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

Toward a better understanding of curve number and initial abstraction ratio values from a large sample of watersheds perspective DOI
Abderraman R. Amorim Brandão, Dimaghi Schwamback, André S. Ballarin

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132941 - 132941

Published: Feb. 1, 2025

Language: Английский

Citations

0
Quantifying the compounding effects of natural hazard events: a case study on wildfires and floods in California DOI Creative Commons
Sam Dulin, Madison Smith,

Beth Ellinport

et al.

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

Published: May 7, 2025

Language: Английский

Citations

0
Uncovering the Dynamic Drivers of Floods Through Interpretable Deep Learning DOI Creative Commons

Yuanhao Xu,

Kairong Lin,

Caihong Hu

et al.

Earth s Future, Journal Year: 2024, Volume and Issue: 12(10)

Published: Oct. 1, 2024

Abstract The formation of floods, as a complex physical process, exhibits dynamic changes in its driving factors over time and space under climate change. Due to the black‐box nature deep learning, use alone does not enhance understanding hydrological processes. challenge lies employing learning uncover new knowledge on flood mechanism. This study proposes an interpretable framework for modeling that employs interpretability techniques elucidate inner workings peak‐sensitive Informer, revealing response floods 482 watersheds across United States. Accurate simulation is prerequisite provide reliable information. reveals comparing Informer with Transformer LSTM, former showed superior performance peak (Nash‐Sutcliffe Efficiency 0.6 70% watersheds). By interpreting Informer's decision‐making three primary flood‐inducing patterns were identified: Precipitation, excess soil water, snowmelt. controlling effect dominant regional, their impact steps shows significant differences, challenging traditional variables closer timing event occurrence have greater impact. Over 40% exhibited shifts between 1981 2020, precipitation‐dominated undergoing more changes, corroborating change responses. Additionally, unveils interplay among variables. These findings suggest through reverse deduction, transforms data‐driven models from merely fitting nonlinear relationships effective tools enhancing characteristics.

Language: Английский

Citations

2
Deep Learning Ensemble for Flood Probability Analysis DOI Open Access

Fred Sseguya,

Kyung Soo Jun

Water, Journal Year: 2024, Volume and Issue: 16(21), P. 3092 - 3092

Published: Oct. 29, 2024

Predicting flood events is complex due to uncertainties from limited gauge data, high data and computational demands of traditional physical models, challenges in spatial temporal scaling. This research innovatively uses only three remotely sensed computed factors: rainfall, runoff temperature. We also employ deep learning models—Feedforward Neural Network (FNN), Convolutional (CNN), Long Short-Term Memory (LSTM)—along with a neural network ensemble (DNNE) using synthetic predict future probabilities, utilizing the Savitzky–Golay filter for smoothing. Using hydrometeorological dataset 1993–2022 Nile River basin, six predictors were derived. The FNN LSTM models exhibited accuracy stable loss, indicating minimal overfitting, while CNN showed slight overfitting. Performance metrics revealed that achieved 99.63% 0.999886 ROC AUC, had 95.42% 0.893218 excelled 99.82% 0.999967 AUC. DNNE outperformed individual reliability consistency. Runoff rainfall most influential predictors, temperature impact.

Language: Английский

Citations

2
Winter climate preconditioning of summer vegetation extremes in the northern hemisphere DOI Creative Commons
Mohit Anand, Raed Hamed, Nora Linscheid

et al.

Environmental Research Letters, Journal Year: 2024, Volume and Issue: 19(9), P. 094045 - 094045

Published: Aug. 20, 2024

Abstract The impact of the spring climate on Northern Hemisphere’s summer vegetation activity and extremes has been extensively researched, but less attention devoted to whether how winter may additionally influence in summer. Here, we provide insights into temperature precipitation Hemisphere. To do this, identify positive negative leaf area index (LAI, a proxy for activity) assess effects those using logistic regression at regional scale. Over quarter regions Hemisphere show strong preconditioning LAI extremes, which is typically stronger croplands than forests. In with preconditioning, mediates link between through ecological memory seasonal legacy effects. Our findings suggest that extremely low both forests preconditioned by colder drier winters, while high associated warmer wetter winters. For croplands, winters are an increased likelihood mid-latitude reduced high-latitude regions. Consideration improve our understanding inter-annual variability support agricultural land management practitioners anticipating detrimental crop yields forest conditions.

Language: Английский

Citations

1
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

Citations

0