Global patterns and drivers of rainfall partitioning by trees and shrubs-Reference-Cited by-同舟云学术

Global patterns and drivers of rainfall partitioning by trees and shrubs

Published:2021-05-06 Issue:14 Volume:27 Page:3350-3357
ISSN:1354-1013
Container-title:Global Change Biology
language:en
Short-container-title:Glob Change Biol

Author:

Yue Kai¹²^ORCID,De Frenne Pieter²^ORCID,Fornara Dario A.³^ORCID,Van Meerbeek Koenraad⁴^ORCID,Li Wang⁵,Peng Xin⁶,Ni Xiangyin¹,Peng Yan⁷,Wu Fuzhong¹^ORCID,Yang Yusheng¹,Peñuelas Josep⁸⁹^ORCID

Affiliation:

1. Key Laboratory for Humid Subtropical Eco‐Geographical Processes of the Ministry of Education School of Geographical Sciences Fujian Normal University Fuzhou China

2. Forest & Nature Lab Ghent University Gontrode Belgium

3. Sustainable Agri‐Food Sciences Division Agri‐Food and Biosciences Institute (AFBI) Belfast UK

4. Department of Earth and Environmental Sciences KU Leuven Leuven Belgium

5. State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of Sciences Beijing China

6. School of Architecture and Civil Engineering Chengdu University Chengdu China

7. Department of Geosciences and Natural Resource Management University of Copenhagen Frederiksberg Denmark

8. CREAF Cerdanyola del Vallès Spain

9. CSICGlobal Ecology UnitCREAF‐CSIC‐UAB Bellaterra Spain

Funder

National Natural Science Foundation of China

H2020 European Research Council

Publisher

Wiley

Subject

General Environmental Science,Ecology,Environmental Chemistry,Global and Planetary Change

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.15644

Reference45 articles.

1. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015

2. Influence of several tree traits on rainfall partitioning in temperate and boreal forests: a review

3. Fitting Linear Mixed-Effects Models Usinglme4

4. Stemflow and throughfall contributions to soil water recharge under trees with differing branch architectures

5. glmulti: AnRPackage for Easy Automated Model Selection with (Generalized) Linear Models

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