Observed divergence in the trends of temperature controls on Chinese ecosystem water use efficiency DOI Creative Commons
Xiaojuan Xu, Fusheng Jiao,

Haibo Gong

et al.

Ecological Indicators, Journal Year: 2023, Volume and Issue: 157, P. 111241 - 111241

Published: Nov. 18, 2023

How have plants addressed the trade-off between carbon gain and water loss in this warming world? Ecosystem water-use efficiency (WUE), defined as ratio of gross primary productivity (GPP) to evapotranspiration (ET), is a key indicator carbon–water relationship. WUE expected change due climate change, yet extent which GPP or ET affects changes remains unclear. Moreover, potential time-varying variations responses recent been overlooked. In study, we assessed relative contributions using variance decomposition investigated trends temperature controls on moving windows partial correlation analysis. Our results include: (1) national multi-year average China increased significantly at rate 0.0174 gC·kg-1H2O·a-1, with 86.88% study area exhibiting trends. Forest ecosystems, except for ENF, had relatively higher WUE, highest value was observed deciduous broad-leaf forest (3.77 gC·kg-1H2O), while grassland ecosystems lowest only 1.05 gC·kg-1H2O. (2) GPP, rather than ET, drove most areas, Northeast Southwest China. always contributed more each land cover type. forests, other types. (3) exhibited positive correlations radiation, 63.73% (14.83% significant level p < 0.05 same below) radiation 74.12% (20.71% significant) area. The control precipitation complex, 54.29% (9.38% significant). (4) coefficients an increasing trend 52.26% areas decreasing 47.74% notable divergence spatial distribution WUE. Warming projected enhance ecosystem functioning northern regions but may adverse effects south. These findings shed light dynamic response ongoing warming, would improve our understanding terrestrial cycle.

Language: Английский

Prediction of global water use efficiency and its response to vapor pressure deficit and soil moisture coupling in the 21st century DOI
Tiantian Chen, Li Peng, Yuxi Wang

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 635, P. 131203 - 131203

Published: April 16, 2024

Language: Английский

Citations

14
Spatial and temporal variations of ecosystem water use efficiency and its response to soil moisture drought in a water-limited watershed of northern China DOI
Ting Zhang,

Wenjie Quan,

Jiyang Tian

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 355, P. 120251 - 120251

Published: Feb. 28, 2024

Language: Английский

Citations

8
Nonlinear influences of climatic, vegetative, geographic and soil factors on soil water use efficiency of global karst landscapes: Insights from explainable machine learning DOI
Chao Li, Shiqiang Zhang, Yongjian Ding

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 965, P. 178672 - 178672

Published: Feb. 1, 2025

Language: Английский

Citations

0
Impacts of the Grain for Green Project on Soil Moisture in the Yellow River Basin, China DOI

Zhen‐Jiang Zhao,

Haijun Huang, Jie Wang

et al.

Hydrological Processes, Journal Year: 2025, Volume and Issue: 39(3)

Published: March 1, 2025

ABSTRACT The Grain for Green Project is a significant environmental protection initiative in China designed to maintain ecological benefits through large‐scale vegetation restoration. Such projects primarily affect cover, which turn influences soil moisture dynamics. This study investigates the changes surface and total Yellow River Basin before after implementation of Project, thereby assessing its impact on conditions. By calculating trends NDVI periods 1982–1998 1999–2014, effects were evaluated. We employed partial correlation analysis obtain relationship between NDVI. Additionally, an Long Short‐Term Memory (LSTM) network model SHapley Additive exPlanations (SHAP) values used identify key factors influencing moisture. results indicated that areas with increase are mainly concentrated middle reaches Basin. Moreover, has resulted decreasing trend across more than 60% Basin, average reduction 0.016 m 3 ·m −3 ·decade −1 0.021 Furthermore, precipitation was found have greatest moisture, while temperature had most influence provides valuable insights into effectiveness promoting growth conservation encourages sustainable management land water resources beyond.

Language: Английский

Citations

0
Resilience Response of China's Terrestrial Ecosystem Gross Primary Productivity under Environmental Stress DOI
Youzhu Zhao, Luchen Wang, Qiuxiang Jiang

et al.

Environmental Research, Journal Year: 2025, Volume and Issue: unknown, P. 121540 - 121540

Published: April 1, 2025

Language: Английский

Citations

0
Machine learning algorithms realized soil stoichiometry prediction and its driver identification in intensive agroecosystems across a north-south transect of eastern China DOI
Xintong Xu, Chao Xiao, Yubing Dong

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 906, P. 167488 - 167488

Published: Sept. 30, 2023

Language: Английский

Citations

8
Dynamic response of vegetation to meteorological drought and driving mechanisms in Mongolian Plateau DOI

Shuhui Gao,

Shengzhi Huang,

Vijay P. Singh

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132541 - 132541

Published: Dec. 1, 2024

Language: Английский

Citations

3
Future response of ecosystem water use efficiency to CO2 effects in the Yellow River Basin, China DOI Creative Commons
Siwei Chen, Yuxue Guo, Yue‐Ping Xu

et al.

Hydrology and earth system sciences, Journal Year: 2024, Volume and Issue: 28(22), P. 4989 - 5009

Published: Nov. 25, 2024

Abstract. Ecosystem water use efficiency (WUE) is pivotal for understanding carbon–water cycle interplay. Current research seldom addresses how WUE might change under future elevated CO2 concentrations, limiting our of regional ecohydrological effects. We present a land–atmosphere attribution framework in the Yellow River basin (YRB), integrating Budyko model with global climate models (GCMs) to quantify impacts and underlying surface changes induced by CO2. Additionally, we further quantitatively decoupled direct secondary radiative biogeochemical Attribution results indicate that YRB projected increase 0.36–0.84 gC kg−1H2O future, being predominant factor (relative contribution rate 77.9 %–101.4 %). However, as carbon emissions intensify, relative importance land becomes increasingly important (respective rates −1.4 %, 14.9 16.9 22.1 % SSP126, SSP245, SSP370, SSP585). Typically, considered reflection an ecosystem's adaptability stress. Thus, analyzed response different scenarios periods various drought conditions. The show distinct “two-stage” pattern YRB, where increases moderate–severe conditions but decreases intensifies across most areas. Furthermore, GCM projections suggest plant stress may improve higher-carbon-emission scenarios. Our findings enhance processes provide insights predictions on terrestrial ecosystems.

Language: Английский

Citations

2
Simulation and Driving Factor Analysis of Satellite-Observed Terrestrial Water Storage Anomaly in the Pearl River Basin Using Deep Learning DOI Creative Commons
Haijun Huang,

Guanbin Feng,

Y. Cao

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(16), P. 3983 - 3983

Published: Aug. 11, 2023

Accurate estimation of terrestrial water storage (TWS) and understanding its driving factors are crucial for effective hydrological assessment resource management. The launches the Gravity Recovery Climate Experiment (GRACE) satellites their successor, GRACE Follow-On (GRACE-FO), combined with deep learning algorithms, have opened new avenues such investigations. In this study, we employed a long short-term memory (LSTM) neural network model to simulate TWS anomaly (TWSA) in Pearl River Basin (PRB) from 2003 2020, using precipitation, temperature, runoff, evapotranspiration, leaf area index (LAI) data. performance LSTM was rigorously evaluated, achieving high average correlation coefficient (r) 0.967 an Nash–Sutcliffe efficiency (NSE) 0.912 on testing set. To unravel relative importance each factor assess impact different lead times, SHapley Additive exPlanations (SHAP) method. Our results revealed that precipitation exerted most significant influence TWSA PRB, one-month time exhibiting greatest impact. Evapotranspiration, LAI also played important roles, interactive effects among these factors. Moreover, observed accumulation effect evapotranspiration TWSA, particularly shorter times. Overall, SHAP method provides alternative approach quantitative analysis natural at basin scale, shedding light dominant influences PRB. combination satellite observations techniques holds promise advancing our dynamics enhancing management strategies.

Language: Английский

Citations

6
Catchment Attributes Influencing Performance of Global Streamflow Reanalysis DOI Open Access

Xinjun Ding

Water, Journal Year: 2024, Volume and Issue: 16(24), P. 3582 - 3582

Published: Dec. 12, 2024

Performance plays a critical role in the practical use of global streamflow reanalysis. This paper presents combined random forest and Shapley additive explanation to examine mechanism by which catchment attributes influence accuracy estimates reanalysis products. In particular, generated Global Flood Awareness System is validated observations provided Catchment Attributes MEteorology for Large-sample Studies dataset. Results highlight that with regard Kling–Gupta efficiency, surpasses mean flow benchmarks 93% catchments across continental United States. addition, twelve are identified as major controlling factors spatial patterns categorized into five clusters. Topographic characteristics climatic indices also observed exhibit pronounced influences. Streamflow performs better low precipitation seasonality steep slopes or wet frequency events. The partial dependence plot most key consistent four seasons but slopes’ magnitudes vary. Seasonal snow exhibits positive effects during melting from March August negative associated snowpack accumulation September February. Catchments very (values less than −1) show strong seasonal variation estimations, June November December May. Overall, this provides useful information applications lays groundwork further research understanding attributes.

Language: Английский

Citations

0