Spatiotemporal changes of vegetation water use efficiency and its influencing factors in Mu Us Sandy Land, China

doi:10.13287/j.1001-9332.202506.007

Abstract

Abstract: Based on the gross primary productivity (GPP) and evapotranspiration (ET) of vegetation, we analyzed the spatiotemporal dynamics of vegetation water use efficiency (WUE) in Mu Us Sandy Land by using linear regression analysis, Hurst index prediction, multiple linear regression, and structural equation modeling, aiming to understand the influence of water and heat conditions on vegetation WUE. The results showed that the average annual values of vegetation GPP and ET in Mu Us Sandy Land from 2001 to 2020 were 264.82 g C·m^-2 and 259.12 mm, respectively, showing a gradually increasing trend with a large fluctuation range. The WUE of vegetation ranged from 0.170 to 0.342 g C·mm^-1·m^-2, with an annual average of 0.279 g C·mm^-1·m^-2, showing a stable decreasing trend. The vegetation WUE exhibited obvious spatial heterogeneity, presenting a changing trend of being lower in the north and higher in the south. The WUE of grassland, cropland, and forest was positively correlated with temperature and solar radiation, and negatively correlated with precipitation and soil moisture. The influence of climatic factors varied across regions of the Mu Us Sandy Land, with temperature being the dominant factor in the eastern region and soil moisture being the dominant factor in the central region. Precipitation, temperature, solar radiation, and soil moisture affected vegetation WUE by altering ET.

Key words: Mu Us Sandy Land, gross primary productivity, water use efficiency, climate change

WANG Zhipeng, SHI Changchun, MA Yali, ZHANG Yan, Yusupukadier ZIMINI, KOU Wei-liang, WEN Zhongming, LIU Yangyang. Spatiotemporal changes of vegetation water use efficiency and its influencing factors in Mu Us Sandy Land, China[J]. Chinese Journal of Applied Ecology, 2025, 36(6): 1731-1739.

References

[1] Beer C, Ciais P, Reichstein M, et al. Temporal and among-site variability of inherent water use efficiency at the ecosystem level. Global Biogeochemical Cycles, 2009, 23: GB2018
[2] 杜晓铮, 赵祥, 王昊宇, 等. 陆地生态系统水分利用效率对气候变化的响应研究进展. 生态学报, 2018, 38(23): 8296-8305
[3] 刘铮, 杨金贵, 马理辉, 等. 黄土高原草地净初级生产力时空趋势及其驱动因素. 应用生态学报, 2021, 32(1): 113-122
[4] Liu Y, Xiao J, Ju W, et al. Water use efficiency of China’s terrestrial ecosystems and responses to drought. Scientific Reports, 2015, 5: 13799
[5] Zhang Z, Jiang H, Liu JX, et al. Assessment on water use efficiency under climate change and heterogeneous carbon dioxide in China terrestrial ecosystems. Procedia Environmental Sciences, 2012, 13: 2031-2044
[6] 刘宪锋, 胡宝怡, 任志远. 黄土高原植被生态系统水分利用效率时空变化及驱动因素. 中国农业科学, 2018, 51(2): 302-314
[7] 秦格霞, 孟治元, 李妮. 黄土高原水分利用效率动态及其对干旱和地表温度的响应. 干旱区地理, 2024, 47(11): 1887-1898
[8] He J, Zhou Y, Liu X, et al. Spatiotemporal changes in water-use efficiency of China’s terrestrial ecosystems during 2001-2020 and the driving factors. Remote Sen-sing, 2025, 17: 136
[9] Zhou D, Zhao X, Hu H, et al. Long-term vegetation changes in the four mega-sandy lands in Inner Mongolia, China. Landscape Ecology, 2015, 30: 1613-1626
[10] 杨梅焕, 李扬, 王涛, 等. 毛乌素沙地植被水分利用效率时空变化特征及其对水热条件的响应. 测绘通报, 2023(7): 44-50
[11] 王志鹏, 石长春, 马雅莉, 等. 毛乌素沙地植被净初级生产力时空变化及其驱动因素. 草地学报, 2024, 23(9): 2962-2972
[12] 罗玉昌, 刘志宏, 刘瑞芳, 等. 鄂尔多斯境内毛乌素沙地近47年气候变化分析. 内蒙古气象, 2007(6): 18-19
[13] 胡永宁, 王林和, 张国盛, 等. 毛乌素沙地1969—2009年主要气候因子时间序列小波分析. 中国沙漠, 2013, 33(2): 390-395
[14] 陈仲新, 张新时. 毛乌素沙化草地景观生态分类与排序的研究. 植物生态学报, 1996, 20(5): 423-437
[15] 翟树琛, 王天娇, 李鑫豪, 等. 毛乌素沙地黑沙蒿水分利用效率环境调控: 从叶片到生态系统. 应用生态学报, 2024, 35(4): 997-1006
[16] Farquhar GD, Sharkey TD. Stomatal conductance and photosynthesis. Annual Review of Plant Biology, 1982, 33: 317-345
[17] 林子琦, 温仲明, 刘洋洋, 等. 基于遥感数据的植被碳水利用效率时空变化和归因分析. 生态学报, 2024, 44(1): 377-391
[18] Ali R, Kuriqi A, Abubaker S, et al. Long-term trends and seasonality detection of the observed flow in Yangtze river using Mann-Kendall and Sen’s innovative trend method. Water, 2019, 11: 1855
[19] Hurst HE. Methods of using long-term storage in reservoirs. Proceedings of the Institution of Civil Engineers, 1956, 5: 519-543
[20] Sen PK. Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 1968, 63: 1379-1389
[21] Neter J, Wasserman W, Kutner M. Applied Linear Regression Models. Hoboken, NJ, USA: John Wiley & Sons, 1983: 50-53
[22] Yu S, Zhu K, Zhang X. Energy demand projection of China using a path-coefficient analysis and PSO-GA approach. Energy Conversion and Management, 2012, 53: 142-153
[23] Ebrahimi E, Bayat H, Fallah M. Relationship of soil moisture characteristic curve and mechanical properties in entisols and inceptisols of Iran. Geoderma Regional, 2021, 27: e434
[24] 曹菲, 周静, 辛雨润, 等. 绿洲化背景下毛乌素沙地植被变化趋势及驱动机制分析. 生态学报, 2025, 45(9): 1-15
[25] 廉泓林, 韩雪莹, 刘雅莉, 等. 基于标准化降水蒸散指数(SPEI)的毛乌素沙地1981—2020年干旱特征研究. 中国沙漠, 2022, 42(4): 71-80
[26] 焦鹏华, 牛健植, 苗禹博, 等. 2001—2020年全球植被对极端气候的响应. 应用生态学报, 2024, 35(11): 2992-3004
[27] 冒吉荣, 曾岩, 徐馨妤, 等. 干旱胁迫及复水对油松渗透调节物质及水力功能的影响. 应用生态学报, 2024, 35(11): 2959-2965
[28] 马明国, 王建, 王雪梅. 基于遥感的植被年际变化及其与气候关系研究进展. 遥感学报, 2006, 10(3): 421-431
[29] 山仑. 植物水分利用效率和半干旱地区农业用水. 植物生理学通讯, 1994(1): 61-66
[30] 陈亚宁, 李稚, 范煜婷, 等. 西北干旱区气候变化对水文水资源影响研究进展. 地理学报, 2014, 69(9): 1295-1304
[31] Wang Z, Liu Y, Wang Z, et al. Quantifying the spatiotemporal changes in evapotranspiration and its components driven by vegetation greening and climate change in the northern foot of yinshan mountain. Remote Sen-sing, 2024, 16: 357
[32] 周伟, 刚成诚, 李建龙, 等. 1982—2010年中国草地覆盖度的时空动态及其对气候变化的响应. 地理学报, 2014, 69(1): 15-30
[33] 俞靓, 李军保, 董强, 等. 陕西关山草原景区生态保护及修复对策. 辽宁林业科技, 2022(6): 61-64
[34] 胡芳. 水资源开发利用与水资源配置问题探究. 水利电力技术与应用, 2024(6): 118-120
[35] 李建兴, 谌芸, 何丙辉, 等. 不同草本的根系分布特征及对土壤水分状况的影响. 水土保持通报, 2013, 33(1): 81-86
[36] Li Y, Pan X, Xu X, et al. Carbon dots as light converter for plant photosynthesis: Augmenting light coverage and quantum yield effect. Journal of Hazardous Materials, 2021, 410: 124534