Radar‐based flood forecasting in small catchments, exemplified by the Goldersbach catchment, Germany

Abstract

Although draining an area of only 75 km², the Goldersbach caused several times severe flooding in the city of Tubingen, Germany. To cope with this, a flood management was set up based on flood forecasting, partial retention in reservoirs and local flood protection. Due to the small catchment size, the anticipated flood‐forecast lead time of three hours could only be achieved by using local, weather‐radar based rainfall forecasts for the next 1.5 hours. In short‐term rainfall forecasting, knowledge of the current rainfield advection is crucial. Therefore, two advection estimation techniques were applied: one based on the Doppler effect the other on covariance maximization. To combine the advantages of the available sources of rainfall observation, namely radar and rain gauges, a method for ‘geostatistical merging’ was developed. It preserves both the relatively reliable mean rainfall measurements from the rain gauges and the high spatial resolution of the radar image. Based on the advection estimates, a short‐term, auto‐regressive forecast model (SCM, or Spectrum‐Corrected Markov chain model) was developed. It follows a two‐step hierarchical approach. A bivariate, auto‐regressive process forecasts the large‐scale development of rainfall in a radar image. The individual development of each gridcell in the image is forecasted by a Markov chain approach. Finally, two rainfall‐runoff models are used for short‐term flood forecasting. The first, Fgmod, is an event‐based model, the second, HBV‐IWS, is a continuous water balance model. Both rainfall‐runoff models, in combination with the rainfall forecast, allow reasonable discharge estimates for up to three hours.

Keywords:

• Weather radar
• rainfall forecast
• flood forecast
• small catchment