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Institute: search.filters.UBSInstitut.Institut für Wasser- und UmweltsystemmodellierungStart date: 2020Author: search.filters.author.Anwar, FaizanSubject: search.filters.subject.624

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Now showing 1 - 3 of 3
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    Hydrological modelling in data sparse environment : inverse modelling of a historical flood event
    (2020) Bárdossy, András; Anwar, Faizan; Seidel, Jochen
    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.
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    Why do our rainfall-runoff models keep underestimating the peak flows?
    (2023) Bárdossy, András; Anwar, Faizan
    In this paper, the question of how the interpolation of precipitation in space by using various spatial gauge densities affects the rainfall-runoff model discharge if all other input variables are kept constant is investigated. The main focus was on the peak flows. This was done by using a physically based model as the reference with a reconstructed spatially variable precipitation model and a conceptual model calibrated to match the reference model's output as closely as possible. Both models were run with distributed and lumped inputs. Results showed that all considered interpolation methods resulted in the underestimation of the total precipitation volume and that the underestimation was directly proportional to the precipitation amount. More importantly, the underestimation of peaks was very severe for low observation densities and disappeared only for very high-density precipitation observation networks. This result was confirmed by using observed precipitation with different observation densities. Model runoffs showed worse performance for their highest discharges. Using lumped inputs for the models showed deteriorating performance for peak flows as well, even when using simulated precipitation.
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    Spatial aspects of hydrological extremes : description and simulation
    (Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2024) Anwar, Faizan; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
    This thesis deals with the development of measures that can identify arbitrary multivariate dependence in space-time. A few new multivariate time series generators are also introduced. The aim was to use these measures and methods to simulate large scale heavy precipitation and river discharge more accurately.