Articles | Volume 20, issue 4
https://doi.org/10.5194/nhess-20-967-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-20-967-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Apr 2020
Research article |
| 09 Apr 2020
| 09 Apr 2020
The role of spatial dependence for large-scale flood risk estimation
Hydrology Section, GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Institute of Environmental Science and Geography, University of
Potsdam, 14476 Potsdam, Germany
Nguyen Viet Dung
Hydrology Section, GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Kai Schröter
Hydrology Section, GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Sergiy Vorogushyn
Hydrology Section, GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Björn Guse
Hydrology Section, GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Heidi Kreibich
Hydrology Section, GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Bruno Merz
Hydrology Section, GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Institute of Environmental Science and Geography, University of
Potsdam, 14476 Potsdam, Germany
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- How Probable Is Widespread Flooding in the United States? M. Brunner et al. 10.1029/2020WR028096
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25 citations as recorded by crossref.
- Process‐Based Flood Risk Assessment for Germany N. Sairam et al. 10.1029/2021EF002259
- Floods and droughts: a multivariate perspective M. Brunner 10.5194/hess-27-2479-2023
- SIMULATION OF FLOOD-PRONE AREAS USING MACHINE LEARNING AND GIS TECHNIQUES IN SAMANGAN PROVINCE, AFGHANISTAN V. Isazade et al. 10.3846/gac.2024.18555
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- Performance evaluation of a multi-site weather generator coupling maximum entropy resampling for estimating the probability distribution of annual maximum daily rainfall in the Loess Plateau D. Mu et al. 10.1007/s00477-023-02630-x
- A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models D. Eilander et al. 10.5194/hess-25-5287-2021
- Drivers of future fluvial flood risk change for residential buildings in Europe M. Steinhausen et al. 10.1016/j.gloenvcha.2022.102559
- RESCUE: A geomorphology‐based, hydrologic‐hydraulic model for large‐scale inundation mapping L. Pavesi et al. 10.1111/jfr3.12841
- Biases in national and continental flood risk assessments by ignoring spatial dependence V. Nguyen et al. 10.1038/s41598-020-76523-2
- Toward Global Stochastic River Flood Modeling O. Wing et al. 10.1029/2020WR027692
- Use of Hydrological Models in Global Stochastic Flood Modeling G. Olcese et al. 10.1029/2022WR032743
- Comparing earth observation and inundation models to map flood hazards L. Hawker et al. 10.1088/1748-9326/abc216
- A spatially-dependent synthetic global dataset of extreme sea level events H. Li et al. 10.1016/j.wace.2023.100596
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- The role of residual risk on flood damage assessment: A continuous hydrologic-hydraulic modelling approach for the historical city of Rome, Italy A. Fiori et al. 10.1016/j.ejrh.2023.101506
- Spatial Dependence of Floods Shaped by Spatiotemporal Variations in Meteorological and Land‐Surface Processes M. Brunner et al. 10.1029/2020GL088000
- Built environment and agricultural value at risk from Hurricane Irma flooding in Florida (USA) A. Miller et al. 10.1007/s11069-021-04880-w
- Flood spatial coherence, triggers, and performance in hydrological simulations: large-sample evaluation of four streamflow-calibrated models M. Brunner et al. 10.5194/hess-25-105-2021
- How Probable Is Widespread Flooding in the United States? M. Brunner et al. 10.1029/2020WR028096
- On the role of floodplain storage and hydrodynamic interactions in flood risk estimation M. Farrag et al. 10.1080/02626667.2022.2030058
- Compound Continental Risk of Multiple Extreme Floods in the United States N. Najibi et al. 10.1029/2023GL105297
- Comparative evaluation of two types of stochastic weather generators for synthetic precipitation in the Rhine basin S. Ullrich et al. 10.1016/j.jhydrol.2021.126544
- A novel hybrid approach to flood susceptibility assessment based on machine learning and land use change. Case study: a river watershed in Vietnam H. Nguyen et al. 10.1080/02626667.2022.2060108
- Spatial dependence of floods shaped by extreme rainfall under the influence of urbanization M. Lu et al. 10.1016/j.scitotenv.2022.159134
- Large-scale flood risk assessment and management: Prospects of a systems approach K. Schröter et al. 10.1016/j.wasec.2021.100109
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Latest update: 24 Apr 2024
Short summary
For effective risk management, flood risk should be properly assessed. Traditionally, risk is assessed by making the assumption of invariant flow or loss probabilities (the chance that a given discharge or loss is exceeded) within the river catchment during a single flood event. However, in reality, flooding is more severe in some regions than others. This study indicates the importance of representing the spatial dependence of flood peaks and damage for risk assessments.
For effective risk management, flood risk should be properly assessed. Traditionally, risk is...
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