Integrated Direct and Indirect Flood Risk Modeling: Development and Sensitivity Analysis

Economic assessment of nature-based solutions to reduce flood risk and enhance co-benefits

Flooding is expected to increase due to climate change, urbanisation, and land use change. To address this issue, Nature-Based Solutions (NBSs) are often adopted as innovative and sustainable flood risk management methods. Besides the flood risk reduction benefits, NBSs offer co-benefits for the environment and society. However, these co-benefits are rarely considered in flood risk management due to the inherent complexities of incorporating them into economic assessments. This research addresses this gap by developing a comprehensive methodology that integrates the monetary analysis of co-benefits with flood risk reduction in economic assessments. In doing so, it aspires to provide a more holistic view of the impact of NBS in flood risk management. The assessment employs a framework based on life-cycle cost-benefit analysis, offering a systematic and transparent assessment of both costs and benefits over time supported by key indicators like net present value and benefit cost ratio. The methodology has been applied to the Tamnava basin in Serbia, where significant flooding occurred in 2014 and 2020. The methodology offers valuable insights for practitioners, researchers, and planners seeking to assess the co-benefits of NBS and integrate them into economic assessments. The results show that when considering flood risk reduction alone, all considered measures have higher costs than the benefits derived from avoiding flood damage. However, when incorporating co-benefits, several NBS have a net positive economic impact, including afforestation/reforestation and retention ponds with cost-benefit ratios of 3.5 and 5.6 respectively. This suggests that incorporating co-benefits into economic assessments can significantly increase the overall economic efficiency and viability of NBS.

Assessing the economic ripple effect of flood disasters in light of the recovery process: Insights from an agent-based model

To assess the economic ripple effect, this study integrates agent-based modeling (ABM) with a multiregional input-output (MRIO) table to develop an assessment model that considers capacity recovery process. The intermediate and final demands in the MRIO table are used to describe the agents' interdependence. Survival analysis is used to construct capacity rate curves. By defining the first- and second-order ripple effects, ABM is used to capture the ripple process in days. To conduct a case study, the service and retail sectors in Enshi in Hubei, China, are selected as disaster-affected sectors (they were severely affected by the July 17, 2020 flood disaster). The main findings are as follows: (1) With the first-order ripple effect, the losses caused by service and retail are concentrated within Enshi. Enshi's final demand, construction, and raw materials manufacturing sectors as well as Wuhan's construction sector are seriously affected. (2) With the second-order ripple effect, the losses caused by the service and retail sectors expand, forming a prominent industrial ripple chain: "service (retail)-raw materials manufacturing-construction." (3) The direct and indirect losses caused by the service sector are more significant than those caused by the retail sector. However, the loss ratio of the service sector is smaller than that of the retail sector because of its sound industrial structure and strong resilience. Hence, the indirect losses caused by different sectors are not entirely determined by their direct losses; instead, they are also related to the degree of perfection of the structures of different sectors.

Assessing the economic impacts of a perfect storm of extreme weather, pandemic control, and export restrictions: A methodological construct

This article investigates the economic impacts of a multi-disaster mix comprising extreme weather, such as flooding, pandemic control, and export restrictions, dubbed a "perfect storm." We develop a compound-hazard impact model that improves on the ARIO model by considering the economic interplay between different types of hazardous events. The model considers simultaneously cross-regional substitution and production specialization, which can influence the resilience of the economy to multiple shocks. We build scenarios to investigate economic impacts when a flood and a pandemic lockdown collide and how these are affected by the timing, duration, and intensity/strictness of each shock. In addition, we examine how export restrictions during a pandemic impact the economic losses and recovery, especially when there is the specialization of production of key sectors. The results suggest that an immediate, stricter but shorter pandemic control policy would help to reduce the economic costs inflicted by a perfect storm, and regional or global cooperation is needed to address the spillover effects of such compound events, especially in the context of the risks from deglobalization.

Spatial spillover effects of natural hazards on energy technology innovation-empirical evidence from provincial panel data

Energy technology innovation is the necessary way and fundamental means to lead, promote, and support the revolution of energy production and consumption. While natural hazards, as an unavoidable external risk in the long-term development of human society, may affect the regional energy innovation level. In this study, we use the SDM bifixed model to investigate the direct, indirect, and aggregate effects of natural hazards on energy technology innovation for data of 31 provinces in China from 2011 to 2020. Results indicate that (1) Natural hazards not only have a significant negative impact on local energy technology innovation, but also inhibit energy technology innovation in neighboring regions, with significant spatial spillover effects. (2) Natural hazards have a more pronounced effect in western cities. (3) Natural hazards can enhance energy technology innovation through increasing environmental regulation and reducing FDI. Our study offers suggestions for strengthening post-natural disaster reconstruction and sub-regional resilience to disaster shocks, improving environmental regulation laws and regulations, and attracting more foreign direct investment.

Evaluating Indirect Economic Losses from Flooding Using Input-Output Analysis: An Application to China's Jiangxi Province

Quantifying total economic impacts of flood disaster in a timely manner is essential for flood risk management and sustainable economic growth. This study takes the flood disaster in China's Jiangxi province during the flood season in 2020 as an example, and exploits the input-output method to analyze indirect economic impacts caused by the agricultural direct economic loss. Based on regional IO data and MRIO data, a multi-dimensional econometric analysis was undertaken in terms of inter-regional, multi-regional, and structural decomposition of indirect economic losses. Our study reveals that the indirect economic losses caused by the agricultural sector in other sectors in Jiangxi province were 2.08 times the direct economic losses, of which the manufacturing sector suffered the worst, accounting for 70.11% of the total indirect economic losses. In addition, in terms of demand side and supply side indirect losses, the manufacturing and construction industries were found to be more vulnerable than other industries, and the flood disaster caused the largest indirect economic loss in eastern China. Besides, the supply side losses were significantly higher than the demand side losses, highlighting that the agricultural sector has strong spillover effects on the supply side. Moreover, based on the MRIO data of the years 2012 and 2015, dynamic structural decomposition analysis was undertaken, which showed that changes in the distributional structure appear to be influential in the evaluation of indirect economic losses. The findings highlight the spatial and sectoral heterogeneity of indirect economic losses caused by floods, and have significant implications for disaster mitigation and recovery strategies.

Flood risk mapping and analysis using an integrated framework of machine learning models and analytic hierarchy process

In this study, a new approach of machine learning (ML) models integrated with the analytic hierarchy process (AHP) method was proposed to develop a holistic flood risk assessment map. Flood susceptibility maps were created using ML techniques. AHP was utilized to combine flood vulnerability and exposure criteria. We selected Quang Binh province of Vietnam as a case study and collected available data, including 696 flooding locations of historical flooding events in 2007, 2010, 2016, and 2020; and flood influencing factors of elevation, slope, curvature, flow direction, flow accumulation, distance from river, river density, land cover, geology, and rainfall. These data were used to construct training and testing datasets. The susceptibility models were validated and compared using statistical techniques. An integrated flood risk assessment framework was proposed to incorporate flood hazard (flood susceptibility), flood exposure (distance from river, land use, population density, and rainfall), and flood vulnerability (poverty rate, number of freshwater stations, road density, number of schools, and healthcare facilities). Model validation suggested that deep learning has the best performance of AUC = 0.984 compared with other ensemble models of MultiBoostAB Ensemble (0.958), Random SubSpace Ensemble (0.962), and credal decision tree (AUC = 0.918). The final flood risk map shows 5075 ha (0.63%) in extremely high risk, 47,955 ha (5.95%) in high-risk, 40,460 ha (5.02%) in medium risk, 431,908 ha (53.55%) in low risk areas, and 281,127 ha (34.86%) in very low risk. The present study highlights that the integration of ML models and AHP is a promising framework for mapping flood risks in flood-prone areas.

Risk caused by the propagation of earthquake losses through the economy

The economy of a country is exposed to disruptions caused by natural and man-made disasters. Here we present a set of probabilistic risk indicators, the Average Annual Loss (AAL) and the Loss Exceedance Curve (LEC), regarding to production, employment, Gross Domestic Product (GDP), Gross Regional Product (GRP), export volume, inflation, tariff revenue, among others, due to earthquakes. All indicators are computed using a systematic probabilistic approach, which integrates the seismic risk assessment with spatial computable general equilibrium models, both robust and well-known frameworks used worldwide in their respective fields. Our approach considers the induced damage and frequency of occurrence of a vast collection of events that collectively describe the entire seismic hazard of a country, giving us a better and more complete understanding of the full consequence of earthquakes. We illustrate this approach with an example developed for Chile.

Practice-relevant model validation: distributional parameter risk analysis in financial model risk management

An objective of model validation within organisations is to provide guidance on model selection decisions that balance the operational effectiveness and structural complexity of competing models. We consider a practice-relevant model validation scenario where a financial quantitative analysis team seeks to decide between incumbent and alternative models on the basis of parameter risk. We devise a model risk management methodology that gives a meaningful distributional assessment of parameter risk in a setting where market calibration and historical estimation procedures must be jointly applied. Such a scenario is typically driven by data constraints that preclude market calibration only. We demonstrate our proposed methodology in a natural gas storage modelling context, where model usage is necessary to support profit and loss reporting, and to inform trading and hedging strategy. We leverage our distributional parameter risk approach to devise an accessible technique to support model selection decisions.

Sensitivity Analysis and Quantification of the Role of Governing Transport Mechanisms and Parameters in a Gas Flow Model for Low-Permeability Porous Media

Recent models represent gas (methane) migration in low-permeability media as a weighted sum of various contributions, each associated with a given flow regime. These models typically embed numerous chemical/physical parameters that cannot be easily and unambiguously evaluated via experimental investigations. In this context, modern sensitivity analysis techniques enable us to diagnose the behavior of a given model through the quantification of the importance and role of model input uncertainties with respect to a target model output. Here, we rely on two global sensitivity analysis approaches and metrics (i.e., variance-based Sobol’ indices and moment-based AMA indices) to assess the behavior of a recent interpretive model that conceptualizes gas migration as the sum of a surface diffusion mechanism and two weighted bulk flow components. We quantitatively investigate the impact of (i) each uncertain model parameter and (ii) the type of their associated probability distribution on the evaluation of methane flow. We then derive the structure of an effective diffusion coefficient embedding all complex mechanisms of the model considered and allowing quantification of the relative contribution of each flow mechanism to the overall gas flow.

Risk Assessment of Terrestrial Transportation Infrastructures Exposed to Extreme Events

Keeping transport links open in adverse conditions and being able to restore connections quickly after extreme events are important and demanding tasks for infrastructure owners/operators. This paper is developed within the H2020 project SAFEWAY, whose main goal is to increase the resilience of terrestrial transportation infrastructure. Risk-based approaches are excellent tools to aid in the decision-making process of planning maintenance and implementation of risk mitigation measures with the ultimate goal of reducing risk and increasing resilience. This paper presents a framework for quantitative risk assessment which guides an integrated assessment of the risk components: hazard, exposure, vulnerability and consequences of a malfunctioning transportation infrastructure. The paper guides the identification of failure modes for transportation infrastructure exposed to extreme events (natural and human-made) and provides models for and examples of hazard, vulnerability and risk assessment. Each assessment step must be made in coherence with the other risk components as an integral part of the risk assessment.

Modeling the Impact of Building-Level Flood Mitigation Measures Made Possible by Early Flood Warnings on Community-Level Flood Loss Reduction

The growing number of flood disasters worldwide and the subsequent catastrophic consequences of these events have revealed the flood vulnerability of communities. Flood impact predictions are essential for better flood risk management which can result in an improvement of flood preparedness for vulnerable communities. Early flood warnings can provide households and business owners additional time to save certain possessions or products in their buildings. This can be accomplished by elevating some of the water-sensitive components (e.g., appliances, furniture, electronics, etc.) or installing a temporary flood barrier. Although many qualitative and quantitative flood risk models have been developed and highlighted in the literature, the resolution used in these models does not allow a detailed analysis of flood mitigation at the building- and community level. Therefore, in this article, a high-fidelity flood risk model was used to provide a linkage between the outputs from a high-resolution flood hazard model integrated with a component-based probabilistic flood vulnerability model to account for the damage for each building within the community. The developed model allowed to investigate the benefits of using a precipitation forecast system that allows a lead time for the community to protect its assets and thereby decreasing the amount of flood-induced losses.

Economic damages from Hurricane Sandy attributable to sea level rise caused by anthropogenic climate change

In 2012, Hurricane Sandy hit the East Coast of the United States, creating widespread coastal flooding and over $60 billion in reported economic damage. The potential influence of climate change on the storm itself has been debated, but sea level rise driven by anthropogenic climate change more clearly contributed to damages. To quantify this effect, here we simulate water levels and damage both as they occurred and as they would have occurred across a range of lower sea levels corresponding to different estimates of attributable sea level rise. We find that approximately $8.1B ($4.7B-$14.0B, 5th-95th percentiles) of Sandy's damages are attributable to climate-mediated anthropogenic sea level rise, as is extension of the flood area to affect 71 (40-131) thousand additional people. The same general approach demonstrated here may be applied to impact assessments for other past and future coastal storms.

An Evaluation of Risk-Based Agricultural Land-Use Adjustments under a Flood Management Strategy in a Floodplain

Agricultural damage due to floods in the Indus basin’s fertile land has been the most damaging natural disaster in Pakistan so far. Earthen dikes are protecting the vast areas of the floodplain from regular flooding. However, the floodplain is attractive to farmers due to its fertility and experiences regular crop production within and out of the dike area. This paper evaluates the flood risk in a floodplain of the Chenab river in Pakistan and recommends land-use changes to reduce the flood risk for crops and associated settlements within the study area. The objective of the land-use change is not just to reduce flood losses but also to increase the overall benefits of the floodplain in terms of its Economic Rent (ER). This preliminary study analyses the economic impacts of the risk-based land-use improvements on existing floodplain land uses. Expected Annual Damage (EAD) maps were developed using hydrodynamic models and GIS data. The developed model identified the areas where maize can be economically more productive compared to rice under flood conditions. Promising results were obtained for the settlement relocations. It was also observed that the infra-structure, running parallel to the river, plays a significant role in curtailing the extent of floods. The results show that a combination of structural and non-structural measures proves more effective. The study also recommends the inclusion of social and environmental damages as well as other types of non-structural measures to develop the most effective flood management strategy.

Fighting the Curse of Sparsity: Probabilistic Sensitivity Measures From Cumulative Distribution Functions

Quantitative models support investigators in several risk analysis applications. The calculation of sensitivity measures is an integral part of this analysis. However, it becomes a computationally challenging task, especially when the number of model inputs is large and the model output is spread over orders of magnitude. We introduce and test a new method for the estimation of global sensitivity measures. The new method relies on the intuition of exploiting the empirical cumulative distribution function of the simulator output. This choice allows the estimators of global sensitivity measures to be based on numbers between 0 and 1, thus fighting the curse of sparsity. For density-based sensitivity measures, we devise an approach based on moving averages that bypasses kernel-density estimation. We compare the new method to approaches for calculating popular risk analysis global sensitivity measures as well as to approaches for computing dependence measures gathering increasing interest in the machine learning and statistics literature (the Hilbert-Schmidt independence criterion and distance covariance). The comparison involves also the number of operations needed to obtain the estimates, an aspect often neglected in global sensitivity studies. We let the estimators undergo several tests, first with the wing-weight test case, then with a computationally challenging code with up to k = 30 , 000 inputs, and finally with the traditional Level E benchmark code.

Flood Footprint Assessment: A Multiregional Case of 2009 Central European Floods

Hydrometeorological phenomena have increased in intensity and frequency in last decades, with Europe as one of the most affected areas. This accounts for considerable economic losses in the region. Regional adaptation strategies for costs minimization require a comprehensive assessment of the disasters' economic impacts at a multiple-region scale. This article adapts the flood footprint method for multiple-region assessment of total economic impact and applies it to the 2009 Central European Floods event. The flood footprint is an impact accounting framework based on the input-output methodology to economically assess the physical damage (direct) and production shortfalls (indirect) within a region and wider economic networks, caused by a climate disaster. Here, the model is extended through the capital matrix, to enable diverse recovery strategies. According to the results, indirect losses represent a considerable proportion of the total costs of a natural disaster, and most of them occur in nonhighly directly impacted industries. For the 2009 Central European Floods, the indirect losses represent 65% out of total, and 70% of it comes from four industries: business services, manufacture general, construction, and commerce. Additionally, results show that more industrialized economies would suffer more indirect losses than less-industrialized ones, in spite of being less vulnerable to direct shocks. This may link to their specific economic structures of high capital-intensity and strong interindustrial linkages.

How to Integrate Labor Disruption into an Economic Impact Evaluation Model for Postdisaster Recovery Periods

Evaluating the economic impacts caused by capital destruction is an effective method for disaster management and prevention, but the magnitude of the economic impact of labor disruption on an economic system remains unclear. This article emphasizes the importance of considering labor disruption when evaluating the economic impact of natural disasters. Based on the principle of disasters and resilience theory, our model integrates nonlinear recovery of labor losses and the demand of labor from outside the disaster area into the dynamic evaluation of the economic impact in the postdisaster recovery period. We exemplify this through a case study: the flood disaster that occurred in Wuhan city, China, on July 6, 2016 (the "7.6 Wuhan flood disaster"). The results indicate that (i) the indirect economic impacts of the "7.6 Wuhan flood disaster" will underestimate 15.12% if we do not consider labor disruption; (ii) the economic impact in secondary industry caused by insufficient labor forces accounts for 42.27% of its total impact, while that in the tertiary industry is 36.29%, which can cause enormous losses if both industries suffer shocks; and (iii) the agricultural sector of Wuhan city experiences an increase in output demand of 0.07% that is created by the introduction of 50,000 short-term laborers from outside the disaster area to meet the postdisaster reconstruction need. These results provide evidence for the important role of labor disruption and prove that it is a nonnegligible component of postdisaster economic recovery and postdisaster reduction.

Probabilistic Multiple Hazard Resilience Model of an Interdependent Infrastructure System

Multiple hazard resilience is of significant practical value because most regions of the world are subject to multiple natural and technological hazards. An analysis and assessment approach for multiple hazard spatiotemporal resilience of interdependent infrastructure systems is developed using network theory and a numerical analysis. First, we define multiple hazard resilience and present a quantitative probabilistic metric based on the expansion of a single hazard deterministic resilience model. Second, we define a multiple hazard relationship analysis model with a focus on the impact of hazards on an infrastructure. Subsequently, a relationship matrix is constructed with temporal and spatial dimensions. Further, a general method for the evaluation of direct impacts on an individual infrastructure under multiple hazards is proposed. Third, we present an analysis of indirect multiple hazard impacts on interdependent infrastructures and a joint restoration model of an infrastructure system. Finally, a simplified two-layer interdependent infrastructure network is used as a case study for illustrating the proposed methodology. The results show that temporal and spatial relationships of multiple hazards significantly influence system resilience. Moreover, the interdependence among infrastructures further magnifies the impact on resilience value. The main contribution of the article is a new multiple hazard resilience evaluation approach that is capable of integrating the impacts of multiple hazard interactions, interdependence of network components (layers), and restoration strategy.

A CGE Framework for Modeling the Economics of Flooding and Recovery in a Major Urban Area

Coastal cities around the world have experienced large costs from major flooding events in recent years. Climate change is predicted to bring an increased likelihood of flooding due to sea level rise and more frequent severe storms. In order to plan future development and adaptation, cities must know the magnitude of losses associated with these events, and how they can be reduced. Often losses are calculated from insurance claims or surveying flood victims. However, this largely neglects the loss due to the disruption of economic activity. We use a forward-looking dynamic computable general equilibrium model to study how a local economy responds to a flood, focusing on the subsequent recovery/reconstruction. Initial damage is modeled as a shock to the capital stock and recovery requires rebuilding that stock. We apply the model to Vancouver, British Columbia by considering a flood scenario causing total capital damage of $14.6 billion spread across five municipalities. GDP loss relative to a no-flood scenario is relatively long-lasting. It is 2.0% ($2.2 billion) in the first year after the flood, 1.7% ($1.9 billion) in the second year, and 1.2% ($1.4 billion) in the fifth year.

Assessing the economic impacts of IT service shutdown during the York flood of 2015 in the UK

In this paper we focus on the ‘Christmas’ flood in York (UK), 2015. The case is special in the sense that little infrastructure was lost or damaged, while a single industry (IT services) was completely knocked out for a limited time. Due to these characteristics, the standard modelling techniques are no longer appropriate. An alternative option is provided by the Hypothetical Extraction Method, or HEM. However, there are restrictions in using the HEM, one being that no realistic substitutes exist for inputs from industries that were affected. In this paper we discuss these restrictions and show that the HEM performs well in the York flood case. In the empirical part of this paper we show that a three-day shutdown of the IT services caused a £3.24 m to £4.23 m loss in York, which is equivalent to 10% of the three days' average GVA (Gross Value Added) of York city. The services sector (excluding IT services) sustained the greatest loss at £0.80 m, where the business support industry which was predominantly hit. This study is the first to apply a HEM in this type of flood on a daily basis.

Understanding the Costs of Inaction–An Assessment of Pluvial Flood Damages in Two European Cities

Today, over 50% of the global population lives near water. Due to population growth, ongoing economic development, and extreme weather events, urban areas are growing more susceptible to flood risks, and the costs of inaction of failing to manage flood risks are high. Research into the benefits of pluvial flood-risk management is needed to spread awareness and motivate investments in pluvial flood-risk reduction. So far, such research is lacking. This research therefore assesses pluvial flood damage from a single 60mm/1-hour rainfall event in the cities of Rotterdam and Leicester using 3Di flood modelling and the flood damage estimation tool (waterschadeschatter; WSS). The results demonstrate that potential pluvial flood damages exceed €10 million in each city. From this research, inhabitants and authorities of Leicester and Rotterdam can learn that preparing for upcoming pluvial floods can save millions of euros resulting from future damages. The application of these tools also makes clear that data availability is a highly relevant bottleneck to the pluvial flood damage assessment process. By addressing data shortages, flood damage estimates can be strengthened, which improves decision support and enhances the chance actions are taken in reducing pluvial flood risks.

Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification

Understanding and quantifying total economic impacts of flood events is essential for flood risk management and adaptation planning. Yet, detailed estimations of joint direct and indirect flood-induced economic impacts are rare. In this study an innovative modeling procedure for the joint assessment of short-term direct and indirect economic flood impacts is introduced. The procedure is applied to 19 economic sectors in eight federal states of Germany after the flood events in 2013. The assessment of the direct economic impacts is object-based and considers uncertainties associated with the hazard, the exposed objects and their vulnerability. The direct economic impacts are then coupled to a supply-side Input-Output-Model to estimate the indirect economic impacts. The procedure provides distributions of direct and indirect economic impacts which capture the associated uncertainties. The distributions of the direct economic impacts in the federal states are plausible when compared to reported values. The ratio between indirect and direct economic impacts shows that the sectors Manufacturing, Financial and Insurance activities suffered the most from indirect economic impacts. These ratios also indicate that indirect economic impacts can be almost as high as direct economic impacts. They differ strongly between the economic sectors indicating that the application of a single factor as a proxy for the indirect impacts of all economic sectors is not appropriate.

Mapping Socioeconomic Exposure for Flood Risk Assessment in Italy

Detailed spatial representation of socioeconomic exposure and the related vulnerability to natural hazards has the potential to improve the quality and reliability of risk assessment outputs. We apply a spatially weighted dasymetric approach based on multiple ancillary data to downscale important socioeconomic variables and produce a grid data set for Italy that contains multilayered information about physical exposure, population, gross domestic product, and social vulnerability. We test the performances of our dasymetric approach compared to other spatial interpolation methods. Next, we combine the grid data set with flood hazard estimates to exemplify an application for the purpose of risk assessment.

Multi-Scale Assessment of the Economic Impacts of Flooding: Evidence from Firm to Macro-Level Analysis in the Chinese Manufacturing Sector

We present an empirical study to systemically estimate flooding impacts, linking across scales from individual firms through to the macro levels in China. To this end, we combine a detailed firm-level econometric analysis of 399,356 firms with a macroeconomic input-output model to estimate flood impacts on China’s manufacturing sector over the period 2003–2010. We find that large flooding events on average reduce firm outputs (measured by labor productivity) by about 28.3% per year. Using an input-output analysis, we estimate the potential macroeconomic impact to be a 12.3% annual loss in total output, which amounts to 15,416 RMB billion. Impacts can propagate from manufacturing firms, which are the focus of our empirical analysis, through to other economic sectors that may not actually be located in floodplains but can still be affected by economic disruptions. Lagged flood effects over the following two years are estimated to be a further 5.4% at the firm level and their associated potential effects are at a 2.3% loss in total output or 2,486 RMB billion at the macro-level. These results indicate that the scale of economic impacts from flooding is much larger than microanalyses of direct damage indicate, thus justifying greater action, at a policy level and by individual firms, to manage flood risk.

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

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.

Comparing the Economic Impact of Natural Disasters Generated by Different Input-Output Models: An Application to the 2007 Chehalis River Flood (WA)

Due to the concentration of assets in disaster-prone zones, changes in risk landscape and in the intensity of natural events, property losses have increased considerably in recent decades. While measuring these stock damages is common practice in the literature, the assessment of economic ripple effects due to business interruption is still limited and available estimates tend to vary significantly across models. This article focuses on the most popular single-region input-output models for disaster impact evaluation. It starts with the traditional Leontief model and then compares its assumptions and results with more complex methodologies (rebalancing algorithms, the sequential interindustry model, the dynamic inoperability input-output model, and its inventory counterpart). While the estimated losses vary across models, all the figures are based on the same event, the 2007 Chehalis River flood that impacted three rural counties in Washington State. Given that the large majority of floods take place in rural areas, this article gives the practitioner a thorough review of how future events can be assessed and guidance on model selection.

Integrated Assessment of Economic Losses in Manufacturing Industry in Shanghai Metropolitan Area Under an Extreme Storm Flood Scenario

In this paper, we developed an integrated methodology for assessing asset damage, production capacity loss, and inter-sector ripple loss using the depth-damage curve, Cobb-Douglas production function and Input-Output model. We applied this methodology to the detailed individual manufacturing firms in Shanghai under an extreme storm floods scenario to simulate the disaster impact propagation from local individual firms to the entire industrial system and comprehensively estimate the resulting economic losses and their spatial distribution. Our results show that given no floodwall protection, a 1000-year storm flood scenario would cause direct asset damage of US $21 billion to the Shanghai manufacturing industry, including fixed asset damage of US $12 billion and inventory damage of US $9 billion. Due to the shortage of input productive factors of asset and labor, it would further lead to production capacity loss of US $24 billion. In addition, affected manufacturing industry would indirectly result in ripple loss of US $60 billion among dependent sectors, which has a significant amplifier effect. Our results have important implications for reasonable cost-benefit analysis of structural flood control measures in coastal areas, as well as for manufacturing firm location planning and resilience strategy decision-making.

Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition

In this article, a new set of multivariate global sensitivity indices based on distance components decomposition is proposed. The proposed sensitivity indices can be considered as an extension of the traditional variance-based sensitivity indices and the covariance decomposition-based sensitivity indices, and they have similar forms. The advantage of the proposed sensitivity indices is that they can measure the effects of an input variable on the whole probability distribution of multivariate model output when the power of distance 0 < α < 2 . When α = 2 , the proposed sensitivity indices are equivalent to the covariance decomposition-based sensitivity indices. To calculate the proposed sensitivity indices, an efficient Monte Carlo method is proposed, which can also be used to calculate the covariance decomposition-based sensitivity indices at the same time. The examples show the reasonability of the proposed sensitivity indices and the stability of the proposed Monte Carlo method.

Dynamic Stochastic Macroeconomic Model of Disaster Risk Reduction Investment in Developing Countries

This research formulates a dynamic stochastic macroeconomic model that includes an optimization problem for the formation of stock of human capital, production capital, and household assets, and quantitatively examines economic impacts of disaster on developing countries. We further investigate the optimal policy of development of disaster risk reduction (DRR) capital by considering costs of DRR investment, and show that the effect of DRR investment on economic growth is like a single-peaked curve with respect to the DRR investment rate, which implies that overaccumulation could decelerate economic growth. Moreover, this study emphasizes an effect that DRR capital increases the shadow values of other types of capital and assets by reducing risks of destruction. Importantly, this effect emerges even in cases of process, in which disaster does not actually occur for a long period. We decompose the effects of DRR investments into two parts: "ex-ante risk reduction effect" (ARRE) and "ex-post damage mitigation effect" (PDME). Furthermore, we develop a method of measuring ARRE and PDME by applying the results of Monte Carlo simulation, and show that the scale of ARRE is nonnegligible using a case study of Pakistan. The results imply that types of models that cannot valuate ARRE underestimate the value of DRR investment.

Which Parameters Are Important? Differential Importance Under Uncertainty

In probabilistic risk assessment, attention is often focused on the expected value of a risk metric. The sensitivity of this expectation to changes in the parameters of the distribution characterizing uncertainty in the inputs becomes of interest. Approaches based on differentiation encounter limitations when (i) distributional parameters are expressed in different units or (ii) the analyst wishes to transfer sensitivity insights from individual parameters to parameter groups, when alternating between different levels of a probabilistic safety assessment model. Moreover, the analyst may also wish to examine the effect of assuming independence among inputs. This work proposes an approach based on the differential importance measure, which solves these issues. Estimation aspects are discussed in detail, in particular the problem of obtaining all sensitivity measures from a single Monte Carlo sample, thus avoiding potentially costly model runs. The approach is illustrated through an analytical example, highlighting how it can be used to assess the impact of removing the independence assumption. An application to the probabilistic risk assessment model of the Advanced Test Reactor large loss of coolant accident sequence concludes the work.

Integrating Entropy-Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard

Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net-water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics-based entropy method. The weighted indices were input into the WNB-based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image-based sampling and validation, cell-by-cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood-related environmental hazard studies.