Regional flood frequency analysis of the sava river in south-eastern Europe
- Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution providesRegional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological-meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure.…
Verfasserangaben: | Igor Lescesen, Mojca SrajORCiD, Biljana Basarin, Dragoslav Pavic, Minucer MesarosORCiD, Manfred MudelseeORCiDGND |
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DOI: | https://doi.org/10.3390/su14159282 |
ISSN: | 2071-1050 |
Titel des übergeordneten Werks (Englisch): | Sustainability |
Verlag: | MDPI |
Verlagsort: | Basel |
Publikationstyp: | Wissenschaftlicher Artikel |
Sprache: | Englisch |
Datum der Erstveröffentlichung: | 28.07.2022 |
Erscheinungsjahr: | 2022 |
Datum der Freischaltung: | 10.05.2024 |
Freies Schlagwort / Tag: | Generalized Extreme Value distribution; L-moments estimation; Sava River; discharge time series; flood risk analysis; regional flood frequency analysis |
Band: | 14 |
Ausgabe: | 15 |
Aufsatznummer: | 9282 |
Seitenanzahl: | 19 |
Fördernde Institution: | ExtremeClimTwin project - European Union [952384] |
Organisationseinheiten: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
DDC-Klassifikation: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
Peer Review: | Referiert |
Publikationsweg: | Open Access / Gold Open-Access |
Lizenz (Deutsch): | ![]() |