Articles | Volume 27, issue 3
https://doi.org/10.5194/hess-27-689-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-689-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A mixed distribution approach for low-flow frequency analysis – Part 1: Concept, performance, and effect of seasonality
Department of Landscape, Spatial and Infrastructure Sciences, Institute of Statistics, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Strasse 82/I, Vienna 1190, Austria
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Short summary
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This study aims to predict long-term averages of low flow on a hydrologically diverse dataset in Austria. We compared seven statistical learning methods and included a backward variable selection approach. We found that separating the low-flow processes into winter and summer low flows leads to good performance for all the models. Variable selection results in more parsimonious and more interpretable models. Linear approaches for prediction and variable selection are sufficient for our dataset.
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Short summary
Knowing the severity of an extreme event is of particular importance to hydrology and water policies. In this paper we propose a mixed distribution approach for low flows. It provides one consistent approach to quantify the severity of summer, winter, and annual low flows based on their respective annualities (or return periods). We show that the new method is much more accurate than existing methods and should therefore be used by engineers and water agencies.
Knowing the severity of an extreme event is of particular importance to hydrology and water...