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 SIMULATED DEPTH TO WATER TABLE The model also predicts the seasonal changes of the water table depth. The simulated depths to water table in both seasons (wet and dry season) over the 20 year period are given in table. The model tends to maintain almost the same depths for each season throughout the simulation period. Obviously the seasonal fluctuation indicated lower depth during the wet (first) season whereby the water table rises close to the surface and deeper during the dry (second) season. However, drastic change of decrease in depth is noticeable just from year zero (entry depth) to the first year which is a common tendency of the model, after which the same trend as explained is maintained. During the whole simulation period none of the predicted depths recedes to the same level or deeper depth than the initial entry depth. Table 1 : Average predicted water table depths (m)/landform GP YEAR_0 YEAR_3 YEAR_10 YEAR_20 Season 1 1 2 1 2 1 2 Pe111 -4.18 -1.39 -1.83 -0.85 -1.38 -0.85 -1.38 Pe112 -2.15 -1.05 -1.33 -1.10 -1.38 -1.19 -1.48 Pe113 -3.46 -0.96 -1.45 -0.81 -1.28 -0.84 -1.26 Pe114 -3.19 -0.87 -1.64 -0.83 -1.22 -1.20 -1.22 Pe115 -2.52 -0.64 -1.08 -0.64 -1.07 -0.64 -1.07 Pe211 -2.89 -0.84 -2.11 -0.80 -2.08 -0.81 -1.68 Pe311 -2.61 -0.80 -1.26 -0.76 -1.20 -0.76 -1.20 Pe412 -3.01 -0.83 -1.23 -0.75 -1.18 -0.75 -1.18 Pe413 -2.30 -0.83 -1.40 -0.82 -1.40 -0.82 -1.49 Pe511 -3.03 -0.82 -1.21 -0.71 -1.16 -0.71 -1.16 Va111 -1.79 -0.81 -1.28 -0.77 -2.19 -0.73 -1.23 Va211 -2.50 -0.78 -1.31 -0.76 -1.54 -1.44 -1.69 Va311 -2.40 -0.66 -1.13 -0.65 -1.12 -0.65 -1.12 * for GP see Geo-pedological map Figure 1 : Estimated water depth for point 36 (S1=season 1, S2 = season 2) The rising groundwater table depth during the first season can be attributed to water percolation due to high torrential rainfall received during this season. While the lowering the water-table depth in the dry season is associated with less rainfall and high evaporation and evapotranspiration rates. The consequence of this kind of cycle between wet and dry season result in capillary rise and salt mobilization to the soil surface which harms crop growth, affect the ecosystems and damage water quality Figure 2 : Map showing salinity (EC) distribution the relief zones for the soil depth Development of Methodologies for Land Degradation Assessment Applied to Land Use Planning in Thailand ^go to top