Spatiotemporal Variation of Water Use Efficiency and Its Responses to Climate Change in the Yellow River Basin from 1982 to 2018
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| Publikašuvnnas: | Remote Sensing vol. 17, no. 2 (2025), p. 316 |
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| Eará dahkkit: | , , , , , , , |
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MDPI AG
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| 001 | 3159539036 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17020316 |2 doi | |
| 035 | |a 3159539036 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Li, Jie |u National Ecological Science Data Center Guangdong Branch, South China Botanical Garden, Chinese Academy of Sciences, 723 Xingke Road, Guangzhou 510650, China<email>lhwang@scbg.ac.cn</email> (L.W.); South China National Botanical Garden, Guangzhou 510650, China | |
| 245 | 1 | |a Spatiotemporal Variation of Water Use Efficiency and Its Responses to Climate Change in the Yellow River Basin from 1982 to 2018 | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The ecosystem water use efficiency (WUE) plays a critical role in many aspects of the global carbon cycle, water management, and ecological services. However, the response mechanisms and driving processes of WUE need to be further studied. This research was conducted based on Gross Primary Productivity (GPP), Evapotranspiration (ET), meteorological station data, and land use/cover data, and the methods of Ensemble Empirical Mode Decomposition (EEMD), trend variation analysis, the Mann–Kendall Significant Test (M-K test), and Partial Correlation Analysis (PCA) methods. Our study revealed the spatio-temporal trend of WUE and its influencing mechanism in the Yellow River Basin (YRB) and compared the differences in WUE change before and after the implementation of the Returned Farmland to Forestry and Grassland Project in 2000. The results show that (1) the WUE of the YRB showed a significant increase trend at a rate of 0.56 × 10−2 gC·kg−1·H2O·a−1 (p < 0.05) from 1982 to 2018. The area showing a significant increase in WUE (47.07%, Slope > 0, p < 0.05) was higher than the area with a significant decrease (14.64%, Slope < 0, p < 0.05). The region of significant increase in WUE in 2000–2018 (45.35%, Slope > 0, p < 0.05) was higher than that of 1982–2000 (8.23%, Slope > 0, p < 0.05), which was 37.12% higher in comparison. (2) Forest WUE (1.267 gC·kg−1·H2O) > Cropland WUE (0.972 gC·kg−1·H2O) > Grassland WUE (0.805 gC·kg−1·H2O) under different land cover types. Forest ecosystem WUE has the highest rate of increase (0.79 × 10−2 gC·kg−1·H2O·a−1) from 2000 to 2018. Forest ecosystem WUE increased by 0.082 gC·kg−1·H2O after 2000. (3) precipitation (37.98%, R > 0, p < 0.05) and SM (10.30%, R > 0, p < 0.05) are the main climatic factors affecting WUE in the YRB. A total of 70.39% of the WUE exhibited an increasing trend, which is mainly attributed to the simultaneous increase in GPP and ET, and the rate of increasing GPP is higher than the rate of increasing ET. This study could provide a scientific reference for policy decision-making on the terrestrial carbon cycle and biodiversity conservation. | |
| 651 | 4 | |a China | |
| 651 | 4 | |a Loess Plateau | |
| 651 | 4 | |a Yellow River | |
| 653 | |a Carbon cycle | ||
| 653 | |a River basins | ||
| 653 | |a Agricultural land | ||
| 653 | |a Datasets | ||
| 653 | |a Water shortages | ||
| 653 | |a Grasslands | ||
| 653 | |a Correlation analysis | ||
| 653 | |a Evapotranspiration | ||
| 653 | |a Rivers | ||
| 653 | |a Land use | ||
| 653 | |a Climate change | ||
| 653 | |a Efficiency | ||
| 653 | |a Precipitation | ||
| 653 | |a Remote sensing | ||
| 653 | |a Forest ecosystems | ||
| 653 | |a Carbon | ||
| 653 | |a Water management | ||
| 653 | |a Terrestrial ecosystems | ||
| 653 | |a Methods | ||
| 653 | |a Ecosystems | ||
| 653 | |a Decision making | ||
| 653 | |a Water use | ||
| 653 | |a Agricultural production | ||
| 653 | |a Trends | ||
| 653 | |a Water use efficiency | ||
| 653 | |a Water resources management | ||
| 653 | |a Time series | ||
| 653 | |a Land cover | ||
| 653 | |a Computer centers | ||
| 653 | |a Vegetation | ||
| 653 | |a Weather stations | ||
| 653 | |a River ecology | ||
| 653 | |a Biodiversity | ||
| 653 | |a Information sources | ||
| 653 | |a Forestry | ||
| 700 | 1 | |a Qin, Fen |u College of Geography and Environmental Science, Henan University, Kaifeng 475004, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China; Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng 475004, China | |
| 700 | 1 | |a Wang, Yingping |u CSIRO Oceans and Atmosphere, Aspendale, VIC 3195, Australia; <email>yingping.wang@csiro.au</email> | |
| 700 | 1 | |a Zhao, Xiuyan |u School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China | |
| 700 | 1 | |a Yu, Mengxiao |u National Ecological Science Data Center Guangdong Branch, South China Botanical Garden, Chinese Academy of Sciences, 723 Xingke Road, Guangzhou 510650, China<email>lhwang@scbg.ac.cn</email> (L.W.); South China National Botanical Garden, Guangzhou 510650, China | |
| 700 | 1 | |a Chen, Songjia |u National Ecological Science Data Center Guangdong Branch, South China Botanical Garden, Chinese Academy of Sciences, 723 Xingke Road, Guangzhou 510650, China<email>lhwang@scbg.ac.cn</email> (L.W.); South China National Botanical Garden, Guangzhou 510650, China | |
| 700 | 1 | |a Jiang, Jun |u National Ecological Science Data Center Guangdong Branch, South China Botanical Garden, Chinese Academy of Sciences, 723 Xingke Road, Guangzhou 510650, China<email>lhwang@scbg.ac.cn</email> (L.W.); South China National Botanical Garden, Guangzhou 510650, China | |
| 700 | 1 | |a Wang, Linhua |u National Ecological Science Data Center Guangdong Branch, South China Botanical Garden, Chinese Academy of Sciences, 723 Xingke Road, Guangzhou 510650, China<email>lhwang@scbg.ac.cn</email> (L.W.); South China National Botanical Garden, Guangzhou 510650, China | |
| 700 | 1 | |a Yan, Junhua |u National Ecological Science Data Center Guangdong Branch, South China Botanical Garden, Chinese Academy of Sciences, 723 Xingke Road, Guangzhou 510650, China<email>lhwang@scbg.ac.cn</email> (L.W.); South China National Botanical Garden, Guangzhou 510650, China | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 2 (2025), p. 316 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159539036/abstract/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3159539036/fulltextwithgraphics/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159539036/fulltextPDF/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |