The landscapes across much of the Midwestern United States are characterized by glacial activity that left water-holding kettles, depressions, and potholes. Until recently, traditional watershed algorithms assumed these depressions to be errors in the elevation data and filled them as a means of correction, when many of these features may rarely fill and have the potential to dramatically affect surface flow patterns. These depressions play an important role in hydrology, water management, site planning, and agronomy. An optimized sequential depression-filling algorithm (SDFA) was developed which fills these water-holding features sequentially based on their respective retention capacity and contributing area. Outputs reflect the state of connectivity following the application of a user-specified amount of rainfall excess (i.e., in excess of infiltration); depressions that have not been filled will remain as their own hydrologically common subcatchments. A performant algorithm is integral to future delivery and utilization by practitioners both in the office and in the field. The optimal set of subroutines were able to fill all of the depressions in a 239 km² watershed in northeastern Indiana in 42 s and 1.5 h on a consumer desktop computer for digital elevation models (DEMs) at 30 m and 3 m resolutions, respectively (O(n²) overall performance).

中文翻译：

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美国印第安纳州东北部流域的基于降雨的连续洼地填充算法和评估

美国中西部大部分地区的景观以冰川活动为特征，留下了盛水的水壶、洼地和坑洼。直到最近，传统的分水岭算法都假设这些洼地是高程数据中的错误，并将其填充作为校正的一种手段，而这些特征中的许多可能很少被填充并有可能显着影响地表流动模式。这些洼地在水文、水资源管理、场地规划和农学方面发挥着重要作用。开发了一种优化的顺序凹陷填充算法 (SDFA)，该算法根据它们各自的保留容量和贡献面积顺序填充这些保水特征。输出反映了在应用用户指定的过量降雨量（即，过度渗透）；尚未被填平的洼地将保留为它们自己的水文共同子汇水面积。高性能算法对于办公室和现场的从业者未来的交付和使用是不可或缺的。最佳子程序集能够填满 239 公里内的所有洼地在 42 秒和 1.5 小时内，分别在 30 m 和 3 m 分辨率下（O ( n ² ) 整体性能）的消费者台式计算机上的²分水岭，用于数字高程模型 (DEM )。