Spatial sensitivity of river flooding to changes in climate and land cover through explainable AI

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dc.contributor.authorSlater, Louise
dc.contributor.authorCoxon, Gemma
dc.contributor.authorBrunner, Manuela
dc.contributor.authorMcMillan, Hilary
dc.contributor.authorYu, Le
dc.contributor.authorZheng, Yanchen
dc.contributor.authorKhouakhi, Abdou
dc.contributor.authorMoulds, Simon
dc.contributor.authorBerghuijs, Wouter
dc.date.accessioned2025-03-25T11:02:51Z
dc.date.available2025-03-25T11:02:51Z
dc.date.freetoread2025-03-25
dc.date.issued2024-05-01
dc.date.pubOnline2024-04-30
dc.description.abstractExplaining 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.
dc.description.journalNameEarth's Future
dc.description.sponsorshipDivision of Earth Sciences, Directorate for Geosciences, UK Research and Innovation, Natural Environment Research Council
dc.identifier.citationSlater L, Coxon G, Brunner M, et al., (2024) Spatial sensitivity of river flooding to changes in climate and land cover through explainable AI. Earth's Future, Volume 12, Issue 5, May 2024, Article number e2023EF004035
dc.identifier.eissn2328-4277
dc.identifier.elementsID540973
dc.identifier.issn2328-4277
dc.identifier.issueNo5
dc.identifier.paperNoe2023EF004035
dc.identifier.urihttps://doi.org/10.1029/2023ef004035
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23637
dc.identifier.volumeNo12
dc.languageEnglish
dc.language.isoen
dc.publisherAmerican Geophysical Union (AGU)
dc.publisher.urihttps://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EF004035
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectfloods
dc.subjectmachine learning
dc.subjectdrivers
dc.subjectclimate impacts
dc.subjectgroundwater
dc.subjecturbanization
dc.subjectafforestation
dc.subject3707 Hydrology
dc.subject3701 Atmospheric Sciences
dc.subject37 Earth Sciences
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectClimate-Related Exposures and Conditions
dc.subject13 Climate Action
dc.subject15 Life on Land
dc.subject3702 Climate change science
dc.titleSpatial sensitivity of river flooding to changes in climate and land cover through explainable AI
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-03-29

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