Xinyu Jiang

Integrated Disaster Risk Management (IDRM) has become a widely accepted concept within disaster risk reduction (DRR), yet it remains insufficiently theorized, critically examined, and empirically evaluated. This paper examines how IDRM has been conceptualized and institutionalized, identifying dominant paradigms and interrogating whose interests they serve. It argues that vulnerability studies have effectively become the dominant framework of IDRM by shifting disaster discourse from hazard-centric to socially constructed perspectives, integrating hazard, exposure, and vulnerability. However, vulnerability-led IDRM remains largely top-down. It relies on expert-defined indicators, gives limited attention to risk perception, and inadequately captures how people make decisions in multi-risk environments. In practice, communities prioritize immediate and persistent risks, such as livelihood, health, and income insecurity, over hazard-specific risks emphasized by experts. This creates a disconnect, as factors defined as “vulnerability” are often perceived as independent and competing risks by communities. While emerging IDRM frameworks increasingly acknowledge interconnected risks, their focus remains largely on exceptional and cascading crises. This paper demonstrates that Global South communities confront overlapping chronic livelihood, environmental, social and disaster risks. It accordingly advocates a participatory, adaptive IDRM framework integrating local risk perception, multi-risk realities and community-determined risk priorities and response strategies.

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.

Risk mitigation policies (like dike rising) are essential to address increasing coastal flood risks due to global warming. Furthermore, the optimal level of risk mitigation policy should be determined by public preferences for risk reduction. However, it is difficult to reveal public preferences for coastal flood risk reduction because projections of coastal flood risks inevitably involve uncertainty. This study aims to estimate household preference for coastal flood reduction under ambiguity and multiple projections of coastal flood risks. By coupling storm surge inundation simulations and stated preference experiments with decision models, we estimate the expected loss reduction, risk premium, and ambiguity premium for coastal flood risk mitigation policies. The study shows that ignoring the ambiguity premium causes significant undervaluation of coastal flood risk mitigation.

A key issue in assessing the spatial distribution of flood risk is considering risk information derived from multiple flood sources (river flooding, drainage inundation, etc.) that may affect the risk assessment area. This study proposes a method for assessing spatial flood risk that includes flooding and inundation in small-basin areas through multivariate design rainfall. The concept of critical rainfall duration, determined by the time of concentration of flooding, is used to represent the characteristics of flooding from different sources. A copula method is adopted to capture the correlation of rainfall amounts in different critical rainfall durations to reflect the correlation of potential flooding from multiple flood sources. Rainfalls for different return periods are designed based on the copula multivariate analysis. Using the design rainfalls as input, flood risk is assessed following the rainfall–runoff–inundation–loss estimation procedure. A case study of the Otsu River Basin, Osaka Prefecture, Japan, was conducted to demonstrate the feasibility and advantages of this method. Compared to conventional rainfall design, this method considers the response characteristics of multiple flood sources, and solves the problem of flood risk assessment from multiple flood sources. It can be applied to generate a precise flood risk assessment to support integrated flood risk management.

Understanding storm surge inundation risk is essential for developing countermeasures and adaptation strategies for tackling climate change. Consistent assessment of storm surge inundation risk that links probability of hazard occurrence to distribution of economic consequence are scarce due to the lack of historical data and uncertainty of climate change, especially at local scales. This paper proposes a simulation-based method to construct exceedance probability (EP) curves for representing storm surge risk and identifying the economic impact of climate change in the coastal areas of Ise Bay, Japan. The region-specific exceedance probability curves show that risk could be different among different districts. The industry-specific exceedance probability curves show that manufacturing, transport and postal activities, electricity, gas, heat supply and water, and wholesale and retail trade are the most affected sectors in terms of property damage. Services also need to be of concern in terms of business interruption loss. Exceedance probability curves provide complete risk information and our simulation-based approach can contribute to a better understanding of storm surge risk, improve the quantitative assessment of the climate change-driven impacts on coastal areas, and identify vulnerable regions and industrial sectors in detail.

The case study we present on estimating business interruption (BI) loss to industrial sectors due to floods in Aichi Prefecture, Japan, involves four steps – estimating the business interruption loss rate (BILR), estimating the spatial distribution of hazard information, identifying the spatial distribution of exposure such as firms and employees, and calculating BI loss based on the BILR, hazards, and exposure information as input. Validation was conducted by comparing estimated BI loss to economic loss calculated by an index of industrial production (IIP). We found that the proposed methodology quickly and feasibly estimates BI loss once water depth is obtained. Estimated BILR and BI loss in the industrial sector provides information enabling individual firms to formulate business continuity plans and design risk management strategies.

In this study, an experiment was performed to assess the trip difficulty for urban residents of different age groups walking in various depths of water, and the data were corroborated with the real urban rainstorm waterlogging scenarios in downtown (Daoli district) Ha-Erbin (China). Mathematical models of urban rainstorm waterlogging were constructed using scenario simulation methods, aided by the GIS spatial analysis technology and hydrodynamic analysis of the waterway systems in the study area. Then these models were used to evaluate the impact of waterlogging on the safety of residents walking in the affected area. Results are summarized as: (1) for an urban rainstorm waterlogging scenario reoccurring once every 10 years, three grid regions would have waterlogging above 0.5 m moving at a velocity of 1.5 m/s. Under this scenario, waterlogging would accumulate on traffic roads only in small areas, affecting the safety and mobility of residents walking in the neighborhood; (2) for an urban rainstorm waterlogging scenario reoccurring once every 20 years, 13 grids experienced the same waterlogging situation affecting a larger area of the city; (3) for an urban rainstorm waterlogging scenario reoccurring once every 50 years, 86 grid regions were affected (waterlogging above 0.5 m moving at 1.5 m/s), and those areas would become impassable for residents.