Browsing by Author "Slater, Louise"

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    Atmospheric rivers and associated extreme rainfall over Morocco
    (Wiley, 2022-05-13) Khouakhi, Abdou; Driouech, Fatima; Slater, Louise; Waine, Toby; Chafki, Omar; Chehbouni, Abdelghani; Raji, Otmane
    Atmospheric rivers (ARs) are long, narrow, and transient corridors of enhanced water vapour content in the lower troposphere, associated with strong low-level winds. These features play a key role in the global water cycle and drive weather extremes in many parts of the world. Here, we assessed the frequency and general characteristics of landfalling ARs over Morocco for the period 1979–2020. We used ECMWF ERA5 reanalysis data to detect and track landfalling ARs and then assessed AR association with rainfall at the annual and seasonal scales, as well as extreme rainfall events (defined as a daily precipitation amount exceeding the 99th percentile threshold of the wet days) at 30 gauging stations located across Morocco. Results indicate that about 36 ARs/year make landfall in Morocco. AR occurrence varies spatially and seasonally with highest occurrences in the autumn (SON) and Winter (DJF) in the northern part of the country and along the Atlantic across northern regions. AR rainfall climatology indicates up to 180 mm·year−1 recorded in stations located in the northwest. High fractional contributions (~28%) are recorded in the north and the Atlantic regions, with the driest regions of the south receiving about a third of their annual rainfall from ARs. For extreme rainfall, the highest AR contributions can attain over 50% in the southern dry regions and along the Atlantic north coast and Atlas highlands.
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    Extreme weather associated with atmospheric rivers over Morocco
    (EGU: European Geophysical Union, 2021-04-30) Khouakhi, Abdou; Driouech, Fatima; Slater, Louise; Waine, Toby; Chafki, Omar; Raji, Otmane
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    Spatial sensitivity of river flooding to changes in climate and land cover through explainable AI
    (American Geophysical Union (AGU), 2024-05-01) Slater, Louise; Coxon, Gemma; Brunner, Manuela; McMillan, Hilary; Yu, Le; Zheng, Yanchen; Khouakhi, Abdou; Moulds, Simon; Berghuijs, Wouter
    Explaining the spatially variable impacts of flood‐generating mechanisms is a longstanding challenge in hydrology, with increasing and decreasing temporal flood trends often found in close regional proximity. Here, we develop a machine learning‐informed approach to unravel the drivers of seasonal flood magnitude and explain the spatial variability of their effects in a temperate climate. We employ 11 observed meteorological and land cover (LC) time series variables alongside 8 static catchment attributes to model flood magnitude in 1,268 catchments across Great Britain over four decades. We then perform a sensitivity analysis to assess how a 10% increase in precipitation, a 1°C rise in air temperature, or a 10 percentage point increase in urban or forest LC may affect flood magnitude in catchments with varying characteristics. Our simulations show that increasing precipitation and urbanization both tend to amplify flood magnitude significantly more in catchments with high baseflow contribution and low runoff ratio, which tend to have lower values of specific discharge on average. In contrast, rising air temperature (in the absence of changing precipitation) decreases flood magnitudes, with the largest effects in dry catchments with low baseflow index. Afforestation also tends to decrease floods more in catchments with low groundwater contribution, and in dry catchments in the summer. Our approach may be used to further disentangle the joint effects of multiple flood drivers in individual catchments.