Evaluation of Satellite Precipitation Products in Simulating Streamflow in a Humid Tropical Catchment of India Using a Semi-Distributed Hydrological Model

Abstract

Precipitation obtained from rain gauges is an essential input for hydrological modelling. It is often sparse in highly topographically varying terrain, exhibiting a certain amount of uncertainty in hydrological modelling. Hence, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. In this study, an attempt was made to evaluate the Tropical Rainfall Measuring Mission (TRMM) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), employing a semi-distributed hydrological model, i.e., Soil and Water Assessment Tool (SWAT), for simulating streamflow and validating them against the flows generated by the India Meteorological Department (IMD) rainfall dataset in the Gurupura river catchment of India. Distinct testing scenarios for simulating streamflow were made to check the suitability of these satellite precipitation data. The TRMM was able to better estimate rainfall than CHIRPS after performing categorical and continuous statistical results with respect to IMD rainfall data. While comparing the performance of model simulations, the IMD rainfall-driven streamflow emerged as the best followed by the TRMM, CHIRPS-0.05, and CHIRPS-0.25. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) were in the range 0.63 to 0.86, 0.62 to 0.86, and −14.98 to 0.87, respectively. Further, an attempt was made to examine the spatial distribution of key hydrological signature, i.e., flow duration curve (FDC) in the 30–95 percentile range of non-exceedance probability. It was observed that TRMM underestimated the flow for agricultural water availability corresponding to 30 percent, even though it showed a good performance compared to the other satellite rainfall-driven model outputs.

Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka

“…Similar analysis was carried out many researchers [52,53]; however, most of them have not incorporated the ANN work to their analyses. In addition, some others have focused on the ANN work to showcase the streamflows with respect to the satellite-based precipitation products.…”

Resolution Dependence of Regional Hydro-Climatic Projection: A Case-Study for the Johor River Basin, Malaysia

“…Global sensitivity analysis shows that Initial SCS CN II value (CN2), soil evaporation compensation factor (ESCO), Manning's n for main channel (CH_N2), maximum canopy storage (CANMX) and groundwater "revap" coefficient (GW_REVAP) are the five most sensitive parameters in the JRB. CN2 that linked to surface runoff has been regarded as the most sensitive parameter in SWAT modeling in many tropical river basins [16,53,54]. A detailed description of each parameter can be found in the SWAT theoretical document written by Neitsch, et al [55].…”

Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India

“…The Western Ghats of India form watersheds of many rivers in India, including the Krishna basin, one of the three most important basins of southern India. The west-flowing rivers originating in the mountainous region of the Western Ghats flow through Konkan coastal plains and have pronounced hydrologic regimes owing to the localized weather and features of the climate (Mudbhatkal & Amai, 2018b;Sharannya et al, 2020).…”