4.7 Article

Ecosystems in China have become more sensitive to changes in water demand since 2001

Journal

COMMUNICATIONS EARTH & ENVIRONMENT
Volume 4, Issue 1, Pages -

Publisher

SPRINGERNATURE
DOI: 10.1038/s43247-023-01105-9

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Changes in heat and moisture significantly affect ecosystem functioning. Limited knowledge exists regarding the dynamics of ecosystem responses to climate change. This study quantifies long-term ecosystem sensitivity and finds that it exhibits large spatial variability and increases with aridity. There is a positive temporal trend in ecosystem sensitivity, mostly attributed to declining vapor pressure deficit and constrained by solar radiation. Carbon dioxide plays a dual role, promoting growth in moderation but suppressing it in excess.
Changes in heat and moisture significantly co-alter ecosystem functioning. However, knowledge on dynamics of ecosystem responses to climate change is limited. Here, we quantify long-term ecosystem sensitivity based on weighted ratios of vegetation productivity variability and multiple climate variables from satellite observations, greater values of which indicate more yields per hydrothermal condition change. Our results show ecosystem sensitivity exhibits large spatial variability and increases with the aridity index. A positive temporal trend of ecosystem sensitivity is found in 61.28% of the study area from 2001 to 2021, which is largely attributed to declining vapor pressure deficit and constrained by solar radiation. Moreover, carbon dioxide plays a dual role; which in moderation promotes fertilization effects, whereas in excess may suppress vegetation growth by triggering droughts. Our findings highlight moisture stress between land and atmosphere is one of the key prerequisites for ecosystem stability, offsetting part of the negative effects of heat. Terrestrial ecosystems in China have become more sensitive to declining water demand, changes in atmospheric carbon dioxide concentration, and solar radiation over the past two decades, suggests an analysis of vegetation productivity data using explainable machine learning over the period 2001-2021.

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