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Lookup NU author(s): Professor Andras BardossyORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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.
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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