Articles | Volume 20, issue 9
https://doi.org/10.5194/nhess-20-2397-2020
© 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-20-2397-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Sep 2020
Research article |
| 11 Sep 2020
| 11 Sep 2020
Uncertainties in coastal flood risk assessments in small island developing states
Matteo U. Parodi
Deltares, Unit Marine and Coastal Systems, Boussinesweg 1, 2629 HV
Delft, the Netherlands
Alessio Giardino
Deltares, Unit Marine and Coastal Systems, Boussinesweg 1, 2629 HV
Delft, the Netherlands
Ap van Dongeren
Deltares, Unit Marine and Coastal Systems, Boussinesweg 1, 2629 HV
Delft, the Netherlands
Dept. of Hydraulic Engineering, Faculty of Civil Engineering and
Geosciences, Delft University of Technology, 2628 CN Delft, the Netherlands
Deltares, Unit Marine and Coastal Systems, Boussinesweg 1, 2629 HV
Delft, the Netherlands
Jeremy D. Bricker
Dept. of Hydraulic Engineering, Faculty of Civil Engineering and
Geosciences, Delft University of Technology, 2628 CN Delft, the Netherlands
Ad J. H. M. Reniers
Dept. of Hydraulic Engineering, Faculty of Civil Engineering and
Geosciences, Delft University of Technology, 2628 CN Delft, the Netherlands
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Cited
15 citations as recorded by crossref.
- On simulating shoreline evolution using a hybrid 2D/one-line model A. Seenath 10.1016/j.coastaleng.2022.104216
- Impact of hydraulic model resolution and loss of life model modification on flood fatality risk estimation: Case study of the Bommelerwaard, The Netherlands A. Brussee et al. 10.1111/jfr3.12713
- Modelling extreme water levels using intertidal topography and bathymetry derived from multispectral satellite images W. Costa et al. 10.5194/nhess-23-3125-2023
- Uncertainty and Bias in Global to Regional Scale Assessments of Current and Future Coastal Flood Risk J. Hinkel et al. 10.1029/2020EF001882
- Participatory risk assessment of pluvial floods in four towns of Niger M. Tiepolo et al. 10.1016/j.ijdrr.2022.103454
- The value of US coral reefs for flood risk reduction B. Reguero et al. 10.1038/s41893-021-00706-6
- Multi-scenario urban flood risk assessment by integrating future land use change models and hydrodynamic models Q. Sun et al. 10.5194/nhess-22-3815-2022
- SaTSeaD: Satellite Triangulated Sea Depth Open-Source Bathymetry Module for NASA Ames Stereo Pipeline M. Palaseanu-Lovejoy et al. 10.3390/rs15163950
- Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes T. Leijnse et al. 10.1016/j.coastaleng.2020.103796
- Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations Y. Zhang et al. 10.5194/hess-27-4529-2023
- Hydrodynamic and Waves Response during Storm Surges on the Southern Brazilian Coast: A Hindcast Study A. de Lima et al. 10.3390/w12123538
- Dynamic Modeling of Coastal Compound Flooding Hazards Due to Tides, Extratropical Storms, Waves, and Sea-Level Rise: A Case Study in the Salish Sea, Washington (USA) K. Nederhoff et al. 10.3390/w16020346
- Accounting for uncertainties in compound flood hazard assessment: The value of data assimilation D. Muñoz et al. 10.1016/j.coastaleng.2021.104057
- Understanding the compound flood risk along the coast of the contiguous United States D. Feng et al. 10.5194/hess-27-3911-2023
- Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models D. Muñoz et al. 10.5194/hess-28-2531-2024
15 citations as recorded by crossref.
- On simulating shoreline evolution using a hybrid 2D/one-line model A. Seenath 10.1016/j.coastaleng.2022.104216
- Impact of hydraulic model resolution and loss of life model modification on flood fatality risk estimation: Case study of the Bommelerwaard, The Netherlands A. Brussee et al. 10.1111/jfr3.12713
- Modelling extreme water levels using intertidal topography and bathymetry derived from multispectral satellite images W. Costa et al. 10.5194/nhess-23-3125-2023
- Uncertainty and Bias in Global to Regional Scale Assessments of Current and Future Coastal Flood Risk J. Hinkel et al. 10.1029/2020EF001882
- Participatory risk assessment of pluvial floods in four towns of Niger M. Tiepolo et al. 10.1016/j.ijdrr.2022.103454
- The value of US coral reefs for flood risk reduction B. Reguero et al. 10.1038/s41893-021-00706-6
- Multi-scenario urban flood risk assessment by integrating future land use change models and hydrodynamic models Q. Sun et al. 10.5194/nhess-22-3815-2022
- SaTSeaD: Satellite Triangulated Sea Depth Open-Source Bathymetry Module for NASA Ames Stereo Pipeline M. Palaseanu-Lovejoy et al. 10.3390/rs15163950
- Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes T. Leijnse et al. 10.1016/j.coastaleng.2020.103796
- Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations Y. Zhang et al. 10.5194/hess-27-4529-2023
- Hydrodynamic and Waves Response during Storm Surges on the Southern Brazilian Coast: A Hindcast Study A. de Lima et al. 10.3390/w12123538
- Dynamic Modeling of Coastal Compound Flooding Hazards Due to Tides, Extratropical Storms, Waves, and Sea-Level Rise: A Case Study in the Salish Sea, Washington (USA) K. Nederhoff et al. 10.3390/w16020346
- Accounting for uncertainties in compound flood hazard assessment: The value of data assimilation D. Muñoz et al. 10.1016/j.coastaleng.2021.104057
- Understanding the compound flood risk along the coast of the contiguous United States D. Feng et al. 10.5194/hess-27-3911-2023
- Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models D. Muñoz et al. 10.5194/hess-28-2531-2024
Latest update: 29 Jun 2024
Short summary
We investigate sources of uncertainty in coastal flood risk assessment in São Tomé and Príncipe, a small island developing state. We find that, for the present-day scenario, uncertainty from depth damage functions and digital elevation models can be more significant than that related to the estimation of significant wave height or storm surge level. For future scenarios (year 2100), sea level rise prediction becomes the input with the strongest impact on coastal flood damage estimate.
We investigate sources of uncertainty in coastal flood risk assessment in São Tomé and...
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