Simulation of the 1953 Storm Surge Flood in the Netherlands and Application of a Method to Estimate Loss of Life

Jonkman, S.N.; Hengel, D. van den; Vrijling, J.K.

doi:10.1142/9789812709554_0118

Cited by 6 publications

(9 citation statements)

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“…Human life is monetized for evacuation decision-making. The value of human life is counted as the GDP per person (GDP P ) and the average longevity (L av ) (Frieser, 2004;Jonkman, 2007). For example, the GDP P and L av in Mianyang City, China, are RMB 13,745 and75 years in 2008 (Mianyang Bureau of Statistics, 2008).…”

Section: The Prior Probability Of the Influence Diagrammentioning

confidence: 99%

“…Despite ethical considerations, a person's life is measured for rational decision-making. The monetized life loss (M L ) is calculated as the product of the probability of life loss and the value of human life (V L ), while V L is calculated as the product of GDP p and the average longevity (L) (Jonkman, 2007). For instance, GDP p and L in Mianyang City in 2008 are RMB 13,745 and 75 years (Mianyang Bureau of Statistics, 2009).…”

Section: Adding Three Utility Function Nodesmentioning

confidence: 99%

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Warning Decision-Making for Landslide Dam Breaching Flood Using Influence Diagrams

et al. 2021

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Warning and evacuation are among the most effective ways for saving human lives and properties from landslide dam hazards. A new warning decision model for landslide dam break is developed using Influence Diagrams to minimize the total losses. An Influence Diagram is a simple visual representation of a decision problem. It analyzes the qualitative (causal) relationships between the variables via a logic diagram and determines the quantitative relationships via conditional probability and Bayes’ theorem. The model is applied for the warning decision-making of the 2008 Tangjiashan landslide dam. The new model unifies the dam failure probability, evacuation, life loss, and flood damage in an Influence Diagram. Besides, a warning criterion is proposed for efficient decision-making. The model is more advanced than the decision tree since the inter-relationships of influence factors are qualitatively analyzed with causality connections and quantitatively analyzed with conditional probabilities. It is more efficient than a dynamic decision-making model (DYDEM) as it can directly calculate the three types of flood loss (i.e., evacuation cost, flood damage, and monetized life loss) and the expected total loss. Moreover, the probabilities of the influence factors leading to known results can be obtained through inversion analysis based on Bayesian theory. The new warning decision model offers an efficient way to save lives from landslide dam breaking and avoid unnecessary expenses from premature warning and evacuation.

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Section: The Prior Probability Of the Influence Diagrammentioning

confidence: 99%

Section: Adding Three Utility Function Nodesmentioning

confidence: 99%

Warning Decision-Making for Landslide Dam Breaching Flood Using Influence Diagrams

et al. 2021

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“…A common choice of DV includes the annual economic loss or the life-cycle cost of the structure-the reason why the computation of the marginal probability distribution of the decision variable is typically known as "loss analysis" (Porter, 2003;Ciampoli et al, 2011;Barbato et al, 2013;Günay and Mosalam, 2013;Attary et al, 2017). Nevertheless, decision variables encompass various aspects of importance for stakeholders and communities, that cover a wide selection of metrics such as downtime, injuries and fatalities (Jonkman, 2007;Günay and Mosalam, 2013), carbon footprint (Dehghani and Shafieezadeh, 2019;Padgett and Tapia, 2013), environmental impact, and the threat to public health in the event of failure (Reible et al, 2006), loss of community service, and even aspects related to psychological trauma after a hazard event.…”

Section: Performance Analysismentioning

confidence: 99%

“…business interruption, loss of household production (Rose et al, 2007)]. Moreover, different studies have proposed methodologies to assess the value of life (Landefeld and Seskin, 1982;Jonkman, 2007;Ditlevsen and Friis-Hansen, 2009;Luechinger and Raschky, 2009;Kousky, 2014;Daniell et al, 2015;Yu and Tang, 2017), however, serious ethical questions arise when estimating the cost of fatalities or injury in the aftermath of natural disasters. CO 2 or other greenhouse gas emissions and embodied energy are typical DVs (Dehghani and Shafieezadeh, 2019;Lounis and McAllister, 2016;Müller et al, 2013;Padgett and Tapia, 2013) to measure the environmental impact of structures and infrastructure systems by associating costs to the emissions of the manufacturing process and transportation of construction materials (Lounis and McAllister, 2016), maintenance and repair (Dehghani and Shafieezadeh, 2019), and any detour of vehicles during the construction phase or in retrofitting, inspection, and repair actions (Lounis and McAllister, 2016;Padgett and Tapia, 2013).…”

Section: Performance Analysismentioning

confidence: 99%

Performance-Based Coastal Engineering Framework

González-Dueñas

Padgett

2021

Front. Built Environ.

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The changing dynamics of coastal regions and climate pose severe challenges to coastal communities around the world. Effective planning of engineering projects and resilience strategies in coastal regions must not only address current conditions but also take into consideration the expected changes in the exposure and multi-hazard risk in these areas. However, existing performance-based engineering frameworks generally neglect time-varying factors and miss the opportunity to leverage related evidence as it becomes available. This paper proposes a Performance-Based Coastal Engineering (PBCE) framework that is flexible enough to accommodate uncertain time-varying factors, multi-hazard conditions, and cascading-effects. Furthermore, using a dynamic Bayesian network approach, the framework can incorporate observed evidence into the model to update the prior conditional distribution of the analyzed variables. As a proof of concept, two case studies—a typical elevated residential structure and a two-frame system—are presented, considering the effects of cascading failure, the incorporation of time-varying factors, and the influence of emerging evidence. Results show that neglecting cascading effects significantly underestimates the losses and that the incorporation of evidence reduces the uncertainty under the assumed distribution of evidence. The resulting PBCE framework can support data collection efforts, optimization of retrofitting strategies, integration of experts and community interests by facilitating interactions and knowledge sharing, as well as the identification of vulnerable regions and critical components in coastal multi-hazard regions.

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“…Flood damage assessment [20,21] is the process of analysing and quantifying the consequences caused by floods. Many scholars have used different methods to evaluate the loss of flood disasters in different areas and carry out flood risk management work.…”

Section: Introductionmentioning

confidence: 99%

Dynamic and loss analysis of flood inundation in the floodplain area of the lower Yellow River considering ecological impact

Chen

2023

J. Eng. Appl. Sci.

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The floodplain area in the lower Yellow River plays the function of flood detention and undertakes the production and living functions of residents. Because the Yellow River basin is one of the most serious flooding areas in China, the Yellow River seriously threatens the safety of people’s lives and property and social stability and development in the floodplain area. It is significant to carry out flood inundation dynamic analysis and flood loss assessment in the lower Yellow River. Taking the zonal flood detention of the Jiahetan-Gaocun section as an example, a two-dimensional flow mathematical model is established by using MIKE 21. Through the numerical simulation of flood routing during the flood detention operation, the flood detention effect of the floodplain area is analysed, and the flood inundation dynamic analysis and flood inundation loss evaluation are carried out. The results show that the maximum absolute error of water level calculated by the model in the measuring station is only 0.77 m. So, it is reliable to use MIKE 21 to simulate the flood process and flood diversion in the lower Yellow River. The flood with a peak discharge of 4000 m3/s and 7000 m3/s basically reaches the maximum submerged area after 100 h, with the maximum submerged areas of 173.72 km2 and 323.47 km2, respectively. Autumn grain, as the main source of income for floodplain residents, is severely lost in shallow water depth, so they are severely lost in both floods. Therefore, when opening a floodplain as a flood storage and detention area, it is important to consider their loss. The flood simulation analysis results and flood inundation loss evaluation results can provide a scientific basis for the rational utilization of flood storage and detention areas in the lower Yellow River.

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Simulation of the 1953 Storm Surge Flood in the Netherlands and Application of a Method to Estimate Loss of Life

Cited by 6 publications

References 0 publications

Warning Decision-Making for Landslide Dam Breaching Flood Using Influence Diagrams

Warning Decision-Making for Landslide Dam Breaching Flood Using Influence Diagrams

Performance-Based Coastal Engineering Framework

Dynamic and loss analysis of flood inundation in the floodplain area of the lower Yellow River considering ecological impact

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