Simulation of streamflow for extreme events using hydro-meteorological approach in the semi-arid river basin of India

The modelling of rainfall-runoff simulation is of great importance for assessment of runoff quantities from rainfall data which is essential for optimizing water storage and mitigating flood hazards. One of the contemporary challenges is to simulate streamflow using the integrated hydro-meteorological approach, which depends on different synoptic conditions and model parameters. HEC-HMS model is run by the rainfall data generated from output of Weather Research Forecasting (WRF) model in the catchment of Mahi dam. Through this approach, the integration of atmospheric and hydrological models is established to evaluate the predictive capabilities of streamflow forecasts. To perform geospatial analysis of the catchment area, ArcGIS extension i.e. HEC-GeoHMS was used to generate the hydrologic elements for the HEC-HMS model. Here, we integrate hydrometeorological approach, in which two WRF simulated (Kain-Goddard-ACM2 parameterization scheme) extreme rainfall events i.e. 25 to 29 July 2015 and 22 to 26 August 2020 were integrated into calibrated HEC-HMS model. Model calibration was done from 1 to 31 August 2019. Daily rainfall, stream flow, and curve number were also used. The catchment is divided into five sub-basins with CWC gauging station over sub-basin 3. The performance and accuracy assessment of the models is calculated using several statistical indices such as the NSE, RMSE, and the coefficient of determination. The results showed that NSE, and R² values 0.89, and 0.89 for calibration, and 0.85, and 0.88 for validation of simulated event 2, were found in good agreement with the observed values. The novelty of this study lies in assessing the performance of the HEC-HMS model using rainfall data simulated by the WRF model, which demonstrated good accuracy in streamflow simulation. The findings of the study indicate that the calibrated HEC-HMS model can be applied for flow forecasting at short time intervals under future climate scenarios in similar environments. This model may serve as a valuable tool for water resource managers to predict and mitigate flood risks.