Articles | Volume 21, issue 6
https://doi.org/10.5194/hess-21-2615-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-2615-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
02 Jun 2017
Research article |
| 02 Jun 2017
Estimating extreme river discharges in Europe through a Bayesian network
Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 11, 2628 CN Delft, the Netherlands
Oswaldo Morales-Nápoles
Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 11, 2628 CN Delft, the Netherlands
Viewed
Total article views: 5,184 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 20 Jun 2016)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 3,546 | 1,480 | 158 | 5,184 | 538 | 199 | 231 |
- HTML: 3,546
- PDF: 1,480
- XML: 158
- Total: 5,184
- Supplement: 538
- BibTeX: 199
- EndNote: 231
Total article views: 4,286 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Jun 2017)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,970 | 1,184 | 132 | 4,286 | 245 | 170 | 200 |
- HTML: 2,970
- PDF: 1,184
- XML: 132
- Total: 4,286
- Supplement: 245
- BibTeX: 170
- EndNote: 200
Total article views: 898 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 20 Jun 2016)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 576 | 296 | 26 | 898 | 293 | 29 | 31 |
- HTML: 576
- PDF: 296
- XML: 26
- Total: 898
- Supplement: 293
- BibTeX: 29
- EndNote: 31
Cited
30 citations as recorded by crossref.
- Assessment of flood risk in Mediterranean catchments: an approach based on Bayesian networks M. Flores et al. https://doi.org/10.1007/s00477-019-01746-3
- Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks R. Ropero et al. https://doi.org/10.1007/s10651-023-00587-2
- Merging modelled and reported flood impacts in Europe in a combined flood event catalogue for 1950–2020 D. Paprotny et al. https://doi.org/10.5194/hess-28-3983-2024
- The influence of spatial variation on the design of foundations of immersed tunnels: Advanced probabilistic analysis C. ’t Hart et al. https://doi.org/10.1016/j.tust.2024.105624
- Probabilistic Estimation of Primary Ship-Induced Wave Heights at Estuary Groins Using a Nonparametric Bayesian Network S. Memar et al. https://doi.org/10.1061/JWPED5.WWENG-2063
- BANSHEE–A MATLAB toolbox for Non-Parametric Bayesian Networks D. Paprotny et al. https://doi.org/10.1016/j.softx.2020.100588
- A Stepwise and Dynamic C-Vine Copula–Based Approach for Nonstationary Monthly Streamflow Forecasts P. Xu et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002145
- Trends in flood losses in Europe over the past 150 years D. Paprotny et al. https://doi.org/10.1038/s41467-018-04253-1
- Revealing the key factors associated with acne development and treatment with a fixed combination adapalene + metronidazole I. Khismatulina et al. https://doi.org/10.21518/ms2025-340
- Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges E. Ragno et al. https://doi.org/10.5194/hess-26-1695-2022
- Structured Expert Elicitation of Dependence Between River Tributaries Using Nonparametric Bayesian Networks G. Rongen et al. https://doi.org/10.1111/risa.70111
- Ecological Restoration as a Means of Managing Inland Flood Hazards C. Nilsson et al. https://doi.org/10.1093/biosci/bix148
- 500-Year Records Demonstrating a Sharp Increase in Intensities of Three Natural Hazards at Multiple Spatiotemporal Scales in China Z. Wang et al. https://doi.org/10.15377/2410-3624.2023.10.3
- Flood-Triggering Rainfall and Potential Losses—The Copula-Based Approach on the Example of the Upper Nysa Kłodzka River A. Perz et al. https://doi.org/10.3390/w15101958
- A Copula-Based Bayesian Network for Modeling Compound Flood Hazard from Riverine and Coastal Interactions at the Catchment Scale: An Application to the Houston Ship Channel, Texas A. Couasnon et al. https://doi.org/10.3390/w10091190
- A Method of Emergent Event Evolution Reasoning Based on Ontology Cluster and Bayesian Network S. Li et al. https://doi.org/10.1109/ACCESS.2019.2894777
- Exploring the uncertainty of weather generators’ extreme estimates in different practical available information scenarios C. Beneyto et al. https://doi.org/10.1080/02626667.2023.2208754
- Copula-based geohazard assessment – case of flood-prone area in Poland A. Perz et al. https://doi.org/10.1016/j.ejrh.2022.101214
- Behaviour of the 2010 flood in Lithuania: management and socio-economic risks M. D. et al. https://doi.org/10.1007/s11027-022-10001-0
- Efficient pan-European river flood hazard modelling through a combination of statistical and physical models D. Paprotny et al. https://doi.org/10.5194/nhess-17-1267-2017
- Pan-European hydrodynamic models and their ability to identify compound floods D. Paprotny et al. https://doi.org/10.1007/s11069-020-03902-3
- Evaluation of speech perception deficit compensation in patients with hearing loss M. Boboshko et al. https://doi.org/10.1080/14992027.2025.2550526
- Modeling Semiarid River–Aquifer Systems with Bayesian Networks and Artificial Neural Networks A. Maldonado et al. https://doi.org/10.3390/math10010107
- On the Use of Weather Generators for the Estimation of Low-Frequency Floods under a Changing Climate C. Beneyto et al. https://doi.org/10.3390/w16071059
- Bayesian Networks for Preprocessing Water Management Data R. Ropero et al. https://doi.org/10.3390/math10101777
- Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis P. Terefenko et al. https://doi.org/10.3390/rs11070843
- A combined hydrological and hydraulic modelling approach for the flood hazard mapping of the Po river basin R. Nogherotto et al. https://doi.org/10.1111/jfr3.12755
- Nonparametric Bayesian Networks as a Tool of Multiscale Time Series Analysis and Remote Sensing Data Integration N. Pyko et al. https://doi.org/10.32603/1993-8985-2023-26-3-32-37
- A probabilistic approach to estimating residential losses from different flood types D. Paprotny et al. https://doi.org/10.1007/s11069-020-04413-x
- The impact of Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation: a natural long-term in situ experiment in a planted pine forest M. Bogachev et al. https://doi.org/10.3389/fenvs.2023.1197657
30 citations as recorded by crossref.
- Assessment of flood risk in Mediterranean catchments: an approach based on Bayesian networks M. Flores et al. https://doi.org/10.1007/s00477-019-01746-3
- Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks R. Ropero et al. https://doi.org/10.1007/s10651-023-00587-2
- Merging modelled and reported flood impacts in Europe in a combined flood event catalogue for 1950–2020 D. Paprotny et al. https://doi.org/10.5194/hess-28-3983-2024
- The influence of spatial variation on the design of foundations of immersed tunnels: Advanced probabilistic analysis C. ’t Hart et al. https://doi.org/10.1016/j.tust.2024.105624
- Probabilistic Estimation of Primary Ship-Induced Wave Heights at Estuary Groins Using a Nonparametric Bayesian Network S. Memar et al. https://doi.org/10.1061/JWPED5.WWENG-2063
- BANSHEE–A MATLAB toolbox for Non-Parametric Bayesian Networks D. Paprotny et al. https://doi.org/10.1016/j.softx.2020.100588
- A Stepwise and Dynamic C-Vine Copula–Based Approach for Nonstationary Monthly Streamflow Forecasts P. Xu et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002145
- Trends in flood losses in Europe over the past 150 years D. Paprotny et al. https://doi.org/10.1038/s41467-018-04253-1
- Revealing the key factors associated with acne development and treatment with a fixed combination adapalene + metronidazole I. Khismatulina et al. https://doi.org/10.21518/ms2025-340
- Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges E. Ragno et al. https://doi.org/10.5194/hess-26-1695-2022
- Structured Expert Elicitation of Dependence Between River Tributaries Using Nonparametric Bayesian Networks G. Rongen et al. https://doi.org/10.1111/risa.70111
- Ecological Restoration as a Means of Managing Inland Flood Hazards C. Nilsson et al. https://doi.org/10.1093/biosci/bix148
- 500-Year Records Demonstrating a Sharp Increase in Intensities of Three Natural Hazards at Multiple Spatiotemporal Scales in China Z. Wang et al. https://doi.org/10.15377/2410-3624.2023.10.3
- Flood-Triggering Rainfall and Potential Losses—The Copula-Based Approach on the Example of the Upper Nysa Kłodzka River A. Perz et al. https://doi.org/10.3390/w15101958
- A Copula-Based Bayesian Network for Modeling Compound Flood Hazard from Riverine and Coastal Interactions at the Catchment Scale: An Application to the Houston Ship Channel, Texas A. Couasnon et al. https://doi.org/10.3390/w10091190
- A Method of Emergent Event Evolution Reasoning Based on Ontology Cluster and Bayesian Network S. Li et al. https://doi.org/10.1109/ACCESS.2019.2894777
- Exploring the uncertainty of weather generators’ extreme estimates in different practical available information scenarios C. Beneyto et al. https://doi.org/10.1080/02626667.2023.2208754
- Copula-based geohazard assessment – case of flood-prone area in Poland A. Perz et al. https://doi.org/10.1016/j.ejrh.2022.101214
- Behaviour of the 2010 flood in Lithuania: management and socio-economic risks M. D. et al. https://doi.org/10.1007/s11027-022-10001-0
- Efficient pan-European river flood hazard modelling through a combination of statistical and physical models D. Paprotny et al. https://doi.org/10.5194/nhess-17-1267-2017
- Pan-European hydrodynamic models and their ability to identify compound floods D. Paprotny et al. https://doi.org/10.1007/s11069-020-03902-3
- Evaluation of speech perception deficit compensation in patients with hearing loss M. Boboshko et al. https://doi.org/10.1080/14992027.2025.2550526
- Modeling Semiarid River–Aquifer Systems with Bayesian Networks and Artificial Neural Networks A. Maldonado et al. https://doi.org/10.3390/math10010107
- On the Use of Weather Generators for the Estimation of Low-Frequency Floods under a Changing Climate C. Beneyto et al. https://doi.org/10.3390/w16071059
- Bayesian Networks for Preprocessing Water Management Data R. Ropero et al. https://doi.org/10.3390/math10101777
- Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis P. Terefenko et al. https://doi.org/10.3390/rs11070843
- A combined hydrological and hydraulic modelling approach for the flood hazard mapping of the Po river basin R. Nogherotto et al. https://doi.org/10.1111/jfr3.12755
- Nonparametric Bayesian Networks as a Tool of Multiscale Time Series Analysis and Remote Sensing Data Integration N. Pyko et al. https://doi.org/10.32603/1993-8985-2023-26-3-32-37
- A probabilistic approach to estimating residential losses from different flood types D. Paprotny et al. https://doi.org/10.1007/s11069-020-04413-x
- The impact of Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation: a natural long-term in situ experiment in a planted pine forest M. Bogachev et al. https://doi.org/10.3389/fenvs.2023.1197657
Saved (final revised paper)
Latest update: 09 Jun 2026
