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Wang L. Characterization of water use efficiency changes in Tibetan Plateau grasslands based on eco-geographic zoning. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:26998-27012. [PMID: 36372860 DOI: 10.1007/s11356-022-23939-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
Water use efficiency (WUE) is an effective indicator to study the coupling of terrestrial carbon and water cycles. The Tibetan Plateau (TP) is the most important ecological security barrier in China, and it is important to understand the characteristics of WUE and the change mechanism to study the carbon and water cycles of plateau ecosystems and the rational use of water resources. This study analyzes the spatial and temporal characteristics of WUE on the TP and the influence of climate factors on WUE based on the gross primary productivity (GPP) and evapotranspiration (ET) data from GLASS. The results show that from 1985 to 2018, the WUE of the TP is on the rise under the combined effect of GPP and ET; the regions with higher mean WUE values are the southeastern and eastern parts of the plateau, and the low value areas are the central and northwestern parts of the plateau. Compared with precipitation, WUE is influenced by temperature over a larger area. The correlations between precipitation and temperature and WUE in different eco-geographic regions are complex, and there is a threshold effect on the correlation between WUE and temperature and precipitation. Temperature is the main driver of WUE changes in HIIA and HIB1 regions, while precipitation has a greater impact on WUE changes in HIIC2, HIIC2, HIC2, HIID3, and HIIC regions. Precipitation, temperature, and elevation are the main factors explaining the variation of WUE in the TP. According to the risk detector, it can be determined that grassland vegetation in warm and humid steep areas of low and medium elevations is more able to maintain efficient use of water. Meanwhile, grasslands located in the shade of northern slopes have weaker transpiration, which is conducive to vegetation accumulation of growth water, and thus can ensure higher WUE. The related study can provide a reference for the response of vegetation WUE to global changes in key climatic regions.
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Affiliation(s)
- Licheng Wang
- Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, 712100, Shaanxi, China.
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Sun H, Bai Y, Lu M, Wang J, Tuo Y, Yan D, Zhang W. Drivers of the water use efficiency changes in China during 1982-2015. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 799:149145. [PMID: 34365270 DOI: 10.1016/j.scitotenv.2021.149145] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 07/01/2021] [Accepted: 07/15/2021] [Indexed: 06/13/2023]
Abstract
This study investigates the drivers of water use efficiency (WUE), a key metric of water resources management, and its changes over eight regions across China from 1982 to 2015 based on gross primary production (GPP) and actual evapotranspiration (AET) datasets. The order of seasonal change of WUE from large to small is autumn, summer, spring and winter. The drivers include seven variables, air temperature, specific humidity, precipitation, short-wave radiation, Normalized Difference Vegetation Index (NDVI), soil moisture and CO2. Our analysis suggests that the sensitivity of annual average NDVI to WUE changes was high nationwide, but there were some differences in seasonal scales. The annual average contribution of air temperature and CO2 affecting WUE change was relatively high in China's largest area (SW, SE, E, NP). Other influencing factors were only relatively high in the local area. Seasonally, NDVI is the driving factor with the highest contribution rate in summer and autumn for NC and NW region. The seasonal contribution rates of driving factors in other regions are significantly different. For the study period (1982-2015), the shrubland ecosystem had the highest annual WUE followed by forest and cropland. The WUE of the farmland ecosystem was higher than that of the grassland ecosystem in most areas.
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Affiliation(s)
- Huaiwei Sun
- School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Yiwen Bai
- School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Mengge Lu
- School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 430074 Wuhan, China.
| | - Jingfeng Wang
- School of Civil and Environmental Engineering, Georgia Institute of Technology, 30318 Atlanta, USA
| | - Ye Tuo
- Chair of Hydrology and River Basin Management, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
| | - Dong Yan
- School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 430074 Wuhan, China.
| | - Wenxin Zhang
- Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 22362 Lund, Sweden
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Lu N, Niu J, Kang S, Singh SK, Du T. A hybrid PCA-SEM-ANN model for the prediction of water use efficiency. Ecol Modell 2021. [DOI: 10.1016/j.ecolmodel.2021.109754] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Spatiotemporal Analysis of Evapotranspiration and Effects of Water and Heat on Water Use Efficiency. WATER 2021. [DOI: 10.3390/w13213019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Water Use Efficiency (WUE) is an important indicator of the carbon cycle in the hydrological and ecological system. It is of great significance to study the response of different hydrological processes to climate and to understand ecosystem carbon sink. However, little is known about the effects and mechanisms of precipitation and temperature on the WUE of different hydrological processes. Thus, three kinds of WUEs (GPP/E (eWUE), GPP/Et (tWUE), and GPP/P (pWUE)) are defined for three different hydrological indicators in semi-arid areas in this study in order to reveal the variation pattern of WUEs based on hydrological indicators and their response to climate. We found that in the past 15 years, the seasonal fluctuation of evapotranspiration in arid areas was large, and the spatial difference of WUE of different hydrological processes was obvious. In semi-arid areas, temperature had a significant effect on WUE (about 68–81%). However, precipitation had a lag effect on WUEs, and the negative impact of precipitation has a great influence (about 84–100%). Secondly, the threshold values of precipitation to WUEs (200 or 300 mm) and temperature to WUEs (2 or 7 °C) are also different from previous studies. This study advances our understanding of the influence of different hydrological processes on ecosystem carbon and climate.
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Determining the Contributions of Vegetation and Climate Change to Ecosystem WUE Variation over the Last Two Decades on the Loess Plateau, China. FORESTS 2021. [DOI: 10.3390/f12111442] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Exploring the variations in the water use efficiency (WUE) is helpful in gaining an in-depth understanding of the regional carbon and water cycles on the Chinese Loess Plateau (CLP). Here, we employed the spatial variations in the WUE and the quantitative contributions of the influencing factors, including the precipitation (P), temperature (Temp), vapor pressure deficit (VPD), sunshine duration (SD), and leaf area index (LAI), with the drought index varying over the last two decades. Results showed that the multiyear average WUE decreased significantly as the drought index increased for all of the vegetation types. Per-pixel interannual variability of WUE trend was 0.024 gC·m−2·mm−1·year−1. As the drought index increased, the WUE initially increased and then decreased for the forests, grassland, and shrubland, and their peaks occurred at drought index values of 2.60–3.10. Among the influencing factors, the WUE was predominantly controlled by the LAI, with an impact and relative contribution of 0.014 gC·m−2·mm−1·year−1 and 58.3%, respectively. The P and SD contributed the least to the trend in WUE, and impact and relative contribution of both were 0.001 gC·m−2·mm−1·year−1 and 4.17%. Our study also demonstrated that the LAI was the dominant factor affecting the WUE trends for grassland and the Yan River due to the structural parameters and geographical location. In addition, the impact and relative contribution of the residual factors on the WUE trend were 0.004 gC·m−2·mm−1·year−1 and 16.7%. Our findings suggested that comprehensive effects such as micro-geomorphic changes and nitrogen deposition could not be ignored except for vegetation and climate change. This study will clarify the spatial and temporal evolution of WUE and its influence mechanism.
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Effects of Climate Factors and Human Activities on the Ecosystem Water Use Efficiency throughout Northern China. REMOTE SENSING 2019. [DOI: 10.3390/rs11232766] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Global climate changes have increased the imbalance of water resources, especially in northern China, which comprises typical arid and semiarid regions. Large-scale afforestation has been implemented over the past three decades in northern China. The ecosystem water use efficiency (WUE) connects the carbon cycle and water cycle of the terrestrial ecosystems and is defined as the ratio of the gross primary productivity (GPP) to the evapotranspiration. However, there are still an insufficient number of studies on the impact of the afforestation on the WUE. In this study, we applied the random forest (RF) model to explore the impacts of climate and nonclimate factors on the WUE in northern China. The results showed that in areas with high precipitation, the forests had the highest WUE, while in the arid areas, the croplands had the highest WUE. Of the total area, 44.34% showed a significant increase, and 5.89% showed a significant decrease in the WUE from 1982–2015 in northern China. The main driving factors for the changes in the WUE were climate factors, including the precipitation, temperature and solar radiation, which contributed to approximately 84% of the WUE trends, while human activities, such as afforestation, contributed to approximately 16% of the WUE trends. Overall, although the climate had a larger impact on the WUE dynamics than the human activities, our results suggested that the impacts of the afforestation programs on forest carbon and water cycles should be considered in the context of climate change.
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Guo L, Shan N, Zhang Y, Sun F, Liu W, Shi Z, Zhang Q. Separating the effects of climate change and human activity on water use efficiency over the Beijing-Tianjin Sand Source Region of China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 690:584-595. [PMID: 31301499 DOI: 10.1016/j.scitotenv.2019.07.067] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 07/04/2019] [Accepted: 07/04/2019] [Indexed: 06/10/2023]
Abstract
Water use efficiency (WUE) is a central parameter for linking carbon and water exchange processes in terrestrial ecosystems. The Beijing-Tianjin Sand Source Region (BTSSR) in China has undergone tremendous vegetation restoration and climate change. Understanding the WUE responses to climate change and human activity and their relative contributions to the trends and inter-annual variations (IAVs) in WUE is necessary to improve water use efficiency and strengthen water resource management. The evapotranspiration (ET) dataset based on the model tree ensemble (MTE) algorithm which was a machine learning approach using flux-tower ET measurements and the GLASS GPP dataset, as well as the variance decomposition method, were used to analyze the spatiotemporal changes in water use efficiency and inherent water use efficiency (IWUE) and the impacts of climate change and human activities. The results showed that the annual WUE and IWUE exhibited significantly increase in most regions of the BTSSR. The trend of human activity played the most important role in the increases of WUE and IWUE, with relative contributions of 88.2% and 85.9%, respectively, followed by the IAV of human activity for WUE (6.1%) and the trend of climate change (8.7%) for IWUE. The contribution of IAV to climate change was relatively small. Moreover, WUE and IWUE were all positively correlated with precipitation and temperature in most regions. Our results indicated that ecological restoration projects had significantly improved water use efficiency in BTSSR and may decrease the water burden in the BTSSR.
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Affiliation(s)
- Limai Guo
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, 210023 Nanjing, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
| | - Nan Shan
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, 210023 Nanjing, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
| | - Yongguang Zhang
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, 210023 Nanjing, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
| | - Fubao Sun
- Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China; Center for Water Resources Research, Chinese Academy of Sciences, Beijing 1001018, China
| | - Wenbin Liu
- Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhongjie Shi
- Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China
| | - Qian Zhang
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, 210023 Nanjing, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
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