Journal Article

FZJ-2020-02738

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Performance of a PDE-Based Hydrologic Model in a Flash Flood Modeling Framework in Sparsely-Gauged Catchments

Poméon, T. (Corresponding author)FZJ* ; Wagner, N.FZJ* ; Furusho, C.FZJ* ; Kollet, S.FZJ* ; Reinoso-Rondinel, R.Extern*

2020
MDPI Basel

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Please use a persistent id in citations: http://hdl.handle.net/2128/25455  doi:10.3390/w12082157

Abstract: Modeling and nowcasting of flash floods remains challenging, mainly due to uncertainty of high-resolution spatial and temporal precipitation estimates, missing discharge observations of affected catchments and limitations of commonly used hydrologic models. In this study, we present a framework for flash flood hind- and nowcasting using the partial differential equation (PDE)-based ParFlow hydrologic model forced with quantitative radar precipitation estimates and nowcasts for a small 18.5 km2 headwater catchment in Germany. In the framework, an uncalibrated probabilistic modeling approach is applied. It accounts for model input uncertainty by forcing the model with precipitation inputs from different sources, and accounts for model parameter uncertainty by perturbing two spatially uniform soil hydraulic parameters. Thus, sources of uncertainty are propagated through the model and represented in the results. To demonstrate the advantages of the proposed framework, a commonly used conceptual model was applied over the same catchment for comparison. Results show the framework to be robust, with the uncalibrated PDE-based model matching streamflow observations reasonably. The model lead time was further improved when forced with precipitation nowcasts. This study successfully demonstrates a parsimonious application of the PDE-based ParFlow model in a flash flood hindcasting and nowcasting framework, which is of interest in applications to poorly or ungauged watersheds.

Note: This study is part of the RealPEP (Near-Realtime Quantitative Precipitation Estimation and Prediction https://www2.meteo.uni-bonn.de/realpep/doku.php) P4 project (Evaluation of QPE and QPN improvements in a flash flood nowcasting framework with data assimilation), funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) under Grant No. FU 1185/1-1.

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Contributing Institute(s):

1. Agrosphäre (IBG-3)

Research Program(s):

1. 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) (POF3-255)
2. SFB 986 MGK - Integriertes Graduiertenkolleg (MGK) (221133179) (221133179)

Appears in the scientific report 2020

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