Physical Sciences and Mathematics Commons^™

Using Satellite-Based Hydro-Climate Variables And Machine Learning For Streamflow Modeling At Various Scales In The Upper Mississippi River Basin, Dongjae Kwon

Theses and Dissertations

Streamflow data are essential to study the hydrologic cycle and to attain appropriate water resource management policies. However, the availability of gauge data is limited due to various reasons such as economic, political, instrumental malfunctioning, and poor spatial distribution. Although streamflow can be simulated by process-based and machine learning approaches, applicability is limited due to intensive modeling effort, or its black-box nature, respectively. Here, we introduce a machine learning (Boosted Regression Tree (BRT)) approach based on remote sensing data to simulate monthly streamflow for three of varying sizes watersheds in the Upper Mississippi River Basin (UMRB). By integrating spatial land …