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

Lookup NU author(s): Professor Andras Bardossy

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Knowledge of spatio-temporal rainfall patterns isrequired as input for distributed hydrologic models used fortasks such as flood runoff estimation and modelling. Normally,these patterns are generated from point observationson the ground using spatial interpolation methods. However,such methods fail in reproducing the true spatio-temporalrainfall pattern, especially in data-scarce regions with poorlygauged catchments, or for highly dynamic, small-scale rainstormswhich are not well recorded by existing monitoringnetworks. Consequently, uncertainties arise in distributedrainfall–runoff modelling if poorly identified spatio-temporalrainfall patterns are used, since the amount of rainfall receivedby a catchment as well as the dynamics of the runoffgeneration of flood waves is underestimated. To addressthis problem we propose an inverse hydrologic modellingapproach for stochastic reconstruction of spatio-temporalrainfall patterns. The methodology combines the stochasticrandom field simulator Random Mixing and a distributedrainfall–runoff model in a Monte Carlo framework. The simulatedspatio-temporal rainfall patterns are conditioned onpoint rainfall data from ground-based monitoring networksand the observed hydrograph at the catchment outlet and aimto explain measured data at best. Since we infer a threedimensionalinput variable from an integral catchment response,several candidates for spatio-temporal rainfall patternsare feasible and allow for an analysis of their uncertainty.The methodology is tested on a synthetic rainfall–runoff event on sub-daily time steps and spatial resolutionof 1 km2 for a catchment partly covered by rainfall. A setof plausible spatio-temporal rainfall patterns can be obtainedby applying this inverse approach. Furthermore, results of areal-world study for a flash flood event in a mountainous aridregion are presented. They underline that knowledge aboutthe spatio-temporal rainfall pattern is crucial for flash floodmodelling even in small catchments and arid and semiaridenvironments.


Publication metadata

Author(s): Grundmann J, Hörning S, Bardossy A

Publication type: Article

Publication status: Published

Journal: Hydrology and Earth System Science

Year: 2019

Volume: 23

Pages: 225-237

Online publication date: 16/01/2019

Acceptance date: 21/12/2018

Date deposited: 05/03/2021

ISSN (print): 1027-5606

ISSN (electronic): 1607-7938

Publisher: Copernicus GmbH

URL: https://doi.org/10.5194/hess-23-225-2019

DOI: 10.5194/hess-23-225-2019


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