Multivariate compound events drive historical floods and associated losses along the U.S. East and Gulf coasts DOI
Javed Ali, Thomas Wahl, Joao Morim

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 26, 2024

Abstract Compound flooding events are a threat to many coastal regions and can have widespread socio-economic implications. However, their frequency of occurrence, underlying flood drivers, direct link past losses largely unknown despite being key supporting risk adaptation assessments. Here, we present an impact-based analysis compound for 203 counties along the U.S. Gulf East coasts by combining data from multiple drivers loss information 1980 2018. We find that ~ 80% all recorded in our study area were rather than univariate. In addition, show historical most driven more two (hydrological, meteorological, and/or oceanographic) distinct spatial clusters exist exhibit variability driver events. Furthermore, over property crop linked flooding. The median cost is 26 times univariate terms 76 loss. Our overcomes some limitations previous compound-event studies based on pre-defined offers new insights into complex relationship between hazards associated impacts.

Language: Английский

How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences DOI Creative Commons
Shijie Jiang, Lily‐belle Sweet,

Georgios Blougouras

et al.

Earth s Future, Journal Year: 2024, Volume and Issue: 12(7)

Published: July 1, 2024

Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering new opportunities to improve our understanding of the complex Earth system. IML goes beyond conventional machine learning by not only making predictions but also seeking elucidate reasoning behind those predictions. The combination predictive power and enhanced transparency makes a promising approach for uncovering relationships data that may be overlooked traditional analysis. Despite its potential, broader implications field have yet fully appreciated. Meanwhile, rapid proliferation IML, still early stages, been accompanied instances careless application. In response these challenges, this paper focuses on how can effectively appropriately aid geoscientists advancing process understanding—areas are often underexplored more technical discussions IML. Specifically, we identify pragmatic application scenarios typical geoscientific studies, such as quantifying specific contexts, generating hypotheses about potential mechanisms, evaluating process‐based models. Moreover, present general practical workflow using address research questions. particular, several critical common pitfalls use lead misleading conclusions, propose corresponding good practices. Our goal is facilitate broader, careful thoughtful integration into science research, positioning it valuable tool capable enhancing current

Language: Английский

Citations

29
Shifted dominant flood drivers of an alpine glacierized catchment in the Tianshan region revealed through interpretable deep learning DOI Creative Commons
Wenting Liang, Weili Duan, Yaning Chen

et al.

npj Climate and Atmospheric Science, Journal Year: 2025, Volume and Issue: 8(1)

Published: Jan. 25, 2025

Language: Английский

Citations

1
Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins DOI Open Access

Haoyuan Yu,

Qichun Yang

Water, Journal Year: 2024, Volume and Issue: 16(15), P. 2199 - 2199

Published: Aug. 2, 2024

Machine learning models’ performance in simulating monthly rainfall–runoff subtropical regions has not been sufficiently investigated. In this study, we evaluate the of six widely used machine models, including Long Short-Term Memory Networks (LSTMs), Support Vector Machines (SVMs), Gaussian Process Regression (GPR), LASSO (LR), Extreme Gradient Boosting (XGB), and Light (LGBM), against a model (WAPABA model) streamflow across three sub-basins Pearl River Basin (PRB). The results indicate that LSTM generally demonstrates superior capability than other five models. Using previous month as an input variable improves all When compared with WAPABA model, better two sub-basins. For simulations wet seasons, shows slightly model. Overall, study confirms suitability methods modeling at scale basins proposes effective strategy for improving their performance.

Language: Английский

Citations

7
Evaluating the affecting factors of glacier mass balance in Tanggula Mountains using explainable machine learning and the open global glacier model DOI
Qiangqiang Xu, Shichang Kang, Xiaobo He

et al.

Journal of Mountain Science, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 8, 2025

Language: Английский

Citations

0
Multivariate compound events drive historical floods and associated losses along the U.S. East and Gulf coasts DOI Creative Commons
Javed Ali, Thomas Wahl, Joao Morim

et al.

npj natural hazards., Journal Year: 2025, Volume and Issue: 2(1)

Published: Feb. 25, 2025

Language: Английский

Citations

0
Exploring the dynamic impact of future land use changes on urban flood disasters: A case study in Zhengzhou City, China DOI Creative Commons

Yuanyuan Bai,

Shao Sun,

Yingjun Xu

et al.

Geography and sustainability, Journal Year: 2025, Volume and Issue: unknown, P. 100287 - 100287

Published: March 1, 2025

Language: Английский

Citations

0
Multivariate indicator-based flood hazard mapping using primary drivers of coastal flood for India DOI
Shelly Singh,

Ankan Chakraborty,

Ravi Ranjan

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 383, P. 125477 - 125477

Published: April 24, 2025

Language: Английский

Citations

0
An increase in the spatial extent of European floods over the last 70 years DOI Creative Commons
Beijing Fang, Emanuele Bevacqua, Oldřich Rakovec

et al.

Hydrology and earth system sciences, Journal Year: 2024, Volume and Issue: 28(16), P. 3755 - 3775

Published: Aug. 20, 2024

Abstract. Floods regularly cause substantial damage worldwide. Changing flood characteristics, e.g., due to climate change, pose challenges risk management. The spatial extent of floods is an important indicator potential impacts, as consequences widespread are particularly difficult mitigate. highly uneven station distribution in space and time, however, limits the ability quantify characteristics and, particular, changes extents over large regions. Here, we use observation-driven routed runoff simulations last 70 years Europe from a state-of-the-art hydrological model (the mesoscale Hydrologic Model – mHM) identify spatiotemporally connected events. Our identified spatiotemporal events compare well against independent impact database. We find that increase by 11.3 % on average across Europe. This occurs most Europe, except for parts eastern southwestern Over northern mainly driven overall magnitude caused increasing precipitation snowmelt. In contrast, trend central can be attributed heavy precipitation. Overall, our study illustrates opportunities combine long-term consistent regional with detection algorithm large-scale trends key their drivers. detected change should considered assessments it may challenge control water resource

Language: Английский

Citations

3
Revising Flood Return Periods by Accounting for the Co‐Occurrence Between Floods and Their Potential Drivers DOI Open Access
Kanneganti Bhargav Kumar, Rajarshi Das Bhowmik, P. P. Mujumdar

et al.

International Journal of Climatology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 13, 2025

ABSTRACT The return period of floods can be influenced by the extreme values their potential drivers, which may vary among catchments. Understanding risk and associated changes in periods due to these drivers is therefore interest flood hydrology. In this study, are considered as compound events resulting from a combination non‐independent factors. estimated using joint distribution functions, accounting for dependence peaks two distinct catchments: (i) an inland catchment‐Warunji Catchment, Krishna basin, India, (ii) coastal catchment‐Usk catchment, United Kingdom (UK). annual maximum (AM) rainfall, soil moisture storm surge variations time occurrence calculated understand co‐occurrence patterns. pairwise frequency estimated, with survival copula function. results indicate that AM variables tend co‐occur within short window, signifying drivers. series observed same year largest series. show significant univariate estimates both catchments, have different flood‐generating mechanisms. This work re‐emphasises findings recent literature traditional assessment methods based only on peak information substantially underestimate/overestimate neglecting effects multivariate viewpoint imperative assessing floods.

Language: Английский

Citations

0
Characterizing Compound Inland Flooding Mechanisms and Risks in North America Under Climate Change DOI Creative Commons
Mohammad Fereshtehpour, Mohammad Reza Najafi, Alex J. Cannon

et al.

Earth s Future, Journal Year: 2025, Volume and Issue: 13(2)

Published: Feb. 1, 2025

Abstract Compound inland flooding (CIF) arises from the concurrent interaction of multiple hydrometeorological drivers. In this study, we characterize key CIF events across North America, including two preconditioned events, rain‐on‐snow (ROS) and saturation excess (SEF) for historical baseline conditions global warming levels 1.5, 2, 4°C relative to preindustrial level. Utilizing high emission climate scenario (RCP8.5) CanRCM4‐LE with 50 members, frequency seasonality compound along probability these leading heavy runoff, role external forcing internal variability are assessed. We convert identified hazards into risk by integrating them exposure vulnerability components. The results suggest that as temperatures increase, overall ROS in causing significant runoff is projected decrease compared individual rainfall. Concurrently, impact SEF occurrences become more pronounced. signal‐to‐noise ratio highlights a high‐confidence change signal events; however, uncertainty related future projections joint These underscore need consider mechanisms, dynamics, risks associated CIFs within systematic approaches flood management.

Language: Английский

Citations

0