Efficient Priority-Flood depression filling in raster digital elevation models |
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| Authors: | Hongqiang Wei Suhua Fu |
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| Institution: | 1. Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China;2. School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China;3. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences, Yangling, People’s Republic of China;4. Faculty of Geographical Science, Beijing Normal University, Beijing, People’s Republic of China |
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| Abstract: | Depressions in raster digital elevation models (DEM) present a challenge for extracting hydrological networks. They are commonly filled before subsequent algorithms are further applied. Among existing algorithms for filling depressions, the Priority-Flood algorithm runs the fastest. In this study, we propose an improved variant over the fastest existing sequential variant of the Priority-Flood algorithm for filling depressions in floating-point DEMs. The proposed variant introduces a series of improvements and greatly reduces the number of cells that need to be processed by the priority queue (PQ), the key data structure used in the algorithm. The proposed variant is evaluated based on statistics from 30 experiments. On average, our proposed variant reduces the number of cells processed by the PQ by around 70%. The speed-up ratios of our proposed variant over the existing fastest variant of the Priority-Flood algorithm range from 31% to 52%, with an average of 45%. The proposed variant can be used to fill depressions in large DEMs in much less time and in the parallel implementation of the Priority-Flood algorithm to further reduce the running time for processing huge DEMs that cannot be dealt with easily on single computers. |
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| Keywords: | Depression filling DEM drainage network |
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