Jiang Ai-Ling, Hsu Kuolin, Sanders Brett F, Sorooshian Soroosh
Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA, USA.
Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA, USA.
Adv Water Resour. 2023 Jun;176. doi: 10.1016/j.advwatres.2023.104449. Epub 2023 Apr 28.
Land surface depressions play a central role in the transformation of rainfall to ponding, infiltration and runoff, yet digital elevation models (DEMs) used by spatially distributed hydrologic models that resolve land surface processes rarely capture land surface depressions at spatial scales relevant to this transformation. Methods to generate DEMs through processing of remote sensing data, such as optical and light detection and ranging (LiDAR) have favored surfaces without depressions to avoid adverse slopes that are problematic for many hydrologic routing methods. Here we present a new topographic conditioning workflow, Depression-Preserved DEM Processing (D2P) algorithm, which is designed to preserve physically meaningful surface depressions for depression-integrated and efficient hydrologic modeling. D2P includes several features: (1) an adaptive screening interval for delineation of depressions, (2) the ability to filter out anthropogenic land surface features (e.g., bridges), (3) the ability to blend river smoothing (e.g., a general downslope profile) and depression resolving functionality. From a case study in the Goodwin Creek Experimental Watershed, D2P successfully resolved 86% of the ponds at a DEM resolution of 10 m. Topographic conditioning was achieved with minimum impact as D2P reduced the number of modified cells from the original DEM by 51% compared to a conventional algorithm. Furthermore, hydrologic simulation using a D2P processed DEM resulted in a more robust characterization on surface water dynamics based on higher surface water storage as well as an attenuated and delayed peak streamflow.
地表洼地在降雨转化为积水、入渗和径流的过程中起着核心作用，然而，用于解析地表过程的空间分布式水文模型所使用的数字高程模型（DEM），在与这种转化相关的空间尺度上很少能捕捉到地表洼地。通过处理遥感数据（如光学和光探测与测距（LiDAR））生成DEM的方法，倾向于选择没有洼地的表面，以避免对许多水文路由方法来说存在问题的不利坡度。在此，我们提出一种新的地形条件处理工作流程——保留洼地的DEM处理（D2P）算法，该算法旨在保留对综合考虑洼地的高效水文建模具有物理意义的地表洼地。D2P包括几个特点：（1）用于划定洼地的自适应筛选间隔；（2）过滤掉人为地表特征（如桥梁）的能力；（3）融合河道平滑（如一般下坡剖面）和洼地解析功能的能力。通过对古德温溪实验流域的案例研究，在10米的DEM分辨率下，D2P成功解析了86%的池塘。由于D2P与传统算法相比，将原始DEM中修改的单元格数量减少了51%，因此在对地形影响最小的情况下实现了地形条件处理。此外，使用经D2P处理的DEM进行水文模拟，基于更高的地表水储量以及衰减和延迟的洪峰流量，对地表水动态进行了更稳健的表征。
