Analysis of the Dominant Factors and Interannual Variability Sensitivity of Extreme Changes in Water Use Efficiency in China from 2001 to 2020

Ecosystem water use efficiency (WUE) is a key indicator of the coupling between carbon and water cycles. With the increasing frequency of extreme climate events, WUE may also show trends of extremization. Understanding the dominant drivers behind extreme WUE variations is crucial for assessing the impact of climate variability on WUE. We investigate the main drivers and regional sensitivity of extreme WUE variations across seven geographical regions in China. The results reveal that extreme WUE variations are collectively influenced by gross primary productivity (GPP) and evapotranspiration (ET) (43.72%). GPP controls extreme WUE variations in 36.00% of the areas, while ET controls 20.17%. Furthermore, as the climate shifts from arid to humid regions, the area where GPP dominates extreme WUE variations increases, while the area dominated by ET decreases, suggesting a relationship with precipitation. Ridge regression analysis shows that vapor pressure deficit (VPD) is the primary driver of interannual WUE variation in China, with an average relative contribution of 38.64% and an absolute contribution of 0.025 gC·m−2·mm−1·a−1. We studied the changes in WUE and its driving mechanisms during extreme disaster events, providing a perspective focused on extreme conditions. In the future, these results may help regulate the carbon–water cycle in different regions, such as by guiding vegetation planting and land use planning based on the spatial characteristics of the dominant factors influencing extreme WUE variations to improve vegetation WUE.

Authors

Institutions

Publication Details

Journal
Forests
Published
2025-03-04
DOI
https://doi.org/10.3390/f16030454
Primary Topic
Hydrology and Drought Analysis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of the Dominant Factors and Interannual Variability Sensitivity of Extreme Changes in Water Use Efficiency in China from 2001 to 2020

Wenli Lai, Feixiang Zhang, Yichen Zhang, Wenjie Liu et al.
Forests
Hydrology and Drought Analysis
article

Analysis of the Dominant Factors and Interannual Variability Sensitivity of Extreme Changes in Water Use Efficiency in China from 2001 to 2020

Wenli Lai, Feixiang Zhang, Yichen Zhang, Wenjie Liu, Jie Zhang, Shuqi Zhang, Shubing Hou
article en

Abstract

Ecosystem water use efficiency (WUE) is a key indicator of the coupling between carbon and water cycles. With the increasing frequency of extreme climate events, WUE may also show trends of extremization. Understanding the dominant drivers behind extreme WUE variations is crucial for assessing the impact of climate variability on WUE. We investigate the main drivers and regional sensitivity of extreme WUE variations across seven geographical regions in China. The results reveal that extreme WUE variations are collectively influenced by gross primary productivity (GPP) and evapotranspiration (ET) (43.72%). GPP controls extreme WUE variations in 36.00% of the areas, while ET controls 20.17%. Furthermore, as the climate shifts from arid to humid regions, the area where GPP dominates extreme WUE variations increases, while the area dominated by ET decreases, suggesting a relationship with precipitation. Ridge regression analysis shows that vapor pressure deficit (VPD) is the primary driver of interannual WUE variation in China, with an average relative contribution of 38.64% and an absolute contribution of 0.025 gC·m−2·mm−1·a−1. We studied the changes in WUE and its driving mechanisms during extreme disaster events, providing a perspective focused on extreme conditions. In the future, these results may help regulate the carbon–water cycle in different regions, such as by guiding vegetation planting and land use planning based on the spatial characteristics of the dominant factors influencing extreme WUE variations to improve vegetation WUE.

ForestsVol. 16(3)
Hainan Normal University (CN), Hainan University (CN), Hainan Meteorology Administration (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 97%
Hydrology and Drought Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.