Development of damage curves for buildings near La Rochelle during storm Xynthia based on insurance claims and hydrodynamic simulations

Loaiza, Andrés Díaz; Bricker, Jeremy D.; Meynadier, Rémi; Duong, Trang Minh; Ranasinghe, Roshanka; Jonkman, Sebastiaan N.

doi:10.5194/nhess-22-345-2022

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…However, the poor structure-wise depth-damage correlation and the alignment of the two considered sets of reported damage claims with different (combinations) of IPS reiterate commonly faced challenges of flood damage modeling (Ahn et al, 2019;Diaz Loaiza et al, 2022). Limited knowledge of the system introduces uncertainty into the damage estimates.…”

Section: Discussionmentioning

confidence: 99%

Developing a framework for the assessment of current and future flood risk in Venice, Italy

Schlumberger

Ferrarin

Jonkman

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. Flooding causes serious impacts on the old town of Venice, its residents, and its cultural heritage. Despite this existence-defining condition, limited scientific knowledge on flood risk of the old town of Venice is available to support decisions to mitigate existing and future flood impacts. Therefore, this study proposes a risk assessment framework to provide a methodical and flexible instrument for decision-making for flood risk management in Venice. We first use a state-of-the-art hydrodynamic urban model to identify the hazard characteristics inside the city of Venice. Exposure, vulnerability, and corresponding damage are then modeled by a multi-parametric, micro-scale damage model which is adapted to the specific context of Venice with its dense urban structure and high risk awareness. Furthermore, a set of individual protection scenarios are implemented to account for possible variability in flood preparedness of the residents. This developed risk assessment framework was tested for the flood event of 12 November 2019 and proved able to reproduce flood characteristics and resulting damage well. A scenario analysis based on a meteorological event like 12 November 2019 was conducted to derive flood damage estimates for the year 2060 for a set of sea level rise scenarios in combination with a (partially) functioning storm surge barrier, the Modulo Sperimentale Elettromeccanico (MOSE). The analysis suggests that a functioning MOSE barrier could prevent flood damage for the considered storm event and sea level scenarios almost entirely. A partially closed MOSE barrier (open Lido inlet) could reduce the damage by up to 34 % for optimistic sea level rise prognoses. However, damage could be 10 % to 600 % higher in 2060 compared to 2019 for a partial closure of the storm surge barrier, depending on different levels of individual protection.

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Section: Discussionmentioning

confidence: 99%

Developing a framework for the assessment of current and future flood risk in Venice, Italy

Schlumberger

Ferrarin

Jonkman

et al. 2022

Nat. Hazards Earth Syst. Sci.

Self Cite

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“…The paper estimates the damage behaviour of mud buildings based on a multivariate probabilistic technique using two pragmatic approaches as described in Fig. 3 , (a) literature-based and (b) laboratory-based approach 50 , 51 . In the literature-based approach, the damage behaviour of mud building is marked using logical assumptions from the available scientific evidence.…”

Section: Methodsmentioning

confidence: 99%

Developing assessment flow for damage estimation of mud housing typology through a case study against coastal floods

Aishwarya

Aniruddha

Sutapa

et al. 2023

Sci Rep

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Rising instances of prolonged inundation due to climate-aggravated high tide flooding are economically burdensome for resource-crunched developing nations that bear enormous damage due to loss of built infrastructure (housing in this case). Regardless of the loss, coastal flood impact on buildings is rarely given precedence. The mud building typology in India captures 34% of existing housing demand primarily within ruralIndia; for instance, 75% of the housing in Sagar Island uses mud as the dominant construction material, making it an ideal case for the proposed research. The multivariate nature of damage and empirical data constraint associated with mud buildings propels the development of two unconventional damage assessment approaches using multivariate-probabilistic technique. The proposed literature-based approach uses logical reasoning based on the available scientific evidence whereas the lab-based approach uses the insights from structural analysis of scaled model. The damage matrix created from both the approaches are used to analyse a common flood data (depth & duration) generated using 1000 Montecarlo simulations. The resultant Damage Stage values confirm the versatility of either approach over spatial (local to regional)—temporal (flood character and intensity) dimensions. The lab-based approach proved to be a better alternative considering the availability of continuous records on damage behaviour and precise information on the flood threshold of dominant building material, a crucial component of the multivariate damage assessment process.

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“…There are different types of damage functions used for this purpose, as introduced below. Fragility functions express the probability of exceeding defined damage levels of a structure or a component at different levels of hazard intensity [25,26]. Vulnerability functions, damage curves, and stage-damage curves instead describe the direct relationship between the probable damage for different severities of a hazard [27,28].…”

Section: Introductionmentioning

confidence: 99%

Systematic Mapping of Global Research on Disaster Damage Estimation for Buildings: A Machine Learning-Aided Study

Rajapaksha,

Siriwardana,

Ruparathna

et al. 2024

Buildings

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Research on disaster damage estimation for buildings has gained extensive attention due to the increased number of disastrous events, facilitating risk assessment, the effective integration of disaster resilience measures, and policy development. A systematic mapping study has been conducted, focusing on disaster damage estimation studies to identify trends, relationships, and gaps in this large and exponentially growing subject area. A novel approach using machine learning algorithms to screen, categorise, and map the articles was adopted to mitigate the constraints of manual handling. Out of 8608 articles from major scientific databases, the most relevant 2186 were used in the analysis. These articles were classified based on the hazard, geographical location, damage function properties, and building properties. Key observations reveal an emerging trend in publications, with most studies concentrated in developed and severely disaster-affected countries in America, Europe, and Asia. A significant portion (68%) of the relevant articles focus on earthquakes. However, as the key research opportunities, a notable research gap exists in studies focusing on the African and South American continents despite the significant damage caused by disasters there. Additionally, studies on floods, hurricanes, and tsunamis are minimal compared to those on earthquakes. Further trends and relationships in current studies were analysed to convey insights from the literature, identifying research gaps in terms of hazards, geographical locations, and other relevant parameters. These insights aim to effectively guide future research in disaster damage estimation for buildings.

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Development of damage curves for buildings near La Rochelle during storm Xynthia based on insurance claims and hydrodynamic simulations

Cited by 6 publications

References 39 publications

Developing a framework for the assessment of current and future flood risk in Venice, Italy

Developing a framework for the assessment of current and future flood risk in Venice, Italy

Developing assessment flow for damage estimation of mud housing typology through a case study against coastal floods

Systematic Mapping of Global Research on Disaster Damage Estimation for Buildings: A Machine Learning-Aided Study

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