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Discussion papers | Copyright
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 Jul 2018

Research article | 02 Jul 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Stochastic reconstruction of spatio-temporal rainfall pattern by inverse hydrologic modelling

Jens Grundmann1, Sebastian Hörning2, and András Bárdossy3 Jens Grundmann et al.
  • 1Technische Universität Dresden, Institute of Hydrology and Meteorology, Dresden, Germany
  • 2University of Queensland, School of Earth and Environmental Sciences, Brisbane, Australia
  • 3Universität Stuttgart, Institute for Modelling Hydraulic and Environmental Systems, Stuttgart, Germany

Abstract. Knowledge about the spatio-temporal rainfall pattern is required as input for distributed hydrologic models to perform several tasks in hydrology like flood runoff estimation and modelling. Normally, these pattern are generated from point observations on the ground using spatial interpolation methods. However, such methods fail in reproducing the true spatio-temporal rainfall pattern especially in data scarce regions with poorly gauged catchments or for highly dynamic, small scaled rainstorms which are not well recorded by existing monitoring networks. Consequently, uncertainties are associated with poorly identified spatio-temporal rainfall pattern in distributed rainfall-runoff modelling since the amount of rainfall received by a catchment as well as the dynamics of the runoff generation of flood waves are underestimated. For addressing this problem we propose an inverse hydrologic modelling approach for stochastic reconstruction of spatio-temporal rainfall pattern. The methodology combines the stochastic random field simulator Random Mixing and a distributed rainfall-runoff model in a Monte-Carlo framework. The simulated spatio-temporal rainfall pattern are conditioned on point rainfall data from ground monitoring networks as well as the observed hydrograph at catchment outlet and aims to explain measured data at best. Since we conclude from an integral catchment response on a three-dimensional input variable, several candidates of spatio-temporal rainfall pattern are possible which also describe their uncertainty. The methodology is testet on a synthetic rainfall-runoff event on subdaily timesteps and spatial resolution of 1km2 for a catchment covered by rainfall partly. Results show that a set of plausible spatio-temporal rainfall pattern can be obtained by applying the inverse approach. Furthermore, results of a real world study for a flash flood event in a mountainious arid region are presented. They underline that knowledge about the spatio-temporal rainfall pattern is crucial for flash flood modelling even in small catchments and arid and semiarid environments.

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