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

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 surface and climate variables that describe the subwatersheds in a basin as an input dataset and streamflow as an output learning dataset in a machine learning model (MLM), relationships between watershed characteristics and streamflow are established. The testing results of NSE with UMRB, IRW, and RRW of 0.8042, 0.7593, and 0.6856, respectively showed the remote sensing-based MLM can be effectively applied to streamflow prediction and has advantages for large basins compared with the performances of process-based approaches. Further, Predictor Importance (PI) analysis revealed the most important remote sensing variables and the most representative subwatersheds