.18
1.39
0.30
2.78
0.75
3.72
3.24
4.80
YEAR_20
0.36
0.69
0.52
0.04
2.60
2.08
1.19
0.28
2.88
1.01
4.12
2.64
7.53
56
Mean Predicted EC per landform
4
3.5
EC (dS/m)
3
2.5
YEAR_0
2
YEAR_10
YEAR_20
1.5
1
0.5
Pe
11
1
Pe
11
2
Pe
11
3
Pe
11
4
Pe
11
5
Pe
21
1
Pe
31
1
Pe
41
2
Pe
41
3
Pe
51
1
Va
11
1
Va
21
1
Va
31
1
0
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
Pe111
Pe112
Pe113
Pe114
Pe115
Pe211
Pe311
Pe412
Pe413
Pe511
Va111
Va211
Va311
0.23
YEAR_3
0.23
YEAR_10
0.21
YEAR_20
0.20
0.52
0..38
0.35
0.43
1.42
1.19
1.11
1.18
0.13
0.10
0.09
0.06
2.73
0.08
0.08
2.31
1.15
0.58
0.55
1.05
2.04
1.96
1.83
1.70
0.30
0.30
0.28
0.25
1.02
0.37
0.36
0.96
0.20
0.20
0.19
0.17
3.56
0.61
0.59
3.37
1.93
0.29
0.27
1.68
2.60
2.57
2.45
2.31
Mean Predicted EC per landform
4
3.5
EC (dS/m)
3
2.5
YEAR_0
2
YEAR_10
YEAR_20
1.5
1
0.5
Va
31
1
a2
11
V
a1
11
V
e5
11
P
Pe
41
3
Pe
31
1
P
e4
12
e2
11
P
e1
15
P
e1
14
P
Pe
11
3
e1
12
P
P
e1
11
0
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
1
Season
Pe111
Pe112
Pe113
Pe114
Pe115
Pe211
Pe311
Pe412
Pe413
Pe511
Va111
Va211
Va311
1
YEAR_3
2
YEAR_10
1
2
1
YEAR_20
2
-4.18
-1.39
-1.83
-0.85
-1.38
-0.85
-1.38
-2.15
-1.05
-1.33
-1.10
-1.38
-1.19
-1.48
-3.46
-0.96
-1.45
-0.81
-1.28
-0.84
-1.26
-3.19
-0.87
-1.64
-0.83
-1.22
-1.20
-1.22
-2.52
-0.64
-1.08
-0.64
-1.07
-0.64
-1.07
-2.89
-0.84
-2.11
-0.80
-2.08
-0.81
-1.68
-2.61
-0.80
-1.26
-0.76
-1.20
-0.76
-1.20
-3.01
-0.83
-1.23
-0.75
-1.18
-0.75
-1.18
-2.30
-0.83
-1.40
-0.82
-1.40
-0.82
-1.49
-3.03
-0.82
-1.21
-0.71
-1.16
-0.71
-1.16
-1.79
-0.81
-1.28
-0.77
-2.19
-0.73
-1.23
-2.50
-0.78
-1.31
-0.76
-1.54
-1.44
-1.69
-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
-1
S1
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 Renvironment 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)
0-30cm
Model
Exp
C0
0.12
C1
0.18
C
0.30
C0/C
0.40
a
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; C0: 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
Statistics
Minimum
1st Quartile
Median
Mean
3rd Quartile
Maximum
Pred
-2.2141
-0.7208
-0.5422
-0.5538
-0.3677
1.7809
0-30 cm
Var
0.1747
0.2937
0.3019
0.3000
0.3072
0.6020
30-60 cm
Pred
Var
-2.0398
1.301
-0.7340
4.480
-0.4680
6.262
-0.4827
6.621
-0.1983
8.201
1.0515
28.620
60-90 cm
Pred
Var
-2.1642
0.0043
-0.6625
-03373
-0.3567
0.3509
-0.3776
0.3372
-0.0381
0,3550
1.0639
0.4606
Pred = kriging prediction; Var = kriging variance
Table 4.8 Summary statistics of back transformed logEC (dS/m) prediction values
Layer
Statistics
Minimum
1st Quartile
Median
Mean
3rd Quartile
Maximum
0-30 cm
Pred
1.092
4.864
5.815
6.037
6.924
59.354
30-60 cm
Pred
1.301
4.80
6.26
6.21
8.201
28.62
60-90 cm
Pred
1.148
5.156
7.00
7.475
9.626
28.977
61
Figure 4.6 Experimental and fitted variogram models for three soil depths
SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT
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
SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT
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 R2 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
Pe111
Pe112
Pe113
Pe114
Pe115
Pe211
Pe311
Pe411
Pe412
Pe413
Va111
Va211
Area (ha)
2441.50
3311.25
5224.00
1217.25
2076.25
2103.00
329.75
390.50
2524.25
657.25
507.50
117.50
0-30 cm
30- 60cm
60-90cm
0.93
2.79
0.24
0.34
0.32
5.70
0.63
12.35
0.06
1.15
15.01
0.19
0.31
3.09
0.89
0.17
0.13
5.67
1.36
5.82
0.26
5.10
17.70
2.69
1.07
2.27
0.89
0.22
1.70
3.83
0.65
3.20
0.51
3.97
12.10
3.07
Area (ha)
Floodplain
74450
15.01
17.70
12.10
Glacis
2106.75
3.23
3.61
2.98
Lateral Vale
3254.50
5.62
4.51
3.69
Old terraces
119.00
0.19
2.69
3.07
Ridge
556.75
1.01
1.20
1.24
12994.50
0.63
1.36
0.65
Vale
0-30
Depth (cm)
30-60 60-90
Relief unit
66
a)
Average EC per Landform units
Average EC per Relief units
b)
20.00
20.00
18.00
18.00
16.00
16.00
14.00
12.00
0-30cm
10.00
30-60cm
8.00
60-100cm
E C (d S / m )
EC(dS/m)
14.00
12.00
10.00
0-30cm
8.00
60-90cm
6.00
6.00
4.00
4.00
2.00
2.00
11
1
41
3
41
2
41
1
31
1
21
1
11
5
11
4
11
3
21
1
Va
Va
Pe
Pe
Pe
Pe
Pe
Pe
Pe
Pe
11
2
0.00
Pe
Pe
11
1
0.00
30-60cm
Floodplain
Glacis
Lateral Vale Old terrace
Ridge
Vale
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
lm(formula = ECE ~ RELIEF, data = EC_P)
Residuals:
Min
1Q
Median
3Q
Max
-5.563
-0.949
-0.749
-0.234
17.953
Coefficients:
Estimate
Std. Error t value
Pr(>|t|)
Intercept
15.010
3.449
4.352
7.66e-05 ***
Glacis]
-11.783
3.911
-3.013
0.004235 **
Lateral Vale -9.387
3.911
-2.400
0.020571 *
Old terraces -14.820
5.973
-2.481
0.016906 *
Ridge
-14.001
3.566
-3.927
0.000293 ***
Vale
-14.382
4.081
-3.524
0.000988 ***
--Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.877 on 45 degrees of freedom
Multiple R-Squared: 0.3061,
Adjusted R-squared: 0.229
F-statistic: 3.969 on 5 and 45 DF, p-value: 0.004564
EC_ 60- 90 cm layer
lm(formula = ECE ~ RELIEF, data = EC_P)
Residuals:
Min
1Q
Median
3Q
Max
-3.1829 -1.1055 -1.0455
-0.3648
13.8500
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)
12.095
2.287
5.289
3.49e-06 ***
Glacis
-9.115
2.593
-3.515
0.001015 **
Lateral Vale -8.402
2.593
-3.240
0.002248 **
Old terraces -9.025
3.961
-2.279
0.027491 *
Ridge
-10.859
2.364
-4.593
3.52e-05 ***
Vale
-11.441
2.706
-4.228
0.000114 ***
--Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.234 on 45 degrees of freedom
Multiple R-Squared: 0.3517, Adjusted R-squared: 0.2797
F-statistic: 4.883 on 5 and 45 DF, p-value: 0.001185
EC _30-60 cm layer
lm(formula = ECE ~ RELIEF, data = EC_P)
Residuals:
Min
1Q
Median
3Q
Max
-4.2500
-1.1960 -1.0666 -0.8466
19.6886
Coefficients:
Estimate Std. Error t value
Pr(>|t|)
Intercept
17.695
3.305
5.354
2.80e-06 ***
Glacis
-14.084
3.747
-3.758
0.000490 ***
Lateral Vale] -13.185
3.747
-3.519
0.001005 **
Old terraces] -15.005
5.724
-2.621
0.011904 *
Ridge]
-16.498
3.417
-4.829
1.62e-05 ***
Vale]
-16.339
3.910
-4.178
0.000133 ***
--Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.674 on 45 degrees of freedom
Multiple R-Squared: 0.36,
Adjusted R-squared: 0.2889
F-statistic: 5.063 on 5 and 45 DF, p-value: 0.0009142
ANOVA(Type II tests)
Response: EC_0-30cm
Sum Sq
Df
F value
Pr(>F)
RELIEF
553.00
5
5.0633
0.0009142 ***
Residuals
982.95
45
Response: EC_30-60cm
Sum Sq
RELIEF
553.00
Residuals
982.95
Df
5
45
F value
5.0633
Pr(>F)
0.0009142 ***
Response: EC_60-90cm
Sum Sq Df
F value
Pr(>F)
RELIEF
255.35
5
4.883
0.001185 **
Residuals
470.65 45
--------------------------------------------------------------------------Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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
Pe111
Pe112
Pe113
Pe114
Pe115
Pe211
Pe311
Pe411
Pe412
Pe413
Va111
Va211
Va311
Area (ha)
2464.00
3346.75
5303.75
1254.25
625.75
2106.75
2120.25
333.75
395.75
2525.00
744.50
556.75
119.00
0-30 cm
30- 60cm
5.22
6.68
5.01
4.49
8.77
6.06
5.10
8.26
7.84
7.40
8.99
6.18
7.31
5.63
6.11
4.68
5.09
8.51
7.56
4.85
9.25
9.50
8.51
11.91
8.05
10.17
60-90cm
6.25
6.32
4.94
5.35
8.92
9.75
4.84
11.02
11.57
10.27
15.14
7.88
10.85
Area (ha)
Floodplain
74450
8.99
11.91
15.14
Glacis
2106.75
6.06
7.56
9.75
Lateral Vale
3254.50
7.54
8.71
10.50
New terraces
119.00
7.31
10.17
10.85
Old terraces
556.75
6.18
8.05
7.88
12994.50
2120.25
5.61
5.10
5.45
4.85
5.78
4.84
Ridge
Vale
0-30
Depth (cm)
30-60 60-90
Relief unit
68
Table 4.12 Area percentages per severity levels for 0-30cm layer
Percentage area per salinity level
Relief Unit
ECe (dS/m)
Floodplain
Glacis
Lateral Vale
New terraces
Old terraces
Ridge
Vale
Total area
(ha)
74450
2106.75
3254.50
119.00
556.75
12994.50
2120.25
Low salinity
0-4
0.57
5.39
0.61
0.42
5.70
15.10
13.05
Moderate
4-8
24.24
78.30
60.79
74.58
87.70
74.47
84.94
High Salinity Severe salinity
8 - 16
> 16
75.18
0.57
16.32
5.39
38.61
0.61
25.00
0.42
6.60
5.70
10.44
15.10
2.00
13.05
Table 4.13 Area percentages per severity levels for 30-60cm layer
Percentage area per salinity level
Relief Unit
ECe (dS/m)
Floodplain
Glacis
Lateral Vale
New terraces
Old terraces
Ridge
Vale
Total area
(ha)
74450
2106.75
3254.50
119.00
556.75
12994.50
2120.25
Low salinity
0-4
0.00
0.19
0.05
0.00
0.27
12.78
15.61
Moderate
4-8
3.09
67.92
40.86
4.41
47.64
80.33
82.44
High Salinity Severe salinity
8 - 16
> 16
96.34
0.57
31.89
0.00
59.08
0.01
95.59
0.00
52.09
0.00
6.89
0.00
1.95
0.00
Table 4.14 Area percentages per severity levels for 60-90cm layer
Percentage area per salinity level
Relief Unit
ECe (dS/m)
Floodplain
Glacis
Lateral Vale
New terraces
Old terraces
Ridge
Vale
Total area
(ha)
74450
2106.75
3254.50
119.00
556.75
12994.50
2120.25
Low salinity
0-4
0.00
0.00
0.00
0.00
0.00
4.85
15.42
Moderate
4-8
0.03
16.93
7.86
0.00
53.08
83.73
82.54
High Salinity Severe salinity
8 - 16
> 16
70.95
29.01
83.07
0.00
92.14
0.00
100.00
0.00
46.92
0.00
11.42
0.00
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 (48dS/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
Zone
ECe (dS/m)
0-30 cm
30-60 cm
60-90 cm
Total area
(ha)
22725
Percentage area per salinity level
Low salinity
0-4
10.72
8.89
4.24
Moderate
4-8
72.28
70.33
62.93
High Salinity Severe salinity
8 - 16
> 16
17.00
0.0
20.76
0.02
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 rootzone and the transition zone. The root-zone covers the two upper measured soil depths (i.e. 0-30 and 3060cm) 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
Root-zone
Transition-zone
Mode
l
Exp
Exp
Model
Sim_EC_10
C0
C1
C
a
C0/C
0.4
0.15
0.28
0.40
0.68
0.55
5953
1294
0.59
0.27
Sph
Sph
Sim_EC_20
C0
C1
C
a
C0/C
0.42
0.19
0.28
0.54
0.70
0.73
2350
2014
0.60
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
Layer
Statistics
Minimum
1st Quartile
Median
Mean
3rd Quartile
Maximum
TENTH YEAR
TWENTIETH YEAR
Root zone (EC_dS/m)
Transition zone(EC_dS/m)
Root zone(EC_dS/m)
Transition zone(EC_dS/m)
Pred
Var
Pred
Var
Pred
Var
Pred
Var
1.60
6.68
9.22
10.30
13.28
43.51
0.25
0.28
0.28
0.29
0.29
0.46
1.896
5.647
7.182
7.444
8.933
14.354
0.338
0.357
0.363
0.369
0.374
0.784
1.190
7.819
11.724
12.589
17.067
56.717
0.4727
0.4962
0.5028
0.5111
0.5144
0.8787
1.599
5.770
7.263
7.438
9.039
31.604
0.007
0.476
0.484
0.475
0.490
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
0.1
0.2
0.3
0.4
0.5
0.6
20
84
129
5000
180
192
Separation Distance (m)
distance
10000
162 194
182 192
159
15000
121
86
86
Fitted Variogram (Exp), log10EC(dS/m), Transition-zone
Figure 4.12 Experimental and fitted variogram models for simulated EC of the tenth year
semivariance
51
23
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
semivariance
0.2
0.4
0.6
5000
distance
10000
243
240
Separation Distance (m)
212 251 263
249
218
112
15000
163
100
60
31
0.2
0.4
0.6
0.8
14
71
132
148
168
distance
10000
162 169
158 157
Separation Distance (m)
5000
112
85 59
15000
129
41
Fitted Variogram (Sph),EC(dS/m) transition-zone
Figure 4.15 Experimental and fitted variogram models for simulated EC of the twentieth year
27
109
166
Fitted Variogram (Sph),EC(dS/m) Root-zone
semivariance
25
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 (10th 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 10th year
Code
Pe111
Pe112
Pe113
Pe114
Pe115
Pe211
Pe311
Pe411
Pe412
Pe413
Va111
Va211
Va311
Area (ha)
2441.50
3311.25
5224.00
1217.25
2076.25
2103.00
329.75
390.50
2524.25
657.25
507.50
117.50
119.00
Root-zone
8.15
10.54
6.95
5.81
18.20
13.37
8.34
14.68
15.39
15.75
19.33
13.43
17.61
Transition zone
6.54
7.71
5.70
5.18
10.81
8.17
6.36
9.00
8.74
9.47
11.98
9.19
10.78
Relief unit
Floodplain
Area (ha)
Root
zone
Transition
zone
744.50
19.33
11.98
Glacis
2106.75
13.37
8.17
Lateral Vale
3254.50
15.60
9.34
New terraces
119.00
17.61
10.78
Old terraces
556.75
13.43
9.19
Ridge
12994.50
8.54
6.57
Vale
2120.25
8.34
6.36
The subsequent tables (4.18- 4.20) give percentages of affected areas for the defined zones (0-60cm & 60100cm) 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
EC (dS/m)
15.60
15.00
13.43
13.37
10.00
8.54
8.34
Ridge
Vale
6.57
6.36
Ridge
Vale
5.00
0.00
Floodplain
Glacis
Lateral
Vale
New
terraces
Old
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
EC(dS/m)
10.00
9.34
9.19
8.17
8.00
6.00
4.00
2.00
0.00
Floodplain
Glacis
Lateral Vale
New
terraces
Old
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 rootzone. 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
Relief Unit
Total area
(ha)
ECe (dS/m)
Floodplain
Glacis
Lateral Vale
New terraces
Old terraces
Ridge
Vale
74450
2106.75
3254.50
119.00
556.75
12994.50
2120.25
Percentage area per salinity level
Low salinity
0-4
0.10
1.27
0.14
0.00
1.21
10.24
7.49
Moderate
4-8
0.67
9.61
3.56
1.26
10.91
45.47
45.71
High Salinity Severe salinity
8 - 16
> 16
24.71
74.51
63.96
25.16
52.36
43.94
32.56
66.18
60.44
27.44
37.04
7.26
43.47
3.33
4.20 Percent area per severity levels for transition zone
Relief Unit
Total area
(ha)
ECe (dS/m)
Floodplain
Glacis
Lateral Vale
New terraces
Old terraces
Ridge
Vale
74450
2106.75
3254.50
119.00
556.75
12994.50
2120.25
Percentage (%) area per salinity level
Low salinity
0-4
0.00
1.34
0.11
0.00
0.31
6.13
5.86
Moderate
4-8
1.71
53.30
27.39
4.41
22.50
71.86
84.46
High Salinity Severe salinity
8 - 16
> 16
98.29
0.00
45.35
0.00
72.50
0.00
95.59
0.00
77.19
0.00
22.02
0.00
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
Zone
ECe (dS/m)
Root-zone
Transition zone
Total area
(ha)
22725
Percentage area per salinity level
Low salinity
0-4
6.74
4.21
Moderate
4-8
32.36
59.58
High Salinity Severe salinity
8 - 16
> 16
43.37
17.53
36.21
0.00
82
Area (% ) per salinity levels (Root-zone)
18%
7%
32%
Low
Moderate
High
Severe
43%
Figure 4.20 Percent area affected for root-zone prediction
Area (% ) per salinity levels (Transition zone)
0% 4%
36%
Low
Moderate
High
60%
Severe
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 20th year
Code
Pe111
Pe112
Pe113
Pe114
Pe115
Pe211
Pe311
Pe411
Pe412
Pe413
Va111
Va211
Va311
Area (ha)
2441.50
3311.25
5224.00
1217.25
2076.25
2103.00
329.75
390.50
2524.25
657.25
507.50
117.50
119.00
Root-zone
10.22
12.23
8.18
7.34
20.41
16.03
10.10
18.91
18.99
18.20
20.60
14.79
19.32
Transition zone
6.86
7.28
5.73
6.23
9.98
8.42
6.59
9.80
9.62
9.29
10.17
7.72
11.51
Relief unit
Floodplain
Area (ha)
Root
zone
Transition
zone
744.50
20.60
10.17
Glacis
2106.75
16.03
8.42
Lateral Vale
3254.50
18.37
9.39
New terraces
119.00
19.32
11.51
Old terraces
556.75
14.79
7.72
Ridge
12994.50
10.12
6.60
Vale
2120.25
10.10
6.59
85
Mean Rootzone EC within Relief zones
25.00
20.60
20.00
19.32
18.37
EC(dS/m)
16.03
14.79
15.00
10.12
10.10
Ridge
Vale
10.00
5.00
0.00
Floodplain
Glacis
Lateral Vale
New
terraces
Old
terraces
Figure 4.23 Average predicted EC values per relief types for the root-zone
Mean transition zone EC within Relief zones
14.00
11.51
12.00
10.17
10.00
9.39
EC(dS/m)
8.42
7.72
8.00
6.60
6.59
Ridge
Vale
6.00
4.00
2.00
0.00
Floodplain
Glacis
Lateral Vale
New
terraces
Old
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
ECe (dS/m)
Floodplain
Glacis
Lateral Vale
New terraces
Old terraces
Ridge
Vale
Total area
(ha)
74450
2106.75
3254.50
119.00
556.75
12994.50
2120.25
Low salinity
0-4
0.00
1.01
0.00
0.00
0.31
4.85
4.00
Moderate
4-8
0.07
3.86
0.32
0.00
5.88
34.85
28.40
High Salinity Severe salinity
8 - 16
> 16
9.94
89.99
47.64
47.49
28.02
71.66
17.23
82.77
56.58
37.22
45.35
14.95
60.70
6.90
Table 4.24 Area percentages per severity levels for transition zone
Percentage area per salinity level
Relief Unit
ECe (dS/m)
Floodplain
Glacis
Lateral Vale
New terraces
Old terraces
Ridge
Vale
Total area
(ha)
74450
2106.75
3254.50
119.00
556.75
12994.50
2120.25
Low salinity
0-4
0.00
1.66
0.02
0.00
0.85
6.04
5.99
Moderate
4-8
8.19
41.51
11.45
3.78
62.24
72.56
75.62
High Salinity Severe salinity
8 - 16
> 16
88.68
3.12
56.62
0.21
88.04
0.49
87.61
8.61
36.24
0.67
21.35
0.05
17.98
0.41
Table 4.25 Percent area per severity levels over entire area of interest
Total area
(ha)
ECe (dS/m)
Root-zone
Transition zone
22725
Percentage area per salinity level
Low salinity
0-4
3.25
4.19
Moderate
4-8
23.25
56.56
High Salinity Severe salinity
8 - 16
> 16
43.47
30.03
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%
23%
30%
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
57%
High
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
salinity
class
(dS/m)
0-4
4-8
8-16
>16
Current
2020.25
15983.25
4717.00
4.50
Area in hectares (total area = 22 725 ha)
Root-zone
Transition zone
Tenth year Changes
Percent
Current
Tenth year Changes
(+/-)
(%)
(ha)
1532.25
-488.00
2.15
964.00
956.50
-7.50
7354.00
-8629.25 37.97
14301.75 1359.25
-762.50
9855.50
+5138.50 22.61
7243.25
8229.25
+986.00
3983.25
+3978.75 17.51
216.00
0.00
-216.00
Percent
(%)
0.03
3.36
4.34
0.95
+: indicate increase; -: indicate decrease
Table 4.27 Predicted area changes of various soil salinity classes from tenth to twentieth year
Soil
salinity
class
(dS/m)
0-4
4-8
8-16
>16
Tenth year
1532.25
7354.00
98.55.50
3983.25
Area in hectares (total area = 22 725 ha)
Root-zone
Transition zone
Changes
Percent Tenth year Twentieth Changes
Twentieth
year
(ha)
(%)
year
(ha)
738.50
-793.75
3.49
956.50
953.00
-3.50
5283.00
-2071.00 9.11
13539.25
12852.50
-686.75
9878.25
+22.75
0.10
8229.25
8836.50
+607.25
6825.25
+2842.00 12.51
0.00
83.00
+83.00
Percent
(%)
0.02
3.02
2.67
0.37
Table 4.28 Predicted area changes of various soil salinity classes over twenty year period
Soil
salinity
class
(dS/m)
0-4
4-8
8-16
>16
Current
2020.25
15983.25
4717.00
4.50
Area in hectares (total area = 22 725 ha)
Root-zone
Transition zone
Twentieth
Changes
Percent
Current
Twentieth Changes
year
(ha)
(%)
year
(ha)
738.50
-1281.75
5.64
964.00
953.00
-11.00
5283.00
-10700.25 47.09
14301.75
12852.50
-1449.25
9878.25
+5161.25
22.71
7243.25
8836.50
+1593.25
68.20
+6820.75
30.01
216.00
83.00
-133.00
Percent
(%)
0.05
6.38
7.01
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
ME
RME
RMSE
RMSE/SD
RMSE/IQR
MSDR
0-30 cm
0.0001
8.883e-05
0.624
0.148
0.469
1.240
30 – 60 cm
0.003
0.002
0.638
0.215
0.689
1.273
60 – 90 cm
0.002
0.001
0.629
0.196
0.635
1.224
Table 4.30 Validation parameters for kriging prediction of simulated EC values
Variable
Statistics
ME
RME
RMSE
RMSE/SD
RMSE/IQR
MSDR
Root-zone
0.0007
0.0002
0.761
0.091
0.3186
1.008
Sim_EC_10
Transition-zone
0.0060
0.0016
0.6959
0.1070
0.1819
1.0416
Root-zone
-0.0020
-0.0002
0.8402
0.0534
0.1301
1.0255
Sim_EC_20
Transition-zone
0.0007
0.0001
0.7808
0.0763
0.1301
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 (R2) 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
Statistics
Units
Root-zone
Transition zone
Validation
Sample (n=51)
Mean
SD
(dS/m)
2.67
2.22
-0.34
0.39
5.54
3.81
ME
RME
RMSE
RMSE/SD
R2
(dS/m)
-0.13
0.18
5.17
3.20
0.93
0.84
0.81
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 (R2) 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 R2 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
Sensitivity index (S)
S
1.5
Area
1
PET
Rain
0.5
Go
Flr
0
-0.5
-20
-10
0
10
20
RZ _EC
TZ_EC
AQ_EC
-1
-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
Sensitvity index (S)
1.5
1
S
0.5
PET
0
-0.5
Rain
-20
-10
0
10
20
Flr
-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
Area
Evapotranspiration
Unit
ha
m
Precipitation
m
Natural Drainage
m
Leaching Efficiency
Initial EC
dS/m
Water table depth
Total porosity
m
S1
A
B
U
S2
A
B
U
S1
S2
S1
S2
RZ
TZ
RZ
TZ
AZ
RZ
TZ
RZ
TZ
Effective porosity
-20
273
-10
307
Baseline
341
+10
375
+20
409
0.46
0.84
1.18
0.51
0.95
1.33
0.57
1.05
1.48
0.63
1.16
1.63
0.68
1.26
1.78
0.08
0.34
1.06
0.70
0.13
0.10
0.12
0.64
0.64
0.080
0.080
0.080
0.96
0.33
0.32
0.06
0.03
0.09
0.39
1.19
0.79
0.14
0.11
0.12
0.72
0.72
0.090
0.090
0.090
1.08
0.37
0.36
0.07
0.04
0.10
0.43
1.32
0.88
0.16
0.12
0.12
0.80
0.80
0.100
0.100
0.100
1.20
0.41
0.40
0.08
0.04
0.11
0.47
1.45
0.97
0.18
0.13
0.12
0.88
0.88
0.110
0.110
0.110
1.32
0.45
0.44
0.09
0.04
0.12
0.52
1.58
1.06
0.19
0.14
0.12
0.96
0.96
0.120
0.120
0.120
1.44
0.49
0.48
0.10
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
S
Area
PET
Rain
Go
Flr
RZ _EC
TZ_EC
AQ_EC
GWD
TPor
EPor
Output (EC)
0.24
0.24
0.24
0.24
0.24
0.24
0.24
0.24
0.24
0.24
0.24
-20
0
0.00
-0.44
0.20
0.00
-0.71
0.02
0.02
0.02
-1.09
-1.48
-1.09
-10
0
0.00
0.06
0.10
0.01
-0.42
0.00
0.00
0.00
-0.53
-0.62
-0.52
0
0
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
10
0
0.00
-0.10
0.03
-0.04
-0.04
0.00
0.00
0.00
0.50
0.44
0.52
20
0
0.00
0.90
1.61
-0.10
-0.09
0.01
0.01
0.01
0.96
0.76
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 hydrogeopedologic processes?
Simulation of EC for a two decadal period indicated progressive increase of salinity with time in the rootzone 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.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
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103
7. APPENDICES
Appendix 1: Input parameters for SaltMod
Season-wise input parameter for use in SALT MOD
No.
Parameters
1
Period of season
Duration of season
2
3
4
5
6
7
8
9
10
11
12
Unit
mont
hs
Crops grown
Water sources
Amount of rainfall
Amount of water used for irrigation
Fraction of area occupied rice (paddy)
Fraction of area occupied other crops
Fraction of area fallowed / barren / non cultivated
Potential evapotranspiration of rice crops
Potential evapotranspiration of other crops
Potential evapotranspiration from non cultivated land
Surface runoff (assumed)
m
m
m
m
m
m
Season 1
Season 2
May to Oct
6
Nov to March
6
Cassava
Maize
Rice
Fallow
Rainfall
0.88
0
0.40
0.52
0.08
0.57
1.05
1.48
0
Rice
Cassava
Maize
Fallow
Rainfall
0.16
0
0.40
0.52
0.08
0.10
0.43
1.32
0
Soil and system input parameters for use in SALTMOD
No
1
2
3
4
5
(i)
(ii)
(iii)
5
(i)
(ii)
(iii)
6
(i)
(ii)
(iii)
12
14
Parameter
Unit Pe111 Pe112 Pe113 Pe114 Pe115 Pe211 Pe311 Pe411 Pe412 Pe413 Va111 Va211 Va311
Storage efficiency
Depth of root zone
m
0.6
0.6
0.6
Depth of transition
m
4
4
4
zone (estimated)
Depth of aquifer
m
15
15
15
(estimated)
Total porosity of the
root zone
0.31 0.33 0.34
transition zone
0.31 0.36 0.35
aquifer
0.35 0.35 0.35
Effective porosity of the
root zone
0.14 0.19 0.15
transition zone
0.12 0.17 0.18
aquifer
0.25 0.25 0.25
Initial salt concentration of the soil moisture in the
Root zone
dS/m
6.94 3.74 3.71
Transition zone
dS/m
5.63 4.67 2.84
Aquifer
dS/m
4..46 3.40 2.55
Depth of water table
m
2.20 1.83 2.20
Critical depth for
m
1.2
1.2
1.2
capillary rise
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
4
4
4
4
4
4
4
4
4
4
15
15
15
1
5
15
15
15
15
15
0.34
0.36
0.35
0.34
0.37
0.35
0.34
0.36
0.35
0.39
0.37
0.35
0.30
0.35
0.35
0.34
0.37
0.35
0.39
0.37
0.35
0.42
0.40
0.35
0.37
0.36
0.35
0.32
0.33
0.35
0.14
0.18
0.25
0.13
0.19
0.25
0.18
0.18
0.25
0.18
0.17
0.25
0.19
0.19
0.25
0.22
0.15
0.25
0.20
0.15
0.25
0.16
0.13
0.25
0.14
0.13
0.25
0.14
0.15
0.25
0.23
0.33
1.80
2.25
0.18
1.60
1.59
2.50
4.69
2.50
2.11
2.03
0.18
0.19
1.14
2.64
2.24
1.36
6.08
1.14
0.25
0.31
1.02
2.28
0.43
0.44
1.66
2.39
0.47
0.70
2.64
2.83
0.16
0.24
1.83
2.27
10.5
7.4
4.6
2.05
1.2
1.2
1.2
1.2
1.2
1.2
1.2
1.2
1.2
1.2
Appendix 2: Land cover types and water table observation points
104
Date
Id
GP
X_Coord
Y_Coord
Land_Cover
WT_Observed
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
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
9/10/2007
8
Pe112
802311
1672472
Cassava
N
GWD_(cm)
100
107
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
9/8/2007
9/13/2007
9/9/2007
12
13
14
15
Pe112
Pe112
Pe113
Pe113
810672
810877
818263
806456
1671315
1673618
1673303
1670389
Marsh (trees/grass)
Paddy rice/Grass
Paddy rice
Cassava
Y
Y
Y
N
110
175
100
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
9/6/2007
20
Pe113
808799
1664197
Cassava
N
9/14/2007
21
Pe113
808420
1662252
Paddy rice/Fallow
Y
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
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
80
100
120
9/14/2007
34
Pe211
816920
1668104
Paddy rice
Y
9/8/2007
35
Pe211
811156
1667521
Cassava
N
85
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
9/9/2007
9/15/2007
9/15/2007
9/7/2007
9/7/2007
9/6/2007
42
43
44
45
46
47
48
Pe311
Pe311
Pe311
Pe311
Pe411
Pe412
Pe413
808034
805523
810387
811341
816593
816014
814586
1670052
1671888
1660380
1660440
1666831
1663150
1670533
Paddy rice
Paddy rice
Cassava
Cassava/Fallow
Marsh/swampy/grass
Cassava/plantation
Cassava
N
N
N
N
Y
N
N
9/7/2007
9/8/2007
49
50
Pe413
Pe413
812748
812593
1667465
1671835
Grass
Paddy rice/Fallow
N
Y
70
115
70
9/13/2007
51
Pe413
813503
1673304
Paddy rice
Y
9/8/2007
52
Pe413
817586
1674203
Maize
N
9/8/2007
57
Va111
809709
1674771
Paddy/grass
Y
170
9/17/2007
9/10/2007
9/16/2007
58
59
60
Va111
Va211
Pe115
813640
803776
815773
1675646
1675096
1671484
Paddy rice
Paddy rice
Paddy rice
Y
N
Y
80
80
105
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
27
29
33
34
35
36
37
38
Pe111
Pe111
Pe111
Pe112
Pe111
Pe111
Pe112
Pe112
Pe112
Pe112
Pe112
Pe111
Pe112
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe114
Pe113
Pe113
Pe113
Pe114
Pe114
Pe114
Pe211
Pe211
Pe211
Pe211
Pe211
GP
808574
807251
818430
809789
806458
814723
812644
803710
812516
803714
816657
809555
811009
818263
806456
810899
809441
809434
817664
808799
808420
813200
806034
807705
805239
812455
814060
815303
816920
811156
812558
817157
804109
X_CORD
1660302
1663913
1663152
1673963
1671839
1668090
1673638
1672238
1669640
1673777
1665755
1671047
1673618
1673271
1670389
1664062
1661439
1664970
1661045
1664197
1662252
1663756
1660127
1666017
1662476
1660232
1660267
1660688
1668104
1667521
1665464
1671259
1660311
Y_CORD
Appendix 3: EC, pH and GWD
6.80
6.58
5.67
5.79
6.79
5.53
7.10
7.00
6.50
6.70
6.61
7.79
7.21
5.24
6.25
5.34
6.93
5.36
5.83
6.49
6.93
6.46
6.39
5.39
6.04
5.60
6.53
7.02
7.19
6.20
6.70
5.20
6.79
PH_SR
0.22
0.02
0.02
0.05
0.03
0.03
0.26
0.01
0.07
0.03
2.39
0.55
0.24
0.02
0.05
0.02
0.05
0.01
0.06
0.04
0.02
0.02
0.06
0.02
0.07
0.04
0.14
0.01
0.08
0.02
0.02
0.02
0.07
EC_SR
1.41
0.13
0.13
0.32
0.19
0.19
1.66
0.06
0.45
0.19
15.30
3.52
1.54
0.13
0.32
0.13
0.32
0.06
0.38
0.26
0.13
0.13
0.38
0.13
0.45
0.26
0.90
0.06
0.51
0.13
0.13
0.13
0.45
ECE_SR
7.49
6.44
5.29
5.60
5.50
4.80
6.06
6.21
6.23
5.71
6.23
6.64
7.37
7.66
5.79
4.84
6.78
5.39
7.80
7.06
5.50
6.01
6.38
5.26
5.46
5.60
6.00
6.01
7.11
5.59
5.56
6.50
7.19
PH_RZ
0.19
0.03
0.01
0.02
0.01
0.03
1.16
0.02
0.02
0.02
0.06
1.53
0.57
1.27
0.04
0.05
0.09
0.03
0.02
0.01
0.02
0.03
0.04
0.03
0.02
0.02
0.04
0.02
0.04
0.03
0.03
0.02
0.16
EC_RZ
1.22
0.19
0.06
0.13
0.06
0.19
7.42
0.13
0.13
0.13
0.38
9.79
3.65
8.13
0.26
0.32
0.58
0.19
0.13
0.06
0.13
0.19
0.26
0.19
0.13
0.13
0.26
0.13
0.26
0.19
0.19
0.13
1.02
ECE_RZ
7.59
5.79
5.29
5.79
5.78
6.41
6.99
4.96
5.13
7.04
6.97
7.23
8.28
7.86
5.51
4.67
5.99
6.60
7.90
5.07
5.80
6.01
6.80
5.57
6.79
4.65
5.66
5.00
5.00
6.58
6.70
7.74
5.21
PH_TZ
0.14
0.03
0.02
0.03
0.02
0.04
1.50
0.02
0.09
0.01
0.05
0.75
0.78
0.93
0.03
0.03
0.31
0.02
0.03
0.01
0.02
0.03
0.05
0.03
0.07
0.03
0.05
0.03
0.05
0.08
0.07
0.15
0.17
EC_TZ
0.90
0.19
0.13
0.19
0.13
0.26
9.60
0.13
0.58
0.06
0.32
4.80
4.99
5.95
0.19
0.19
1.98
0.13
0.19
0.06
0.13
0.19
0.32
0.19
0.45
0.19
0.32
0.19
0.32
0.51
0.45
0.96
1.09
ECE_TZ
107
3.00
3.00
1.00
3.00
3.00
3.00
1.07
3.00
3.00
3.00
0.60
1.10
1.75
1.00
3.00
3.00
3.00
3.00
0.80
3.00
1.00
3.00
3.00
1.20
3.00
3.00
3.00
3.00
0.85
3.00
1.20
1.70
3.00
WTD (m)
ID
39
40
41
42
43
44
45
46
47
48
49
50
51
52
57
58
59
60
Pe211
Pe211
Pe311
Pe311
Pe311
Pe311
Pe311
Pe411
Pe412
Pe115
Pe211
Pe413
Pe413
Pe413
Va111
Va111
Va211
Pe115
GP
811344
817634
804898
808034
805523
810387
811341
816593
817828
814586
812408
812593
813503
817586
809709
813650
803779
815773
X_CORD
1669609
1669457
1663795
1670052
1671967
1660380
1660440
1666831
1664168
1670952
1667465
1671835
1673304
1674203
1674771
1674919
1674998
1671484
Y_CORD
6.59
6.84
5.62
7.39
7.29
6.80
7.39
6.80
5.47
6.80
7.33
5.57
6.66
6.80
7.15
7.34
6.09
6.21
PH_SR
3.31
0.01
0.02
0.02
0.38
0.05
0.02
1.93
0.01
0.08
3.59
0.15
0.25
0.14
2.32
2.37
0.03
0.02
EC_SR
21.18
0.06
0.13
0.13
2.43
0.32
0.13
12.35
0.06
0.51
22.98
0.96
1.60
0.90
14.85
15.17
0.19
0.13
ECE_SR
7.66
6.01
4.91
7.11
9.59
5.81
7.11
7.08
6.25
7.39
7.80
6.16
7.07
7.06
6.79
7.05
7.20
7.02
PH_RZ
3.64
0.03
0.03
0.03
0.95
0.02
0.03
0.91
0.04
0.19
3.14
0.14
0.42
0.09
3.02
2.51
0.42
0.02
EC_RZ
23.30
0.19
0.19
0.19
6.08
0.13
0.19
5.82
0.26
1.22
20.10
0.90
2.69
0.58
19.33
16.06
2.69
0.13
ECE_RZ
7.49
5.96
4.60
5.00
9.79
5.32
6.60
7.10
5.73
6.85
7.59
6.52
7.35
7.68
6.79
7.15
6.89
6.69
PH_TZ
2.63
0.11
0.02
0.04
0.40
0.03
0.02
0.50
0.08
0.08
1.52
0.28
1.41
0.17
1.81
1.97
0.48
0.45
EC_TZ
16.83
0.70
0.13
0.26
2.56
0.19
0.13
3.20
0.51
0.51
9.73
1.79
9.02
1.09
11.58
12.61
3.07
2.88
ECE_TZ
108
0.65
1.00
3.00
3.00
3.00
3.00
3.00
0.70
3.00
3.00
3.00
0.70
1.15
3.00
1.70
0.80
3.00
0.80
WTD (m)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
ID
13.60
27.72
43.09
50.00
57.36
58.71
61.58
72.39
75.21
75.88
75.95
76.57
77.61
77.83
78.86
79.43
81.50
82.03
82.20
82.72
87.73
83.00
83.00
65.00
65.00
65.00
83.00
91.00
50.00
65.00
Sand %
49.19
43.48
13.92
29.05
20.13
19.24
17.14
6.71
9.84
7.82
9.44
12.95
11.91
7.18
13.04
0.00
6.33
4.74
11.50
7.05
2.09
5.00
5.00
10.00
10.00
10.00
6.00
5.00
41.00
10.00
Clay %
C
C
L
SCL
SL
SL
SL
SL
SL
SL
SL
SL
SL
LS
LS
LS
LS
LS
LS
LS
S
LS
LS
SL
SL
SL
LS
S
SC
SL
Class
0.43
0.39
0.21
0.28
0.22
0.21
0.2
0.11
0.12
0.11
0.12
0.14
0.13
0.1
0.13
0.13
0.12
0.12
0.12
0.12
0.1
0.11
0.11
0.18
0.18
0.18
0.12
0.1
0.37
0.18
FC
0- 30 cm
0.44
0.44
0.34
0.43
0.39
0.39
0.39
0.39
0.39
0.39
0.39
0.39
0.39
0.17
0.17
0.17
0.17
0.17
0.17
0.17
0.38
0.39
0.34
0.41
0.44
0.38
0.4
0.38
0.48
0.38
Tot_Por
0.01
0.05
0.13
0.15
0.17
0.18
0.19
0.28
0.27
0.28
0.27
0.25
0.26
0.07
0.04
0.04
0.05
0.05
0.05
0.05
0.28
0.28
0.23
0.23
0.26
0.2
0.28
0.28
0.11
0.2
Eff_Por
13.79
21.37
64.80
38.60
65.08
54.22
57.28
80.39
70.39
0.00
66.50
69.38
77.61
75.58
73.11
72.46
77.10
76.89
66.85
71.77
87.73
66.00
66.00
60.00
60.00
66.00
66.00
91.00
43.00
61.00
Sand%
48.18
46.73
10.81
24.46
16.10
22.65
24.82
3.88
14.31
0.00
19.17
19.85
11.91
6.48
8.89
0.00
8.89
10.88
26.95
13.04
6.00
10.00
10.00
27.00
27.00
10.00
10.00
5.00
28.00
27.00
Clay%
C
C
SL
L
SL
SCL
SCL
LS
SL
SL
SL
SL
SL
LS
SL
SL
SL
SL
SCL
SL
LS
SL
SL
SCL
SCL
SL
SL
S
CL
SCL
Class
0.42
0.41
0.15
0.29
0.18
0.24
0.25
0.15
0.16
0.11
0.19
0.2
0.13
0.1
0.12
0.13
0.12
0.13
0.24
0.15
0.12
0.18
0.18
0.29
0.29
0.18
0.18
0.1
0.31
0.28
FC
30-60 cm
Appendix 4(A): Texture (sand and clay percent), field capacity and porosity
0.44
0.44
0.3
0.36
0.34
0.39
0.39
0.34
0.34
0.38
0.38
0.38
0.34
0.34
0.2
0.2
0.2
0.2
0.39
0.3
0.34
0.39
0.3
0.44
0.43
0.37
0.42
0.18
0.35
0.39
Tot_Por
0.02
0.03
0.15
0.07
0.16
0.15
0.14
0.19
0.18
0.27
0.19
0.18
0.21
0.24
0.08
0.07
0.08
0.07
0.15
0.15
0.22
0.21
0.12
0.15
0.14
0.19
0.24
0.08
0.04
0.11
Eff_Por
13.79
23.09
64.80
26.20
65.08
54.22
56.33
34.22
70.39
0.00
63.20
0.00
0.00
0.00
73.11
0.00
77.10
71.90
66.85
71.77
84.68
65.00
60.00
60.00
52.00
60.00
65.00
92.00
60.00
11.00
Sand%
48.18
45.58
10.81
24.46
16.10
22.65
23.56
14.33
14.31
0.00
23.39
0.00
0.00
0.00
8.89
0.00
8.89
16.27
26.95
13.04
6.00
10.00
28.00
28.00
42.00
28.00
10.00
5.00
28.00
33.00
Clay%
C
C
SL
SiL
SL
SCL
SL
SiL
SCL
SL
SCL
SL
SL
LS
SL
SL
SL
SL
SCL
SL
LS
SL
SCL
SCL
SC
SCL
SL
S
C
SiCL
Class
0.42
0.4
0.15
0.3
0.18
0.24
0.19
0.24
0.21
0.11
0.28
0.2
0.13
0.1
0.12
0.13
0.12
0.17
0.24
0.15
0.12
0.18
0.28
0.28
0.37
0.28
0.18
0.09
0.28
0.38
FC
60-90 cm
0.44
0.44
0.19
0.4
0.34
0.36
0.34
0.36
0.39
0.17
0.36
0.34
0.34
0.17
0.34
0.34
0.34
0.34
0.36
0.34
0.17
0.4
0.36
0.36
0.43
0.41
0.34
0.34
0.31
0.4
Tot_Por
109
0.02
0.04
0.04
0.1
0.16
0.12
0.15
0.12
0.18
0.06
0.08
0.14
0.21
0.07
0.22
0.21
0.22
0.17
0.12
0.19
0.05
0.22
0.08
0.08
0.06
0.13
0.16
0.25
0.03
0.02
Eff_Por
31
32
33
34
ID
SL
LS
SL
LS
Class
0.18
0.12
0.11
0.19
FC
0.49
0.17
0.37
0.43
Tot_Por
0.31
0.05
0.26
0.24
Eff_Por
61.00
66.00
77.61
76.89
Sand%
27.00
10.00
11.91
10.88
Clay%
FC = Field capacity; Tot_Por = Total porosity; Eff_Por = Effective porosity
10.00
6.00
6.71
4.74
Clay %
SCL
SL
LS
SCL
Class
0.28
0.18
0.13
0.27
FC
30-60 cm
0.41
0.2
0.31
0.38
Tot_Por
809789
809555
808420
806034
816010
811344
816593
817828
814586
812408
808984
813650
806734
1673963
1671047
1662252
1660127
1674772
1669609
1666831
1664168
1670952
1667465
1674692
1674919
1674439
Y
Pe112
Pe111
Pe311
Pe113
Va111
Pe413
Pe411
Pe412
Pe115
Pe211
Va311
Va111
Va211
GPU
1.68
1.93
1.55
1.48
1.73
1.60
1.69
1.51
1.33
1.69
1.67
1.66
1.65
BDs
1.60
1.77
1.56
1.56
1.56
1.59
1.65
1.44
1.67
1.67
1.63
1.73
1.71
SBD
0.08
0.16
-0.01
-0.08
0.17
0.01
0.04
0.07
-0.34
0.02
0.04
-0.07
-0.06
Error
2.76
2.76
2.78
2.60
2.76
2.76
2.07
2.59
2.06
2.76
2.73
2.18
2.06
PDs
0.39
0.30
0.44
0.43
0.37
0.42
0.18
0.37
0.35
0.39
0.38
0.24
0.20
Por
1.59
1.73
1.66
1.56
1.73
1.64
1.74
1.50
1.45
1.73
1.66
1.45
1.74
BDs
1.62
1.69
1.68
1.68
1.69
1.65
1.72
1.47
1.68
1.68
1.70
1.75
1.63
SBD
BDs = Measure bulk density; SBD = simulated bulk density; PDs = Particle density; Por = Total porosity
4
12
21
23
29
39
46
47
48
49
57
58
59
X
0-30 cm
-0.03
0.04
-0.02
-0.12
0.04
-0.01
0.02
0.03
-0.23
0.05
-0.04
-0.30
0.11
Error
30-60 cm
2.61
2.64
2.79
2.79
2.77
2.73
2.81
2.65
2.78
2.81
2.76
2.82
2.11
PDs
0.13
0.02
0.18
0.11
Eff_Por
Appendix 4 (B): Measured and predicted bulk density, particle density and porosity
Point
65.00
82.00
72.39
82.03
Sand %
0- 30 cm
0.39
0.34
0.41
0.44
0.38
0.40
0.38
0.37
0.48
0.38
0.38
0.49
0.17
Por
60.00
60.00
63.20
71.90
Sand%
1.68
1.69
1.79
1.67
1.62
1.88
1.80
1.62
1.44
1.72
1.71
1.69
1.74
BDs
1.68
1.73
1.73
1.62
1.65
1.70
1.75
1.64
1.46
1.67
1.67
1.62
1.44
0.00
-0.04
0.06
0.05
-0.03
0.18
0.05
-0.02
-0.02
0.05
0.04
0.07
0.30
Error
2.82
2.73
2.80
2.92
2.74
2.84
2.74
2.19
2.09
2.85
2.80
2.85
2.02
0.40
0.33
0.36
0.43
0.41
0.34
0.34
0.32
0.31
0.40
0.41
0.41
0.14
Por
0.41
0.39
0.35
0.41
Tot_Por
PDs
0.28
0.28
0.1
0.26
FC
60-90 cm
SCL
SCL
LS
CL
Class
SBD
27.00
27.00
23.39
16.27
Clay%
60-90 cm
110
0.13
0.11
0.25
0.15
Eff_Por
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
Name
---------Unclassified
Paddy rice
Water 7
Saltt2
Cassava
Bareland
Plantation 3
Paved4
Reference
Totals
---------0
18
5
1
28
0
2
3
Classified
Totals
---------0
28
4
0
24
1
0
0
Totals
57
57
Overall Classification Accuracy =
Number
Correct
------0
16
4
0
19
0
0
0
Producers
Accuracy
----------88.89%
80.00%
--67.86%
-------
Users
Accuracy
------57.14%
100.00%
--79.17%
-------
39
68.42%
----- End of Accuracy Totals -----
KAPPA (K^) STATISTICS
--------------------Overall Kappa Statistics = 0.5002
Conditional Kappa for each Category.
-----------------------------------Class Name
---------Unclassified
Paddy rice
Water 7
Saltt2
Cassava
Bareland
Plantation 3
Paved4
Kappa
----0.0000
0.3736
1.0000
0.0000
0.5905
0.0000
0.0000
0.0000
-------------End of Kappa Statistics---------------------
111
a) pH
Appendix 6: Histograms of pH, texture and porosity for the three soil depth
112
b) Texture
113
c) Porosity
114
a). pH
Appendix 7 : Box plots for pH, texture and porosity of the primary dataset
115
b). Texture
116
Appendix 8: Calibration results of root-zone leaching efficiency (Flr)
Point
3
7
11
12
12
14
19
21
24
34
36
37
39
40
46
50
51
57
58
60
47
1
2
4
5
6
8
9
10
15
16
17
18
20
22
23
27
33
35
38
41
42
43
44
45
48
49
52
59
25
29
59
Observed EC
0.13
4.54
7.84
0.25
0.67
2.60
4.13
0.26
0.13
0.16
0.13
0.16
1.30
22.24
0.13
9.09
0.16
0.93
2.69
17.09
15.62
0.13
0.29
0.58
1.32
0.16
21.54
0.13
0.19
0.10
0.29
0.16
0.29
0.23
0.45
0.13
0.16
0.16
0.32
0.20
0.10
0.16
0.74
0.16
0.16
4.26
0.23
0.16
0.87
0.23
0.74
1.44
0.10
0.22
3.83
9.20
0.21
0.58
1.89
4.14
0.35
0.19
0.25
0.47
0.20
2.71
10.20
1.45
9.20
0.22
0.51
2.43
9.01
16.00
1.06
0.30
0.16
0.02
0.14
8.15
0.78
0.15
0.18
0.41
0.40
0.42
0.15
0.16
0.20
0.15
0.17
0.12
0.16
0.15
1.31
0.12
0.15
0.42
5.24
0.61
0.16
0.78
1.23
2.23
1.37
0.20
0.17
3.83
8.70
0.21
0.55
1.02
3.89
0.33
0.14
0.24
0.45
0.14
1.62
16.90
0.98
8.70
0.16
0.69
1.57
14.50
4.55
0.64
0.41
0.23
0.31
0.18
12.90
1.07
0.20
0.26
0.62
0.62
0.64
0.21
0.22
0.30
0.20
0.25
0.14
0.23
0.20
2.82
0.14
0.21
0.64
8.78
0.82
0.23
1.07
2.05
3.27
3.35
0.40
0.11
3.82
7.80
0.20
0.52
1.02
3.50
0.23
0.08
0.22
0.41
0.08
0.60
29.90
0.38
7.80
0.10
1.06
0.66
25.90
1.73
0.24
0.65
0.36
0.52
0.25
22.30
1.69
0.31
0.43
1.04
1.05
1.08
0.34
0.31
0.50
0.31
0.40
0.19
0.36
0.30
5.24
0.19
0.32
1.08
15.80
1.25
0.36
1.69
3.71
5.34
6.65
0.60
0.08
3.83
7.04
0.20
0.48
0.71
3.07
0.33
0.05
0.20
0.37
0.05
0.24
42.20
0.16
7.04
0.07
1.42
0.29
37.90
0.77
0.10
0.90
0.50
0.73
0.39
31.70
2.32
0.43
0.73
1.46
1.50
1.53
0.47
0.47
0.71
0.42
0.55
0.24
0.50
0.41
7.61
0.24
0.44
1.53
22.60
1.69
0.50
2.32
5.34
7.34
9.71
0.80
0.06
3.83
6.42
0.18
0.45
0.62
2.75
0.35
0.04
0.18
0.34
0.04
0.11
53.90
0.07
6.42
0.05
1.79
0.14
50.60
0.30
0.05
1.16
0.63
0.94
0.46
41.00
2.95
0.54
0.78
1.88
1.96
1.98
0.60
0.59
0.92
0.53
0.71
0.29
0.63
0.53
9.93
0.29
0.56
1.98
29.30
2.14
0.63
2.95
6.96
9.29
12.80
1.00
0.05
3.83
5.88
0.17
0.42
0.55
2.48
0.38
0.03
0.18
0.31
0.04
0.06
65.10
0.03
5.88
0.04
2.14
0.07
64.20
0.14
0.03
1.44
7.63
1.36
0.52
50.10
3.58
0.66
0.96
2.28
2.41
2.42
0.74
0.72
1.14
0.65
0.86
0.35
0.76
0.64
12.20
0.35
0.68
2.42
35.70
2.60
0.76
3.58
8.55
11.10
15.90
117
Appendix 9: Calibration results of natural drainage (Go)
Point
1
2
3
4
5
6
7
8
9
10
11
12
12
14
15
16
17
18
19
20
21
22
23
24
25
27
29
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
57
58
59
60
Calibrated_Flr
1.00
0.10
0.40
0.40
0.10
0.20
0.20
0.10
0.10
0.10
0.40
0.10
0.10
0.10
0.10
0.20
0.60
0.10
0.40
0.10
0.20
0.10
0.80
0.80
0.10
0.20
0.60
0.10
0.20
0.10
0.20
0.20
1.00
0.40
0.60
0.10
0.10
0.10
0.10
0.10
0.10
0.20
0.10
0.10
0.40
0.10
0.10
0.20
0.10
0.10
0.60
Obs_GWD
3.00
3.00
1.00
3.00
3.00
3.00
1.07
3.00
3.00
3.00
0.60
1.10
1.75
1.00
3.00
3.00
3.00
3.00
0.80
3.00
1.00
3.00
3.00
1.20
3.00
3.00
3.00
3.00
1.00
3.00
1.20
1.70
3.00
0.65
1.00
3.00
3.00
3.00
3.00
3.00
0.70
1.00
3.00
3.00
0.70
1.15
3.00
1.70
0.80
3.00
0.80
0.00
0.74
-0.02
0.67
0.08
0.32
0.59
-0.07
0.38
0.46
0.04
0.64
0.45
0.45
0.45
0.53
0.55
0.58
0.59
0.39
0.59
0.51
0.51
0.72
0.55
0.43
0.53
0.53
0.56
0.41
0.12
0.32
0.40
0.72
0.31
-0.05
0.54
0.53
0.06
0.57
0.53
0.39
1.11
0.32
0.08
0.34
-0.12
0.42
-0.05
0.19
-0.05
-0.03
0.08
0.59
0.47
0.94
0.67
0.71
0.84
0.06
0.76
0.75
0.67
0.64
0.93
0.93
0.56
0.78
0.79
0.79
0.82
0.50
0.81
0.58
0.76
0.92
0.67
0.65
0.77
0.77
0.84
0.61
0.35
0.60
0.61
0.92
0.42
0.34
0.80
0.78
0.54
0.79
0.77
0.62
1.12
0.75
0.67
0.57
0.37
0.71
0.50
0.52
0.61
0.45
0.16
0.93
0.63
1.34
0.94
1.05
0.92
0.41
1.07
0.98
1.05
0.71
1.06
1.07
0.68
0.99
0.88
0.87
0.94
0.61
0.93
0.64
0.89
0.92
0.83
0.86
0.90
0.99
0.98
0.87
0.63
1.08
0.96
1.04
0.53
0.82
0.94
0.90
0.74
0.92
0.90
0.91
1.14
0.90
1.02
9.00
0.88
0.90
0.86
0.81
0.84
0.83
0.24
2.15
0.87
2.34
2.33
2.42
2.36
0.61
2.68
2.74
2.69
0.80
1.21
1.27
0.81
1.58
2.56
1.19
2.77
0.72
2.09
0.70
1.73
3.80
1.05
1.50
1.73
1.73
2.48
1.76
2.74
3.22
3.14
3.80
0.66
5.91
2.60
1.58
1.72
2.09
1.73
1.72
1.17
2.42
2.33
2.62
2.46
2.34
2.06
1.32
1.73
1.93
0.32
5.83
1.11
3.34
4.00
4.08
4.03
0.91
4.24
5.01
4.26
1.02
1.70
1.82
1.01
3.66
5.06
3.79
5.54
0.87
4.87
0.85
3.65
5.95
2.32
4.00
4.23
4.23
3.95
4.11
5.52
5.57
5.92
5.95
0.90
7.21
4.68
3.66
3.95
4.87
4.23
3.87
1.29
4.08
4.00
5.65
5.17
4.62
4.72
3.82
3.30
4.80
118
Appendix 10: Simulation Results for root-zone salinity
POINT
3
4
10
31
44
46
13
18
33
52
54
63
16
30
35
37
38
40
43
45
51
58
59
61
65
66
1
41
26
32
6
7
8
14
17
60
70
9
15
34
36
39
50
64
67
GP
Pe111
Pe111
Pe111
Pe111
Pe111
Pe111
Pe112
Pe112
Pe112
Pe112
Pe112
Pe112
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe114
Pe114
Pe115
Pe115
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe311
Pe311
Pe311
Pe311
Pe311
Pe311
Pe311
Pe311
X
816288
816156
814473
808599
804981
804504
812517
810523
809836
804137
817479
807294
808548
805033
806961
804174
805724
812557
804179
804366
818540
808038
807584
810951
818218
809717
815316
813258
817083
812841
817153
817155
815948
813219
809656
811662
810755
816032
810957
804896
806870
808271
816791
810931
812622
Y
1663192
1663209
1668455
1672631
1661113
1666705
1668792
1672141
1670828
1673100
1674121
1670605
1665289
1671892
1669083
1665801
1663079
1663596
1669859
1663596
1672340
1667174
1661415
1662722
1672585
1664909
1662192
1660897
1671497
1672527
1668228
1668230
1668815
1666240
1667782
1667236
1668581
1660808
1664599
1668820
1665471
1662537
1661316
1664520
1663496
YEAR_0
0.13
0.40
0.10
0.10
0.10
0.40
0.50
0.96
0.06
0.20
1.20
0.20
0.10
0.50
0.10
0.10
0.20
0.10
0.20
0.20
4.20
13.00
0.10
0.40
0.20
0.30
0.10
0.10
1.30
0.19
0.50
2.56
0.10
0.13
0.20
0.40
3.33
0.10
0.20
0.10
3.50
0.10
0.22
0.10
0.10
YEAR_3
0.09
0.28
0.07
0.07
0.04
0.28
0.17
0.76
0.24
0.17
1.12
0.17
0.09
0.43
0.07
0.07
0.19
0.09
0.14
0.16
2.87
17.80
0.14
0.35
0.15
0.26
0.21
0.08
1.48
0.61
0.69
7.76
0.08
0.49
0.14
0.76
9.17
0.08
0.18
0.08
3.06
0.10
0.41
0.08
0.09
YEAR_10
0.25
0.20
0.10
0.07
0.09
0.28
0.48
0.78
0.70
0.36
1.20
0.13
0.09
0.62
0.12
0.08
0.22
0.17
0.11
0.16
9.63
30.90
0.19
0.37
0.19
0.27
0.27
0.10
2.11
3.53
2.08
10.73
0.14
2.00
0.73
1.95
20.35
0.09
0.40
0.13
13.15
0.15
2.02
0.13
0.11
YEAR_20
0.32
0.13
0.13
0.10
0.11
0.34
0.55
0.82
1.46
0.61
1.31
0.10
0.09
0.87
0.20
0.10
0.26
0.26
0.08
0.17
20.05
52.65
0.24
0.39
0.24
0.29
0.01
0.11
2.84
23.30
5.86
17.15
0.21
5.98
1.70
3.44
51.05
0.10
0.65
0.17
21.00
0.20
9.28
0.15
0.12
119
POINT
68
5
2
22
25
69
71
11
12
24
29
53
28
GP
Pe311
Pe412
Pe413
Pe413
Pe413
Pe413
Pe413
Va111
Va111
Va111
Va211
Va211
Va311
X
816764
816516
815945
817954
815747
813281
810090
814183
814132
816010
806793
807338
809196
Y
1661399
1665723
1662545
1673236
1671574
1673331
1669166
1674780
1675041
1674772
1673956
1674695
1674661
YEAR_0
14.40
9.60
0.10
4.40
1.70
3.70
2.70
0.20
13.00
1.10
0.10
0.10
3.50
YEAR_3
8.14
3.34
0.07
5.55
2.05
5.83
5.39
4.05
17.80
1.11
0.10
0.17
3.09
YEAR_10
40.30
55.35
0.03
13.50
3.86
9.78
10.85
5.07
30.90
1.15
0.26
0.40
9.24
YEAR_20
69.80
56.25
0.01
28.50
6.46
14.80
15.50
5.32
52.65
1.21
0.47
0.65
17.55
120
Appendix 11: Simulation Results for the transition zone
Point
3
4
10
31
44
46
13
18
33
52
54
63
16
30
35
37
38
40
43
45
51
58
59
61
65
66
1
41
49
57
26
32
6
7
8
14
17
60
70
27
55
56
62
9
15
34
36
39
GP
Pe111
Pe111
Pe111
Pe111
Pe111
Pe111
Pe112
Pe112
Pe112
Pe112
Pe112
Pe112
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe114
Pe114
Pe114
Pe114
Pe115
Pe115
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe311
Pe311
Pe311
Pe311
Pe311
X
816288
816156
814473
808599
804981
804504
812517
810523
809836
804137
817479
807294
808548
805033
806961
804174
805724
812557
804179
804366
818540
808038
807584
810951
818218
809717
815316
813258
818557
815025
817083
812841
817153
817155
815948
813219
809656
811662
810755
818665
810965
805838
810669
816032
810957
804896
806870
808271
Y
1663192
1663209
1668455
1672631
1661113
1666705
1668792
1672141
1670828
1673100
1674121
1670605
1665289
1671892
1669083
1665801
1663079
1663596
1669859
1663596
1672340
1667174
1661415
1662722
1672585
1664909
1662192
1660897
1656243
1658844
1671497
1672527
1668228
1668230
1668815
1666240
1667782
1667236
1668581
1670203
1657742
1659510
1657965
1660808
1664599
1668820
1665471
1662537
Year_0
0.13
0.40
0.20
0.10
0.40
0.40
1.00
1.00
0.06
1.20
1.20
0.20
0.10
0.50
0.06
0.10
0.06
0.10
0.20
0.10
4.20
0.01
0.20
0.40
0.20
1.00
0.10
0.20
0.20
0.20
4.40
0.19
0.10
2.56
0.10
0.10
0.20
1.70
3.30
8.30
0.50
0.50
0.20
0.50
0.20
0.10
0.13
0.13
Year_3
0.15
0.37
0.17
0.08
0.37
0.34
0.77
0.87
0.13
1.17
1.17
0.24
0.08
0.51
0.13
0.09
0.13
0.09
0.17
0.09
3.50
0.10
0.18
0.37
0.17
0.87
0.15
0.18
0.17
0.18
5.37
0.33
0.08
6.09
0.09
0.15
0.17
5.04
6.53
9.70
0.46
0.43
0.17
0.45
0.18
0.08
0.27
0.19
Year_10
4.15
0.73
0.18
0.06
0.36
0.26
1.03
1.00
0.32
1.37
1.37
0.43
0.10
0.80
0.32
0.08
0.32
0.13
0.12
0.09
6.29
0.41
0.23
0.36
0.17
1.00
0.27
0.16
0.23
0.16
11.50
2.04
0.07
9.35
0.11
0.86
0.40
15.26
12.02
23.09
0.50
0.24
0.17
0.47
0.31
0.09
1.38
0.43
Year_20
19.27
1.38
0.28
0.09
0.38
0.32
1.50
1.33
0.49
1.67
1.67
0.72
0.13
1.28
0.49
0.09
0.49
0.22
0.09
0.10
15.48
0.65
0.33
0.38
0.22
1.33
0.22
0.17
0.37
0.15
19.91
11.56
0.07
14.01
0.18
1.88
1.29
24.17
12.87
41.85
0.57
0.09
1.49
0.52
0.54
0.16
7.27
0.77
121
Point
50
64
67
68
5
2
22
25
69
71
23
47
48
11
12
24
21
42
29
53
19
20
28
GP
Pe311
Pe311
Pe311
Pe311
Pe412
Pe413
Pe413
Pe413
Pe413
Pe413
Pe413
Pe511
Pe511
Va111
Va111
Va111
Va111
Va111
Va211
Va211
Va211
Va211
Va311
X
816791
810931
812622
816764
816516
815945
817954
815747
813281
810090
819877
807495
809347
814183
814132
816010
819670
817627
806793
807338
803665
801618
809196
Y
1661316
1664520
1663496
1661399
1665723
1662545
1673236
1671574
1673331
1669166
1673907
1657703
1657703
1674780
1675041
1674772
1678104
1675366
1673956
1674695
1678242
1678078
1674661
Year_0
0.22
0.10
0.10
13.00
9.60
0.10
4.40
1.70
3.70
2.70
0.10
0.60
1.70
0.20
13.00
1.10
0.30
0.20
0.10
0.10
6.10
9.60
3.50
Year_3
0.26
0.13
0.09
15.29
11.24
0.09
4.59
2.84
4.80
4.54
0.19
0.53
1.51
4.21
15.29
1.11
0.33
0.18
0.09
0.13
6.22
10.66
3.87
Year_10
1.24
0.24
0.11
25.22
19.16
0.08
10.02
3.65
8.15
11.60
0.95
0.76
1.67
4.72
25.22
1.14
0.44
0.14
0.19
0.31
7.27
13.96
10.64
Year_20
5.18
0.39
0.14
42.42
22.00
0.07
21.23
3.96
12.53
19.28
2.03
1.32
2.21
5.29
42.42
1.19
0.51
0.10
0.38
0.54
8.11
16.45
20.65
122
Appendix 12: Simulation Results for the aquifer
Point
3
4
10
31
44
46
13
18
33
52
54
63
16
30
35
37
38
40
43
45
51
58
59
61
65
66
1
41
49
57
26
32
6
7
8
14
17
60
70
27
55
56
62
9
15
34
36
39
GP
Pe111
Pe111
Pe111
Pe111
Pe111
Pe111
Pe112
Pe112
Pe112
Pe112
Pe112
Pe112
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe113
Pe114
Pe114
Pe114
Pe114
Pe115
Pe115
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe211
Pe311
Pe311
Pe311
Pe311
Pe311
X
816288
816156
814473
808599
804981
804504
812517
810523
809836
804137
817479
807294
808548
805033
806961
804174
805724
812557
804179
804366
818540
808038
807584
810951
818218
809717
815316
813258
818557
815025
817083
812841
817153
817155
815948
813219
809656
811662
810755
818665
810965
805838
810669
816032
810957
804896
806870
808271
Y
1663192
1663209
1668455
1672631
1661113
1666705
1668792
1672141
1670828
1673100
1674121
1670605
1665289
1671892
1669083
1665801
1663079
1663596
1669859
1663596
1672340
1667174
1661415
1662722
1672585
1664909
1662192
1660897
1656243
1658844
1671497
1672527
1668228
1668230
1668815
1666240
1667782
1667236
1668581
1670203
1657742
1659510
1657965
1660808
1664599
1668820
1665471
1662537
Year_0
0.17
0.30
0.10
0.10
0.40
0.30
0.60
0.59
0.10
0.80
0.80
0.20
0.10
0.13
0.10
0.10
0.10
0.10
0.20
0.10
15.00
0.10
2.64
0.40
0.20
0.59
0.10
0.20
0.10
0.10
5.30
0.16
0.20
1.54
0.20
0.10
0.40
1.30
3.30
5.20
0.10
0.10
0.20
0.50
0.20
0.10
0.13
0.13
Year_3
0.17
0.29
0.10
0.10
0.39
0.30
0.00
0.58
0.10
0.79
0.79
0.00
0.00
0.00
0.10
0.10
0.10
0.10
0.20
0.10
14.74
0.10
0.00
0.39
0.20
0.58
0.10
0.20
0.10
0.00
0.00
0.16
0.20
0.00
0.20
0.00
0.40
0.00
0.00
5.14
0.10
0.10
0.20
0.49
0.20
0.10
0.00
0.00
Year_10
0.18
0.27
0.09
0.09
0.37
0.28
0.00
0.53
0.09
0.74
0.74
0.00
0.00
0.00
0.09
0.09
0.09
0.09
0.18
0.09
13.71
0.10
0.00
0.37
0.18
0.53
0.10
0.18
0.09
0.00
0.00
0.16
0.18
0.00
0.18
0.00
0.39
0.00
0.00
4.91
0.09
0.11
0.18
0.46
0.19
0.09
0.00
0.00
Year_20
0.19
0.24
0.08
0.08
0.33
0.25
0.52
0.47
0.09
0.67
0.67
0.17
0.08
0.22
0.08
0.08
0.08
0.08
0.16
0.08
12.27
0.09
2.37
0.33
0.17
0.47
0.09
0.16
0.00
0.00
4.45
0.18
0.16
1.83
0.16
0.09
0.36
1.21
2.84
4.54
0.09
0.11
0.10
0.41
0.17
0.09
0.22
0.11
123
Point
50
64
67
68
5
2
22
25
69
71
23
47
48
11
12
24
21
42
29
53
19
20
28
GP
Pe311
Pe311
Pe311
Pe311
Pe412
Pe413
Pe413
Pe413
Pe413
Pe413
Pe413
Pe511
Pe511
Va111
Va111
Va111
Va111
Va111
Va211
Va211
Va211
Va211
Va311
X
816791
810931
812622
816764
816516
815945
817954
815747
813281
810090
819877
807495
809347
814183
814132
816010
819670
817627
806793
807338
803665
801618
809196
Y
1661316
1664520
1663496
1661399
1665723
1662545
1673236
1671574
1673331
1669166
1673907
1657703
1657703
1674780
1675041
1674772
1678104
1675366
1673956
1674695
1678242
1678078
1674661
Year_0
0.17
0.10
0.10
16.90
0.30
0.10
0.10
1.30
2.64
1.90
0.10
0.10
0.30
0.20
16.90
0.20
0.20
0.30
1.10
0.10
2.30
4.20
2.60
Year_3
0.00
0.10
0.10
16.64
0.30
0.10
0.11
0.00
0.00
1.88
0.10
0.10
0.30
2.57
0.00
0.20
0.00
0.30
1.08
0.10
0.00
0.00
2.57
Year_10
0.00
0.09
0.09
15.55
0.28
0.09
0.16
0.00
0.00
1.81
0.10
0.09
0.28
2.47
0.00
0.18
0.00
0.28
1.00
0.09
0.00
0.00
2.45
Year_20
0.17
0.08
0.08
13.96
0.25
0.08
0.21
1.33
2.37
1.67
0.10
0.09
0.25
2.32
13.96
0.16
0.17
0.25
0.90
0.08
2.01
3.73
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