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A comparative analysis of urban forests for storm-water management

Large-scale urban growth has modified the hydrological cycle of our cities, causing greater and faster runoff. Urban forests (UF), i.e. the stock of trees and shrubs, can substantially reduce runoff; still, how climate, tree functional types influence rainfall partitioning into uptake and runoff is...

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Autores principales: Rahman, Mohammad A., Pawijit, Yanin, Xu, Chao, Moser-Reischl, Astrid, Pretzsch, Hans, Rötzer, Thomas, Pauleit, Stephan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879978/
https://www.ncbi.nlm.nih.gov/pubmed/36702865
http://dx.doi.org/10.1038/s41598-023-28629-6
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author Rahman, Mohammad A.
Pawijit, Yanin
Xu, Chao
Moser-Reischl, Astrid
Pretzsch, Hans
Rötzer, Thomas
Pauleit, Stephan
author_facet Rahman, Mohammad A.
Pawijit, Yanin
Xu, Chao
Moser-Reischl, Astrid
Pretzsch, Hans
Rötzer, Thomas
Pauleit, Stephan
author_sort Rahman, Mohammad A.
collection PubMed
description Large-scale urban growth has modified the hydrological cycle of our cities, causing greater and faster runoff. Urban forests (UF), i.e. the stock of trees and shrubs, can substantially reduce runoff; still, how climate, tree functional types influence rainfall partitioning into uptake and runoff is mostly unknown. We analyzed 92 published studies to investigate: interception (I), transpiration (T), soil infiltration (IR) and the subsequent reduction in runoff. Trees showed the best runoff protection compared to other land uses. Within functional types, conifers provided better protection on an annual scale through higher I and T but broadleaved species provided better IR. Regarding tree traits, leaf area index (LAI) showed a positive influence for both I and T. For every unit of LAI increment, additional 5% rainfall partition through T (3%) and I (2%) can be predicted. Overall, runoff was significantly lower under mixed species stands. Increase of conifer stock to 30% in climate zones with significant winter precipitation and to 20% in areas of no dry season can reduce runoff to an additional 4%. The study presented an overview of UF potential to partition rainfall, which might help to select species and land uses in different climate zones for better storm-water management.
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spelling pubmed-98799782023-01-28 A comparative analysis of urban forests for storm-water management Rahman, Mohammad A. Pawijit, Yanin Xu, Chao Moser-Reischl, Astrid Pretzsch, Hans Rötzer, Thomas Pauleit, Stephan Sci Rep Article Large-scale urban growth has modified the hydrological cycle of our cities, causing greater and faster runoff. Urban forests (UF), i.e. the stock of trees and shrubs, can substantially reduce runoff; still, how climate, tree functional types influence rainfall partitioning into uptake and runoff is mostly unknown. We analyzed 92 published studies to investigate: interception (I), transpiration (T), soil infiltration (IR) and the subsequent reduction in runoff. Trees showed the best runoff protection compared to other land uses. Within functional types, conifers provided better protection on an annual scale through higher I and T but broadleaved species provided better IR. Regarding tree traits, leaf area index (LAI) showed a positive influence for both I and T. For every unit of LAI increment, additional 5% rainfall partition through T (3%) and I (2%) can be predicted. Overall, runoff was significantly lower under mixed species stands. Increase of conifer stock to 30% in climate zones with significant winter precipitation and to 20% in areas of no dry season can reduce runoff to an additional 4%. The study presented an overview of UF potential to partition rainfall, which might help to select species and land uses in different climate zones for better storm-water management. Nature Publishing Group UK 2023-01-26 /pmc/articles/PMC9879978/ /pubmed/36702865 http://dx.doi.org/10.1038/s41598-023-28629-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Rahman, Mohammad A.
Pawijit, Yanin
Xu, Chao
Moser-Reischl, Astrid
Pretzsch, Hans
Rötzer, Thomas
Pauleit, Stephan
A comparative analysis of urban forests for storm-water management
title A comparative analysis of urban forests for storm-water management
title_full A comparative analysis of urban forests for storm-water management
title_fullStr A comparative analysis of urban forests for storm-water management
title_full_unstemmed A comparative analysis of urban forests for storm-water management
title_short A comparative analysis of urban forests for storm-water management
title_sort comparative analysis of urban forests for storm-water management
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879978/
https://www.ncbi.nlm.nih.gov/pubmed/36702865
http://dx.doi.org/10.1038/s41598-023-28629-6
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