Global critical soil moisture thresholds of plant water stress

Zheng Fu (), Philippe Ciais, Jean-Pierre Wigneron, Pierre Gentine, Andrew F. Feldman, David Makowski, Nicolas Viovy, Armen R. Kemanian, Daniel S. Goll, Paul C. Stoy, Iain Colin Prentice, Dan Yakir, Liyang Liu, Hongliang Ma, Xiaojun Li, Yuanyuan Huang, Kailiang Yu, Peng Zhu, Xing Li, Zaichun Zhu, Jinghui Lian and William K. Smith
Additional contact information
Zheng Fu: Chinese Academy of Sciences
Philippe Ciais: Université Paris-Saclay
Jean-Pierre Wigneron: Bordeaux Sciences Agro
Pierre Gentine: Columbia University
Andrew F. Feldman: Biospheric Sciences Laboratory
David Makowski: Unit Applied Mathematics and Computer Science (UMR MIA-PS) INRAE AgroParisTech Université Paris-Saclay
Nicolas Viovy: Université Paris-Saclay
Armen R. Kemanian: 116 Agricultural Science and Industries Building
Daniel S. Goll: Université Paris-Saclay
Paul C. Stoy: University of Wisconsin—Madison
Iain Colin Prentice: Imperial College London
Dan Yakir: Weizmann Institute of Science
Liyang Liu: Université Paris-Saclay
Hongliang Ma: UMT CAPTE
Xiaojun Li: Bordeaux Sciences Agro
Yuanyuan Huang: Chinese Academy of Sciences
Kailiang Yu: Université Paris-Saclay
Peng Zhu: The University of Hong Kong
Xing Li: Seoul National University
Zaichun Zhu: Peking University
Jinghui Lian: Université Paris-Saclay
William K. Smith: University of Arizona

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-49244-7

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