Hydrological ensemble forecasting at ungauged basins: using neighbour catchments for model setup and updating

Abstract

In flow forecasting, additionally to the need of long time series of historic discharges for model setup and calibration, hydrological models also need real-time discharge data for the updating of the initial conditions at the time of the forecasts. The need of data challenges operational flow forecasting at ungauged or poorly gauged sites. This study evaluates the performance of different choices of parameter sets and discharge updates to run a flow forecasting model at ungauged sites, based on information from neighbour catchments. A cross-validation approach is applied on a set of 211 catchments in France and a 17-month forecasting period is used to calculate skill scores and evaluate the quality of the forecasts. A reference situation, where local information is available, is compared to alternative situations, which include scenarios where no local data is available at all and scenarios where local data started to be collected at the beginning of the forecasting period. To cope with uncertainties from rainfall forecasts, the model is driven by ensemble weather forecasts from the PEARP-Météo-France ensemble prediction system. The results show that neighbour catchments can contribute to provide forecasts of good quality at ungauged sites, especially with the transfer of parameter sets for model simulation. The added value of local data for the operational updating of the hydrological ensemble forecasts is highlighted.