Hydrological Analysis Using Satellite Remote Sensing Big Data and CREST Model

Abstract

Hydrological modeling significantly contributes to the understanding of catchment water balance and water resource management and mitigates negative impacts of flooding. Considering the advantages of satellite remote sensing big data and the coupled routing and excess storage (CREST) model, this paper investigates the hydrological modeling in the Shehong basin during 2006–2013. The results show that humid Shehong basin has main rainfalls in summer (From May to September). For the monthly average rainfall and streamflow, there is a remarkable increase (+52%) in discharge and a smaller increase (+18%) in rainfall in the second period (2010–2013) relative to the first period (2006–2009). The CREST model was calibrated using China gauge-based daily precipitation analysis for the period of 2006–2009, followed by a favorable performance with Nash-Sutcliffe coefficient efficiency (NSCE) of 0.77, correlation coefficient (CC) up to 0.88 and −11% Bias. The model validation shows an error metric with NSCE of 0.74, CC of 0.87 and −11.7% Bias. In terms of water balance modeling results at Shehong basin, the runoff and rainfall estimates from CREST model coincide well with the gauge observations, indicating the model captures the appropriate signature of soil moisture variability. Therefore, the satellite-based precipitation product is feasible in hydrological prediction, and the CREST models the interaction between surface and subsurface water flow process in the Shehong basin.