环境科学
干燥
生态系统
蒸腾作用
干旱
焊剂(冶金)
降水
含水量
干旱指数
蒸散量
土壤质地
引爆点(物理)
气候变化
灌溉
水文学(农业)
大气科学
土壤水分
土壤科学
地理
农学
生态学
气象学
地质学
化学
生物
生物化学
有机化学
光合作用
岩土工程
免疫学
工程类
电气工程
作者
Zheng Fu,Philippe Ciais
标识
DOI:10.5194/egusphere-egu23-4507
摘要
During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits transpiration, defining a critical SM threshold (θcrit). Better quantification of θcrit is needed for understanding recent dryness trends and 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 first observation-based global map of θcrit. We find an average global θcrit of 0.19 m3/m3, with a large gradient ranging from 0.12 m3/m3 in arid ecosystems to 0.26 m3/m3 in humid ecosystems. Compared to observations, θcrit simulated by Earth System Models is underestimated in wet areas and overestimated in dry areas, leading to an erroneous spatially uniform pattern. 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 key implications for improving the representation of water stress in models and identifying SM tipping points that could result in impaired ecosystem functioning during prolonged dry-downs.
科研通智能强力驱动
Strongly Powered by AbleSci AI