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Global critical soil moisture thresholds of plant water stress.
Fu, Zheng; Ciais, Philippe; Wigneron, Jean-Pierre; Gentine, Pierre; Feldman, Andrew F; Makowski, David; Viovy, Nicolas; Kemanian, Armen R; Goll, Daniel S; Stoy, Paul C; Prentice, Iain Colin; Yakir, Dan; Liu, Liyang; Ma, Hongliang; Li, Xiaojun; Huang, Yuanyuan; Yu, Kailiang; Zhu, Peng; Li, Xing; Zhu, Zaichun; Lian, Jinghui; Smith, William K.
Afiliação
  • Fu Z; Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. fuzheng@igsnrr.ac.cn.
  • Ciais P; Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, 91191, France. fuzheng@igsnrr.ac.cn.
  • Wigneron JP; Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, 91191, France.
  • Gentine P; ISPA, INRAE, Université de Bordeaux, Bordeaux Sciences Agro, F-33140, Villenave d'Ornon, France.
  • Feldman AF; Department of Earth and Environmental Engineering, Columbia University, New York, NY, 10027, USA.
  • Makowski D; NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, MD, 20771, USA.
  • Viovy N; Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA.
  • Kemanian AR; Unit Applied Mathematics and Computer Science (UMR MIA-PS) INRAE AgroParisTech Université Paris-Saclay, Palaiseau, 91120, France.
  • Goll DS; Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, 91191, France.
  • Stoy PC; Department of Plant Science, The Pennsylvania State University, 116 Agricultural Science and Industries Building, University Park, PA, 16802, USA.
  • Prentice IC; Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, 91191, France.
  • Yakir D; Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, USA.
  • Liu L; Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, SL5 7PY, UK.
  • Ma H; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Li X; Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel.
  • Huang Y; Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, 91191, France.
  • Yu K; INRAE, Avignon Universit´e, UMR 1114 EMMAH, UMT CAPTE, F-84000, Avignon, France.
  • Zhu P; ISPA, INRAE, Université de Bordeaux, Bordeaux Sciences Agro, F-33140, Villenave d'Ornon, France.
  • Li X; Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
  • Zhu Z; Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, 91191, France.
  • Lian J; Department of Geography, The University of Hong Kong, Hong Kong, SAR, China.
  • Smith WK; Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, South Korea.
Nat Commun ; 15(1): 4826, 2024 Jun 06.
Article em En | MEDLINE | ID: mdl-38844502
ABSTRACT
During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θcrit). Better quantification of θcrit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θcrit. We find an average global θcrit of 0.19 m3/m3, varying from 0.12 m3/m3 in arid ecosystems to 0.26 m3/m3 in humid ecosystems. θcrit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θcrit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θcrit, has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Solo / Água / Ecossistema Idioma: En Revista: Nat Commun Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Solo / Água / Ecossistema Idioma: En Revista: Nat Commun Ano de publicação: 2024 Tipo de documento: Article