scholarly journals Uncertainties in Coastal Flood Risk Assessments in Small Island Developing States

2019 ◽  
Author(s):  
Matteo U. Parodi ◽  
Alessio Giardino ◽  
Ap van Dongeren ◽  
Stuart G. Pearson ◽  
Jeremy D. Bricker ◽  
...  
Keyword(s):  
Flood Risk ◽  
Input Data ◽  
Flood Damage ◽  
Small Island ◽  
Small Island Developing States ◽  
Damage Functions ◽  
Current Time ◽  
Coastal Flood ◽  
Coastal Flood Risk ◽  
The Impact

Abstract. Considering the likely increase of coastal flooding in Small Island Developing States (SIDS), coastal managers at the local and global level have been developing initiatives aimed at implementing Disaster Risk Reduction (DRR) measures and adapting to climate change. Developing science-based adaptation policies requires accurate coastal flood risk (CFR) assessments, which are often subject to the scarcity of sufficiently accurate input data for insular states. We analysed the impact of uncertain inputs on coastal flood damage estimates, considering: (i) significant wave height, (ii) storm surge level and (iii) sea level rise (SLR) contributions to extreme sea levels, as well as the error-driven uncertainty in (iv) bathymetric and (v) topographic datasets, (vi) damage models and (vii) socioeconomic changes. The methodology was tested through a sensitivity analysis using an ensemble of hydrodynamic models (XBeach and SFINCS) coupled with an impact model (Delft-FIAT) for a case study at the islands of São Tomé and Príncipe. Model results indicate that for the current time horizon, depth damage functions (DDF) and digital elevation model (DEM) dominate the overall damage estimation uncertainty. We find that, when introducing climate and socioeconomic uncertainties to the analysis, SLR projections become the most relevant input for the year 2100 (followed by DEM and DDF). In general, the scarcity of reliable input data leads to considerable predictive error in CFR assessments in SIDS. The findings of this research can help to prioritise the allocation of limited resources towards the acquisitions of the most relevant input data for reliable impact estimation.

scholarly journals Uncertainties in coastal flood risk assessments in small island developing states

2020 ◽  
Vol 20 (9) ◽  
pp. 2397-2414 ◽  
Author(s):  
Matteo U. Parodi ◽  
Alessio Giardino ◽  
Ap van Dongeren ◽  
Stuart G. Pearson ◽  
Jeremy D. Bricker ◽  
...  
Keyword(s):  
Flood Risk ◽  
Input Data ◽  
Small Island ◽  
Small Island Developing States ◽  
Damage Functions ◽  
Adaptation Measures ◽  
Current Time ◽  
Coastal Flood ◽  
Coastal Flood Risk ◽  
The Impact

Abstract. Considering the likely increase in coastal flooding in small island developing states (SIDSs) due to climate change, coastal managers at the local and global levels have been developing initiatives aimed at implementing disaster risk reduction (DRR) and adaptation measures. Developing science-based adaptation policies requires accurate coastal flood risk (CFR) assessments, which in the case of insular states are often subject to input uncertainty. We analysed the impact of a number of uncertain inputs on coastal flood damage estimates: (i) significant wave height, (ii) storm surge level and (iii) sea level rise (SLR) contributions to extreme sea levels, as well as the error-driven uncertainty in (iv) bathymetric and (v) topographic datasets, (vi) damage models, and (vii) socioeconomic changes. The methodology was tested through a sensitivity analysis using an ensemble of hydrodynamic models (XBeach and SFINCS) coupled with a direct impact model (Delft-FIAT) for a case study of a number of villages on the islands of São Tomé and Príncipe. Model results indicate that for the current time horizon, depth damage functions (DDFs) and digital elevation models (DEMs) dominate the overall damage estimation uncertainty. When introducing climate and socioeconomic uncertainties to the analysis, SLR projections become the most relevant input for the year 2100 (followed by DEM and DDF). In general, the scarcity of reliable input data leads to considerable predictive uncertainty in CFR assessments in SIDSs. The findings of this research can help to prioritize the allocation of limited resources towards the acquisitions of the most relevant input data for reliable impact estimation.

scholarly journals Understanding epistemic uncertainty in large-scale coastal flood risk assessment for present and future climates

2018 ◽  
Author(s):  
Michalis I. Vousdoukas ◽  
Dimitrios Bouziotas ◽  
Alessio Giardino ◽  
Laurens M. Bouwer ◽  
Evangelos Voukouvalas ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Flood Risk ◽  
Large Scale ◽  
Epistemic Uncertainty ◽  
Coastal Protection ◽  
Flood Risk Assessment ◽  
Coastal Flood ◽  
Coastal Flood Risk ◽  
The Impact

Abstract. An upscaling of flood risk assessment frameworks beyond regional and national scales has taken place during recent years, with a number of large-scale models emerging as tools for hotspot identification, support for international policy-making and harmonization of climate change adaptation strategies. There is, however, limited insight on the scaling effects and structural limitations of flood risk models and, therefore, the underlying uncertainty. In light of this, we examine key sources of epistemic uncertainty in the Coastal Flood Risk (CFR) modelling chain: (i) the inclusion and interaction of different hydraulic components leading to extreme sea-level (ESL); (ii) inundation modelling; (iii) the underlying uncertainty in the Digital Elevation Model (DEM); (iv) flood defence information; (v) the assumptions behind the use of depth-damage functions that express vulnerability; and (vi) different climate change projections. The impact of these uncertainties to estimated Expected Annual Damage (EAD) for present and future climates is evaluated in a dual case study in Faro, Portugal and in the Iberian Peninsula. The ranking of the uncertainty factors varies among the different case studies, baseline CFR estimates, as well as their absolute/relative changes. We find that uncertainty from ESL contributions, and in particular the way waves are treated, can be higher than the uncertainty of the two greenhouse gas emission projections and six climate models that are used. Of comparable importance is the quality of information on coastal protection levels and DEM information. In the absence of large-extent datasets with sufficient resolution and accuracy the latter two factors are the main bottlenecks in terms of large-scale CFR assessment quality.

scholarly journals Understanding epistemic uncertainty in large-scale coastal flood risk assessment for present and future climates

2018 ◽  
Vol 18 (8) ◽  
pp. 2127-2142 ◽  
Author(s):  
Michalis I. Vousdoukas ◽  
Dimitrios Bouziotas ◽  
Alessio Giardino ◽  
Laurens M. Bouwer ◽  
Lorenzo Mentaschi ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Flood Risk ◽  
Large Scale ◽  
Epistemic Uncertainty ◽  
Coastal Protection ◽  
Flood Risk Assessment ◽  
Coastal Flood ◽  
Coastal Flood Risk ◽  
The Impact

Abstract. An upscaling of flood risk assessment frameworks beyond regional and national scales has taken place during recent years, with a number of large-scale models emerging as tools for hotspot identification, support for international policymaking, and harmonization of climate change adaptation strategies. There is, however, limited insight into the scaling effects and structural limitations of flood risk models and, therefore, the underlying uncertainty. In light of this, we examine key sources of epistemic uncertainty in the coastal flood risk (CFR) modelling chain: (i) the inclusion and interaction of different hydraulic components leading to extreme sea level (ESL), (ii) the underlying uncertainty in the digital elevation model (DEM), (iii) flood defence information, (iv) the assumptions behind the use of depth–damage functions that express vulnerability, and (v) different climate change projections. The impact of these uncertainties on estimated expected annual damage (EAD) for present and future climates is evaluated in a dual case study in Faro, Portugal, and on the Iberian Peninsula. The ranking of the uncertainty factors varies among the different case studies, baseline CFR estimates, and their absolute and relative changes. We find that uncertainty from ESL contributions, and in particular the way waves are treated, can be higher than the uncertainty of the two greenhouse gas emission projections and six climate models that are used. Of comparable importance is the quality of information on coastal protection levels and DEM information. In the absence of large datasets with sufficient resolution and accuracy, the latter two factors are the main bottlenecks in terms of large-scale CFR assessment quality.

scholarly journals Global LiDAR land elevation data reveal greatest sea-level rise vulnerability in the tropics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
A. Hooijer ◽  
R. Vernimmen
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Flood Risk ◽  
Land Area ◽  
Area At Risk ◽  
Population Number ◽  
Coastal Flood ◽  
Elevation Data ◽  
Coastal Flood Risk ◽  
The Tropics

AbstractCoastal flood risk assessments require accurate land elevation data. Those to date existed only for limited parts of the world, which has resulted in high uncertainty in projections of land area at risk of sea-level rise (SLR). Here we have applied the first global elevation model derived from satellite LiDAR data. We find that of the worldwide land area less than 2 m above mean sea level, that is most vulnerable to SLR, 649,000 km2 or 62% is in the tropics. Even assuming a low-end relative SLR of 1 m by 2100 and a stable lowland population number and distribution, the 2020 population of 267 million on such land would increase to at least 410 million of which 72% in the tropics and 59% in tropical Asia alone. We conclude that the burden of current coastal flood risk and future SLR falls disproportionally on tropical regions, especially in Asia.

scholarly journals Large-scale application of the flood damage model RAilway Infrastructure Loss (RAIL)

2016 ◽  
Vol 16 (11) ◽  
pp. 2357-2371 ◽  
Author(s):  
Patric Kellermann ◽  
Christine Schönberger ◽  
Annegret H. Thieken
Keyword(s):  
Risk Management ◽  
Risk Aversion ◽  
Flood Risk ◽  
Structural Damage ◽  
Large Scale ◽  
Damage Model ◽  
Flood Damage ◽  
Railway Infrastructure ◽  
Increased Risk ◽  
The Impact

Abstract. Experience has shown that river floods can significantly hamper the reliability of railway networks and cause extensive structural damage and disruption. As a result, the national railway operator in Austria had to cope with financial losses of more than EUR 100 million due to flooding in recent years. Comprehensive information on potential flood risk hot spots as well as on expected flood damage in Austria is therefore needed for strategic flood risk management. In view of this, the flood damage model RAIL (RAilway Infrastructure Loss) was applied to estimate (1) the expected structural flood damage and (2) the resulting repair costs of railway infrastructure due to a 30-, 100- and 300-year flood in the Austrian Mur River catchment. The results were then used to calculate the expected annual damage of the railway subnetwork and subsequently analysed in terms of their sensitivity to key model assumptions. Additionally, the impact of risk aversion on the estimates was investigated, and the overall results were briefly discussed against the background of climate change and possibly resulting changes in flood risk. The findings indicate that the RAIL model is capable of supporting decision-making in risk management by providing comprehensive risk information on the catchment level. It is furthermore demonstrated that an increased risk aversion of the railway operator has a marked influence on flood damage estimates for the study area and, hence, should be considered with regard to the development of risk management strategies.

Investigation of long-term changes of coastal wave directionality patterns and their connections with NAO climatic index: UK case study

2021 ◽  
Author(s):  
Antonia Chatzirodou
Keyword(s):  
Climate Change ◽  
Flood Risk ◽  
Wave Climate ◽  
Wave Direction ◽  
Climatic Index ◽  
Coastal Flood ◽  
Time Period ◽  
Wave Directionality ◽  
Coastal Flood Risk ◽  
Offshore Wave

<p>The effects of climate change are at the spotlight of scientific research. In coastal science the effects of sea-level rise (SLR) on coastal areas, mainly as a result of melting of ice sheets and thermal volume expansion consist an intensive area of research. As well the changing ocean wave field due to greenhouse effect and interactions of atmospheric processes is under investigation. Researchers have placed focus on significant wave height changes and their associated impacts on the coastal environment, with evidence suggesting that the number, intensity and location of storms will change. It is suggested that equal attention should be placed on the mean wave direction changes and the effects that these changes may have on the coastlines and surrounding coastal infrastructure. Following that, this study investigated the changes in wave direction data since 1979 to 2019 covering 40 years’ time period at 11 offshore UK coastal locations. The selected locations lie close to WaveNet, Cefas’ strategic wave monitoring network points for the UK. Stakeholders use the data to provide advice and guidance to all involved parties including responders and communities about coastal flood risk. On a longer timescale the data provide evidence to coastal engineers and scientists of the wave climate change patterns and the implications this may have on coastal structures and flood defences design. Based on this initiative, this study investigated UK offshore wave climate changes by performing a longer timescale analysis of changes of wave direction patterns. The wave direction data were taken from ECMWF ERA5 6-hour hind cast data catalogue which covers 40 years’ time period from 1797-2019 (Copernicus Climate Change Service (C3S), 2017). MATLAB software coding was primarily utilized for data processing and analyses. Following that, inferential statistics were applied to map inter-decadal statistical changes in wave direction patterns, suggesting that wave directionality patterns have presented changes at 11 offshore locations tested.  The connections of wave directions with North Atlantic Oscillation (NAO) Climatic Index are currently investigated through use of machine learning approaches. The results of this study can be confidently used in wave transformation computational models coupled with hydro-morphodynamic models to downscale offshore wave direction changes to UK coastal areas. This can help identify susceptible coasts to offshore wave climate change. Susceptibility is regarded in form of coastal erosion and accretion rates changes as a result of altered offshore wave conditions, which might affect coastal flood risk with potential impacts on critical infrastructure.  </p>

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