ABSTRACT Streamflow modeling is essential to investigate processes in the hydrologic cycle and important for water resource management application. However, in-situ hydrologic data paucity, because of various factors such as economic, political, instrument malfunctioning, and poor spatial distribution, makes the modeling process challenging. To overcome this limitation, we introduced a satellite remote sensing-based machine learning approach – boosted regression tree (BRT) – that integrates spatial land surface and climate variables that describe the sub-units, and applied it in three variable size watersheds in the Upper Mississippi River Basin (UMRB), USA. The model simulation results were tested using an independent dataset and showed Nash–Sutcliffe efficiency values of 0.80, 0.76, and 0.69 for the UMRB, Illinois River Watershed, and Raccoon River Watershed, respectively. In addition, we compared the performance of the machine learning models with existing process-based modeling results. Overall performance is comparable with the process-based approaches, but with significantly less modeling effort and resources.

中文翻译：

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基于卫星的水文气候变量和机器学习在各种规模流域水流建模中的适用性

摘要 溪流建模对于研究水文循环过程至关重要，对水资源管理应用也很重要。然而，由于经济、政治、仪器故障和空间分布不良等各种因素，现场水文数据缺乏，使得建模过程具有挑战性。为了克服这一限制，我们引入了一种基于卫星遥感的机器学习方法——增强回归树 (BRT)——它整合了描述子单元的空间地表和气候变量，并将其应用于上游的三个可变大小的流域。美国密西西比河流域 (UMRB)。使用独立数据集对模型模拟结果进行了测试，结果显示伊利诺伊州河流域 UMRB 的 Nash-Sutcliffe 效率值为 0.80、0.76 和 0.69。和浣熊河流域。此外，我们将机器学习模型的性能与现有的基于过程的建模结果进行了比较。整体性能与基于过程的方法相当，但建模工作和资源要少得多。