scholarly journals Unravelling the Importance of Uncertainties in Global-Scale Coastal Flood Risk Assessments under Sea Level Rise

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 774
Author(s):  
Jeremy Rohmer ◽  
Daniel Lincke ◽  
Jochen Hinkel ◽  
Gonéri Le Cozannet ◽  
Erwin Lambert ◽  
...  

Global scale assessments of coastal flood damage and adaptation costs under 21st century sea-level rise are associated with a wide range of uncertainties, including those in future projections of socioeconomic development (shared socioeconomic pathways (SSP) scenarios), of greenhouse gas concentrations (RCP scenarios), and of sea-level rise at regional scale (RSLR), as well as structural uncertainties related to the modelling of extreme sea levels, data on exposed population and assets, and the costs of flood damages, etc. This raises the following questions: which sources of uncertainty need to be considered in such assessments and what is the relative importance of each source of uncertainty in the final results? Using the coastal flood module of the Dynamic Interactive Vulnerability Assessment modelling framework, we extensively explore the impact of scenario, data and model uncertainties in a global manner, i.e., by considering a large number (>2000) of simulation results. The influence of the uncertainties on the two risk metrics of expected annual damage (EAD), and adaptation costs (AC) related to coastal protection is assessed at global scale by combining variance-based sensitivity indices with a regression-based machine learning technique. On this basis, we show that the research priorities in terms of future data/knowledge acquisition to reduce uncertainty on EAD and AC differ depending on the considered time horizon. In the short term (before 2040), EAD uncertainty could be significantly decreased by 25 and 75% if the uncertainty of the translation of physical damage into costs and of the modelling of extreme sea levels could respectively be reduced. For AC, it is RSLR that primarily drives short-term uncertainty (with a contribution ~50%). In the longer term (>2050), uncertainty in EAD could be largely reduced by 75% if the SSP scenario could be unambiguously identified. For AC, it is the RCP selection that helps reducing uncertainty (up to 90% by the end of the century). Altogether, the uncertainty in future human activities (SSP and RCP) are the dominant source of the uncertainty in future coastal flood risk.

2020 ◽  
Vol 20 (4) ◽  
pp. 1025-1044 ◽  
Author(s):  
Timothy Tiggeloven ◽  
Hans de Moel ◽  
Hessel C. Winsemius ◽  
Dirk Eilander ◽  
Gilles Erkens ◽  
...  

Abstract. Coastal flood hazard and exposure are expected to increase over the course of the 21st century, leading to increased coastal flood risk. In order to limit the increase in future risk, or even reduce coastal flood risk, adaptation is necessary. Here, we present a framework to evaluate the future benefits and costs of structural protection measures at the global scale, which accounts for the influence of different flood risk drivers (namely sea-level rise, subsidence, and socioeconomic change). Globally, we find that the estimated expected annual damage (EAD) increases by a factor of 150 between 2010 and 2080 if we assume that no adaptation takes place. We find that 15 countries account for approximately 90 % of this increase. We then explore four different adaptation objectives and find that they all show high potential in cost-effectively reducing (future) coastal flood risk at the global scale. Attributing the total costs for optimal protection standards, we find that sea-level rise contributes the most to the total costs of adaptation. However, the other drivers also play an important role. The results of this study can be used to highlight potential savings through adaptation at the global scale.


2019 ◽  
Author(s):  
Timothy Tiggeloven ◽  
Hans de Moel ◽  
Hessel C. Winsemius ◽  
Dirk Eilander ◽  
Gilles Erkens ◽  
...  

Abstract. Coastal flood hazard and exposure are expected to increase over the course of the 21st century, leading to increased coastal flood risk. In order to limit the increase in future risk, or even reduce coastal flood risk, adaptation is necessary. Here, we present a framework to evaluate the future benefits and costs of structural protection measures at the global scale, which accounts for the influence of different flood risk drivers (namely: sea-level rise, subsidence, and socioeconomic change). Globally, we find that the estimated expected annual damage (EAD) increases by a factor of 150 between 2010 and 2080, if we assume that no adaptation takes place. We find that 15 countries account for approximately 90 % of this increase. We then explore four different adaptation objectives and find that they all show high potential to cost-effectively reduce (future) coastal flood risk at the global scale. Attributing the total costs for optimal protection standards, we find that sea-level rise contributes the most to the total costs of adaptation. However, the other drivers also play an important role. The results of this study can be used to highlight potential savings through adaptation at the global scale.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
A. Hooijer ◽  
R. Vernimmen

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.


2008 ◽  
Vol 55 (12) ◽  
pp. 1062-1073 ◽  
Author(s):  
Matthew J. Purvis ◽  
Paul D. Bates ◽  
Christopher M. Hayes

2016 ◽  
Vol 137 (3-4) ◽  
pp. 347-362 ◽  
Author(s):  
Maya K. Buchanan ◽  
Robert E. Kopp ◽  
Michael Oppenheimer ◽  
Claudia Tebaldi

2018 ◽  
Vol 87 ◽  
pp. 92-101 ◽  
Author(s):  
Timothy David Ramm ◽  
Christopher Stephen Watson ◽  
Christopher John White

2020 ◽  
Author(s):  
Jeremy Rohmer ◽  
Daniel Lincke ◽  
Jochen Hinckel ◽  
Goneri Le Cozannet ◽  
Erwin Lambert

<p>Global scale assessment of coastal flood damage and adaptation costs under 21st century sea-level rise are associated with a wide range of uncertainties including those in future projections of socioeconomic development (SSP scenarios), of greenhouse gas emissions (RCP scenarios), and of sea-level rise (SLR). These uncertainties also include structural uncertainties related to the modeling of extreme sea levels, vulnerability functions, and the translation of flooding-induced damage to costs. This raises the following questions: what is the relative importance of each source of uncertainty in the final global-scale results? Which sources of uncertainty need to be considered? What uncertainties are of negligible influence? Hence, getting better insights in the role played by these uncertainties allows to ease their communication and to structure the message on future coastal impacts and induced losses. Using the integrated DIVA Model (see e.g. Hinkel et al., 2014, PNAS), we extensively explore the impact of these uncertainties in a global manner, i.e. by considering a large number (~3,000) of scenario combinations and by analyzing the associated results using a regression-based machine learning technique (i.e. regression decision trees). On this basis, we show the decreasing roles, over time, of the uncertainties in the extremes’ modeling together with the increasing roles of SSP and of RCP after 2030 and 2080 for the damage and adaptation costs respectively. This means that mitigation of climate change helps to reduce uncertainty of adaptation costs, and choosing a particular SSP reduces the uncertainty on the expected damages. In addition, the tree structure of the machine learning technique allows an in-depth analysis of the interactions of the different uncertain factors. These results are discussed depending on the SLR data selected for the analysis, i.e. before and after the recently released IPCC SROCC report on September 2019.</p>


2021 ◽  
Author(s):  
Alejandra Rodríguez Enríquez ◽  
Thomas Wahl ◽  
Hannah Baranes ◽  
Stefan A Talke ◽  
Philip Mark Orton ◽  
...  

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