Spatiotemporal Changes in Water-Use Efficiency of China’s Terrestrial Ecosystems During 2001–2020 and the Driving Factors

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(1), P. 136 - 136

Published: Jan. 3, 2025

Water-use efficiency (WUE) is an important indicator for understanding the coupling of carbon and water cycles in terrestrial ecosystems. It provides a comprehensive reflection ecosystems’ responses to various environmental factors, making it essential how ecosystems adapt complex changes. Using satellite-based estimates gross primary productivity (GPP) evapotranspiration (ET), our study investigated spatiotemporal variations WUE across China’s from 2001 2020. We employed geographic detector method, partial correlation analysis, ridge regression assess contributions different factors (temperature, precipitation, solar radiation, vapor pressure deficit, leaf area index, soil moisture) GPP, ET, WUE. The results show significant increases during period, with increase rates 6.70 g C m−2 yr−1, 2.68 kg H2O 0.007 respectively. More than three-quarters regions trends (p < 0.05) displayed notable 0.05). Among all driving index (LAI) made largest contribution WUE, particularly warm temperate semi-humid regions. Precipitation radiation were climatic influences arid northern China humid southwestern China,

Language: Английский

Spatiotemporal Variation of Water Use Efficiency and Its Responses to Climate Change in the Yellow River Basin from 1982 to 2018

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(2), P. 316 - 316

Published: Jan. 17, 2025

The ecosystem water use efficiency (WUE) plays a critical role in many aspects of the global carbon cycle, management, and ecological services. However, response mechanisms driving processes WUE need to be further studied. This research was conducted based on Gross Primary Productivity (GPP), Evapotranspiration (ET), meteorological station data, land use/cover methods Ensemble Empirical Mode Decomposition (EEMD), trend variation analysis, Mann–Kendall Significant Test (M-K test), Partial Correlation Analysis (PCA) methods. Our study revealed spatio-temporal its influencing mechanism Yellow River Basin (YRB) compared differences change before after implementation Returned Farmland Forestry Grassland Project 2000. results show that (1) YRB showed significant increase at rate 0.56 × 10−2 gC·kg−1·H2O·a−1 (p < 0.05) from 1982 2018. area showing (47.07%, Slope > 0, p higher than with decrease (14.64%, 0.05). region 2000–2018 (45.35%, 1982–2000 (8.23%, 0.05), which 37.12% comparison. (2) Forest (1.267 gC·kg−1·H2O) Cropland (0.972 (0.805 under different cover types. has highest (0.79 gC·kg−1·H2O·a−1) 2000 increased by 0.082 gC·kg−1·H2O (3) precipitation (37.98%, R SM (10.30%, are main climatic factors affecting YRB. A total 70.39% exhibited an increasing trend, is mainly attributed simultaneous GPP ET, ET. could provide scientific reference for policy decision-making terrestrial cycle biodiversity conservation.

Language: Английский

Quantitative Analysis of Vegetation Dynamics and Driving Factors in the Shendong Mining Area under the Background of Coal Mining

Forests, Journal Year: 2024, Volume and Issue: 15(7), P. 1207 - 1207

Published: July 12, 2024

Elucidating the response mechanism of vegetation change trends is great value for environmental resource management, especially in coal mining areas where climate fluctuations and human activities are intense. Taking Shendong area as an example, based on Google Earth Engine cloud platform, this study used kernel Normalized Vegetation Index (kNDVI) to spatiotemporal characteristics cover during 1994–2022. Then, it carried out attribution analysis through partial derivative method explore driving behind greening. The results showed that (1) growth rate from 1994 2022 was 0.0052/a. with upward trend kNDVI accounted 94.11% total area. greening effect obvious, would continue rise. (2) Under scenario regional warming humidifying, responds slightly differently different climatic factors, positively correlated temperature precipitation 85.20% average contribution precipitation, temperature, were 0.00094/a, 0.00066/a, 0.0036/a, respectively. relative rates 69.23% 30.77%, Thus, main factor changing area, secondary factor. (3) dynamic land use presents increase forest under ecological restoration project. can provide a scientific basis future construction help realization green sustainable development goals.

Language: Английский