Hyper-resolution mapping of regional storm surge and tide flooding: comparison of static and dynamic models

Ramı́rez, Jorge A.; Lichter, Michal; Coulthard, Tom; Skinner, Chris

doi:10.1007/s11069-016-2198-z

Cited by 88 publications

(106 citation statements)

References 53 publications

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“…The present study does not include hydraulic models in the studied inundation approaches, as they are computationally expensive and complex to implement. Dynamic inundation simulations have been shown to be more reliable (Ramirez et al, 2016). However, Vousdoukas et al (2016) have shown that VI can be a good surrogate when computational efficiency is the priority, as also demonstrated by Breilh et al (2013).…”

Section: Discussionmentioning

confidence: 95%

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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: Discussionmentioning

confidence: 95%

“…ESLs are used as forcing for the inundation calculations at 100 m resolution and are based on land surface elevation data provided from the Shuttle Radar Topography Mission (SRTM) DEM (Reuter et al, 2007). The inundation calculations are limited to 50 km from the coastline.…”

Section: Coastal Inundationmentioning

confidence: 99%

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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“…Water is routed over the landscape in two dimensions (2D) from raster cell to cell using a simplification of the shallow water equations (Bates et al, 2010). CAESAR-Lisflood is open source, has low computational demands, low data requirements and has been used to replicate fluvial (Coulthard et al, 2013a(Coulthard et al, , 2013b and storm surge (Ramirez et al, 2016;5 Skinner et al, 2015) flooding. The model operates on inexpensive PC hardware, is parallelised to utilise multiple processor cores and is driven by a GUI front end making model set up and operation simpler.…”

Section: Modellingmentioning

confidence: 99%

“…For example, recent advances in reduced-10 complexity flood models (Bates et al, 2010;Coulthard et al, 2013a) with simplified physics have shown promise in estimating flood depths, extent and velocity in urban areas (Fewtrell et al, 2011;Ramirez et al, 2016;Sampson et al, 2014). These models are particularly suited for data sparse locations, with large spatial extents (> 500 km 2 ), and urban topography represented by fine spatial resolution (≤ 30 m) digital elevation models (DEMs).…”

mentioning

confidence: 99%

Flood modeling can make a difference: Disaster risk-reduction and resilience-building in urban areas

Ramı́rez

Rajasekar²,

Patel

et al. 2016

Preprint

Self Cite

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Abstract. Surat, India is a coastal city with a population of approximately 4.5 million people that lies on the banks of the river 10Tapi and is located 100 km downstream from the Ukai dam. Given Surat's geographic location the city is repeatedly exposed to flooding caused by large emergency dam releases into the Tapi river combined with high tide water levels. Flood events of this type occur twice a decade, but their frequency and magnitude may increase due to the urbanization, encroachment in flood plain and climate change. A first step towards strengthening resilience in Surat requires a robust method for mapping flood exposure at fine spatial resolution. Here, in this study we have developed such a method for Surat using a reduced-complexity 15 hydrodynamic model to simulate flooding, but is easily transferable to other urban locations. Our method features three distinct phases that involve: 1) modelling dam release discharge from the Ukai dam arriving at Surat, 2) modelling flooding within Surat caused by the combination of dam release and tides, and, 3) identifying Surat critical infrastructure, population, and income groups exposed to flooding. Our flood model of Surat utilizes topography produced using elevation data collected from an extensive survey. Within the city we have modelled flood scenarios that represent the uncertainty in flood peak discharge 20 and duration resulting from possible climate change. These scenarios include catastrophic conditions that flood 50% of the city and expose > 60% of the population and critical infrastructure to deep flooding. Finally, we highlight how our modelling has contributed to changes in flood risk management within the city following a major flood and resulted in actions that have increased community resilience to flood hazard.

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“…However, given the high complexity of coastal flooding processes, several recent studies showed that the static approach results in substantial overestimation of the flood extent compared to dedicated hydraulic models, especially in flatter terrains (Breilh et al, 2013;Gallien, 2016;Ramirez et al, 2016;Seenath et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Developments in large-scale coastal flood hazard mapping

Vousdoukas

Voukouvalas

Mentaschi

et al. 2016

Nat. Hazards Earth Syst. Sci.

169

157

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Abstract. Coastal flooding related to marine extreme events has severe socioeconomic impacts, and even though the latter are projected to increase under the changing climate, there is a clear deficit of information and predictive capacity related to coastal flood mapping. The present contribution reports on efforts towards a new methodology for mapping coastal flood hazard at European scale, combining (i) the contribution of waves to the total water level; (ii) improved inundation modeling; and (iii) an open, physics-based framework which can be constantly upgraded, whenever new and more accurate data become available. Four inundation approaches of gradually increasing complexity and computational costs were evaluated in terms of their applicability to large-scale coastal flooding mapping: static inundation (SM); a semidynamic method, considering the water volume discharge over the dykes (VD); the flood intensity index approach (Iw); and the model LISFLOOD-FP (LFP). A validation test performed against observed flood extents during the Xynthia storm event showed that SM and VD can lead to an overestimation of flood extents by 232 and 209 %, while Iw and LFP showed satisfactory predictive skill. Application at panEuropean scale for the present-day 100-year event confirmed that static approaches can overestimate flood extents by 56 % compared to LFP; however, Iw can deliver results of reasonable accuracy in cases when reduced computational costs are a priority. Moreover, omitting the wave contribution in the extreme total water level (TWL) can result in a ∼ 60 % underestimation of the flooded area. The present findings have implications for impact assessment studies, since combination of the estimated inundation maps with population exposure maps revealed differences in the estimated number of people affected within the 20-70 % range.

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Hyper-resolution mapping of regional storm surge and tide flooding: comparison of static and dynamic models

Cited by 88 publications

References 53 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

Flood modeling can make a difference: Disaster risk-reduction and resilience-building in urban areas

Developments in large-scale coastal flood hazard mapping

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