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Open AccessArticle

Hydrological Modelling in Data Sparse Environment: Inverse Modelling of a Historical Flood Event

Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, D-70569 Stuttgart, Germany
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Water 2020, 12(11), 3242; https://doi.org/10.3390/w12113242
Received: 14 October 2020 / Revised: 10 November 2020 / Accepted: 17 November 2020 / Published: 19 November 2020
(This article belongs to the Section Hydrology and Hydrogeology)
We dealt with a rather frequent and difficult situation while modelling extreme floods, namely, model output uncertainty in data sparse regions. A historical extreme flood event was chosen to illustrate the challenges involved. Our aim was to understand what the causes might have been and specifically to show how input and model parameter uncertainties affect the output. For this purpose, a conceptual model was calibrated and validated with recent data rich time period. Resulting model parameters were used to model the historical event which subsequently resulted in a rather poor hydrograph. Due to the bad model performance, a spatial simulation technique was used to invert the model for precipitation. Constraints, such as taking the precipitation values at historical observation locations in to account, with correct spatial structures and following the observed regional distributions were used to generate realistic precipitation fields. Results showed that the inverted precipitation improved the performance significantly even when using many different model parameters. We conclude that while modelling in data sparse conditions both model input and parameter uncertainties have to be dealt with simultaneously to obtain meaningful results. View Full-Text
Keywords: inverse modelling; data uncertainty; parameter uncertainty; data scarcity inverse modelling; data uncertainty; parameter uncertainty; data scarcity
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MDPI and ACS Style

Bárdossy, A.; Anwar, F.; Seidel, J. Hydrological Modelling in Data Sparse Environment: Inverse Modelling of a Historical Flood Event. Water 2020, 12, 3242. https://doi.org/10.3390/w12113242

AMA Style

Bárdossy A, Anwar F, Seidel J. Hydrological Modelling in Data Sparse Environment: Inverse Modelling of a Historical Flood Event. Water. 2020; 12(11):3242. https://doi.org/10.3390/w12113242

Chicago/Turabian Style

Bárdossy, András; Anwar, Faizan; Seidel, Jochen. 2020. "Hydrological Modelling in Data Sparse Environment: Inverse Modelling of a Historical Flood Event" Water 12, no. 11: 3242. https://doi.org/10.3390/w12113242

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