Integration of geographic information systems and agricultural nonpoint-source pollution model to estimate runoff sediment of Huai Som sub-basin / Sutthasini Glawgitigul = การรวมระบบสารสนเทศทางภูมิศาสตร์กับแบบจำลองมลพิษแบบแพร่กระจายจากแหล่งเกษตรกรรมเพื่อใช้ในการประเมินตะกอนที่ถูกพัดพาในลุ่มน้ำย่อยห้วยส้ม
Integration of GIS and AGNPS have been done to predict the runoff sediment in Huai Som sub-basin, Chiangmai. The programs used in the integration of AGNPS and GIS were pc ARC/INFO, SURFER, IDRISI and ID2AGS. The spatial data of soil type, topographic, land cover and hydrography were digitized as vector data. Then the contour vectors were interpolated to create DEM (digital elevation model). After that, these maps were converted to grid maps with IDRISI tool. Finally, these databases were converted to form an array of model input data by ID2AGS. The output from the AGNPS model was compared to observed data. Teh result showed that the simulated data were higher than the observed data. The overestimate is the effect of several external and internal influence such as the structure of soil, slope gradient, slope length, etc. This study investigated the sensitivity analysis of model input parameters to runoff sediment. These parameters were manning's roughness coefficient, Cropping factor (C-factor), Practice factor (P-factor) Soil erodibility factor (K-factor) and duration of rainfall. It found that the lower Manning's roughness coefficient value will increase runoff sediment; the higher C-factor, P-factor and K-factor and value will increase the runoff sediment. For the storm duration, it is found that the increase in duration of rainfall, the decrease in runoff sediment. The results leads to a conclusion that the C-factor is the most sensitive to runoff sediment. Additionally, it found that other five parameters of the AGNPS model namely, the surface condition constant, manning's roughness coefficient, cover and management factor, runoff curve number and COD which are based on the land use condition of the area, play significant roles in governing the model responses.