Climate‐driven vegetation greening further reduces water availability in drylands

Abstract

Climate change alters surface water availability (WA; precipitation minus evapotranspiration, P − ET) and consequently impacts agricultural production and societal water needs, leading to increasing concerns on the sustainability of water use. Although the direct effects of climate change on WA have long been recognized and assessed, indirect climate effects occurring through adjustments in terrestrial vegetation are more subtle and not yet fully quantified. To address this knowledge gap, here we investigate the interplay between climate‐induced changes in leaf area index (LAI) and ET and quantify its ultimate effect on WA during the period 1982–2016 at the global scale, using an ensemble of data‐driven products and land surface models. We show that ~44% of the global vegetated land has experienced a significant increase in growing season‐averaged LAI and climate change explains 33.5% of this greening signal. Such climate‐induced greening has enhanced ET of 0.051 ± 0.067 mm year−2 (mean ± SD), further amplifying the ongoing increase in ET directly driven by variations in climatic factors over 36.8% of the globe, and thus exacerbating the decline in WA prominently in drylands. These findings highlight the indirect impact of positive feedbacks in the land–climate system on the decline of WA, and call for an in‐depth evaluation of these phenomena in the design of local mitigation and adaptation plans.

Timing and magnitude of drought impacts on carbon uptake across a grassland biome

“…For example, rising atmospheric CO 2 could ameliorate drought impacts on C uptake by reducing plant water demand (Swann et al, 2016). Simultaneously, ongoing and widespread greening across the Northern Great Plans, often in the most arid locations (Brookshire et al, 2020), could exacerbate drought impacts on C uptake through increased plant water use associated with higher leaf area (Chen et al, 2022). The pace of these changes and the complexity of their interactions poses a fundamental challenge to our ability to project the ecological impacts of climate change (Adler et al, 2020; Charney et al, 2016; Felton et al, 2022).…”

Locating Hydrologically Unsustainable Areas for Supporting Ecological Restoration in China's Drylands

“…Regarding the self‐sufficiency of water resources at the local scale, we developed WSS index to determine whether the water demand (ET) of dryland ecosystems is met by the water supply (precipitation): $$$\begin{array}{c}\text{WSS}=\frac{\text{Water}\,\text{availability}}{P}=\frac{P-ET}{P}\end{array}$$$ where P is precipitation and ET is actual evapotranspiration. The numerator of this indicator is the water availability calculated as the difference between the ecosystem water supply ( P ) and demand ( ET ), and the denominator is the water supply (P. Chen et al., 2023; Z. Chen et al., 2023; F. Zhao et al., 2021). As a water balance indicator, the relative index of the WSS enabled us to compare the water availability across different precipitation levels.…”

“…These ERs have obvious greening characteristics (C. Chen et al., 2019; C. Zhang et al., 2023), and their implementation has accelerated and even dominated greening in China's drylands (Z. Li et al., 2022; Song et al., 2022). However, the process of greening in drylands has the potential to impact water availability (Z. Chen et al., 2023), even leading to a shift in the ecosystem's self‐sufficiency in water resources—from a state of natural balance to an unsustainable condition, especially for prevalent ER‐induced greening (M. Zhao et al., 2021). This hydrologically unsustainable phenomena are symbolized by vegetation exceeding the local carrying capacity (H. Liu et al., 2022).…”

Seasonal variation of chlorophyll concentration in the Yongding New River Estuary from 2017 to 2021

“…Firstly, this paper normalized all data sources to eliminate the influence of different units on the analysis results [4] . Secondly, multiple linear regression method [5] was carried out between each factor and chlorophyll concentration, and the degree of influence of each factor was determined based on the absolute value of the regression coefficient. The variable with the largest absolute value in the regression coefficient is considered as the primary influencing factor.…”