Data assimilation (DA) has proven to be a useful technique in real-time hydrological modeling and forecasting. Jointly assimilating both surface water and groundwater data has promising application value for hydrological simulations in areas where surface water and groundwater are closely linked; however, such studies have not been intensively reported. In addition, the role of the quality of precipitation forecast has not been fully addressed in real-time forecasting using a coupled surface water - groundwater model, where the model evaluation includes both deterministic and probabilistic forecasts. In the present study, we use the MIKE SHE hydrological model code in conjunction with the Ensemble Transform Kalman Filter DA technique. The study area is a small urbanized catchment in Denmark. The model is run in simulated real-time using historical numerical weather prediction forecasts. The results show that DA can significantly reduce model bias and thereby improve model performance for both surface water and groundwater simulations. Comparing the impact of DA and rainfall forecast quality, it is found that, for streamflow forecasts, the most important factor is the quality of the rainfall data; whereas for groundwater head forecasts, the initial state at time of forecast is more important. We also find that inclusion of rainfall forecast uncertainty may be important for simulating a single event, however, it is not vital if long-term average model performance is of interest.
- Programområde 2: Vandressourcer