Benefits of upstream data for downstream streamflow forecasting: data assimilation in a semi-distributed flood forecasting model

Abstract

An hourly hydrological forecasting model (GRP), used for flood forecasting in France, has been enhanced by developing a semi-distributed version (GRPS). This new model addresses some limitations of the original lumped approach by integrating flow observations from upstream stations to improve downstream flood predictions. A comparison of GRP and GRPS was conducted on a large set of flood events in nested catchments in France. Results indicate that GRPS slightly outperforms GRP up to the lead time of the last upstream flow observations reaching the downstream station, though performance gains vary widely across events. Event classification identified detrimental interactions between data assimilation methods and river routing model errors. A sensitivity test suggested that enhancing both the propagation model and the assimilation scheme could reduce poor performance at short lead times for some events. The study also demonstrated that upstream forecast quality significantly impacts downstream forecast accuracy. Overall, the findings confirm the benefits of incorporating multiple flow observations into a semi-distributed model and suggest several avenues for further improvement.

Keywords

• Flood forecasting
• hydrological model
• spatial discretisation
• data assimilation
• prévision des crues
• modélisation hydrologique