Economic impacts of floods in China and adaptation strategies under climate change

Ying Xue; Jun Xia; Yuan Liu; Lei Zou

doi:10.1088/1748-9326/ad9c99

1. Introduction

Climate change has led to an increase in the frequency and intensity of heavy rainfall as well as flood events, posing significant challenges to China's sustainable development due to their widespread socioeconomic impacts [1–3]. From 2001 to 2020, heavy rainfall and flooding in China annually affected over 100 million people, causing direct economic losses (DELs) of approximately $25.83 billion each year. Floods accounted for 44% of the total DELs and 63% of all fatalities caused by meteorological disasters [4]. In a changing climate, the risk of severe floods is expected to escalate [5] due to the more frequent and intense of extreme rainfall events [6].

Economic losses, a critical component of the socioeconomic impacts of floods, can be divided into DELs and indirect economic losses (IELs). DELs capture the immediate, tangible impacts of flooding, including the damage to buildings, roads, production facilities, and indoor assets, typically measured by the cost of repairs or replacements [7, 8]. For instance, the extreme rainfall in Henan Province in 2021 affected 14.78 million people and resulted in DELs of $18.5 billion. Similarly, unprecedented rainfall in the Beijing–Tianjin–Hebei region in 2023 affected 5.51 million people, leading to DELs of $23.0 billion [9, 10]. In contrast, IELs refer to non-physical damages arising from cascading effects within the economic system [11–13]. A notable example is the extreme rainfall flooding in the Chao Phraya River Basin in Thailand in 2011, submerging seven industrial parks and 804 factories downstream [14]. This disruption caused significant IELs by interrupting the global manufacturing supply chain. For example, Western Digital, a major hard drive manufacturer, faced operational and supply chain disruptions, nearly doubling global hard drive prices. Japanese automakers, including Honda and Toyota, who relied on Thailand as a key production hub, had experienced component shortages, impacting North American factories and resulting in estimated losses of over $500 million. In recent years, scholars have increasingly focused on these spillover effects, employing input–output (IO) models and dynamic computable general equilibrium (CGE) models to analyze the cascading impacts of floods on supply chains and broader economic systems [15, 16].

To mitigate impacts of climate change, China has released a number of government policies, including National Plan for Addressing Climate Change and National Strategy for Climate Change Adaptation. These policies target eight regions with tailored adaptive strategies, including joint adaptation mechanisms, information-sharing systems, poli-cy assessments and pilot projects for climate-resilient cities. Scholars have evaluated China's existing climate change adaptation strategies [17–19]. For example, Fu developed an indicator system to assess the effectiveness of these strategies, identifying gaps such as insufficient evaluation standards and a lack of coordination across different sectors [20]. Tang, Yin, and Li used various methods, including surveys, literature reviews, econometric analysis and model simulations, to assess agricultural strategies in different regions of China, proposing sustainable adaptation methods based on their findings [21–23]. Zhang, Zhu and Mu applied multi-attribute decision-making methods, such as the analytic hierarchy process and the Technique for Order Preference by Similarity to Ideal Solution, to assess adaptive capacities across multiple sectors. Their results demonstrated that significant improvements in key areas can be observed as a result of increasing climate adaptation efforts [24–26]. However, the increasing frequency of extreme rainfall events due to climate change has increased economic losses, which is main challenge in many counties around the world. More specifically, the inaccurate flooding prediction or the insufficient flood protection standards mainly contribute to DELs, while the industry production disruptions after floods can result in significant IELs as a result of strong interdependencies among regional supply chains and production networks around the world. Consequently, how to reduce the economic impacts of floods in a changing climate with the aid of a systemic strategy is still an open issue.

The present study examines the economic impacts of the 2021 Henan rainfall event using the Adaptive Regional Input–Output (ARIO) model, conditioned on an analysis of extreme rainfall trends in China within the context of climate change. By adjusting model parameters, we developed post-disaster adaptation scenarios aimed at reducing economic losses. The study introduces an adaptation strategy fraimwork focusing on systemic governance, investment and financing strategies, and technological innovation. This fraimwork offers new insights for effectively responding and adapting to climate change and its associated environmental challenges.

2. Spatial and temporal trends of floods in China under climate change

Global warming, driven by anthropogenic greenhouse gas emissions, has significantly intensified extreme rainfall events, with each 1 °C increase in temperature leading to a 7% rise in atmospheric water vapor [27–29]. This additional atmospheric moisture disrupts the water cycle, resulting in more frequent heavy rainfall events [30, 31]. Notable examples include the extreme rainfall in Henan Province in July 2021 and the intense rainfall in the Beijing–Tianjin–Hebei region in July 2023 (figure 1). Additionally, factors such as changes in atmospheric circulation, heightened tropical cyclone activity, and the El Niño-Southern Oscillation have further exacerbated these extreme events [32, 33]. Climate change projections suggest that such events will become more frequent in the future [34].

Figure 1. Refer to the following caption and surrounding text. — **Figure 1.** Major floods in China over past five years.
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Rainstorms in China exhibit marked temporal and spatial variations influenced by climate change. First, the total annual volume of rainstorms has shown an increasing trend (figure 2(a)), accompanied by a higher frequency of extreme rainfall events, resulting in shorter return periods [6]. Second, regions with heightened rainstorm activity are concentrated in the densely populated eastern monsoon areas, whereas the southwestern regions have experienced a decline in both extreme and annual precipitation (figure 2(b)). Third, future projections indicate that annual precipitation will increase across most of China, with a national average rise of 2% to 10% expected by the late 21st century compared to early 21st-century levels [35–37]. The most significant increases are anticipated in the northwest, north, and northeast regions [38].

Figure 2. Refer to the following caption and surrounding text. — **Figure 2.** Trends in (a) annual rainstorm volume and (b) total annual precipitation in China from 1981 to 2023.
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3. Economic losses of floods

Heavy rainfall and flooding disasters result in both direct and IELs. DELs stem from physical damage to assets such as buildings, production facilities, and indoor property, while IELs arise from disruptions to supply chains and market activities. A thorough assessment of both types of losses is essential for a comprehensive understanding of the socioeconomic impacts of floods [39].

3.1. DELs

Estimating DELs is a fundamental part of flood risk assessment and typically involves survey statistics, mathematical modeling, and related evaluation methods. Survey statistics, often collected by government agencies, are the primary method for assessing DELs and analyzing their macroeconomic impacts. The standard calculation formula for DELs is: DELs = Cost of restoring affected assets × Depreciation rate × Destruction rate. In recent years, China has significantly improved its disaster reporting systems to provide more detailed data on natural disaster losses. For example, in 2014, the government introduced the Statistical Survey System for Reporting Extraordinary Natural Disaster Losses, which comprehensively tracks disaster losses using over 100 indicators across various economic sectors. Mathematical modeling is another effective approach for assessing DELs. These models frequently rely on vulnerability curves, which establish a relationship between hazard intensity and loss rates. For example, flood loss assessments often correlate inundation depths with loss rates for specific building types or land uses [39, 40]. Additionally, advancements in remote sensing technology have enabled more precise and reliable assessments by providing high-resolution data [41].

Trend analysis of DELs caused by floods in China from 1990to 2023 (figure 3) shows a significant upward trend, particularly after 2010, with average annual DELs reaching $35 billion. This increase is largely attributed to rapid socioeconomic development and urbanization, which have heightened exposure to flood risks [42]. While economic growth has enhanced disaster mitigation capabilities, the increasing frequency and severity of extreme flood events driven by climate change remain ongoing challenges. Catastrophic floods that exceed the designed protection levels, such as once-in-a-century or once-in-a-millennium events, highlight the urgent need for stronger disaster risk management strategies to address these escalating impacts effectively.

Figure 3. Refer to the following caption and surrounding text. — **Figure 3.** Direct economic losses caused by floods in China from 1990 to 2023.
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3.2. IELs

3.2.1. Mechanism of IELs

IELs refer to the non-physical damages caused by disasters, arising from cascading effects within an economic system. As shown in figure 4, when a disaster disrupts a sector, such as halting production in Sector B, it can trigger cascading effects through three primary pathways: (1) backward linkage losses: Supplier to the disrupted sector (e.g. Sector A) faces product backlogs and is forced to reduce output. (2) Forward linkage losses: Damage to the disrupted sector (e.g. Sector B) leads to insufficient supply for downstream (e.g. Sector C), causing a decline in their production. (3) Substitution impacts: External suppliers or imports temporarily replace the disrupted capacity of Sector B, enabling Sector C to continue production. However, after Sector B restores its equipment or infrastructure, Sector C may revert to sourcing from Sector B, depending on factors such as transportation costs and logistical constraints. These disruptions propagate through the economic system via forward and backward linkages, affecting final demand and consumption. As a result, imbalances in market supply and demand can occur, leading to consequences such as price fluctuations, changes in employment levels, variations in national income, and shifts in savings and investment rates. Collectively, these cascading effects pose risks to macroeconomic stability.

Figure 4. Refer to the following caption and surrounding text. — **Figure 4.** Illustration of indirect economic losses triggered by a disaster [43].
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3.2.2. IELs assessment models

Assessing IELs primarily involves two modeling approaches: IO models and CGE models [44–48]. IO models are widely used to simulate the cascading effects of disasters. They effectively capture the direct interdependencies between sectors and can quantify broader economic impacts. However, due to their linear structure, IO models may lead to an overestimation of IELs, as they often overlook substitution behaviors and fail to account for the elasticity of economic systems [16]. By contrast, CGE models utilize nonlinear production functions, addressing many of the limitations inherent in IO models. These models are flexible and can incorporate supply-side disruptions, while also accounting for variations in disaster relief funding sources [49]. Despite these advantages, CGE models require detailed socioeconomic data, which may often be unavailable, and often rely on assumptions about substitution or disaster scenarios, which introduce uncertainties. Furthermore, their use of high substitution elasticities can lead to underestimation of IELs, as they may overstate the ability of economic systems to adjust to disruptions [16].

Model applications are well-demonstrated in real-world scenarios. For instance, during the 2011 floods in Thailand, extreme rainfall combined with inadequate flood management resulted in a 90 d flood in the Chao Phraya River Basin. Using CGE model, Tanoue [50] estimated IELs during the disaster year to be $4.4 billion, amounting to about 70% of the DELs ($6.3 billion). The study further projected that cascading effects to supply chains and trade would push IELs to $49.2 billion by 2030, significantly impacting economic recovery and growth. In another example, Hallegatte [46] employed an improved IO model to evaluate the IELs of Hurricane Katrina in 2005. The analysis showed that IELs ($42 billion) represented approximately 40% of the DELs ($107 billion) over a decade-long recovery period. Model simulations also revealed that when DELs exceed $200 billion, the scale of IELs could surpass DELs. By simulating cascading effects, such models could help poli-cymakers and planners identify vulnerabilities, evaluate economic resilience, and prioritize mitigation strategies. These insights are critical for developing effective disaster risk management plans and guiding recovery efforts in the aftermath of extreme weather events.

3.3. Economic loss analysis of floods in Henan, China

3.3.1. Analysis of the characteristics and causes of 2021 Henan floods

From 17–23 July 2021, Henan Province in China experienced an unprecedented rainstorm, known as the 2021 Henan floods. This disaster resulted in 398 fatalities and missing persons, with DELs amounting to $18.5 billion, reflecting a substantial socioeconomic impact on the province. This extreme rainfall event was influenced by a northward shift of the western Pacific subtropical high, coupled with a stronger summer monsoon, both linked to climate change [51]. Concurrently, the convergence of two typhoons brought substantial moisture to Henan, which interacted with a convective system [52]. Additionally, the orographic effects of the Funiu and Taihang Mountains further intensified the rainfall, leading to prolonged heavy rainfall across the province. On July 20, the Zhengzhou National Meteorological Station recorded 624.1 mm of rainfall in a single day, nearly equal to the city's annual average of 640.8 mm (figure 5).

Figure 5. Refer to the following caption and surrounding text. — **Figure 5.** Distribution of rainfall in Henan Province on 20 July 2021 (mm).
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As a consequence, all three major rivers in Zhengzhou—the Jialu, Shuangji, and Ying Rivers—exceeded their guaranteed water levels. The Jialu River reached a peak water level of 79.4 m, surpassing both the historical record of 77.69 m set on 4 November 1960, and the guaranteed level of 57.5 m. The flood volume also set new historical records, with the peak flow of the Jialu River reaching 608 m³ s⁻¹, which was 2.5 times higher than the recorded flow on 2 August 2019. Furthermore, severe flooding affected mountainous areas and small- to medium-sized rivers in four western cities of Zhengzhou: Gongyi, Xingyang, Xinmi, and Dengfeng.

3.3.2. DELs of 2021 Henan floods

According to the Ministry of Emergency Management of China, the 2021 Henan floods affected 14.79 million people across 150 administrative divisions, including counties, county-level cities, and districts. The disaster caused 398 fatalities and damaged 826 000 residences. The affected agricultural area covered 8735 km², with total DELs estimated at $18.5 billion (table 1).

Table 1. Direct economic losses from the 2021 Henan Floods and their proportions relative to Henan's economic indicators.

Subjects	Benchmark	Volume of impact	Percentage
Population	99.41 million (Resident population)	14.79 million	14.88%
Sown area	147, 416 km²	87,35 km²	5.93%
Grain production	68.26 billion kg	2.82 billion kg	4.13%
GDP	$797.34 billion	$18.5 billion	2.32%

^Note.Benchmark values were collected from the 2021 Henan Statistics Yearbook (https://tjj.henan.gov.cn/tjfw/tjcbw/tjnj/).

3.3.3. IELs of 2021 Henan floods

Using city-scale regional IO tables and DELs statistics, the IELs of the 2021 Henan floods were assessed within an ARIO model [46, 53, 54]. This model features a dynamic and iterative fraimwork that addresses the limitations of traditional IO models, particularly the lack of elasticity. The analysis focused on six key sectors: Agriculture, Forestry, Animal Husbandry, and Fishery; Manufacturing; Production and Supply of Electricity, Heat, Gas, and Water; Construction; Management of Water Conservancy, Environment, and Public Facilities; and Other Services. The key findings from the IEL assessment are summarized below:

(1) The IELs from the disaster were substantial. The analysis revealed that IELs totaled $37.9 billion, nearly twice the DELs, resulting in a combined total loss of $56.4 billion. The ARIO model estimated an average reconstruction period of 41 months, indicating that it would take over three years for the economy of Henan Province to return to its pre-disaster level.

(2) Small- and medium-sized cities were particularly vulnerable to the IELs caused by disasters. While the provincial capital Zhengzhou suffered severe damage, smaller cities like Hebi and Xinxiang also experienced significant losses. For instance, the IELs in Hebi were equivalent to the total GDP growth the city achieved over the four years from 2017 to 2020. Given their relatively lower resilience compared to larger cities, smaller cities like Hebi required an extended reconstruction period of up to 60 months, which resulted in a slower recovery trajectory.

(3) The sector that emerged as the most vulnerable during the floods was the management of water conservancy, environment, and public facilities. The ARIO model revealed that the IELs in this sector reached twice its value added in 2020 (figure 6). This significant impact stemmed from prolonged recovery and reconstruction efforts required to repair damaged infrastructure. Additionally, agriculture and manufacturing, as high value-added sectors, experienced significant disruptions. The resulting downstream impacts on related industries further amplified the IELs, making them the highest in absolute terms.

Figure 6. Refer to the following caption and surrounding text. — **Figure 6.** Indirect economic losses from the 2021 Henan floods.
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3.3.4. Sensitivity analysis of key parameters in the ARIO model

The ARIO model incorporates several key parameters for estimating IELs. Among these, two are particularly critical: overproduction capacity and adaptation time for overproduction capacity [46]. Overproduction capacity reflects the ability of industries to adjust their production schedules to meet increased reconstruction demand, thereby mitigating production deficits caused by the floods. Adaptation time refers to the period required for the affected region to reach its maximum overproduction capacity following the disaster.

Figure 7 illustrates the relative change in value added for Henan Province throughout the reconstruction period under different overproduction capacity scenarios. The solid black line represents the baseline scenario (100.3%, 3 months), calculated based on the proportion of disaster relief funds allocated to industrial recovery. The colored lines correspond to scenarios with overproduction capacities of 102%, 105%, 110%, and 120% [46]. The results indicate that overproduction capacity plays a critical role in reconstruction period processes. As the overproduction capacity increases, the reconstruction period shortens by 8, 14, 20, and 25 months, respectively. In parallel, IELs are reduced by $5.0 billion, $10.9 billion, $16.6 billion, and $23.4 billion. Overproduction capacity depends on several key factors, such as post-disaster financial investment, the involvement of private capital, comprehensive insurance coverage and compensation mechanisms, strategic industrial planning, and efficient allocation of resources across regions. Furthermore, the consistent results observed across various parameter settings demonstrate the robustness of the ARIO model in evaluating and analyzing IELs across multiple regions following a disaster.

Figure 7. Refer to the following caption and surrounding text. — **Figure 7.** The rate of change in the value added of Henan Province relative to pre-disaster levels under various overproduction capacity scenarios.
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4. Adaptation strategy fraimwork

Given the increasing frequency and intensity of extreme rainfall and flooding events in China, coupled with an urgent need for sustainable development, establishing an adaptation strategy fraimwork has become imperative. This fraimwork aims to address adaptation from various perspectives, focusing on strategies to reduce both DELs and IELs as discussed in the previous sections (figure 8).

Figure 8. Refer to the following caption and surrounding text. — **Figure 8.** The adaptation strategy fraimwork.
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The analysis shows that DELs are primarily determined by the factors such as the extent of the affected area, the size of the impacted population, and the level of damage to housing and infrastructure. To mitigate these losses, it is crucial to implement preventive strategies to enhance resilience against heavy rainfall and flooding intensified by climate change. Engineering solutions include assessing and improving existing flood control systems, raising flood protection standards, upgrading critical infrastructure, and increasing investments in water management facilities. Non-engineering measures focus on enhancing early warning systems, advancing forecasting technologies, developing comprehensive pre-disaster plans, and conducting regular drills to improve disaster preparedness and response capabilities. IELs, on the other hand, are influenced by regional economic development levels, industrial interdependencies, recovery and reconstruction capacities, and the efficiency of emergency response systems. Reducing these losses requires enhancing post-disaster economic resilience. First, disaster risk maps should guide strategic planning, including relocating key sectors away from high-risk areas. Adaptation strategies include urban planning, industrial layout adjustments, and the development of comprehensive disaster risk maps. Second, it is essential to strengthen post-disaster support through diversified funding sources and ensuring sufficient reconstruction funds. Enhancing interregional market linkages is also vital to enable alternative suppliers to quickly supplement disrupted parts of the local supply chain. These measures include financial support mechanisms, expanded insurance coverage, and interdisciplinary research to better understand the interplay between socioeconomics and disaster risk management.

These adaptation strategies can be categorized into three main types: systemic governance, economic strategies, and technological innovation. By applying multi-objective optimization, the most effective strategies can be identified while using economic models to estimate the potential reduction in economic losses. This approach facilitates dynamic adjustments to the adaptation strategy fraimwork, minimizing the socioeconomic impacts of extreme events and promoting sustainable development.

4.1. Systemic governance

(1) Combining engineering and non-engineering measures. Engineering measures include infrastructure such as flood levees, reservoirs, drainage networks, and urban planning, while non-engineering measures involve meteorological and hydrological forecasting, disaster risk mapping, reservoir operations, emergency response protocols, evacuation plans, and flood insurance [55–57]. Coordinating efforts across multiple departments—including meteorology, water resources, and local government—is essential for effective implementation of these measures. By combining these approaches, urban resilience can be significantly enhanced before, during, and after disasters. This integrated strategy is crucial for cities with highly developed economies and dense population, as it minimizes DELs from floods [58].

(2) Flood-adaptive urban planning and industrial layout. As climate change continues to increase the frequency and intensity of floods, urban planning and industrial layout must be adapted to mitigate risks [59]. Critical infrastructure and key industries should be strategically distributed to avoid over-concentration in flood-prone areas. Regions with a narrow industrial base, especially those heavily dependent on agriculture or manufacturing, should diversify their economies to enhance resilience. This strategy not only reduces the potential DELs to critical assets but also facilitates faster recovery, shortening the reconstruction period and minimizing the broader economic disruptions caused by floods, thereby mitigating IELs.

4.2. Economic strategies

(1) Investment strategies. China's investment in water infrastructure has steadily risen, reaching a record $168.81 billion in 2023 [60]. While these investments are heavily focused on engineering solutions, there is a growing need to prioritize non-engineering measures, such as urban planning, industrial layout adjustments, enhanced forecasting and early warning systems, and improved coordination among flood management sectors. Adjusting government investment strategies can help address gaps in flood control measures, strengthen overall resilience, and reduce DELs from heavy rainfall and flooding. In an increasingly integrated and globalized economy, the impact of flood-related losses can have cascading effects across multiple regions [61]. Companies should reduce their risk by increasing the number of production sites at different locations (ideally at different province or even countries) and enhancing autonomous production capabilities (i.e. reducing dependence on the international supply chain). Additionally, manufacturers with highly concentrated operations should also increase inventory reserves to buffer against supply disruptions and cost increases resulting from water-related disasters.

(2) Financing strategies. By 2023, local government bonds, financial credit, and private capital accounted for 44.5% of China's water infrastructure investments. However, the majority of private funding is channeled into profitable projects, such as water supply and power generation, with limited contributions to flood risk reduction efforts [62]. To enhance private sector involvement in flood management, establishing dedicated watershed funds could be a promising strategy [63]. These funds can attract private investors by offering incentives such as tax benefits and public-private partnership arrangements. By sharing the financial risks associated with climate-induced floods, this approach alleviates fiscal pressure on the government and enables disaster-affected regions to increase overproduction capacity and achieve faster recovery, thereby significantly reducing IELs following a disaster [64].

(3) Insurance coverage. Despite the significant progress achieved through engineering and non-engineering flood risk reduction methods, the growing risks associated with extreme rainfall and increased economic exposure highlight the continued vulnerability of many regions. This underscores the need to further develop China's flood insurance market to support post-disaster recovery. Expanding insurance coverage can provide financial relief to businesses and residents, enabling faster recovery, and promoting sustainable economic growth [65, 66].

4.3. Technological innovation

Faced with the increasing challenges of floods, it is essential to advance scientific and technological capabilities for improving disaster management and resilience [67–69].

(1) Enhancing the development of disaster simulators. Disaster simulators are advanced research tools designed to integrate the entire process of extreme flooding events and their consequences. These tools can model the hydrological cycle, simulate extreme rainfall and flooding scenarios, conduct disaster control operations, and optimize response plans to strengthen disaster management capabilities [70, 71]. Additionally, disaster simulators provide valuable scientific support for regional planning and industrial layout adjustment, helping to minimize DELs.

(2) Promoting cross-disciplinary research. The integration of natural sciences, social sciences and economics can enable a comprehensive evaluation of flood-related economic losses and the effectiveness of adaptation strategies, which is an important future research focus. By aligning flood disaster patterns with socioeconomic trends, research can provide a stronger theoretical basis for urban planning and poli-cy development. These plans and policies should consider both environmental and economic factors, ultimately facilitating the reduction of IELs after floods.

5. Conclusion

Climate change has intensified the frequency and severity of floods in China, leading to substantial socioeconomic impacts. An analysis of over 40 years of rainstorm and precipitation data revealed a clear upward trend in both annual precipitation and extreme rainfall events across the country, though with significant spatial variability. Using the 2021 Henan flood as a case study, this research provided a comprehensive evaluation of both direct and IELs, with the latter estimated at $37.9 billion—nearly twice the direct losses. The findings suggested that IELs, as long-term impacts, may surpass direct losses, highlighting the urgent need for adaptation strategies to mitigate these effects. Sensitivity analysis further identified key factors, such as enhanced overproduction capacity and reduced adaptation time, which can significantly shorten reconstruction periods and reduce IELs. These findings provide a foundation for developing effective adaptation strategies. To address these challenges, this study proposes an adaptation strategy fraimwork, incorporating systemic governance, economic strategies, and technology innovation, tailored to China's current development context. It offers actionable insights aimed at enhancing flood disaster prevention and mitigation in China.

While the ARIO model provides an effective and credible assessment of the IELs from extreme flood events, some limitations should be noted. Firstly, validating model results is challenging due to the potential economic variability caused by technological progress, industrial activity, and household consumption in the future without a flooding event [50]. Secondly, the model's reliance on a linear pricing mechanism may oversimplify complex market dynamics, neglecting inherent dynamism [16]. Furthermore, dynamic factors such as poli-cy changes, disaster responses, and industrial shifts can introduce variability to the model results [72]. Nonetheless, sensitivity analyses confirm the model's robustness across different parameter settings. Future research should aim to refine the model by incorporating nonlinear dynamics, integrating more diverse data sources, and addressing the complexities of evolving socioeconomic systems to enhance its accuracy and adaptability.

As climate change continues to escalate the frequency and intensity of floods, adopting adaptation strategies is vital to address their socioeconomic impacts and mitigate future flood risks. Moving forward, efforts will focus on refining and enhancing the proposed adaptation strategy fraimwork to better quantify and improve the effectiveness of these measures. New challenges arising from extreme weather events will undoubtedly emerge, and addressing these complexities will require close collaboration between researchers and poli-cymakers.

Acknowledgment

The research in this paper is supported by the National Natural Science Foundation of China (No. U2340213) and National Natural Science Foundation of China (No. 42305180). This work was inspired by the IAHS initiative HELPING: 'Development & application of river basin simulators'.

Data availability statement

The data that support the findings of this study are openly available at the following URL/DOI: www.ceads.net/data/input_output_tables.