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<p>The traditional approaches for reservoir inflow forecasting are deterministic forecasts with shorter lead times. The present study aims to carry out an in-depth analysis of control as well as ensemble forecasts for reservoir inflow prediction. The ensemble approach has been utilised to provide a range of predictions moving beyond the deterministic forecasts. The ensemble weather forecasts have been generated by Numerical Weather Prediction (NWP) models and provided by various agencies such as ECMWF (European Centre for Medium Range Weather Forecasting), UK Meteorological Office (UKMO), National Centers for Environmental Prediction (NCEP), India Meteorological Department (IMD), and China Meteorological Administration (CMA), etc. TIGGE (The International Grand Global Ensemble) provides the deterministic control forecasts along with the perturbed ensemble forecasts. A hydrodynamic model is developed that leverages ensemble forecasts and control forecasts of GEFS (Global Ensemble Forecasting System) and NEPS (NCMRWF Ensemble Precipitation System). The model integrates hydrometeorological precipitation forecasts with the hydrological processes to predict reservoir inflows, enabling water resources management and flood risk assessment. The model-simulated inflows have been bias-corrected to improve their accuracy and compared with the observed inflows. The whole framework has been evaluated for Ukai reservoir situated across river Tapi, India. The comparison of simulated inflows has been made with observed inflows based on the performance measures. The ensemble-based inflow forecasting approach outperforms the control forecast-based inflow predictions for higher lead times. The accuracy of ensemble forecast is better than control forecasts. NEPS outperforms the GEFS for a 1-day lead time, while GEFS performs better for two to five-day lead times. The results indicate that the ensemble forecasts enhance inflow prediction by providing a range of expected inflows, including both maximum and minimum estimates. This enables reservoir operators to plan releases with a lead time of one to five days, thereby supporting the timely initiation of advance flood mitigation measures to avoid damages and loss of lives.</p>

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Analysis of ensemble and control forecasts from GEFS and NEPS for reservoir inflow prediction

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Ayushi Panchal,
  • S. M. Yadav

摘要

The traditional approaches for reservoir inflow forecasting are deterministic forecasts with shorter lead times. The present study aims to carry out an in-depth analysis of control as well as ensemble forecasts for reservoir inflow prediction. The ensemble approach has been utilised to provide a range of predictions moving beyond the deterministic forecasts. The ensemble weather forecasts have been generated by Numerical Weather Prediction (NWP) models and provided by various agencies such as ECMWF (European Centre for Medium Range Weather Forecasting), UK Meteorological Office (UKMO), National Centers for Environmental Prediction (NCEP), India Meteorological Department (IMD), and China Meteorological Administration (CMA), etc. TIGGE (The International Grand Global Ensemble) provides the deterministic control forecasts along with the perturbed ensemble forecasts. A hydrodynamic model is developed that leverages ensemble forecasts and control forecasts of GEFS (Global Ensemble Forecasting System) and NEPS (NCMRWF Ensemble Precipitation System). The model integrates hydrometeorological precipitation forecasts with the hydrological processes to predict reservoir inflows, enabling water resources management and flood risk assessment. The model-simulated inflows have been bias-corrected to improve their accuracy and compared with the observed inflows. The whole framework has been evaluated for Ukai reservoir situated across river Tapi, India. The comparison of simulated inflows has been made with observed inflows based on the performance measures. The ensemble-based inflow forecasting approach outperforms the control forecast-based inflow predictions for higher lead times. The accuracy of ensemble forecast is better than control forecasts. NEPS outperforms the GEFS for a 1-day lead time, while GEFS performs better for two to five-day lead times. The results indicate that the ensemble forecasts enhance inflow prediction by providing a range of expected inflows, including both maximum and minimum estimates. This enables reservoir operators to plan releases with a lead time of one to five days, thereby supporting the timely initiation of advance flood mitigation measures to avoid damages and loss of lives.