Natural Hazards Research最新文献

Assessment of three satellite precipitation products for hydrological studies in a data-scarce context: Ouarzazate basin, southern Morocco 在数据匮乏的情况下对水文研究中三个卫星降水产品的评估：摩洛哥南部的瓦尔扎扎特盆地

Natural Hazards Research

Pub Date : 2025-12-01 DOI: 10.1016/j.nhres.2025.02.008

Khalid En-nagre , Mourad Aqnouy , Abdelmounim Bouadila , Chaimaa Et-Takaouy , Morad Chahid , Ismail Bouizrou , Ismail Hilal , Jamal Eddine Stitou El Messari , Aqil Tariq

Arid and semi-arid regions, such as southern Morocco, face challenges due to limited precipitation data availability, posing significant obstacles for climate and hydrological studies. To bridge this data gap, this study directly evaluates the reliability of Satellite Precipitation Products (SPPs) from the latest version (V06) of the 3-hourly Integrated Multi-satellite Retrievals (3IMERG-V06) for Global Precipitation Measurement (GPM), The Tropical Rainfall Measuring Mission (TRMM-3B42-V7), and Climate Hazards Group Infrared Rainfall Station (CHIRPSV2.0). The assessment involves comparing ground-based measurements in the Ouarzazate basin to estimate precipitation characteristics. Additionally, SPPs are indirectly evaluated for their ability to simulate runoff using two hydrological models (GR4J and HEC-HMS). The analysis employs the point-to-pixel method, and various performance criteria, including R², NSE, PBIAS, RMSE, and Distance between Simulation and Observation Indices (DISO), are utilized for result comparison. Findings indicate that all three products struggle to accurately replicate daily precipitation amounts, with R² values < 0.5 and positive PBIAS values suggesting precipitation overestimation. However, when evaluating hydrological performance, the HEC-HMS model correctly simulates the observed flow when using ground-based data, with NSE (R²) values of 0.76 (0.63) during the validation phase. In contrast, the GR4J model performs poorly, with NSE (R²) values of 0.30 (0.45). When the three SPPs forced both models, HEC-HMS acceptably simulates flows using TRMM data, with NSE (R²) values of 0.57 (0.56) during the calibration phase and 0.63 (0.67) during the validation phase, along with positive PBIAS values below 15.30%. Additionally, flow irregularities and outliers affect simulation results in continuous mode, which can lead to biased estimates. However, the hydrological evaluation method is considered a complementary approach to assess the performance of SPPs, particularly in arid regions where data is scarce.

干旱和半干旱地区，如摩洛哥南部，由于降水数据有限而面临挑战，这对气候和水文研究构成了重大障碍。为了弥补这一数据差距，本研究直接评估了全球降水测量（GPM）最新版本（V06）、热带降雨测量任务（TRMM-3B42-V7）和气候危害组红外雨量站（CHIRPSV2.0）的卫星降水产品（SPPs）的可靠性。评估包括比较瓦尔扎扎特盆地的地面测量数据，以估计降水特征。此外，利用两种水文模型（GR4J和HEC-HMS）间接评估了spp模拟径流的能力。分析采用点到像素法，采用R2、NSE、PBIAS、RMSE、模拟与观测指数之间的距离（DISO）等多种性能标准对结果进行比较。研究结果表明，这三种产品都难以准确地复制日降水量，R2值为<； 0.5， PBIAS值为正，表明降水高估。然而，在评估水文性能时，HEC-HMS模型在使用地基数据时正确地模拟了观测流量，在验证阶段NSE （R2）值为0.76（0.63）。相比之下，GR4J模型表现较差，NSE （R2）值为0.30（0.45）。当三个SPPs强制两个模型时，HEC-HMS使用TRMM数据可以接受地模拟流动，在校准阶段的NSE （R2）值为0.57(0.56)，在验证阶段的NSE （R2）值为0.63 (0.67)，PBIAS值低于15.30%。此外，流动不规则性和异常值会影响连续模式下的模拟结果，从而导致估计偏差。然而，水文评价方法被认为是评估spp性能的补充方法，特别是在数据匮乏的干旱地区。

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引用次数: 0

Investigation of an unusual extreme precipitation event in the Jaru biological reserve, Brazilian Amazonia 巴西亚马逊Jaru生物保护区异常极端降水事件的调查

Natural Hazards Research

Pub Date : 2025-12-01 DOI: 10.1016/j.nhres.2025.04.003

Bárbara Antonucci , José Felipe Gazel Menezes , Nara Luísa Reis de Andrade , Gabriel de Oliveira , Celso Augusto Guimarães Santos , Michel Eustáquio Dantas Chaves , Guilherme Mataveli , Steven R. Schultze , Alberto Dresch Webler

This study analyzes the seasonality and magnitude of precipitation at three strategically positioned stations near Ji-Paraná in the Brazilian Amazon, aiming to assess the representativeness of data from the Jaru Biological Reserve (Rebio Jaru). A significant precipitation event was recorded in December 2018, with Rebio Jaru receiving approximately 700 mm of rainfall. Comparative analyses revealed notable discrepancies in precipitation across the stations, with Ji-Paraná ANA and Fazenda Nossa Senhora (FNS) measuring 500 mm and 397 mm, respectively. These variations underscore the influence of regional hydrological and climatic factors, including the Ji-Paraná River system. The findings highlight the critical need to expand and enhance the rainfall monitoring network within the Amazon Basin to improve meteorological data accuracy and strengthen the region's resilience against climate change impacts. The study emphasizes the need for advanced predictive capabilities and robust, real-time monitoring systems to mitigate the risks associated with extreme weather events, thereby supporting biodiversity conservation and the socio-economic well-being of local and riverside communities.

本研究分析了巴西亚马逊河流域ji - paranar 附近三个战略位置站点的降水季节性和强度，旨在评估Jaru生物保护区（Rebio Jaru）数据的代表性。2018年12月记录了一次重大降水事件，热比奥贾鲁的降雨量约为700毫米。对比分析显示，ji - paranana和Fazenda Nossa Senhora （FNS）的降水量差异显著，分别为500 mm和397 mm。这些变化强调了区域水文和气候因素的影响，包括冀帕拉河水系。这些发现强调了扩大和加强亚马逊流域降雨监测网络的迫切需要，以提高气象数据的准确性，并加强该地区对气候变化影响的适应能力。该研究强调需要先进的预测能力和强大的实时监测系统，以减轻与极端天气事件相关的风险，从而支持生物多样性保护以及当地和河边社区的社会经济福祉。

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引用次数: 0

Debris flow characteristics investigation and trend prediction of Mocao Gully in Huidong Country, Sichuan Province, China 四川惠东县摩草沟泥石流特征调查及趋势预测

Natural Hazards Research

Pub Date : 2025-12-01 DOI: 10.1016/j.nhres.2025.06.004

Bu Xianghang , Fan Songhai , Zhang Zongxi , Zhu Ke , Ma Xiaomin

In June 2017, heavy rainfall triggered a debris flow disaster in Mocao Gully, Huidong County, Liangshan Prefecture. This event resulted in casualties, with 10–29 people killed or missing, and over 30 seriously injured. The debris flow also caused blockage of steel culverts on the high-level overpass road spanning Mocao Gully and damaged facilities of the Lower Baimachuan sand and gravel processing system. To characterize this debris flow event in Mocao Gully, a geo-environmental dataset of the study area was developed by integrating regional geological, hydrogeological, and remote sensing monitoring data. Subsequently, the development and evolution of mudslide source areas and channels within the study area were comprehensively analyzed using remote sensing interpretation and field investigation techniques. Based on this analysis, the dynamic characteristics and development trend of debris flows were simulated using FLO-2D software. The simulation results indicate that Mocao Gully remains susceptible to large-scale debris flows under 50 to 100-year rainstorm conditions, potentially causing significant damage. This study provides a scientific basis for comprehensive debris flow prevention and control measures in Mocao Gully and serves as a reference for the prediction and assessment of debris flow events in similar environments.

2017年6月，梁山州惠东县摩草沟强降雨引发泥石流灾害。事件造成人员伤亡，10-29人死亡或失踪，30多人重伤。泥石流还造成跨摩草沟高架桥钢涵洞堵塞，下白马川砂石加工系统设施受损。为刻画此次摩草沟泥石流事件，综合区域地质、水文地质和遥感监测数据，建立了研究区地质环境数据集。随后，利用遥感解译和野外调查技术，综合分析了研究区内泥石流源区和河道的发育演变。在此基础上，利用FLO-2D软件对泥石流的动态特征和发展趋势进行了模拟。模拟结果表明，在50 ~ 100年的暴雨条件下，摩草沟仍易发生大规模泥石流，可能造成重大破坏。本研究为摩草沟泥石流综合防治措施的制定提供了科学依据，为类似环境下泥石流事件的预测与评价提供了参考。

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引用次数: 0

Exploring the dynamics of extreme rainfall in the Cauvery river basin, Southern India: Spatio-temporal insights and adaptive strategies 探索印度南部高韦里河流域极端降雨的动态：时空洞察和适应策略

Natural Hazards Research

Pub Date : 2025-12-01 DOI: 10.1016/j.nhres.2025.03.004

V.S. Manivasagam , Vishwesh Raja Kanagaraj , Navin Marimuthu , Kavitha Srinivasan Shaanjai , Sudheesh Manalil

Understanding the complexity of severe climatic events holds immense significance for policymakers and local communities in the Indian subcontinent, which is heavily reliant on agriculture. This study examines the spatio-temporal dynamics of extreme rainfall occurrences in the Cauvery basin, a crucial water source in Southern India, for the period 1901–2022. The investigation is based on 0.25-degree gridded data from the India Meteorological Department, aiming to unveil temporal trends, frequency, and spatial distribution patterns of extreme rainfall events across the basin. The results reveal that around 265 extreme events happen each year across the whole basin, with an upward trend. Notably, 51 % (135 events) of these occurrences coincide with the Northeast Monsoon, occurring across diverse locations in the basin. November emerges as a significant month, contributing 21 % (55 events) to the total observed extreme rainfall events. The Cauvery delta region demonstrates a higher susceptibility to these extreme rainfall events compared to other areas within the basin. These findings underscore the necessity for customized adaptation measures at specific times and locations, ranging from micro to larger regions. The findings are crucial for water resource management, agricultural planning, and implementing adaptation strategies in the basin, providing valuable insights for policymakers, researchers, and stakeholders.

了解严重气候事件的复杂性对严重依赖农业的印度次大陆的政策制定者和当地社区具有重大意义。本研究考察了1901-2022年印度南部重要水源高韦里盆地极端降雨发生的时空动态。这项调查基于印度气象部门的0.25度网格数据，旨在揭示整个盆地极端降雨事件的时间趋势、频率和空间分布模式。结果表明，整个流域每年发生的极端事件约265起，并呈上升趋势。值得注意的是，这些事件中有51%（135次）与东北季风同时发生，发生在盆地的不同位置。11月是一个重要的月份，占观测到的极端降雨事件总数的21%（55次）。与盆地内的其他地区相比，高韦里三角洲地区对这些极端降雨事件表现出更高的敏感性。这些发现强调了在特定时间和地点（从微观到更大的区域）采取定制化适应措施的必要性。这些发现对于该流域的水资源管理、农业规划和实施适应战略至关重要，为政策制定者、研究人员和利益相关者提供了有价值的见解。

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引用次数: 0

Geometry and late Quaternary slip rate of the Tuolai Shan-Hala Hu segment of the Haiyuan fault, northeastern Tibetan Plateau

Natural Hazards Research

Pub Date : 2025-12-01 DOI: 10.1016/j.nhres.2025.03.001

Jianxiong Tan , Kang Li , Wenjun Kang , Zhanfei Li , Pengkang Liang , Xiwei Xu

The location and long-term slip rate of active faults are key to elucidating regional deformation distribution and seismicity. The Haiyuan fault zone is an important left-lateral strike-slip fault zone on the northeastern margin of the Tibetan Plateau. However, the fault geometry and long-term slip rate of its western segment, especially in the Tuolai Shan–Hala Hu region, remain poorly constrained. To address this issue, this study utilizes high-resolution remote sensing images and regional geological maps, combined with unmanned aerial vehicle (UAV) surveys, to identify multiple left-lateral offset landforms and obtain a large number of left-lateral offset features and fault displacements. Based on these data, the fault location and geometric distribution of the Tuolai Shan–Hala Hu segment were preliminarily determined. Field investigations involved collecting radiocarbon (¹⁴C) and optically stimulated luminescence (OSL) samples from an offset alluvial fan (F1), which yielded a displacement of 28.5 ± 0.5 m and abandonment ages between 10.92 ± 0.45 ka and 9221 ± 100 a. By correlating these data, we quantified the late Quaternary left-lateral slip rate of the Tuolai Shan segment to be 2.8 ± 0.3 mm/yr, which is slightly smaller than the latest geodetic research result of 3.7 ± 0.2 mm/yr. Our findings also show a westward decrease in the slip rate along the Haiyuan fault zone, a pattern that mirrors the slip rate decrease along the eastern segment of the Altyn Tagh fault. This suggests that the Qilian Shan orogenic belt may absorb the horizontal strike-slip deformation of the Haiyuan fault zone, implying a consistent structural deformation mechanism with Altyn Tagh fault.

活动断层的位置和长期滑动速率是揭示区域形变分布和地震活动性的关键。然而，其西段，特别是沱莱山-哈拉湖地区的断层几何形态和长期滑动速率仍然没有得到很好的控制。为了解决这一问题，本研究利用高分辨率遥感图像和区域地质图，结合无人机（UAV）调查，识别了多个左侧偏移地貌，获得了大量左侧偏移特征和断层位移。在此基础上，初步确定了沱莱山—哈拉湖段的断层位置和几何分布。通过对偏置冲积扇（F1）的放射性碳（14C）和光学激发发光（OSL）样品的野外调查，得到了28.5±0.5 m的位移，废弃年龄在10.92±0.45 ka ~ 9221±100 a之间。通过这些数据的对比，我们量化了沱来山段晚第四纪的左侧滑动速率为2.8±0.3 mm/yr，略小于最新的大地测量结果3.7±0.2 mm/yr。我们的研究结果还表明，沿海原断裂带的滑动速率向西减小，这一模式反映了沿阿尔金断裂带东段的滑动速率减小。这表明祁连山造山带可能吸收了海原断裂带的水平走滑变形，与阿尔金断裂带的构造变形机制一致。

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引用次数: 0

Report on the 2024 annual academic conference of the committee on earthquake hazard chain, Seismological Society of China,6–9 December 2024, Shanghai, China 地震灾害链委员会2024年学术年会报告，中国地震学会，2024年12月6-9日，中国上海

Natural Hazards Research

Pub Date : 2025-12-01 DOI: 10.1016/j.nhres.2025.02.005

Wuwei Mao , Yu Huang , Hu Zheng , Xingyue Li , Xiangli He , Chong Xu

The 2024 Annual Academic Conference of the Committee on Earthquake Hazard Chain, Seismological Society of China was successfully convened at Tongji University in Shanghai from December 6 to 9, 2024. The conference focused on theoretical advancements, technical applications and public education pertaining to prevention, mitigation, and relief of seismic disasters. Participants engaged in discussions on cutting-edge research across various domains, including deep earth, deep sea, deep space and Earth systems. Additionally, new challenges, theories, and methodologies concerning multi-hazard interactions were introduced. Emphasizing a demand-driven approach, the conference underscored the necessity of enhancing seismic design and disaster prevention strategies for vital infrastructure in large engineering projects. Experts engaged in extensive exchanges regarding the improvement of engineering resilience and the management of secondary disasters induced by earthquakes, such as landslides, debris flows and submarine landslides. The importance of interdisciplinary integration was highlighted, with calls for the integration of seismology, engineering, data science, and artificial intelligence to address the complexities of disaster prevention and control. Innovative approaches for real-time monitoring, early warning, and risk assessment of disaster chains have been introduced. These methods leverage advanced technologies such as big data and artificial intelligence, incorporating data from satellite imagery, ground-based sensors, and social media feeds. Such advancements provide fresh insights and technological support for future disaster chain management. The conference concluded on the morning of December 8, following which participants undertook a technical visit to the State Key Laboratory of Disaster Reduction in Civil Engineering at Tongji University.

中国地震学会地震灾害链委员会2024年学术年会于2024年12月6日至9日在上海同济大学成功召开。会议重点讨论了地震防灾减灾的理论进展、技术应用和公众教育。与会者就深地、深海、深空和地球系统等各个领域的前沿研究进行了讨论。此外，介绍了有关多危害相互作用的新挑战、新理论和新方法。会议强调了需求驱动的方法，强调了加强大型工程项目中重要基础设施的抗震设计和防灾战略的必要性。专家们就提高工程抗灾能力和滑坡、泥石流、海底滑坡等地震次生灾害的治理进行了广泛交流。会议强调了跨学科整合的重要性，呼吁整合地震学、工程学、数据科学和人工智能，以解决灾害预防和控制的复杂性。引入了实时监测、预警和灾害链风险评估的创新方法。这些方法利用了大数据和人工智能等先进技术，结合了来自卫星图像、地面传感器和社交媒体馈送的数据。这些进步为未来的灾害链管理提供了新的见解和技术支持。会议于12月8日上午结束，随后与会代表对同济大学土木工程减灾国家重点实验室进行了技术参观。

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引用次数: 0

Preliminary analysis of the July 30, 2024, Wayanad landslide disaster in India: Causes and impacts 2024年7月30日印度瓦亚纳德滑坡灾害的初步分析：原因和影响

Natural Hazards Research

Pub Date : 2025-12-01 DOI: 10.1016/j.nhres.2025.04.005

Qinxia Wang , Chong Xu , Junxue Ma , Yuandong Huang , Shuhui Zhang , Huiran Gao

In recent years, with the increasingly pronounced trend of global climate warming, extreme weather events have become more frequent and intense, leading to a significant rise in the occurrence of natural disasters. Landslides, as a common and highly destructive type of geological hazard, are often extremely damaging when triggered by short-duration intense rainfall. At 2:00 a.m. on July 30, 2024, a large-scale landslide occurred in the Wayanad district of Kerala, India, induced by continuous heavy rainfall. This landslide disaster resulted in at least 373 deaths, over 200 injuries, and 218 people reported missing, making it the most devastating disaster in Kerala's history in terms of both casualties and damage, and causing severe human and economic losses. This study utilizes multi-source data and remote sensing imagery to review the evolution of the disaster event in detail and conducts a preliminary analysis of the landslide's causes based on Disaster System Theory. The results indicate that this large-scale landslide was a reactivation of a pre-existing failure. While extreme tropical rainfall was the primary triggering factor, the geological setting and anthropogenic activities further intensified the disaster's occurrence and impacts. Specifically, the synergistic coupling among the disaster-breeding environment (unstable geological structures and anthropogenic disturbances), disaster-inducing factors (intensified rainfall under climate change), and vulnerability of exposed elements (high population density, insufficient emergency response, and generally high societal risk) collectively led to the disaster. Despite active emergency response efforts following the event, the management process still revealed several shortcomings, including inaccurate early warnings, inadequate in-disaster coordination, and insufficient post-disaster recovery. This study summarizes the experience and lessons from India's response to the rainfall-induced landslide, and, in combination with China's context, proposes targeted countermeasures, aiming to provide scientific evidence and practical guidance for improving emergency management and disaster risk reduction capacities in China and other countries facing similar hazards.

近年来，随着全球气候变暖趋势的日益明显，极端天气事件更加频繁和强烈，导致自然灾害的发生明显增加。山体滑坡是一种常见的、具有高度破坏性的地质灾害类型，在短时间强降雨引发时往往具有极大的破坏性。2024年7月30日凌晨2时许，印度喀拉拉邦瓦亚纳德地区受持续强降雨影响，发生大规模滑坡。这次山体滑坡灾害造成至少373人死亡，200多人受伤，218人失踪，使其成为喀拉拉邦历史上伤亡和破坏最严重的灾难，造成严重的人员和经济损失。本研究利用多源数据和遥感影像详细回顾了灾害事件的演变过程，并基于灾害系统理论对滑坡成因进行了初步分析。结果表明，这次大规模滑坡是先前破坏的再激活。热带极端降雨是主要触发因素，地质环境和人为活动进一步加剧了灾害的发生和影响。具体来说，灾源环境（不稳定的地质结构和人为干扰）、致灾因素（气候变化下的强降雨）和暴露因素的脆弱性（人口密度高、应急响应不足、普遍较高的社会风险）三者之间的协同耦合共同导致了灾害的发生。尽管在事件发生后作出了积极的应急反应，但管理过程仍然暴露出若干缺陷，包括早期预警不准确、灾中协调不足和灾后恢复不足。本研究总结了印度应对降雨引发的山体滑坡的经验教训，并结合中国的具体情况，提出了针对性的应对措施，旨在为中国和其他面临类似灾害的国家提高应急管理和减灾能力提供科学依据和实践指导。

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引用次数: 0

Data-driven ground motion model for aftershock seismic hazard assessment in the Himalayan region 喜马拉雅地区余震地震危险性评价的数据驱动地震动模型

Natural Hazards Research

Pub Date : 2025-12-01 DOI: 10.1016/j.nhres.2025.03.009

Naga Tejasri M, Ravi Kanth Sriwastav, S.T.G. Raghukanth

Aftershock Earthquake poses a significant risk to structures already weakened by a mainshock, potentially leading to further damage or collapse. Despite their critical impact, aftershocks are often overlooked in seismic design practices due to the limited availability of comprehensive mainshock-aftershock datasets. Existing ground motion models (GMMs) primarily focus on mainshock events and do not fully capture the distinct characteristics of aftershock sequences. To address this gap, this study aims to develop a GMM for 5 % damped elastic aftershock response spectra for the Himalayan region using an artificial neural network (ANN) algorithm. The dataset contains numerous sequences including the 1999 Chamoli, 2011 Sikkim, 2013 Uttarkashi, and 2015 Nepal earthquakes, supplemented by records from the NGA-West2 database. Mainshock spectral ordinate and other parameters such as the difference between mainshock and aftershocks magnitude, difference between their hypocentral depth, logarithm of mainshock to aftershocks representative distance ratio, ratio of mainshock to aftershocks magnitude ratio to logarithm of mainshock rupture distance and a site classification flag are taken as predictors. Distinct effect of all the considered predictor variables on the aftershock ordinates are observed, with the most important predictor being the mainshock Spectral ordinates followed by difference between hypocentral depth of mainshock and aftershock. The model achieved high accuracy in predicting aftershock spectra, with a coefficient of determination (R²) of 0.861, mean square error (MSE) of 0.078 and mean absolute error (MAE) of 0.215. The model outperforms existing global GMMs in capturing aftershock spectral characteristics, particularly in the Himalayan region. Mixed-effects regression framework is adopted to account for inter- and intra-event variabilities and the respective standard deviations are reported. Residual analysis confirms the robustness of model and demonstrated minimal bias. The total standard deviation is reported to lie between 0.268 and 0.488.

余震地震对已经被主震削弱的结构构成了巨大的风险，可能导致进一步的破坏或倒塌。尽管余震具有重要的影响，但由于综合主震-余震数据集的可用性有限，余震在抗震设计实践中经常被忽视。现有的地面运动模型（gmm）主要关注主震事件，并没有完全捕捉到余震序列的独特特征。为了解决这一差距，本研究旨在利用人工神经网络（ANN）算法开发喜马拉雅地区5%阻尼弹性余震反应谱的GMM。该数据集包含许多序列，包括1999年查莫利地震、2011年锡金地震、2013年乌塔尔卡什地震和2015年尼泊尔地震，并补充了NGA-West2数据库的记录。以主震谱纵坐标、主震与余震震级之差、震源深度之差、主震与余震代表距离比值的对数、主震与余震震级比值与主震破裂距离对数的比值、站点分类标志等参数作为预测指标。所有考虑的预测变量对余震纵坐标的影响都很明显，其中最重要的预测变量是主震谱纵坐标，其次是主震和余震的震源深度差。该模型预测余震谱具有较高的精度，决定系数（R2）为0.861，均方误差（MSE）为0.078，平均绝对误差（MAE）为0.215。该模型在捕获余震频谱特征方面优于现有的全球GMMs，特别是在喜马拉雅地区。采用混合效应回归框架来解释事件间和事件内的变量，并报告了各自的标准差。残差分析证实了模型的稳健性和最小的偏差。据报道，总标准差介于0.268和0.488之间。

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引用次数: 0

2024 Brazil floods: Mapping the extent and impacts in Eastern Rio Grande do Sul using geospatial techniques 2024年巴西洪水：利用地理空间技术绘制巴西南部大德州东部洪水的范围和影响

Natural Hazards Research

Pub Date : 2025-12-01 DOI: 10.1016/j.nhres.2025.03.011

Jitender Rathore , Sheetal Kumari , Pratyush Tripathy , Shanti Shwarup Mahto , Preet Lal

The May 2024 flood event in Porto Alegre, Brazil, marked one of the most severe hydrological disasters in the region's history. This study provides a high-resolution flood inundation assessment by integrating Sentinel-1 SAR, Sentinel-2 multispectral imagery, and Copernicus DEM-derived contours, overcoming the limitations of individual datasets. A key challenge in urban flood detection is SAR's insensitivity due to the double bounce effect and the limited spatial resolution of Sentinel-derived flood maps. To address this, we integrated high-resolution PlanetScope imagery for manual validation and used DEM-based contours to refine the flood extent in urban areas. Further, daily precipitation from the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for the (GPM-IMERG) is considered for assessing the impact of precipitation on flooding. The analysis from precipitation revealed multi-day extreme rainfall from April 27 to May 4, with three consecutive days (April 30 – May 2) exceeding the 99.9^th percentile as the primary driver of the flooding. The flood extent map shows that 1481 km² of cropland and 76.2 km² of urban areas were inundated, significantly impacting critical infrastructures, including the Salgado Filho International Airport. These findings highlight the vulnerability of densely populated and agriculturally vital regions to extreme weather events exacerbated by climate change. The findings emphasize the urgent need for resilient infrastructure, adaptive urban planning, and robust mitigation strategies to manage the increasing risks of extreme weather events driven by climate change.

2024年5月发生在巴西阿雷格里港的洪水事件是该地区历史上最严重的水文灾害之一。该研究通过整合Sentinel-1 SAR、Sentinel-2多光谱图像和哥白尼dem衍生等高线，克服了单个数据集的局限性，提供了高分辨率洪水淹没评估。城市洪水探测面临的一个关键挑战是，由于双重反弹效应和sentinel衍生洪水地图有限的空间分辨率，SAR不敏感。为了解决这个问题，我们整合了高分辨率的PlanetScope图像进行手动验证，并使用基于dem的等高线来细化城市地区的洪水范围。此外，考虑了全球降水测量综合多卫星检索（GPM-IMERG）的日降水量来评估降水对洪水的影响。降水分析显示，4月27日至5月4日多日极端降水，其中连续3天（4月30日至5月2日）超过99.9%是此次洪涝的主要驱动因素。洪水范围图显示，1481平方公里的农田和76.2平方公里的城市地区被淹没，严重影响了包括萨尔加多菲尔霍国际机场在内的关键基础设施。这些发现突出表明，人口稠密和农业重要地区易受气候变化加剧的极端天气事件的影响。研究结果强调，迫切需要有弹性的基础设施、适应性的城市规划和强有力的缓解战略，以管理由气候变化驱动的极端天气事件日益增加的风险。

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引用次数: 0

State, market, or community? Exploring public perceptions of disaster management strategies 国家、市场还是社区？探讨公众对灾害管理策略的看法

Natural Hazards Research

Pub Date : 2025-12-01 DOI: 10.1016/j.nhres.2025.03.012

Madeline Craig-Scheckman , Mikio Ishiwatari , Daniel P. Aldrich

Effective disaster management strategies are essential for strengthening community resilience and reducing risks, particularly as climate change intensifies the frequency and severity of natural hazards. This study explores the factors that influence how nearly 1000 respondents across the United States perceive the effectiveness of different disaster management strategies, taking into account demographic variables, regional differences, past disaster experiences, levels of social capital, and individual expertise. Using a new, nationally representative survey of U.S. residents, the study investigates the perceived effectiveness of state-led approaches (e.g., early warning systems, levees, managed retreat), market-based mechanisms (e.g., insurance), and community-focused efforts (e.g., social cohesion) for disaster risk reduction (DRR). Demographic factors—such as age, race, and gender—as well as social capital levels and experiences of disaster-induced displacement significantly influence preferences for particular DRR strategies. Most notably, women, older age groups, individuals with high social capital, and those who have been displaced by disaster tend to favor community-focused approaches. The findings highlight the need for tailored DRR policies that consider local cultural contexts, engage diverse populations, and leverage both community and expert insights. By offering concrete policy recommendations, this study underscores the importance of an inclusive, multi-layered approach to disaster preparedness and management—one that integrates public perception and expertise to enhance overall climate resilience in the United States and beyond.

有效的灾害管理战略对于加强社区复原力和减少风险至关重要，特别是在气候变化加剧自然灾害发生频率和严重程度的情况下。本研究探讨了影响美国近1000名受访者如何看待不同灾害管理策略有效性的因素，考虑到人口变量、地区差异、过去的灾害经验、社会资本水平和个人专业知识。该研究对美国居民进行了一项新的、具有全国代表性的调查，调查了国家主导的方法（如预警系统、堤坝、管理撤退）、市场机制（如保险）和以社区为中心的努力（如社会凝聚力）在减少灾害风险（DRR）方面的感知有效性。人口因素（如年龄、种族和性别）以及社会资本水平和灾害导致的流离失所经历显著影响对特定DRR战略的偏好。最值得注意的是，妇女、年龄较大的群体、拥有较高社会资本的个人以及因灾难而流离失所的人倾向于采用以社区为中心的方法。研究结果强调，需要制定有针对性的DRR政策，考虑当地文化背景，让不同人群参与进来，并充分利用社区和专家的见解。通过提供具体的政策建议，本研究强调了一种包容的、多层次的灾害准备和管理方法的重要性，这种方法将公众的认知和专业知识结合起来，以增强美国及其他地区的整体气候适应能力。

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引用次数: 0