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

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SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT M. MADYAKA February, 2008 SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT Spatial Modelling and Prediction of Soil Salinization Using SaltMod in a GIS Environment by Mthuthuzeli Madyaka Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation: (Natural Resource Management – Soil Information Systems for Sustainable Land Management: NRM-SISLM) Thesis Assessment Board Prof. Dr. V.G.Jetten: Chairperson Prof. Dr.Ir. A. Veldkamp: External Examiner B. (Bas) Wesselman: Internal Examiner Dr. A. (Abbas) Farshad: First Supervisor Dr. D.B. (Dhruba) Pikha Shrestha: Second Supervisor INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute. Abstract One of the problems commonly associated with agricultural development in semi-arid and arid lands is accumulation of soluble salts in the plant root-zone of the soil profile. The salt accumulation usually reaches toxic levels that impose growth stress to crops leading to low yields or even complete crop failure. This research utilizes integrated approach of remote sensing, modelling and geographic information systems (GIS) to monitor and track down salinization in the Nung Suang district of Nakhon Ratchasima province in Thailand. Though salinization in this region is attributed to underlying parent material and climatic conditions, it is aggravated by human activities through poor agricultural practices, deforestation, salt making, and construction of roads and reservoirs. The area was selected for this study because greater part of its population depends on agriculture and thus agricultural development is imperative for socio-economic upliftment of the area. Moreover the study area falls under one of the highly salinized regions in Thailand. The collaboration of LDD and ITC for capacity building, research and development projects in Thailand is another reason. Two Aster images (11/2006 & 01/2007, topographic (1: 50 000), geopedologic map, EC datasets from previous studies (2003 & 2004) coupled with field observations served as the basic sources of data. These data sources were used to generate input parameters required by SaltMod model for long term prediction of salinization over 20 year period. Other parameters were logically estimated while others were estimated by a trial and error calibration of the model. Some soil related parameters were estimated from pedotransfer functions using SPAW and CropWat computer programs. SaltMod is a one dimensional point model based on three component systems, viz. water balance (hydrological) model, salt balance model and seasonal agronomic aspects. Geostatistical analysis was used for interpolation of EC measured and simulated values. GIS was used for reclassification and mapping of salinity affected areas based on the FAO (USDA) classification systems. Regression kriging was the basic interpolation method applied with auxiliary predictors derived from the prior mentioned data sources. The auxiliary predictors included relief zones (polygon map) from the geopedologic map, relief parameters (DEM, slope in degrees, mean curvature, profile and plan curvature) derived from digitized 10 m contours (from 1:50 000 topographic map) and land-cover/use map from supervised classification of aster image, with all the processing done in Ilwis and ArcGIS. According to the prediction output results the original saline zones of the study area will, on one hand decrease from 10% and 71% to 3% and 23% for low and moderate saline zones respectively after 20 years under present cropping patterns. On the other hand the high and severe saline soils will increase from 17% and 0% to 43% and 30% respectively. However, the lack of historical and difficulty to obtain existing salinity and groundwater data in the area has presented difficulties and uncertainty of the results. The prediction of salinity in the transition zone (60-100cm) was rather poor. Despite validation results suggesting suitability of the model for root-zone salinity prediction, concerns and uncertainties regarding the relevance and applicability of the model to the applied spatial scale remain. Nevertheless integration of the model into a GIS environment and geostatistical methods helped in upscaling from point to area scale level. The sensitivity analysis results indicated that the SaltMod model was sensitive to five out of eleven selected input parameters. The approach presented in the study is fundamental to responding to questions related to soil salinity management thereby way of prognostic analysis to detect salinization at early stages thus providing prevention measures rather than damage control measures. However, the results presented should be taken as indicative due to uncertainties associated with large assumptions rather measured data. Besides, though accuracy of prediction may be uncertain, it is useful when the trend of prediction is clear. i SPATIAL MODELLING AND PREDICTION OF SOIL SALINIZATION USING SALTMOD IN A GIS ENVIRONMENT Acknowledgements I’m very grateful to The Netherlands Fellowship Programme (Nuffic) for financial assistance of my studies. I’m also thankful to South African government, Department of Agriculture for allowing me the opportunity to further my studies. I would like to express my sincere gratitude to my supervisor Dr Abbas Farshad for his guidance and invaluable comments to this work. Without his supervision and constructive criticism I would not have managed to accomplish this study. I’m also thankful to my co-supervisor Dr Druba P. Shrestha for his invaluable guidance during my fieldwork and useful suggestion towards completion of this study. I would like to thank the LDD staff in Thailand, especially Mr Anukul Suchinai for providing all the necessary support needed for fieldwork. Many more thanks to Mr Thoi and Ms Waei for their assistance during field data collection and the driver (Pee Nai), who was so keen to take us for point to point without hesitation. I would like further extend my gratefulness to the LDD staff in Khon Kaen and laboratory staff who were so welcoming and helpful during my laboratory analysis and for finalizing the analytical analysis. Besides, their hospitality and humanity made my few days in Khon Kaen the fabulous experience in Thailand. Further I would to thank Montoon, Poo and Koi who made us feel at home and treated us like their brothers in a foreign country where very few people could understand our language. Special thanks to all my colleagues, especially cluster mates and course mates, Edward, Yirgalem and Raju who were so courageous and helping throughout the duration of our research work. Thanks to all my friends who made my stay in Netherlands such a wonderful experience. Thanks to ITC community for all the efforts of creating a social environment with all social gatherings and activities organized. I would like to extend my greatest appreciation to my family and friends with their kind words of encouragement and building my confidence to finish my studies. Special thanks to Thandi (my son’s mother), who never complained while leaving her to raise a three months old baby alone. Lastly and the most all, I would like to thank the Lord for giving me strength, without His grace nothing would have been possible. ii Table of contents 1. INTRODUCTION............................................................................................................................1 1.1. General Background ...............................................................................................................1 1.1.1. Soil Salinity ........................................................................................................................1 1.1.2. Impacts of Soil Salinization................................................................................................2 1.1.3. Soil Salinity Issue in Thailand............................................................................................3 1.1.4. Soil Salinity Detection Problem.........................................................................................4 1.1.5. Modeling Salinization ........................................................................................................5 1.2. Problem Formulation and Research Justification...................................................................6 1.3. Research Objectives................................................................................................................8 1.3.1. Broad Research Objective..................................................................................................8 1.3.2. Specific Objectives.............................................................................................................8 1.4. Research Questions.................................................................................................................8 1.5. Research Hypothesis...............................................................................................................9
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