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

Date published

2024-05-01

Free to read from

2025-03-25

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

American Geophysical Union (AGU)

Department

Type

Article

ISSN

2328-4277

Format

Citation

Slater 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

Abstract

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.

Description

Software Description

Software Language

Github

Keywords

floods, machine learning, drivers, climate impacts, groundwater, urbanization, afforestation, 3707 Hydrology, 3701 Atmospheric Sciences, 37 Earth Sciences, Machine Learning and Artificial Intelligence, Climate-Related Exposures and Conditions, 13 Climate Action, 15 Life on Land, 3702 Climate change science, 3707 Hydrology

DOI

Rights

Attribution 4.0 International

Relationships

Relationships

Resources

Funder/s

Division of Earth Sciences, Directorate for Geosciences, UK Research and Innovation, Natural Environment Research Council