Abstract
Abstract. Hydrological modelling of floods relies on precipitation data with a high resolution in space and time. A reliable spatial representation of short time step rainfall is often difficult to achieve due to a low network density. In this study hourly precipitation was spatially interpolated with the multivariate geostatistical method kriging with external drift (KED) using additional information from topography, rainfall data from the denser daily networks and weather radar data. Investigations were carried out for several flood events in the time period between 2000 and 2005 caused by different meteorological conditions. The 125 km radius around the radar station Ummendorf in northern Germany covered the overall study region. One objective was to assess the effect of different approaches for estimation of semivariograms on the interpolation performance of short time step rainfall. Another objective was the refined application of the method kriging with external drift. Special attention was not only given to find the most relevant additional information, but also to combine the additional information in the best possible way. A multi-step interpolation procedure was applied to better consider sub-regions without rainfall. The impact of different semivariogram types on the interpolation performance was low. While it varied over the events, an averaged semivariogram was sufficient overall. Weather radar data were the most valuable additional information for KED for convective summer events. For interpolation of stratiform winter events using daily rainfall as additional information was sufficient. The application of the multi-step procedure significantly helped to improve the representation of fractional precipitation coverage.
Highlights
Precipitation data with a high resolution in space and time are the driving forces for hydrological modelling of floods
After the “Introduction” the section “Methods” follows with a description of the semivariogram estimation techniques and a description of the interpolation method kriging with external drift (KED) considering the special cases which were applied here
Kriging with external drift (KED) is a simple and efficient algorithm allowing the incorporation of one or more secondary variables, which are assumed to be linearly related to the expected value of the primary variable
Summary
Precipitation data with a high resolution in space and time are the driving forces for hydrological modelling of floods. To obtain high space-time resolution fields of precipitation for flood studies it is necessary to apply sophisticated interpolation methods on the short time step rainfall data in combination with radar information and other available additional information. (2008) have developed a merging method combining a mean precipitation field interpolated from rain gauge observations with information about the spatial variability from radar data. After the “Introduction” the section “Methods” follows with a description of the semivariogram estimation techniques and a description of the interpolation method kriging with external drift (KED) considering the special cases which were applied here. In the final section the main findings are concluded and an outlook is presented
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