Yan-Fang Sang
Lingling Shen
Xiya Zhang
Wentao Yang- 1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- 2Key Laboratory of Compound and Chained Natural Hazards, Ministry of Emergency Management of China, Beijing, China
- 3Yarlung Zangbo Grand Canyon Water Cycle Monitoring and Research Station, Linzhi, China
- 4Beijing Meteorological Information Center, Beijing, China
- 5Institute of Urban Meteorology, China Meteorological Administration, Beijing, China
- 6School of Geography, Faculty of Environment, University of Leeds, Leeds, United Kingdom
Editorial on the Research Topic
Risk assessment and resilience of extreme weather-induced disasters
Introduction
Extreme weather events include unexpected, unusual, severe, or unseasonable rainstorms, droughts, and extreme temperatures, among others. They are important triggering factors that cause various natural hazards, including mountain flash floods (Figure 1), landslides, debris flows, urban flooding waterlogging, and agro-meteorological hazards, etc (Field et al., 2012; Sang et al., 2018; Sajadi, et al., 2022; Ren et al., 2024; Shi, et al., 2024). Such extreme weather-induced hazards pose a significant global threat to sustainable socioeconomic development. For instance, flooding generated by heavy rainstorms has become a serious “urban disease” in many cities worldwide, posing a serious threat to the safety of people’s lives and property and the normal operation of cities (Yang et al., 2020). Because of climate change, the frequency and intensity of extreme weather events and related disasters will worsen (Stott, 2016). Thereby, it is vital to focus on risk assessment and resilience management of extreme weather-induced disasters (Easterling et al., 2000), to inform policymaking and mitigate natural disasters. This is the motivation for proposing this Research Topic.
Figure 1. Mountain flash flood disaster in a small basin in Southwest China on 18–19 August 2020, following extreme rainstorms.
In this Research Topic, scholars contributed their latest findings on useful methods and techniques for forecasting, providing early warning and assessing the risks of extreme weather-induced disasters. Moreover, they provided in-depth scientific insights and improved our understanding of the resilience and mitigation of extreme weather-induced disasters. Their solid scientific contributions will significantly promote research on extreme weather-induced disasters.
Overview of the articles
As guest editors, we would like to thank the authors who submitted very interesting articles for this Research Topic. Thanks to the valuable collaboration between the reviewers and authors, eight articles are featured in this Research Topic, which are briefly summarized below.
In the first study, “Objective identification and forecast method of PM2.5 pollution based on medium- and long-term ensemble forecasts in Beijing-Tianjin-Hebei region and its surrounding areas”, Liu et al. developed an objective identification and forecast method for PM2.5 pollution (OIF-PM2.5) in the Beijing-Tianjin-Hebei region and its surrounding areas. The authors reported that the observed PM2.5 pollution ratio increased with the aggravating PM2.5 pollution. Statistical results indicated that the OIF-PM2.5 method is highly reliable for forecasts with a leading forecasting time of 1–15 days.
In the second article by Assi et al.,“Homeowner flood risk and risk reduction from home elevation between the limits of the 100- and 500-year floodplains”, the authors proposed a systematic approach to predicting flood risk for single-family homes using the average annual loss in the shaded X Zone–the area immediately outside the Special Flood Hazard Area (i.e., the 500-year floodplain) in the United States. The results enhanced the understanding of flood risk and the benefits of elevating homes above the first floor in the shaded X Zone.
The third article “Gaps in the governance of floods, droughts, and heatwaves in the United Kingdom” was contributed by Carvalho and Spataru. The authors presented the current state of the art of flood, drought, and heatwave governance in the United Kingdom, with a focus on pre-emergency phases and the lack of indicators for the assessment of the effectiveness of adaptation to all three disasters. Gaps and challenges are discussed, along with providing actions for adapting to and building resilience against these three types of disasters.
In the fourth contribution “Analysis of urban necessities reserve index and reserve quantity under emergency conditions”, Jiang et al. assessed urban safety, and classified the emergency materials of urban necessities in Shanghai, by establishing a corresponding reserve list. To better handle emergencies, the authors provided countermeasures and suggestions for optimizing the material structure of emergency reserves, managing material reserves at different levels, reasonably planning the amount of emergency materials, reducing the cost of reserves and improving the efficiency of emergency reserves.
The fifth study “Sedimentary records of giant landslide-dam breach events in western Sichuan, China” was contributed by Ma et al. The authors conducted a detailed investigation of large-scale landslide-dammed lake outburst deposits in two typical River Basins on the Western Sichuan Plateau in China. They found that the sedimentary characteristics of outburst deposits (ODs) explain the hydrodynamic changes during the propagation of outburst floods, and are important records for distinguishing ODs and “normal” floods.
The sixth study, by Liu et al., is titled “A comparative study of regional rainfall-induced landslide early warning models based on RF, CNN and MLP algorithms”. The authors focused on Fujian Province in China, and proposed a four-step process for building a regional landslide early warning model based on machine learning. The process includes data integration and cleaning, sample set construction, model training and validation, and practical application. This study will be valuable for landslide disaster warning research.
In the seventh contribution “Construction and preliminary analysis of landslide database triggered by heavy storm in the parallel range-valley area of western Chongqing, China, on 8 June 2017”, Liu and Xu identified landslide disasters triggered by extreme rainfall events in the parallel range-valley area of western Chongqing, China, and established a historical landslide database. This database provides scientific support for investigating landslide mechanisms in western Chongqing and mitigating the associated risks.
The eighth study “Exploring Bayesian network model with noise filtering for rainfall-induced landslide susceptibility assessment in Fujian, China” was contributed by Zhou et al. The researchers employed a Bayesian network to analyze the factors influencing landslides in Fujian Province, China, which is prone to typhoons and landslides. They introduced a progressive noise filtering method to mitigate the mislabeling effects of non-landslide points. This study provides useful guidance for reliable landslide susceptibility mapping in the study area.
For this Research Topic, further critical and constructive debate, viewpoints and opinions are welcome: they will contribute to more resilient and sustainable strategies and practices for adapting to extreme weather-induced disasters. We suggest you freely use and discuss these articles—including their methods, solid datasets, key findings and propositions, to promote research on extreme weather-induced disasters.
Author contributions
Y-FS: Writing – review and editing, Writing – original draft. LS: Writing – review and editing. XZ: Writing – review and editing. WY: Writing – review and editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This work is financially supported by the National Natural Science Foundation of China (Grants 42471029, 2311530063), and the Science and Technology Projects of Xizang Autonomous Region (Grants XZ202501ZY0004, XZ202401JD0001).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Easterling, D. R., Meehl, G. A., Parmesan, C., Changnon, S. A., Karl, T. R., and Mearns, L. O. (2000). Climate extremes: observations, modeling, and impacts. Science 289 (5487), 2068–2074. doi:10.1126/science.289.5487.2068
C. B. Field, V. Barros, T. F. Stocker, and Q. Dahe (2012). Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change (Cambridge University Press).
Ren, Z., Sang, Y. F., Cui, P., Chen, D., Zhang, Y., Sun, S., et al. (2024). Temporal scaling characteristics of sub-daily precipitation in Qinghai-Tibet Plateau. Earth’s Future 465, 133177. doi:10.1029/2024ef004417
Sajadi, P., Sang, Y. F., Gholamnia, M., Bonafoni, S., and Mukherjee, S. (2022). Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms. Geosci. Lett. 9 (1), 9–25. doi:10.1186/s40562-022-00218-x
Sang, Y. F., Singh, V. P., Hu, Z., Xie, P., and Li, X. (2018). Entropy-aided evaluation of meteorological droughts over China. J. Geophys. Research-Atmospheres 123 (2), 740–749. doi:10.1002/2017jd026956
Shi, J., Sang, Y. F., Sun, S., Aghakouchak, A., Hu, S., and Dash, S. S. (2024). Development of a leaf area index-based relative threshold method for identifying agricultural drought areas. J. Hydrology 641, 131846. doi:10.1016/j.jhydrol.2024.131846
Stott, P. (2016). How climate change affects extreme weather events. Science 352 (6293), 1517–1518. doi:10.1126/science.aaf7271
Keywords: extreme weather, risk assessment, resilience management, natural disasters, sustainable development
Citation: Sang Y-F, Shen L, Zhang X and Yang W (2025) Editorial: Risk assessment and resilience of extreme weather-induced disasters. Front. Earth Sci. 13:1623074. doi: 10.3389/feart.2025.1623074
Received: 05 May 2025; Accepted: 14 May 2025;
Published: 21 May 2025.
Edited and reviewed by:
Gordon Woo, Risk Management Solutions, United KingdomCopyright © 2025 Sang, Shen, Zhang and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yan-Fang Sang, c2FuZ3lmQGlnc25yci5hYy5jbg==