home    land degradation    project outcome    Gallery Flooding Flood Hazard Assessment Pilot Area • The Pilot Areas • Climate • Cover factor (C) estimation • Digital terrain analysis • Relationship between soil properties • Relationship of soil properties with different land use/cover types Methodology • Methodologies • LISEM Results • Result Soil Properties and Land Use • Runoff rate for different land cover types • Model calibration and validation • Sensitivity analysis • Land use scenarios • Rainfall size scenarios • Flooding Scenario - 2 years return period • Flooding Scenario - 10 years return period • Flooding Scenario - 20 years return period • Flooding Scenario - 50 years return period • Summary tables for each flood characteristic • Flood hazard mapping • Conclusions and Recommendation Erosion Erosion Assessment Pilot Area • The Pilot Areas • Climate • Cover factor (C) estimation • Digital terrain analysis • Relationship between soil properties • Relationship of soil properties with different land use/cover types Methodology • Methodologies • Erosion Models Result • Results • C-Factor Mapping for erosion assessment • Soil erosion assessment • Assessment of land use/cover change effect on soil erosion • Assessing critical zones for ephemeral gully formation • Erosion Assessment by Different Models • The recommended erosion models • RMMF Landslide Case I • Landslide hazard assessment • The area (Pilot 3) • Landslide Mapping in Wang Chin using digital image analysis Techniques. • Results • Discussions Case II • Doi Ang Khang (Ang Khang) (Pilot area 4) • Landslide mapping in Ang Khang using aerial photos • Landslide Inventory Mapping • Landslide modelling and Susceptibility mapping • Landslide Susceptibility Maps from the three Scenarios (models) • Comparison between Ang Khang and Wang Chin (with respect to weights of evidence modelling using morphometric parameters) Salinization Case I • Pilot area II • Soil salinity • Salinization • Geomorphology • Soils • Geopedologic map • Results Case II • CASE II - The study of Yadav, R.D. (2005), • Methodology • Crop Simulation Model • PS 123 • Results • Mathematical (Regression) Model for Crop Yield Estimation • Conclusion Case III • Case III • Modeling Salinization • Geostatistics and Interpolation (GIS and Kriging) • Model Simulation and Prediction of Salinity • Simulated Depth to water table • Tenth Years Prediction • Twenty Years Prediction • Conclusion Runoff • Surface Runoff • Methodology • Modelling Runoff • Modelling Runoff Using Empirical Approach • Modelling runoff using the Semi-physical approach • Estimation with the Curve Number Method • Results and Discussion • Generation of Scenarios • Comparison of runoff rates with soil loss map • Conclusions • Presentation of papers based on project work at international • Workshop in Lomsak • Hua Hin seminar • Training of LDD staff • GIS and RS based predictive soil mapping • Soil erosion assessment • Landslide hazard assessment • Flood hazard assessment • Soil salinity study • List of completed MSc. theses on project SENSITIVITY ANALYSIS Sensitivity analysis is the act of determining the change in model behavior due to a predetermined adjustment of model parameter. A sensitivity analysis was used to find out which parameters had the largest influence on the models prediction. As can be concluded from the calibration results shown in the previous sections, these are differences between the measured and simulated discharges. A sensitivity analysis was performed on the three calibrated events to identify the most sensitive parameters of the model. Jetten et al (1998) showed that the sensitivity to certain parameters might depend on the level of other parameters. Thus model sensitivity can be more completely evaluated by changing combinations of parameters. Nonetheless, a simple sensitivity analysis in which only one parameter value is changed at a time is the easiest way to determine which individual parameters will be most important (Hessel, 2002). Therefore, in this study, simulations were carried out by symmetrically and uniformly subtracting 10% from and adding 10% to the calibration parameters including:       Saturated hydraulic conductivity The initial soil moisture content Surface roughness ( Manning’s n and random roughness) Interception parameter (plant cover and crop height)       The results of sensitivity analysis on all parameters indicate that the most important parameters and uncertainty are saturated conductivity and initial moisture content. These parameters should be measured as accurately as possible in the field to improve the model predictions. The same conclusion has been reached in other sensitivity analyses of the LISEM model. For example, De Roo (1996) performed sensitivity analyses and found that the most sensitive variable in the prediction of runoff is saturated conductivity and also De Roo and Jetten (1999) identified saturated conductivity and initial moisture content as being very sensitive parameters.   References De Roo, A.P.J., Wesseling, C.G. and Ritsema, C.J., 1996. LISEM: A SINGLE-EVENT PHYSICALLY BASED HYDROLOGICAL AND SOIL EROSION MODEL FOR DRAINAGE BASINS. I: THEORY, INPUT AND OUTPUT. Hydrological Processes, 10(8): 1107-1117. http://dx.doi.org/10.1002/(SICI)1099-1085(199608)10:8<1107::AIDHYP415> 3.0.CO;2-4 Hessel, R., 2002. Modelling soil erosion in small catchment on the Chinese Loess Plateau; Applying LISEM to extreme conditions, Utrecht University, Utrecht, 318 pp. Jetten, V.G., De Roo, A.P.J. and Gu?rif, J., 1998. Sensitivity of the model Lisem to Variables related to agriculture. In: J.D.F.-M. Boardman (Editor), Modelling soil erosion by water. Springer, NATO ASI Series I 55 Berlin, pp. 339-349. Development of Methodologies for Land Degradation Assessment Applied to Land Use Planning in Thailand ^go to top