Quick viewing(Text Mode)

Tsegay Fithanegest Desta 09318

Tsegay Fithanegest Desta 09318

Spatial Modelling and Timely Prediction of Salinization using SAHYSMOD in GIS Environment (a case study of Nakhon Ratchasima,Thailand)

Tsegay Fithanegest Desta March, 2009

Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

by

Tsegay Fithanegest Desta

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: (Applied Earth Sciences: Geo-Hazards )

Thesis Assessment Board

Prof. Dr.V. G.Jetten Chairman Dr.T.W.J. Van Asch External Examiner Dr.A. (Abbas) Farshad First Supervisor Dr.D. B. (Dhruba) Pikha Shrestha Second Supervisor

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

Disclaimer

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

Abstract

Salinization is a complex process, often initiated from subsurface. This is one of the reasons why it is so difficult in tracing it at its earliest stage of development, using a wide range of techniques and methods. For that matter agricultural productivity is observed to be hampered by the development of root zone through the use of brackish water during spate and capillary rise of saline . The salinity problem of the northeast of Thailand in general, and that of the Korat area in particular is due to rise of saline table and of salts on the surface through capillary action. Thus it is difficult to trace back its early stage using optical remote sensing (RS) alone. In this study we apply an integration of (hydrological) modelling and remote sensing (RS) to track down the temporal and spatial soil salinization process in the area.

The numeric , SAHYSMOD, is used to image the subsurface solute movement by parameterizing the surfaces and subsurface water movement through the interaction of climate, soil, and human responses factors. Use is also made of RS, which addresses the status of the event at time of surveillance. Through the ‘give and take’ type process of both techniques in GIS environment modelling of the salinization process became possible, both in space and time dimension. Here, GIS plays the role integration the results of the two techniques. The results are then extrapolated to the unsampled section of the study area using the devised decision support system (decision tree). By doing so the intended objectives of the study: detecting salinity change in time and spaces and modelling salinization as a process have been achived.

The model has answered two basic questions of the research, 1), to define the source of salinity of the area; the rise of saline groundwater table, and 2) geopedological mapping units which are prone to salinization. The supervised image classification has made it clear that the low lying areas have been invaded at time of imaging. The model predictions have identified those areas which were less salt affected soils or not at all in the image classification are now prone to be affected due to the rise of saline groundwater table.

According to 20 years of prediction from the model salinization route (development) is towards the west side of the study area, which is currently none saline, with a rate of 20% per year per geomorphic unit area coverage. That areas which are currently (non saline=NS) and have total area coverage of 234.20 km2, are endangered and could be changed into salt affected soils if and only if the existing conditions have not changed to the favour of the environment.

Key words: spatial modelling, prediction, SAHYSMOD, ECFC and DSS

i Acknowledgements

First and foremost I would like to forward my special thanks to The Netherlands Government and The Dutch people in general and to my Institute ITC, in particular. I am here at the last virtue of academically endeavour through the sponsorship given to me by ITC. My heart moved so much by the lovely and kind treatment that my INSTITUTE and its STAFF renders to me. I feel so much indebted for all sorts of chances that ITC have open for my future life. I always hear people say that words are poor to explain sensational feelings but it is a pity that at under such condition I always lost my poor words. In this case even now I have lost them and can not able to pass all what I want to say. Anyway thanks so much.

Secondly I would like to extend especial thanks and gratitude to my organization: Tigray Water, Mines and Energy Resource Development Bureau, for supporting my study at ITC and giving me long leave of absence with all my rights respected.

I am deeply indebted to my first supervisor Dr. Abbas Farshad for his supervision, encouragement and guidance he has provided me throughout my research. His critical comments and helpful guidance gives me a chance to explore further, to improve my English and above all to go in-depth to what is research work . My deepest gratitude goes to my second supervisor Dr. D. B. (Dhruba) Pikha Shrestha. His valuable guidance during the field work and frequent follow-up thereafter was vey essential. I have learned a lot from them. Their kind support and encouragements gives me strength right starting from the field work. My stay with them for the last six months I hardly remember the time that I left their office being depressed.

I gratefully acknowledge the supports from LDD both in Korat and Kon kean, my special thanks goes to Mr. Somsak Sukchan (head of soil survey at khon kean regional laboratory) and all his staffs. With unlimited support of Mr.Somsak and his staffs all laboratory analysis and secondary data required are achieved. My thanks also go to my guiders and translators Mr. Lek and Ode of korat at the same time our driver Mr. Asenay.

I would like to thank my beloved and much more missed wife Tsige Gidey and my sons Abie and Alief. I would also like to thank W/r Kiros Gebregergs, Memhir Gidey Berhe, and W/r Yalem Desta for taking care of my family during my long absence.

I praise and acknowledge the support of Mr. Oosterbaan the programmer throughout my modelling works. His supports have helped me to learn the model with less difficulty. At the same time I would like to thank Mr. Aliakbar Noorazi for his last minute support in data presentation of my research works. The love, social and academically event that I share with my class mates is also unforgettable event that have to be acknowledged here. At last but certainly not least my thanks also go to every staff member in the Applied Earth Sciences program.

ii

Dedicated to my late grandfather

YENETA GEBRESLASE TEDLA

iii Table of contents

1. Introduction ...... 1 1.1. Ggeneral introduction on Salinization and its mapping...... 1 1.2. General Background ...... 2 1.3. Soil Salinity and Salinization...... 3 1.4. Soil Salinity of the the world ...... 4 1.5. Soil salinity in Thailnd...... 5 1.6. Irrigation Practise in the study area ...... 6 1.7. Problems in salinity detection...... 8 1.8. Modelling salinization ...... 8 1.9. Problem statement and research justification ...... 9 1.10. Research objective ...... 10 1.10.1. General objective ...... 10 1.10.2. Specific objective...... 10 1.11. Research questions and hypotheses ...... 11 1.11.1. Research questions...... 11 1.11.2. Research hypotheses ...... 11 2. Literature review ...... 13 2.1. Soil salinity and pH...... 13 2.2. Soil salinity and electrical conductivity...... 14 2.3. Relationship of EC1:5 with ECe and ECFc...... 16 2.4. Model types for salinization ...... 18 2.4.1. Seasonal models ...... 19 2.4.2. Transit models ...... 19 2.5. SAHYSMOD Model...... 20 2.5.1. Model rational and description...... 20 2.5.2. Model possibilities and conditions for applications...... 21 2.5.3. Reservoir concept and water flow ...... 22 2.5.4. Model data requirement and data components...... 23 2.5.5. Polygonal network...... 23 2.5.6. Computational time step...... 24 2.5.7. Hydrological data ...... 24 2.5.8. Agricultural cropping pattern-...... 25 2.5.9. ...... 25 2.5.10. Salt balance ...... 26 2.5.11. Output data...... 26 2.6. Model calibration...... 26 2.7. Model sensitivity analysis...... 28 2.8. Model validation...... 29 2.9. Soil salinity change detection ...... 30 2.9.1. Exploratory data analysis ...... 30

iv 3. The Study area...... 33 3.1. General information...... 33 3.2. Location ...... 33 3.2.1. Geographical location...... 33 3.2.2. Administrative location ...... 33 3.3. Study area selection justification...... 34 3.3.1. Problem oriented ...... 34 3.3.2. Facility related...... 35 3.3.3. Previous study ...... 35 3.4. Climate...... 35 3.5. Geology...... 36 3.6. Geomorphology ...... 38 3.7. Land cover ...... 39 3.8. Soils ...... 39 4. Materials and methods ...... 41 4.1. Detecting soil salinization process and modelling it ...... 41 4.2. Pre-field work ...... 44 4.2.1. Establishment of Database system ...... 44 4.2.2. Study basics of the model...... 45 4.2.2.1. Nodal network alignment...... 45 4.3. At Field work...... 46 4.3.1. Large scale survey ...... 46 4.3.2. Small scale survey ...... 47 4.3.3. Sample laboratory analysis...... 48 4.3.3.1. Soil conductivity analysis...... 48 4.3.3.2. Water conductivity analysis...... 49 4.4. Materials used...... 49 4.5. Post Field work ...... 49 4.5.1. Soil laboratory result interpretation ...... 49 4.5.2. Water laboratory result interpretation ...... 50 4.5.3. Model input data preparation ...... 52 4.5.4. Model calibration ...... 53 4.5.4.1. Determination of efficiency ...... 54 4.5.4.2. Initial Groundwater flow determination ...... 55 4.5.5. Model sensitivity analysis ...... 56 5. Result and discussion ...... 58 5.1. General Statistical description of observed ECFC values ...... 58 5.2. Justification to higher EC from observation point 4...... 60 5.2.1. Scenario 1: Heterogeneous saline parent rock ...... 62 5.2.2. Scenario 2: Rise of saline groundwater table...... 63 5.2.3. Scenario 3: surface washout and accumulation...... 64 5.3. Model calibration, sensitivity and validation analysis...... 67 5.3.1.1. Preamble to model works ...... 67 5.3.1.2. Specific note for model outputs interpretations...... 68 5.3.2. Model Calibration...... 68

v 5.3.2.1. Evaluation of calibrated model...... 69 5.3.3. Model Sensitivity Analysis...... 71 5.3.4. Model validity Analysis...... 76 5.3.4.1. Descriptive statistical analysis of data used for validation...... 76 5.3.4.2. Statistical evaluation of model validation...... 78 5.3.5. Cross validation using confusion matrix ...... 80 5.4. End result discussion ...... 81 5.4.1. End result procedures followed...... 81 5.4.2. Simulated groundwater table and salinization...... 87 5.4.3. Simulated numerical soil salinity result ...... 90 5.4.3.1. Simulated salinity numerical results for Root zone...... 90 5.4.3.2. Simulated salinity numerical results for Transitional zone ...... 92 5.4.3.3. Simulated salinity numerical results for zone salinity ...... 93 5.4.4. Map result of soil salinity...... 94 5.4.5. Spatial Distribution of Simulated Salinity within the Geomorphic Units...... 99 5.5. Salinity change detection ...... 100 5.6. Limitation of the model ...... 102 5.6.1. Out put data presentation...... 103 5.6.2. Grid alignment...... 104 5.6.3. Other General limitations ...... 105 5.7. Conclusion ...... 106 5.8. Recommendation ...... 107 5.8.1. Soil salinity hazard related ...... 107 5.8.2. Model related for future users ...... 108

vi List of figures

Figure 1Global distribution of saline soils Cited in (Farifteh et al., 2007)...... 3 Figure 2 Soil salinity classes of the study area (LDD, 1994) ...... 5 Figure 3 Land cover/ use map...... 7 Figure 4 Major types of salinity Rengasamy (2006)...... 15 Figure 5 Landuse types, Soil strata vs. hydrological factors (Oosterbaan, 2005)...... 22 Figure 6 Nodal network alignments of SAHYSMOD in the study area...... 24 Figure 7 GPS locations observation data used for model validation and change detection ...... 31 Figure 8 Location of the study area location...... 34 Figure 9 Monthly meteorological data of the area...... 36 Figure10 Three dimensional views of the folded basins...... 37 Figure 11 Rocksalt and the geological map of the study area in one (Sukchan, 2003) ...... 37 Figure 12 Geomorphology of the study area (Soliman, 2004a)...... 38 Figure 13 Soil textural classes of the study area (LDD, 1994)...... 40 Figure 14 Methodological of full activity process...... 43 Figure 15 Observation points and their location on the study area...... 48 Figure 16 Soil profiles at which clay and sand soils separate in the study area LDD (1994) ...... 53 Figure 17 Graphical representation of determined leaching efficiency...... 55 Figure 18 Histogram distributions of the ECFc data used for model to run...... 59 Figure 19Observation points overlay on ASTER image of November 03, 2004...... 61 Figure 20 Observation points overlaid on the land cover/use map...... 61 Figure 21 Observation point overlay on the rock formation of the study area ...... 63 Figure 22 Catchment area and topographic feature of observation points with higher EC value ...... 66 Figure 23 Pattern Analysis of Observed vs simulated ECFc ...... 70 Figure 24 Scatter plot of measured against simulated ECFc values of calibration...... 70 Figure 25 Model sensitivity analyses for installation of subsurface ...... 72 Figure 26 Histogram distributions of the ECFc data used for model validation ...... 77 Figure 27 Validation pattern Analysis of Observed versus simulated ECFc...... 79 Figure 28 Scatter plot of measured against simulated ECFc values ...... 79 Figure 29 Aquifer salinity map for 10th year prediction from the model...... 82 Figure 30 Aquifer salinity map for 10th and extrapolated for study area...... 83 Figure 31 localized red-light spots for salinization (Shrestha and Farshad, 2009)...... 88 Figure 32 Soil salinity of root zone (Cr4) versus groundwater inflow-outflow (Gaq) ...... 89 Figure 33 Depth of water table [DW] in each polygon...... 92 Figure 34 Model predictions for 20 years salinization even of the Root zone ...... 96 Figure 35 Model predictions for 20 years salinization even of the Transition zone...... 97 Figure 36 Model predictions for 20 years salinization even of the Aquifer zone...... 98 Figure 38 Change detection of root zone salinity for year 0 ...... 102 Figure 39 Output data type generated by the model ...... 104 Figure 40 Grid sampling and internal nodal network representation of the model ...... 105

vii List of Equations

Equation 1 EC conversion equation...... 18 Equation 2 Principles of conservation of mass of the model...... 21 Equation 3 balance equation ...... 27 Equation 4 Groundwater balance equation ...... 27 Equation 5 Combined surface and subsurface hydrological movement equation...... 28 Equation 6 Conversion relationship of ECe and ECFc...... 52 Equation 7 Top soil water balance equations...... 73 Equation 8 Root zone salinity balance equations ...... 73 Equation 9 Change in salt concentration...... 75 Equation 10 Salt concentration below and above subsurface drainage network line ...... 75 Equation 11 Net horizontal flow in the aquifer...... 90

viii List of tables

Table 1 Conversion factor to estimate ECe from EC1:5 Slavich and Petterson (1993) ...... 17 Table 2 Climatic data (1971-2007) of Nakhon Ratchasima...... 35 Table 3 Developing research items into experimental design ...... 42 Table 4 Soil texture affect on EC weight/volume measures and salinity class(DAF, 2006)...... 50 Table 5 Conductivity of irrigation water and their location ...... 51 Table 6 value used for leaching efficiency determination ...... 55 Table 7 Summery of descriptive of EC [dS/m] data collected to run model ...... 58 Table 8 Standardized ECe value of the observation points ...... 62 Table 9 Mean error of measured Vs simulated root zone salinity of calibrated model...... 71 Table 10 Summery of descriptive statics of EC [dS/m] data used for model validation...... 77 Table 11 Mean error of measured Vs simulated root zone salinity of validation...... 78 Table 12 Model validation through confusion matrix /cross validation...... 80 Table 13 Part of the designed decision supporting system ...... 86 Table 14 Average yearly predicted root zone salinity [dS/m] ...... 91 Table 15 Average yearly predicted Transition zone salinity [dS/m] ...... 93 Table 16 Average yearly predicted Aquifer zone salinity [dS/m] ...... 94 Table 17 Root zone salinization as a function of time and geomorphologic unit for year 0 ...... 99 Table 18 Salinity change within a year for (year zero)...... 101

ix List of abbreviations used

ASTER Advanced Space borne Thermal Emission and Reflection Radiometer CSV Comma separated values DAF Department of and Food Western Australia. DEDP Department of Energy Development and Promotion: DEM Digital Elevation model DNR Department of Natural Resources DSS Decision Supporting System EC Electrical Conductivity of soil ECe Electrical Conductivity of soil extracted soil paste ECFc Electrical Conductivity of soil at field capacity ECw Electrical conductivity of water ETo Reference crop evapotranspiration GAS Groundwater associated salinity GIS Geographic Information systems GPS Global Positioning System IAS Irrigation associated salinity LDD Department of Land Development Mha Million hectares MSDOS Microsoft Disk Operating System NAS Non-groundwater-associated salinity NIST National institute of Standards and Technology (USA) SAH17 Sample collected from nodal network code 17 of SAYSMOD model

x Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

1. Introduction

1.1. Ggeneral introduction on Salinization and its mapping Salinity hazard of the area is believed to have been aggravated by the deforestation of the natural forests in the early 1960s. Even though mismanagement of natural sources has got the lion share accountability but factors such as: salt rock weathering, geomorphology and other anthropogenic activities, rise of saline groundwater table and climate are also there playing role in the salinization process in northeast of Thailand, Nong sung area district. Accordingly what is currently seen in the study area is losing hectares of land every year.

Majority of the population of the region makes their living from agriculture. As agriculture is also a source of employment, hence, the importance of supporting fed agriculture by irrigation, is not questionable. Although it is practised traditionally both in spate and pressurised type of irrigation still there seems a need of expanding it. But as the interview results show the sustainability is questionable. That is the farmers only harvest good yield only during heavy rainy season, due to leaching effect of salts accumulated on the root zone from the heavy rain. But this heavy rain has also a chance of recharging effect to the groundwater table which is shallow, and to saline solution too. To keep life going on, a sort of balancing between the two [leaching effect and rise of water table] is required.

The global coverage is so significant and even its development rate is accelerating at an alarming rate. As a result of this a number of countries/areas where they were free of the problem in the recent past are hanged up like rope at the neck conditions. And yet in most research works the hazard is treated as especial treat for Arid and semi arid areas only. Regardless of the current global view, factors of soil salinization are being favoured and will be favoured by the paved conditions for it: population increase and climate change. Now days this chronic problem is escalating to conditions that mitigating circumstances look blurred and beyond capacity for the world in general and agrarian countries in particular. The challenging problem from salinization is not only its development is fast but also recovering out of the problem is quite rare than giving up the land and everything in its storehouse .

To the 21st technology level the challenge from salinity hazard is the detection of salinization in the early stage. Up to now there is no absolute technique or methodology which is capable of doing this. Scientific findings such as Remote sensing is effective and cheap in detecting and mapping salt affected area but lacks the subsurface information from which salinity begins. Where as ground based salinity detection techniques of geophysics such as EM survey. Even though they are power full to detected salinity hazard from cropped area and extract subsurface information which is lacked by RS still its efficiency is influenced by different factors. To name few: soil texture, bulk density and are among the major limiting factors.

1 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

This appraises the importance of hydrological models for they address surfaces and subsurface movement of water movement thereby soil salinity and salinization. Currently hydrological models are widely accepted to overcome the limitation of geophysics subsurface survey.

Nevertheless, much of the on which hydrological models are constructed are not measurable and known confidentially. Thus modellers are usually forced to estimate and assume at conditions that could not know and measure it to their capacity being confident. Hence model users are also expected to carry out sensitivity analysis and validation to estimate the error of confidence at which the hydrological models are capable of representing the truth on ground. This quests and shows that study of soil salinity and salinization can not be done efficiently without the integration of techniques, methods and even collaboration of professionals from related disciplines.

This research based thesis on soil salinization process is on integration result of RS and hydrological model in GIS environment. The SAHYSMOD, model, is a numerical computer program for prediction of the salinity of soil moisture, ground water and drainage water, depth of water table, and drain in irrigated agricultural lands, using different (geo) hydrologic conditions, varying water management options, including use of ground water for irrigation, and several cropping rotation schedules.

The model recognizes the project sites in geographical space and accounted spatial variations through a network of polygons. But it does neither accept map as input nor produces an out map, all the outputs are comma separated values [CSV] and ordinary graph. However, it is a powerful model in the area of hydrological modelling. Thus in this study an integration of Remote sensing and SAHYSMOD hydrological model in GIS environment was done to overcome the weakness of one technique by the other and maximizing the strength of the two techniques/methods. By doing so the spatial and temporal soil salinization condition of the northeast region of Thailand has been modelled. Following, this overall brief prefatory about the challenges facing in the realm of soil salinity identification, mapping, assessment and management, is the full thesis on spatial modelling and prediction of soil salinization process.

1.2. General Background It is true that there are naturally occurred saline soils, but human-induced salt-affected soils cover extensive areas throughout the world too, in particular in the arid regions. The extension is so that one can say there is no country without salt-affected soils. The difference is in extent and in degree of salinization. In this general background, we will first deal with soil salinity and salinization and then try to give a short review of the soil salinity status in the globe in general and that of Thailand in particular.

2 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Figure 1Global distribution of saline soils Cited in (Farifteh et al., 2007)

1.3. Soil Salinity and Salinization Saline soils are characterized by containing excess soluble salts. As a result soil salinity is a measure of the total amount of soluble salt of Na+, K+, Ca2+, Mg2+ and Cl- in the soil. Usually salt affected soils are identified by their electrical conductivity (EC), which indicates the salinity of the saturated soil extract at 25oC, and by their sodium absorption ratio (SAR), exchangeable sodium percentage (ESP) and the degree of acidity (pH) of the saturated soil extract (Greiner, 1997b). A soil is considered saline if the electrical conductivity of its saturation extract (ECe) is above 4 mS/m, the threshold value above which deleterious effects occur, varying depending on plant type (variety), soil-water regime and climatic conditions.

Salt becomes a problem when it excessively accumulates at the soil surface for it starts to affect crop production, environmental health and economic welfare. For example hazardous effect of water soluble salts in crop production is explained by affecting the water flow condition in the root zone through the development of osmotic effect. Thereby it limits plant growth. In general plants grown in salt-affected areas are stunted, because as salinity level increases, plants extract water hardly from soil. Intern this excessive water stress conditions causes nutrient imbalances. According to Rengasamy (2006), this low osmotic potentials resulted from soil salinity holds back the water uptake by plants and reduces their ability to survive and give yield.

Salinization as a process where by concentration of total dissolved solids in the soil are increased, have so many deleterious effects. Greiner (1997b) has summarized these factors of salinization process into three: salt itself, water and a mechanism by which salt is moved. Salt, being mobile in nature, is heavily influenced by factors like: topography, geology, and geomorphology, human interference with nature, climate and the surface and sub surface water

3 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

movement. This mobility and subsurface process nature have complicated the tracing of soil salinity development just before the threshold level for mitigation passes.

1.4. Soil Salinity of the the world Even the single pin of today’s civilisation is based back to agriculture. That is the beginning of using land and resources within it opens the gate for civilization. However, land resources regeneration is very slow while population growth is very fast which leads to imbalance of the equilibrium between demand and supply. This gap was and still is continuing being the fertile ground for land degradation through unmanaged forest clearance and poor irrigation water use.

Therefore, the challenges of mankind in this century will be ensuring sufficient food production at level that meets the ever increasing population density. According to Wild (2003) the world need to ensure sufficient food production to meet the demand of an extra two and three billion people by the year 2025 and 2050 respectively

However, Rengasamy (2006) has pointed out that most of the suitable land has been cultivated and expansion into new areas is hardly possible. Thus the only way of achieving this global food plan target is intensive agriculture that is maximizing yield per unit area using the existing arable land. Yet, the bad side of this possibility as to estimations of Wild (2003) is that about 15% of the total arable land area of the world has been degraded by physical and chemical degradations types. On top of this as to the explanations by Paniconi et al. (2001) the world is losing, on average, 10 hectares of land suitable for cultivation every minute, Of which 3 hectares is due to salinization which is equivalent to 1.5 million hectare per year. In line to this as to the estimation by Eswaran et al.(2001) 950 million ha of salt-affected lands occur in arid and semi-arid regions that is nearly 33% of the potentially arable land area of the world.

Nevertheless, according to Ghassmi et al. (1991) salinization is somewhat an extensively researched but fairly understood environmental hazard. Despite the general awareness and knowledge of this problem, salinization has remained increasing at an alarming rate. And its continued existence has put a number of negative impacts on the environment, society, and economy of affected countries

During early development stages of salinity and its effects are manifested through yield reduction and decreasing any aspect of soil productivity. But at advanced stages salinity destroys overall vegetation in the soil resulting in loss of habitat and reduced biodiversity, and totally renders the soil barren. In terms of social side aspect for the food security is greatly challenged by the hampered yield reduction the farming community in particular and that of the nation in general will be scrambled. As a result to avoid the overall social and economical crises countries faced with this problem spent hundreds to thousands of million dollars per year in production losses and rehabilitation of damaged land and water supply structures.

4 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

1.5. Soil salinity in Thailnd Shrestha (2006) has pointed out that about one quarter of the 5.8 million hectares [Mha] of salt- affected soils in Southeast Asia occur in Thailand. And this accounts for about 2.7 percent of the country’s total area coverage. Based on the findings of Ghassemi et al.(1995) most of saline soils in Thailand occur in the Northeast region and accounts for approximately 2.85 million hectares while the south coastal plain and central plain account for 0.58 Mha and 0.18 Mha respectively. Refer the figure 2 for the salinity distribution of the area

Figure 2 Soil salinity classes of the study area (LDD, 1994)

The fundamental cause of salinization in northeast region as ascribed by Ghassemi et al. (1995) are climate and extensively underlying salt-bearing rocks which include shale, siltstone and sandstone. 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 carry salty water flowing from groundwater layers. And this is accelerated and widely spread by human activities which are associated with poor agricultural practices, deforestation, salt making, and construction of roads and reservoirs. Agreeing with the above authors on the fundamental causes of salinity of the region Shrestha (2006) has added that the salinization of the area is aggravated by miss management of natural resources and is becoming sources of declining agricultural productivity of the region.

Nowadays climate change is expected to increase and lower rainfall. That means driving forces for groundwater table rise will be favoured by the change. Stepping from this unless good conservation measures are put in place, pending the salt rich rock material and water table rise, the future of the region will be so challenging. So far as to the witness of

5 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Yadav (2005) and Shrestha (2006) salinity is the main causes of low and unstable agricultural productivity of the region. Farmers of the area are in problem due to soil salinization. As to these authors through its threatening effect to agricultural production, which is a source of employment and livelihood, salinity become problematic hazard for 18 million people of the region, which is one third of the total population of the nation.

Northeast region of Thailand has a tropical climate. And as Wild (2003) the properties of soils in the tropics and subtropics cover a very wide range, and their quality greatly depends particularly on climate and topography. But due to higher temperatures and rainfall weathering is more extreme in tropics and subtropics than at higher latitudes. Therefore, giving the climate, topography, and higher weathering conditions soils of the area are by far fragile and susceptible to wider range of degradation unless they are well treated.

To put it in a nutshell the natural factors of salinization are highly favoured by the natural conditions of the area. On top of this the management of natural resources can said to be inefficient. For instance it is anonymously accepted by all researches of the area that the deforestation of the natural forest is the main cause for all sort of salinization. Therefore, management and rehabilitation measures that recover the land cover and backup the natural ecosystem which improves soil plant growth conditions and ensure agricultural sustainability currently are so essential for region than ever they were.

1.6. Irrigation Practise in the study area

In the , farmers undergo spate irrigation practices using well built harvesting diversion structures. That is by diverting from the peneplain and surrounding hills and mountains farmers irrigate their paddy fields. This spate irrigation activity covers 100% in the paddy fields.

But in peneplain area the irrigation practices are localized to patches of flood harvesting structures [ponds and small dams] and/or shallow as a result the type of irrigation is mostly pressurized irrigation.

The crop types that grow in the irrigated farms of the peneplain are cassava, , pepper, flowers and very limited commercial trees for industry consumption. But in the valley the dominant (perhaps the only) crop is . Cassava, being the dominant crop in the peneplain, does not grow in the paddies for it is not water loving crop.

The irrigation schemes in the paddy fields in general have no tertiary/field . Because the farm lands are completely underwater every rainy season, but in contrary the field canals in the peneplain areas are well prepared and maintained.

According to Ayers and Westcot (1994) in irrigated areas, salts often originate from saline groundwater table or from salts in the applied water. In the project area, topographically the

6 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

salinity distribution has violently invaded the low laying valleys. Relatively speaking elevated areas of the valley in particular and peneplain areas found western part of the project area in general are less affected by salinity. The main factor for the invasion of low-lying topographies is an addition of salt with surface washout and accumulation from the upper-basin.

Causes of salinization in valley areas are: in-situ salinization from capillary rise of saline groundwater and salt accumulation with run off from higher mountains and peneplain. As it can be seen in figure 3 below significant part of the project area is affected by salt and this affected area is, topographically, the low-lying part. For instance Farifteh et al.(2007) have mentioned that more than 50% of the lowland areas in the northeast region are highly affected by salinization. According to these authors parts of the lowland that have not been affected by salinity are mainly used as paddy fields. And the upland areas, mainly, are not saline are used for plantations of cassava. Refer figure 3 for the landuse and cover in relation to salinity distribution in the study area

Figure 3 Land cover/ use map

As it was mentioned in the climate description part, the project area have double rainfall pattern with one dry spell month between them. Moreover, depending on the on set and off set of those rainy seasons, farmers exercise supplementary and complementary irrigation activity. This means that there is not enough time that the raised saline groundwater table could drop down significantly below the critical depth of plant root growth. Hence, the fluctuation of groundwater table between the rain and dry seasons is so minimal, which makes the valley to be inundated by water after one to three significant rainy days during the wet season.

7 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

1.7. Problems in salinity detection

Salinization is easy to get worsen by slightest shift in the soil solute dynamic due to several factors; however, its reclamation for crop production and environment rehabilitation is by far challenging and too difficult. Because salinization starts from subsurface and is so difficult to trace it before it appears on the surface, therefore, remote sensing being a relatively cheaper detecting technique it can effectively map the surface salinity conditions only. At this time the degree of severity of salt affected soil reach/pass threshold level, a stage at which reclamation measures can not restore it. In short the optical remote sensing falls to give information about the subsurface conditions where salinity begins is a challenge for the researcher. Furthermore, Metternicht and Zinck (2003) have also added that use of remote sensing data for mapping salt- affected areas have other limitation in connection to the spectral behaviour of salt types, spatial distribution of salts on the terrain surface, temporal changes of salinity, interference of vegetation, and spectral confusions with other terrain surfaces features. Thus it is difficult to study and fully detect and monitor soil salinization using remote sensing alone.

Several studies have been carried out focusing on detection the spatial and temporal distribution of soil salinity looking for remedial measures. Nevertheless, it is only the general process of formation of salinization are well understood, there has been, and still is, a lack of knowledge concerning the complex biophysical relationships of specific locations and catchments. Knowledge about the costs and the effectiveness of possible remedial measures is sketchy, and hence actions are inherently risky. And this risky hampers salinity management once the problem is recognized(Greiner, 1997b).

In conclusion in the process of soil salinity detection and mitigation the challenges are too immense and the remedial measures are meagre thus the need for research in the area of soil and agricultural challenges in general and in the sphere of soil salinization in particular is quite enormous and demands an integrated approach both from technically and professional aspect.

1.8. Modelling salinization

Knowledge on spatial and temporal variation of soil salinity is crucial base for developing appropriate management strategies to mitigate and monitor farther salinization. This acquired knowledge on the cause and effect of soil salinization must be supported by efficient and reliable methods and techniques to monitor the overall negative impact of salinization both to the environment and social dimension life economics. According to the explanations by Metternicht and Zinck (2003) monitoring of soil salinity includes identifying places where salt accumulate first, and then detect its temporal and spatial distribution to track its changes and anticipate further expansion.

As it have been discussed in 1.7 above in the processes of detection already salt affected area remote sensing technique plays an important role, but it lacks capabilities of extracting

8 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

information from soil subsurface. However models appear being efficient fundamental technique to overcome the limitation of remote sensing in the third dimension.

Due to the close relationships between soil surface and hydrological movement, hydrological models are becoming the best choices for responding both to surface and subsurface movement of soluble salts. In this regard Xu and Shao (2002b) have confirmed the closely relatedness of salinization with surface-soil and groundwater hydrological processes. Further more, from hydrological models point of view Madyaka (2008) have mentioned three main regions of interest that have to be considered in modelling salinization, namely:-

1. The vertical exchange of salts between the groundwater system and unsaturated zone; 2. The accumulation of salt in the vegetation root, , and 3. The horizontal transportation of salts through groundwater movement, and flow.

This author believes that the complexes interaction of the above mentioned hydrological process with soil properties imposes modelling salinization so difficult and challenging. However, the negative impact from unregulated activity of man and other climatic factors is also huge burden for soil salinization modeller.

From challenging nature of soil salinity detection and the lack of absolute detector of salinity and salinization as mentioned in last paragraph of subtitle 1.7 above, the overall identifying and management of soil salinization as a process needs an integration of techniques, methods and different disciplines of study to work together.

1.9. Problem statement and research justification

As mentioned above, several techniques and methods of salinity detection were implemented, each with advantages and also with limitations. Unless one technique is supported by the other it is difficult to detect salinity development at early stage. Thus soil salinity detection using remote sensing should be supported by different other supplemental techniques. For instance, near surface geophysics is used to detect soil (as a 3D body) salinity via electromagnetic induction and bulk soil electrical conductivity. As Farifteh et al. (2006) have highlighted estimation of salinity using this method is influenced by soil solution, , moisture content, and type and amount of clay in the soil. Thus the authors advocates the use of hydrological modelling would be an appropriate integral method with remote sensing for hydrological models better estimates soil solute movement in the subsurface.

As a matter of fact, soil salinity plays a crucial role in determining of vegetation distribution, plant productivity, and biogeochemical processes in the agricultural soils. At the same time associations of soil salinity-gradient and soil salinity-vegetation in the agricultural fields have often been observed but rarely explained. Wang et al. (2007) have mentioned that there are few quantitative and systematic studies on the effects of various factors: such as climate, soil,

9 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

topography, and vegetation, on soil salinization process and gradient of saline areas. That means much work is yet to be done or required to understand the challenges from the leading driving force of soil salinization process.

The difficulty of soil salinity map using remote sensing is due to naturally moving property of salinity. This movement is both vertical and horizontal directions. As a result there are conditions at which saline areas of today might be washed away and become free tomorrow or in contrary the one which were less severe saline area now become worst saline in the future. Thus the important idea of the study lies on addressing the following three problem definitions.

1. Evaluation of the effect of major driving forces of soil salinity (climate in terms of ETO and crop production, soil properties, agricultural management, topography and saline ground water rise) on soil salinization process. 2. Assessing soil salinity distribution and magnitude along the topographic gradient of saline agricultural fields and see the main biophysical factors that affect the distribution and magnitude of soil salinity in the study area. 3. Estimating soil salinization process via change detections on the bases of previous studies.

1.10. Research objective

1.10.1. General objective The main objective of this study is to test the applicability of SAHYSMOD model to detect soil salinity and to examine the possibility of mapping the process of salinization using the model. To apply GIS techniques to indicate and map potentially salt affected areas. Based on the current agricultural practices, soil, water and salinity management levels to Model and Predict long term salinization outcomes in the study area based on the prevailing landforms. With ultimate goal aiming at formulating or proposing means which help in mitigation and management of soil salinization.

1.10.2. Specific objective

• To calibrate SAHYSMOD model using laboratory soil salinity analysis results. • To validate the calibrated SAHYSMOD model using independent input data. • To identify and model existing saline areas using SAHYSMOD. • To predict Geopedological mapping units those are prone to salinization. • To model soil salinization in time functions using SAHYSMOD model. • Give a recommendation on mitigation and management practises of salinization.

10 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

1.11. Research questions and hypotheses

1.11.1. Research questions • Can input data from soil laboratory data analysis be used to calibrate SAHYSMOD model reliably? • Could the independent data validate the calibrated model to expected mean errors? • Which geopedological mapping units are most prone to salinization? • Could SAHYSMOD model be able to model change of salinization with time? • How severe is soil salinity in the study area?

1.11.2. Research hypotheses • An input from soil laboratory analysis can calibrate SAHYSMOD model reliably. • An independent data can validate SAHYSMOD model reliably. • SAHYSMOD model can be used to model existing salt affected soils. • Geopedological mapping units that are prone to salinization could be detected. • SAHYSMOD could help in modelling soil salinity change with time. • It is possible to evaluate severity of salinity level in the area.

11

Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

2. Literature review

In this chapter subjects related to salt affected soils, salinization, problems related in soil salinity detection, mapping and modelling have been reviewed which can be serve as base foundation for the whole study process.

2.1. Soil salinity and pH Overall distribution of soil salinity is influenced by soil texture and topography, while at the local scale terrain attributes such as curvature, plan and profile curvatures, and solar radiation were the most influential factors(Akramhanov, 2005). Accordingly the distribution of soil salinity of the study area, as reported by Wada et al (1994), is higher in hilly and undulating regions than in the low-lying flat regions. Although the area coverage by salt affected soils is smaller in the former than in latter. According to these authors’ salt affected soils are a mosaic of denuded patches and vegetated patches, which is true even for one small salt affected part. And these denuded patches are called salt patches.

Noble et al. (2008) have ascribed the significant changes in northeast Thailand is associated with the removal of climax Dipterocarp forests. This clearance has resulted in a major decline in soil chemical, physical and biological attributes. Accordingly wongpokhom et al (2008) have found that the salt affected fine textured soils of Nakhon Ratchasima have wide range of pH value from extremely acid to strongly alkaline which is consistent with the soil pH of tropical soils. However, the availability of nutrients is directly affected by soil pH. If the soil pH is too high or too low, some nutrients become insoluble, limiting the availability of these nutrients to the plant root system.

The 1:1 soil laboratory analysis results show that the pH value of the study area is found between 4.0 and 9.6, which are minimum and maximum respectively. Based on the average pH value, 6.05, the soils of the area can be categorized into medium acidic soil classes. In relation to soil pH Noble et al (2008), from their 1:5 soil to water ratio pH analysis, have fond that the soils of the region are inherently acid and their acidic nature increase with depth as a result they have very low internal buffering capacity and low surface charge characteristics. Keeping the weight to volume ration among us as constant, both studies show that the soil is acidic than be basic.

Under in situ conditions pH in general affects availability of nutrients and growth of plants. The effect of soil pH is great on the solubility of minerals or nutrients. Most minerals and nutrients are more soluble or available in acid soils than in neutral or slightly alkaline soils. The soil pH values does not have precise significant by themselves but by their role in limiting the availability of some readily usable minerals and others in less quantity needs attention in crop production. Focusing to salinization, Landon (1991b), have mentioned that as pH values becomes greater than 8 the presence of Calcium, phosphate, tends to be converted to calcium phosphate and availability of P to plants is reduced. Above all soils with pH values >8 indicates an exchangeable sodium percentage of > 15 that leads to the development of NaCl salt and finally soil deflocculating that destroys the of .

13 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Thus in conclusion the soil lab results indicate that some soil samples have a pH > 8 meaning the potential finding calcium phosphate and NaCl salts in the soils of the area is unquestionable. But for the lab analysis does not include elemental analysis it is difficult to tell the type of salt present in the area.

2.2. Soil salinity and electrical conductivity

According to Landon (1991a), saline soils occur where the supply of salts in cases such as from rock weathering, capillary rise, rainfall or flooding, exceeds their removal for example by leaching or flooding. And saline soils tend to coincide with areas where evapotranspiration exceeds precipitation and where there is no lengthy rainy season.

The dominant sources of natural salt are rainfall, weathering of parent rock material, wind-transported salt from saline water bodies (sea, lake) and salt-affected surfaces, poor irrigation water and sea water intrusion to land. According to Rengasamy (2006), Farifteh et al. (2007) and Madyaka (2008) there is no climatic zone in the world free of salinity problem. Salinity is one of the most common and frequently observed soil degradation processes, in arid and semi-arid areas.

Shortage of water in these areas due to lack of regular precipitation is the main reason why irrigation must be practiced. But the inefficient water use and poor quality gave rise to salts-affected soils of human induced type. The process is known as salinization. Referring to explanations by Farifteh et al. (2007) salinization, is defined as a series of processes, a complex interaction of various factors that cause changes within a time period about a decade and generally irreversible.

Soliman et al. (2005b) and Rengasamy (2006) have mentioned that secondary salinization is largely related to hydrological processes. And factors like unregulated landuse change and inefficient irrigation water use, which are causes for shifting of hydrological balance in a basin and increase accessions to groundwater system, are considered to be responsible for its development. But according to the explanations by Eldiery et al. (2005) primary salinization, also known as natural salinization, is a result of long term influence of natural process.

Unlike to the usual classification methods, primary and secondary salinity or Saline and Sodic soils, Rengasamy (2006) has used other soil salinity classification, based on soil and groundwater process. This classification considers three major types of salinity classes refer to figure 4.

14 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Figure 4 Major types of salinity Rengasamy (2006)

These groundwater based salt affected soil class are:-

1. Groundwater associated salinity (GAS) which is observed in discharge areas of the landscape, where water exits from groundwater to the soil surface bringing the salts dissolved in it. And the driving force for upward movement of water and salts is evaporation from the soil plus plant transpiration.

2. Non-groundwater-associated salinity (NAS) usually happens in areas and/or landscapes where the water table is deep and drainage is poor. Under this condition salts are introduced and stored within the soil solum by rain, weathering, and Aeolian. The poor hydraulic properties of shallow solum layers can lead to the accumulation of salts in the topsoil and subsoil layers affecting agricultural productivity.

3. Irrigation associated salinity (IAS), is caused by irrigation water. IAS salinity is stored within the root zone due to insufficient leaching. IAS is accelerated by conditions like poor quality irrigation water use, low soil layers and high evaporative climate. In areas with this condition use of highly saline wastewater and the improper drainage, poor soil management increase the risk of development of IAS salinity in irrigated soils.

Both cases, human induced and natural, the salt forming process combined with the influences from climate, negative interference of man and topographic features of an area determines where salt is likely to be accumulated in the landscape. Usually soil salinity generally occurs in discharge areas where the water table is high including edges of closed depressions and low lying areas where evaporation is high.

15 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

The electrical conductivity, EC, measurements are used as indications of total quantities of soluble salts in soils. But the quantities of salts which pass into solution depend on the relative amounts of soil and water used. Thus, Slavich and Petterson (1993) have made it clear that in order to assess the effects of salinity on plant growth a measure of the concentration of soluble salts in the soil-water solution, at a reference water content, is required. The most desirable reference water content is the water content at field capacity. However, for simplicity reasons the most commonly used reference water is the saturation water content. And this saturation water content is more diluted than field capacity.

According to Landon (1991a) many interpretations of EC values have been devised, but no universal, precise interpretation is possible because the effects of salinity are modified greatly by factors such as quality of irrigation water, soil texture, crop growth stage and climate.

2.3. Relationship of EC1:5 with ECe and ECFc

As discussed under 2.2, EC is an indicator of total quantities of soluble salts in the soil. Furthermore the soil and water ratio prepared determines the quantity of salt in the solution prepared.

In according with Landon(1991a) the routine measurements of soil salinity are made on extracts from saturated soil pastes starting from 1:1 up to 1:5 soil-water mixtures. In field and laboratories soil- water solutions are made in the ratio of 1:2.5 and below because they are easier to handle. However, based on the findings of Slavich and Petterson (1993), these reference ratios of soil-water solutions determined in the field and laboratory, are 2 to 3 times higher than the field capacity water content. Supporting this Landon (1991a), and Lambert and Turner (2000) in their studies have showed that the above EC measured weight/volume solutions are more diluted than saturation extract paste and can not be interpreted directly from salinity scale for crop tolerance. Therefore, reading of theses EC’s saturation extract is easy to manage it at field level. But there must be a way of converting them to the standard crop tolerance limit.

Likewise EC is also affected by soil texture. For example EC1:5 suspension of sand does not have equal salinity level with an EC1:5 solutions of clay. On top of this Prior et al. (1992) have showed that EC values also vary with soil salinity managements. To have common understanding all these influences on EC measurement should be minimized and there should be a standardized soil salinity reading level, in this case ECe. But up to now there is no foolproof standardized conversion factor that compares EC of different soil: water ratios. Some use single constant factor indiscriminately for all soil types while others use different constants depending on the soil textures. For example Slavich and Petterson (1993), and Lambert and Turner (2000) use soil-water ratio and texture parameters to convert EC to ECe but Landon (1991a) uses single constant number regardless of soil texture.

16 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Table 1 Conversion factor to estimate ECe from EC1:5 Slavich and Petterson (1993)

Even though there is a common consensuses that EC reading is also influenced by soil salinity management but up to now not encountered with any conversion factor that brings into account the variation due to soil salinity management. However, this might be due to the following reasons:-

1. Management is quite variable and highly dependant on level of technology and skill. Therefore, it is difficult to single out a conversion factor that treats different management capacity with different skill and technology in one level equally. 2. This management is more qualitative than quantitative so there is always difficulty of quantify management and single out one representative factor out of it. 3. Otherwise the school of thought that says “soil salinity management is explained by soil management” may overrule the salinity management not to be treated independently.

During the field work the soil conductivity test was carried out using weight-volume ratio of EC1:5. This study focuses in finding out factors that cause salinization using EC as instrument and any factor that creates variability on EC is appreciated. Hence, conversion of EC1:5 to ECe that accounts soil texture difference and solution ratio is adopted. The texture based conversion factor (f) given by Slavich and Petterson (1993) in table 1 is accepted, extracted and used through the analysis and modelling process. Refer the box below for the equation of conversion of EC1:5 to ECe. But for the model case still needs another conversation which is demand by the model itself, for this matter refer chapter 4 under subtitle 4.5.3 model data preparation.

17 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

ECe = f * EC1:5 Where

f = texture dependant conversion factor ECe = standardized electrical conductivity

EC1:5 = Electrical conductivity of 1gm of soil in 5 ml of distilled water.

Equation 1 EC conversion equation

2.4. Model types for salinization

According to Hidenori et al.(1994) salt development in northeast Thailand is mainly due to Mahasarakham formation, which is a salt bearing rock stratum and is found shallowest to the surface. Likewise Shrestha et al.(2005b) believe that salinization can be caused by groundwater rise but is aggravated due to improper land use practices. Meaning capillary raise and adverse accumulation of salt in the root zone can greatly be influenced by management practice.

Generally, in salinization process, dissolution of saline rock and capillary rise of saline groundwater account for salt affected soil in the surface. Besides surface and subsurface hydrological movement is a driving force for the transport of salt, soil and groundwater. That is the spatial and temporal variation of groundwater table is the main mechanism for exchange of salt between saturated and unsaturated zones in the groundwater system. And man’s influence through landuse management and salt mining activity is significant agent of the processes. Therefore, soil salinity identification and mapping with consideration of the above factors is so crucial to mitigate it.

Soil salinity mapping through various remote sensing and GIS techniques have been implemented by several researchers (Metternicht and Zinck, 2003; Shrestha et al., 2005a; Soliman et al., 2004) however, salinity is a dynamic process and may vary seasonally. Thus, it is not only identification, detection and mapping but also understanding the causes of the process is by far important for undertaking appropriate management and mitigating measures.

Farifteh et al.(2007) believe that to understand soil salinization process and devise management practices to control its spread, modelling of solute transport in the soil provides vital information on dynamics of salt movement regime and salt accumulation under various conditions. However, practically several approaches are in use for modelling soil salinization that attempt for better understanding of salinization distribution and dynamics. Most of these approaches involve mathematical models to describe and quantify the basic hydrological processes and phenomena. Thus model selection is a serious business and needs clear understanding the mathematical equations representing the physical system and the dynamics system itself.

For example, according to Rushton (2003) hydrological models, to best approximate the natural fact on the ground, they must include all parts of the aquifer system. And the effectiveness and efficiency of hydrological models depends on the understanding of the dynamism of water. Equally important,

18 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

for good approximations of soil moisture and moisture , Xu and Shao (2002a) strongly recommend that the model must to be capable of simulating surface and subsurface processes. Because modelling solute transport in unsaturated zone depends very much on simulation of soil moisture and moisture fluxes that are strongly influenced by precipitation, evapotranspiration, surface runoff and surface soil hydrological processes that are explained by hydrological modelling.

As have been discussed in several publications various models have been developed for simulating salinization dynamics and solute transport in the soil. In view of that Castrignano et al. (2002) have showed that 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. However, basing on the models’ operating system Madyaka (2008) has classified models into two main broad categories, seasonal and transient models.

Under any categorical circumstance models address many different flow conditions. That ranges from pipe drains to surface drainage and from systems to ground-water basins of irrigation systems. Thus model selection needs critical reviewing and understanding of the model characteristics in relation to availability of data and objective of the study.

2.4.1. Seasonal models

The selection of appropriate techniques and methods of salinity control requires the quantifying of the movement of salts and water in the soil, the response of the crop to soil water and salinity, and how the environment and management conditions affect these interactions. Mathematical models can help to integrate these interactions and are useful tool to define the best management of a system for saline conditions.

Seasonal models has been described by Smith et al.(1996) to consist of basically an equation that relates yield to the amount of seasonal applied water of a given salinity. Similarly Castrignano et al.(2002) have reported that the yield and applied seasonal water relationship results from the combination of the relation between yield and evapotranspiration, yield and average root zone salinity, and average root salinity and leaching fraction. However, Smith et al. (1996) have reported that seasonal models assume a steady-state conditions for the soil, and do not include the effects of soil salinity variation in space and time on the crop response. At the same time conclusions by Castrignano et al.(2002) showed that steady-state models are not suitable for in saline conditions.

2.4.2. Transit models

It is reported by Castrignano et al. (2002) that transient models generally use sophisticated numerical solutions to compute water and solute flow in the soil, and predict soil profile conditions with greater details. These models compute water and solute flow in the soil, and include a water uptake term. However, as to the explanations by Madyaka (2008) the available transient models differ in their conceptual approach, degree of complexity, and in their application for research or management

19 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

purposes. For instances, Smith et al. (1996),have also showed that transient models for management and research applications in saline conditions require a mechanistic treatment of relevant processes in the soil-water-plant-atmosphere system. But conclusions on the transit drawn by Castrignano et al. (2002) distinguishes that water and solute flow in the soil and root water uptake are modelled in detail although the crop growth is simple and not fully modelled because crop growth interactions with environmental variables and agronomic management do not consider by the model

2.5. SAHYSMOD Model

This computer program model is used for prediction of salinity of soil moisture, ground water and drainage water, depth of water table, and drain discharge in irrigated agricultural lands, using different geo-hydrologic conditions, varying water management options, including the use of ground water for irrigation, and several cropping rotation schedules, whereby the spatial variations are accounted through a network of polygons(Oosterbaan, 2005)

2.5.1. Model rational and description

According to Farifteh et al.(2007) hydrological models are used to predict average values of solute concentration as a function of depth and time, through highly variable field systems. These hydrological models aim at predicting subsurface salt distribution as a result of water percolation, groundwater changes and groundwater flow. The spatial and temporal modelling of soil salinization using such models has several advantages over subsurface geophysics salinity survey. Accordingly Kupper et al.(2002) have explained that controlling of salinization in a system with irrigation, drainage and interaction with an aquifer system is a complex operation, in which hydrological models can be useful as they simulate the water flow in the saturated zone, unsaturated zones and surface water in an integrated manner.

Water is a means of transport for salt to move both on surface and subsurface of the earth. Referring to explanations by Yardley et al.(2004), the behaviour of water in the crust is controlled by and direction of change of temperature of its host rock. Climate is not only the major regulating factor for the movement of water but also for crustal deformation. As a result being driven by climate water interacts with rocks and dictates both the fluid chemistry and sites of focused fluid flow. Thus modelling soil salinization process and nature could be not be made better by excluding the processes, dependencies and impact nature of water resource dynamics. Moreover, quantifying the likely impact from different conjunctive related to water management and crop production options are useful to understand the spatial and temporal distribution of soil salinity.

Up to recent past, most ground-water models do not include the unsaturated zone and agronomic aspects. Hence the continuity flow of groundwater contribution through and within the aquifer was overlooked. In addition, most groundwater models also lack to show the neighbourhoods relationship effect of soil salinity. But now all these limitations that have a significant role in soil salinization dynamics are addressed by SAHYSMOD (Oosterbaan, 2002a).

20 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Oosterbaan (2005) have described that SAHYSMOD, permits a maximum 240 internal and 120 external polygons with a minimum of 3 and a maximum of 6 sided polygons each. And these nodal networks indicate neighbouring relationship that gives chance to calculate surface area of each polygon and the distance between them using the Thiessen polygon principle. And this polygonal function helps the model to handle salinization dynamics in 3D aspects. On top of that the model considers agronomic and beneficiary attitude parameters which make it comprehensive in characterizing the salinization to significant degree.

Nevertheless, in limiting the size and number of polygons to be used by the model needs care and is regulated by the prevailing topographic feature of area. That is under sloppy and undulating topography feature the size and number of network polygon used must be smaller and fewer respectively. The alignment of polygonal network needs an abutment at both ends, the valley being at the centre.

As displayed in the DEM below the study area is sloppy. The mountains and old levees are resided in the southwest direction. That is its elevation gradually decreases from southwest to northeast, east and southeast directions. Thus the only location for right abutment that is required by the model is found southeast of the quadrant of the image. Therefore, considering the sloping nature of the topography the number and size of the polygonal networks considered for the study are 24 and 23.5 km2 respectively. Refer figure 6 for the nodal network grids.

2.5.2. Model possibilities and conditions for applications

The model assumes uniform distribution of cropping practices for various grown in the study area. There are four soil strata in the model, namely surface reservoir, root zone, transition zone and main aquifer.

For each reservoir a water balance can be made with the hydrologic components. All quantities of the components are expressed as seasonal volumes per unit surface area, giving a seasonal depth of water. A water balance is made based on the principle of the conservation of mass for boundaries defined in space and time and can be written as:

Inflow = Outflow + Storage

Equation 2 Principles of conservation of mass of the model

When the storage is positive the water content increases and, when negative that is there is depletion instead of storage, it decreases. As mentioned in the above the model computes water based on the principle of conservation of mass hence the solute movement is taken in the assumptions of mass flow.

21 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

The numbers of seasons per year that can be chosen are between a minimum of one and a maximum of four. And the computation time step of the model is on daily bases for this purpose, the seasonal water-balance factors given with the input are reduced automatically to daily values.

The following application conditions are incorporated in the ground water flow, agricultural water balance and aquifer recharge parts of the model:-

1. The main aquifer is bounded at the bottom by an impermeable layer, but an inflow condition, e.g. through faults, can be imposed. 2. The upper boundary of the aquifer is free water table, phreatic or unconfined aquifer or a relatively slowly permeable layer with respect to the underlying semi-confining layer. 3. Darcy's law and Dupuit's assumptions, resistance to vertical flow in the subsoil can be neglected, are applicable in the main aquifer. 4. In semi-confined the resistance to vertical flow in the top layer is taken into account, but the horizontal flow is excluded; 5. The aquifer has head-controlled or flow-controlled boundaries, which may vary from, season to season; 6. The depth of the water table at the end of the previous time step, calculated from the water balances, is assumed to be the same within each polygon. 7. Aquifer recharge corresponds to the steady state groundwater flow and recharge assuming that no fluctuations occur and that the observed ground-water situation is permanent. Under this assumption, the net outflow of ground water equals the recharge.

2.5.3. Reservoir concept and water flow As displayed below in figure 6 the model accepts four different reservoirs of which three are located in the soil profile: 1. s: a surface reservoir

2. r: a root zone reservoir

3. x: a transition zone reservoir

4. q: Main aquifer. The surface reservoir is located on top of the soil. But the root zone reservoir is defined by the soil depth from which water can evaporate or be taken up by plant roots. It can be taken equal to the root zone. It can be saturated, unsaturated, or partly saturated, depending on the water balance. All water movements in this zone are vertical, either upward or downward, depending on the water balance. Figure 5 Landuse types, Soil strata vs. hydrological factors (Oosterbaan, 2005)

22 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

The transition zone is found below the root zone and it can also be saturated, unsaturated or partly saturated. And all flows in this zone are horizontal, except the flow to subsurface drains, which is radial. If a horizontal subsurface drainage system is present, this must be placed in the transition zone, which is then divides the transition zone into two parts. That is the upper transition zone and a lower transition zone found above and below the drain level respectively.

The aquifer has mainly horizontal flow and is found below the transition zone. Pumped wells, if present, receive their water from the aquifer only. The flow in the aquifer is determined depending the spatially varying depths of the aquifer, levels of the water table, and hydraulic conductivity.

2.5.4. Model data requirement and data components

As it has been described in the model rational subtitle the polygonal network should be aligned between the two abutments of a valley. Then after for model input data collection the study area should be divided into a nodal network of triangles, rectangles, or any other polygons with a maximum of 6 sides. During data collection all soil data must be collected along the profile of these abutments so as to see the topographical impact on the hydrological movement thereby salinity distribution.

The model which is divided into a nodal network of polygons consists of further three parts or components, these are:-

1. An agronomic water balance model, which calculates for each polygon the downward and/or upward water fluxes in the soil profile depending on the fluctuations of the water table. 2. A of the aquifer, which calculates the groundwater flows into and from each polygon and the groundwater levels per polygon depending on the upward and/or downward water fluxes. The parts 1 and 2 are interactive as they influence each other. 3. A salt balance model, which runs parallel to the water, balance model and determines the salt concentrations in the soil profile, and of the drainage, well and ground water.

The general model polygonal network relations, input data requirements’, salt and water balance principles and assumptions based on which the model was developed as given by Oosterbaan (2005) are depicted below.

2.5.5. Polygonal network

The subdivision of the area into polygons, based on nodal points with known coordinates, is governed by the characteristics of the distribution of the cropping pattern, irrigation and drainage network, and groundwater characteristics over the study area. At the same time nodes must be numbered and their relation should also be defined. Depending on size of the polygon and physiographic feature of the area even though part of a polygon could fall in a valley and the rest other part in hill, parameters like depth of water table, rainfall and salt concentration of the deeper layers are assumed to be uniform through out the whole polygon. For the selected site for the model alignment and the final grids to which an output is assigned (8 black points) are displayed in figure 6.

23 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Figure 6 Nodal network alignments of SAHYSMOD in the study area

2.5.6. Computational time step

The model is based on seasonal input data of agricultural water balance and climate. Four seasons in one year can be distinguished, example dry, wet, cold, hot, irrigation or fallow seasons and returns seasonal outputs. The number of seasons per year can be chosen between a minimum of one and a maximum of four. The duration of each season is given in numbers of months.

The model performs daily calculations. For this purpose, the seasonal water balance factors given as an input are reduced automatically to daily values. And the calculated seasonal water-balance factors, as given in the output, are obtained by summations of the daily calculated values. Groundwater levels and soil salinity at the end of the season are found by accumulating the daily changes of water and salt storage.

2.5.7. Hydrological data

The model uses seasonal water balance components as input data. These are related to the surface (like rainfall, potential evaporation, irrigation, use of drain and well water for irrigation, runoff), and the aquifer hydrology (for example pumping from wells). The other water balance components (like actual evaporation, downward percolation, upward capillary rise, subsurface drainage, ground water flows) are predicted as output. The quantity of drainage water, as output, is determined by two drainage intensity factors for drainage above and below drain level respectively and this (to be given with the input data) and the height of the water table above the given drain level. This height results from the computed water balance further, a drainage reduction factor can be

24 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

applied to simulate a limited operation of the drainage system. Variation of the drainage intensity factors and the drainage reduction factor gives the opportunity to simulate the impact of different drainage options.

2.5.8. Agricultural cropping pattern-water balance

The agricultural water balances are calculated for each soil reservoir separately. The excess water leaving one reservoir is converted into incoming water for the next reservoir. The three soil reservoirs can be assigned different thickness and storage coefficients, to be given as input data. The depth of the water table at the end of the previous time step, calculated from the water balances, is assumed to be the same within each polygon.

The cropping pattern related input data on irrigation, potential evapotranspiration, and surface runoff are specified per season for three kinds of agricultural practices, which can be chosen at free will of the user:

1. A: irrigated land with crops of group A 2. B: irrigated land with crops of group B 3. U: non-irrigated land with rain-fed crops or fallow land

These rotational cropping patterns are expressed in fractions of the total area. Each cropping pattern may consist of combinations of crops or just of a single kind of crop but with different management practise. The area fraction of rain fed (not irrigated) land is specified by deducting from 1 the sum of area fractions under cropping patterns of A and B. Further, specification must be given of the seasonal rotation of the different land uses over the total area, example full rotation, no rotation at all, or incomplete rotation. This occurs with a rotation index. The rotations are taken over the seasons within the year. According to the variation of the area fractions and/or the rotational schedule gives the opportunity to simulate the impact of different agricultural practices on the water and salt balance during model calibration.

2.5.9. Groundwater flow

The model calculates the ground water levels and the incoming and outgoing ground water flows between the polygons by a numerical solution of Boussinesq equation. And each level and flows influence each other mutually.

The ground water situation is further determined by the vertical recharge that is calculated from the agronomic water balances. These depend again on the levels of the ground water. When semi- confined aquifers are present, the resistance to vertical flow in the slowly permeable top-layer and the overpressure in the aquifer, if any, are taken into account. Hydraulic boundary conditions are given as hydraulic heads in the external nodes in combination with the hydraulic conductivity between internal and external nodes. Further, aquifer flow conditions can be given for the internal nodes. These are

25 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

required when a geological fault line is present at the bottom of the aquifer or when flow occurs between the main aquifer and a deeper aquifer separated by a semi-confining layer.

2.5.10. Salt balance

The salt balances are calculated for each soil reservoir separately. They are based on their water balances, using the salt concentrations of the incoming and outgoing water. The initial salt concentrations of the water in the different soil reservoirs, in the irrigation water and the incoming ground water from the aquifer are required as input to the model. The concentrations are expressed in terms of electric conductivity (EC in dS/m). Salt concentrations of outgoing water (either from one reservoir into the other or by subsurface drainage) are computed on the basis of salt balances, using different leaching or salt mixing efficiencies to be given with the input data.

The effects of different leaching efficiencies can be simulated by varying their input value. If drain or well water is used for irrigation, the model computes the salt concentration of the mixed irrigation water in the course of the time and the subsequent impact on the soil and ground water salinity, which again influences the salt concentration of the drain and well water. By varying the fraction of used drain or well water (through the input), the long term impact of different fractions can be simulated.

2.5.11. Output data

Output in SAHYSMOD is given for each season of any year during any number of years, as specified with the input data. The output data comprise hydrological and salinity aspects. The data are filed in the form of tables that can be inspected directly, through the user menu that calls selected groups of data either for a certain polygon over time or for a certain season over the polygons. Also, the program has the facility to store the selected data in a spreadsheet format for further analysis and for import into a mapping program. But in spite that the model recognizes the study area in its geographical coordinate but its out put data does not yield any map at all.

2.6. Model calibration

Models are conceptual descriptions or approximations that describe physical systems through the use of mathematical equations. And they are useful tools in predicting future conditions or the transport of contaminants for risk evaluation purposes. And yet they are not exact descriptions of physical system or processes. As a result the usefulness of a model depends on how closely the mathematical equations approximate the physical system being modelled. Rushton (2003) have stated that hydrological models, to best approximate the natural fact on the ground, they must include all parts of the aquifer system. Hence before using the model it is necessary to have a thorough understanding of the physical system and the assumptions embedded in the derivation of the mathematical equations.

Likewise selection and proper use of a model must be on the bases of thorough understanding of the significance of relevant flow or solute transport processes both in the surfaces and subsurface of the

26 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

earth. In turn this requires proper resource characterization which involves collection of site-specific data that accurately describes the movement of water and disposition of solute transport.

Particularly in korat area landuse change from forest to agricultural landuse in the uplands favours the water flow to the valley to pass through evaporite beds (mainly halite) and be deposited in the Cretaceous. And this water flow becomes the main reason for the salinization of the soil and water along its path (Grunwald S. and Norton L.D., 2000). Therefore, if surface runoff and sediment transport influence soil quality and the quality of receiving water, the mathematical models to be chosen for soil and water salinity assessments in the area must have useful equations that describe the soil and water chemistry, surface and subsurface transport of solute behaviour and the impact they impose.

In SAHYSMOD the water balance is made with the hydrologic components. All quantities of the components are expressed as seasonal volumes per unit surface area, based on the principles of conservation of mass for boundaries defined in space and time. Usually the water balance equations are treated independently for each of the four reservoirs. But due to close interrelationship between surface and subsurface hydrology and depending on the location of the water table there are conditions that the four equations combined in to one (Oosterbaan, 2005).

For example, given the water table is located in the aquifer, the water balance of the first three reservoirs viz. surface reservoir, root zone and transition zone are combined and gives rise to agronomic water balances. And the water balance, depicted in equation 3, is expected to approximate factors of salinity and salinization process due to climate, activity of man, vertical and horizontal hydrological movement.

Pp + Ig+ Lc+Gti= Io +So + Ea + Gd+ Gto + ∆Ws + ∆Wr + ∆Wx

Equation 3 Surface water balance equation

At the same time the Geo-hydrological water balance (groundwater flow) model works based on method to calculate the water flow between the nodal polygons. And this is represented by equation 4.

Lr+Lc+Gti+Gqi+Qinf = Rr+Gto+Gqo+Qout+Gd+Gw+∆Wx

Equation 4 Groundwater balance equation

According to Oosterbaan (2005) when the water table remains above the soil surface, like in the paddy fields conditions all linkage components that exist between the reservoirs will be disappeared. Hence the values of ∆Ws, ∆Wr and ∆Wx become zero for the soil is fully saturated. Similarly the water flow from the subsoil into the surface reservoir and the in-filtration becomes negative.

27 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Then finally the water balances equations for all the reservoirs that approximate salinity of soil moisture, groundwater and drainage water, depth of water table and drain discharge in agricultural fields are combined to equation 5 below.

In hydrological modelling, Anderson and Woessner (1992) have mentioned that flow model calibration refers to demonstration that the model is capable of producing field measured parameters which are the calibration values. And is accomplished by finding a set of parameters, boundary conditions and stresses that produces simulated heads and fluxes that match field measured values with a pre-established range of error. Accordingly calibrating SAHYSMOD could mean finding out the best combinations of the values of equation 5 to simulate field condition.

Pp+Ig+Lc+Gti+Gqi+Qinf = Ea+Io+So+Gto+Gqo+Qout+Gd+Gw+∆Ws

Equation 5 Combined surface and subsurface hydrological movement equation

Where

Pp is amount of water vertically reaching the soil surface Ig is gross irrigation inflow (includes irrigation of surface, drain and well water) Lc is percolation loss from the irrigation canal system Gti is horizontally incoming flow of groundwater Io is Water leaving the area through the irrigation canal system. So is amount of surface runoff or surface drainage leaving the area. Ea is total actual evapo-transpiration Gd is total amount of natural/ artificial drainage of groundwater to pipe drains Gto is horizontally outgoing flow of groundwater ∆Ws is change in amount of water stored in the surface reservoir. ∆Wr is storage of water in the root zone between field capacity and full saturation. ∆Wx is water storage in transition zone between field capacity and wilting point Lr is amount of percolation losses from the root zone Gqi is amount horizontal groundwater inflow through the main aquifer Qinf is inflow condition of groundwater Rr is amount of capillary rise into the root zone Gqo is amount of horizontal groundwater outflow through the aquifer Qout is an outflow condition of groundwater

2.7. Model sensitivity analysis

In the real world, there are many parameters in system dynamics model that represent quantities which are very difficult, or even impossible to measure to a great deal of accuracy. As well, some parameter values change from time to time. Therefore, when building a system dynamics model, the modeller is usually at least somewhat uncertain about the parameter values he chooses and must use estimates (Anderson M. P. and Woessner W. W., 1992; Breierova and Choudhari, 2001).

28 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Accordingly during calibration a number of changes in the value of the parameters of the model made are not known. Even to try to be certain about the changes based on the parameter of the input is quite challenging and needs other study period by itself. Instances such as the uncertainties of lithology, stratigraphy, soil texture and structure that determine the hydraulic conductivity are also other sources of uncertainty. Consequently sensitivity analysis is performed to evaluate the model's ability to account for fluctuations of these factors. And these fluctuations help the user to know how sensitive a model is and what degree of error is tolerable for the work.

Parameter sensitivity is usually performed through a series of tests in which the sets of different parameter values are changed to see how a change in the parameter causes a change in the dynamic behaviour of the simulated results. By showing how the model responds to changes of input parameter, values sensitivity analysis is a useful tool for model evaluation. For example if a small change in a parameter results in relatively large changes in the simulated outcome, the outcomes are said to be sensitive to that parameter. As a result, this may mean that the model has to be determined very accurately for low sensitivity of that parameter.

Anderson and Woessner (1992) advise that identifying the most sensitivity parameter and rectifying this parameter through recalibration processes helps the user to build confidence in the model. Thereby allows one to determine level of accuracy and make the model valid. With this objective, sensitivity analysis of SAHYSMOD model was conducted by changing a single parametric value of the well calibrated parameters at a time. Then the magnitudes of change in the parameters from the calibrated solutions were tested for shift/deviations from the pre-sated simulation result as a measured sensitivity of the model.

At time of calibration, even though it was challenging enough, great care was taken in order for parameters not to go off their limit, by this means it gives chance to learn to what parameters of the model are sensitive enough and to what others not. Due to this reason during calibration process parameters that are related to each other and parameters that are easily sensitive were identified. To this end leaching efficiency of root and transition zones is insensitive parameter of the model.

2.8. Model validation

Owing to uncertainty in the calibration, the set of parameter values used in the calibration model may not accurately represent field values. Consequently, the calibrated parameters may not accurately represent the system under a different set of boundary conditions. Thus to avoid this uncertainty Anderson and Woessner (1992) recommend that the calibrated model must be validated for its accuracy and predictive capability to lie within acceptable limits of error through tests using independent data that have not been used for calibration. Macal (2005) has reported that the ultimate goal of model validation is to make the model useful in the sense that the model addresses the right problem and provides accurate information about the system being modelled. And to make the model actually used model validation ensures that the model meets its intended requirements in terms of the methods employed and the results obtained. According to this author one way of model validation is

29 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

replication of special cases that is previously validated using the model and look if it provides accurate information about the early validated data. However, according to National institute of Standards and Technology, NIST for short (2006) model validation is an overlooked process and it could also be accomplished by using different statistical methods like: numerical methods such as R2 statistic and graphical residual analysis.

2.9. Soil salinity change detection

2.9.1. Exploratory data analysis

Of the data type collected this exploratory analysis of primary data focuses on electrical conductivities namely: ECFc, and Ec change (EC of laboratory result and that of Madyaka (2008) model predicted results). Two of these datasets (especially the ECFc and lab test results) are considered in descriptive statistical analysis because the first is used for model calibration and sensitivity analysis simulations while the latter is used for change detection and model validation respectively. The variable of interest is soil salinity and it is explained by the conductivity of soil paste extract, EC for short, hence it is considered important to give the descriptive statistics of these variable inputs(EC) used for salinity detection. The collected data are of three soil depth based observation points: 0-30, 30-60 and 60-90 cm. Thus the statistical description of the parameter values is done based on the variables categorized by depths; refer on chapter 5 under subtitles 5.1 and 5.3.3.1 respectively.

Salinity change detection is the one objective of the study for that matter the coordinates of EC for change detection and validation are of previous study through which the progress of the salinity level will be detected. Moreover, the early work by Madyaka (2008) was carried out in relation to geopedological mapping units so as to find out the influence of geopedology on soil salinity.

Stepping from this the current study is intended to evaluate the progress of salinization on each geopedological units. However, for shortfall in time and accessibility reasons of 71 Madyaka’s GPS locations only 30 locations are sampled. The sampled locations are few but are systematically selected. They are selected in such away that they can represent the general stratification on the bases of landform units. Figure 7 A and B represent the spatial distribution of the observation points for current and previous studies respectively

30 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

A B

Figure 7 GPS locations observation data used for model validation and change detection

31

Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

3. The Study area

This chapter deals with the general description of the study area. That is the physical and environmental aspect and the background history of soil salinity and those related with salinity are discussed separately under subtitles of their own.

3.1. General information 1. Name: Nong Sung. 2. Area coverage: 816 km2

3.2. Location

3.2.1. Geographical location 1. General: 101o 45’ to 102o East and 14.47o to 15o 15’ North 2. Specific: 795858.51--821786.79 N and 1659635.42--1688797.85 E 3. UTM WGS 84 Zone 47 Northern hemisphere 4. Situated at 35.4km and 67km aerial and ground distance to the northwest of Korat city. 5. Elevation range 114 to 209 meter above mean sea level.

3.2.2. Administrative location 1. Country: Thailand 2. Region: Northeast 3. Province: Nakhon Ratchasima 4. Sub district: Nong Sung

33

Figure 8 Location of the study area location

3.3. Study area selection justification

3.3.1. Problem oriented

A significant part of Thailand is prone, and the Northeast region is already a victim of several environmental degradations. The parent material of the soil, Mahasarakham formation, is the source of the salinity. This rock formation occurs at about 80 to 100 m deep (see under 3.5 Geology). Salinity problem is being aggravated by mismanagement of natural resources (see under 3.7 land cover).

Clearing forested areas for agriculture, industrial plantation and shrimp farming are a few causes to name here. These all salt hazards occur in the study area can justify this study to be conducted in this particular area, which is on spatial modelling and timely prediction of soil salinization. On top of these facts, the immediate following facts (3.3.2 and 3.3.3.) have a great contribution to complete the research work successfully.

34 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

3.3.2. Facility related The regional offices of Land Development Department [LDD] at Korat and Khon Kaen provided a number of facilities related to data collection, laboratory equipments vehicles, and all sort of sample analysis facilities which make the field work successful within the limited available time and budget facilities.

3.3.3. Previous study The combined effect of all factors of salinization in the area: geological, climate and human factors have drawn an attention of several researchers’ in the area. Thus various research projects with quite wider scope of objective have already been carried out. Thus relevant points to this study that are highly needed to enrich this research can be found in abundance.

3.4. Climate Based on the meteorological data collected, Nong Sung is characterized by having maximum and minimum daily average temperature of 33oC and 20oC, respectively. The yearly average daily temperature hardly drops from 27oC throughout the year, except for November and December. Mean annual rainfall received is about 1030 mm of which only 800 mm is effective (using USDAS manipulation method) that is the amount stored in the soil.

But due to higher temperature and wind prevalence (the only minimum wind speed is 90 km/day and it occurs on January and February) there is higher evapotranspiration, 1948mm/year which is equivalent to 5.5 mm/day. At the same time the pan evaporation is equally high that depletes out much of effective precipitation and aggravates the capillary rise of saline groundwater table.

Table 2 Climatic data (1971-2007) of Nakhon Ratchasima (Index: 48431, station 431201, Lat. 14o 57’46” N and Long. 102o 4’ 36”E)

Max. Mini. Wind Sun Solar Temp Temp Humidity Speed Shine Radiation Rainfall ETo Month [oC ] [oC] [%] [Km/d] [Hours] [MJ/m2/d] [mm/mth] [mm/d] Jan. 31.0 18.4 64.0 90.0 11.0 22.0 6.3 4.2 Feb. 33.6 20.0 61.0 90.0 11.0 23.8 18.1 4.9 Mar. 35.9 23.1 61.0 103.0 11.9 27.0 34.1 6.1 Apr. 36.7 24.8 65.0 110.0 12.3 28.5 62.1 6.6 May 35.0 25.0 72.0 116.0 12.6 28.7 149.8 6.4 June 34.3 25.0 72.0 149.0 12.8 28.6 108.8 6.5 July 33.8 25.0 72.0 149.0 12.7 28.5 116.9 6.4 Aug. 33.2 24.4 75.0 142.0 12.4 28.4 149.9 6.2 Sep. 32.2 23.9 80.0 90.0 12.0 27.3 221.3 5.6 Oct. 30.9 23.0 78.0 123.0 11.6 25.2 136.9 5.1 Nov. 30.1 20.9 71.0 136.0 11.3 22.7 23.9 4.6 Dec. 29.3 18.1 66.0 136.0 11.1 21.4 2.7 4.2 Ave. 33.0 22.6 69.8 119.5 11.9 26.0 1030.8 5.6

35

As it can be seen from the graph in figure 9, rainfall amounts <100 mm in six months in a year, <150 mm in 4 months, and > 150mm in about 2 months in a year, during which rainfall exceeds pan evaporation (mid August up to mid October). This is a good indication of temporal uneven distribution of rainfall. In addition to this the dry spell period of the area is quite long, that is about 75% of the months in the year, which is equivalent to 9 months in a year.

Meteorological data of project area

250.0

200.0

150.0

Amount Amount 100.0

50.0

0.0 1 2 3 4 5 6 7 8 9101112

Months Temperature (Celsius) Dew point Temp(celcius) Relative Humidity(%) wind(km/hr) Pan Evaporation(mm) Rainfall (mm)

Figure 9 Monthly meteorological data of the area

This long dry spell period and uneven distribution of rainfall both in space and time in the northeast region is described by Yadav (2005) as the primary factor of crop production limitation by aggravating the process of soil salinization. The sharp alternation of rainy season and dry season in the region is due to the influence of tropical monsoon climate. Thus based on the Köppen’s system the climate of the region at which the project area is located is classified under tropical savannah (AW) (Wada H. et al., 1994)

3.5. Geology

Northeast Thailand is located in a geographical province of Khorat Plateau with a substratum of igneous, metamorphic and sedimentary rocks. According to Satarugsa et al. (2005) the sedimentary rocks of the Khorat Plateau were deposited during the Paleozoic, Mesozoic and Cenozoic. The sedimentation of Mesozoic era begins in Triassic period at a series of half-graben rift basins were developed periodically. The depositions in these rift basins are mainly lacustrine and fluvial clastic sediments.

36 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Figure10 Three dimensional views of the folded basins

As displayed in figure 10 the geological setting is composed of two folded basins of Sakon Nakhon and Khorat in the north and south respectively. These two basins are separated by the Phu Phan Range in the middle. Wada et al.(1994) have mentioned that these basins are underlain by Mahasarakham formation which is the youngest member of the khorat group and includes three rock salt strata (upper, middle and lower). Results from geophysical investigations conducted by Wannakomol (2005) indicate that the rock salt beneath the moderately to the severely salt-affected areas is present at a depths range from 80 to 100 m. While other members, older than Mahasarakham formation of khorat group (e.g. Khok kruat formation), are exposed in the mountain ranges. Extending his explanation, Wannakomol has reported that the salt bearing rock have a relatively flat to broadly anticlinal surface. These results imply that beneath the elevated areas the rock salt is situated at greater depths than under the low-lying areas, troughs or swampy areas.

Figure 11 Rocksalt and the geological map of the study area in one (Sukchan, 2003)

37

Two way travel time [twt] seismic map study on Mahasarakham formation area coverage by Satarugsa et al. (2005) shows that this formation covers a total area of 50,000 km2, which is about 30% of the entire northeast region. Of this area coverage about 25,620 km2 and 20,323 km2 occupies underneath ground surface of Khorat and Sakon Nakorn basins, respectively.

This low-lying geological settings of area, Mahasarakham and Khok Kruat rock formations, are the common rock type that includes variety of sedimentary rocks like sandstone, siltstone, shale, clay stone and conglomerate, which are mainly from the Khorat group.

3.6. Geomorphology

According to the studies by Pramojanee (1982), also cited in Soliman(2004) and in Yadav (2005), the region can be divided into four units namely alluvial plain, plateau, mountainous and intra- mountainous areas. Geopedologically, there are basically two main landscapes distinguished, namely, peneplain and valley. Their development is attributed to two main formation processes: denudation and depositional processes. Peneplain landscape is the largest in size, as compared with the valley.

According to Pramojanee (1982) the dissected ridge is gently folded sandstone bedrock which is the oldest strata of the Mahasarakham formation. This formation belongs to the Tertiary era and the folding action was happened in the Quaternary and approximately at the Pleistocene period. The farms of the upland ridges are covered by infertile sandy to sandy loam textured soils, which show sort of evidence to the geomorphologic hypothesis. Almost all of these farms were under cassava plantation during the time of field survey.

Figure 12 Geomorphology of the study area (Soliman, 2004a)

38 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Depositional landforms are very few in the area. They are only represented in the valley landscape, and the system of old and new terraces surrounding some few rivers and channels coming across the northeast shield. The alluvial depositional clayey soil in the north is the result of this active sedimentation process.

Geomorphology of this area indicates an undulating topography with top hills found in the south and west and the low land in the north and east. But their elevation difference is not such significant. That is the highest is 209 m height and lowland area forming wide flat valley bottoms at height of approximately less than 172 metres. Although the difference in height is minor according Farifteh et al.(2007) the effects on salinity spatial distribution are large. In general, the lowlands in this area are highly affected by salinization.

3.7. Land cover

The project area was under rapid changes during the industrialization of Thailand. Greater area was subjected to change due to road construction and introduction of new crops. According to Sukchan (2003) the native land cover of Korat region was Dipterocarps forest and a major part of the area has been deforested and converted into agricultural land. Maize and kenaf were introduced to the local farmers in the 1960’s, Cassava in the 1970’s, and in the 1980’s. whereas at time of field survey the dominant crops on the field were cassava, Pepper and maize

In a very general sense a replacement of deep rooted trees with shallow rooted crops increases recharge. Because decreased transpiration and interception due to deforestation substantially increases surface runoff and results in excess discharge to the valleys. Findings of the following authors revels the condition. That is according to Williamson et al (1989) salinization of paddy fields and water reservoirs in the area is a recent development and is associated with deforestation of upland trees for production of cash crops. These authors have found that groundwater response to significant rainfall events occurs even within one day.

3.8. Soils

The Mesozoic sandstone and its weathering products dominate most of the surface geology of northeast Thailand. According to Bell and Seng (2004), northeast Thailand has the highest proportion of Luvisols, Arenosols, Solonetz and Lixisols, but the lowest proportions of Cambisols, Ferralsols, and Plinthosols soil series. The comparison is made with the neighbouring countries and concentrated in their rice fields. As the reports indicate the paddy fields of northeast Thailand are mainly in fluvisols.

Texturally according Wada et al.(1994) large part of the soils in northeast Thailand is sandy texture (the name holds true for: sand, loamy sand and sandy loam) and the chemistry of these soils is acidic and infertile. But the exceptions are clayey soils in the flood plains of comparatively larger rivers, and locally in areas which are affected by limestone or basic igneous rocks.

39

The nature of these sandy soils somewhat vary depending to their geomorphologic positions. Where catena is recognized, soil colour changes along the slope from red trough brownish or red and reddish brown to brown. Obviously, catenas are studied in the higher dissected parts (called by some authors as high terrace, which is equivalent to the term glacis terrace used by US when using geopedologic approach to soil survey, refer photo in figure 13 Kalasin Catchment found in northeast region and taken from www.solutions.ird.fr). Large parts of the soil on the low terrace are utilized as paddy field. In the flood plain of the large rivers, the sandy soils with varying thickness are present. They are often underlying by thick fluvial clayey sediments. In the flat plain both the hills and the plain are also covered with the sandy soils. At the plain, the sandy cover is rather thin and is usually underlain by the mottled zone or the pallid zone.

Figure 13 Soil textural classes of the study area (LDD, 1994)

As described by Noble et al.(2004) the light textured sandy soils of the region are dominated by low organic matter and clay contents hence they have low water holding capacity, CEC because of which they do have limited buffering capacity to both anthropogenic and natural stresses.

Rice production in the valleys or lower paddies is more stable. However the upland soils have been extensively leached and eroded. This results in impoverishing and lowering the water holding capacity of the soils. Because of which farmers have moved from rice growing towards low-input and long duration crops such cassava and kenaf. The leaching and process not only degraded the soil fertility but also paves away for expansion of soil salinity hazard in the region.

40 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

4. Materials and methods

4.1. Detecting soil salinization process and modelling it This research presented in this thesis focuses on detecting the soil salinization process over time and modelling the topographical relationship of soil salinity. On the other hand, to achieve this theme there are challenges. For that matter soil salinity is a gradual process that takes places in the soil subsurface and appears to the surfaces after the threshold for mitigation measures have passed. And the available materials for detecting the spatial informations such as satellite images are only useful to traces soil salinity after the time for mitigation has escaped. In similar manner subsurface geophysics, as in the case of EM survey is suffered from several similar drawbacks. For instance its accuracy is influenced by soil texture and soil moisture availability. Moreover, the information acquired using EM survey is bulk property, which is another challenge. Owing to these reasons it is still compelling to find out other methods. Thus aiming at hydrological models which address the driving force of soluble salts through mathematical equations is believed to be the best integral with remote sensing. Hence, the study utilizes the integration of remote sensing and hydrological model techniques in detecting, mapping and modelling soil salinity and salinization process.

Analysing the research questions results in a number of issues that could be translated in to experiments in the proposed methodology:-

1. Detecting soil salinity variation over landscape that can show spatial salinity hazard assessment and can be explained by simulating and ability of the model in modelling the surface conditions. 2. Detecting salinity trends over time that can show temporal salinity hazard assessment and can be introduced by predicting the model out comes over given time, twenty years. 3. Identifying geopedological mapping units that are severely suffered from salinity that can be introduced through reading the simulated result of the model against the standardized soil salinity rating parameters by FAO (1988).

To attain these theme ideas the research adopts measuring soil and water salinity as they can be investigated for model input data set.

The methodology was divided in to two different data collection scales.

1. Small scale survey in which a representative number of soil samples were collected from the whole study area. These sampling observation points are locations of previous study conducted by madyaka (2008) and used to compare the salinization appraisal over time function and for validating the model results.

41

2. Large scale/ Experimental grid plot of 24 km2 were used to apply SAHYSMOD model and collect samples such as soil, water, crop and cropping pattern, irrigation practise and irrigation network facilities and limitations. These samples are used to calibrate and carry out sensitivity analysis of the model. At the same time to verify the applicability of SAHYSMOD model in modelling and predicting soil salinity along the topographical features.

Finally using the calibrated and sensitivity analysed model the soil salinity was simulated and predicted for 20 years and this result data set was used for comparing different spatial and temporal prediction techniques.

Table 3 Developing research items into experimental design

No Research theme Experimental design test 1 Detecting soil salinity Nodal network Polygons for soil, Simulating calibrated and variation along the water, crop, irrigation activity and sensitivity analysed model landscape facility survey and collecting samples 2 Determine soil salinity Comparison between previous and Raster calculation in GIS change over time current salinity level environment. And model prediction for different time series 3 Identifying severely salt Soil and water sample collection Read lab and model results affected mapping units and carry out Laboratory analysis against standardized salinity rating class level

42 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Figure 14 Methodological of full activity process

43

4.2. Pre-field work

During the pre-field work phase the major tasks were:

1. Establishment of Database system 2. Study basics of SAHYSMOD model

4.2.1. Establishment of Database system

This part of the study entailed database system establishment consists of collection and synthesis of available data from previous research projects by ITC and Department of Land Development, LDD for short. The collected data were used to get into the project features and parameters used in the model to represent salinity. That is the developed database system was helpful to realize and understand the soil salinity patterns in relation to landforms, geomorphologic process and landuse/cover systems.

The understanding of these process and availability of geopedological map helped to devise the designing of sampling methods taking into account the time and logistics constraints. The existing GPS data of EC locations and land cover from observation points of previous studies was considered during this process for purposes of detecting process of salinization change over time. At the same time designing of nodal network alignments of the model was devised based on the available data and basic principles and assumptions of the model.

To understand the physical terrain of the study area digital elevation model of ASTER image was collated from ITC image warehouse and image mosaic processes was carried out. The geopedological map developed by Soliman(2004) was used as basis for sampling design. This geopedological map shows two basic landscapes occur in the area: peneplain and valley. Thus especially the data types for model validation and salinity change detection were collected through the help of this geopedological sampling units.

The established database system includes data types such as 1:50,000 scaled digital geomorphology and topography map developed by ITC and LDD respectively. At the same time other maps (such as: soil series map, contour map and land cover map), DTM, TM image, and Aerial photo, attribute tables and documents on geology, soil, salinity, land cover and hydrology were also collected. Moreover, additional soil information that holds resource full maps and attributes tables of the study area prepared by ITC under the name of korat soil database was also used. During the field work, new topographic map sheet 5339II of the area (Thailand 1:50,000 WGS84, 1-RSTD, L7018) developed in 2004 was also collected from the LDD office and used during field survey. These all were used as base maps for locating the observation points in the field. The land cover maps by Soliman(2004), Madyaka (2008) and Google earth image of the study area were also considered during image classification for field work basis.

44 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

4.2.2. Study basics of the model

During pre-field work preparation the basic principles and data requirements of the model were studied. As a new model it is it was hard to find earlier research works for reference or to learn the model how it works. This was the most time and effort demanding portion of the study.

Using the examples given in the model itself it was possible to run the model before field work. At the same time by learning the basic principles and assumptions in the model and defining the data requirements of the nodal network for the model was designed before field work and semi-structured data collection sheets was devised according the data input requirement for farmers interview.

4.2.2.1. Nodal network alignment

Selection of specific site for the model nodal network alignment and data collection based on that alignment is dictated by the specifications of the model itself. The model is mainly meant to simulate: soil moisture salinity, ground water and drainage water salinity, ground water depth, drainage discharge from irrigated agricultural schemes. The subdivision of the area into polygons of known coordinates is governed by either of the characteristics of the following factors in the study area. Namely distribution of the cropping pattern, irrigation, drainage and groundwater, presences of right and left abutments so as to collected data along the profiles (Oosterbaan, 2005). Taking these requirements into consideration and aided by the DTM of the area a nodal network was defined using the presences of abutments. In the field work it was also verified that if the selected site fulfils the other criteria of site selection. And it was found that criteria for alignment of nodal network such as cropping pattern and irrigation practices are also met. At the end observation points for each polygon were labelled for data collection by taking the central part of the each individual polygon. Refer figure 6 for nodal network map.

Main steps during site selection for nodal network alignment preparation were:-

1. Hill shed was generated for good visualization of the topographic feature of the area. 2. Based on the model requirements a boundary condition is defined by taking the right and left abutments. 3. The bounded site is subdivided into polygonal grids. 4. Then the intersection point of nodal network was taken for observation point. 5. Using UTM coordinate system and WGS 84 projection these observation points were prepared for soil sample collection. 6. An attribute table was prepared for the polygonal network. 7. And finally these observation points were laid over the topographic map and DEM. 8. The coordinates of these observation points were chased using Garmen-12X during soil sample collection in the field work.

45

4.3. At Field work

The 20 days field work was conducted between September 4 and 28, 2008 and it covers two major components: soil survey for field data collection and Laboratory work for soil and water electrical conductivity and positive Hydrogen, EC and pH testes for short respectively.

The first components of the field work comprises of soil survey, interviewing of farmers, ground truth of the landuse/cover and collection of secondary data required to run the model and salinization process detection. To achieve this mission a reconnaissance survey was carried out in the first day to get a general overview of the physiographic features, know the accessibility and have clear understanding of the landuse/cover part of study area both in the valley and in the peneplain. Then in the succeeding days data collection was executed by chasing the tracks of these GPS locations that are overlaid on 1:50,000 topographic map and DEM of the study area. That is out of the 24 days effective 16 days were used for soil and water sample, primary and secondary data collection based on the semi-structured questionnaires prepared before field work.

And the second part of the fieldwork was accomplished within four days at the LDD regional soil laboratory office in Khon Kaen. In this laboratory the collected 172 soil and water samples were analyzed. In addition, other secondary data on soils, salinity, salinity management and borehole data the study area were also collected.

For ease of data collection and handling the field work was arranged in two categories. That is data collection made concerning the model it is taken as large scale survey, for it is in smaller area but in detail survey input parameters. And the second category that concerns the salinization change detection in the over all project area is considered small scale for it focuses in collection of fewer predetermined data from a bigger area.

4.3.1. Large scale survey

Out of the study area which has an approximately 816 km2 the selected nodal network site makes surface area coverage of 24 km2. This specific site which is found at the centre of the project area is the only best fit to the model requirements. And this nodal network is subdivided in to 24 square polygons and is aligned in the centre of given degree, about 450, from y-axis of the map. This area was chosen for the model requires an abutments area both in the right and left. But this position 450 had a problem in relation to the model (refer limitation of the model under 5.5).

Each 24 polygons were represented by three soil samples from single observation point. That is one observation point per polygon. Thus a total of 72 soil samples were collected from the nodal network. But irrigation water salinity data required by the model was collected only from ten observation points.

The observation points for water sample collection are less because most of irrigation sites in the polygons use from the same water sources, surface flooding. Moreover, due to mobile nature of the

46 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

water some polygons have irrigation water sources within their boundaries where as others use irrigation water from nearby polygons. Thus the water sample collection is done at random but care was done to make it representative for all sources of irrigation water and landscapes.

Other input data of the model such as: cropping pattern and crop production practise, irrigation practise, irrigation network and drainage facility, borehole data, were collected but not nodal network specific. These data types including the climate and hydro-geological data types are study area specific in their input behaviour.

4.3.2. Small scale survey

The objective of this study is soil salinity change detection over time and space. In short it focuses mainly on spatial modelling and prediction of salinization processes. However, to detect change or to know what has been changed over time and what is not, there must be a known base reference or history of the subject matter in focus or prediction should be done based keeping current conditions remain constant. Thus to know the change in salinization over a year a GPS locations of observation points of previous studies were chased and sampled for EC and pH tests.

Thus, out of the three previous salinity studies carried out in the area GPS locations of Madyaka (2008) were chosen because his study focused on soil salinity detection and modelling using hydrological models of an earlier version of SAHYSMOD model, SALTMOD. For the groundwater balance equations of SAHYSMOD is an inbuilt of SALTMOD equations. Therefore, there could not be a difference in parameters that approximate soil salinity that could give rise to a significant deviation between the two studies. However, out of 71 observation points by Madyaka, for time constraints and inaccessibility reasons due to heavy rainfall, only 30 observation points were surveyed and three samples from each observation point within three depths were taken.

Of these surveyed madyak’s observation points with the exception of 1 observation point, ID-17, three soil samples were collected. But the soil profile of this exceptional sampling point stops at a depth of 60cm. just after this depth a moderately weathered parent rock material appears. For this reason only two soil samples from depths, 0-30cm and 30-60cm were collected, during model validation work this observation point which have no data for the aquifer depth it is filled by averaging method.

About 100 gram of soil sample was collected from each profile depths and the soil samples were packed in polyethylene bags, marked and taken to the laboratory of LDD office at Khon Kaen, where the analyses process was carried out. For the specific location of each observation points both for the large and small scale surveys refer figure16 below.

47

Figure 15 Observation points and their location on the study area

4.3.3. Sample laboratory analysis To use the available resources from the field and put additional time for data analysis, at the same time the sample size was too much in number, heavier in weight and socked by rain which makes them inconvenient/difficult for air transportation. So that both the soil and water laboratory analysis was carried out in khon kean regional central laboratory of LDD.

4.3.3.1. Soil conductivity analysis

The samples were mud at time of sampling. So as to attain well mixed-up sample and form a representative solution of known weight to volume ratio of soil and distilled water the samples were air dried in a greenhouse. Then the air dried samples were mortared and sieved. At the end by mixing defined weight to volume ratio of soil and distilled water an appropriate soil: water ratio solution was formed depending on the type of analysis to be done.

That is 10gm of soil sample was weighed and mixed with 10ml volume of distilled water to form 1:1 weight: volume solution of soil and water. This soil: water suspensions were stirred / agitated for 30 minutes using a machine and allow settling. Finally at the end it was tested for positive Hydrogen (pH 1:1) using a calibrated pH meter of Aqua Lytic pH18 for its pH value.

After pH test was carried out 40ml of distilled water was added to the solution. Through the addition of this distilled water a solution of 1:5 weight-volumes was prepared for electric conductivity test. Then after the solution extract was re-agitated again for 30 minutes in the machine and allowed to settle, at the end the solution was measured for electrical conductivity test of EC1:5 with a portable EC meter of Aqua Lytic L-17.

48 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

All the soil solution was prepared and test measurements were done in the laboratory at a temperature range of 250C to 270C. Furthermore, to let uniform elemental availability in the solutions through out the tests of pH and EC both reading were carried out within 24 hours. For all EC 1:5 and pH tests of 171 samples the reference material for lab analyses was the Handbook of references methods by Ward (1999) according to the quality control of local LDD office.

4.3.3.2. Water conductivity analysis

Irrigation water source is one of the factors for salinity development in irrigation farms, either by rising saline groundwater table due to inefficient water application or using poor quality water. Hence the model takes the quality of irrigation water as one input parameter. For this reason, ten water samples were collected from both landscapes and analyzed for their pH and EC value. That is those water samples were collected from representative sources such as: rice paddy fields, ponds, canals, perennial rivers, surfaces floods and reservoir thanks.

Like to soil samples, the water samples were analyzed for their pH and ECw content and the instrument used to conduct for pH and EC test were pH meter of Hanna HI 111 model and an EC meter of TDA model CM-7B respectively.

4.4. Materials used

The list of materials used in the study includes geopedological map, land use map, topographic map, aerial photo, and DTM, as well as some attribute data on groundwater, soils, and climate and landuse types. A Garmin GPS 12X was used during field data collection to locate and record coordinates of observation sites.

A number of software programs used include Erdas Imagine 9.2 for image processing and classification, ArcGIS 9.3 for spatial data management and map development, and SPSS16.0 for non- spatial statistical analysis. SAHYSMOD, 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 organization. ILWIS 3.3 was also used to some extent for hydrological works, visualization, Decision support system analysis and for exporting secondary data, images and maps to other spatial programs (GIS and ERDAS).

4.5. Post Field work

4.5.1. Soil laboratory result interpretation

As it has been mentioned in the literature review, the standardization of the laboratory results that is conversion of EC1:5 to ECe for the study is decided to consider all factors or sources of variation of soil electrical conductivity. That is rather than using single blanket multiplier factor, soil texture and

49

soil: water suspension based mid conversion factor(f) of Slavich and Petterson (1993) was adopted for the reasons mentioned below:-

1. The single constant multiplier treats all samples equally and it does not consider variability due to texture and soil and water suspension. But it is pointed out earlier under subtitle 2.3 that EC values of similar suspension of soil-water ratio, but of different texture are not equal. And this variation is needed to be considered by the study because it is one objective of the study, spatial variability. 2. At the same time soil textural variability is highly dependant on topographical features. So adopting textural based conversion of EC to ECe is expected to hint Geopedological distribution of soil salinity.

However, the end result of standardization, ECe values, is very small with exception of observation point SAH 17 which is significantly higher than any observation point. By virtue of which the salinity rating class or level done using the reference parameters presented in table 4 below shows that almost all the soil samples lay between slightly saline to moderately salinity classes. And this is much lower than the soil salinity rating result by LDD (1994) refer soil salinity figure in section 1.5 There are several reasons for this to happen but the main possible causes could be:-

1. The heavy rainfall during sampling gave rise to formation of heavy and frequent flooding as a result significant amount of soil salinity are expected to be washed out from in-situ condition. 2. The salinity study by LDD is carried out during the dry spell time at which the salts are not dissolved and washed away from their original place. 3. The salinity level may change over time for there is 14 years gap between the two studies. Table 4 Soil texture affect on EC weight/volume measures and salinity class(DAF, 2006)

Salinity Soil salinity measurements using EC1:5 [w/v] in mS/m Rating Sand Sandy loam Loam Clay loam L/Med Clay Heavy Clay Non-saline <13 <17 <20 <22 <25 <33 Slightly 13-26 17-33 20-40 22-44 25-50 33-67 Moderately 26-52 33-67 40-80 44-89 50-100 67-133 Very 52-106 67-133 80-160 89-178 100-200 133-267 Extremely >106 >133 >160 >178 >200 >267

4.5.2. Water laboratory result interpretation

Of the collected and analyzed water samples the ECw value result of observation point SAH17 is significantly unique of its kind. It is 1.2 up to 7.8 times higher than the rest. However, its pH value test is the lowest of all ten samples.

This water sample is collected from Perennial River which cross polygonal network 4, refer figure 23 the location of the sample is represented by star numbered 4. For the heavy rain reasons, all the rivers and were with soil suspension. However, unlike other the river where SAH 17 was collected

50 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

was pure of soil suspension, but it looks very light bluish in colour. For this reason even though there is no any crop under irrigation nearby the water sample was collected for curiosity and with the expectation that it could represent the groundwater table.

And its ECw value is found so higher than others, 3.6 mS/m, while all the rest being less than one micro Siemens per meter. According to Ayers and Westcot (1994) in irrigated areas, salts often originate from saline, high water table or from salts in the applied water. That means quality of irrigation water is the most critical factor not only in managing and planning of mitigation measures of salt-affected soils but also predicting the salinization condition in case the irrigation water used is poor quality.

The over all laboratory result shows that except the above mentioned sample the rest water samples are sweet. This indicates how the saline groundwater table damages the soil when it appears the surface of the soil. By quantifying the amount of salt in irrigation water Bauder et al. (2008) put the relationship of ECw of water with the amount of salt it contains. That is according to those authors water with electrical conductivity of only 1.15 dS/m contains approximately 2,000 pounds of salt for every acre foot of water. Based on this relationship the water sample with an ECw of 3.6mS/m contains more than two times pounds of the mentioned quantity of salt. Table 5 Conductivity of irrigation water and their location

Sample Sample area Description Coordinate ECw ID X_coor Y_coor pH (mS/m) 1 pond 4( Irrigation water source 2) 810806 1669396 6.02 0.28 2 pond 2 (Irrigation water canal) 811054 1667050 6.04 0.24 3 Drip irrigation 811427 1664486 6.03 0.26 4 SAH 20 813765 1667047 6.22 0.14 5 SHA 15 811502 1666533 6.40 0.05 6 POND 3 802230 1667655 6.27 0.04 7 SHA 17(Perennial river) 813077 1667801 5.74 3.60 8 POND 2 (water logged area) 805256 1665365 6.54 0.07 9 Pond 1( irrigation water sources 1) 805206 1665444 6.29 0.07 10 SHA 16 812281 1667080 6.16 0.21

NOTE: The water samples are collected from different locations and water sources used for irrigation. Thus than demolishing their original sampling names, which is associated with their location and type of water sources, it is preferred to keep the names hold unchanged. And interested researches can locate them using the coordinates given. But this “SAH” has nothing with the area or sources type. They are taken from the first three letters of the model’s name because they overlay with the nodal network of the polygon numbered after them.

According to Ayers and Westcot (1994) irrigation water with an ECw value of greater than 3.0 mS/m affecting crop severely and should be rejected from use. From this it is not difficult to speculate the

51

salinity rating level of the groundwater and there by the salinity hazard impact it puts to the soils of the project area. The input data for the model concerning the quality of irrigation water is then taken the average of all the samples, including the water sample with high ECw value.

4.5.3. Model input data preparation

The standard soil electrical conductivity is, electrical conductivity of soil paste extract (ECe) hence any salt concentration of the soil in relation to crop tolerances is made based on ECe soil salinity ratings. As a result all EC of soil are converted from EC1:5 to this generally accepted soil conductivity extract. By contrast, as reported by Oosterbaan (2005) the soil electrical conductivity input for SAHYSMOD needs other conversion. Because according to the above author the model works based on electrical conductive at filed capacity (ECfc). So any electrical conductivity measures should be converted to first to ECe and then to ECfc based on the following relationship of ECfc to ECe is 2 to 1. In this case the ECe which was a result of similar process was also converted to ECfc to use as input data for the model.

ECFc = 2 * ECe

Equation 6 Conversion relationship of ECe and ECFc

Numbers of input data are fixed at time of model calibration through estimation but under the boundary conditions defined by the model. However, input data such as the thickness of the three soil strata were fixed at this stage. For example according Oosterbaan (2005) the depths of the root zone and transition zone must be fixed at depths where evapotranspiration takes place, and clay and sand layer separated respectively. For this reasons the transition zone of the soil reservoir was limited at 3 meters from the surface based on the borehole data displayed in figure 18 below.

However, the root zone needs care for it demands a combination of factors, depth at which ETo takes places and root depth of crops grown. According to Steve et al. (2005) evaporation from a shallow water table is driven by the vapour pressure gradient between the soil water and atmosphere and is generally proportional to groundwater depth. However, evaporation rate decreases substantially at some depth below land surface and this depth is significantly soil texture dependant. In line to this Sorman and Abdulrazzak (1995) have showed that evaporation from bare soil is also dependant on availability of moisture in the upper layers. Moreover, Salhotra et al.(1987) have find out that salinity decreases the evaporation rate relative to fresh water due to a reduction in the vapour pressure gradient and increasing albedo. But works of Donald et al. (2004) have showed that evaporation affects water that infiltrates to the upper 1 to 3 meters of the soil during the relatively wet, cool, and humid winter months. That means evaporation occurs shallowest at 1 meter and deepest up to 3 meter from the surface.

Like to the evapotranspiration root growth performance, rooting behaviour and distribution of roots are affected greatly by soil and the moisture on it. In general field crops and have shallower root depth where as orchards and tree plants have deeper roots. But according to the explanations of Dhyani et al. (1990) the vertical distribution of fine roots decreases with increasing

52 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

depth from surface. And majority of most plant roots are found within 90-120cm depth from the surface. In accordance to this Ande et al (2008 ) have found that that the average root depth for cassava is considered to be 100 cm.

Therefore, the root zone for the model is fixed at 90cm by taking the shallowest depth at which evaporation can takes place and lowest range at which majority of crop roots found. Because this depth is assumed to satisfy the root depth of crops proposed to compute crop water requirement, rice and cassava, and depth at which ETO occurs.

3 meter mark

Figure 16 Soil profiles at which clay and sand soils separate in the study area LDD (1994)

4.5.4. Model calibration

Deciding and selecting a model with equations and assumptions that properly characterize and approximate the target of study alone does not pay. Because the accuracy of predictions made to see future conditions and appraising mitigation measures is highly dependant on the degree of successful calibration and verification of the model simulations. This is due to errors in the model used for predictive simulations, even though small, can result in gross errors in the projections to be made.

As to the explanations by Anderson and Woessner (1992) model calibration is proving a prove that the model is capable of producing field measured parameters which are the calibration values. And is accomplished by finding a set of parameters, boundary conditions, and stresses that produces simulated heads and fluxes that match field measured values with a pre-established range of error. Thus to find out the best combinations of the parameter values during model calibration several run have to be done and equally simulated results have to be analysed for their representing the field condition.

53

The first step during SAHYSMOD model calibration process starts with determining factors that could not be measured directly in the field. That is factors like leaching efficiency of the root-zone (Flr) and transition zone (Flx) should be calibrated before application of the model. This can be done by running trials with the model using different values of Flr and Flx and choosing those values that produce soil that correspond with the actual measured values on the ground (Idris Bacheci and Suat Nacara, 2007; Madyaka, 2008; Man Sing et al., 2002; Oosterbaan, 1992; Oosterbaan and Abu Senna, 1990; Silberstein et al., 1999). Thus, the process of calibration was started after determining leaching efficiency of the root zone.

4.5.4.1. Determination of leaching efficiency

Leaching efficiency of the root (Flr) or transition zones (Flx) is defined as the ratio of salt concentration of the water percolating from the root or transition zone to the average concentration of the soil water at saturation(Oosterbaan, 2002b; Oosterbaan, 2005). Based on the model input data specification, leaching efficiency is a fraction found between 0.01 and 1. Thus, for best fraction that simulates the root zone and transitions salinity all possible fractions within these mentioned boundaries were tested by assigning values (0.01-1) for leaching efficiency for root and transition zones and running the model. At the end the outputs of simulated root-zone salinity levels were compared with measured values. After several simulations it is found that the model is not sensitive to various values of leaching efficiency.

Nevertheless, according to Tanji and Kielen (2003) this leaching efficiency property is highly dependant on soil texture, structure and methods irrigation application and is subjected to change with these limiting factors. Based on this property of leaching, the efficiency of the model is expected to respond for different values of leaching efficiencies inputs provided. But it did not. The result of similar study using the model in Bonn University Germany shows that they have used a leaching efficiency of 0.75 and 1 for root and transition zone respectively (http://www.hydrogeologie.uni- bonn.de/de/pdf/DiplArbBreuer.pdf). This shows that the model’s insensitivity to leaching efficiency values lower than 0.75.

Therefore, at the end based on these findings and trials carried out the leaching efficiency for model calibration was determined to be 1. The results of leaching efficiencies calibrating results made are presented both in graphical and tabular formats in table 6 and figure 17 respectively. The leaching efficiency for the transition zone was calculated the same way and the result is the same.

54 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Table 6 value used for leaching efficiency determination

No Data type ECFC[dS/m] 1 0.01 0.34 1.13 4.50 109 0.17 0.84 6.95 2.47 2 0.10 0.34 1.13 4.50 109 0.17 0.84 6.95 2.47 3 Leaching 0.20 0.34 1.13 4.50 109 0.17 0.84 6.95 2.47 4 efficiency 0.30 0.34 1.13 4.50 109 0.17 0.84 6.95 2.47 5 values 0.40 0.34 1.13 4.50 109 0.17 0.84 6.95 2.47 6 0.50 0.34 1.13 4.50 109 0.17 0.84 6.95 2.47 7 0.60 0.34 1.13 4.50 109 0.17 0.84 6.95 2.47 8 0.70 0.34 1.13 4.50 109 0.17 0.84 6.95 2.47 9 0.80 0.34 1.13 4.50 109 0.17 0.84 6.95 2.47 10 0.90 0.34 1.13 4.50 109 0.17 0.84 6.95 2.47 11 1.00 0.34 1.13 4.50 109 0.17 0.84 6.95 2.47 12 Observed Ecfc [mS/m] 0.91 1.82 7.26 175.75 0.45 2.27 11.21 3.99

Determining of leaching efficiency

120 ECFC_0.1 ECFC_0.2 100 ECFC_0.3 80 ECFC_0.4 60 ECFC_0.5 40

E CF C(d S /m ) ECFC_0.6 20 ECFC_0.7 0 ECFC_0.8 0.01 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 ECFC_0.9 percentage of leaching efficency ECFC_1

Figure 17 Graphical representation of determined leaching efficiency

4.5.4.2. Initial Groundwater flow determination Consistent with Oosterbaan (2005) in SAHYSMOD, natural subsurface drainage (Gn = Go – Gi) is defined as excess horizontally outgoing groundwater (Go, m3/season per m2 total area of the polygon) over the horizontally incoming groundwater (Gi, m3/season per m2 total area of respective polygon) in a given season. According to this author, under steady state in SAHYSMOD the incoming and outgoing groundwater flows are given as instantaneous values and there is no need to determine them during calibration process. Due to lack of time series climatic and soil data record the model is calibrated to steady state using long term average data of climatic and currently record of EC data. So as the groundwater flow is given instantaneous conditions no need to calibrate or determine the initial

55

groundwater flow (recharge or discharge) before calibration process begins. Thus after determining the value for leaching efficiency by the above process the over all calibration of the model has begun. During calibration processes input parameters values like ECfc, rainfall, potential evapotranspiration, elevation, crop behaviour and the like which are known with a high degree of certainty were modified only slightly. At the same time the input parameters for over all system geometry, nodal network relations, thickness of the soil reservoirs and total porosity once they were put to the model they kept constant, not modified at all. Because according to Oosterbaan (2005) changing the input values of these parameters bring unexpected result which does not represent the truth on the ground and affect the over all results.

Looking for reasonable matching between simulated and observed ECfc data, regular changing values of input parameters within a reasonable range was done. After each model simulation results were compared with calibration targets. This trial of best fit comparisons between simulated and observed root zone salinity was accompanied by trend line fitting and root mean square of error computation in the excel sheet refer the final calibration result refer under subtitle 5.1.3.2

4.5.5. Model sensitivity analysis

The growing interest in simulation of water and solute movement in soils is in response to the need for development of solutions for various agricultural and environmental management problems. In order to be able to adopt models for simulation of the effects of various soil management practices with confidence, it is important that the capabilities of these models and credibility of their results be tested.

The actual sensitivity analysis was started using the feedbacks from model calibration. And from the sensitivity analysis work it is found that the model is easily sensitive to aquifer depth change and drainage installation. But according to Oosterbaan (2005) it is important to note that aquifer depth is among parameters that their value must not be changed. As a result any change in input data was not done in the aquifer parameters.

Furthermore, parameters such as hydraulic conductivity and hydraulic resistance, and total porosity and effective porosity are inversely related to each other. Hence, decreasing or increasing the value of one of those pairs of parameters was accompanied by increasing or decreasing value of the other to keep pace with earlier analysis activity. As it has been reported before with out drainage network installation leaching efficiency of the model is not respondent parameter. However after installation of subsurface drainage network it starts to respond to changes.

The calibration was done for 10 years. In the evaluation of the calibrated model using patterns analysis method, it was preferred to present the output based on polygon data preseason, because here it is necessary to see fit variation in space. However, the evaluation of sensitivity analysis using pattern analysis method the time output data per polygon was selected, because this choice can show two things. First the time output data per polygon shows the time of response by the model when any change is made in the input parameters. From this it is easy to speculate since when the model starts to

56 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

calculate the real calculation it is calibrated for. Second the display of the graphs is clear and can easily identifiable if the graph is made using 22 seasonal output results than using 8 polygonal output results. This is because unlike polygon data preseason the time data per polygon have got 22 outputs. Following the model sensitivity analysis the model validation and prediction have been carried out.

57

5. Result and discussion

5.1. General Statistical description of observed ECFC values The electrical conductivity at field capacity, ECfc, data used to run the model was analyzed for descriptive summary statistics in terms of minimum, maximum, mean, range, variance and . The statistical result parameters such as , and histograms are helpful for better descriptions of the nature of the data distribution and variation.

Table 7 Summery of descriptive statistics of EC [dS/m] data collected to run model

Data type Depth Min Max Range Mean Std. v Vari. Skewness kurtosi [cm] s Root Zone 0-30 0.454 175.7 175.2 12.18 36.01 1.304E 4.422 20.397 [ECFc] 30-60 0.454 154.4 154.0 9.93 31.38 984.91 4.622 21.317 Transition [ECFc] Aquifer 60-90 0.454 31.35 30.89 6.05 8.11 65.83 1.94 3.317 [ECFc]

The statistical result displayed in table 7 shows high standard deviation, and variance. The deviation/variation is significant for the first two upper depths (0-30 and 30-60 cm) as compared to the aquifer zone (60-90 cm). This shows that the first two zones are under the influence of wash-out and accumulation process from surface run-off and percolation, whereas the aquifer zone is more or less stable. This is natural that the aquifer is relatively stable when it is compared to the top surfaces and shallow subsurface soil profiles conditions. Aquifer change is gradual and cumulative effects of surfaces parameters such as climate and other hydrological factors such as evaporation and rainfall amount.

58 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

ECFC 30 ECFC 60

ECFC 90

Figure 18 Histogram distributions of the ECFc data used for model to run

The representation of all other statistical parameters of the ECFc data is in congruent with the standard deviation/ variance meaning all show that the data is not normally distributed. Even the histogram distribution for all parameters show that it is skewed positive left (figure 18). Had the data to be analysed geo-statistically in the R-environment it should be transformed, using logarithmic function to normal distribution. Because better results are obtained from normally distributed data (Hengl, 2001).

The main cause of abnormality of the data is an EC value from polygon 4, which is observation point 4 (refer table 8). This observation has originally got an EC1:5 value of 9.25 mS/m, which through conversions first to ECe and then to ECFc, at the end it has got an ECFc value of 175.75 dS/m.

This abnormally distributed data have imposed significant difficulties in the early stage/process of model calibration but finally the needed result was achieved, refer to the model calibration and sensitivity analysis part under 5.3 This EC with higher value seems an outlier for the one who does not know the study area, but it is a real value on the ground. Thus during analysis it is not rejected as an outlier for the following main reasons:-

1. This observation point is located in the internal polygon of the nodal network so rejecting this will affect the model in general. At the same time it was possible to consider this value as missing data and refill it again with other value that goes with rest of the collected data using the missing data acquiring techniques. But this option is not taken because it does not go with the ethics of research, “facing reality and opting solutions through challenges of scientific approach.”

2. The reading is real so it must not be rejected, rejecting the data as an outlier is going away from challenges the truth and digging out why it happens. So working for truth to find out the reason behind until time and capacity is a limitation and paving the way for others to follow

59

and finish it is the very essence of a research. Up to the current findings the succeeding subtitle are used as background proof for not rejecting the data as an outlier.

5.2. Justification to higher EC from observation point 4

The soil samples collected for the model are of 24 observation points. From each observation point three samples were taken at three depths: 0-30, 30-60, and 60-90 cm. The laboratory EC1:5 results from the rest of observation points is less than 1 mS/m. From all the samples (161 samples out of 54 observation points) only one observation point has exceptionally higher value than others. This observation point has an EC1:5 of 9.25 mS/m but the rest have a value less than one. However, its electrical conductivity decreases with increasing depth. Referring to standardized ECe values in table 8 one can notice and easy speculate back what its initial values at EC1:5 was by comparing with the rest of ECe values in the table. And from the table it is easy to see that ECe value corresponding to observation point 4 is significantly higher than others, almost ten times.

As it has been mentioned in study area description the time of data collection was a rainy season and according to the farmers of the study area this year rain was quite higher than previous years. As a result significant part of the peneplain was heavily washed out and the valley was under water, at least for a large extent (refer the photos in the appendix23). This heavy rain could be one possible reason for the imbalanced distribution of electrical conductivity.

Observation points overly on ASTER image taken during the dry season (of 03/11/2004) shows that the sample area is covered by salt patches. It is seen that sample points 4, 7 and 12 lay completely on salt affected soils whereas sample point 19 falls partially on a salt patch (figure 19, below). Thus even though it is not vivid on the surface like it is on the image but the samples might be collected before the dissolved salt leached away from the aquifer. This could give possibilities of higher salinity from the observation point. For this possibility, it was decided to continue for farther work with this higher EC values, than reject it as an outlier. The image hints existence of salt patches but taking the limitation of optical remote sensing with subsurface data capturing. It was not tried to know what the conditions was during data collection using image processing but opt for hydrological reasons and factors that influence surface movement.

60 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Figure 19Observation points overlay on ASTER image of November 03, 2004

Similarly, the supervised image classification of landuse/cover map supports the argument that observation points 4, 7, 12 and 19 occur in the saline patches. Thus from both images in, figure 19 and 20, observation point 4 is expected to have higher EC value.

Figure 20 Observation points overlaid on the land cover/use map

The answer for this question using image processing also triggers other questions. That is other similar observations that occur in salt patches do have lower EC value. This re-challenges the hypothetical answer of image processing. That is why other samples, particularly observation points 7, 12, and 19, occurring in a saline patch both in the ASTER image and landuse/cover maps have less EC values. See an ECe value of each candidate observation points in table 8.

61

Table 8 Standardized ECe value of the observation points

UTM Coordinates pH value ECe value(mS/m) Obse_ID E N 0-30cm 30-60cm 60-90cm 0-30cm 30-60cm 60-90cm 4 813077 1667800 7.69 8.09 7.78 87.88 77.24 15.68 7 811634 1667834 9.30 9.85 10.07 5.61 9.98 10.83 8 812455 1668389 8.42 9.02 8.91 2.00 3.23 5.70 9 811336 1665139 5.48 5.56 4.53 9.76 0.91 0.23 12 813765 1667046 6.20 5.76 8.06 0.68 0.68 7.72 19 811037 1668488 5.81 7.54 9.73 0.29 0.57 4.09

Therefore, the most possible causes of variability in EC values among observation points that occur in similar environment in the image could be any of the following three factors:- 1. Heterogeneity in the parent material ,see also geology under 3.7 2. The rise of saline groundwater table, see also under 5.4.2 below, and finally 3. The displacement of materials due to surface washout and accumulation process.

5.2.1. Scenario 1: Heterogeneous saline parent rock

The geological study has shown that the massive base rock, Korat group, underlies the geological settings of northeast region of Thailand. The korat group consists of six formations, of which the Mahasarakham formation is a salt bearing one, composed of halite, gypsum, anhydrite, carnallite and sylvite of varying thickness.

In summary, weathering of saline parent material in the area could be the cause of the high EC value. But it could not be a source of variation in EC among the observation points because subsurface of each of them is uniformly underlined by single formation, Mahasarakham. Thus at this level, using rock formation data only, it is difficult to find out heterogeneity between the samples, as a result no information can be drown that supports the hypothesis. Refer figure 21 that shows all sampling locations occur in one rock unit. For that matter conditions with sample points found so clothe with each other it can only be addressed using lithology unfortunately this data is lacking.

62 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Figure 21 Observation point overlay on the rock formation of the study area

5.2.2. Scenario 2: Rise of saline groundwater table

The most affirmative reasoning for the problem comes from Montoroi (2006), that says the salt affected soils of northeast of Thailand are groundwater associated salinity. The lowland paddy soils are still under groundwater influence. Confirming this, DEDP (1997) reports that in the alluvial unit of the region saline groundwater table depth is found below 20 to 30 meter. But during dry season it rises up to 1 to 2 meter of the surfaces. In line with this, Madyaka (2008) has mentioned that during the end of the dry season there is little fresh water in the profiles, and the rivers carry saline water that is fed by the groundwater. This salt is then conveyed from the rivers to fields during the next monsoon. That means the washing out and accumulation process play a role in the variability of an EC of the soil surface depending on the topographical feature of the site in question.

However, in contrary to Madyaka (2008) Last et al. ( 2003) have reported that during the rainy season the saline groundwater table of the is pushed back to the soil profile due to pressure difference. To some degree it is true because the ECw from the valley and the peneplain shows difference, ECw from the valleys tends to have higher EC value. Accordingly EC value of each soil samples also show a decreasing trend with depth. But it is not convincing to take it as absolute truth that works for all conditions in the area. Even though it is single observation an ECw result from SAH 17 of the water sample collected from a perennial river (refer Figure 22) which has significantly higher value that ruled out the statement by Last and his study crew. This sample is collected and analyzed during the rain season thus as to the above authors it should not have higher ECw value because the saline groundwater by this time is expected to be pushed down. .

63

This water sample of SAH 17, is located next to the soil observation point 4 has and is collected from a perennial river. Even though it is one observation and should not lead to full conclusion, but it is an indicator that the saline water table is already at the surface by now just five years after the study by Last et al( 2003).

That is unlike other water samples collected; SAH17 is sampled from the found around 50 meters away from observation point 4. The special character for this stream is it was pure while other streams were with soil suspension. Thus it was sampled to represent the groundwater table conditions but for there is no immediate irrigation fields near the stream it was not taken to represent irrigation water salinity condition.

The water laboratory analysis outcome of the water samples have showed that water sample from SAH17 is the highest of all samples, with an electrical conductivity value of 3.6 mS/m. But the rest water samples have quite smaller ECw value, all below 1 mS/m. Therefore, from these findings it can be concluded that both the soils and water bodies found in polygon four, where observation point 4 and SAH 17 are located, is under the influence of shallow saline groundwater which makes it to be higher than the surrounding polygons.

In accordance to, the simulated groundwater depth (refer under subtitle 5.4.2) also supports the rise of ground water table in the polygon. Hence the source of variation in EC for this sample point 4 is due to the saline water table which is on the surface. That means this polygon is a hotspot for change in salinization of the study area.

5.2.3. Scenario 3: surface washout and accumulation

From surface wash out and accumulation point of view observation points 4 and 12 are located on river embankments, but the rest points are away from the river, located in the erosional sites. In other words, observation points 4 and 12 are ‘accumulation’ type. Therefore corollary to soil erosion and sedimentation that regulates shallow and thick soil profile respectively here surface runoff and accumulation could play an EC concentration value variation role among the points. On top of this surfaces phenomena the subsurface capillary rise and accumulation on bowl type surface features could also be a sources of variation in EC readings of the samples. Even though the exaggeration is not as such high to see clearly the differences the lower part of figure 22 could give hint on the location and surface feature of the two points which is bowl shaped. This bowl shaped feature of the two observations show micro relief change is significant on these observations than in the rest.

It is free of any reasonable doubt that topography regulates all sort of variation ranging from living to none living creatures on the Earth. But if it is a source of variation for soil texture and plant species what factors hinders it from being sources of variation for an EC which is closely tied with soil texture and organic matter? There will not be any scientific evidences that stop it to do so. For one

64 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

thing it is topography that determines characteristics of a landscape thereby the soil property thus certainly not indirectly but directly determines the properties of EC too.

The question that follows would be to what extent that this micro-topography/relief is a cause of variations of EC among observation points of shortly apart among each other? This solely depends on the quantity and chemical property of the washed off and accumulated surface material. Along their way to the discharge area surfaces washed materials are expected to be in excess in one part and lesser in the other, in the accumulation and erosion micro sink areas respectively.

And yet, this answer also provokes additional question that if both points (4 and 12) are located in accumulation sites why point 4 has higher EC than point 12? As both observation points occur along one bigger river, refer the upper part of figure 22, at least both should have equal conductivity but not. Thus there should be undefined certain anomalies which cause the variation.

As it has been explained in the irrigation water sample analysis part (under 4.3.3.2), SAH 17 (observation point 4 with star mark in figure 22) that the saline water table is at the surface as a result it starts affecting the soils. But why this saline water of the river does not go down to observation point 12. As competent answer “lithologic anomaly” in the area between the two points can be taken as a reason but for known reasons it doesn’t take long.

But it is wise to note the additional catchment area observation point 12 has both to the right and left of the main river. And this area is not part of catchment area of observation point 4. Thus the water from these rivers must be with big discharge capacity and lower conductivity to overcome the salinity concentration influence from observation point 4.

The findings by DNR (2000) supports this hypothesis “Water quality varies throughout the catchment, but EC typically declines and turbidity increase with distance downstream in the larger catchments. Further more EC also typically declines with increasing flow.” This means the additional stream flow from the additional catchment size in observation point 12 and EC innate property are responsible factors for dilution in concentration of EC at observation 12.

65

4

Internal node

12 External node

Figure 22 Catchment area and topographic feature of observation points with higher EC value

In conclusion, the higher EC value in observation point 4 is supported directly by scenario 2 and indirectly by scenario 3. The lower concentration of observation point 12 is fully supported by scenario 3 and the decreasing property of EC concentration with downstream distance and increasing volume of flow. In short the sources of variation of EC among the so closely located sample location are micro topography, surface hydrological flow and diluting nature of EC all together.

On top of the above justification, soil salinity is observed being influenced by physical and environmental factors which vary in space and time. As have been tried to explain in the above paragraphs, in relation to topography, soil of a given landscape is always under the influences soil forming factors (Clorpt). Thus occurrence of outliers of EC values must be understood to occur as the property of their storage house, soil, is kept being influenced by factors such as climate, weathering of parent rock material, topography, vegetation, groundwater table depth and human activities.

Those all factors are not expected to be evenly found anywhere, but used to vary not only from places to places but also within a place. Thus it is natural EC to vary from point to point as the soils do. This

66 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

is a basic reason why the standardization of EC value was made to adopt soil texture and in addition to weight to volume ratio of the paste extracts. Therefore basing all the preceding facts forwarded the model calibration and further analysis observation point 4 is not excluded (as outlier) even though it looks to be. It is also important to mention that according to the salt affected soil maps’ of LDD(1994) observation point 4 occurs in the salinity class representing areas with EC of greater than 50 mS/m, refer figure 2.

5.3. Model calibration, sensitivity and validation analysis

5.3.1.1. Preamble to model works Irrigation activity of the area is run traditionally by farmers as a result there are no historic data related to irrigation practices that influence the water and salt balance activity of the model. Thus irrigation demand and field application, storage and irrigation efficiency, reuse of seepage and drain water, irrigation scheduling and initial water level of soil layers are assumed to be uniform through out the prediction.

Under normal circumstances for their seasonal change is not so drastic and it is difficult to measure landuse/cover changes within a year the input parameters from landuse/cover and the farming practices such as are also assumed to be uniform both in space and time of the prediction made.

The model has been run for a prediction period of twenty years at each location using the input parameters as given in appendix 1 through 4. The salinity prediction outputs in terms of ECFc are given for each season (seasons 1 and 2) in a year with SI unit of dS/m. thus unless the conductivity is either from EC1:5 or ECe all simulated outputs of the model are with SI unit of dS/m through out the report. The simulated variables include root-zone, transition zone, aquifer and groundwater salinities based on of location the groundwater table salt accumulated on soil surface.

The seasonal results are averaged to give yearly value based on time space of five year. That is the predicted simulation result on the basis of landform units was made for time series of year zero, fifth, tenth, fifteenth and twentieth. The time scale interval considered during crop water requirement and ETo calculation for input data generation was based on decadal/ten days (refer appendix 8 through 11) but the five year time scale based prediction output was made for the purposes simplification of the evaluation to understand the progress of the salinization within years and it is easy data management at yearly level than day scale of decadal bases.

At the same time the first output data, indicated by year 0, gives the initial conditions for those output variables whose initial values are not defined in the input file, example initial groundwater flow. Thus as no calculations have yet taken place, the values of the output variables that still have to be calculated by the program will remain zero.

67

The model predicts a number of irrigation and salinity related parameters. But of the many functions by the model in the succeeding subtitle sections the simulated prediction salinity of each soil layers, groundwater salinity and surface soil accumulation will be discussed. Outputs of these parameters are enough to meet the research objectives and questions.

5.3.1.2. Specific note for model outputs interpretations

This specific prefatory paragraph is crucial to understand the overall relationship in the output of the model results. That is the degree of salinization of a polygon depends with proximity or interconnection of the internal nodes that has either gain or loss in water table. Meaning in one or the other polygons even they are closely located to one another they used to vary in their topographic elevation even though it is small. Thereby the horizontal gravitational water flow among polygons is free unless there is area specific obstacle among them, like faults and dykes.

Thus based on the free movement of water and low of conservation of mass, the loss in one polygon should be gained by that of the other. But at conditions where the aquifer slope is in contrary to the surface slope or none phreatic aquifer is present the relationship among polygons will vanities. Actually the model have a solution for such prior known conditions using the semi-confined or none phreatic functionality, that block gravitational flow of water. At the same time there also a boundary condition effect like in polygon 8 where significant amount water comes from adjacent polygon which is not among the internal nodal polygons so you can not traces back the reason behind. As a result, the chance of tracing back the sources of the water could not be done. Due to this reasons polygon 8 having thicker water depth on the surface but it has lesser salinity condition than polygon 4. For clarity compare and contrast figures 32 and 33.

When it comes to the vertical movement of the water, according to Oosterbaan (2005) the model calculates groundwater level and the incoming and outgoing groundwater flows between polygons using Boussinesq equations. The net vertical flow (Gnt) of the aquifer (m3 /m2 of a polygon) is determined with the considerations of these incoming (Gi) and outgoing (Go) flow of groundwater and is represented by Gnt = Gi – Go. That means when Gnt is greater than zero (Gnt > 0) it indicates a positive net vertical inflow or net downward recharge to the ground. However, if Gnt is less than zero (Gnt < 0) this indicates a negative net vertical out flow which is an upward discharge to surface ground hence salinization is to be expected depending the chemistry of the groundwater table.

5.3.2. Model Calibration

After the input data for the model calibration are prepared and leaching efficiency was determined the calibration was started. The general model calibration was done to a steady-state. Because according to Oosterbaan (2005) when SAHYSMOD is run in year 0 it will give the instantaneous ground water flow. This corresponds to the steady state groundwater flow and recharge assuming that no fluctuations occur and that the observed ground-water situation is permanent. Under this assumption,

68 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

the (steady state) net outflow of groundwater (i.e. the outflow less the inflow) equals the (steady state) of recharge.

The attained steady state of the net outflow groundwater varies in space. That is being negative 0.156m/season in polygon three it become positive 0.484m/season in polygon eight. This shows there is a potential of salinity in polygon three due to the capillary apprise, note that negative sign indicates that the outgoing groundwater flow is higher than the inflow into the aquifer. Likewise the absences of time series in hydrological and EC data will also hinder if calibrate of the model in to transient state be attempted.

According to Oosterbaan (2005) the calibration process of the model can be done using either one of or combination of the following three parameters: leaching efficiency, area ratio(fraction) and crop rotations. But as it has been mentioned in chapter four under subtitle 4.5.4.1, leaching efficiency is not responsive. Thus the whole calibration was accomplished by varying the input parameters in area fraction, hydraulic conductivity and crop rotation. And out of the three it is found that model is well responsive to area fraction refer the fitting graph in figure 23.

During calibration process not only several simulations were done but also simulations have been tested for best fitting. During calibration looking for reasonable matching between simulated and observed ECfc data, regular changing values of input parameters within a reasonable range of limitation of the parameter in focus was done. After each model simulation results were compared with calibration targets. This trial of best fit comparisons between simulated and observed root zone salinity was accompanied by trend line fitting and root mean square of error computation in the excel sheet. The very challenging during calibration was the highest value from observation point 4. That is it used to affect the whole process by depressing the contribution from other input observation points. But finally the calibration activity was ended when the simulation trail reaches at an acceptable agreement with the observed values.

5.3.2.1. Evaluation of calibrated model

The calibrated model has to be evaluated both qualitatively and quantitatively. But up to date, there is not any standard protocol for evaluating the calibration process. The commonly used qualitative measures of evaluation for trail and error calibrations processes are: first comparison between contour maps of measured and simulated values this provides a visual, qualitative measure of the similarity between patterns, thereby giving some idea of the spatial distributions of error in the calibration. The second method that shows calibration fit is the scatter plot of measured against simulated values.

69

200 180 160 140 120 100 80 60 40

Simulated_ECFc[dS/m] 20 0 0.908 1.816 7.264 175.75 0.454 2.27 11.21 3.99 Observed_ECFc[dS/m]

observed_ECFc[dS/m] Simulated_ECFc[dS/m]

Figure 23 Pattern Analysis of Observed vs simulated ECFc

160 y = 0.833x - 0.3642 140 R2 = 0.9999 120 100 Series1 80 Linear (Series1) 60 40 20 0 0 50 100 150 200

Figure 24 Scatter plot of measured against simulated ECFc values of calibration

The objective of the calibration is to minimize the error which is also called calibration criterion. Therefore according to Anderson and Woessner (1992) the three quantitative ways of expressing the average differences between simulated and measured model result are the mean error (ME), mean absolute error (MAE) and root mean squared (RMS). Thus the calibration error of the model based on the three statistically analyzed and the result is tabulated in table 9 below.

70 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Table 9 Mean error of measured Vs simulated root zone salinity of calibrated model

Poly_ID Root Zone Salinity (dS/m) ME MAE RMS Observed Simulated 1 0.908 0.146 0.762 0.762 0.581 2 1.816 1.51 0.306 0.306 0.094 3 7.264 6.03 1.234 1.234 1.523 4 175.75 146 29.75 29.75 885.063 5 0.454 0.072 0.382 0.382 0.146 6 2.27 0.363 1.907 1.907 3.637 7 11.21 9.3 1.91 1.91 3.648 8 3.99 3.31 0.68 0.68 0.462 Mean of Errors 4.62 4.62 10.6

The overall calibration process, even though time consuming at the end, was calibrated perfectly. This can be seen from the fitting of the pattern and the R2 value of 0.9999, read in figures 23 and 24, respectively. On top of these, all the computed mean of errors displayed in table 9 is smaller even the highest error in RMS is in the acceptable range. However, replacing of an EC value of observation point 4 by an average value of all the observation point used for model calibration lowers the RMS from 10.6 to 2.1 and ME and MAE to less than 2 but for the mentioned reasons it was kept like that

5.3.3. Model Sensitivity Analysis

The ultimate goal of analysis is to find out the most sensitive parameter of the model and finely calibrate the parameter so as to minimize the error that could be incurred during prediction. Thus through changing the values of the input and internal parameters of the model system geometry has been done to determine the effect upon the model’s behaviour and its output. The same relationships should occur in the model as in the real system. Those parameters that are sensitive are identified and finely calibrated to because significant changes in the model’s behaviour or output should be made sufficiently accurate prior to prediction made using the model.

From the calibration it was found that parameters such as hydraulic conductivity and hydraulic resistance, and total porosity and effective porosity are inversely related to each other. Hence, decreasing or increasing the value of one of those pairs of parameters was done in accordance to the other which inversely related to it. For further information refer chapter 4 under subtitle 4.5.4 through 4.5.5.

Figure 25 shows sensitivity analysis carried out for subsurface drainage installation. From this graph it is easy to conclude that subsurface drainage installation in the area will bring tremendous change in

71

depleting the level of soil salinity of the root zone starting from the second season of the second year. Look the red circle on the graph, the root zone soil salinity level drops significantly to about 1dS/m and stabilizes at this ECFc level. This means the root zone salinity hazard level will be incredible reduced by a value greater than four times through the installation of subsurface drainage.

Model sensitivity analysis for drainage installation

6.00 5.00 4.00 3.00 2.00

E C F C1.00 (d S /m ) 0.00

1 3 5 7 9 11 13 15 17 19 21 Time

Dd_1m Dd_1.5m Dd_2m calibrated line

Figure 25 Model sensitivity analyses for installation of subsurface drainage

However, it can be seen from the graph, the model is not responding to various thickness at which the drainage network pipe is placed. That is to find out the safest depth for sensitivity of the model 3 depths were taken (namely at meters 1, 1.5 and 2) and all follow similar trend and overlay one over the other in one line.

Srinivasulu et al. (2004) have used SALTMOD model to see the effects of drainage depth and spacing on root zone salinity. From their work they have found that the model is not responding to various drainage depth inputs but is so sensitive for variations of drainage spacing.

However, from crop root safety and yield reduction point of view the depth at which the subsurface drainage should be placed have significant impact and have to be fixed for successful salinity mitigation process. On top of that the root zone of the soil reservoir is fixed at a depth of 0.9 meter. Hence crop roots taller than this fixed depth are natural to go deeper in to the subsoil and 10cm clearance will not safeguard the crops from salinity hazard. For these facts mentioned and total mitigation of salinity problem the reason why the model is not responding to various depth options should be discovered. If this model has a weakness then a recommendation should be made based on the results so an appropriate model that responds to depth of drainage pipe placement could be proposed for the area as a recommendation and conclusion.

72 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Why the model doesn’t respond to depth of drainage pipe placement?

According to Oosterbaan (2005) the salt balance of the root zone depends on three situations that relate with the location of water table and can be summarized as:- 1. The water table is below the soil surface in the present and previous time step, or it is above the soil surface while it was below in the previous time step. 2. The water table is above the soil surface in the present and previous time step. 3. The water table is below the soil surface in the season while it was previously above it. Out of these three scenarios the third scenario fully applies to this study problem condition, insensitivity of the model to drain depth, so let us see why the model is insensitive to various depths of drainage pipe placements. Therefore, Oosterbaan (2005) has made it clear that the salt balance of the root zone is computed on the basis of the topsoil water balance equation.

Pp + Ig + Lc = Ea + Io + So +∆ Wr + ∆Wx

Equation 7 Top soil water balance equations

∆Zr4 = PpCp + Ci [Ig-Io] –So [0.2Cr4i+ Ci] + RrTCxki - LrTCL4

Equation 8 Root zone salinity balance equations

Where Pp = the amount of water vertically reaching the soil surface. Ig = gross irrigation inflow. Lc = percolation loss from the irrigation canal system. Ea = total actual evapo-transpiration. Io = gross irrigation outflow So = amount of surface runoff or surface drainage leaving the area. ∆ Wr = storage of water in root zone between field capacity and full saturation. ∆Wx = water storage in transition zone between field capacity and wilting point. ∆Zr4 = salt storage in the root zone. PpCp = salinity of rainwater. Ci = salt concentration of irrigation water. Cr4i = salt concentration of the soil moisture in the root zone when saturated. RrTCxki = salt concentration of capillary rise based on soil salinity in transition zone. LrTCL = salt concentration of percolated water at the end of the previous time step

The above two equations, equations 7 and 8, are parameters in the model to approximate the hydrological water balance in the topsoil and salt balance of the root zone respectively. In equation 7

73

the water movement between the soil reservoirs is considered to be free. That is groundwater inflow and out flow to and from the aquifer is assumed to be free to pass either through a geological fault or semi-pervious layers. This assumption holds true for the horizontal flow within a given reservoir too.

Basing the above assumptions the factors of root zone soil salinity are approximated by the second equation (equation 8). That is the terms (components of the equation) in the equation whose net balance determines salt concentrations of the root zone soil reservoir are:-

1. Rain water which is represented in the equation by a term PpCp. 2. Net field irrigation water represented by (Ci [Ig-Io]) 3. Outgoing surface runoff/surface drainage leaving the area(So[0.2Cr4i+ Ci]) 4. Capillary rise from the water table which is designated by (RrTCxki ) 5. Percolation from the root zone which is also represented by (LrTCL4).

Therefore, from those above five parameters of the equation terms (components) represented by number 3 and 5 function a role of reducing salinity hazard at least in the root zone reservoir, for that matter they will not be point of discussion. Other terms of the equation (designated by numbers 1, 2 and 4) function as causes of salinity development in the reservoir(root zone), thus identifying and examining the terms that have relationship with drainage network is essential to see their impact on insensitivity of the model for drainage depth placement network.

Of the three parametric terms of salinity development equation in the root zone, salinity due to rain water (term 1) is not applicable to the study area, because rain water salinity is a coastal region phenomenon. As a result the root zone salinization equation remained with terms of salinization from applied irrigation water and capillary rise from groundwater table only.

However, according to Oosterbaan (2005), salt concentration due to capillary rise from the transition zone, term 4, is a subsurface drainage system dependant. That means if the installed subsurface drainage is efficient enough to drain off the saline groundwater salinity contribution to the root zone would reduced significantly. That means with the installations of drainage network there is no any role of salinization that can be contributed from this term (component) of the equation. As drainage pipe are already installed, and by now looking which effective depth is best effective for salt reduction, the contributions of salinity from this component is stopped. At the end the salt concentration of the root zone salinization is left on the term of the equation that comes from the applied irrigation water only which is unchanged thorough the all steps in the above.

Oosterbaan (2005) has also made clear in the users manual that the model accepts the subsurface drainage pipe to be placed in the transition zone only. Meaning below the root zone for the study case it is placed 1 meter from the surface that is 10 centimetres below the root zone. And the final change in salt balance concentration after each balance is calculated using equation 9 given below.

74 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Change in salt concentration of the soil = salt storage ÷ amount of water in the soil.

Equation 9 Change in salt concentration

Thus based on equation 9 the amount of the groundwater in the transition zone reservoir is divide in to two parts by the drainage network. That is above the drainage and below the drainage pipe. The same holds true for salt concentration, Cxki, it also divided into Cxai and Cxbi that are salt concentration above and below the drainage pipe or ditches respectively.

Giving that the drainage network is efficient enough to holdback the capillary rise of saline groundwater table the only possible salt concentration part of the transition zone to join root zone salinity is Cxai. And its final salinity concentration is computed by the first equation given in equation 10 box below which is an addition and division of very smaller number. Even the multiplier Ptx total pore space is also a fraction that can not bring significant change to the overall process.

Cxaf = Cxai + ∆Zr4 ÷ [Ptx *(Dd-Dr)] ------eqn. 1

Cxbf = Cxbi + ∆Zr4 ÷ [Ptx *(Dr +Dx-Dr)] ------eqn. 2

Equation 10 Salt concentration below and above subsurface drainage network line

Where

Ptx is total pore space of transition zone Dd is depth at which the subsurface drainage is placed Dr is the thickness of the root zone soil reservoir Dx is the thickness of “a” part of the transition zone

The big contributor of salinity of the root zone [Cxbf] is drained out at the transition zone before it reaches the root zone and also salt concentration contribution from the Cxai is negligible. Therefore, the root zone salinity balance remained under the influence of applied irrigation water only.

Therefore, to conclude the answers for why the model did not respond with depth question, as discussed above the equation of the model that approximates the root zone salinity do not have a powerful term that sensitizes the model to respond to variations of drainage depths at which the subsurface drainage pipe locations can be placed. As a result the model fails to fix depth at which drainage network must be placed for best and safest salinity control. And this might be considered as limitation of the model but basing the purpose it is programmed for according to Oosterbaan(2005)”SAHYSMOD is A predictive computation method for soil and groundwater salinity and the water table depth in agricultural lands using varying hydrologic conditions and water management options” and not for drainage installation purposes, thus it can not considered as

75

weakness because during programming the business in focus was salt concentration not drainage depth.

So the focal objective of the programming of the model is achieved its target. Refer figures 37 and 38 of this chapter it clearly meets the phrases put in quotation above. That is the model have proved and modelled that the salinity of the study area is highly associated with rise and fail of groundwater table depending the topographical location of individual polygons. However, considering the drainage network placements the model is not responsive for the mathematical equations representing the physical parameters are not meant to approximate drainage specification requirements.

5.3.4. Model validity Analysis

According to the definition given by Sargent (1998) model validation is substantiation that a computerized model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model. In view of model validation Greiner(1997a) has also reported that reliable output generated by the model determines the dependability of the model for use . Thus for this reason the author recommends that model validation must be taken as best integral part of modelling activity.

These validation data sets before using them to the model they have to be converted into the EC format that the model requires. So using the relationships given in chapter four, under subtitle 4.5.3, all the ECe values have been converted to ECFc format. Unlike the evaluation made for calibration test, here the evaluation for validation was conducted in two ways. First using pattern analysis and mean of errors test of the observed and simulated data, second using cross validation using attributes of maps in ILWIS

5.3.4.1. Descriptive statistical analysis of data used for validation

The EC data used to validate the calibrated model was collected from the study area based on the governing geomorphologic unit exists. That means unlike EC data used to run the model these data have wider area coverage which can be visualized in the figure given in chapter 2 under 2.9

These validating data were analyzed for descriptive summary statistics in terms of minimum, maximum, mean, range, variance and standard deviation. Especially results from descriptive statistics such as skewness, Kurtosis and histograms better describes the distribution and variation of the observation points.

76 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Table 10 Summery of descriptive statics of EC [dS/m] data used for model validation

Data type Depth Min Max Range Mean Std. vat Vari. Skewness kurtosis [cm] Root Zone 0-30 0.19 97.7 97.51 14.89 28.07 787.97 2.1 3.51 [ECFc] 30-60 0.17 96.6 96.43 16.08 24.52 601.32 1.88 3.8 Transition [ECFc] Aquifer 60-90 0.15 42.68 42.53 8.82 13.97 195.29 1.51 0.89 [ECFc]

The statistical result displayed in table 10 shows high standard deviation, and variance. The deviation/variation is significant for the first two upper depths [0-30 and 30-60 cm] as compared to the aquifer zone [60-90 cm]. This shows that the first two zones are under the influence of wash-out, and accumulation from both the surface run-off and percolation, whereas the aquifer zone is more or les stable for its change is gradual.

ECFc 30 ECfc 60

ECfc 90

Figure 26 Histogram distributions of the ECFc data used for model validation

This descriptive statistics shows both data used for calibration and validation are not homogenous or normally distributed, but to avoid redundancy it is only the histogram of one set of data displayed here. Three of the data groups in the two data sets have a histogram which is skewed towards left.

77

That means majority of the data sets are under the influences of a single case which is very different from the rest as a result all the scores are squeezed up to left end.

For instance according Field (2005) in normally distributed data skewness and kurtosis should be zero but neither have zero value of skewness and kurtosis. But of these two data sets the skewness and kurtosis of the data used for validation are smaller and approaches the zero than data used for calibration. This shows that the data used for calibration data are more unmoral than the data used for validation. The influence of this is shown on the mean error of tests carried out during evaluation of both the calibration and validation tests.

The data used to calibrate the model is collected from relatively small area but data used for validation is collected from bigger area under similar condition and time of sampling with the first data set. Keeping the sample area size alone the first data is expected to be homogenous but to the contrary it is not

5.3.4.2. Statistical evaluation of model validation During model validation process by keeping other parameters intact simulation was made by changing only conductivity input data of the model. The root zone, transition zone and aquifer zone salinity level was tested and find interestingly working excellent. But for output presentation to keep consistency with the calibration process and make the model visualization clear the root zone salinity test is displayed below. Table 11 Mean error of measured Vs simulated root zone salinity of validation

Poly_ID Root Zone Salinity (dS/m) ME MAE RMS Observed Simulated 1 0.91 0.15 0.76 0.76 0.76 2 1.66 1.38 0.28 0.28 0.28 3 5.90 4.90 1.00 1.00 1.00 4 97.70 81.10 16.60 16.60 16.60 5 0.55 0.09 0.46 0.46 0.46 6 1.66 0.27 1.39 1.39 1.39 7 15.18 12.60 2.58 2.58 2.58 8 4.14 3.44 0.70 0.70 0.70 Mean of Errors 2.97 2.97 1.72

The main objective of model validation is to confirm or verify that the set of parameter values used in the calibrated model are accurately representing the system under a different set of boundary conditions.

Model validation will help establish greater confidence in the calibration. According to Anderson and Woessner (1992) a model is verified or validated “if its accuracy and predictive capability have been

78 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

proven to lie within acceptable limits of error by tests independent of calibration data”. Basing the quoted suggestions of the above authors the validation work was carried out using EC data which were not used during calibration and which can be proven by looking the second column of table 9 and 11. The result of model validation has certified that the calibration process was excellent. As it can be seen from the degree of mean of errors which was higher at time of calibration has significantly lowered down. This is mainly due to relatively homogenous nature of the EC data used for validation.

In addition to the quantitative analysis of the validation result was also tested for pattern analysis and scatter plot test which are displayed below in figures 27 and 28. As it can be seen vividly the qualitative and quantitative test analysis of the validation are in accordance to the calibration test.

120 100 80 60 40 20 0 0.9 1.7 5.9 97.7 0.6 1.7 15.2 4.1 Observ_ECFc(dS/M) Simulated_ECFc(dS/M) Obse_Validation Simul_Validation

Figure 27 Validation pattern Analysis of Observed versus simulated ECFc

Scaterplot for validation y = 0.834x - 0.3222 100 R2 = 0.9998 80 60 40 20 0 Simulated_ECFc_valid 0 50 100 150 Obse_ECFc_validation

Validation satter Linear (Validation satter)

Figure 28 Scatter plot of measured against simulated ECFc values

79

5.3.5. Cross validation using confusion matrix

In addition to the pattern analysis and statistical test (see under 5.3.4.2) the validation of the model calibration was also done through the confusion matrix in ILWIS (here, below). The data used for this validation are from the following sources:-

1. Results of model prediction (values) extracted from the MSc thesis of Madyaka (2008), 2. From laboratory analysis; conductivity results.

Both of these data are of the same area. None of them was used for calibration that is to say that they are independent from input data used in the current study for model calibration.

The prediction values reported by Mr. Madyaka were made for three ‘times’t0, t10 and t20, and for the three soil zones, namely root zone, transition zone and aquifer zone. This means there are totally 9 entrees. The laboratory analysis results used here belong to the three soil section (for model validations EC refer appendix 2 for lab results and appendix 16 up to 18 for previous study prediction). Thus the cross validation was carried out with 12 entrees from the independent data against 9 entrees from SAHYSMOD model predictions. As noticed, the five soil ratings of FAO (1988) namely: none saline, slightly saline, moderately saline, strongly saline and very strongly saline are used. The cross validation/confusion matrix result is summarized by soil strata (section of the profile) in table 12, below. Table 12 Model validation through confusion matrix /cross validation

No Model Previous study Laboratory predictions [2007/8] Analysis result 1 Root zone 52% 27% 2 Transition zone 80% 33%% 3 Aquifer 95% 95%

From the result of the confusion matrix displayed in the above table the model prediction accuracy level increases when it goes down from the surface. Especially the validation accuracy percentage value of the aquifer zone (95%) hits three times for strongly saline class for the prediction years (t0, t10 and t20) with the laboratory results. But the percentage accuracy values of root and transition zones are one event reading for either salinity class rating. This shows that rainfall does not affect the aquifer salinity level as it does in the above two soil zones.

It is true that the influences from capillary and deep percolation to aquifer is not swift that happens overnight. But it is amazing that the higher accuracy percentage hit (95%) in both validation data confirms that under different circumstances the salinity concentration groundwater table remains constant which is an indicator for the aquifer not being diluted from percolation from the surface. The

80 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

fieldwork of the both studies (of Mr Madyaka, and the current one) took place in September, one in 2007 and the current one in 2008. However, the rainfall received in 2008, when sampling for this study was conducted, was higher than the amount received in 2007. Thus, if under both conditions the aquifer salinity was the same that means that the fluctuation of the groundwater table and its concentration is insignificant and is found at shallower depth. This goes parallel with the arguments made to justify the higher value of the observation point 4. Refer under subtitle 5.2.

In similar cases this validation result strengthens the result of the model prediction in that As it is depicted in figures 32 and 33 result of the model prediction suggests that the main cause of salinity for the area is the rise of saline groundwater table. Thus, highest score for aquifer zone in the validation means confirming consistence of the data collected and simulated. Leads to the tendency that in both years the aquifer salinity is highly concentrated saline and is detected by both model predictions. Second, even though both validation values show an increase in percentage of accuracy towards the aquifer refer table 12. But in the root and transition zone value by Madyaka is almost double than the validation percentage value from the laboratory analysis. For instance the root zone validation value by Madyaka is 52% but that of the laboratory is 27%, the first case shows the trend of rising of the groundwater table to the surface but in the second is the reverse. This difference has to be accounted to percolation effect from heavy rainfall influence during the sampling period.

In conclusion the accuracy of the model calibration was validated using: statistical test, pattern analysis and scatter plot (refer the immediate preceding subtitle) and the cross validation of confusion matrix. The result, in all the three ways, shows that the calibration process is meets the requirements. That is the validation from mean of errors, using independent data gives the highest score, that is, the output of the validation mean errors value is much below than it was in the same evaluation was done in the calibration evaluations. For instance during calibration RMS between calibrated and simulated was 10.6 but during validation using independent input value RMS drops to 1.72 which is so significant than expected, refer tables 9 and 11. Even the validation done using the pattern analysis and the scatter plot is quite perfect. Thus keeping the present conditions are kept constant the predictions made will met the truth to be occurred in the coming 15 to 20 years.

5.4. End result discussion

5.4.1. End result procedures followed

Through critically and seriously calibration activity the required stage of calibration was achieved and a 20 year prediction was made. But the prediction result demands an extrapolation.

The SAHYSMOD is an updated version of SALTMOD model. The latter is a point-based model, vertically with 1D extension to addresses the solute transport in z-axis but lacks the possibility to associate the horizontal relationship of points in the plain. Oosterbaan, the modeller of the two models has upgraded the SALTMOD into the SAHYSMOD by overcoming the horizontal dimension

81

limitation of the SALTMOD. From this angle, SAHYSMOD can be considered as a 2D model, whereby the spatial variations are accounted through a network of polygons. These horizontal variations are represented based on fixations made on the overall system geometry of the model. The horizontal relation of the assumed polygons are determined in the model itself by assigning in the index Of polygon inputs either 1 or 2, which should represent internal and external polygons of the nodal network relationship respectively (for detail see under input data in appendex4).

The model answers/solves quite a number of questions/ issues associated with irrigation water and salinity management problems. The mathematical equations are powerful, and there are appropriate numbers of parameters that approximate the true condition on ground. However, from GIS and RS point of view, where use of maps (raster), both for input as well as output, the model has a limitation. For instance, the model is able to recognize the study site in the geographical space to which they are geo-referenced, but it does neither take nor produces a map. The program offers only a limited number of standard graphics and the rest of output data are in comma separated values of text format that can be taken in to Excel. Thus by integrating the result of RS and that of the model prediction in GIS environment the final result was produced, and is discussed hereafter.

The result is produced through compensating the weakness of the model to produce map was solved by taking the output of the model in to GIS. And finally the map in figure 34 A was produced.

A A B

Figure 29 Aquifer salinity map for 10th year prediction from the model

Nevertheless, from the advanced feature in the SAHYSMOD that maximally can be produced is a defined geometry of polygon, with 2D representation. This polygon is defined initially during data collection or input data to the model. Yet this geometry is not flexible with actual conditions on the ground, as it can not approximate the fuzzy nature of salinity on the ground, using predefined shape. However, this upgraded model is by far better than the SALTMOD, which is 1D point model because it gives a chance to the user to choose out of the five geometrical shapes(that occurs within the ranges

82 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

as from three sided triangle up to hexagonal polygon) that best fits to the ground condition. For instance, figure 29 A is the end result of the 10th year salinity prediction for aquifer zone salinity made of almost 8 square shaped polygonal networks of SAHYSMOD but its equivalences in SALTMOD is displayed in the figure 29 B. In the earlier generation, similar prediction results were indicated as it is presented in the figure; like 8 points (pay attention to the colours of the points and compare them to the figure A polygons). Thus in SALTMOD, in order to show horizontal relationship among the observation points additional extrapolation process is needed. In this context Madyaka (2008) work can be taken as an example, therefore, the upgrading made by the modeller on SAHYSMOD is quite significant.

Figure 30 Aquifer salinity map for 10th and extrapolated for study area

However, from GIS and RS point of view a lot of work is need even in SAHYSMOD. For one thing the polygon results and their attributes were produced in GIS environment by collecting the CSV output of the model in an Excel sheet, for already obvious reason that the model does not produce map. Second the output area coverage is smaller than the area covered by the overall system geometry during data input. Both from the area coverage in this study and from the inbuilt examples under worst condition, SAHYSMOD reduces the output result by two third of the input data. For instance, the figure 29 A, 8 polygon is an output result of 20 polygonal input data given to the model, refer figure 6 under subtitle 2.5.5 in the map the all the polygons need an input data except the four corners but the output is for the internal polygons represented by black points. This in turn is a challenge in extrapolating the results.

This output reduction problem becomes so serious if the area coverage is small and the numbers of data collected are few. Furthermore, this extrapolating problem, due to output reduction, appears to be complicated by the topography(physiography) features at which soil salinity develops, for instance in the peneplain (almost plain) and homogeneous environment there are no variations that can be maximized for intersection of parameters, such as slope, elevation and landuse .

83

For example the topographic feature of the study area where the elevation difference among the peak (204m.asl) and bottom is insignificant, that is, only 32 meters. It was challenging to extrapolate and produce the map of figure 30, which is similar to the result presented in figure A; prediction result of the 10th year salinity in aquifer zone.

Thus to produce figure 30 from the model prediction attribute data, several GIS-oriented activities were done in the ArcGIS and ILWIS environment. The map is obtained through the several images processing on the following maps: 1m resolution DEM which is generated from 10 by 10 m2 pixel raster map, landuse/cover map produced through supervised image classification on ASTER image, slope contour map generated from the DEM, and geomorphology map of the study area.

Steps followed to prepare feature map for extrapolation

1. Six DEM class were created using histogram frequency on the DEM. Class 1 = holds all areas below an elevation of 192m.asl Class 2 = areas located within the elevation range of 192 to 195m.asl Class 3 = areas located within the elevation range of 195 to 197m.asl Class 4 = areas located within the elevation range of 197 to 200m.asl Class 5 = areas located within the elevation range of 200 to204m.asl Class 6 = holds all areas below an elevation of greater than 204m.asl 2. Four slope class was created from the slope generated from the DEM through a histogram frequency Class 1 = slope ranges between 0 to 0.5% Class 2 = slope ranges between 0.5 to 1% Class 3 = slope ranges between 1 to 5% Class 4 = slope ranges between greater than 5%

3. Landuse/cover map of six classes was created using supervised image classification being aided by higher resolution image of Google on ASTER image Class 1 = Water Class 2 = salt patch Class 3 = Bare land Class 4 = Rice field Class 5 = Cassava field Class 6 = Riverian tree

The above three maps were converted into feature classes and in the GIS environment were combined to give the best possible size of intersection map size used for extrapolation. For the area is so homogeneous and a number of tiny polygons were created so all process of elimination, merging and dissolving were carried out, and finally the last possible to a reduced size on which the prediction was

84 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

made was created. All polygons of less than 1ha were eliminated or merged. As a result of this process the possible visible polygon was established. Finally the map result of this process was joined with the polygon that has salinity attributes from the model predictions and the geomorphology map of the area. Finally a map with a lot of null fields and rows in its attribute table was formed.

At this stage extrapolating of mechanism was needed to cover the whole study area which was developed through the intersection of the above four maps, DEM class map, Slop class map, Landuse class map and Geomorphology map with an attributes of model prediction salinity classes.

However, for extrapolation to be made a system that helps to make decision unbiased or objectively a decision tree (DSS environment) was designed and developed. The basis for this DSS is personal professional experience in irrigation, agronomy and soils, and predicted attributes of the model were utilized.

From crop physiological response to salinity it is found that cassava is relatively more tolerant than rice does. According to Pessaraki (1999) unlike its kind, cereal crops, rice is the most sensitive to salt next to maize. At the same time from soil salinity distribution in the landform it is found that topographic elevation of 200 meter above sea level. is a break through between the highly salt affected and less affected soils of the study area. That is at relativity cases of comparison landforms of the study area found below this elevation are aggressively invaded by salinity than those found above this elevation. Using this relationship, the following DSS was developed and used to fill the attributes for the land forms with null data. Then after filling the data the maps were developed by dissolving the entire map using the attribute map partially filled by the DSS and partially from the model attributes of all.

85

Table 13 Part of the designed decision supporting system

Yearly model predictions for aquifer DEM Slope landuse Expertise zone salinity Class Class LU1 LU2 LU3 LU4 LU5 LU6 Salt class Yr_0 yr_5 yr_10 yr_15 yr_20

< 0.5 1 WW WW WW WW WW WW 192 < 0.5 2 VSS VSS VSS VSS VSS VSS < 0.5 3 STS MS MS MS MS MS < 0.5 4 MS MS MS MS MS MS < 0.5 5 NS NS NS NS NS NS < 0.5 6 SLS SLS SLS SLS SLS SLS

192 < 1 1 WW WW WW WW WW WW < 1 2 VSS VSS VSS VSS VSS VSS < 1 3 STS MS MS MS MS MS < 1 4 MS MS MS MS MS MS < 1 5 NS NS NS NS NS NS < 1 6 SLS SLS SLS SLS SLS SLS

192 < 5 1 WW WW WW WW WW WW < 5 2 VSS VSS VSS VSS VSS VSS < 5 3 STS MS MS MS MS MS < 5 4 MS MS MS MS MS MS < 5 5 NS NS NS NS NS NS < 5 6 SLS SLS SLS SLS SLS SLS

192 > 5 1 WW WW WW WW WW WW > 5 2 VSS STS STS STS STS STS > 5 3 MS MS MS MS MS MS > 5 4 SLS MS MS MS MS MS > 5 5 NS NS NS NS NS NS > 5 6 NS SLS SLS SLS SLS SLS

Remark and key for the table:-

1. The colours are taken from the landuse land cover map produced by supervised image classification, thus they represent land use classes. Respective numbers represent landuse class number and the colours Blue, Red, light Pink, Yellow, Dark green, Light green stand for Water, Salt patches, bare land, Rice field, Riverian tree and Cassava fields in similar order.

86 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

2. The abbreviation in the decision supporting system are salt rating class and they stand for VSS= Very strong saline, STS = Strongly saline, MS = Moderately saline, SLS = Slightly saline, NS = None saline

Here it is important to note that while building the null attributes if there is an attribute obtained from the model prediction, due consideration is given to the model result. In other words in such a case expertise classification is given less weight. At the same time as it is mentioned in the above paragraph that elevation plays a greater role in distribution of salt; hence DEM classes were given higher weight. When the decision needs critical understanding from the landuse point of view especial focus was given to salt patches for they are renowned at confidence level, however more than the other classes such as bare lands. Similarly, if decision is being needed to be made from landuse point of view but the comparison was to be made among other group other than salt patches, less attention was given to cassava. Cassava only grows in the peneplain area at surfaces which are above the limiting elevation that means it grows in less saline area. At the same time the stand for landuse type of bare land is conditional. If the comparison is with other landuse classes, to decide which weigh best matches needs additional information from slope and topographic elevation. That is most the focus for bare land depends on the slope and elevation on which it is located. The landuse type, LU5, represents a “riverian tree” (trees grow along the sides of the river embankments) in spite its root expected to go deeper than the root depth defined in the model and reaches the saline groundwater table but still keeps growing. This is believed to be due to the leaching effect of the running water in the rivers. That is yearly flows of the rivers that dilute the salt concentration. So land use in this case is considered to be none saline (NS).

From table 13 it is easy to see that of the 28 rows and 5 fields (of the attributes) for the intersection maps But for category DEM class 1 and aquifer salinity prediction it is only two rows (in red colour) have got a prediction result from the model. The other rows and fields are filled based on the DSS. The ones in blue colour are changed from the prior expertise salt class based on slope or landuse. So the remaining 6 Tables with extended fields of root and transition are larger in size than table 13 but they are field this way. For the remaining DSS tables refer to Appendix 16-21

5.4.2. Simulated groundwater table and salinization Detection and mapping soil salinity is not an easy process especially during its early stage. On top of that due to its subsurface process and mobile nature sometimes it imposes other difficulty on the reliability of the techniques deployed to detect it. For example the presence of salt crust in the surfaces simplifies the detection of soil salinity using satellite imagery. But salt curst of the dry season dissolves by water and disappears in wet season. So image taken during wet season say nothing about the salt crust. This shows an association of events and integration of techniques in detecting and mapping soil salinity is by far compulsory.

87

Simultaneously, detection and mapping of soil salinity via ground survey presences of symptoms (e.g. salt crust) may not be mandatory for it could be done using signs. For instance in irrigated area, if there is water logging problem thereby by default there is a salinity problem especially in arid and semi arid areas. Because both water-logging and salinization are twin problems in agricultural environment, however, the signs of water logging and its association with salinization could give rise for soil salinity survey to be conducted but they are not end results by themselves. From this, it is possible to deduct that detection of groundwater salinity change could be an essential step to identify the causal relationships of salinity change.

Many ecological and environmental studies have proven that accelerated changes often occur in some localized areas, while the majority of land surface showed gradual transitions from salt free to salt affected state. Thus identifying of such red-light spots greatly enhances the identifying ability of the significant process that undergoes in the subsurface.

These red-light spots area, cited by Shrestha and Farshad (2009) as saline spots from which spreading of salts in the paddy field takes place. According to these authors the occurrences of saline spots is due to the presence of densipan around 50-70 cm depth from the soil surface. By nature this densipan is impervious layer but its breakage result in an opening that outpours the saline ground water to the soil surface hence the creation of red-light spots.

Local spot for salinization,

Figure 31 localized red-light spots for salinization (Shrestha and Farshad, 2009)

For instance the ECe from soil sample in polygon 4 which is designated by observation point 4 tells us that the soil surface in this polygon is quite different than other samples found adjacent to it. Thus in one way or the other the ECe value is changed quite significantly. Thus, this sample can be considered as being collected from a red-light spot which signals the of the polygon. So if some drastic change is to happen in and/or around the area for truth the point of change must be from this

88 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

polygon. Because this polygon has already signalized the soon coming event by showing that its soil chemistry is under influence of a process below it. This higher ECe value of observation point 4 is nothing but casting of the salinization that takes place within the polygon in general. And observation point 4 is a red-light spot of the process.

As it has already been discussed in the subtitle water sample analysis collected within this polygon 4 has showed that this nodal network polygon is with highest ECw test value. Based on the evidences provided, the possible cause for this highest ECw value was speculated to be capillary rise of saline groundwater. Incongruent to the earlier speculations made in the preceding subtitle, the simulated result of the model also shows similar results. That is the soil salinity of the area is highly associated with rise and fall of saline ground water. Hence polygon 4 has got the highest simulated salinity value, refer the figure 34 below.

Correlation between root zone salinity[Cr4] and Excess of groundwater inflow overoutflow[Gaq]

30.00 25.00 20.00 15.00 Cr4 10.00 Gaq 5.00 0.00 -5.00

Cr4 (dS/m)Cr4 and Gaq (cm/season) -10.00 0 2 4 6 8 Polygon nr.

Figure 32 Soil salinity of root zone (Cr4) versus groundwater inflow-outflow (Gaq)

Gaq represents an excess of groundwater inflow in relation to the over outflow. Thus negative Gaq means that there is more outflow of water from the groundwater or aquifer to the transition or root zone soil layer. But positive Gaq means that there is more inflow of water to the groundwater table or aquifer. Thus, based on the up and down-ward fluctuations of the groundwater table the soil salinity concentration in the root zone increases and decreases respectively.

According to principles of the model the root zone salinity is calculated at field capacity or field saturation with defined rotation key (rotation key, Kr, ranges from 1 up to 4 and is included in the model to address farmers’ response to salinity through irrigation activities such as crop rotation). Thus the root zone salinity displayed in figure 37 is simulated at field capacity or field saturation of full rotation (rotation key Kr=4).

89

Based on this principle the net horizontal flow in the aquifer (m3/season/m2 of nodal area) is manipulated as:-

Gaq = Gqi+Qinf-Gqo-Qout-Gw

Equation 11 Net horizontal flow in the aquifer Where Qinf is inflow condition of the aquifer Qout is outflow condition of the aquifer, Gw is pumping from wells Gaq is vertical flow from aquifer into transition zone or root zone storages.

Therefore, the overall result/ net positive Gaq means that there is a vertical flow to the transition or root zone storage from the aquifer thereby to the soil surface, hence salinization. But if the net Gaq is negative the reverse will be true.

Thus as have been displayed in figure 37, as Gaq is remained above the soil surface the salinity increases and as the Gaq is below zero the salinity decreases depending the depth of location of Gaq. For instance, at Gaq depth of -5 cm per season the salinity level of the root zone (Cr4) shows 0dS/m whereas when the Gaq reaches 12.6 metre per season the root zone salinity (Cr4) goes up to 27.7 dS/m.

5.4.3. Simulated numerical soil salinity result

5.4.3.1. Simulated salinity numerical results for Root zone

According to the model context and assumptions taken during input data preparation (refer 4.5.3) the root-zone refers to the first two upper soil depths (0-30 and 30 -60cm). But the input data of the first soil depth was used for root zone while the input data of the second profile used for transition zone. This is mainly because of lack of driller to collect representative sample at three meters depth from the soil surface (at which the transition zone is fixed). The results of the predicted root-zone salinity (ECFc_dS/m) are given in table 14.

90 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Table 14 Average yearly predicted root zone salinity [dS/m]

Root zone salinity(dS/m) Poly_ID Observed Yearly average Simulated ECFc ECFc Year_0 Year_5 Year_10 Year_15 Year_20 1 0.908 0.15 1.42 5.47 10.75 16.70 2 1.816 1.51 3.28 6.58 10.00 13.40 3 7.264 6.03 1.48 0.42 0.19 0.13 4 175.75 146 40.35 30.05 27.85 26.65 5 0.454 0.07 4.58 4.55 4.44 4.32 6 2.27 0.36 0.17 0.13 0.13 0.12 7 11.21 9.30 20.50 19.70 18.75 17.85 8 3.99 3.31 10.45 9.82 9.24 8.71

The trend showing over the time span is variable. That is some polygons increase (1, 2, 5, 7 and 8) while others decrease (3, 4 and 6). Here it is good to recall the introductory paragraph made in 5.3.1.1 above. That is the conditions of the groundwater balance assumptions and topographical relationship of the individual polygons. Here the change in salinization (salt balance) is made based on the groundwater balance. Whereas this groundwater balance brings in to account horizontal water movement within polygons based on topographical locations.

At the same time the basic assumption of the mathematical equations that parameterized the groundwater and salinity balance conditions is conservation of mass. That is the amount/volume of water leave from one polygon should be gained by other polygon which receives it. For clarity refer to figure 32, the relationship between the water table depth and salinity concentration at the root zone level of table 14 goes hand in hand with the figure. That is when the groundwater table is found below the soil surface the salinity of the root zone decrease. But when the water table occurs above the soil surface (or if it is located close to the surface, like the situation in the polygon 1 and 2, in the figure 32) the salinity increases. Here it is also nice to bring in to account the maximum depth at which ETo can take place, refer to model input data determination in chapter 4.

At the same time, the power of the model has showed off by reducing and stabilizing the higher soil salinity in the polygon 4. For clear understanding of this effect refer to the yearly seasonal based prediction result in the annexed tables 4, 5 and 6.

91

Figure 33 Depth of water table [DW] in each polygon

Pay attention to the measuring unit of the water depth in figure 38 it is an image from the output graph directly from the model. According to this graph polygon 8 has 12 meter effective depth of water on the surface, which is almost equivalent to height of live water in micro-dam and this is not true on the ground. However, this has already been mentioned as one limitation of the model. This shows that the surface water balance equations have limitations, or else during updating the program from MSDOS to windows for the user manual have not been updated this silly error might have occurred due.

5.4.3.2. Simulated salinity numerical results for Transitional zone

The predicted salinity results for transition zone are given in table 15. The transition zone salinity in each polygon follows more or less similar pattern with that of root zone salinity prediction. The basic difference among them is in the polygons 2 and 7. Both polygons show an increment in the root zone but they decrease in the transition zone. Even though polygon 2 (refer either of appendix 4 to 6 for elevation of each observation points) is located relatively at higher topography than polygon 7 but it is clear that role of topography in this case is not a cause of the difference in EC between them. Because there is no a trend of losing and gaining in EC value between the polygons. Thus the possible cause could be deep percolation due to the rainfall during data collection and boundary condition. At the same time capillary rise could not be a cause of this effect because it is not evidenced by similar pattern from the aquifer salinity prediction.

Even if these two profiles would be used to follow the same trend (either increasing or decreasing) parallel wise, the type of change in the root zone is more significant than it does in the transition zone. Moreover the change in the root zone tends rather to increase than to decrease the salinization, but the

92 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

situation in the transition zone is the reverse. This could due to surfaces accumulation and percolation effect in both zones respectively.

Table 15 Average yearly predicted Transition zone salinity [dS/m]

Root zone salinity(dS/m) Poly_ID Observed Yearly average Simulated ECFc ECFc Year_0 Year_5 Year_10 Year_15 Year_20 1 0.91 0.91 1.34 1.87 2.19 2.41 2 1.82 1.82 1.70 1.59 1.56 1.57 3 2.27 2.27 2.53 1.90 1.31 0.89 4 154.47 154.00 37.30 29.45 27.60 26.45 5 0.91 0.91 4.60 4.53 4.41 4.29 6 3.18 3.18 2.13 1.44 1.00 0.72 7 19.95 20.00 20.40 19.55 18.55 17.65 8 6.46 6.46 10.35 9.70 9.12 8.61

5.4.3.3. Simulated salinity numerical results for Aquifer zone salinity

The predicted simulated results for the aquifer salinity that shows soil variation/changes over time and space is given in table 16. This zone shows a decrease in salinity in polygons thought to be found in the valleys but increasing in the peneplain. That is the elevation range of the eight polygons is found between 197 and 172 meters above sea level. And polygons with elevation less than 193 have showed decreasing in their salinity but polygons above this elevation keeps increasing. This is due to the inundated water in the valleys, where leaching takes place. Besides, in all polygons change between the observed and simulated throughout the prediction period is not significant but just fractions. Polygons 7 and 8 are exceptional to this generalization and this is due to thicker water inundation on their surface. At the same time the difference in degree of salinity between transition-aquifer and root- aquifer is significant in the later pairing. In the first pairing (transition-aquifer zones) it is still minimal and stable. Thus observed insignificant difference and stability throughout the prediction time (polygons 7 and 8 are still exceptional) indicates that salt is not leaching down from root and transition zone into the aquifer. Another factor that can be highlighted is that the horizontally incoming groundwater that was not taken into consideration is due to lack of data.

93

Table 16 Average yearly predicted Aquifer zone salinity [dS/m]

Root zone salinity(dS/m) Poly_ID Observed Yearly average Simulated ECFc ECFc Year_0 Year_5 Year_10 Year_15 Year_20 1 1.82 1.82 1.99 2.15 2.30 2.43 2 1.36 1.36 1.39 1.44 1.49 1.54 3 6.36 6.36 6.38 6.43 6.50 6.55 4 31.35 31.40 29.90 28.65 27.65 26.60 5 4.99 4.99 4.73 4.58 4.44 4.32 6 2.72 2.72 2.73 2.74 2.75 2.75 7 21.66 21.70 20.55 19.45 18.55 17.65 8 11.40 11.40 10.45 9.81 9.23 8.71

5.4.4. Map result of soil salinity For sake of an easy comparison and to make a better visualization, the five prediction maps were stacked together and are presented under figures 34 through 36 below. At the same time for easy understanding the three stacked soil maps are treated indifferent subtitle than put them together with the respective numeric prediction results under subtitle 5.4.3

The five time series maps (see figure 34) of the root zone salinity shows that the left part of the blue Line which was not saline has started to be affected by salts from the groundwater rise, after five years. That is moderately affected soils which were seen as small spots in the left side of the line at year zero (t0) have developed to patches of moderately saline appearances in the year 5 (t5). Then after 15 years (meaning in the 20th in the scale of prediction time) in this area none saline area becomes saline, with a rate of development, to very strong salinity level, at which no crops can grow. The rate of salinization progress in this zone is 25% per annum of a respected geomorphic unit.

Here it is nice to notice that the valley area which is found extended from north east to wards east and south east (right of the blue line in prediction map for year 0). Except for the salt patches, which are indicated by red colour in the supervised image classification, the rest is none saline. In this area, it is found that the saline water table is already at the surface, like in polygon 4 where observation point is found. Thus, this contradicts to the fact in the ground and this happens due to the surfaces washout and accumulation process affected the situation. However, the history of this area in the transition zone and aquifer zone reflects the ground condition. That is the salinity is reflected back to the true conditions by being changed in to moderately saline and moderately to strongly saline respectively in the deepest zone (aquifer zone).

As mentioned earlier the aquifer is less affected by temporal events, and needs time to be changed. The process in aquifer is gradual, because of which it is possible to observe the presence of strongly

94 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

saline area in the aquifer zone prediction only. In other words, the aquifer zone is not yet influenced by the heavy rainfall, during time of soil sampling is carried out; hence the reading is expected to show the true conditions. However, the root zones fully and transition partially are relatively affected by the surfaces washout and percolation from the rainfall.

From the preceding paragraph it becomes clear that salinity concentration is more stable in the aquifer than it does in the root zone, because the latter is subject to external factors that easily affect its chemistry. The aquifer salinity concentration takes enough time to show change. As far as the groundwater table is highly saline and is less affected by external factors the system is stable and this stability decreases when moves away from the aquifer and reach the soil surface. For this matter the change of salinity from strongly saline to moderately saline and then slightly saline change is seen on the prediction map results of the three soil strata throughout the prediction years. Refer to maps of figures 39 up to 41 by taking the right side of the blue line on the maps.

Inline with the preceding paragraph (in the specified part of the map, right side of the line) an interesting stable feature is observed. That taking the assumption that says salinity concentration becomes less affected as it goes deep to the aquifer. It is observed that the area is invaded by salinity that occurs with the ranges of moderate to very strong saline level. And this is in accordance with image result displayed in figures 34, 35 and 36. This proves that the speculation made that saline groundwater table is already on the top of the soil surface.

For one who looks closely to the five stacked maps in the three figures the significant change of salinization with time series is observed in areas to left of the blue line of each map. This area can be taken as a summit of the study area. This area is less affected by salinity as it can be seen in the year 0 prediction map (t0) of each zone. But with time it will be drastically changed. Of the polygons polygon 1, 2, 5 and 6 are found towards this summit for they are adjacent polygons to polygons which put to the left abutment. In these polygons the water table is either very shallow or already on the surface. Comparatively speaking only polygon 5 has got deeper water table (refer to figure 32). This means that rise of groundwater is responsible for all the drastic changes in this area.

95

Figure 34 Model predictions for 20 years salinization even of the Root zone

96 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Figure 35 Model predictions for 20 years salinization even of the Transition zone

97

Figure 36 Model predictions for 20 years salinization even of the Aquifer zone

98 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

5.4.5. Spatial Distribution of Simulated Salinity within the Geomorphic Units During soil sample collection few geomorphologic units were not sampled for time concentrate. The units which are not sampled are Pe114 and Pe511 from the peneplain and Va211 and Va311 from the valley landforms.. That is from the peneplain geomorphic units Pe114, Pe311 and Pe412 are severely affected than the rest. While from the valley areas the severely affected area is Va111 only. According to the model prediction for the root zone the rate of average salinization of all types of salinity ratings in these units is 25% km2 per year per geomorphic unit. The rate of salinization for the rest geomorphic units is 20%km2 per year per geomorphic unit. According to the classification of the geomorphologic made by soleman(2004) areas the geomorphic units with highest EC reading or higher rate of salinization prediction belong to summit, slope complex , side complex and levee overflow complex landforms respectively. Table 17 is one of the five salinization rate table that are made based on the model prediction by taking root zone only as an exemplary. These salinization rates are made based the geomorphic units taking prediction year as time dimensions. That is the rows show spatial distribution of salinization and the year of prediction as time dimensions. Table 17 is displayed here as demonstration the other four tables are annexed under table series from appendix 21 to 24 respectively.

Table 17 Root zone salinization as a function of time and geomorphologic unit for year 0

Year Area (km2) Salinity rating class yearly average 0 coverage MS NS SLS STS VSS area affected Pe111 27.53 1.2% 65.0% 29.4% NA 4.2% 25.0% Pe112 34.89 5.0% 51.0% 35.3% 1.7% 7.0% 20.0% Pe113 56.16 0.7% 91.5% 5.0% 0.8% 2.1% 20.0% Pe114 14.77 3.5% 71.2% 23.7% 0.7% 0.9% 20.0% pe115 6.25 0.8% 3.2% 77.9% 0.8% 17.3% 20.0% Pe211 23.32 0.7% 46.0% 42.6% 0.4% 10.2% 20.0% Pe311 22.03 1.7% 79.9% 15.2% 1.3% 1.9% 20.0% Pe411 3.53 NA 5.1% 6.1% 22.0% 66.9% 25.0% Pe412 4.83 NA 6.4% 45.3% 1.0% 47.4% 25.0% Pe413 26.26 0.7% 7.1% 49.2% 6.9% 36.2% 20.0% Pe511 0.07 NA 100.0% NA NA NA It is one event Va111 7.41 NA 4.7% 19.1% 16.2% 60.0% 25.0% Va211 5.96 6.0% 30.2% 60.8% 2.6% 0.5% 20.0% Va311 1.19 1.4% 0.0% 57.8% 0.0% 40.8% 20.0%

99

5.5. Salinity change detection This change detection was carried out in GIS environment through the raster calculator by deducting from the current study prediction that of last years (by madyaka) created map. Both these prediction maps are for time series year zero. That is this year prediction map is based on data collected on September 2008 while the later prediction salinity was based on sample collected on September 2007. Meaning the change in salinity (either the increase or decrease) is a solid one year phenomena.

However, of the number of years to which prediction was made by both studies (the five time series prediction years, namely t_0, t_5, t_10, t_15, t_20) only year zero change is taken to be discussed here. The tabular data in table 18 and the map data in figure 38 which were produced based on the mentioned maps could provide enough highlight that a change occurred in time space of one year for the three soil strata.

Through intersection activities first for extrapolation of the model output the area coverage of the study area have decreased significantly. For this change detection for both maps should be of equal size an intersection activity was carried out and this size also decreased. That is from original size of 816km2 at time of data collection it reduced finally to 244.21km2 at time of change detection. Thus this salinity detection is carried out on the on smaller area (244.21km2) than the intended size.

From the table it can be seen that area covered by the net change (+ 174, 608,100m2 and +150,407,100m2 in the root zone and aquifer zone respectively) are almost equal to the total area coverage (244.21km2). This shows that almost all area is under the influences of salinization process, which is true with the supervised image classification carried out and the model prediction. The trend of change either decrease or increase is uniform. That is in all soil strata the dynamic movement of the system is parallel. This shows that the association of the salinity movement with its driving force, water. And water by nature can not go two ways at time so the salinity concentration change is uniform through out and the direction is one. This can strengthen the idea that rise of saline groundwater is the sources of salinity.

The decrease and increase condition in the root zone is higher than all zones and this would be due to additional sources of salinity for the root zone from the surfaces washout and accumulation from the mountains and peneplain area.

Farther more as the transition zone is a joker between the to determinant sources of salinity (surfaces washout and accumulation in root zone case and saline groundwater and parent material in that of the aquifer zone) its value of change in area coverage remains smaller. Perhaps the in between condition of transition zone could be for the main reasons that the change covers one solid year at which two major events of groundwater table observed, rise and fail, so it is difficult to have equivalent area coverage with either strata. Unless the time should be the rise of water table to the surface at which all could have more or less uniform salinity depending the soil texture and micro topography of the area in focus.

100 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Table 18 Salinity change within a year for (year zero) Area covered by salinity change @ time scales of year zero for 3 profiles (M2) No Change Root zone Transition zone Aquifer zone rate Decrease Increase Decrease Increase Decrease Increase 1 Minimum 115,021,800 20,651,400 16,175,700 98,967,600 2,124,900 81,081,900 2 Average 5,382,000 60,659,100 19,654,200 12,247,200 1,548,900 41,836,500 3 Maximum 27,900 24,988,500 2,891,700 24,988,500 989,100 32,151,600 4 No change 17,475,300 69,281,100 84,473,100 Sum of change 26,061,300 200,669,400 38,721,600 136,203,300 4,662,900 155,070,000 Net change + 174, 608,100 + 97,481,700 +150,407,100

101

Figure 37 Change detection of root zone salinity for time series year 0

5.6. Limitation of the model For the purposes it is meant for, the model represent very well in performing both the hydrological and salt balance analysis. That is the model mathematical equations/parameterized to approximate or represent salinity of soil moisture, water table, depth of groundwater, irrigation and other salinity distributions both in space and time are powerful. For these reasons the first version of the model

102 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

(SALTMOD) is now popular and is widely used. The groundwater and salinity balance of this model, SAHYSMOD, is also an updated version of SALTMOD. Despite that it being latest version and is open computer program can be downloaded freely from www.waterlog.info it is less used so far since it has been opened for free to the public in 2005. Up to now only two works are done using this model. Its unpopularity might be lack of awareness however it is worth mentioning the following two basic limitations the model has. Namely the output data presentation is not in map form (also mentioned in the user manual) and model works only for samples collected from grids of either horizontal or vertically aligned.

As it has been mentioned in the above the model approximates very well the surfaces parameters soil salinity and groundwater depth hence, discussing the limitation in detail could motive others either to upgrade it or use it by knowing its limitation before hand. Is not said that and is always true too “well known problem is half done”.

5.6.1. Out put data presentation

The output is given for each season of any year during any number of years, as specified with the input data. What makes the program so attractive is it tries to address a number of problems related to hydrological and salinity aspects. But the input data are filed in the form of tables manually and almost all of the output data generated are text format with CSV function to take them to excel sheet. In spite it only accepts and represents grid data with know location in geographical space it does neither accept nor generate map which is highly required in GIS environment. The programmer, Oosterbaan(2005), have confirmed this by putting the following phrase in the user manual “the program offers only a limited number of standard graphics”.

These outputs of the model are either time data per polygon or polygon data preseason. Thus based on the selection of the user the graph types vary from segment to zigzag lines, refer figure 39 A. This figure 40 represents an output data graph generated for polygons per season which is segment type. While figures that represent time output data per polygon, or output data in text and tabular form refer in the appendix figures 14 through 17 respectively.

103

A

Figure 38 Output data type generated by the model

5.6.2. Grid alignment The model requires from grid wise collected and geographically well defined an input data. Thus by taking the coordinates of these sampling points in the forms of centimetre unit length and scale of the map together it represents both the output and the nodal network to the space they are geo-referenced for. However this gridding or network facility works only for either horizontally or vertically aligned grids, not to this study type, which is aligned about 450 from north.

This problem was discovered after data collection. So during data input a serious problem was encountered. By that time it was must to approach and consult the program. Even the help functions of the model are so limited for few functions too, and the user manual is attached with the model is not updated with the model form MSDOS to windows and it was so hard to understand windows’ program using manuals prepared for MSDOS. Even the response from the programmer by that time has not paid off for he needs time to reprogram the model again.

Thus finally the problem was solved in GIS environment through the data frame tools function facilities of ArcGIS 9.3. That is the grid was rotated by 3200, to produce 1:30,000 scale map (here to make the area outside the grid the scale is not kept) and printing only but the analysis is carried out using the original alignment of the map. For clarity refer figures 6 and figure 42 that represent the grid network before and after rotation respectively.

104 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Figure 39 Grid sampling and internal nodal network representation of the model

In both figures the black points are represents the internal node network of the model to which the final output is generated and this equal to the refer figure in appendix 14. Here it is important to note that out of the 72 samples collected from 24 observation grid points. At early time of data entry the four corner data are rejected for they don’t have relation with the internal polygon. Second the rest other 12 polygons were excluded during output generation for their role is boundary condition only. Thus output data generated is reduced to only most central 8 internal nodes. As a result it was challenging to bring the output and extrapolate so as to represent the study area in general. 1. Attempts in the GIS environment was not attractive because being influenced by the gently flat to flat topography there is no enough difference in landuse, slope and DEM property thus for the similarity signify most of the eight points half of them lie in one category and the rest polygons of intersection remain with no data. 2. To use geo-statistics there are no enough output data required by geo-statistical extrapolation techniques.

5.6.3. Other General limitations 1. Input parameters which are not easily measured in the field. A. Both horizontal and vertical hydraulic conductivity between each polygon both in the aquifer and transition zone. B. Similarly the hydraulic resistance or hydraulic conductivity of semi confined layer. C. Leaching efficiency of each soil strata. D. Deep percolation loss from canals (assume open canals like the project case). E. Storage efficiency of each polygon 2. Parameters that are susceptible to subjectivity A. Under the absence of borehole data fixing the depth of each soil strata are more subjective and uncertain. B. For reasons of not easily availability measuring gauges for every project the seasonal surface inflow-outflow and surface drainage are more subjective too.

105

3. Use of Area fraction of cropping pattern [with only two crops] In case the proposed cropping patterns have only two crops the area fraction is not fixable to 100% area coverage because the program is fixed to be less than 1. Assume you have area A and B covered by crops C and D. Then according to the model the area cover ration by the two crops must be A + B < 1. And this remained fraction is expected for fallow land. If the fallow area is not treated this remained area fraction from water balance point of view especially in big catchment areas it reduces significant volume of water. 4. Use of seasonal rainfall To be in the safest sides as well for all rainfall received by a catchment is not stored and ready available to crops, usually it is the effective part of rainfall is used during crop water computation. However, the model uses monthly mean of rainfall and thus the model adds extra water which is not used by the crops. Hence, in case this water balance is used for crop water requirement budget the proposed crops might die of water deficit. Especially this problem could be so serious in arid areas.

5.7. Conclusion

The ultimate goal of this research is to model the spatial and temporal progress of soil salinization in the study area. To reach to this goal there were earlier satiated research questions and objectives that lead to this end. And these questions deal with successes full calibration, sensitivity analysis, validation and prediction the model. Thus by calibrating and analysing its sensitivity and validating the model successfully a prediction was made. And through this prediction questions related to identification of geomorphologic units which are prone to salinization and the salinity rate was also achieved. From the five year based salinity prediction made for the three soil strata/root zone , transition zone and aquifer zone the model have able to model the change of salinization both in space and in time.

As a matter of fact the basic factor for salinization of the study area is rise of groundwater table which is saline in its chemistry. And the biophysical factors that aggravate this are uncontrolled irrigation practise (that is irrigation water application is inefficient). There is no irrigation scheduling and canal maintenance works. Thus water simply moves from one plot to the other regardless that recipient plot requires water or not. This poor water use efficiency leads the valley to be inundated almost through out the wet season. As a result of which the saline water table is recharged and starts to rise,

Similarly near the farming plot where there are leaks and perennial rivers there are also traditional salt making ruminants on which farmers make salt during the dry season. And this is expected to give rise for Aeolian salt type even though the quantity could be less enough.

Like wise the prevalence of hot climate drying the dry season is the main cause of higher evapotranspiration (refer the climate of the area) and hence rise of groundwater table to occur. The sum total of all by taking the area ratio of the geomorphologic units affected by salinity of each rating class an average area which is yearly affected by salinity was estimated to be 20% per year in

106 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

each unit. However two to three units are found to have an annual rate of 25%, for farther information refer 5.4.5 in these special area are taught to be the severely affected geomorphic units.

5.8. Recommendation

5.8.1. Soil salinity hazard related

Most researches works carried out in the area agree in that the salinity of the area is being aggravated by the change in landuse. Means the changed landuse has resulted in a decline in the nutrient status of these soils which has been large due to declining soil organic mater. And this negative impact does not stop in places. It is life system that keeps moving within or out of the landscape it has been occurred. As a result of which part of the landscape will be affected by deficit and the other by surplus. In this regard it is possible to exemplify the up Lander and lowlander situation of chemical erosion conditions. This shows system dynamics in the agricultural sphere is not localized to in-situ condition only.

It is true that a system is a successful integration and functionality of different components at a time. And affecting in one or the other part of a system it is difficult to restore to its original natural dynamics, at least it demands holistic approach, time and energy consuming effort. The natural way to heal nature is to go back to innocence. As the “industrialization” have eaten away all the natural forests afforesting must restore the system. But this is, time and resources consuming which is always beyond the capacity of poor farmers.

However, agronomic packages that improve the soil property such as using organic manure and growing of salt resistance crop Varity could help. Other limitation observed from the farmers’ interview is the farmers used to grow rice every year in a given plot of land. And this has two negative impacts to the soil. First it impoverish specific soil nutrients in the soil system and second the practices build up pests and diseases thereby limits crop development and diminishes addition of organic matter to the soil which can improve the ECE of the soil. Therefore, adoption of rational crop rotation that goes with the food habit and marketability should be adopted.

Above all from the model result it becomes clear that soil salinity of the area is due to the rise of groundwater table which is saline in its chemical composition. Inline to this the sensitivity analysis part of the model have also depicted that an installation of drainage network in the area will reduces soil salinity from four up to five times than the present salinity in the area. Thus the installation of drainage network, even it can be deep surfaces ditch cutting (this is to minimize cost) should be adopted.

107

5.8.2. Model related for future users

The model is not suit to topographic feature which are perfectly plain that have on abutments (hilly topography in two ends). That is as the basic principle behind the model is surfaces and subsurface movement of water (saline groundwater table). And in plain areas where the surface movement of water is hampered could have difficulty with the model result. It is a pity that most of saline areas are found in almost flat topography. But this should not be misquoted and lead to a wrong generalization that say the model does not work with salinity detection at all. However, I am trying to alert future users to pay attention in area selection. That is what ever the flat the study area is what the model requires is the existence of abutments in both ends being valley between them. Thus area selection should be given due focus.

Further more the model requires data collected in grid format and this grid should not be at an angle for the model only accepts either vertically or horizontally aligned grid data. Future users of the model should pay attention to the data reduction of the model. That is the model does not as much out put as it have been given. So the data collection quantity/ density should be by paying attention the internal node size. That is the external polygons are ignored in the output for they are there for boundary conditions reasons. There should be a balance between the size to be studied and the size of the internal node. The good quality of the model lies on integrating different factors that have influences in salinity development. To mention some the factors it incorporates:-

1. Crop impact (through crop ration factor), 2. Crop production management( through farmers responses) 3. Different irrigation practise, such as pressurised and gravity all run at the same the time (Through the water well, seepage and drainage water use) input parameters.

So if different types of irrigation are practised in the study area it is good to collect them to see which factor of salinity have much impact than the others. In case of mixed irrigation water in the course of the time and the subsequent impact on the soil and ground water salinity, this again influences the salt concentration of the drain and well water. But the model can treat these influences by varying the fraction of used in the drain or well water inputs. And this helps in detecting the primary sources of irrigation which thereby helps in prioritizing of rehabilitation programs to tackle salinity.

Final remark The time spent in modelling was significant because of the problems faced from data entry up to extrapolating the output of the model prediction. So to accomplishes this work reference materials are by far mandatory however the model new it is there is no any references materials to use as a references to read or adopt techniques and methods how others they use. For this reason the change detection part is not work than giving a highlight. Above all Change detection is a big subject by itself which needs a research. Thus at the end of this I recommend a change detection research to be carried out in the area especially using this

108 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

model for the following reasons. First as it can be seen in figure 22 which shows the 3D of the study area enough grid size bigger than size can be created and detail work can be conducted. Second there are two year hydrological salinity modelling (this inclusive) data available for the area which can be enough for thesis work. Third as it has been highlighted by this study the change of salinity (process of salinization) in the area is fast so it needs monitoring.

109

Reference

(DEDP), D.o.E.D., 1997. Establishment of a hydrogeological model for the mangement of salt contamination in the khong-Chi-Mun project area in Northeast Thailand. Progress report IV by Kaen University, Department of Energy Development and promotion, Thailand. A.D. Noble, S. Ruaysoongnern, F.W.T. Penning de Vries, C. Hartmann and Webb, M.J., 2004. Enhancing the Agronomic Productivity of Degraded Soils in Northeast Thailand through Clay-based Interventions. Water and Agriculture, ACIAR Proceedings No. 116: 147-160. A.Srinivasulu, CH. Sujanirao, G.V.Lakshmi, T.V. Satyanarayana and J.Boonstra, 2004. Model studies on salt and water balances at konanki pilot area, Andhra Paradesh, India. Irrigation and drainage Systems, 18: 1-17. Akramhanov, A., 2005. spatial Distribution of soil salinity: Detection and prediction. Ande, O.T., Adediran J. A., Ayoola, O.T. and Akinlosotu, T.A., 2008 Effects of land quality, management and cropping systems on cassava production in southern western Nigeria. African Journal of Biotechnology, 7 (14): 2368-2374. Anderson M. P. and Woessner W. W., 1992. Applied groundwater modeling : simulation of flow and advective transport. Academic Press, San Diego etc., 381 pp. Ayers R.S. and D.W., W., 1994. Water quality for agriculture. FAO for Irrigation and Drainge Paper, 29. Bauder, T.A., Waskom R.M. and G., D.J., 2008. Irrigation Water Quality Criteria. Colorado State University, 506. Bell R.W. and Seng, V., 2004. Rainfed lowland rice-growing soils of Cambodia,Laos, and North-east Thailand. Water in agriculture ACIAR Proceedings No. 116e. Breierova, L. and Choudhari, M., 2001. An Introduction to Sensitivity Analysis. Massachusetts Institute of Technology, 2(D-4526): 41-107. Castrignano, A., Katerji N. and Mastrorilli M. , 2002. Modelling crop response to soil salinity: review and proposal of a new approach, in Mediterranean crop responses to water and soil salinity. eco-physiological and agronomic analysis (Options méditerranéennes Série B Number 36) , Lavoisier 2000-2008. D.R.Williamson, A.J.Peck, J.V.Turner and S.Arunin, 1989. Groundwater hydrology and Salinity in a valley in Northeast Thailand. DAF, 2006. Salinity measures, units and classes. Department of Agriculture and Food western Australia(DAF). Dhyani, S.K., Narain, P. and K., S.R., 1990. Studies on root distribution of five multipurpose tree species in Doon Valley, India. Agroforestry systems. DNR, 2000. Condamine_balonne WAMP Environmental Flows Techincal Report. Department of Natural Resources Australian Queensland Gobernment website, Brisbane(163pp). Donald J. DePaolo, Mark E. Conrad, Katharine Maher and Gee, G.W., 2004. Evaporation Effects on Oxygen and Hydrogen Isotopes in Deep Vadose Zone Pore Fluids at Hanford, Washington. Vadose Zone Journal(3): 220 - 232. . Eldiery, A., A.Garcia, L. and Reich, R.M., 2005. Estimating soil salinity from remote sensing data in corn fields. Hydrology days, Colorado state univesity. Eswaran, H., Lal, R. and Reich, P.F., 2001. Land degradation: An overview. In Response to Land Degradation, E.M. Bridges, I.D. Hannam, L.R. Oldeman, F.W.T.P. de Vries, S.J. Scherr, and S. Sombatpanit (Eds.),Oxford and IBH Publishing, New Delhi=Calcutta, India. Farifteh, J., Farshad, A. and George, R.J., 2006. Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma, 130(3-4): 191-206. Farifteh, J., van der Meer, F.D., de Jong, S.M., van der Meijde, M. and Carranza, E.J.M., 2007. Imaging spectroscopy of salt - affected soils : model - based integrated method, ITC, Enschede, 215 pp. Field, A., 2005. Discovering statistics using SPSS. Sage, London etc., 779 pp.

110 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Ghassemi, F., Jakeman, A.J. and Nix, H.A., 1995. Salinisation of land and water resources : human causes, extent, management and case studies. The Australian National University ; CAB International, Canberra ; Wallingford Oxon, 526 pp. Ghassemi F., Jakeman A.J. and Nix H.A., 1991. Human induced salinisation and the use of quantitative methods. . Environment International, 17(6): p. 581-594. Greiner, R., 1997a. Optimal farm management responses to emerging soil salinisation in a dryland catchment in eastern Australia. Land Degradation & Development, 8(4): 281-303. Greiner, R., 1997b. Optimal farm management responses to emerging soil salinisation in a dryland catchment in eastern Australia. . Land Degradation & Development, 8(4): 281-303. Grunwald S. and Norton L.D., 2000. Calibration and validation of a non-point source pollution model. Agricultural Water Management, 45: 17-39. Hengl, T., 2001. A Practical Guide to Geostatistical Mapping of Environmental Variables, 2007. Luxembourg: Office for Official Publications of the European Communities, Italy. Hidenori, W., Pichai, W. and Paiboon, P., 1994. Salt Affected Area in Northeast Thailand Nature, Properties and Management. Technical Paper No 15. Idris Bacheci and Suat Nacara, A., 2007. Estimation of root zone salinity, using SaltMod, in the arid region of Turkey. Irrigation and Drainage, 56(5): 601-614. J.P.Montoroi, 2006. Ground Geophysics applied to salt-affected soils of Northeastern Thailand. Geophysical research 8, 06026(SRef_ID: 1607-7962/gra/EGU06-A-06026). Kenneth K. Tanji and Kielen, N.C., 2003. Agricultural Drainage Water Management in Arid and Semi-Arid Areas. FAO IRRIGATION AND DRAINAGE PAPER 61. Kupper, E., Querner, E.P., Morábito, J.A. and Menenti, M., 2002. Using the SIMGRO regional hydrological model to evaluate salinity control measures in an irrigation area. Agricultural Water Management, 56(1): 1-15. Lambert, M. and Turner, J., 2000. Commerical Forest Plantations on Saline Lands. 216. landon, J.R., 1991a. Booker Tropical Soil Manual. book(A handbook for soil survey and agricultural land evaluation in the tropics ans subrtopics). Landon, J.R.e., 1991b. Booker tropical soil manual : a handbook for soil survey and agricultural land evaluation in the tropics and subtropics. Longman Scientific & Technical ; Booker Tate, Harlow ; Thame, 474 pp. Last R., e.a., 2003. Bio-Economic Modelling towards Optimising Land Use and Salinity Management in South-Eastern NSW and North-Eastern Thailand. in Salinity under the sun - investing in prevention and rehabilitation of salinity in Australia. . Queensland Department of Natural Resources and Mine, Australia: . LDD, 1994. Field note of the study of ground water flow by piezometer in Kamtaleso Nakornratchasima. Land Development Department, Ministry of agriculture and coopperative. Macal, C.M., 2005. Model Verification and Validation. The University of Chicago and Argonne National Laboratory, Workshop on "Threat Anticipation: Social Science Methods and Models". Madyaka, M., 2008. Spatial modelling and prediction of soil salinization using Saltmod in a GIS environment, ITC, Enschede, 128 pp. Man Sing, A.K. Bhattacharya, Singh, A.K. and Singh, A., 2002. Application of SALTMOD in coastal clay soil in India. Irrigation and Drainage Systems 16: 213 - 231. Metternicht, G.I. and Zinck, J.A., 2003. Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment, 85(1): 1-20. NIST, 2006. SEMATECH e-Handbook of Statistical Methods. http://www.itl.nist.gov/div898/handbook. Noble, A.D., Suzuki, S., Soda, W., Ruaysoongnern, S. and Berthelsen, S., 2008. Soil acidification and carbon storage in fertilized pastures of Northeast Thailand. Geoderma, 144(1-2): 248-255. Oosterbaan, R.J., 1992. in Farmers fields: Analysis of Data On website. Oosterbaan, R.J., 2002a. Drainge Research in Farmers' Fields: Analysis of Data. Oosterbaan, R.J., 2002b. SALTMOD: Description of Principles, User Manual, and Examples of Application, Wageningen, The Netherlands.

111

Oosterbaan, R.J., 2005. Description of Principles, User Manual and Case Studies. 181. Oosterbaan, R.J. and Abu Senna, M., 1990. Drainage and salinity predictions in the Nile Delta, Using SALTMOD. Drainage Research Institute for Land Reclamation and Improvement, , Wageningen, Netherlands. Paniconi, C., Khlaifi, I., Lecca, G., Giacomelli, A. and Tarhouni, J., 2001. A Modelling study of seawater intrusion in the Korba coastal plain, Tunisia. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(4): 345-351. Pessaraki, M., 1999. Handbook of plant and crop stress second edition: 1254. Pramojanee., P., 1982. Study of the relationship between salt affected soils and landforms in Amphoe Kam Sakae Saeng area, Nakorn Ratchasima province, Thailand. Natural Resource Management. Enschede, ITC. MSc: 155. Prior L. D., Grieve A. M., Slavich P. G. and R., C.B., 1992. Sodium chloride and soil texture interactions in irrigated field grown sultana grapevines. III. Soil and root system effects. Australian Journal of Agricultural Research, 43: 1085–1100. Rengasamy, P., 2006. World salinization with emphasis on Australia. Experimental Botany, 57(5): 1017-1023. Rushton, K.R., 2003. Groundwater Hydrology: Conceptual and Momputational Models. Salhotra A. M., Adams E. E. and F., H.D.R., 1987. The alpha, beta, gamma of evaporation from saline water bodies. WATER RESOURCES RESEARCH, 23: 1769-1774. Sargent, R.G., 1998. VERIFICATION AND VALIDATION OF SIMULATION MODELS. College of Engineering and Computer Science Syracuse University, New York 13244, U.S.A.(Proceedings of the 1998 Winter Simulation Conference). Satarugsa P., Youngmee W. and S., M., 2005. New Regional Boundary of Maha Sarakham Formation in the Northeastern Thailand: Results from 2D Seismic Mapping. GEOINDO 2005(International Conference on Geology, Geotechnology and Mineral Resources of Indochina). Shrestha, D.P. and Farshad, A., 2009. Mapping Salinity Hazard: An integrated Application of Remote Sensing and Modeling-Based Techniques. In: A.J. Zinck and G. Metternich (Editors), Remote sensing of soil salinization : impact on land management. CRC Press, Boca Raton, pp. 374. Shrestha, D.P., Margate, D.E., van der Meer, F.D. and Anh, H.V., 2005a. Analysis and classification of hyperspectral data for mapping land degradation : an application in southern Spain. International journal of applied earth observation and geoinformation : JAG, 7(2). Shrestha, D.P., Soliman, A.S. and Farshad, A., 2005b. Salinity mapping using geopedologic and soil line approach. In: ACRS 2005 : proceedings of the 26th Asian conference on remote sensing, ACRS 2005, 7-11 November 2005, Hanoi, Vietnam. Hanoi : Asian Association on Remote Sensing (AARS), Geoinformatics Center, Asian Institute of Technology, 2005. 6 p. Shrestha, R.P., 2006. Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand. Land Degradation & Development, 17(6): 677-689. Silberstein, R.P., Vertessy, R.A., Morris, J. and Feikema, P.M., 1999. Modelling the effects of soil moisture and solute conditions on long-term tree growth and water use: a case study from the Shepparton irrigation area, Australia. Agricultural Water Management, 39(2-3): 283-315. Slavich, P.G. and Petterson, G.H., 1993. Estimating the electrical conductivity of saturated paste extracts from 1:5 soil, water suspensions and texture. Australian Journal of Soil Research, 31(1): 73-81. Smith M. et al., 1996. Irrigation Scheduling: From to Practice - Proceedings M-56. FAO - Food and agriculture Organization of the United Nations. Soliman, A.S., 2004. Detecting salinity in early stages using electromagnetic survey and multivariate geostatistical techniques : a case study of Nong Suang district, Nakhon Ratchasima, Thailand. Unpublished MSc thesis, Thesis, Unpublished MSc thesis, ITC, Enschede, 90 pp. Soliman, A.S., Farshad, A., Sporry, R.J. and Shrestha, D.P., 2004. Predicting salinization in its early stage, using electro magnetic data and geostatistical techniques : a case study of Nong Suang district, Nakhon Ratchasima, Thailand. In: ACRS 2004 : Proceedings of the 25th Asian

112 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

conference on remote sensing, ACRS 2004 Silver jubilee: November 22-26, 2004, Chiang Mai, Thailand. Chiang Mai : Geo-Informatics and Space Technology Development Agency (GISTDA), 2004. pp. 847-852. Sorman, A.U. and Abdulrazzak, M.J., 1995. Estimation of actual evaporation using precipitation and soil moisture records in arid climates. Hydrological Processes, 9(7): 729-741. Steve Deverel et al., 2005. Groundwater Evaporation Estimates Using Stable Isotope and Chloride Data, Yelland Playa, Valley, Nevada. Science Environment Solutions. Sukchan, S., 2003. Land suitability analysis concerning water resources and soil property. Land Development Department, Khon Kaen. . Wada, H., Wichaidit, P. and Pramojanee, P., 1994. Salt Affected Area in Northeast Thailand Nature, Properties and Managment. Technical paper No. 15. Wada H., Wichaidit P. and P., P., 1994. Salt Affected Area in Northeast Thailand Nature, Properties and Managment. Technical paper No. 15. Wada Hidenori, Wichanidit Pichai and Pramojanee Paiboon, 1994. Salt Affected Area in Northeast Thailand Nature, Properties and Management. Technical Paper No 15. Wang, H., Hsieh, Y.P., Harwell, M.A. and Huang, W., 2007. Modeling soil salinity distribution along topographic gradients in tidal salt marshes in Atlantic and Gulf coastal regions. Ecological Modelling, 201(3-4): 429-439. Wannakomol, A., 2005. Soil and Groundwater Salinization Problems in the Khorat Plateau, NE Thailand - Integrated Study of Remote Sensing, Geophysical and Field Data. PHD Dissertation(Angefertigt mit Genehmigung des Fachbereiches Geowissenschaften der Freien Universität Berlin). Ward, R., 1999. Soil analysis handbook of reference methods. Crc press: 247. Wild, A., 2003. Soils, Land and Food: Managing the Land during the Twenty First Century press syndicate of the university of cambridge. Wongpokhom, N., Kheoruenromne, I., Suddhiprakarn, A. and Gilkes, R.J., 2008. Micromorphological properties of salt affected soils in Northeast Thailand. Geoderma, 144(1-2): 158-170. Xu, P. and Shao, Y., 2002a. salt-transport model within a land-surface scheme for studies of salinisation in irrigated areas. Environmental Modelling & Software, 17(1): 39-49. Xu, P. and Shao, Y., 2002b. A salt-transport model within a land-surface scheme for studies of salinisation in irrigated areas. Environmental Modelling & Software, 17(1): 39-49. Yadav, R.D., 2005. Modeling salinity affects in relation to soil fertility and crop yield : a case study of Nakhon Ratchasima, Nong Suang district, Thailand, ITC, Enschede, 150 pp. YARDLEY, B.W.D., BENNETT, A.J. and BANKS, D.A., 2004. Role of Water in Controlling Metal Concentration in the Crust. School of Earth Sciences, University of Leeds, Leeds LS2 9JT, UK.

113

Appendix 1 Input data used for model calibration

ID X-COOR Y-COOR Elevation soil EC1:5 (mS) EC1:5 (mS) EC1:5 (mS) Ece (dS/m) Ece (dS/m) Ece (dS/m) Ecfc(dS/m) Ecfc(dS/m) Ecfc (dS/m) metere testure Root Zone Transtion Zo Aquifer Root Zone Transtion Zo Aquifer Root Zone Transtion Zo Aquifer Zo 1 810648 1665803 197 sandy loam 0.02 0.02 0.04 0.45 0.45 0.91 0.908 0.908 1.816 2 811502 1666532 190 sandy loam 0.04 0.04 0.03 0.91 0.91 0.68 1.816 1.816 1.362 3 812281 1667079 186 sandy clay loam 0.16 0.05 0.14 3.63 1.14 3.18 7.264 2.27 6.356 4 813077 1667800 176 loam 9.25 8.13 1.65 87.88 77.24 15.68 175.75 154.47 31.35 5 810026 1666524 188 sandy loam 0.01 0.02 0.11 0.23 0.45 2.50 0.454 0.908 4.994 6 810814 1667237 193 sandy clay loam 0.05 0.07 0.06 1.14 1.59 1.36 2.27 3.178 2.724 7 811634 1667834 182 loam 0.59 1.05 1.14 5.61 9.98 10.83 11.21 19.95 21.66 8 812455 1668389 172 loam 0.21 0.34 0.60 2.00 3.23 5.70 3.99 6.46 11.4 9 811336 1665139 197 sandy loam 0.43 0.04 0.01 9.76 0.91 0.23 19.522 1.816 0.454 10 812148 1665769 189 sandy loam 0.04 0.02 0.02 0.91 0.45 0.45 1.816 0.908 0.908 11 812944 1666358 172 sandy loam 0.98 0.56 0.43 22.25 12.71 9.76 44.492 25.424 19.522 12 813765 1667046 179 sandy loam 0.03 0.03 0.34 0.68 0.68 7.72 1.362 1.362 15.436 13 809819 1665148 209 sandy loam 0.11 0.07 0.09 2.50 1.59 2.04 4.994 3.178 4.086 14 813997 1668356 198 sandy loam 0.03 0.02 0.04 0.68 0.45 0.91 1.362 0.908 1.816 15 809172 1665919 203 sandy loam 0.01 0.02 0.02 0.23 0.45 0.45 0.454 0.908 0.908 16 813234 1669210 197 sandy loam 0.11 0.10 0.08 2.50 2.27 1.82 4.994 4.54 3.632 17 809462 1667311 199 sandy clay loam 0.01 0.01 0.02 0.23 0.23 0.45 0.454 0.454 0.908 18 810250 1667842 192 sandy clay loam 0.02 0.01 0.02 0.45 0.23 0.45 0.908 0.454 0.908 19 811037 1668488 183 loam 0.03 0.06 0.43 0.29 0.57 4.09 0.57 1.14 8.17 20 811825 1669143 182 sandy loam 0.01 0.03 0.02 0.23 0.68 0.45 0.454 1.362 0.908 21 812679 1669815 200 sandy loam 0.12 0.05 0.04 2.72 1.14 0.91 5.448 2.27 1.816 22 814652 1667668 188 sandy loam 0.01 0.02 0.02 0.23 0.45 0.45 0.454 0.908 0.908 23 810515 1664451 208 sandy loam 0.02 0.04 0.04 0.45 0.91 0.91 0.908 1.816 1.816 24 808576 1666549 203 sandy loam 0.01 0.02 0.03 0.23 0.45 0.68 0.454 0.908 1.362

Appendix 2 Input data used for model validation from laboratory analysis

ID N E GPU Landscape Relief Texture EC1:5 0-30 Ece_textue Ece 0_30 ECFc30 1 806456 1670389 Pe113 Peneplain Ridge Loamy Sand 0.02 0.45 0.28 0.91 2 803776 1675096 Va111 Valley Flood Plain Sandy loam 0.06 0.83 0.76 1.66 3 816657 1665475 Pe112 Peneplain Ridge loamy sand 0.13 2.95 15.59 5.90 4 812558 1665464 Pe211 Peneplain Ridge Sandy loam 3.54 48.85 1.81 97.70 5 808420 1662252 Pe113 Peneplain Ridge Sandy loam 0.02 0.28 0.14 0.55 6 813614 1675646 Va111 Valley Flood Plain Sandy loam 0.06 0.83 0.91 1.66 7 817155 1661822 Pe113 Peneplain Ridge Sandy loam 0.55 7.59 1.93 15.18 8 805838 1659510 Pe211 Peneplain Ridge Sandy loam 0.15 2.07 0.10 4.14 9 807338 1674695 Pe413 Peneplain Lateral vale Sandy clay loam 0.08 0.76 3.04 1.52 10 816014 1663150 Pe411 Peneplain Lateral vale Sand clay loam 0.19 1.81 0.45 3.61 11 810669 1657965 Pe113 Peneplain Ridge VG_clay loam 0.60 8.28 0.83 16.56 12 818218 1672585 Pe311 Peneplain Ridge loam 1.95 18.53 0.14 37.05 13 816920 1668104 Pe114 Peneplain Ridge Sandy loam 1.54 21.25 48.85 42.50 14 817627 1675366 Pe311 Peneplain Ridge loam 3.15 29.93 2.95 59.85 15 809196 1674661 Pe114 Peneplain Ridge Loamy Sand 1.87 42.45 0.23 84.90 16 809789 1672642 Pe111 PenePlain Ridge VG_clay loam 0.03 0.41 0.83 0.83 17 804898 1663795 Pe211 Peneplain Ridge loam 0.02 0.19 0.14 0.38 18 804109 1660311 Pe211 Peneplain Ridge loam 0.02 0.19 0.26 0.38 19 811344 1669609 Pe211 Peneplain Ridge Sandy loam 0.01 0.14 29.93 0.28 20 815303 1660688 Pe115 Peneplain Ridge Sandy loam 0.01 0.14 0.19 0.28 21 812748 1667465 Pe115 Peneplain Ridge Sandy loam 0.01 0.14 0.19 0.28 22 813503 1673304 Pe413 Peneplain Lateral vale Sand clay loam 0.01 0.10 7.59 0.19 23 807705 1666017 Pe113 Peneplain Ridge Sandy clay loam 0.01 0.10 21.25 0.19 24 818263 1673303 Pe112 Peneplain Ridge Sandy loam 0.01 0.14 0.14 0.28 25 812516 1669640 Pe112 Peneplain Ridge Sandy loam 0.01 0.14 42.45 0.28

114 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Appendix 3 part of input data used in model calibration used for PRIDICTION20.INP ********************************************************* Internal system geometry FROM PREDICTION20: Node SL Dr Dx Ksc 1 197.000 0.900 3.000 0 2 190.000 0.900 3.000 0 3 186.000 0.900 3.000 0 4 176.000 0.900 3.000 0 5 188.000 0.900 3.000 0 6 193.000 0.900 3.000 0 7 182.000 0.900 3.000 0 8 172.000 0.900 3.000 0 ******************************************************** Hydraulic_conductivity FROM PREDICTION20: From_Node to_Node K hori K top K vert D top 1 5 3.000 n.a. n.a. n.a. 2 3.000 n.a. n.a. n.a. 9 3.000 n.a. n.a. n.a. 13 3.000 n.a. n.a. n.a. 2 6 2.000 n.a. n.a. n.a. 3 2.000 n.a. n.a. n.a. 10 2.500 n.a. n.a. n.a. 1 3.000 n.a. n.a. n.a. 3 7 2.000 n.a. n.a. n.a. 4 2.000 n.a. n.a. n.a. 11 2.000 n.a. n.a. n.a. 2 2.000 n.a. n.a. n.a. 4 8 1.000 n.a. n.a. n.a. 14 1.000 n.a. n.a. n.a. 12 1.000 n.a. n.a. n.a. 3 1.000 n.a. n.a. n.a. 5 17 1.500 n.a. n.a. n.a. 6 1.500 n.a. n.a. n.a. 1 2.000 n.a. n.a. n.a. 15 2.000 n.a. n.a. n.a. 6 18 1.000 n.a. n.a. n.a. 7 1.000 n.a. n.a. n.a. 2 1.000 n.a. n.a. n.a. 5 1.000 n.a. n.a. n.a. 7 19 1.500 n.a. n.a. n.a. 8 1.500 n.a. n.a. n.a. 3 2.000 n.a. n.a. n.a. 6 4.000 n.a. n.a. n.a. 8 20 4.000 n.a. n.a. n.a. 16 4.000 n.a. n.a. n.a. 4 1.500 n.a. n.a. n.a. 7 1.500 n.a. n.a. n.a. ***************************************************************** Total_porosity FROM PREDITION20: Node Ptr Ptx Ptq 1 0.800 0.800 0.800 2 0.800 0.800 0.800 3 0.800 0.800 0.800 4 0.800 0.800 0.800 5 0.800 0.800 0.800 6 0.800 0.800 0.800 7 0.800 0.800 0.800 8 0.800 0.800 0.800 **************************************************************** Effective_porosity/storage_coefficient FROM PREDICTION20: Node Per Pex Peq Psq 1 0.010 0.010 0.010 n.a. 2 0.010 0.010 0.010 n.a. 3 0.010 0.010 0.010 n.a. 4 0.010 0.010 0.010 n.a.

115

5 0.010 0.010 0.010 n.a. 6 0.010 0.010 0.010 n.a. 7 0.010 0.010 0.010 n.a. 8 0.010 0.010 0.010 n.a. *************************************************************** Boundary_conditions FROM PREDICTION20: Node Cq0 H(s=1) H(s=2) 9 0.450 196.00 195.00 10 0.910 188.00 187.00 11 19.520 171.00 170.00 12 15.440 178.00 177.00 13 4.100 208.00 207.00 14 1.820 197.00 196.00 15 0.910 202.00 201.00 16 3.630 196.00 195.00 17 0.910 196.00 195.00

18 0.910 191.00 190.00 19 8.170 182.00 181.00 20 0.910 181.00 180.00

Appendix 4 internal system geometry (k i/e determines internal and external boundary)

116 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Appendix 5 Year average model prediction salinity for the three soil strata

Appendix 1 Predicted root zone salinity (dS/m)

Poly_ID Obsereved Year_0 Year_5 Year_10 Year_15 Year_20 (dS/m) average average average average average 1 0.91 0.15 1.42 5.47 10.75 16.70 2 1.82 1.51 3.28 6.58 10.00 13.40 3 7.26 6.03 1.48 0.42 0.19 0.13 4 175.75 146.00 40.35 30.05 27.85 26.65 5 0.45 0.07 4.58 4.55 4.44 4.32 6 2.27 0.36 0.17 0.13 0.13 0.12 7 11.21 9.30 20.50 19.70 18.75 17.85 8 3.99 3.31 10.45 9.82 9.24 8.71

Appendix 1 Predicted transtion zone salinity (dS/m)

Poly_ID Obse Year_0 Year_5 Year_10 Year_15 Year_20 (dS/m) average average average average average 1 0.91 0.91 1.34 1.87 2.19 2.41 2 1.82 1.82 1.70 1.59 1.56 1.57 3 2.27 2.27 2.53 1.90 1.31 0.89 4 154.47 154.00 37.30 29.45 27.60 26.45 5 0.91 0.91 4.60 4.53 4.41 4.29 6 3.18 3.18 2.13 1.44 1.00 0.72 7 19.95 20.00 20.40 19.55 18.55 17.65 8 6.46 6.46 10.35 9.70 9.12 8.61

Appendix 1 Predicted aquifer zone salinity (dS/m)

Polygon Obsereved Year_0 Year_5 Year_10 Year_15 Year_20 ID (dS/m) average average average average average 1 1.82 1.82 1.99 2.15 2.30 2.43 2 1.36 1.36 1.39 1.44 1.49 1.54 3 6.36 6.36 6.38 6.43 6.50 6.55 4 31.35 31.40 29.90 28.65 27.65 26.60 5 4.99 4.99 4.73 4.58 4.44 4.32 6 2.72 2.72 2.73 2.74 2.75 2.75 7 21.66 21.70 20.55 19.45 18.55 17.65 8 11.40 11.40 10.45 9.81 9.23 8.71

117

Appendix 6 Seasonal model prediction salinity for root zone soil strata ID E N Elev. ECFC Year 0 Year 5 Year 10 Year 15 Year 20 UTM UTM M [dS/m] Se 1 Se 2 Se 1 Se 2 Se 1 Se 2 Se 1 Se 2 Se 1 Se 2 1 810648 1665803 197.0 0.9 0.1 0.1 1.2 1.6 5.2 5.8 10.4 11.1 16.3 17.1 2 811502 1666532 190.0 1.8 1.5 1.5 3.1 3.4 6.4 6.8 9.8 10.2 13.2 13.6 3 812281 1667079 186.0 7.3 6.0 6.0 1.5 1.4 0.4 0.4 0.2 0.2 0.1 0.1 4 813077 1667800 176.0 175.8 146.0 146.0 41.5 39.2 30.2 29.9 27.9 27.8 26.7 26.6 5 810026 1666524 188.0 0.5 0.1 0.1 4.6 4.6 4.6 4.5 4.4 4.4 4.3 4.3 6 810814 1667237 193.0 2.3 0.4 0.4 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 7 811634 1667834 182.0 11.2 9.3 9.3 20.5 20.5 19.7 19.7 18.8 18.7 17.9 17.8 8 812455 1668389 172.0 4.0 3.3 3.3 10.5 10.4 9.8 9.8 9.3 9.2 8.7 8.7

Appendix 7 Seasonal model prediction salinity for transition soil strata ID E N Elev. ECFC Year 0 Year 5 Year 10 Year 15 Year 20 UTM UTM M [dS/m] Se 1 Se 2 Se 1 Se 2 Se 1 Se 2 Se 1 Se 2 Se 1 Se 2 1 810648.0 1665803.0 197.0 0.9 0.9 0.9 1.3 1.4 1.9 1.9 2.2 2.2 2.4 2.4 2 811502.0 1666532.0 190.0 1.8 1.8 1.8 1.7 1.7 1.6 1.6 1.6 1.6 1.6 1.6 3 812281.0 1667079.0 186.0 2.3 2.3 2.3 2.5 2.5 1.9 1.9 1.3 1.3 0.9 0.9 4 813077.0 1667800.0 176.0 154.5 154.0 154.0 38.2 36.4 29.6 29.3 27.7 27.5 26.5 26.4 5 810026.0 1666524.0 188.0 0.9 0.9 0.9 4.6 4.6 4.5 4.5 4.4 4.4 4.3 4.3 6 810814.0 1667237.0 193.0 3.2 3.2 3.2 2.2 2.1 1.5 1.4 1.0 1.0 0.7 0.7 7 811634.0 1667834.0 182.0 20.0 20.0 20.0 20.4 20.4 19.6 19.5 18.6 18.5 17.7 17.6 8 812455.0 1668389.0 172.0 6.5 6.5 6.5 10.4 10.3 9.7 9.7 9.2 9.1 8.6 8.6

Appendix 8 Seasonal model prediction salinity for transition soil strata

ID E N Elev. ECFC Year 0 Year 5 Year 10 Year 15 Year 20 UTM UTM [Met] [dS/m] Se 1 Se 2 Se 1 Se 2 Se 1 Se 2 Se 1 Se 2 Se 1 Se 2 1 810648.0 1665803.0 197.0 1.8 1.8 1.8 2.0 2.0 2.1 2.2 2.3 2.3 2.4 2.4 2 811502.0 1666532.0 190.0 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.5 1.5 1.5 1.5 3 812281.0 1667079.0 186.0 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.5 6.5 6.5 6.6 4 813077.0 1667800.0 176.0 31.4 31.4 31.4 30.0 29.8 28.7 28.6 27.7 27.6 26.6 26.6 5 810026.0 1666524.0 188.0 5.0 5.0 5.0 4.7 4.7 4.6 4.6 4.5 4.4 4.3 4.3 6 810814.0 1667237.0 193.0 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.8 2.8 2.8 7 811634.0 1667834.0 182.0 21.7 21.7 21.7 20.6 20.5 19.5 19.4 18.6 18.5 17.7 17.6 8 812455.0 1668389.0 172.0 11.4 11.4 11.4 10.5 10.4 9.8 9.8 9.3 9.2 8.7 8.7

118 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Appendix 9 Seasonal model prediction salinity with salinity class ratings

Predicted Root zone salinity (dS/m) year 0 5 10 15 20 polygon Obsereved Se_1 ClassSe_2 Class Se_1 Class Se_2 Class Se_1 Class Se_2 Class Se_1 Class Se_2 Class Se_1 Class Se_2 Class 1 0.908 0.146NS 0.146NS 1.24NS 1.6NS 5.17MS 5.76MS 10.4STS 11.1STS 16.3VSS 17.1 VSS 2 1.816 1.51NS 1.51NS 3.12SLS 3.44SLS 6.39MS 6.77MS 9.8STS 10.2STS 13.2STS 13.6 STS 3 7.264 6.03MS 6.03MS 1.52NS 1.44NS 0.426NS 0.416NS 0.182NS 0.189NS 0.128NS 0.139 NS 4 175.75 146VSS 146VSS 41.5VSS 39.2VSS 30.2VSS 29.9VSS 27.9VSS 27.8VSS 26.7VSS 26.6 VSS

5 0.454 0.072NS 0.072NS 4.57MS 4.58MS 4.55MS 4.54MS 4.44MS 4.43MS 4.32MS 4.31 MS 6 2.27 0.363NS 0.363NS 0.17NS 0.177NS 0.127NS 0.137NS 0.12NS 0.131NS 0.119NS 0.13 NS 7 11.21 9.3STS 9.3STS 20.5VSS 20.5VSS 19.7VSS 19.7VSS 18.8VSS 18.7VSS 17.9VSS 17.8 VSS 8 3.99 3.31SLS 3.31SLS 10.5STS 10.4STS 9.84STS 9.8STS 9.25STS 9.22STS 8.72STS 8.69 STS

Predicted Transtion zone salinity (dS/m) year 0 5 10 15 20 polygon Obsereved Se_1 ClassSe_2 Class Se_1 Class Se_2 Class Se_1 Class Se_2 Class Se_1 Class Se_2 Class Se_1 Class Se_2 Class 1 0.908 0.91NS 0.91NS 1.31NS 1.37NS 1.85NS 1.89NS 2.18SLS 2.2SLS 2.4SLS 2.41 SLS 2 1.816 1.82NS 1.82NS 1.71NS 1.69NS 1.59NS 1.58NS 1.56NS 1.55NS 1.57NS 1.56 NS 3 2.27 2.27SLS 2.27SLS 2.54SLS 2.52SLS 1.91NS 1.88NS 1.32NS 1.29NS 0.893NS 0.878 NS 4 154.47 154VSS 154VSS 38.2VSS 36.4VSS 29.6VSS 29.3VSS 27.7VSS 27.5VSS 26.5VSS 26.4 VSS 5 0.908 0.91NS 0.91NS 4.6MS 4.6MS 4.53MS 4.52MS 4.41MS 4.4MS 4.29MS 4.28 MS 6 3.178 3.18SLS 3.18SLS 2.15SLS 2.1SLS 1.45NS 1.42NS 1.01NS 0.989NS 0.726NS 0.713 NS

7 19.95 20VSS 20VSS 20.4VSS 20.4VSS 19.6VSS 19.5VSS 18.6VSS 18.5VSS 17.7VSS 17.6 VSS 8 6.46 6.46MS 6.46MS 10.4STS 10.3STS 9.73STS 9.66STS 9.15STS 9.09STS 8.63STS 8.58 STS

Predicted Aquifer zone salinity (dS/m) year 0 5 10 15 20 polygon Obsereved Se_1 ClassSe_2 Class Se_1 Class Se_2 Class Se_1 Class Se_2 Class Se_1 Class Se_2 Class Se_1 Class Se_2 Class 1 1.816 1.82NS 1.82NS 1.98NS 2NS 2.14SLS 2.15SLS 2.29SLS 2.3SLS 2.42SLS 2.43 SLS 2 1.362 1.36NS 1.36NS 1.39NS 1.39NS 1.43NS 1.44NS 1.48NS 1.49NS 1.53NS 1.54 NS 3 6.356 6.36MS 6.36MS 6.37MS 6.38MS 6.42MS 6.44MS 6.49MS 6.5MS 6.54MS 6.55 MS 4 31.35 31.4VSS 31.4VSS 30VSS 29.8VSS 28.7VSS 28.6VSS 27.7VSS 27.6VSS 26.6VSS 26.6 VSS 5 4.994 4.99MS 4.99MS 4.74MS 4.72MS 4.58MS 4.57MS 4.45MS 4.43MS 4.32MS 4.31 MS 6 2.724 2.72SLS 2.72SLS 2.73SLS 2.73SLS 2.74SLS 2.74SLS 2.74SLS 2.75SLS 2.75SLS 2.75 SLS

7 21.66 21.7VSS 21.7VSS 20.6VSS 20.5VSS 19.5VSS 19.4VSS 18.6VSS 18.5VSS 17.7VSS 17.6 VSS 8 11.4 11.4STS 11.4STS 10.5STS 10.4STS 9.83STS 9.78STS 9.25STS 9.2STS 8.73STS 8.68 STS

Key 1 NS======Non saline 2 MS======Moderately saline 3 Slightly S======Slightly saline 4 Strongly S===== Strongly saline 5 VSS======Very strongly saline 6 Salinity class based on Abrol I.P., Yadav J.S.P., and M. F.I. 1988. Salt-Affected Soils and their Management. FAO Land and Water Development FAO SOILS BULLETIN 39.

119

Appendix 10 Crop water computation as input for the model from Rice crop for wet season irrigation

ID Date ETo Area Crop CWR Total Effec Irriga. FWS Ratio Kc [ETM] Rain Rain Req

1 1-Apr 61.2 50 0.17 10.71 13.83 11.01 0 0 2 11-Apr 62.74 50 0.17 10.98 15.52 12.37 0 0 3 21-Apr 64.07 50 0.17 11.21 16.52 13.29 0 0 4 1-May 65.15 50 0.23 14.79 16.97 13.84 0.95 0.02 5 11-May 65.97 50 0.32 21.21 17.08 14.14 7.06 0.12 6 21-May 66.51 50 0.42 27.66 17.1 14.32 13.34 0.22 7 31-May 66.78 50 0.51 34.07 17.22 14.5 19.57 0.32 8 10-Jun 66.75 50 0.59 39.42 17.62 14.79 24.63 0.41 9 20-Jun 66.45 50 0.6 39.87 18.44 15.25 24.62 0.41 10 30-Jun 65.87 50 0.6 39.52 19.72 15.92 23.6 0.39 11 10-Jul 65.04 50 0.6 39.03 21.44 16.81 22.21 0.37 12 20-Jul 63.98 50 0.6 38.39 23.5 17.87 20.51 0.34 13 30-Jul 62.7 50 0.59 37.06 25.73 19.01 18.05 0.3 14 9-Aug 61.24 50 0.54 32.89 27.89 20.11 12.78 0.21 15 19-Aug 59.63 50 0.48 28.45 29.71 21.02 7.43 0.12 16 29-Aug 57.91 50 0.42 24.16 30.89 21.57 2.58 0.04 17 8-Sep 56.11 50 0.36 20.04 31.13 21.61 0 0 18 18-Sep 27.37 50 0.31 8.54 15.3 10.62 0 0 19 Total 1105.46 478 375.59 288.06 197.35 [0.19] 20 1-Apr 61.2 50 0.17 10.71 13.83 11.01 0 0 21 11-Apr 62.74 50 0.17 10.98 15.52 12.37 0 0 22 21-Apr 64.07 50 0.17 11.21 16.52 13.29 0 0

120 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Appendix 11 Crop water computation as input for the model from Rice crop for dry season irrigation

Date ETo Planted Crop CWR Total Effect. Irr. FWS Area Kc (ETm) Rain Rain Req. (mm/period) (%) (mm/period) (l/s/ha) 1-May 65.15 50 0.53 34.2 16.97 13.84 20.36 0.34 11-May 65.97 50 0.53 34.63 17.08 14.14 20.49 0.34 21-May 66.51 50 0.53 34.92 17.1 14.32 20.59 0.34 31-May 66.78 50 0.54 35.98 17.22 14.5 21.47 0.36 10-Jun 66.75 50 0.56 37.63 17.62 14.79 22.84 0.38 20-Jun 66.45 50 0.59 39.12 18.44 15.25 23.87 0.39 30-Jun 65.87 50 0.6 39.52 19.72 15.92 23.6 0.39 10-Jul 65.04 50 0.6 39.03 21.44 16.81 22.21 0.37 20-Jul 63.98 50 0.6 38.39 23.5 17.87 20.51 0.34 30-Jul 62.7 50 0.6 37.62 25.73 19.01 18.61 0.31 9-Aug 61.24 50 0.6 36.74 27.89 20.11 16.63 0.28 19-Aug 59.63 50 0.6 35.78 29.71 21.02 14.76 0.24 29-Aug 57.91 50 0.55 32.1 30.89 21.57 10.53 0.17 8-Sep 56.11 50 0.47 26.43 31.13 21.61 4.82 0.08 18-Sep 54.28 50 0.39 21.05 30.2 20.98 0.06 0 Total 944.36 523.13 344.63 261.76 261.38 [0.29]

121

Appendix 12 Crop water computation as input for the model from Cassava for wet season irrigation

Date ETo Planted Crop CWR Total Effect. Irr. FWS Area Kc (ETm) Rain Rain Req. (mm/period) (%) (mm/period) (l/s/ha) 1-Apr 61.2 50 0.07 4.59 13.83 11.01 0 0 11-Apr 62.74 50 0.07 4.71 15.52 12.37 0 0 21-Apr 64.07 50 0.11 7.23 16.52 13.29 0 0 1-May 65.15 50 0.18 11.83 16.97 13.84 0 0 11-May 65.97 50 0.25 16.52 17.08 14.14 2.37 0.04 21-May 66.51 50 0.32 21.22 17.1 14.32 6.9 0.11 31-May 66.78 50 0.35 23.37 17.22 14.5 8.87 0.15 10-Jun 66.75 50 0.35 23.36 17.62 14.79 8.58 0.14 20-Jun 66.45 50 0.35 23.26 18.44 15.25 8.01 0.13 30-Jun 65.87 50 0.35 23.06 19.72 15.92 7.13 0.12 10-Jul 65.04 50 0.35 22.76 21.44 16.81 5.95 0.1 20-Jul 63.98 50 0.35 22.39 23.5 17.87 4.52 0.07 30-Jul 62.7 50 0.35 21.94 25.73 19.01 2.93 0.05 9-Aug 61.24 50 0.32 19.76 27.89 20.11 0 0 19-Aug 59.63 50 0.27 16.26 29.71 21.02 0 0 29-Aug 57.91 50 0.22 12.89 30.89 21.57 0 0 8-Sep 56.11 50 0.17 9.69 31.13 21.61 0 0 18-Sep 54.28 50 0.12 6.66 30.2 20.98 0 0 Total 1132.37 291.5 390.49 298.43 55.26 [0.05]

Appendix 13 Crop water computation as input for the model from Cassava for dry season irrigation

Date ETo Planted Crop CWR Total Effect. Irr. FWS Area Kc (ETm) Rain Rain Req. (mm/period) (%) (mm/period) (l/s/ha) 1-Oct 51.92 50 0.07 3.89 26.95 19 0 0 11-Oct 50.16 50 0.07 3.76 22.88 16.52 0 0 21-Oct 48.5 50 0.11 5.46 17.65 13.31 0 0 31-Oct 46.98 50 0.18 8.52 11.73 9.56 0 0 10-Nov 45.62 50 0.25 11.41 5.91 5.56 5.85 0.1 20-Nov 44.46 50 0.32 14.18 1.37 1.37 12.8 0.21 30-Nov 43.51 50 0.35 15.23 0 0 15.23 0.25 10-Dec 42.78 50 0.35 14.97 0 0 14.97 0.25 20-Dec 42.28 50 0.35 14.8 0 0 14.8 0.24 30-Dec 43.45 50 0.35 15.21 0 0 15.21 0.25 9-Jan 45.25 50 0.35 15.84 0 0 15.84 0.26 19-Jan 47.06 50 0.35 16.47 0.61 0.5 15.97 0.26 29-Jan 49.01 50 0.35 17.15 0.16 0.16 17 0.28 8-Feb 51.05 50 0.32 16.45 0 0 16.45 0.27 18-Feb 53.13 50 0.27 14.47 0.87 0.87 13.6 0.22 28-Feb 55.2 50 0.22 12.27 4.04 3.92 8.35 0.14 10-Mar 57.21 50 0.17 9.86 7.56 6.51 3.35 0.06 20-Mar 59.11 50 0.12 7.23 10.79 8.78 0 0 Total 876.67 217.19 110.52 86.07 169.42 [0.16]

122 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Appendix 14 Internal polygon on which the end result is given or assigned at

Appendix 15 Model text format output type

123

Appendix 16 Model tabular format output type

Appendix 17 model graphical format output type

124 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Appendix 18 Simulation Results for the aquifer [Madyaka(2008)]

Point GP X Y Year_0 Year_3 Year_10 Year_20 3 Pe111 816288 1663192 0.17 0.17 0.18 0.19 8 Pe211 815948 1668815 0.2 0.2 0.18 0.16 10 Pe111 814473 1668455 0.1 0.1 0.09 0.08 13 Pe112 812517 1668792 0.6 0 0 0.52 14 Pe211 813219 1666240 0.1 0 0 0.09 16 Pe113 808548 1665289 0.1 0 0 0.08 18 Pe112 810523 1672141 0.59 0.58 0.53 0.47 19 Va211 817155 1661822 2.3 0 0 2.01 20 Va211 801618 1678078 4.2 0 0 3.73 23 Pe413 819877 1673907 0.1 0.1 0.1 0.1 27 Pe211 818665 1670203 5.2 5.14 4.91 4.54 29 Va211 806793 1673956 1.1 1.08 1 0.9 32 Pe115 812841 1672527 0.16 0.16 0.16 0.18 33 Pe112 809836 1670828 0.1 0.1 0.09 0.09 34 Pe311 804896 1668820 0.1 0.1 0.09 0.09 35 Pe113 806961 1669083 0.1 0.1 0.09 0.08 37 Pe113 804174 1665801 0.1 0.1 0.09 0.08 38 Pe113 805724 1663079 0.1 0.1 0.09 0.08 40 Pe113 812557 1663596 0.1 0.1 0.09 0.08 41 Pe114 813258 1660897 0.2 0.2 0.18 0.16 46 Pe111 804504 1666705 0.3 0.3 0.28 0.25 48 Pe511 809347 1657703 0.3 0.3 0.28 0.25 50 Pe311 816791 1661316 0.17 0 0 0.17 51 Pe113 818540 1672340 15 14.74 13.71 12.27 52 Pe112 804137 1673100 0.8 0.79 0.74 0.67 55 Pe211 810965 1657742 0.1 0.1 0.09 0.09 57 Pe114 815025 1658844 0.1 0 0 0 58 Pe113 808038 1667174 0.1 0.1 0.1 0.09 61 Pe113 810951 1662722 0.4 0.39 0.37 0.33 64 Pe311 810931 1664520 0.1 0.1 0.09 0.08

125

Appendix 19 Simulation Results for the transition zone [Madyaka(2008)]

Point GP X Y Year_0 Year_3 Year_10 Year_20 3 Pe111 816288 1663192 0.13 0.15 4.15 19.27 8 Pe211 815948 1668815 0.10 0.09 0.11 0.18 10 Pe111 814473 1668455 0.20 0.17 0.18 0.28 13 Pe112 812517 1668792 1.00 0.77 1.03 1.50 14 Pe211 813219 1666240 0.10 0.15 0.86 1.88 16 Pe113 808548 1665289 0.10 0.08 0.10 0.13 18 Pe112 810523 1672141 1.00 0.87 1.00 1.33 19 Va211 817155 1661822 6.10 6.22 7.27 8.11 20 Va211 801618 1678078 9.60 10.66 13.96 16.45 23 Pe413 819877 1673907 0.10 0.19 0.95 2.03 27 Pe211 818665 1670203 8.30 9.70 23.09 41.85 29 Va211 806793 1673956 0.10 0.09 0.19 0.38 32 Pe115 812841 1672527 0.19 0.33 2.04 11.56 33 Pe112 809836 1670828 0.06 0.13 0.32 0.49 34 Pe311 804896 1668820 0.10 0.08 0.09 0.16 35 Pe113 806961 1669083 0.06 0.13 0.32 0.49 37 Pe113 804174 1665801 0.10 0.09 0.08 0.09 38 Pe113 805724 1663079 0.06 0.13 0.32 0.49 40 Pe113 812557 1663596 0.10 0.09 0.13 0.22 41 Pe114 813258 1660897 0.20 0.18 0.16 0.17 46 Pe111 804504 1666705 0.40 0.34 0.26 0.32 48 Pe511 809347 1657703 1.70 1.51 1.67 2.21 50 Pe311 816791 1661316 0.22 0.26 1.24 5.18 51 Pe113 818540 1672340 4.20 3.50 6.29 15.48 52 Pe112 804137 1673100 1.20 1.17 1.37 1.67 55 Pe211 810965 1657742 0.50 0.46 0.50 0.57 57 Pe114 815025 1658844 0.20 0.18 0.16 0.15 58 Pe113 808038 1667174 0.01 0.10 0.41 0.65 61 Pe113 810951 1662722 0.40 0.37 0.36 0.38 64 Pe311 810931 1664520 0.10 0.13 0.24 0.39

126 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Appendix 20 Simulation Results for root-zone salinity [madyaka(2008)]

POINT GP X Y YEAR_0 YEAR_3 YEAR_10 YEAR_20 3 Pe111 816288 1663192 0.13 0.09 0.25 0.32 8 Pe211 815948 1668815 0.1 0.08 0.14 0.21 10 Pe111 814473 1668455 0.1 0.07 0.1 0.13 13 Pe112 812517 1668792 0.5 0.17 0.48 0.55 14 Pe211 813219 1666240 0.13 0.49 2 5.98 16 Pe113 808548 1665289 0.1 0.09 0.09 0.09 18 Pe112 810523 1672141 0.96 0.76 0.78 0.82 19 No data From start 20 No data From start 23 No data From start 27 No data From start 29 Va211 806793 1673956 0.1 0.1 0.26 0.47 32 Pe115 812841 1672527 0.19 0.61 3.53 23.3 33 Pe112 809836 1670828 0.06 0.24 0.7 1.46 34 Pe311 804896 1668820 0.1 0.08 0.13 0.17 35 Pe113 806961 1669083 0.1 0.07 0.12 0.2 37 Pe113 804174 1665801 0.1 0.07 0.08 0.1 38 Pe113 805724 1663079 0.2 0.19 0.22 0.26 40 Pe113 812557 1663596 0.1 0.09 0.17 0.26 41 Pe114 813258 1660897 0.1 0.08 0.1 0.11 46 Pe111 804504 1666705 0.4 0.28 0.28 0.34 48 No data From start 50 Pe311 816791 1661316 0.22 0.41 2.02 9.28 51 Pe113 818540 1672340 4.2 2.87 9.63 20.05 52 Pe112 804137 1673100 0.2 0.17 0.36 0.61 55 No data from Start 57 No data From start 58 Pe113 808038 1667174 13 17.8 30.9 52.65 61 Pe113 810951 1662722 0.4 0.35 0.37 0.39 64 Pe311 810931 1664520 0.1 0.08 0.13 0.15

127

Appendix 21 Yearly average salinization rate in Geopedological units under year5 condition

Year Area (km2) Salinity rating class yearly average 5 coverage MS NS SLS STS VSS area affectd Pe111 27.53 22.8% 43.5% 28.9% 0.7% 4.2% 20.0% Pe112 34.89 20.1% 35.9% 24.9% 12.1% 7.0% 20.0% Pe113 56.16 27.7% 64.5% 4.2% 1.5% 2.1% 20.0% Pe114 14.77 30.8% 43.9% 10.4% 14.0% 0.9% 20.0% pe115 6.25 0.8% 3.2% 75.7% 3.0% 17.3% 20.0% Pe211 23.32 24.1% 22.7% 24.8% 18.2% 10.2% 20.0% Pe311 22.03 29.3% 52.3% 8.4% 8.1% 1.9% 20.0% Pe411 3.53 NA 5.1% 5.6% 22.4% 66.9% 25.0% Pe412 4.83 NA 6.4% 45.3% 1.0% 47.4% 25.0% Pe413 26.26 3.7% 4.1% 26.3% 29.7% 36.2% 20.0% Pe511 0.07 NA 100.0% NA NA NA NA Va111 7.41 NA 4.7% 19.1% 16.2% 60.0% 25.0% Va211 5.96 26.2% 10.0% 22.3% 41.0% 0.5% 20.0% Va311 1.19 1.4% 0.0% 57.8% 0.0% 40.8% 20.0%

Appendix 22 Yearly average salinization rate in Geopedological units under year 10 condition

Year Area (km2) Salinity rating class yearly average 10 coverage MS NS SLS STS VSS area affectd Pe111 2753.5% 55.3% 11.0% 28.9% 0.7% 4.2% 20.0% Pe112 3488.9% 44.6% 11.5% 24.9% 12.1% 7.0% 20.0% Pe113 5615.7% 69.3% 23.0% 4.2% 1.5% 2.1% 20.0% Pe114 1477.0% 69.6% 5.1% 10.4% 14.0% 0.9% 20.0% Pe115 625.3% 0.8% 3.2% 75.7% 3.0% 17.3% 20.0% Pe211 2332.1% 26.5% 20.2% 24.8% 18.2% 10.2% 20.0% Pe311 2202.8% 52.5% 29.1% 8.4% 8.1% 1.9% 20.0% Pe411 352.6% NA 5.1% 5.6% 22.4% 66.9% 25.0% Pe412 482.6% NA 6.4% 45.3% 1.0% 47.4% 25.0% Pe413 2625.7% 3.7% 4.1% 26.3% 29.7% 36.2% 20.0% Pe511 7.3% NA 100.0% NA NA NA NA Va111 741.3% NA 4.7% 19.1% 16.2% 60.0% 25.0% Va211 595.7% 32.7% 3.5% 22.3% 41.0% 0.5% 16.8% Va311 119.5% 1.4% NA 57.8% NA 40.8% 49.3%

128 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Appendix 23 Yearly average salinization rate in Geopedological units under year 15 condition

Year Area (km2) Salinity rating class yearly average 15 coverage MS NS SLS STS VSS area affectd Pe111 27.53 22.8% 1.4% 28.9% 42.7% 4.2% 20.0% Pe112 34.89 20.1% 7.7% 24.9% 40.4% 7.0% 20.0% Pe113 56.16 27.7% 3.0% 4.2% 63.1% 2.1% 20.0% Pe114 14.77 30.8% 3.8% 10.4% 54.1% 0.9% 20.0% Pe115 6.25 0.8% 3.2% 75.7% 3.0% 17.3% 20.0% Pe211 23.32 24.1% 11.4% 24.8% 29.5% 10.2% 20.0% Pe311 22.03 29.3% 4.4% 8.4% 56.0% 1.9% 20.0% Pe411 3.53 NA 5.1% 5.6% 22.4% 66.9% 25.0% Pe412 4.83 NA 6.4% 45.3% 1.0% 47.4% 25.0% Pe413 26.26 3.7% 4.1% 26.3% 29.7% 36.2% 20.0% Pe511 0.07 NA NA NA 100.0% NA NA Va111 7.41 NA 4.7% 19.1% 16.2% 60.0% 25.0% Va211 5.96 26.2% 1.9% 22.3% 49.1% 0.5% 20.0% Va311 1.19 1.4% 0.0% 57.8% 0.0% 40.8% 20.0%

Appendix 24 Yearly average salinization rate in Geopedological units under year 20 condition

Year Area (km2) Salinity rating class yearly average 20 coverage MS NS SLS STS VSS area affectd pe111 27.53 1.2% 1.4% 28.9% 4.3% 42.6% 15.7% Pe112 34.89 5.0% 7.7% 24.9% 12.5% 34.9% 17.0% Pe113 56.16 0.7% 3.0% 4.2% 2.9% 62.3% 14.6% Pe114 14.77 3.5% 3.8% 10.4% 14.0% 41.0% 14.5% pe115 6.25 0.8% 3.2% 75.7% 3.0% 17.3% 20.0% Pe211 23.32 0.7% 11.4% 24.8% 18.4% 21.3% 15.3% pe311 22.03 1.7% 4.4% 8.4% 8.6% 49.4% 14.5% Pe411 3.53 NA 5.1% 5.6% 22.4% 66.9% 25.0% Pe412 4.83 NA 6.4% 45.3% 1.0% 47.4% 25.0% Pe413 26.26 0.7% 4.1% 26.3% 29.7% 36.2% 19.4% Pe511 0.07 NA NA NA NA 100.0% NA Va111 7.41 NA 4.7% 19.1% 16.2% 60.0% 25.0% Va211 5.96 6.0% 1.9% 22.3% 41.0% 8.6% 16.0% Va311 1.19 1.4% 0.0% 57.8% 0.0% 40.8% 20.0%

129

Appendix 25 various events during field survey from the eye of the camera photo

130 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

131

132 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Appendix 26 Decision supporting system based on DEM class 1

EXP PREDICTED AQUIFER ZONE PREDICTED TRANSITION ZONE SLO ERTI SALINITY SALINITY PREDICTED ROOT ZONE SALINITY DEM P LANDUSE SE CLA CLAS Salt SS S 1 2 3 4 5 6 class Yr0 Yr5 Yr10 Yr15 Yr20 Yr0 Yr5 Yr10 Yr15 Yr20 Yr0 Yr5 Yr10 Yr15 Yr20

1 < 0.5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < < 0.5 2 VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS 192 < 0.5 3 STS MS MS MS MS MS MS MS MS MS MS SLS SLS SLS SLS SLS < 0.5 4 MS MS MS MS MS MS MS MS MS MS MS SLS SLS SLS SLS SLS < 0.5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 0.5 6 SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS NS NS NS NS NS 2 < 1 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < 1 2 VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS < 1 3 STS MS MS MS MS MS MS MS MS MS MS SLS SLS SLS SLS SLS < 1 4 MS MS MS MS MS MS MS MS MS MS MS SLS SLS SLS SLS SLS < 1 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 1 6 SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS NS NS NS NS NS 3 < 5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < 5 2 VSS VSS VSS VSS VSS VSS STS STS STS STS STS STS STS STS STS STS < 5 3 STS MS MS MS MS MS SLS SLS SLS SLS SLS NS NS NS NS NS < 5 4 MS MS MS MS MS MS SLS SLS SLS SLS SLS NS NS NS NS NS < 5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 5 6 SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS NS NS NS NS NS 4 > 5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW > 5 2 VSS STS STS STS STS STS STS STS STS STS STS MS MS MS MS MS > 5 3 MS MS MS MS MS MS SLS SLS SLS SLS SLS NS NS NS NS NS > 5 4 SLS MS MS MS MS MS SLS SLS SLS SLS SLS NS NS NS NS NS > 5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS > 5 6 NS SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS NS NS NS NS NS 133

Appendix 27 Decision supporting system based on DEM class 2

EXP PREDICTED AQUIFER ZONE PREDICTED TRANSITION ZONE SLO ERTI SALINITY SALINITY PREDICTED ROOT ZONE SALINITY DEM P LANDUSE SE CLA CLAS Salt SS S 1 2 3 4 5 6 class Yr0 Yr5 Yr10 Yr15 Yr20 Yr0 Yr5 Yr10 Yr15 Yr20 Yr0 Yr5 Yr10 Yr15 Yr20

1 < 0.5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < < 0.5 2 VSS STS STS VSS STS STS MS STS STS STS STS SLS STS STS STS STS 195 < 0.5 3 STS MS MS MS MS MS SLS SLS SLS SLS SLS NS NS NS NS NS < 0.5 4 SLS MS MS MS MS MS SLS SLS SLS SLS SLS NS NS NS NS NS < 0.5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 0.5 6 SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS NS NS NS NS NS 2 < 1 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < 1 2 VSS STS STS STS STS STS MS STS STS STS STS SLS STS STS STS STS < 1 3 STS STS STS STS STS STS MS MS MS MS MS SLS SLS SLS SLS SLS < 1 4 SLS STS STS STS STS STS MS MS MS MS MS SLS SLS SLS SLS SLS < 1 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 1 6 SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS NS NS NS NS NS 3 < 5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < 5 2 VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS VSS < 5 3 STS STS STS STS STS STS MS MS MS MS MS MS MS MS MS MS < 5 4 SLS STS STS STS STS STS MS MS MS MS MS MS MS MS MS MS < 5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 5 6 SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS NS NS NS NS NS 4 > 5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW > 5 2 VSS STS STS STS STS STS STS STS STS STS STS STS STS STS STS STS > 5 3 STS MS MS MS MS MS SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS > 5 4 SLS MS MS MS MS MS SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS > 5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS > 5 6 SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS NS NS NS NS NS 134 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Appendix 28 Decision supporting system based on DEM class 3 PREDICTED TRANSITION ZONE EXPERT PREDICTED AQUIFER ZONE SALINITY SALINITY PREDICTED ROOT ZONE SALINITY DEM SLOP LANDUSE ISE CLAS CLAS Salt S S 1 2 3 4 5 6 class Yr0 Yr5 Yr10 Yr15 Yr20 Yr0 Yr5 Yr10 Yr15 Yr20 Yr0 Yr5 Yr10 Yr15 Yr20 1 < 0.5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < < 0.5 2 VSS STS STS STS STS STS MS STS STS STS STS SLS STS STS STS STS

197 < 0.5 3 STS STS STS STS STS STS MS STS STS STS STS SLS STS STS STS STS < 0.5 4 SLS STS STS STS STS STS MS STS STS STS STS SLS STS STS STS STS < 0.5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 0.5 6 SLS STS STS STS STS STS SLS SLS SLS SLS SLS NS NS NS NS NS 2 < 1 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < 1 2 VSS STS STS STS STS STS MS STS STS STS STS SLS STS STS STS STS < 1 3 STS STS STS STS STS STS MS STS STS STS STS SLS STS STS STS STS < 1 4 SLS STS STS STS STS STS MS STS STS STS STS SLS STS STS STS STS < 1 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 1 6 SLS STS STS STS STS STS MS MS MS MS MS SLS SLS SLS SLS SLS 3 < 5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < 5 2 VSS STS STS STS STS STS MS STS STS STS STS SLS STS STS STS STS < 5 3 SLS STS STS STS STS STS MS STS STS STS STS SLS STS STS STS STS < 5 4 SLS STS STS STS STS STS MS MS MS MS MS SLS SLS SLS SLS SLS < 5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 5 6 SLS MS MS MS MS MS MS MS MS MS MS SLS SLS SLS SLS SLS 4 > 5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW > 5 2 VSS VSS VSS VSS VSS VSS STS STS STS STS STS STS STS STS STS STS > 5 3 STS STS STS STS STS STS MS MS MS MS MS SLS SLS SLS SLS SLS > 5 4 SLS STS STS STS STS STS MS MS MS MS MS SLS SLS SLS SLS SLS > 5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS > 5 6 SLS STS STS STS STS STS MS MS MS MS SLS SLS SLS SLS SLS SLS

135

Appendix 29 Decision supporting system based on DEM class 4 EXP PREDICTED AQUIFER ZONE PREDICTED TRANSITION ZONE SLO ERTI SALINITY SALINITY PREDICTED ROOT ZONE SALINITY DEM P LANDUSE SE CLA CLAS Salt SS S 1 2 3 4 5 6 class Yr0 Yr5 Yr10 Yr15 Yr20 Yr0 Yr5 Yr10 Yr15 Yr20 Yr0 Yr5 Yr10 Yr15 Yr20

< 0.5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < < 0.5 2 VSS STS STS STS STS STS STS STS STS STS STS STS STS STS STS STS 200 < 0.5 3 STS MS MS MS MS MS NS MS MS MS MS NS MS MS MS MS < 0.5 4 STS MS MS MS MS MS NS MS MS MS MS NS MS MS MS MS < 0.5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 0.5 6 SLS MS MS MS MS MS NS MS MS MS MS NS MS MS MS MS 4 < 1 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < 1 2 VSS MS MS MS MS MS MS MS MS MS MS MS MS MS MS MS < 1 3 STS SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS NS NS NS NS NS < 1 4 SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS SLS NS NS NS NS NS < 1 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 1 6 SLS SLS SLS SLS SLS SLS NS NS NS NS NS NS NS NS NS NS 4 < 5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < 5 2 VSS STS STS STS STS STS STS STS STS STS STS STS STS STS STS STS < 5 3 SLS STS STS STS STS STS STS STS STS STS STS STS STS STS STS STS < 5 4 SLS STS STS STS STS STS MS STS STS STS STS SLS STS STS STS STS < 5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 5 6 SLS STS STS STS STS STS MS MS MS MS MS MS SLS SLS SLS SLS 4 > 5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW > 5 2 VSS STS STS STS STS STS STS STS STS STS STS STS STS STS STS STS > 5 3 MS SLS SLS SLS SLS SLS NS NS NS NS NS NS NS NS NS NS > 5 4 SLS SLS SLS SLS SLS SLS NS NS NS NS NS NS NS NS NS NS > 5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS > 5 6 SLS SLS SLS SLS SLS SLS NS NS NS NS NS NS NS NS NS NS

136 Spatial Modelling and Timely Prediction of Salinization Process using SAHYSMOD model In GIS Environment

Appendix 30 Decision supporting system based on DEM class 5 EXPE PREDICTED AQUIFER ZONE SALINITY PREDICTED TRANSITION ZONE SALINITY PREDICTED ROOT ZONE SALINITY DEM SLOP LANDUSE RTISE CLAS CLAS Salt S S 1 2 3 4 5 6 class Yr0 Yr5 Yr10 Yr15 Yr20 Yr0 Yr5 Yr10 Yr15 Yr20 Yr0 Yr5 Yr10 Yr15 Yr20

1 < 0.5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < < 0.5 2 VSS STS STS STS STS STS MS MS MS MS MS MS MS MS MS MS

204 < 0.5 3 SLT SLS SLS SLS SLS SLS SLS SLS NS NS NS NS NS NS NS NS < 0.5 4 SLT SLS SLS SLS SLS SLS SLS SLS NS NS NS NS NS NS NS NS < 0.5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 0.5 6 NS SLS SLS SLS SLS SLS SLS SLS NS NS NS NS NS NS NS NS 2 < 1 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < 1 2 VSS STS STS STS STS STS STS STS STS STS STS STS STS STS STS STS < 1 3 SLT MS MS MS MS MS NS MS MS MS MS NS MS MS MS MS < 1 4 SLT MS MS MS MS MS NS MS MS MS MS NS MS MS MS MS < 1 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 1 6 NS MS MS MS MS MS NS NS NS NS NS NS NS NS NS NS 3 < 5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < 5 2 VSS STS STS STS STS STS MS MS MS MS MS MS MS MS MS MS < 5 3 SLS SLS SLS SLS SLS SLS NS NS NS NS NS NS NS NS NS NS < 5 4 SLS SLS SLS SLS SLS SLS NS NS NS NS NS NS NS NS NS NS < 5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 5 6 NS SLS SLS SLS SLS SLS NS NS NS NS NS NS NS NS NS NS 4 > 5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW > 5 2 VSS MS MS MS MS MS MS MS MS MS MS MS MS MS MS MS > 5 3 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS > 5 4 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS > 5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS > 5 6 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS

137

Appendix 31 Decision supporting system based on DEM class 6 EXP

SLOP ERTI PREDICTED AQUIFER ZONE SALINITY PREDICTED TRANSITION ZONE SALINITY PREDICTED ROOT ZONE SALINITY E LANDUSE SE DEM CLAS Salt CLASS S 1 2 3 4 5 6 class Yr0 Yr5 Yr10 Yr15 Yr20 Yr0 Yr5 Yr10 Yr15 Yr20 Yr0 Yr5 Yr10 Yr15 Yr20 1 < 0.5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW > < 0.5 2 VSS STS STS STS STS STS MS STS STS STS STS STS STS STS STS STS 204 < 0.5 3 SLS MS MS MS MS MS NS MS MS MS MS NS MS MS MS MS < 0.5 4 SLS MS MS MS MS MS NS MS MS MS MS NS MS MS MS MS < 0.5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 0.5 6 NS MS MS MS MS MS NS MS MS MS MS NS MS MS MS MS 2 < 1 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < 1 2 VSS STS STS STS STS STS MS MS MS MS MS MS MS MS MS MS < 1 3 SLS MS MS MS MS MS NS MS MS MS MS NS MS MS MS MS < 1 4 SLS MS MS MS MS MS NS MS MS MS MS NS MS MS MS MS < 1 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 1 6 NS MS MS MS MS MS NS MS MS MS MS NS NS NS NS NS 3 < 5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW < 5 2 VSS STS STS STS STS STS MS MS MS MS MS MS MS MS MS MS < 5 3 SLS NS NS SLS SLS SLS NS NS NS SLS SLS NS NS MS STS VSS < 5 4 SLS NS NS SLS SLS SLS NS NS NS SLS SLS NS NS MS STS STS < 5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS < 5 6 NS NS NS SLS SLS SLS NS NS NS SLS SLS NS NS MS STS STS 4 > 5 1 WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW WW > 5 2 VSS MS MS MS MS MS MS MS MS MS MS MS MS MS MS MS > 5 3 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS > 5 4 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS > 5 5 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS > 5 6 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS

138

Top View