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《Journal of Lake Science》 2003-03
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The Methods and Application of Automatically Extracting Stream Network of Watershed

LI Changfeng1,2, FENG Xuezhi3 & ZHAO Rui (1:Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences, Nanjing 210008,P.R.China; 2:Graduate School,Chinese Academy of Sciences,Beijing 100039,P.R.China; 3:Dept. of Urbanology and Resource,Nanjing Univ.,Nanjing 210093,P.R.China)  
At present, with digital terrain analysis technologies, calculating topographic information directly, modeling hydrological process and extracting hydrological data automatically from digital elevation models (DEM) is the focus of digital hydrology, and the present Geographic Information System (GIS) software based on raster data structure provide solid foundation for the modeling hydrological process, too. Automated extraction of drainage features from DEM is a very important research item of them, and has been widely used in actual works. The paper discussed the principles, methods and process of automated extraction of natural stream networks of watershed from DEM. The upper reaches watershed of Xitiaoxi as a case of study, a raster DEM is used to define surface drainage and extract the stream networks. Through comparing the stream networks delineated from DEM with the actual stream networks, it can be conclude that in the mountains area, especially the area of mean slope no less than 3 degrees, the delineated stream networks would be reliable. In the flat region of mean slope less than 3 degrees, the delineated stream is much different from the actual stream networks. In order to solve the problem, we put forward the restricted river channel method by digitizing the trunk streams and the flat land rivers. With the method, we got the delineated steam networks same as the actual stream networks, and make the simulation of hydrologic factor has more practical value. The method has universal reference value for hydrological modeling of other watersheds, and can improve the efficiency of hydrological modeling obviously.
【Fund】: 中国科学院知识创新工程重大项目(KZCX1-SW-12-1-2);; 领域前沿项目(CXNIGLAS- A02- 11)联合资助.
【CateGory Index】: P334
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