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

In this article, we propose an integrated direct and indirect flood risk model for small‐ and large‐scale flood events, allowing for dynamic modeling of total economic losses from a flood event to a full economic recovery. A novel approach is taken that translates direct losses of both capital and labor into production losses using the Cobb‐Douglas production function, aiming at improved consistency in loss accounting. The recovery of the economy is modeled using a hybrid input‐output model and applied to the port region of Rotterdam, using six different flood events (1/10 up to 1/10,000). This procedure allows gaining a better insight regarding the consequences of both high‐ and low‐probability floods. The results show that in terms of expected annual damage, direct losses remain more substantial relative to the indirect losses (approximately 50% larger), but for low‐probability events the indirect losses outweigh the direct losses. Furthermore, we explored parameter uncertainty using a global sensitivity analysis, and varied critical assumptions in the modeling framework related to, among others, flood duration and labor recovery, using a scenario approach. Our findings have two important implications for disaster modelers and practitioners. First, high‐probability events are qualitatively different from low‐probability events in terms of the scale of damages and full recovery period. Second, there are substantial differences in parameter influence between high‐probability and low‐probability flood modeling. These findings suggest that a detailed approach is required when assessing the flood risk for a specific region.     

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.     

An Adaptive Regional Input-Output Model and its Application to the Assessment of the Economic Cost of Katrina

This article proposes a new modeling framework to investigate the consequences of natural disasters and the following reconstruction phase. Based on input-output tables, its originalities are (1) the taking into account of sector production capacities and of both forward and backward propagations within the economic system; and (2) the introduction of adaptive behaviors. The model is used to simulate the response of the economy of Louisiana to the landfall of Katrina. The model is found consistent with available data, and provides two important insights. First, economic processes exacerbate direct losses, and total costs are estimated at $149 billion, for direct losses equal to $107 billion. When exploring the impacts of other possible disasters, it is found that total losses due to a disaster affecting Louisiana increase nonlinearly with respect to direct losses when the latter exceed $50 billion. When direct losses exceed $200 billion, for instance, total losses are twice as large as direct losses. For risk management, therefore, direct losses are insufficient measures of disaster consequences. Second, positive and negative backward propagation mechanisms are essential for the assessment of disaster consequences, and the taking into account of production capacities is necessary to avoid overestimating the positive effects of reconstruction. A systematic sensitivity analysis shows that, among all parameters, the overproduction capacity in the construction sector and the adaptation characteristic time are the most important.     

Modeling the Role of Inventories and Heterogeneity in the Assessment of the Economic Costs of Natural Disasters

Estimates of the cost of potential disasters, including indirect economic consequences, are an important input in the design of risk management strategies. The adaptive regional input‐output (ARIO) inventory model is a tool to assess indirect disaster losses and to analyze their drivers. It is based on an input‐output structure, but it also (i) explicitly represents production bottlenecks and input scarcity and (ii) introduces inventories as an additional flexibility in the production system. This modeling strategy distinguishes between (i) essential supplies that cannot be stocked (e.g., electricity, water) and whose scarcity can paralyze all economic activity; (ii) essential supplies that can be stocked at least temporarily (e.g., steel, chemicals), whose scarcity creates problems only over the medium term; and (iii) supplies that are not essential in the production process, whose scarcity is problematic only over the long run and are therefore easy to replace with imports. The model is applied to the landfall of Hurricane Katrina in Louisiana and identifies two periods in the disaster aftermath: (1) the first year, during which production bottlenecks are responsible for large output losses; (2) the rest of the reconstruction period, during which bottlenecks are inexistent and output losses lower. This analysis also suggests important research questions and policy options to mitigate disaster‐related output losses.     

Modeling a low-carbon garment supply chain

This article attempts to introduce indirect carbon emission and trade-credit concept in a network optimisation model for sustainable supply chain. The proposed model optimises total cost, total direct carbon emission, total indirect emission in the form of embodied carbon footprint of the raw material and total trade-credit amount over the purchased item in a supply chain. The model calculates the total cost by considering purchasing cost, logistics cost, handling cost and manufacturing cost. It attempts to measure the direct emission involved in manufacturing and logistics operations. The model has the capability to consider dissimilar trucks used for transportation according to their operating cost and carbon emission. Multi-objective goal programming is applied to deal with four objectives to find a tradeoff among these objectives. The result suggests that managers should capture the direct as well as the indirect emission which helps in arriving at appropriate strategy for a sustainable supply chain. The effectiveness of the proposed model is demonstrated through a case of a garment supply chain. This model also supports in deciding appropriate goal for carbon emission, supply chain costs, etc.     

Evaluating the consequences of abandoning unprofitable customers: A comparison of direct and indirect abandonment strategies

Due to the increasing use of customer databases in many industries, companies nowadays have high visibility about the profitability of individual-level client relationships. This has led some researchers to recommend the implementation of value-based customer management strategies, including the abandonment of unprofitable customer relationships. In this context firms are faced with the choice between direct and indirect abandonment approaches. While direct abandonment strategies tend to be more efficient than indirect ones (i.e., immediate termination of the relationship vs. hoping that the unprofitable customer may leave on her own) they are also claimed to be associated with more severe negative reactions and higher abandonment cost. Based on two surveys among 965 US customers we provide evidence that, despite these beliefs, direct and indirect abandonment strategies do not differ in their negative impact on the abandoning firm. Given that (indirect) abandonment costs are therefore likely to be similar for both approaches, direct abandonment appears to be the recommendable approach for relationship dissolution.     

基于ITIM模型的政治争端事件对产业经济系统的影响评估

本文在考虑事件恢复期的情景下,扩展了Jung^[7]的针对进出口国际贸易的非正常投入产出模型。将2012年中日"钓鱼岛事件"视为一类政治争端事件,在几个假设前提下,评估该事件给中国的产业经济系统带来的间接经济损失,并筛选出对该事件较为敏感的产业。结果表明:"钓鱼岛事件"严重影响了中日贸易,2012年中日进出口贸易总额同比减少134.3716亿美元,考虑到产业经济系统内部的技术经济关联性,估算出"钓鱼岛事件"带来的静态间接经济损失区间为;然后假设"钓鱼岛事件"在1年、2年、3年、5年、10年和15年内得以解决,分别计算了该事件带来的间接经济损失区间;筛选出了"钓鱼岛事件"的高敏感行业:通用、专用设备制造业、电气机械及器材制造业、化学工业、金属冶炼及压延加工业和金属制品业等。最后提出了相应的政策建议。本文的研究方法可为类似事件的损失评估提供借鉴,研究结果可为政府、行业管理部门和相关企业提供参考。     

An Integrated Approach for Assessing the Impact of Large-Scale Future Floods on a Highway Transport System

The negative impact of climate change continues to escalate flood risk. Floods directly and indirectly damage highway systems and disturb the socioeconomic order. In this study, we propose an integrated approach to quantitatively assess how floods impact the functioning of a highway system. The approach has three parts: (1) a multi-agent simulation model to represent traffic, heterogeneous user demand, and route choice in a highway network; (2) a flood simulator using future runoff scenarios generated from five global climate models, three representative concentration pathways (RCPs), and the CaMa-Flood model; and (3) an impact analyzer, which superimposes the simulated floods on the highway traffic simulation system, and quantifies the flood impact on a highway system based on car following model. This approach is illustrated with a case study of the Chinese highway network. The results show that (i) for different global climate models, the associated flood damage to a highway system is not linearly correlated with the forcing levels of RCPs, or with future years; (ii) floods in different years have variable impacts on regional connectivity; and (iii) extreme flood impacts can cause huge damages in highway networks; that is, in 2030, the estimated 84.5% of routes between provinces cannot be completed when the highway system is disturbed by a future major flood. These results have critical implications for transport sector policies and can be used to guide highway design and infrastructure protection. The approach can be extended to analyze other networks with spatial vulnerability, and it is an effective quantitative tool for reducing systemic disaster risk.     

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.     

Stakeholder Views on Flood Risk Management in Hungary''s Upper Tisza Basin

With escalating costs of flood mitigation and relief, a challenge for the Hungarian government is to develop a flood mitigation and insurance/relief system that is viewed as efficient and fair by the many stakeholders involved. To aid policymakers in this task, this article reports on a recent study to elicit stakeholder views on flood risk management in the Upper Tisza Basin, including views on appropriate means of reducing losses and for transferring the residual losses from the direct victims to taxpayers or an insurance pool. This study is part of a project to develop an integrated approach to flood risk management coordinated by the International Institute of Applied Systems Analysis (IIASA) in collaboration with Swedish and Hungarian researchers. The discussion begins by describing the background of flood risk management problems in the Upper Tisza Basin. The results of interviews carried out with selected key stakeholders and the results of a public survey eliciting views on flood risk management are reported. The final section draws conclusions on incorporating stakeholder views into a flood risk management model, which will be used to illustrate policy paths at an upcoming stakeholder workshop. The conclusions are also of direct interest to Hungarian policymakers.     

How Do Disaster Characteristics Influence Risk Perception?

The main purpose of this study is to examine how risk perception is influenced by the type of disaster (flood or landslide) and victim characteristics. The data reported here are based on the National Risk Perception Survey (NRPS) that was administered for the victims and the general public in Taiwan in 2004. In that year, many towns in Taiwan were seriously affected by floods and landslides, resulting in huge economic losses and fatalities. The primary findings are: (1) the victims and the general public are concerned about the different potential hazards that might affect their residential area, (2) the negative associations between the sense of controllability and the perceived impact is high for landslide victims, but not for flood victims, and (3) disaster type, gender, and previously experienced disasters are good predictors of victims' attitudes toward natural disasters.     

Disaster Risk Management Policies and the Measurement of Resilience for Philippine Regions

How can a government prioritize disaster risk management policies across regions and types of interventions? Using an economic model to assess welfare risk and resilience to disasters, this article systematically tackles the questions: (1) How much asset and welfare risks does each region in the Philippines face from riverine flood disasters? (2) How resilient is each region to riverine flood disasters? (3) What are, per region, the possible interventions to strengthen resilience to riverine flood disasters and what will be their measured benefit? We study the regions of the Philippines to demonstrate the channels through which macroeconomic asset and output losses from disasters translate to consumption and welfare losses at the micro-economic level. Apart from the regional prioritizations, we identify a menu of policy options ranked according to their level of effectiveness in increasing resilience and reducing welfare risk from riverine floods. The ranking of priorities varies for different regions when their level of expected value at risk is different. This suggests that there are region-specific conditions and drivers that need to be integrated into considerations and policy decisions, so that these are effectively addressed.     

A Blueprint for Full Collective Flood Risk Estimation: Demonstration for European River Flooding

Floods are a natural hazard evolving in space and time according to meteorological and river basin dynamics, so that a single flood event can affect different regions over the event duration. This physical mechanism introduces spatio‐temporal relationships between flood records and losses at different locations over a given time window that should be taken into account for an effective assessment of the collective flood risk. However, since extreme floods are rare events, the limited number of historical records usually prevents a reliable frequency analysis. To overcome this limit, we move from the analysis of extreme events to the modeling of continuous stream flow records preserving spatio‐temporal correlation structures of the entire process, and making a more efficient use of the information provided by continuous flow records. The approach is based on the dynamic copula framework, which allows for splitting the modeling of spatio‐temporal properties by coupling suitable time series models accounting for temporal dynamics, and multivariate distributions describing spatial dependence. The model is applied to 490 stream flow sequences recorded across 10 of the largest river basins in central and eastern Europe (Danube, Rhine, Elbe, Oder, Waser, Meuse, Rhone, Seine, Loire, and Garonne). Using available proxy data to quantify local flood exposure and vulnerability, we show that the temporal dependence exerts a key role in reproducing interannual persistence, and thus magnitude and frequency of annual proxy flood losses aggregated at a basin‐wide scale, while copulas allow the preservation of the spatial dependence of losses at weekly and annual time scales.     

Public Perception of the Risks of Floods: Implications for Communication

Floods in the U.S. kill an average of 162 people each year and cause $3.4 billion in property damage. Flood control programs have been successful in lowering, but not eliminating, the risks to lives and property. Since the late 1960s, the federal government has emphasized flood insurance as a primary tool for improving location and flood-proofing decisions, as well as for reimbursing flood losses. Since only 12.7% of houses in flood plain areas are covered by flood insurance, the program has been ineffective. We interviewed people living in three communities that had recently been flooded. Most people had little knowledge of the cause of floods or what could be done to prevent damage. People who work and who are better educated know more and are more likely to have flood insurance. Current government publications about flood risks are not likely to be understood by those at risk. There is little effective communication about the nature and magnitude of the risks and what individuals can do to protect their lives and property and lower their financial risks. The risk management program should both emphasize communication and enforcement of the current law requiring people at risk who hold federally funded loans to be insured.     

多受灾点应急物资动态多阶段分配模型研究

针对传统的单阶段物资分配模型可能导致应急物资分配的局部冗余或短缺、高成本、系统无法达到全局最优等现实情况,通过在指数效用函数中引入灾民物资需求的比例短缺测度公平,以物资短缺的延迟损失最小化与物资分配的总成本最小化为目标构建考虑多集散点、多配送中心和多受灾点的三级配送网络的应急物资动态多阶段分配模型,设计了目标转化与线性近似相结合的模型求解方法,并通过算例对所提出模型的有效性和可行性进行了验证。结果表明：所提出的多阶段模型能够兼顾物资分配的效率与公平,最大程度地降低物资短缺的延迟损失以及物资分配的总成本;运用灾民物资需求的比例短缺量化公平,避免了由于各受灾点的需求量差异而对公平分配产生的影响,可以使各受灾点即使在应急救援初期物资有限、中期物资持续供不应求等情况下,仍然能够在每阶段获得一定比率的所需物资,进而避免较大的物资短缺损失,确保多受灾点之间多阶段应急物资分配的公平性,更符合灾害救援实际,可为现实大规模灾害应急救援物资多阶段分配提供决策支持。     

Manpower requirements of scientists and engineers in high technology industries

This paper analyzes the impact on job requirements for engineers, scientists, technicians and skilled workers of the output of high technology and research intensive industries. The manpower requirements for 22 occupations resulting from a billion dollars of output of each of 12 industries are estimated for 1983. The manpower impacts of the total output of each industry are then simulated to determine the importance of economic activity in these industries in creating employment within each occupation in 1983. Occupational manpower requirements generated by the industries' 1983 outputs are analyzed in relation to total industry output and employment and total employment within each occupation. It is found that the industries analyzed here, although accounting for only 19% of GNP, are responsible for creating between 25% and 60% of many science, engineering and related jobs. These results are detailed for each industry and for every occupation, and the direct and the indirect manpower requirements for each occupation are computed. The implications of these findings are briefly discussed.     

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.     

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.     

Policy Learning for Flood Mitigation: A Longitudinal Assessment of the Community Rating System in Florida

Floods continue to inflict the most damage upon human communities among all natural hazards in the United States. Because localized flooding tends to be spatially repetitive over time, local decisionmakers often have an opportunity to learn from previous events and make proactive policy adjustments to reduce the adverse effects of a subsequent storm. Despite the importance of understanding the degree to which local jurisdictions learn from flood risks and under what circumstances, little if any empirical, longitudinal research has been conducted along these lines. This article addresses the research gap by examining the change in local flood mitigation policies in Florida from 1999 to 2005. We track 18 different mitigation activities organized into four series of activities under the Federal Emergency Management Agency's (FEMA) Community Rating System (CRS) for every local jurisdiction in Florida participating in the FEMA program on a yearly time step. We then identify the major factors contributing to policy changes based on CRS scores over the seven-year study period. Using multivariate statistical models to analyze both natural and social science data, we isolate the effects of several variables categorized into the following groups: hydrologic conditions, flood disaster history, socioeconomic and human capital controls. Results indicate that local jurisdictions do in fact learn from histories of flood risk and this process is expedited under specific conditions.     

Flood Protection Diversification to Reduce Probabilities of Extreme Losses

Recent catastrophic losses because of floods require developing resilient approaches to flood risk protection. This article assesses how diversification of a system of coastal protections might decrease the probabilities of extreme flood losses. The study compares the performance of portfolios each consisting of four types of flood protection assets in a large region of dike rings. A parametric analysis suggests conditions in which diversifications of the types of included flood protection assets decrease extreme flood losses. Increased return periods of extreme losses are associated with portfolios where the asset types have low correlations of economic risk. The effort highlights the importance of understanding correlations across asset types in planning for large‐scale flood protection. It allows explicit integration of climate change scenarios in developing flood mitigation strategy.