Model-based assessment of flood generation mechanisms over Poland: The roles of precipitation, snowmelt, and soil moisture excess

The Science of The Total Environment, Год журнала: 2023, Номер 891, С. 164626 - 164626

Опубликована: Июнь 5, 2023

Hydrometeorological variability, such as changes in extreme precipitation, snowmelt, or soil moisture excess, Poland can lead to fluvial flooding. In this study we employed the dataset covering components of water balance with a daily time step at sub-basin level over country for 1952-2020. The data set was derived from previously calibrated and validated Soil & Water Assessment Tool (SWAT) model 4000 sub-basins. We applied Mann Kendall test circular statistics-based approach on annual maximum floods various potential flood drivers estimate trend, seasonality, relative importance each driver. addition, two sub-periods (1952-1985 1986-2020) were considered examine mechanism recent decades. show that northeast decreasing, while south trend showed positive behavior. Moreover, snowmelt is primary driver flooding across country, followed by excess precipitation. latter seemed be dominant only small, mountain-dominated region south. gained mainly northern part, suggesting spatial pattern generation mechanisms also governed other features. found strong signal climate change large parts Poland, where losing second sub-period favor which explained temperature warming diminishing role snow processes.

Язык: Английский

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Changes in the Climate System Dominate Inter‐Annual Variability in Flooding Across the Globe

Geophysical Research Letters, Год журнала: 2024, Номер 51(6)

Опубликована: Март 21, 2024

Abstract Extreme flood events have regional differences in their generating mechanisms due to the complex interaction of different climate and catchment processes. This study aims examine capability drivers capture year‐to‐year variability global extremes. Here, we use a statistical attribution approach model seasonal annual maximum daily discharge for 7,886 stations worldwide, using season‐ basin‐averaged precipitation temperature as predictors. The results show robust performance our climate‐informed models describing inter‐annual discharges regardless geographical region, type, basin size, degree regulation, impervious area. developed enable assessment sensitivity changes, indicating potential reliably project changes magnitude

Язык: Английский

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Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method

Journal of Hydrology, Год журнала: 2024, Номер 642, С. 131867 - 131867

Опубликована: Авг. 23, 2024

Язык: Английский

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An increase in the spatial extent of European floods over the last 70 years

Hydrology and earth system sciences, Год журнала: 2024, Номер 28(16), С. 3755 - 3775

Опубликована: Авг. 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

Язык: Английский

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The Value of Large‐Scale Climatic Indices for Monthly Forecasting Severity of Widespread Flooding Using Dilated Convolutional Neural Networks

Earth s Future, Год журнала: 2024, Номер 12(2)

Опубликована: Фев. 1, 2024

Abstract Spatially co‐occurring floods pose a threat to the resilience and recovery of communities. Their timely forecasting plays crucial role for increasing flood preparedness limiting associated losses. In this study we investigated potential dilated Convolutional Neural Network (dCNN) model conditioned on large‐scale climatic indices antecedent precipitation forecast monthly severity widespread flooding (i.e., spatially floods) in Germany with 1 month lead time. The was estimated from 63 years daily streamflow series as sum concurrent exceedances at‐site 2‐year return periods within given across 172 mesoscale catchments (median area 516 km 2 ). trained individually whole country three diverse hydroclimatic regions provide insights heterogeneity performance drivers. Our results showed considerable using dCNN especially length training increases. However, event‐based evaluation skill indicates large underestimation rainfall‐generated during dry conditions despite overall lower these events compared rain‐on‐snow floods. Feature attribution wavelet coherence analyses both indicated difference major drivers regions. While North‐Eastern region is strongly affected by Baltic Sea, North‐Western more global patterns El‐Niño activity. Southern addition detected effect Mediterranean while less important region.

Язык: Английский

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Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application

Environmental Modelling & Software, Год журнала: 2025, Номер unknown, С. 106350 - 106350

Опубликована: Янв. 1, 2025

Язык: Английский

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Identification of time-varying parameters of a monthly Budyko function in US MOPEX catchments and its implications

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 59, С. 102348 - 102348

Опубликована: Апрель 11, 2025

Язык: Английский

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Basin-informed flood frequency analysis using deep learning exhibits consistent projected regional patterns over CONUS

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 13, 2025

Climate change poses a significant threat to flood-prone areas by altering precipitation patterns and the water cycle. Here, we analyzed impact of climate on future flood trends. We trained Long Short-Term Memory (LSTM) model estimate long term discharge at 638 river sites over contiguous United States (CONUS) based inputs from gridMET meteorological datasets, downscaled bias-corrected Coupled Model Intercomparison Project 5 (CMIP5) projections. Our results indicate that LSTM can replicate observed with reliable accuracy. The projected changes in magnitude for 10-year 100-year return periods reveal consistent geographical robust across models, increasing trends approximately + 10 40% East West coastal regions decreasing about - 30% Southwestern areas. exhibiting an trend are likely driven increase total seasonal extreme timing amount peak flow. In contrast, result reduction snowpack. To support adaptation planning, developed interactive map providing historical 10- floods selected basins CONUS.

Язык: Английский

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Enhancing flood forecasting performance using effective and transparent explainable hybrid deep learning model

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Май 28, 2025

Язык: Английский

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Extreme and compound ocean events are key drivers of projected low pelagic fish biomass

Global Change Biology, Год журнала: 2023, Номер 29(23), С. 6478 - 6492

Опубликована: Окт. 10, 2023

Ocean extreme events, such as marine heatwaves, can have harmful impacts on ecosystems. Understanding the risks posed by events is key to develop strategies predict and mitigate their effects. However, underlying ocean conditions driving severe ecosystems are complex often unknown arise not only from hazards but also interactions between hazards, exposure vulnerability. Marine may be impacted in single drivers rather compounding effects of moderate anomalies. Here, we employ an ensemble climate-impact modeling approach that combines a global fish model with output large simulation Earth system model, identify ecosystem associated most total biomass 326 pelagic species. We show low net primary productivity influential driver extremely over 68% area considered especially subtropics mid-latitudes, followed high temperature oxygen eastern equatorial Pacific latitudes. Severe loss generally driven anomalies at least one driver, except tropics, where combination sufficient drive impacts. Single never biomass. Compound either or necessary condition for 78% ocean, compound variable 61% ocean. Overall, our results highlight crucial role

Язык: Английский

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