An efficient depression processing algorithm for hydrologic analysis

Depression filling and direction assignment over flat areas are critical issues in hydrologic analysis. This paper proposes an efficient approach for the treatment of depressions and flat areas, based on gridded digital elevation models. Being different from the traditional raster neighborhood process which is time consuming, a hybrid method of vector and raster manipulation is designed for depression filling, followed by a neighbor-grouping scan method to assign the flow direction over flat areas. The results from intensive experiments show that there is a linear relationship between time efficiency and data volume, and the extracted hydrologic structures of flat areas are also more reasonable than those proposed by the existing methods.