A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Multivariate flood frequency analysis in large river basins considering tributary impacts and flood types
In contrast to the basic assumption of a homogeneous population underlying common approaches to flood frequency analysis, flood events often arise from different runoff-generating processes. In many large river basins, the diversity of these processes within tributary basins and the superposition of their flood waves increase the complexity of statistical flood modeling. Under these circumstances, the allocation of the most effective flood protection measures requires a spatially explicit analysis of flood-generating processes and the determination of the probability of downstream flood scenarios. For large basins, flood scenarios are often derived using individual historical floods along with model-based simulations. We, instead, performed hydrograph-based flood-type classification and volume-based runoff analyses for the Upper Danube River to estimate the contributions of subbasins to floods at downstream locations. Using this information, we generated long synthetic samples of peak-volume-pairs to apply a multivariate statistical flood-frequency model that yields a conditional probability of a flood peak given the peaks in tributary stations. The results show that only certain combinations of flood types may result in extreme peaks downstream of confluences. They also highlight the need to distinguish runoff-generation mechanisms for the larger floods from ones that drive smaller, more frequent events. Through an example with the Rhine River, we demonstrate how the statistical model can be generalized for complex river networks featuring several tributary confluences. Finally, design floods for different scenarios of flood-type combinations and assigned probabilities are derived, an approach that can be used to possible climate impacts to flood frequency.
Multivariate flood frequency analysis in large river basins considering tributary impacts and flood types
In contrast to the basic assumption of a homogeneous population underlying common approaches to flood frequency analysis, flood events often arise from different runoff-generating processes. In many large river basins, the diversity of these processes within tributary basins and the superposition of their flood waves increase the complexity of statistical flood modeling. Under these circumstances, the allocation of the most effective flood protection measures requires a spatially explicit analysis of flood-generating processes and the determination of the probability of downstream flood scenarios. For large basins, flood scenarios are often derived using individual historical floods along with model-based simulations. We, instead, performed hydrograph-based flood-type classification and volume-based runoff analyses for the Upper Danube River to estimate the contributions of subbasins to floods at downstream locations. Using this information, we generated long synthetic samples of peak-volume-pairs to apply a multivariate statistical flood-frequency model that yields a conditional probability of a flood peak given the peaks in tributary stations. The results show that only certain combinations of flood types may result in extreme peaks downstream of confluences. They also highlight the need to distinguish runoff-generation mechanisms for the larger floods from ones that drive smaller, more frequent events. Through an example with the Rhine River, we demonstrate how the statistical model can be generalized for complex river networks featuring several tributary confluences. Finally, design floods for different scenarios of flood-type combinations and assigned probabilities are derived, an approach that can be used to possible climate impacts to flood frequency.
Multivariate flood frequency analysis in large river basins considering tributary impacts and flood types
Fischer, Svenja (Dr. rer. nat.) (author) / Schumann, Andreas H. (Prof. Dr. rer. nat. habil.) (author)
2021-07-26
Article (Journal)
Electronic Resource
English
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