Stream flow prediction using TIGGE ensemble precipitation forecast data for Sabarmati river basin

Abstract

Abstract Flooding is the most prevalent natural disaster globally. Increasing flood frequency affects developing nations as these countries lack strong forecasting systems. The most flood-prone urban regions are near the coast or riverbanks. Using The International Grand Global Ensemble (TIGGE) data, a coupled atmospheric-hydrologic ensemble flood forecasting model for the Sabarmati river was developed. Incorporating numerical weather prediction (NWP) information into flood forecasting systems can increase lead times from hours to days. When predicting the weather, we employed numerous NWP models from various prediction centers. European Center for Medium Range Weather Forecasts (ECMWF), United Kingdom Meteorological Office (UKMO) and National Centers for Environmental Prediction (NCEP) data with a 5-day advance time are coupled with the HEC-HMS model to provide ensemble stream flow predictions. The ensemble flood forecasting model uses the 2015 flood season as a test scenario. In this research, we discovered that TIGGE ensemble prediction data can be useful for prediction of stream flow and results showed effective flood forecasting for Sabarmati river. HEC-HMS, a semi-distributed hydrologic model, uses ECMWF, NCEP, and UKMO precipitation ensembles. ECMWF shows that 90% of the correlation with observed data and peak time and peak discharge is also match with the observed discharge with a peak on 29 July 2015 with 9,300 cumecs. Danger probability may be accurately predicted based on peak time and flood warning probability distributions.