Articles | Volume 23, issue 3
https://doi.org/10.5194/hess-23-1305-2019
© Author(s) 2019. 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-23-1305-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Mar 2019
Research article |
| 07 Mar 2019
| 07 Mar 2019
Climate influences on flood probabilities across Europe
Eva Steirou
CORRESPONDING AUTHOR
Section Hydrology, GFZ German Research Center for Geosciences,
Potsdam, 14473, Germany
Lars Gerlitz
Section Hydrology, GFZ German Research Center for Geosciences,
Potsdam, 14473, Germany
Heiko Apel
Section Hydrology, GFZ German Research Center for Geosciences,
Potsdam, 14473, Germany
Key Laboratory of Geographic Information Science (Ministry of
Education), East China Normal University, 200241, Shanghai, China
Columbia Water Center, Earth Institute, Columbia University, New York,
NY 10027, USA
Bruno Merz
Section Hydrology, GFZ German Research Center for Geosciences,
Potsdam, 14473, Germany
Institute of Environmental Science and Geography, University of
Potsdam, Potsdam, 14476, Germany
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- Global Changes in 20‐Year, 50‐Year, and 100‐Year River Floods L. Slater et al. 10.1029/2020GL091824
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- Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
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- Do small and large floods have the same drivers of change? A regional attribution analysis in Europe M. Bertola et al. 10.5194/hess-25-1347-2021
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- Detecting Flood‐Rich and Flood‐Poor Periods in Annual Peak Discharges Across Europe D. Lun et al. 10.1029/2019WR026575
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37 citations as recorded by crossref.
- Causes, impacts and patterns of disastrous river floods B. Merz et al. 10.1038/s43017-021-00195-3
- River channel conveyance capacity adjusts to modes of climate variability L. Slater et al. 10.1038/s41598-019-48782-1
- A tiered stochastic framework for assessing crop yield loss risks due to water scarcity under different uncertainty levels V. Uddameri et al. 10.1016/j.agwat.2020.106226
- Potential for seasonal flood forecasting in West Africa using climate indexes J. Hounkpè et al. 10.1111/jfr3.12833
- A Global Map for Selecting Stationary and Nonstationary Methods to Estimate Extreme Floods Z. Li et al. 10.3390/w15213835
- An investigation on the non-stationarity of flood frequency across the UK M. Chen et al. 10.1016/j.jhydrol.2021.126309
- Towards seasonal forecasting of flood probabilities in Europe using climate and catchment information E. Steirou et al. 10.1038/s41598-022-16633-1
- Comparing Flood Projection Approaches Across Hydro‐Climatologically Diverse United States River Basins K. Schlef et al. 10.1029/2019WR025861
- Current European flood-rich period exceptional compared with past 500 years G. Blöschl et al. 10.1038/s41586-020-2478-3
- Climate impact on flood changes – an Austrian-Ukrainian comparison S. Snizhko et al. 10.2478/johh-2023-0017
- Individual Influence of Climate Variability Indices on Annual Maximum Precipitation Across the Global Scale L. Belkhiri & T. Kim 10.1007/s11269-021-02882-8
- Intra-annual dendrogeomorphic dating and climate linkages of flood events in headwaters of central Europe R. Tichavský et al. 10.1016/j.scitotenv.2020.142953
- Nonstationary stochastic simulation method for the risk assessment of water allocation S. Chen et al. 10.1039/D0EW00695E
- On the need of ensemble flood forecast in India J. Nanditha & V. Mishra 10.1016/j.wasec.2021.100086
- Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability G. Papacharalampous & H. Tyralis 10.1016/j.jhydrol.2020.125205
- Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity G. Papacharalampous et al. 10.1016/j.scitotenv.2020.144612
- Modelling non-stationary flood frequency in England and Wales using physical covariates D. Faulkner et al. 10.2166/nh.2024.134
- An updated national-scale assessment of trends in UK peak river flow data: how robust are observed increases in flooding? J. Hannaford et al. 10.2166/nh.2021.156
- Climate and rivers G. McGregor 10.1002/rra.3508
- Quantifying the effect of climate variability on seasonal precipitation using Bayesian clustering approach in Kebir Rhumel Basin, Algeria L. Belkhiri & N. Krakauer 10.1007/s00477-023-02488-z
- Global Changes in 20‐Year, 50‐Year, and 100‐Year River Floods L. Slater et al. 10.1029/2020GL091824
- A Hidden Climate Indices Modeling Framework for Multivariable Space‐Time Data B. Renard et al. 10.1029/2021WR030007
- Flood trends in Europe: are changes in small and big floods different? M. Bertola et al. 10.5194/hess-24-1805-2020
- Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions R. Graf & V. Vyshnevskyi 10.3390/resources11120111
- What Will the Weather Do? Forecasting Flood Losses Based on Oscillation Indices G. Guimarães Nobre et al. 10.1029/2019EF001450
- Trends in Compound Flooding in Northwestern Europe During 1901–2014 P. Ganguli & B. Merz 10.1029/2019GL084220
- An Overview of Flood Concepts, Challenges, and Future Directions A. Mishra et al. 10.1061/(ASCE)HE.1943-5584.0002164
- Comparison of time trend- and precipitation-informed models for assessing design discharges in variable climate M. Šraj & N. Bezak 10.1016/j.jhydrol.2020.125374
- Forecasting Magnitude and Frequency of Seasonal Streamflow Extremes Using a Bayesian Hierarchical Framework Á. Ossandón et al. 10.1029/2022WR033194
- Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
- Synchronization and Delay Between Circulation Patterns and High Streamflow Events in Germany F. Conticello et al. 10.1029/2019WR025598
- Do small and large floods have the same drivers of change? A regional attribution analysis in Europe M. Bertola et al. 10.5194/hess-25-1347-2021
- Using climate information as covariates to improve nonstationary flood frequency analysis in Brazil G. Anzolin et al. 10.1080/02626667.2023.2182212
- Detecting Flood‐Rich and Flood‐Poor Periods in Annual Peak Discharges Across Europe D. Lun et al. 10.1029/2019WR026575
- Combined predictive and descriptive tests for extreme rainfall probability distribution selection A. Ballarin et al. 10.1080/02626667.2022.2063725
- A method for detecting the non-stationarity during high flows under global change Z. Zhang et al. 10.1016/j.scitotenv.2022.158341
- The NAO Variability Prediction and Forecasting with Multiple Time Scales Driven by ENSO Using Machine Learning Approaches B. Mu et al. 10.1155/2022/6141966
1 citations as recorded by crossref.
Discussed (final revised paper)
Latest update: 23 Apr 2024
Short summary
We investigate whether flood probabilities in Europe vary for different large-scale atmospheric circulation conditions. Maximum seasonal river flows from 600 gauges in Europe and five synchronous atmospheric circulation indices are analyzed. We find that a high percentage of stations is influenced by at least one of the climate indices, especially during winter. These results can be useful for preparedness and damage planning by (re-)insurance companies.
We investigate whether flood probabilities in Europe vary for different large-scale atmospheric...
