Regionalizing non-parametric precipitation amount models on different temporal scales
Tobias Mosthaf and András Bárdossy
Institute for Modeling Hydraulic and Environmental Systems, Universität Stuttgart, Stuttgart, Germany
Received: 02 Sep 2016 – Accepted for review: 09 Oct 2016 – Discussion started: 17 Oct 2016
Abstract. Parametric distribution functions are commonly used to model precipitation amounts. Precipitation amounts themselves are crucial for stochastic rainfall generators or weather generators. Non-parametric kernel density estimates (KDEs) offer a more flexible way to model precipitation amounts. As it is already stated in their name, these models do not exhibit a parameter which can easily be regionalized to run rainfall generators at ungauged locations as well as at gauged locations. To overcome this deficiency we present a new interpolation scheme for non-parametric models and evaluate it for different temporal resolutions ranging from hourly to monthly. During the evaluation the non-parametric methods are compared to commonly used parametric models like the two parameter gamma and the mixed exponential distribution. As water volume is considered to be an essential parameter for applications like flood modeling, a Lorenz-curve based criterion is also introduced. To add value to the estimation of data at sub-daily resolutions, we incorporated the plentiful daily measurements in the interpolation scheme and this idea was evaluated. The study region is the federal state of Baden-Württemberg in the southwest of Germany with more than 500 rain gauges. The validation results show that the newly proposed non-parametric interpolation scheme works, and additionally seems to be more robust compared to parametric interpolation schemes, and that the incorporation of daily values in the regionalization of sub-daily models is very beneficial.
Mosthaf, T. and Bárdossy, A.: Regionalizing non-parametric precipitation amount models on different temporal scales, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-458, in review, 2016.