Streamflow estimation using satellite-retrieved water fluxes and machine learning technique over monsoon-dominated catchments of India

摘要

In this study, advanced scatterometer (ASCAT) soil moisture data is employed to compute the basin water index (BWI) over six river basins of India for 10 years (2007-2016). The BWI time series is assessed for the development of its relationship with observed streamflow. Further, a popular ensemble learning technique, random forest, is employed to compute the 10-d streamflow using the BWI time series. Moreover, the results are compared with the classical rainfall-runoff model forced with satellite-based precipitation and evapotranspiration, BWI-rainfall-runoff model, and Global Flood Awareness System (GloFAS). The performance of the model is evaluated in terms of multiple efficiency measures, viz. Nash-Sutcliffe efficiency (NSE), correlation coefficient (R) and root mean square error (RMSE). The results reveal the BWI-rainfall-runoff model is the most accurate model for prediction of discharge. The performance of the BWI-rainfall-runoff model is very good over four of six catchments and good to satisfactory over the remaining two catchments.