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.
Original language | English |
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Article number | e2023EF004035 |
Pages (from-to) | 1-14 |
Number of pages | 14 |
Journal | Earth's Future |
Volume | 12 |
Issue number | 5 |
Early online date | 30 Apr 2024 |
DOIs | |
Publication status | Published - May 2024 |
Bibliographical note
Publisher Copyright:© 2024 The Authors. Earth's Future published by Wiley Periodicals LLC on behalf of American Geophysical Union.
Funding
The authors would like to thank the Reviewers and Associate Editors for their insightful and helpful comments on the manuscript. Zhenrong Du is thanked for assistance with the FROM-GLC Plus data set. Bailey Anderson and Marcus Buechel are thanked for insightful discussions. LS and SM were supported by a UKRI Future Leaders Fellowship award to LS (MR/V022008/1). LS was additionally supported by NERC (NE/S015728/1), the John Fell Fund, and the Returning Carers' Fund at the University of Oxford. GC and YZ were supported by a UKRI Future Leaders Fellowship award to GC (MR/V022857/1). HM was supported by the NSF Hydrologic Sciences Program, NSF Division of Earth Sciences (Award 2124923). The authors wish to thank the Centre for Environmental Data Analysis and the National River Flow Archive for providing the data used for the analyses. One of the authors is Associate Editor at Earth's Future.
Funders | Funder number |
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Neurosciences Foundation | |
UK Research and Innovation | MR/V022008/1 |
UK Research and Innovation | |
Natural Environment Research Council | NE/S015728/1 |
Natural Environment Research Council | |
John Fell Fund, University of Oxford | MR/V022857/1 |
John Fell Fund, University of Oxford | |
Earth Sciences Division | 2124923 |
Earth Sciences Division |
Keywords
- afforestation
- climate impacts
- drivers
- floods
- groundwater
- machine learning
- urbanization