New estimates of potential impacts of sea level rise and coastal floods in Poland

Paprotny, Dominik; Terefenko, Paweł

doi:10.1007/s11069-016-2619-z

Cited by 58 publications

(58 citation statements)

References 40 publications

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“…An alternative but more computationally expensive approach is to explore the full range of uncertainty from all ESL components, expressing them first as PDFs and combining them through Monte Carlo simulations in order to generate probabilistic projections of ESLs (Vousdoukas et al, 2018b). The study comes in agreement with findings from Paprotny et al (2018), who highlighted that the accuracy of DEM and coastal protection data apply strong control to flood mapping efforts. Such uncertainty factors are more challenging to deal with, despite their criticality.…”

Section: Discussionsupporting

confidence: 71%

“…Existing protection datasets suffer from multiple sources of errors such as (i) the fact that protection standards are often reported in return periods and conversion in ESLs can include artefacts, among others, from the components considered, the extreme value analysis, and model errors (Vousdoukas et al, 2018b); (ii) most datasets provide one value for extensive regions covering several kilometres along which protection levels can vary substantially; (iii) there is no centralized system to collect and update information on available coastal protection in most countries, not to mention at continental or global scales. Still, present and previous findings (Paprotny et al, 2018) highlight the urgent need to generate large-scale but highly detailed datasets of coastal protection standards, as the absence of such information introduces substantial uncertainty in any CFR analysis.…”

Section: Discussionmentioning

confidence: 90%

“…Similar to the tidal elevation uncertainty analysis (see Sect. 3.2.1), the range of the applied z crest perturbation was based on the observed errors of reported flood protection levels against in situ measurements (Scussolini et al, 2016;Paprotny and Terefenko, 2017).…”

Section: Coastal Flood Protectionmentioning

confidence: 99%

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

Vousdoukas

Bouziotas

Giardino

et al. 2018

Nat. Hazards Earth Syst. Sci.

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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.

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Section: Discussionsupporting

confidence: 71%

Section: Discussionmentioning

confidence: 90%

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

Vousdoukas

Bouziotas

Giardino

et al. 2018

Nat. Hazards Earth Syst. Sci.

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“…LiDAR measurements can provide centimetre-scale accuracy that facilitate flood exposure assessments for smaller SLR increments resolvable over decades; however, DEM coverage is often limited to sub-national levels [16]. LiDAR DEM application can also support detailed assessments of individual assets at-risk to coastal flooding such as buildings, roads and pipelines [17,18].…”

Section: Introductionmentioning

confidence: 99%

National-Scale Built-Environment Exposure to 100-Year Extreme Sea Levels and Sea-Level Rise

et al. 2020

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Coastal flooding from extreme sea levels will increase in frequency and magnitude as global climate change forces sea-level rise (SLR). Extreme sea-level events, rare in the recent past (i.e., once per century), are projected to occur at least once per year by 2050 along many of the world's coastlines. Information showing where and how built-environment exposure increases with SLR, enables timely adaptation before damaging thresholds are reached. This study presents a first national-scale assessment of New Zealand's built-environment exposure to future coastal flooding. We use an analytical risk model framework, "RiskScape", to enumerate land, buildings and infrastructure exposed to a present and future 100-year extreme sea-level flood event (ESL 100 ). We used high-resolution topographic data to assess incremental exposure to 0.1 m SLR increases. This approach detects variable rates in the potential magnitude and timing of future flood exposure in response to SLR over decadal scales. National built-land and asset exposure to ESL 100 flooding doubles with less than 1 m SLR, indicating low-lying areas are likely to experience rapid exposure increases from modest increases in SLR expected within the next few decades. This highlights an urgent need for national and regional actions to anticipate and adaptively plan to reduce future socio-economic impacts arising from flood exposure to extreme sea-levels and SLR.

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“…Many recent studies have focused on various types of floods in risk analysis. Paprotny and Terefenko [11], for instance, used a topographic objects database to estimate the potential impacts of sea-level rise and coastal floods in Poland. Considering flood frequency, inundation depth, and depth-damage functions, Oubennaceur and colleagues [12] assessed the expected annual damage to each individual building due to river flooding in a reach of the Richelieu River, Quebec, Canada.…”

Section: Introductionmentioning

confidence: 99%

Building Asset Value Mapping in Support of Flood Risk Assessments: A Case Study of Shanghai, China

Wang

et al. 2019

Sustainability

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Exposure is an integral part of any natural disaster risk assessment, and damage to buildings is one of the most important consequence of flood disasters. As such, estimates of the building stock and the values at risk can assist in flood risk management, including determining the damage extent and severity. Unfortunately, little information about building asset value, and especially its spatial distributions, is readily available in most countries. This is certainly true in China, given that the statistical data on building floor area (BFA) is collected by administrative entities (i.e. census level). To bridge the gap between census-level BFA data and geo-coded building asset value data, this article introduces a method for building asset value mapping, using Shanghai as an example. This method consists of a census-level BFA disaggregation (downscaling) by means of a building footprint map extracted from high-resolution remote sensing data, combined with LandScan population density grid data and a financial appraisal of building asset values. Validation with statistical data and field survey data confirms that the method can produce good results, but largely constrained by the resolution of the population density grid used. However, compared with other models with no disaggregation in flood exposure assessment that involves Shanghai, the building asset value mapping method used in this study has a comparative advantage, and it will provide a quick way to produce a building asset value map for regional flood risk assessments. We argue that a sound flood risk assessment should be based on a high-resolution-individual building-based-building asset value map because of the high spatial heterogeneity of flood hazards.Röthlisberger and colleagues [13] compared five building value models for flood risk analysis, and proposed that estimating exposed-building values should be based on individual buildings rather than on areas of land-use types. Higher quality exposure data is needed to perform validations of flood risk models [14].However, for flood risk analysis, quite often a spatial mismatch exists between hazard intensity data (e.g., inundation depth), which are frequently modelled on a high-resolution raster level, and exposure data, which are usually only available at coarse census units (e.g., counties) or aggregated land-use/land cover classes [14][15][16][17]. Flood risk assessments often invest much more in hazard modelling of water depths or inundation areas at a spatially explicit raster level [18,19], while only a limited number of studies have explicitly focused on the estimations of assets and their disaggregation to overcome the spatial mismatch between the quality of hazard and exposure data [20][21][22][23]. Meanwhile, the quality of the exposure data is one of the most important uncertainties in flood risk assessments [24][25][26][27]. Because of the high spatial resolution of water inundation depth estimation, which is mainly a function of topography [28,29], flood risk assessments are much more sensi...

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New estimates of potential impacts of sea level rise and coastal floods in Poland

Cited by 58 publications

References 40 publications

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

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

National-Scale Built-Environment Exposure to 100-Year Extreme Sea Levels and Sea-Level Rise

Building Asset Value Mapping in Support of Flood Risk Assessments: A Case Study of Shanghai, China

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