Improving the reservoir inflow prediction using TIGGE ensemble data and hydrological model for Dharoi Dam, India

Abstract

Flooding occurs frequently compared to other natural disasters. Less developed countries are severely affected by floods. This research provides an integrated hydrometeorological system that forecasts hourly reservoir inflows using a full physically based rainfall–runoff and numerical weather models. This study develops a 5-day lead time reservoir inflow prediction using TIGGE ensemble datasets from ECMWF, UKMO, and NCEP for the Dharoi Dam in Gujarat, India. The ensemble data were post-processed using censored non-homogeneous Linear Regression and Bayesian model averaging approach. These post-processed data were used in a hydrological model to simulate hydrological processes and predict Dharoi Dam reservoir inflows. Results show that ECMWF with a BMA approach and HEC-HMS hydrological model can predict reservoir inflows in the Sabarmati River basin. The correlation result of an observed reservoir inflow is 0.91. This research can help regional water resource managers and government officials to plan and manage water resources. HIGHLIGHTS A novel ensemble approach was proposed to predict reservoir inflows.; Spatial, hydrological, and meteorological data were used as the input for the semi-distributed hydrological model.; Three ensemble data (ECMWF, NCEP, and UKMO) were used by combining the runoff results of the semi-distributed models to boost the overall efficiency.; Developed 1- to 5-day lead time reservoir inflow prediction using a hybrid ensemble and hydrological model.;

Keywords

• ensemble
• hydrological model
• precipitation
• reservoir inflow prediction
• sabarmati
• tigge