Effects of spatial variability of precipitation for process-orientated hydrological modelling: results from two nested catchments

D. Tetzlaff (1) , U. Uhlenbrook (2)

1 Department of Geography and Environment
2 Institute of Hydrology

The importance of considering the spatial distribution of rainfall for process-oriented hydrological modelling is well-known. However, the application of rainfall radar data to provide such detailed spatial resolution is still under debate. In this study the process-oriented TAC^D (Tracer Aided Catchment model, Distributed) model had been used to investigate the effects of different spatially distributed rainfall input on simulated discharge and runoff components on an event base. TAC^D is fully distributed (50x50 m² raster cells) and was applied on an hourly base. As model input rainfall data from up to 11 ground stations and high resolution rainfall radar data from an operational C-band radar were used. For seven rainfall events the discharge simulations were investigated in further detail for the mountainous Brugga catchment (40 km²) and the St. Wilhelmer Talbach (15.2 km²) sub-basin, which are located in the Southern Black Forest Mountains, south-west Germany. The significance of spatial variable precipitation data was clearly demonstrated. Dependent on event characteristics, localized rain cells were occasionally poorly captured even by a dense ground station network, and this resulted in insufficient model results. For such events, radar data can provide better input data. However, an extensive data adjustment using ground station data is required. Therefore, a new method was developed that considers the rainfall intensity distribution. The use of the distributed catchment model allowed further insights into spatially variable impacts of different rainfall estimates. Impacts for discharge predictions are the largest in areas that are dominated by the production of fast runoff components. To conclude, the improvements for distributed runoff simulation using high resolution rainfall radar input data are strongly dependent on the investigated scale, the event characteristics, the existing monitoring network and, last but not least, the applied model.

Domaines: Interfaces continentales, environnement; Océan, Atmosphère; Sciences de la Terre

D. Tetzlaff, U. Uhlenbrook. Effects of spatial variability of precipitation for process-orientated hydrological modelling: results from two nested catchments. Hydrology and Earth System Sciences Discussions, 2005, 2 (1), pp.119-154. ⟨hal-00298620⟩