Soil moisture and streamflow data assimilation for streamflow prediction in the Narmada river basin

Abstract

An accurate streamflow forecast is vital for flood prediction and early warning systems. Notwithstanding the rising frequency and intensity of floods during the summer monsoon season in India, efforts to examine the utility of data assimilation (DA) for streamflow prediction remain limited. We examine soil moisture and streamflow DA to improve streamflow simulations in the Narmada River basin, considered a testbed. Data assimilation was performed using the Variable Infiltration Capacity (VIC) model at four gauge stations in the basin. First, we used the ensemble Kalman filter (EnKF) to assimilate the satellite soil moisture from the European Space Agency Climate Change Initiative (ESA-CCI) to the initial state of the VIC model. We examined the usefulness of observed streamflow from the India Water Resources Information System (India-WRIS) to improve the initial hydrological conditions of the VIC model in the streamflow DA during the summer monsoon (June-September) season from 1980 to 2018. The assimilation of ESA-CCI soil moisture showed less improvement in percent error reduction (PER) and efficiency index (EFF) (less than 2%) than the streamflow DA at all of the four gauge locations in the Narmada basin. On the other hand, the streamflow DA showed a significant improvement in PER and EFF (more than 10%) at all the gauge stations for both mean and high-flow conditions. Streamflow data assimilation improved errors in the magnitude and timing for the major floods in 1994 and 2013.