Climate-driven variability in the occurrence of major floods across North America and Europe

Journal of Hydrology, Journal Year: 2017, Volume and Issue: 552, P. 704 - 717

Published: July 14, 2017

Language: Английский

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Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments

Water Resources Research, Journal Year: 2021, Volume and Issue: 58(1)

Published: Dec. 27, 2021

Abstract Long short‐term memory (LSTM) networks represent one of the most prevalent deep learning (DL) architectures in current hydrological modeling, but they remain black boxes from which process understanding can hardly be obtained. This study aims to demonstrate potential interpretive DL gaining scientific insights using flood prediction across contiguous United States (CONUS) as a case study. Two interpretation methods were adopted decipher machine‐captured patterns and inner workings LSTM networks. The by expected gradients method revealed three distinct input‐output relationships learned LSTM‐based runoff models 160 individual catchments. These correspond flood‐inducing mechanisms—snowmelt, recent rainfall, historical rainfall—that account for 10.1%, 60.9%, 29.0% 20,908 flow peaks identified data set, respectively. Single flooding mechanisms dominate 70.7% investigated catchments (11.9% snowmelt‐dominated, 34.4% rainfall‐dominated, 24.4% rainfall‐dominated mechanisms), remaining 29.3% have mixed mechanisms. spatial variability dominant reflects catchments' geographic climatic conditions. Moreover, additive decomposition unveils how network behaves differently retaining discarding information when emulating different types floods. Information inputs within previous time steps partially stored predict snowmelt‐induced rainfall‐induced floods, while only is retained. Overall, this provides new perspective processes extremes demonstrates prospect artificial intelligence‐assisted discovery future.

Language: Английский

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CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil

Earth system science data, Journal Year: 2020, Volume and Issue: 12(3), P. 2075 - 2096

Published: Sept. 8, 2020

Abstract. We introduce a new catchment dataset for large-sample hydrological studies in Brazil. This encompasses daily time series of observed streamflow from 3679 gauges, as well meteorological forcing (precipitation, evapotranspiration, and temperature) 897 selected catchments. It also includes 65 attributes covering range topographic, climatic, hydrologic, land cover, geologic, soil, human intervention variables, data quality indicators. paper describes how the hydrometeorological were produced, their primary limitations, main spatial features. To facilitate comparisons with catchments other countries, follow same standards previous CAMELS (Catchment Attributes MEteorology Large-sample Studies) datasets United States, Chile, Great Britain. CAMELS-BR (Brazil) complements by providing hundreds tropics Amazon rainforest. Importantly, precipitation evapotranspiration uncertainties are assessed using several gridded products, quantitative estimates water consumption provided to characterize impacts on resources. By extracting combining these different products making publicly available, we aim create opportunities research Brazil inclusion Brazilian basins continental global studies. envision that this will enable community gain insights into drivers behavior, better extreme hydroclimatic events, explore climate change activities resources The is freely available at https://doi.org/10.5281/zenodo.3709337 (Chagas et al., 2020).

Language: Английский

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An extremeness threshold determines the regional response of floods to changes in rainfall extremes

Communications Earth & Environment, Journal Year: 2021, Volume and Issue: 2(1)

Published: Aug. 26, 2021

Precipitation extremes will increase in a warming climate, but the response of flood magnitudes to heavier precipitation events is less clear. Historically, there little evidence for systematic increases magnitude despite observed extremes. Here we investigate how change warming, using large initial-condition ensemble simulations with single climate model, coupled hydrological model. The model chain was applied historical (1961–2000) and warmer future (2060–2099) conditions 78 watersheds Bavaria, region comprising headwater catchments Inn, Danube Main River, thus representing an area expressed heterogeneity. For majority catchments, identify ‘return interval threshold’ relationship between increases: at return intervals above this threshold, further extreme frequency clearly yield increased magnitudes; below modulated by land surface processes. We suggest that threshold behaviour can reconcile climatological perspectives on changing risk climate. Germany rainfall processes not above,

Language: Английский

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Global Changes in 20‐Year, 50‐Year, and 100‐Year River Floods

Geophysical Research Letters, Journal Year: 2021, Volume and Issue: 48(6)

Published: March 9, 2021

Abstract Concepts like the 100‐year flood event can be misleading if they are not updated to reflect significant changes over time. Here, we model observed annual maximum daily streamflow using a nonstationary approach provide first global picture of in: (a) magnitudes 20‐, 50‐, and floods (i.e., flows given exceedance probability in each year ); (b) return periods floods, as assessed 1970 fixed magnitude (c) corresponding probabilities. Empirically, find 20‐/50‐year have mostly increased temperate climate zones, but decreased arid, tropical, polar, cold zones. In contrast, arid/temperate zones exhibit mixed trends results influenced by small number stations with long records, highlight need for continued updating hazard assessments.

Language: Английский

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An Overview of Flood Concepts, Challenges, and Future Directions

Journal of Hydrologic Engineering, Journal Year: 2022, Volume and Issue: 27(6)

Published: March 24, 2022

This review provides a broad overview of the current state flood research, challenges, and future directions. Beginning with discussion flood-generating mechanisms, synthesizes literature on forecasting, multivariate nonstationary frequency analysis, urban flooding, remote sensing floods. Challenges research directions are outlined highlight emerging topics where more work is needed to help mitigate risks. It anticipated that systems will likely have significant risk due compounding effects continued climate change land-use intensification. The timely prediction floods, quantification socioeconomic impacts developing mitigation strategies continue be challenging. There need bridge scales between model capabilities end-user needs by integrating multiscale models, stakeholder input, social citizen science input for monitoring, mapping, dissemination. Although much progress has been made in using applications, recent upcoming Earth Observations provide excellent potential unlock additional benefits applications. community can benefit from downscaled, as well ensemble scenarios consider changes. Efforts also data assimilation approaches, especially ingest local, citizen, media data. Also enhanced compound hazards assess reduce vulnerability impacts. dynamic complex interactions climate, societal change, watershed processes, human factors often confronted deep uncertainty highlights transdisciplinary science, policymakers, stakeholders vulnerability.

Language: Английский

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Reconciling disagreement on global river flood changes in a warming climate

Nature Climate Change, Journal Year: 2022, Volume and Issue: 12(12), P. 1160 - 1167

Published: Nov. 28, 2022

Language: Английский

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RETRACTED ARTICLE: A critical assessment of extreme events trends in times of global warming

The European Physical Journal Plus, Journal Year: 2022, Volume and Issue: 137(1)

Published: Jan. 13, 2022

Abstract This article reviews recent bibliography on time series of some extreme weather events and related response indicators in order to understand whether an increase intensity and/or frequency is detectable. The most robust global changes climate extremes are found yearly values heatwaves (number days, maximum duration cumulated heat), while trends heatwave not significant. Daily precipitation stationary the main part stations. Trend analysis tropical cyclones show a substantial temporal invariance same true for tornadoes USA. At time, impact warming surface wind speed remains unclear. then extended meteorological events, namely natural disasters, floods, droughts, ecosystem productivity yields four crops (maize, rice, soybean wheat). None these clear positive trend events. In conclusion basis observational data, crisis that, according many sources, we experiencing today, evident yet. It would be nevertheless extremely important define mitigation adaptation strategies that take into account current trends.

Language: Английский

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Hydroclimate volatility on a warming Earth

Nature Reviews Earth & Environment, Journal Year: 2025, Volume and Issue: 6(1), P. 35 - 50

Published: Jan. 9, 2025

Language: Английский

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Recent changes in extreme floods across multiple continents

Environmental Research Letters, Journal Year: 2017, Volume and Issue: 12(11), P. 114035 - 114035

Published: Aug. 24, 2017

Analyses of trends in observed floods often focus on relatively frequent events, whereas changes rare are only studied for a small number locations that have exceptionally long observational records. Understanding is especially relevant as these events most damaging and influence the design major structures. Here, we provide an assessment largest flood (~0.033 annual exceedance probability) during period 1980−2009 1744 catchments located Australia, Brazil, Europe United States. The occurrence spatial aggregate shows strong temporal variability peaked around 1995. During 30 year period, there overall increases both frequency magnitude extreme floods. These strongest States, weakest Brazil Australia. Physical causes reported short-term longer-term currently remain elusive, because key drivers vary between catchments. Nonetheless, this approach provides basis more spatially representative occurrence.

Language: Английский

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