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10 July 2009 Development of a soil moisture prediction model based on Xinanjiang model and GIS
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Proceedings Volume 7491, PIAGENG 2009: Remote Sensing and Geoscience for Agricultural Engineering; 74910L (2009) https://doi.org/10.1117/12.836762
Event: International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), 2009, Zhangjiajie, China
Abstract
Soil moisture conditions are very important in agriculture - they control crops growth and development and are used directly to assess irrigation needs for a variety of crops. In this paper, a new soil moisture prediction model was developed based on GIS technology and the hydrological model Xinanjiang, which has been successfully and widely applied in humid and semi-humid regions in China since its development. The original Xinanjiang model uses a single parabolic curve to represent the spatial distribution of the soil moisture storage capacity over the catchment, where the exponent parameter b measures the non-uniformity of this distribution. It was extended to be a distributed hydrological model by using GIS technology. The watershed is divided into a number of regular grids, corresponding to the grids of DEM, and each grid is viewed as a sub-basin. So the surface runoff production was calculated for each grid. The runoff in each grid cell is routed along the stream flow direction to the main watershed outlet respectively at different velocity depending on the slop of this grid and watershed-average routing velocity . The soil moisture is predicted using the new distributed hydrological model. The new model was tested in Linyi watershed, Shandong province, China. The results show that the soil moisture predicted by the new model agrees with the field observed.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jingwen Xu, Wanchang Zhang, Changquan Wang, Ziyan Zheng, and Jiongfeng Chen "Development of a soil moisture prediction model based on Xinanjiang model and GIS", Proc. SPIE 7491, PIAGENG 2009: Remote Sensing and Geoscience for Agricultural Engineering, 74910L (10 July 2009); https://doi.org/10.1117/12.836762
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