Projected changes in Rhine River flood seasonality under global warming

Hydrology and earth system sciences, Journal Year: 2021, Volume and Issue: 25(5), P. 2353 - 2371

Published: May 3, 2021

Abstract. Climatic change alters the frequency and intensity of natural hazards. In order to assess potential future changes in flood seasonality Rhine River basin, we analyse streamflow, snowmelt, precipitation evapotranspiration at 1.5, 2.0 3.0 ^∘C global warming levels. The mesoscale hydrological model (mHM) forced with an ensemble climate projection scenarios (five general circulation models under three representative concentration pathways) is used simulate present conditions both pluvial nival regimes. Our results indicate that characteristics basin are controlled by increases antecedent diminishing snowpacks. pluvial-type sub-basin Moselle River, increasing due increased encounters declining snowpacks during winter. decrease snowmelt seems counterbalance precipitation, resulting only small transient streamflow maxima. For Basin Basel, rising temperatures cause from solid liquid which enhance overall increase sums, particularly cold season. At gauge strongest maxima show up winter, when strong encounter almost unchanged snowmelt-driven runoff. analysis events for Basel suggests no point time season does a result risk flooding. Snowpacks increasingly depleted course We do not find indications merging floods warming. To refine attained results, next steps need be representation glaciers lakes set-up, coupling simulations component independent validation snow routine using satellite-based cover maps.

Language: Английский

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Evaluation of snow depth and snow cover represented by multiple datasets over the Tianshan Mountains: Remote sensing, reanalysis, and simulation

International Journal of Climatology, Journal Year: 2021, Volume and Issue: 42(8), P. 4223 - 4239

Published: Nov. 17, 2021

Abstract Mountain snowpacks play important roles in water resource and ecological system. However, existing snow depth products show great uncertainties across the Tianshan Mountains, Central Asia. This study evaluated compared four datasets over including from ERA5, ERA5‐Land, passive microwave, as well a dynamically downscaled simulation by Weather Research Forecasting (WRF) model. The was against situ observations while cover extent interactive multisensor ice mapping system (IMS) cover. Furthermore, snow‐related metrics, such annual mean days, were among datasets. results showed that spatial patterns of metrics relatively consistent datasets, although discrepancies existed magnitude. Additionally, temporal variations depended on dataset employed. simulated WRF got lowest bias daily value (mean = 0.12 cm, root square error 2.08 cm), additionally, it had best performance classifying correct grids with IMS (probability detection 0.766). It also outperformed days 47.2 13.3% ERA5‐Land depth. highlights confidence characterizing both pattern based model simulation.

Language: Английский

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Future Changes in Snowpack, Snowmelt, and Runoff Potential Extremes Over North America

Geophysical Research Letters, Journal Year: 2021, Volume and Issue: 48(22)

Published: Nov. 6, 2021

Abstract Snowpack and snowmelt‐driven extreme events (e.g., floods) have large societal consequences including infrastructure failures. However, it is not well understood how projected changes in the snow‐related extremes differ across North America. Using dynamically downscaled regional climate model (RCM) simulations, we found that magnitudes of snow water equivalent, snowmelt, runoff potential (RP; snowmelt plus precipitation) decrease by 72%, 73%, 45%, respectively, over continental United States southern Canada but increase up to 8%, 53%, 41% Alaska northern late 21st century. In California Pacific Northwest, there a notable RP 21% contrary 31% These regions could be vulnerable larger rain‐on‐snow floods warmer climate. Regions with variability among RCM ensembles are identified, which require further investigation reduce uncertainties.

Language: Английский

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River flooding mechanisms and their changes in Europe revealed by explainable machine learning

Hydrology and earth system sciences, Journal Year: 2022, Volume and Issue: 26(24), P. 6339 - 6359

Published: Dec. 16, 2022

Abstract. Climate change may systematically impact hydrometeorological processes and their interactions, resulting in changes flooding mechanisms. Identifying such is important for flood forecasting projection. Currently, there a lack of observational evidence regarding trends mechanisms Europe, which requires reliable methods to disentangle emerging patterns from the complex interactions between drivers. Recently, numerous studies have demonstrated skill machine learning (ML) predictions hydrology, e.g., predicting river discharge based on its relationship with meteorological The relationship, if explained properly, provide us new insights into hydrological processes. Here, by using novel explainable ML framework, combined cluster analysis, we identify three primary that drive 53 968 annual maximum events around thousand European catchments. can be associated catchment-wide mechanisms: recent precipitation, antecedent precipitation (i.e., excessive soil moisture), snowmelt. results indicate over half studied catchments are controlled combination above mechanisms, especially moisture, dominant mechanism one-third Over past 70 years, significant been detected within number Generally, snowmelt-induced floods has decreased significantly, whereas driven increased. consistent expected climate responses, highlight risks seasonality magnitude. Overall, study offers perspective understanding weather extreme demonstrates prospect future scientific discoveries supported artificial intelligence.

Language: Английский

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Projecting changes in flood event runoff coefficients under climate change

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 615, P. 128689 - 128689

Published: Nov. 9, 2022

Language: Английский

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