Development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for Dharoi Dam

摘要

The most frequent natural disaster is flooding. Advanced forecasting systems are lacking in developing countries. The majorityof urban areas are located close to flood plains for rivers. Accurate flood forecasting is necessary for reservoir planning andflood management. The Sabarmati River’s atmospheric-hydrologic ensemble flood forecasting model has been developedusing TIGGE data. Precipitation can be reliably predicted by TIGGE’s global ensemble numerical weather prediction (NWP) systems.By using NWP data, flood forecasting systems may be extended from hours to days. Ensemble weather forecasts areproduced using the European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Predictiontogether with 5-day lead times from TIGGE. The flood occurrences from 2015, 2017, and 2020 were used for the calibration andvalidation of the ensemble flood forecasting model. Bias was corrected using Bayesian model averaging (BMA), heterogeneousextended linear regression, censored non-homogeneous linear regression (cNLR), and other statistical downscaling techniques.Forecasted and downscaled precipitation data were checked using the Brier score and rank likelihood score. For cNLR, Brier’sscore performed admirably. The specificity vs. sensitivity performance of the cNLR and BMA approaches is 91.87 and 91.82,respectively, according to receiver operating characteristic and area under the curve diagrams. Models with the hybrid hydrologiccoupling approach accurately predict floods. Users may predict peak time and peak discharge hazard likelihood withreliability using peak time and flood warning probability distributions.