Estimation of industrial and commercial asset values for hazard risk assessment

<p><strong>Abstract.</strong> Uncertainty analyses of flood damage assessments generally require a large amount of model evaluations. This is often hampered by the high computational costs necessary to calculate flood extents and depths using 2-dimensional flow models. In this paper we developed a new approach to estimate flood inundation depths that can be incorporated in a Monte Carlo uncertainty analysis. This allows estimation of the uncertainty in flood damage estimates and the determination of which parameters contribute the most to this uncertainty. The approach is applied on three breach locations on the west coast of the Netherlands. In total, uncertainties in 12 input parameters were considered in this study, related to the storm surge, breach growth and the damage calculation. We show that the uncertainty in flood damage estimates is substantial, with the bounds of the 95% confidence range being more than four times smaller or larger than the median. The most influential parameter is uncertainty in depth-damage curves, but five other parameters also contribute substantially. The contribution of uncertainty in parameters related to the damage calculation is about equal to the contribution of parameters related to the volume of the inflowing water. Given the emphasis of most risk assessments on the estimation of the hazard, this implies that the damage calculation aspect deserves more attention in flood risk research efforts. Given the large uncertainties found in this study, it is recommended to always perform multiple calculations in flood simulations and damage assessments to capture the full range of model outcomes.</p>

Section: Resultsmentioning

confidence: 99%

Uncertainty and sensitivity analysis of coastal flood damage estimates in the west of the Netherlands

Moel

Asselman

Aerts

2012

“…Branch-specific Germany Derivation of branch-specific asset values for Meso 60 commercial and assets with three sizes of production sites and 60 economic industrial sectors dasymetric mapping activities based on stock data; municipal values (mobile and immobile; Seifert et al (2010) were further disaggregated with the help of gross/net values) CORINE land cover data and a mapping approach modified from Mennis (2003).…”

Section: Exposure Analysis and Asset Assessmentmentioning

confidence: 99%

“…Australia Construction costs (replacement costs) per square meter Micro All building types cost ratios of different building types as published by the Australian Blong (2003b) authorities are related to construction costs of a medium-sized family house (cost ratios mapping of population density (Eicher and Brewer, 2001;Gallego and Peedell, 2001;ICPR, 2001;Mennis, 2003;Chen et al, 2004;Meyer, 2005;Thieken et al, 2006;Seifert et al, 2010). In these studies topographic maps, traffic networks, satellite or land use and land cover data sets have been proved suitable for disaggregation purposes, since their information reveal an explicit relation to population and, therefore, to asset distribution as well.…”

Section: Constructionmentioning

confidence: 99%

“…This approach is feasible for small or medium sized areas, but not for a countrywide approach, since the analysis of aerial photographs for a huge area would be too time-consuming. Other approaches as shown in Thieken et al (2006) and Seifert et al (2010) are also applicable in large areas. Wünsch et al (2009) compared three different disaggregation methods and two land use data sets in the framework of damage estimation and concluded that it is better to invest in land use data than in more sophisticated mapping techniques.…”

Section: Constructionmentioning

confidence: 99%

See 1 more Smart Citation

Review article "Assessment of economic flood damage"

Merz

Kreibich

Schwarze

et al. 2010

1,045

1,085

Abstract. Damage assessments of natural hazards supply crucial information to decision support and policy development in the fields of natural hazard management and adaptation planning to climate change. Specifically, the estimation of economic flood damage is gaining greater importance as flood risk management is becoming the dominant approach of flood control policies throughout Europe. This paper reviews the state-of-the-art and identifies research directions of economic flood damage assessment. Despite the fact that considerable research effort has been spent and progress has been made on damage data collection, data analysis and model development in recent years, there still seems to be a mismatch between the relevance of damage assessments and the quality of the available models and datasets. Often, simple approaches are used, mainly due to limitations in available data and knowledge on damage mechanisms. The results of damage assessments depend on many assumptions, e.g. the selection of spatial and temporal boundaries, and there are many pitfalls in economic evaluation, e.g. the choice between replacement costs or depreciated values. Much larger efforts are required for empirical and synthetic data collection and for providing consistent, reliable data to scientists and practitioners. A major shortcoming of damage modelling is that model validation is scarcely performed. Uncertainty analyses and thorough scrutiny of model inputs and assumptions should be mandatory for each damage model development and application, respectively. In our view, flood risk assessments are often not well balanced. Much more attention is given to the hazard assessment part, whereas damage assessment is treated as some kind of appendix within the risk analysis. Advances in floodCorrespondence to: B. Merz (bmerz@gfz-potsdam.de) damage assessment could trigger subsequent methodological improvements in other natural hazard areas with comparable time-space properties.

“…These two aspects are linked via damage functions or loss models, which quantitatively describe how hazard characteristics affect specific elements at risk. This kind of damage or loss modelling typically provides an estimate of the expected monetary loss (Seifert et al, 2009;Luna et al, 2014;van Westen et al, 2014;Mazzorana et al, 2012). However, more holistic approaches go further, incorporating social, economic, cultural, institutional and educational aspects, and their interdependence (Fuchs, 2009).…”

Section: Introductionmentioning

confidence: 99%

Regional prioritisation of flood risk in mountainous areas

Rogelis

Werner

Obregón

et al. 2016

Abstract. In this paper a method is proposed to identify mountainous watersheds with the highest flood risk at the regional level. Through this, the watersheds to be subjected to more detailed risk studies can be prioritised in order to establish appropriate flood risk management strategies. The prioritisation is carried out through an index composed of a qualitative indicator of vulnerability and a qualitative flash flood/debris flow susceptibility indicator. At the regional level, vulnerability was assessed on the basis of a principal component analysis carried out with variables recognised in literature to contribute to vulnerability, using watersheds as the unit of analysis. The area exposed was obtained from a simplified flood extent analysis at the regional level, which provided a mask where vulnerability variables were extracted. The vulnerability indicator obtained from the principal component analysis was combined with an existing susceptibility indicator, thus providing an index that allows the watersheds to be prioritised in support of flood risk management at regional level. Results show that the components of vulnerability can be expressed in terms of three constituent indicators: (i) socio-economic fragility, which is composed of demography and lack of well-being; (ii) lack of resilience and coping capacity, which is composed of lack of education, lack of preparedness and response capacity, lack of rescue capacity, cohesiveness of the community; and (iii) physical exposure, which is composed of exposed infrastructure and exposed population. A sensitivity analysis shows that the classification of vulnerability is robust for watersheds with low and high values of the vulnerability indicator, while some watersheds with intermediate values of the indicator are sensitive to shifting between medium and high vulnerability.