Evaluating uncertainties of future marine flooding occurrence as sea-level rises

Cozannet, Gonéri Le; Rohmer, Jérémy; Cazenave, Anny; Idier, Déborah; Wal, R. S. W. van de; Winter, Renske de; Pedreros, Rodrigo; Balouin, Yann; Vinchon, Charlotte; Oliveros, Carlos

doi:10.1016/j.envsoft.2015.07.021

Cited by 71 publications

(76 citation statements)

References 72 publications

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“…While it is desirable to specify statistical uncertainty ranges for some parameters, this may not always be possible. The longer the planning timeframe, the increasing dominance of SLR on the outcome [45], and neither a best estimate, nor statistical uncertainty, can be robustly derived for SLR [18,29]. In any case the optimal risk will occur at a higher SLR than the best estimate of the hazard, due to the tail in the probability distribution for SLR for any of the RCPs [12].…”

Section: How Certain Are We? Uncertainty Is Importantmentioning

confidence: 99%

Section: How Certain Are We? Uncertainty Is Importantmentioning

confidence: 99%

“…Additionally, Le Cozannet et al [45] showed that the relative importance of the various sources of uncertainties changes over the time-local coastal processes such as storm-tide and wave runup are the most important during the first part of this century, whereas uncertainties of future SLR scenarios largely dominate beyond the year 2080. In other words, level 2 statistical uncertainty is relatively important over short-term planning timeframes (before year 2060), but after a transition period (2060-2080), level 3-5 scenario and deep uncertainties become dominant over longer planning timeframes (after the year 2080), driven mainly by the increasing uncertainty in the rates of SLR [45]. For coastal hazard modeling, we suggest that uncertainty surrounding future rates of SLR must be dealt with by evaluating the hazard from various SLR scenarios (scenario uncertainty), and using higher H + type scenario(s), e.g., [46] as a proxy for exploring some implications of deep uncertainty in our understanding of the hazard from possible upper-range SLR, and evaluating their consequence within the DAPP [16].…”

Section: How Certain Are We? Uncertainty Is Importantmentioning

confidence: 99%

See 2 more Smart Citations

Applying Principles of Uncertainty within Coastal Hazard Assessments to Better Support Coastal Adaptation

Stephens

Bell

Lawrence

2017

JMSE

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Abstract:Coastal hazards result from erosion of the shore, or flooding of low-elevation land when storm surges combine with high tides and/or large waves. Future sea-level rise will greatly increase the frequency and depth of coastal flooding and will exacerbate erosion and raise groundwater levels, forcing vulnerable communities to adapt. Communities, local councils and infrastructure operators will need to decide when and how to adapt. The process of decision making using adaptive pathways approaches, is now being applied internationally to plan for adaptation over time by anticipating tipping points in the future when planning objectives are no longer being met. This process requires risk and uncertainty considerations to be transparent in the scenarios used in adaptive planning. We outline a framework for uncertainty identification and management within coastal hazard assessments. The framework provides a logical flow from the land use situation, to the related level of uncertainty as determined by the situation, to which hazard scenarios to model, to the complexity level of hazard modeling required, and to the possible decision type. Traditionally, coastal flood hazard maps show inundated areas only. We present enhanced maps of flooding depth and frequency which clearly show the degree of hazard exposure, where that exposure occurs, and how the exposure changes with sea-level rise, to better inform adaptive planning processes. The new uncertainty framework and mapping techniques can better inform identification of trigger points for adaptation pathways planning and their expected time range, compared to traditional coastal flooding hazard assessments.

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Section: How Certain Are We? Uncertainty Is Importantmentioning

confidence: 99%

Section: How Certain Are We? Uncertainty Is Importantmentioning

confidence: 99%

Section: How Certain Are We? Uncertainty Is Importantmentioning

confidence: 99%

See 1 more Smart Citation

Applying Principles of Uncertainty within Coastal Hazard Assessments to Better Support Coastal Adaptation

Stephens

Bell

Lawrence

2017

JMSE

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“…The relative importance of the sources of uncertainties change over the time: local coastal processes are the most important during the first part of this century, whereas uncertainties of future SLR scenarios largely dominate beyond 2080 [12]. Statistical uncertainty is relevant over short-term planning timeframes (≤ 2050), but after a transition period, scenario and deep uncertainties become dominant over longer planning timeframes (≥ 2080), driven mainly by the increasing uncertainty in the rates of SLR up to and beyond 2100 [9,12].…”

Section: How Certain Are We? Uncertainty Is Importantmentioning

confidence: 99%

Applying principles of uncertainty within coastal hazard assessments to better support coastal adaptation

Stephens¹,

Bell²,

Lawrence³

2017

Preprint

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Abstract:Coastal inundation is an increasing problem. Sea-level rise will greatly increase the frequency and depth of inundation, forcing vulnerable communities to adapt. Communities will need to decide when and how to adapt. The process of decision-making along adaptive pathways is now being used internationally to plan for adaptation over time by anticipating decision points in the future however it unfolds. This process requires risk and uncertainty considerations to be transparent in the scenarios used in such planning. We outline a framework for uncertainty identification and management within coastal hazard assessments which recognizes different types of decision and identifies the types of uncertainty that must be accounted for, such as statistical, scenario and deep uncertainty types. We show how coastal-inundation hazard can be mapped and presented in a way that clearly separates sources of uncertainty, so that they are transparent within a dynamic adaptive pathways planning process. Traditional coastal inundation maps show inundated area only. We present maps of inundation depth and frequency which clearly show the degree of exposure, where that exposure occurs, and how much sea-level rise can be tolerated. The new uncertainty framework and mapping techniques can better identify decision points and their expected time range, which provides more useful input to the adaptation process than traditional coastal inundation assessments.

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“…For coastal flooding, uncertainty relates not only to the amount or rate of sea-level rise (SLR) and socio-economic development, but also to the input data used in the analysis. While scenario uncertainty is generally explored in coastal impact assessments, data uncertainty has not received as much attention in the literature (Le Cozannet et al, 2015). Initial work carried out (Lichter et al, 2011;Mondal and Tatem, 2012) has shown that variations in estimates of area and population exposure are highly dependent on the input datasets.…”

Section: Introductionmentioning

confidence: 99%

Effects of Scale and Input Data on Assessing the Future Impacts of Coastal Flooding: An Application of DIVA for the Emilia-Romagna Coast

et al. 2016

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This paper assesses sea-level rise related coastal flood impacts for Emilia-Romagna (Italy) using the Dynamic Interactive Vulnerability Assessment (DIVA) modeling framework and investigate the sensitivity of the model to four uncertainty dimensions, namely (1) elevation, (2) population, (3) vertical land movement, (4) scale and resolution of assessment. A one-driver-at-a-time sensitivity approach is used in order to explore and quantify the effects of uncertainties in input data and assessment scale on model outputs. Of particular interest is the sensitivity of flood risk estimates when using datasets of different resolution. The change in assessment scale is implemented through the use of a more detailed digital coastline and input data for the coastline segmentation process. This change leads to a 35-fold increase in the number of coastal segments and in a more realistic spatial representation of coastal flood impacts for the Emilia-Romagna coast. Furthermore, the coastline length increases by 43%, considerably influencing adaptation costs (construction of dikes). With respect to input data our results show that by the end of the century coastal flood impacts are more sensitive to variations in elevation and vertical land movement data than to variations in population data in the study area. The inclusion of local information on human induced subsidence rates increases the relative sea-level by 60 cm in 2100, resulting in coastal flood impacts that are up to 25% higher compared to those generated with the global DIVA values, which mainly account for natural processes. The choice of one elevation model over another can result in differences of ∼45% of the coastal floodplain extent and up to 50% in flood damages by 2100. Our results emphasize that the scale of assessment and resolution of the input data can have significant implications for the results of coastal flood impact assessments. Understanding and communicating these implications is essential for effectively supporting decision makers in developing long-term robust and flexible adaptation plans for future changes of highly uncertain scale and direction.

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Evaluating uncertainties of future marine flooding occurrence as sea-level rises

Cited by 71 publications

References 72 publications

Applying Principles of Uncertainty within Coastal Hazard Assessments to Better Support Coastal Adaptation

Applying Principles of Uncertainty within Coastal Hazard Assessments to Better Support Coastal Adaptation

Applying principles of uncertainty within coastal hazard assessments to better support coastal adaptation

Effects of Scale and Input Data on Assessing the Future Impacts of Coastal Flooding: An Application of DIVA for the Emilia-Romagna Coast

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