Application of Ensemble approach for Stream flow forecasting for Indian River basin

Flooding poses a significant threat to human life, property, and the environment, especially in semi-arid river basins where the occurrence of intense rainfall events can lead to flash floods. Early warning systems are crucial for mitigating the impact of floods, and accurate streamflow prediction is a key component of these systems. This research focuses on developing an ensemble approach for streamflow prediction to enhance the effectiveness of early warning systems in Indian river basin. Traditional deterministic models may struggle to capture the complex hydrological processes and uncertainties associated with these regions. In response to this challenge, ensemble methods, which combine multiple models or data sources, have gained popularity for improving the accuracy and reliability of predictions. The Indian River Basin faces unique challenges in water resource management due to its diverse hydrological characteristics and the impact of climate variability. This research presents an innovative application of an ensemble approach for streamflow forecasting tailored specifically for the complex dynamics of the Indian River Basin. The research employs a combination of hydrological models, meteorological data, and machine learning techniques to develop an ensemble streamflow prediction system. A Hydrological model such as the HEC-HMS is integrated into the ensemble to leverage their strengths and compensate for individual weaknesses. Additionally, machine learning was applied for post processing of the ensemble data. These are incorporated to capture non-linear relationships and improve the overall predictive performance. The study area selected for this research is a semi-arid Sabarmati river basin with a history of past flood. Historical streamflow data, meteorological observations and remote sensing data are utilized to calibrate and validate the ensemble prediction system. TIGGE Ensemble data from ECMWF, NCEP, IMD and NCMRWF were used. Research covers machine learning approaches post processing methods such as BMA, cNLR, HXLR, OLR, logreg, hlogreg, etc were applied. The probabilistic forecasts were validated using the Brier Score (BS), Area Under Curve (AUC) of Receiver Operator Characteristics (ROC) plots and reliability plots. The cNLR and BMA strategies for postprocessing performed exceptionally well with Brier score value 0.10 and RPS value 0.11 at all grid points for both methods.  The ROC-AUC values for the cNLR and BMA methods were found to be 91.87% and 91.82% respectively. Furthermore, the research focuses on developing an effective flood early warning system based on the ensemble predictions. The results of the ensemble streamflow prediction system are evaluated against traditional deterministic models and individual hydrological models. Performance metrics such as accuracy, precision, and lead time are analysed to assess the effectiveness of the ensemble approach in comparison to single-model predictions. The findings demonstrate the superiority of the ensemble method in capturing the variability of streamflow by improving the lead time for flood warnings. In conclusion, this research contributes to the advancement of flood prediction methods in Indian river basin by introducing an ensemble approach that combines hydrological models and machine learning techniques. The findings have implications for water resource management, disaster preparedness, and the sustainable development of semi-arid regions.