A Streamflow Bias Correction and Validation Method for GEOGloWS ECMWF Streamflow Services

Abstract:

The GEOGloWS ECMWF Streamflow Service is a global streamflow prediction system (GSPS) that provides access through web services to 15-day global streamflow forecast based on the ensemble predictions of the European Centre for Medium-Range Weather Forecasts (ECMWF) and a historical simulation based on ERA-5 precipitation data. One of the main concerns for decision-makers is the accuracy and uncertainty of hydrologic models, especially a global model largely. This uncertainty is due to the challenge of gathering and processing the needed local data to validate these large-scale models. For global models, this is a challenge as the ability to perform validation over large domains is limited by both data and human resources. In this presentation, we will describe the extension of a previously published method to correct the bias in the GEOGloWS historical simulated streamflow. The method is based on flow duration curves of the observed and simulated data. We present case studies of several different countries where observed data were available for demonstration. We can apply this approach to bias correction using monthly flow duration curves to account for temporal variations in bias that may result. Temporal variations mean that both high and low biases can occur at different times of the year at the same stations. As using the GEOGloWS streamflow forecast is an important element in the SERVIR-Amazonia efforts to increase capacity in water resources and disaster management, our presentation focuses on case studies in the Americas. For each case study, we demonstrate an improvement in the bias-corrected historical simulation. These results are encouraging and suggest that our bias correction method can be used to locally improve global forecasts where historical observations are available.