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| DOI | 10.1029/2023EF003553 |
| Natural Hazards in a Changing World: Methods for Analyzing Trends and Non-Linear Changes | |
| 发表日期 | 2024 |
| EISSN | 2328-4277 |
| 起始页码 | 12 |
| 结束页码 | 5 |
| 卷号 | 12期号:5 |
| 英文摘要 | Estimating the frequency and magnitude of natural hazards largely hinges on stationary models, which do not account for changes in the climatological, hydrological, and geophysical baseline conditions. Using five diverse case studies encompassing various natural hazard types, we present advanced statistical and machine learning methods to analyze and model transient states from long-term inventory data. A novel storminess metric reveals increasing European winter windstorm severity from 1950 to 2010. Non-stationary extreme value models quantify trends, seasonal shifts, and regional differences in extreme precipitation for Germany between 1941 and 2021. Utilizing quantile sampling and empirical mode decomposition on 148 years of daily weather and discharge data in the European Alps, we assess the impacts of changing snow cover, precipitation, and anthropogenic river network modifications on river runoff. Moreover, a probabilistic framework estimates return periods of glacier lake outburst floods in the Himalayas, demonstrating large differences in 100-year flood levels. Utilizing a Bayesian change point algorithm, we track the onset of increased seismicity in the southern central United States and find correlation with wastewater injections into deep wells. In conclusion, data science reveals transient states for very different natural hazard types, characterized by diverse forms of change, ranging from gradual trends to sudden change points and from altered seasonality to overall intensity variations. In synergy with the physical understanding of Earth science, we gain important new insights into the dynamics of the studied hazards and their possible mechanisms. According to global databases on natural hazard events and associated risks, there has been a noteworthy escalation in the extent of economic losses during past decades. It is important but difficult to distinguish and disentangle trends due to changing hazard occurrence or damage potential. Accurately quantifying altered hazards requires high-quality data sets and robust statistical methodologies. Here, we present recent progress in earth and data science toward a quantitative assessment of natural hazards in a changing world. We show that winter storms have become more frequent and more severe in Europe; that extreme precipitation in Germany shows seasonal shifts and changing intensities with regional variation; that river runoff in Central Europe is changing due to modifications of the river network, declining snowpacks, and changes in precipitation; that frequency of glacier lake outburst floods in the Himalayas have remained unchanged over the past 30 years despite rapid glacier melt and lake growth; and that earthquake activity in Oklahoma (USA) has increased with the onset of wastewater injection wells. We infer that recent advances in data science can efficiently provide new knowledge from big data sets, but interpreting these results needs a solid understanding and rather detailed analysis of the underlying processes. Time-dependent approaches are key to capture changing hazards, with diverse altered frequencies, intensities, timing or spatial occurrences Advances in data-driven methods and increasing availability of geodata foster new ways to detect changes in natural hazards Over-simplification or averaging over large scales in time or space cause severe information loss and may hide the mechanisms for change |
| 英文关键词 | non-stationary hazard models; European winter windstorms; extreme precipitation in Germany; river runoff in European Alps; Himalayan glacier lake outburst floods; induced seismicity in Oklahoma |
| 语种 | 英语 |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences |
| WOS类目 | Environmental Sciences ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:001230169600001 |
| 来源期刊 | EARTHS FUTURE
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/306740 |
| 作者单位 | University of Potsdam; Federal Institute for Materials Research & Testing; University of Potsdam; Free University of Berlin; University of Innsbruck |
| 推荐引用方式 GB/T 7714 | . Natural Hazards in a Changing World: Methods for Analyzing Trends and Non-Linear Changes[J],2024,12(5). |
| APA | (2024).Natural Hazards in a Changing World: Methods for Analyzing Trends and Non-Linear Changes.EARTHS FUTURE,12(5). |
| MLA | "Natural Hazards in a Changing World: Methods for Analyzing Trends and Non-Linear Changes".EARTHS FUTURE 12.5(2024). |
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