Weather radar-based quantitative precipitation estimation (QPE) is in principle superior to the areal precipitation estimated by using rain gauge data only, and therefore has become increasingly popular in applications such as hydrological modeling. The present study investigates the potential of using multiannual radar QPE data in coupled surface water - groundwater modeling with emphasis given to the groundwater component. Since the radar QPE is partly dependent on the rain gauge observations, it is necessary to evaluate the impact of rain gauge network density on the quality of the estimated rainfall and subsequently the simulated hydrological responses. A headwater catchment located in western Denmark is chosen as the study site. Two hydrological models are built using the MIKE SHE code, where they have identical model structures expect for the rainfall forcing: one model is based on rain gauge interpolated rainfall, while the other is based on radar QPE which is a combination of both radar and rain gauge information. The two hydrological models are inversely calibrated and then validated against field observations. The model results show that the improvement introduced by using radar QPE data is in fact more obvious to groundwater than to surface water at daily scale. Moreover, substantial negative impact on the simulated hydrological responses is observed due to the cut down in operational rain gauge network between 2006 and 2010. The radar QPE based model demonstrates the added value of the extra information from radar when rain gauge density decreases; however it is not able to sustain the level of model performance preceding the reduction in number of rain gauges. Key Points Radar data have strong impact on simulating groundwater head. Radar data are more valuable when fewer rain gauges are available. Loss of model performance happens when rain gauge density decreases.
- Programområde 2: Vandressourcer