Articles | Volume 23, issue 1
https://doi.org/10.5194/hess-23-107-2019
© 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-23-107-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modeling the spatial dependence of floods using the Fisher copula
Manuela I. Brunner
CORRESPONDING AUTHOR
Department of Geography, University of Zurich, Zurich, Switzerland
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf ZH, Switzerland
Reinhard Furrer
Department of Mathematics and Department of
Computational Science, University of Zurich, Zurich, Switzerland
Anne-Catherine Favre
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
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Cited
37 citations as recorded by crossref.
- Spatial Dependence Analysis of Weekly Moving Cumulative Rainfall for Flood Risk Assessment P. Chomphuwiset et al. 10.3390/atmos14101525
- A General Construction of Multivariate Dependence Structures with Nonmonotone Mappings and Its Applications J. Quessy 10.1214/23-STS916
- Analyzing the conditional behavior of rainfall deficiency and groundwater level deficiency signatures by using copula functions M. Tahroudi et al. 10.2166/nh.2020.036
- 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
- Spatial dependence of floods shaped by extreme rainfall under the influence of urbanization M. Lu et al. 10.1016/j.scitotenv.2022.159134
- Rising risk and localized patterns of Indian Summer Monsoon rainfall extremes K. Athira et al. 10.1016/j.atmosres.2024.107554
- Swarm‐based optimizer for convolutional neural network: An application for flood susceptibility mapping T. Chou et al. 10.1111/tgis.12715
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- R-statistic based predictor variables selection and vine structure determination approach for stochastic streamflow generation considering temporal and spatial dependence X. Wang & Y. Shen 10.1016/j.jhydrol.2023.129093
- A spatially-dependent synthetic global dataset of extreme sea level events H. Li et al. 10.1016/j.wace.2023.100596
- Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
- Spatial Dependence of Floods Shaped by Spatiotemporal Variations in Meteorological and Land‐Surface Processes M. Brunner et al. 10.1029/2020GL088000
- On the Systematic Occurrence of Compound Cold Spells in North America and Wet or Windy Extremes in Europe G. Messori & D. Faranda 10.1029/2022GL101008
- Trivariate joint frequency analysis of water resources deficiency signatures using vine copulas M. Nazeri Tahroudi et al. 10.1007/s13201-022-01589-4
- Processes and controls of regional floods over eastern China Y. Yang et al. 10.5194/hess-28-4883-2024
- Copulas for hydroclimatic analysis: A practice‐oriented overview F. Tootoonchi et al. 10.1002/wat2.1579
- Stability of spatial dependence structure of extreme precipitation and the concurrent risk over a nested basin Z. Liu et al. 10.1016/j.jhydrol.2021.126766
- How Probable Is Widespread Flooding in the United States? M. Brunner et al. 10.1029/2020WR028096
- Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment M. Dal Molin et al. 10.5194/hess-24-1319-2020
- Copula geoadditive modelling of anaemia and malaria in young children in Kenya, Malawi, Tanzania and Uganda D. Roberts & T. Zewotir 10.1186/s41043-020-00217-8
- Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas k. yin & A. Mostafavi 10.2139/ssrn.4631611
- Hotspot movement of compound events on the Europe continent S. Doshi et al. 10.1038/s41598-023-45067-6
- Uni- and multivariate bias adjustment of climate model simulations in Nordic catchments: Effects on hydrological signatures relevant for water resources management in a changing climate F. Tootoonchi et al. 10.1016/j.jhydrol.2023.129807
- Floods and droughts: a multivariate perspective M. Brunner 10.5194/hess-27-2479-2023
- Changing correlations: a flexible definition of non-Gaussian multivariate dependence A. Bárdossy 10.1007/s00477-023-02408-1
- A Framework of Dependence Modeling and Evaluation System for Compound Flood Events X. Wang & Y. Shen 10.1029/2023WR034718
- Dynamic long-term streamflow probabilistic forecasting model for a multisite system considering real-time forecast updating through spatio-temporal dependent error correction R. Mo et al. 10.1016/j.jhydrol.2021.126666
- A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow Á. Ossandón et al. 10.5194/hess-26-149-2022
- Generating synthetic rainfall fields by R‐vine copulas applied to seamless probabilistic predictions P. Schaumann et al. 10.1002/qj.4751
- Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs. weather-generator-based approach B. Winter et al. 10.5194/nhess-20-1689-2020
- The testing of a multivariate probabilistic framework for reservoir safety evaluation and flood risks assessment in Slovakia: A study on the Parná and Belá Rivers R. Výleta et al. 10.2478/johh-2023-0027
- Study of the Flood Frequency Based on Normal Transformation in Arid Inland Region: A Case Study of Manas River in North-Western China C. Qiao et al. 10.1155/2022/5229348
- Spatio-temporal clustering of extreme floods in Great Britain G. Formetta et al. 10.1080/02626667.2024.2367167
- Application of Copula Functions for Bivariate Analysis of Rainfall and River Flow Deficiencies in the Siminehrood River Basin, Iran M. Nazeri Tahroudi et al. 10.1061/(ASCE)HE.1943-5584.0002207
- Dynamical systems theory sheds new light on compound climate extremes in Europe and Eastern North America P. De Luca et al. 10.1002/qj.3757
- Multivariate fire risk models using copula regression in Kalimantan, Indonesia M. Najib et al. 10.1007/s11069-022-05346-3
36 citations as recorded by crossref.
- Spatial Dependence Analysis of Weekly Moving Cumulative Rainfall for Flood Risk Assessment P. Chomphuwiset et al. 10.3390/atmos14101525
- A General Construction of Multivariate Dependence Structures with Nonmonotone Mappings and Its Applications J. Quessy 10.1214/23-STS916
- Analyzing the conditional behavior of rainfall deficiency and groundwater level deficiency signatures by using copula functions M. Tahroudi et al. 10.2166/nh.2020.036
- 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
- Spatial dependence of floods shaped by extreme rainfall under the influence of urbanization M. Lu et al. 10.1016/j.scitotenv.2022.159134
- Rising risk and localized patterns of Indian Summer Monsoon rainfall extremes K. Athira et al. 10.1016/j.atmosres.2024.107554
- Swarm‐based optimizer for convolutional neural network: An application for flood susceptibility mapping T. Chou et al. 10.1111/tgis.12715
- Extreme floods in Europe: going beyond observations using reforecast ensemble pooling M. Brunner & L. Slater 10.5194/hess-26-469-2022
- Integrated real-time flood risk identification, analysis, and diagnosis model framework for a multireservoir system considering temporally and spatially dependent forecast uncertainties B. Xu et al. 10.1016/j.jhydrol.2021.126679
- R-statistic based predictor variables selection and vine structure determination approach for stochastic streamflow generation considering temporal and spatial dependence X. Wang & Y. Shen 10.1016/j.jhydrol.2023.129093
- A spatially-dependent synthetic global dataset of extreme sea level events H. Li et al. 10.1016/j.wace.2023.100596
- Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
- Spatial Dependence of Floods Shaped by Spatiotemporal Variations in Meteorological and Land‐Surface Processes M. Brunner et al. 10.1029/2020GL088000
- On the Systematic Occurrence of Compound Cold Spells in North America and Wet or Windy Extremes in Europe G. Messori & D. Faranda 10.1029/2022GL101008
- Trivariate joint frequency analysis of water resources deficiency signatures using vine copulas M. Nazeri Tahroudi et al. 10.1007/s13201-022-01589-4
- Processes and controls of regional floods over eastern China Y. Yang et al. 10.5194/hess-28-4883-2024
- Copulas for hydroclimatic analysis: A practice‐oriented overview F. Tootoonchi et al. 10.1002/wat2.1579
- Stability of spatial dependence structure of extreme precipitation and the concurrent risk over a nested basin Z. Liu et al. 10.1016/j.jhydrol.2021.126766
- How Probable Is Widespread Flooding in the United States? M. Brunner et al. 10.1029/2020WR028096
- Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment M. Dal Molin et al. 10.5194/hess-24-1319-2020
- Copula geoadditive modelling of anaemia and malaria in young children in Kenya, Malawi, Tanzania and Uganda D. Roberts & T. Zewotir 10.1186/s41043-020-00217-8
- Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas k. yin & A. Mostafavi 10.2139/ssrn.4631611
- Hotspot movement of compound events on the Europe continent S. Doshi et al. 10.1038/s41598-023-45067-6
- Uni- and multivariate bias adjustment of climate model simulations in Nordic catchments: Effects on hydrological signatures relevant for water resources management in a changing climate F. Tootoonchi et al. 10.1016/j.jhydrol.2023.129807
- Floods and droughts: a multivariate perspective M. Brunner 10.5194/hess-27-2479-2023
- Changing correlations: a flexible definition of non-Gaussian multivariate dependence A. Bárdossy 10.1007/s00477-023-02408-1
- A Framework of Dependence Modeling and Evaluation System for Compound Flood Events X. Wang & Y. Shen 10.1029/2023WR034718
- Dynamic long-term streamflow probabilistic forecasting model for a multisite system considering real-time forecast updating through spatio-temporal dependent error correction R. Mo et al. 10.1016/j.jhydrol.2021.126666
- A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow Á. Ossandón et al. 10.5194/hess-26-149-2022
- Generating synthetic rainfall fields by R‐vine copulas applied to seamless probabilistic predictions P. Schaumann et al. 10.1002/qj.4751
- Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs. weather-generator-based approach B. Winter et al. 10.5194/nhess-20-1689-2020
- The testing of a multivariate probabilistic framework for reservoir safety evaluation and flood risks assessment in Slovakia: A study on the Parná and Belá Rivers R. Výleta et al. 10.2478/johh-2023-0027
- Study of the Flood Frequency Based on Normal Transformation in Arid Inland Region: A Case Study of Manas River in North-Western China C. Qiao et al. 10.1155/2022/5229348
- Spatio-temporal clustering of extreme floods in Great Britain G. Formetta et al. 10.1080/02626667.2024.2367167
- Application of Copula Functions for Bivariate Analysis of Rainfall and River Flow Deficiencies in the Siminehrood River Basin, Iran M. Nazeri Tahroudi et al. 10.1061/(ASCE)HE.1943-5584.0002207
- Dynamical systems theory sheds new light on compound climate extremes in Europe and Eastern North America P. De Luca et al. 10.1002/qj.3757
1 citations as recorded by crossref.
Latest update: 22 Nov 2024
Short summary
Floods often affect a whole region and not only a single location. When estimating the rarity of regional events, the dependence of floods at different locations should be taken into account. We propose a simple model that considers the dependence of flood events at different locations and the network structure of the river system. We test this model on a medium-sized catchment in Switzerland. The model allows for the simulations of flood event sets at multiple gauged and ungauged locations.
Floods often affect a whole region and not only a single location. When estimating the rarity of...