Climate Risk Management最新文献_第6页

Non-stationary precipitation design standards for stormwater infrastructure modernization at USAF installations 美国空军设施雨水基础设施现代化的非固定降水设计标准

IF 4.8 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Climate Risk Management

Pub Date : 2025-01-01 DOI: 10.1016/j.crm.2025.100718

Douglas C. Jaks , Ashish Shrestha , Christopher M. Chini

The resilience of defense infrastructure systems to a changing climate is critical for national security. Climate induced recurrent flooding is already impacting over 20 U.S. Air Force installations, underscoring the urgency of revisiting precipitation standards and stormwater infrastructure design. Despite growing scientific knowledge and an expanding set of tools for updating outdated precipitation standards based on the assumption of climate stationarity, the adoption of climate informed analyses remain limited in practice. This study utilizes an existing framework to update Intensity (or Depth)-Duration-Frequency (DDF) curves using an ensemble of future climate projections. Change factors in precipitation estimates are derived and applied to six USAF installations across the U.S. The analysis is further extended to evaluate the implications of climate-informed DDFs on stormwater infrastructure performance and flood analysis at Tyndall AFB. Results indicate that the current design precipitation estimates are likely to become obsolete in all six USAF bases by the end of the century. The wide range of change factors across 32 GCM ensembles highlights the need to integrate uncertainty and evolving scientific data into infrastructure planning. The study also finds that the impacts of a changing climate vary spatially and temporally, emphasizing the value of localized analysis for infrastructure decision-making. The work advances ongoing DoD and societal efforts to implement adaptation strategies aimed at enhancing infrastructure resilience.

国防基础设施系统对气候变化的适应能力对国家安全至关重要。气候引起的经常性洪水已经影响了20多个美国空军设施，强调了重新制定降水标准和雨水基础设施设计的紧迫性。尽管科学知识不断增长，基于气候平稳性假设的过时降水标准的更新工具也越来越多，但在实践中采用气候信息分析仍然有限。本研究利用现有框架，利用未来气候预测的集合更新强度（或深度）-持续时间-频率（DDF）曲线。降水估算中的变化因子被导出并应用于美国六个空军基地。该分析进一步扩展到评估气候信息ddf对廷德尔空军基地雨水基础设施性能和洪水分析的影响。结果表明，到本世纪末，目前的设计降水估计可能在所有六个美国空军基地中过时。32个GCM整体的变化因素范围广泛，突出了将不确定性和不断发展的科学数据整合到基础设施规划中的必要性。研究还发现，气候变化的影响在空间和时间上存在差异，强调了基础设施决策的本地化分析的价值。这项工作推进了正在进行的国防部和社会努力，以实施旨在增强基础设施弹性的适应战略。

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引用次数: 0

From climate risk to action: Analysing adaptation decision robustness under uncertainty 从气候风险到行动：不确定性下的适应决策稳健性分析

IF 5 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Climate Risk Management

Pub Date : 2025-01-01 DOI: 10.1016/j.crm.2025.100751

Cecina Babich Morrow , Laura Dawkins , Francesca Pianosi , Dennis Prangle , Dan Bernie

Climate adaptation decisions are made under great uncertainty, arising from uncertainties about both the level of climate risk and the attributes of decision options. Decision-makers must understand how uncertainties in the input factors of risk assessment and decision models affect the ultimate adaptation decision, and whether the modelling yields a robust decision, i.e. one that is consistently identified as optimal over a range of uncertain input factors. Here, we present a framework for analysing the robustness of climate adaptation decisions. We apply a Bayesian Decision Analysis framework to determine the optimal output decision in a region based on both climate risk and decision-related attributes. Then, we present an approach for performing global uncertainty and sensitivity analysis on the optimal adaptation decision itself to assess robustness and understand which input factors most influence the decision in a particular region. We demonstrate this framework on an idealised example of adaptation decision-making to mitigate the risk of heat-stress on outdoor physical working capacity in the UK. In this application, we find that regions with high uncertainty in climate risk can still exhibit greater robustness in the optimal decision, and the decision is often more sensitive to variations in decision-related attributes rather than risk-related attributes. Previous research often stops short at assessing uncertainty and sensitivity in climate risk alone. These results highlight the necessity of conducting uncertainty and sensitivity analysis on the ultimate decision output itself in order to understand what factors drive decision robustness.

由于气候风险水平和决策选择属性的不确定性，气候适应决策是在很大的不确定性下做出的。决策者必须了解风险评估和决策模型的输入因素的不确定性如何影响最终的适应决策，以及建模是否产生稳健决策，即在一系列不确定的输入因素中始终被确定为最优决策。在这里，我们提出了一个框架来分析气候适应决策的稳健性。我们应用贝叶斯决策分析框架，在气候风险和决策相关属性的基础上确定一个地区的最优产出决策。然后，我们提出了一种对最优适应决策本身进行全局不确定性和敏感性分析的方法，以评估鲁棒性并了解哪些输入因素对特定区域的决策影响最大。我们在一个理想化的适应决策的例子上展示了这个框架，以减轻英国户外体力工作能力的热应激风险。在此应用中，我们发现气候风险不确定性较高的地区在最优决策中仍然表现出更强的鲁棒性，并且决策往往对决策相关属性的变化比风险相关属性的变化更敏感。以往的研究往往在评估气候风险的不确定性和敏感性方面止步不前。这些结果强调了对最终决策输出本身进行不确定性和敏感性分析的必要性，以便了解驱动决策鲁棒性的因素。

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引用次数: 0

Factors affecting the application of protective measures before flood occurrence among local communities in Iran 影响伊朗当地社区在洪水发生前采取保护措施的因素

IF 4.8 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Climate Risk Management

Pub Date : 2025-01-01 DOI: 10.1016/j.crm.2025.100730

Esmaiel Askari , Moslem Savari , Marzieh Rezaei

Floods rank among the most destructive natural hazards, inflicting extensive damage on rural areas by compromising infrastructure, destroying crops, and undermining livelihoods. Beyond the immediate economic losses, such events often trigger rural outmigration and erode social cohesion. Effective flood prevention strategies are therefore critical and can be advanced through public awareness campaigns, the reinforcement of social capital, the development of flood-resilient infrastructure, and the implementation of integrated crisis management plans. Despite the importance of these measures, previous research has largely concentrated on assessing community vulnerability, with limited attention given to proactive protective factors. Addressing this gap, the present study aims to identify the determinants influencing the adoption of protective measures among rural communities in Iran prior to flood events. The statistical population of this study comprised all rural households in Shushtar County, located in Khuzestan Province, southwest Iran. A sample of 353 rural household heads was selected using the Krejcie and Morgan table, applying a multi-stage stratified sampling method to ensure representation across different villages. Data were collected through a structured questionnaire, the validity of which was confirmed by a panel of subject matter experts, while reliability was assessed using Cronbach’s alpha coefficient. Data analysis was conducted in two phases—descriptive and inferential statistics—using SPSS and SmartPLS software. The findings demonstrated that the variables of psychological distance, social media use, place attachment, flood experience, social capital, and flood risk perception had significant and positive effects on protective behaviors prior to flooding. Collectively, these variables accounted for 76.3% of the variance in protective measures. These findings offer critical insights for policymakers seeking to enhance the resilience and safety of rural communities in flood-prone regions. By proactively addressing flood risks, such measures can contribute to the sustainability of rural livelihoods and the strengthening of local resilience.

洪水是最具破坏性的自然灾害之一，通过破坏基础设施、摧毁作物和破坏生计，对农村地区造成广泛破坏。除了直接的经济损失外，这类事件往往引发农村人口外流，侵蚀社会凝聚力。因此，有效的防洪战略至关重要，可以通过提高公众意识、加强社会资本、发展抗洪基础设施以及实施综合危机管理计划来推进。尽管这些措施很重要，但以前的研究主要集中在评估社区脆弱性上，对主动保护因素的关注有限。为了解决这一差距，本研究旨在确定在洪水事件发生之前影响伊朗农村社区采取保护措施的决定因素。本研究的统计人口包括位于伊朗西南部胡齐斯坦省Shushtar县的所有农村家庭。采用Krejcie和Morgan表选取了353个农村户主样本，采用多阶段分层抽样方法，以确保不同村庄的代表性。数据通过结构化问卷收集，其有效性由主题专家小组确认，而可靠性则使用Cronbach 's alpha系数进行评估。使用SPSS和SmartPLS软件进行数据分析，分为描述性统计和推断性统计两个阶段。研究结果表明，心理距离、社交媒体使用、地方依恋、洪水经历、社会资本和洪水风险感知对洪水前保护行为有显著的正向影响。总的来说，这些变量占保护措施方差的76.3%。这些发现为寻求提高洪水易发地区农村社区的抗灾能力和安全性的政策制定者提供了重要的见解。通过积极应对洪水风险，这些措施有助于农村生计的可持续性和加强当地的抵御能力。

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引用次数: 0

The role of remittances in building resilience through adaptive capacities amid environmental and socioeconomic vulnerabilities: A systematic literature review 汇款在环境和社会经济脆弱性中通过适应能力建设复原力的作用：系统文献综述

IF 5 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Climate Risk Management

Pub Date : 2025-01-01 DOI: 10.1016/j.crm.2025.100769

Ikram Ullah , Niraj Prakash Joshi , Luni Piya

This systematic review examines how financial and social remittances contribute to resilience under environmental and socioeconomic stress by synthesizing 79 peer-reviewed studies published between 2002 and 2025. Guided by a Disaster Resilience Integrated Framework for Transformation (DRIFT)-informed, stage-based framework, we analyze resilience before, during, and after disasters, linking each phase to capacities such as consumption smoothing, livelihood diversification, asset-based adaptation, social capital, and institutional empowerment. Remittance-resilience research expanded rapidly after 2020, with most studies focusing on Asia and Africa and fewer in Latin America, Europe, and Pacific Small Island Developing States. Financial remittances primarily support immediate stabilization and asset repair, whereas social remittances strengthen skills, networks, risk awareness, and collective action that underpin longer-term adjustment. Regional patterns differ: Asian cases emphasize consumption smoothing and housing upgrades, while African studies highlight diversification and institutional pathways. Our review contributes by mapping remittance roles across household, community, and system levels; linking micro-level mechanisms to governance and market conditions; and offering a comparative regional synthesis. Key constraints include dependency risks, unequal access by gender and income, and market or institutional volatility. Policy priorities include integrating remittances into national adaptation and disaster risk reduction strategies, reducing transfer costs through digital rails, leveraging diaspora co-financing, and strengthening financial and digital literacy to enhance inclusive, shock-responsive resilience aligned with Sustainable Development Goals 11, 13, and 16.

本系统综述通过综合2002年至2025年间发表的79项同行评议研究，考察了金融和社会汇款如何促进环境和社会经济压力下的复原力。在灾害复原力转型综合框架（DRIFT）的指导下，我们分析了灾前、灾中和灾后的复原力，并将每个阶段与消费平滑、生计多样化、基于资产的适应、社会资本和机构赋权等能力联系起来。2020年后，对汇款复原力的研究迅速扩大，大多数研究集中在亚洲和非洲，对拉丁美洲、欧洲和太平洋小岛屿发展中国家的研究较少。金融汇款主要支持即时稳定和资产修复，而社会汇款则加强技能、网络、风险意识和集体行动，为长期调整奠定基础。区域模式不同：亚洲案例强调消费平滑和住房升级，而非洲研究强调多样化和制度路径。我们的评估通过映射汇款在家庭、社区和系统层面的作用做出了贡献；将微观层面的机制与治理和市场条件联系起来；并提供了一个比较的区域综合。主要制约因素包括依赖风险、性别和收入不平等以及市场或体制波动。政策重点包括将汇款纳入国家适应和减少灾害风险战略，通过数字轨道降低转移成本，利用侨民共同融资，加强金融和数字素养，以增强符合可持续发展目标11、13和16的包容性、抗冲击能力。

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引用次数: 0

How beneficial are seasonal climate forecasts for climate risk management? An appraisal for crop production in Tanzania 季节气候预报对气候风险管理有多大益处？坦桑尼亚作物生产评估

IF 4.8 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Climate Risk Management

Pub Date : 2025-01-01 DOI: 10.1016/j.crm.2024.100686

Jacob Emanuel Joseph , K.P.C Rao , Elirehema Swai , Anthony M. Whitbread , Reimund P. Rötter

Understanding growing period conditions is crucial for effective climate risk management strategies. Seasonal climate forecasts (SCF) are key in predicting these conditions and guiding risk management in agriculture. However, low SCF adoption rates among smallholder farmers are due to factors like uncertainty and lack of understanding. In this study, we evaluated the benefits of SCF in predicting growing season conditions, and crop performance, and developing climate risk management strategies in Kongwa district, Tanzania. We used sea surface temperature anomalies (SSTa) from the Indian and Pacific Ocean regions to predict seasonal rainfall onset dates using the k-nearest neighbor model. Contrary to traditional approaches, the study established the use of rainfall onset dates as the criterion for predicting and describing growing period conditions. We then evaluated forecast skills and the profitability of using SCF in crop management with the Agricultural Production System sIMulator (APSIM) coupled with a simple bio-economic model. Our findings show that SSTa significantly influences rainfall variability and accurately predicts rainfall onset dates. Onset dates proved more effective than traditional methods in depicting key growing period characteristics, including rainfall variability and distribution. Including SCF in climate risk management proved beneficial for maize and sorghum production both agronomically and economically. Not using SCF posed a higher risk to crop production, with an 80% probability of yield losses, especially in late-onset seasons. We conclude that while SCF has potential benefits, improvements are needed in its generation and dissemination. Enhancing the network of extension agents could facilitate better understanding and adoption by smallholder farmers.

了解生长期条件对于有效的气候风险管理策略至关重要。季节性气候预报（SCF）是预测这些条件和指导农业风险管理的关键。然而，由于不确定性和缺乏了解等因素，小农对SCF的采用率较低。在这项研究中，我们评估了SCF在预测坦桑尼亚Kongwa地区生长季节条件和作物性能以及制定气候风险管理策略方面的益处。我们利用印度洋和太平洋地区的海温异常（SSTa），利用k近邻模型预测了季节性降雨的开始日期。与传统方法相反，该研究建立了使用降雨开始日期作为预测和描述生长期条件的标准。然后，我们利用农业生产系统模拟器（APSIM）和一个简单的生物经济模型，评估了在作物管理中使用SCF的预测技能和盈利能力。研究结果表明，海温对降水变率有显著影响，并能准确预测降水发生日期。事实证明，在描述关键生长期特征（包括降雨变率和分布）方面，发病日期比传统方法更有效。事实证明，将SCF纳入气候风险管理对玉米和高粱生产在农艺和经济上都是有益的。不使用SCF对作物生产构成更高的风险，产量损失的可能性为80%，特别是在晚发季节。我们的结论是，虽然SCF有潜在的好处，但它的产生和传播需要改进。加强推广机构网络可以促进小农更好地理解和采用。

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引用次数: 0

Nexus of climate change adaptation and household wealth in climate risk hotspots – Insights from rural farm households of Pakistan 气候风险热点地区气候变化适应与家庭财富的关系——来自巴基斯坦农村农户的见解

IF 4.8 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Climate Risk Management

Pub Date : 2025-01-01 DOI: 10.1016/j.crm.2025.100704

Shangao Wang , Panhwar Ghulam Mustafa , Gershom Endelani Mwalupaso , Zhou Li

The agricultural sector is a cornerstone of economic development, particularly in low-income countries where smallholder farming supports livelihoods and food security. However, the increasing unpredictability of climatic factors poses significant challenges, threatening its capacity to bolster smallholder farmers’ household wealth through food production. While climate adaptation measures have been widely promoted, there remains a notable lack of empirical evidence establishing the relationship between climate adaptation and household wealth. Addressing this critical research gap, this study examines the relationship between climate adaptation and household wealth among 400 wheat smallholder farmers in Sindh province, Pakistan. Using endogenous switching regression (ESR) and propensity score matching (PSM) for robustness, we estimate the average treatment effect on the treated (ATT). The results consistently show that climate adaptation significantly increases per capita household wealth, primarily through enhanced crop production. Counterfactual analysis reveals that non-adopting households could have reduced poverty severity by 15% and extreme poverty by 17% had they adopted adaptation measures. These findings provide compelling empirical evidence for policymakers to prioritize adaptation support frameworks—such as subsidized inputs or training programs—which are indispensable for safeguarding food production, reducing climate vulnerability, and lifting smallholders out of poverty. By demonstrating the dual benefits of adaptation—wealth accumulation and poverty alleviation—this study underscores the urgency of scaling up climate-resilient agricultural practices as a key strategy for reducing vulnerability and fostering sustainable livelihoods.

农业部门是经济发展的基石，特别是在小农农业支持生计和粮食安全的低收入国家。然而，气候因素日益增加的不可预测性构成了重大挑战，威胁到其通过粮食生产增加小农家庭财富的能力。虽然气候适应措施已得到广泛推广，但仍明显缺乏建立气候适应与家庭财富之间关系的经验证据。为了弥补这一重要的研究缺口，本研究调查了巴基斯坦信德省400名小麦小农的气候适应与家庭财富之间的关系。利用内源性转换回归（ESR）和倾向评分匹配（PSM）的稳健性，我们估计了对被治疗者（ATT）的平均治疗效果。结果一致表明，气候适应显著增加了人均家庭财富，主要是通过提高作物产量。反事实分析表明，如果采取适应措施，非收养家庭可以将贫困严重程度降低15%，将极端贫困程度降低17%。这些发现为政策制定者优先考虑适应支持框架（如补贴投入或培训计划）提供了令人信服的经验证据，这些框架对于保障粮食生产、降低气候脆弱性和帮助小农脱贫至关重要。通过展示适应的双重效益——积累财富和减轻贫困——本研究强调了扩大气候适应型农业实践作为减少脆弱性和促进可持续生计的关键战略的紧迫性。

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引用次数: 0

The Australian housing affordability trap – How environmental, institutional, and structural factors can immobilize Australian households in the face of extreme weather events – A case study on flooding 澳大利亚住房负担能力陷阱——环境、制度和结构因素如何使澳大利亚家庭在面对极端天气事件时无法行动——以洪水为例

IF 4.8 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Climate Risk Management

Pub Date : 2025-01-01 DOI: 10.1016/j.crm.2025.100713

Julia Plass , Jens O. Zinn

With climate change a growing number of increasingly severe hazards such as floods and bushfires affect populated regions in Australia. As a result, insurance premiums rise, and hazard-prone regions might even become uninsurable. Using the example of floods, this article examines how under conditions of the Australian housing crisis these risks affect households unequally. After major floods, un- and underinsured households often lack the capacity to recover. At the same time, they become immobilized because they cannot afford to move out of regions at risk. Based on 26 semi-structured interviews with (re-) insurance, legal, financial and urban planning experts conducted in 2022, the article provides empirical insights into the under-researched interconnection of household immobilization and vulnerability to extreme weather events from an expert perspective. The experts identify four factors which combine in producing vulnerability and at the same time immobilizing people: location and urban planning, the privatization of risk, socio-economic factors as well as awareness and the distribution of information. Current political strategies address the challenge of moving people out of at-risk locations but do neither sufficiently address the housing and insurance situation nor how people’s personal attachment to a region affects their housing decision.

随着气候变化，越来越多的日益严重的灾害，如洪水和森林大火，影响着澳大利亚的人口稠密地区。其结果是，保险费上涨，而易发生灾害的地区甚至可能无法投保。本文以洪水为例，考察了在澳大利亚住房危机的条件下，这些风险对家庭的影响是如何不平等的。大洪水过后，未投保和保险不足的家庭往往缺乏恢复能力。与此同时，他们也无法行动，因为他们负担不起离开危险地区的费用。基于2022年对（再）保险、法律、金融和城市规划专家进行的26次半结构化访谈，本文从专家的角度对尚未得到充分研究的家庭固定化与极端天气事件脆弱性之间的相互关系提供了实证见解。专家们确定了造成易受害性同时又使人无法行动的四个因素：地点和城市规划、风险私有化、社会经济因素以及认识和信息分发。目前的政治战略解决了将人们从风险地区转移出去的挑战，但既没有充分解决住房和保险状况，也没有充分解决人们对一个地区的个人依恋如何影响他们的住房决定。

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引用次数: 0

Ex-ante and ex-post adaptation: Farmers’ fertilizer strategies for extreme weather events in China 前后适应：中国农民应对极端天气事件的肥料策略

IF 5 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Climate Risk Management

Pub Date : 2025-01-01 DOI: 10.1016/j.crm.2025.100745

Yueqing Ji, Yongyi Fu, Liping Kong, Price M. Amanya, Zongyao Yang

This study investigates farmers’ fertilizer strategies as ex-ante and ex-post adaptations to extreme weather events in China. Using plot-level data from 778 maize farmers across three provinces collected in 2015 and 2018, we employ two-way fixed effects models to analyze how farmers adjust fertilizer quantity both before and during the growing season in response to floods, droughts, and windstorms. The results show that farmers increase ex-ante fertilizer application under flood risk but reduce input when anticipating drought and windstorm risks. After disaster shocks, they supplement fertilizer as an ex-post coping strategy. Heterogeneity analysis reveals that smallholders and nitrogen fertilizers are more sensitive to extreme weather, and that the effects of disaster severity are nonlinear and vary by disaster type. Mechanism analysis suggests that ex-ante fertilizer use can mitigate flood risk but may increase vulnerability to drought and windstorms, whereas ex-post use broadly aids recovery. Extended analysis uncovers a substitution effect between public interventions and private adaptation. These findings provide micro-level empirical evidence and policy insights for balancing food security and environmental sustainability amid rising extreme weather.

本研究考察了中国农民在极端天气事件发生前和发生后的施肥策略。利用2015年和2018年收集的来自三个省份778名玉米农户的地块数据，我们采用双向固定效应模型分析了农民如何在生长季前和生长季中调整肥料用量，以应对洪水、干旱和风暴。结果表明，在洪涝风险下，农民增加了化肥的预施量，而在干旱和风暴风险下，农民减少了化肥的预施量。灾害发生后，他们补充肥料作为事后应对策略。异质性分析表明，小农和氮肥对极端天气更为敏感，灾害严重程度的影响是非线性的，且随灾害类型的不同而不同。机制分析表明，事前施肥可以减轻洪水风险，但可能增加对干旱和风暴的脆弱性，而事后施肥则广泛有助于恢复。扩展分析揭示了公共干预和私人适应之间的替代效应。这些发现为在极端天气不断增加的情况下平衡粮食安全和环境可持续性提供了微观层面的经验证据和政策见解。

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引用次数: 0

Towards wildfire risk reduction goals and targets for Europe – Opportunities and challenges 实现欧洲减少野火风险的目标和目标-机遇和挑战

IF 5 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Climate Risk Management

Pub Date : 2025-01-01 DOI: 10.1016/j.crm.2025.100744

C. Berchtold , K. Petersen , M. Kaskara , M.L. Pettinari , J. Vinders , J. Schlierkamp , N. Kalapodis , G. Sakkas , P. Brunet , J. Soldatos , A. Lazarou , D. Casciano , K. Chandramouli , T. Deubelli , A. Scolobig , H. Silva , E. Plana , M. Garofalo

The impact of wildfires is increasing worldwide. The root causes of these effects are manifold, encompassing among others climate change and the accumulation of fuels and increasing settlements in wildland-urban interfaces (WUI). Reports and initiatives to better understand and govern these developments have been launched and call for more integrated approaches to wildfire risk management, including the use of targets or Key Performance Indicators (KPIs).

However, despite some examples such as Portugal, wildfire risk management targets are still mainly lacking in Europe. This is surprising since they find wider application in the U.S. and are also more widely applied for flooding in Europe.

This perspective hence takes a closer look at the use of targets in reducing disaster risk for different hazards worldwide and reflects about the opportunities and challenges for wildfire risk reduction targets for Europe. It concludes with some suggestions for the application of wildfire risk reduction targets for Europe.

世界范围内野火的影响正在增加。造成这些影响的根本原因是多方面的，其中包括气候变化、燃料的积累以及荒地-城市界面（WUI）定居点的增加。为了更好地了解和管理这些发展，报告和倡议已经启动，并呼吁采取更综合的野火风险管理方法，包括使用目标或关键绩效指标（kpi）。然而，尽管有葡萄牙等一些例子，欧洲仍主要缺乏野火风险管理目标。这是令人惊讶的，因为它们在美国得到了更广泛的应用，在欧洲也得到了更广泛的应用。因此，这一视角更仔细地审视了目标在减少全球不同灾害风险方面的使用情况，并反映了欧洲减少野火风险目标的机遇和挑战。最后对欧洲野火风险降低目标的应用提出了一些建议。

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引用次数: 0

Ai-driven approaches to flood risk management: overcoming data bias and enhancing decision-making 人工智能驱动的洪水风险管理方法：克服数据偏差和加强决策

IF 5 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Climate Risk Management

Pub Date : 2025-01-01 DOI: 10.1016/j.crm.2025.100752

Peigen Wang , Xiaoxu Wu , Yichen

The growing number and severity of climate-related hazards have made flooding an important issue. It is crucial to use new, data-driven approaches to flood risk management (FRM) due to the constraints of traditional analytical and computational methodologies. The purpose of this study is to investigate the impact that artificial intelligence (AI), specifically machine learning and deep learning approaches, has on the enhancement of FRM. This assessment, which draws on over 300 papers on artificial intelligence in FRM, examines various types of floods, spatial scales, AI models, input data reports, and practical applications. One of the primary objectives is to develop AI-driven solutions that enhance flood estimations and early detection and response systems, working in conjunction with existing technology. This paper demonstrates how AI can help improve adaptive FRM and protect infrastructure and communities from the increasing risks of floods by addressing these challenges and exploring various possibilities for further research. According to the results, AI may significantly enhance flood risk predictions and provide more accurate real-time evaluations of floods. A variety of data types, including satellite images, hydrological data, and real-time weather reports, can be processed by machine learning and deep learning models. These models help to improve decision-making and emergency response by providing short-term flood estimates. Major obstacles to their broad use, however, include data bias, the difficulty of interpreting models, and the requirement for substantial computational resources. Moreover, this research suggests several policy changes. It is crucial to develop open-source AI systems that can be utilised in flood-prone areas with diverse socioeconomic contexts. Second, policymakers should prioritise expanding access to and inclusion of AI-driven solutions, particularly for marginalized and disadvantaged populations. Ultimately, it is crucial to make AI models more transparent and explainable to enhance trust among stakeholders and ensure that FRM decisions are well-informed..

与气候有关的灾害的数量和严重程度不断增加，使洪水成为一个重要问题。由于传统分析和计算方法的限制，使用新的数据驱动方法进行洪水风险管理（FRM）至关重要。本研究的目的是探讨人工智能（AI），特别是机器学习和深度学习方法对FRM增强的影响。该评估借鉴了300多篇关于FRM中人工智能的论文，研究了各种类型的洪水、空间尺度、人工智能模型、输入数据报告和实际应用。主要目标之一是开发人工智能驱动的解决方案，与现有技术相结合，增强洪水估计、早期检测和响应系统。本文展示了人工智能如何通过应对这些挑战和探索进一步研究的各种可能性，帮助改善适应性FRM，保护基础设施和社区免受日益增加的洪水风险。根据研究结果，人工智能可以显著增强洪水风险预测，并提供更准确的洪水实时评估。各种数据类型，包括卫星图像、水文数据和实时天气报告，都可以通过机器学习和深度学习模型进行处理。这些模型通过提供短期洪水估计，有助于改进决策和应急反应。然而，它们广泛使用的主要障碍包括数据偏差、解释模型的困难以及对大量计算资源的需求。此外，这项研究还提出了几项政策变化。开发可用于具有不同社会经济背景的洪水易发地区的开源人工智能系统至关重要。其次，政策制定者应优先考虑扩大人工智能驱动的解决方案的获取和包容性，特别是针对边缘化和弱势群体。最终，至关重要的是使人工智能模型更加透明和可解释，以增强利益相关者之间的信任，并确保FRM决策得到充分的信息。

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