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Catchment woodland planting and the benefits to flood reduction

Abstract:
Tree planting is considered to be an effective, low maintenance approach for reducing flood risk in catchments, primarily by interception, increasing water infiltration and surface roughness. Identifying optimal tree planting scenarios for hydrological benefits is an important aspect of developing wider and long-term woodland planting strategies in relation to natural flood management and nature-based solutions interventions. In this study, we apply a spatially distributed hydrological model to explore six different woodland planting scenarios that are limited to between 10% and 2% of the catchment area. We evaluate which approach is the most effective in reducing overland flow, reducing downstream flow volumes and flood peaks and delaying those peaks for three UK catchments: the Lowther, Ennerdale, and Colne valleys. We further explore whether the possibility of identifying target areas for tree planting can be achieved by exploring the physical properties of the areas of woodland that individually achieve the greatest flood attenuation. Large randomly located areas of planting are shown to have the highest overall benefit (10% peak flow reduction); however, planting schemes that focus on river corridors are more effective when considering the hydrological benefit per hectare (~2–3 times more effective). No single underlying characteristic or combination of characteristics are found to define better performing regions of planting, indicating that designing optimized planting schemes will be location specific, and require detailed modeling.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1093/forestry/cpaf055

Authors


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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0003-0103-1805


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Funder identifier:
https://ror.org/02b5d8509


Publisher:
Oxford University Press
Journal:
Forestry More from this journal
Article number:
cpaf055
Publication date:
2025-09-24
Acceptance date:
2025-08-17
DOI:
EISSN:
1464-3626
ISSN:
0015752X and 0015-752X


Language:
English
Keywords:
Source identifiers:
3309596
Deposit date:
2025-09-24
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