中国西北干旱区植被水分利用效率变化对气象要素的响应——以新疆为例

doi:10.12118/j.issn.1000-6060.2022.545

Abstract

Abstract:

Water use efficiency (WUE) links the processes of carbon and water cycling in terrestrial ecosystems and is a crucial indicator for understanding the response of vegetated ecosystems to climate change. In this study, the spatial and temporal patterns of vegetation WUE in Xinjiang of China from 1990 to 2020 were systematically analyzed based on the Carnegie-Ames-Stanford approach model inversions of net primary productivity and evapotranspiration (ET). In this method, remote sensing images and reanalysis data products from past 31 years were combined. The results revealed that vegetation WUE in Xinjiang has been decreasing for 31 years and 2003 was a pivotal year with a fluctuating downward trend before the turning point and a subsequent fluctuating upward trend. The spatial pattern of vegetation WUE in Xinjiang has not changed considerably over the past 31 years, with high values concentrated in plains, especially in the oasis and desert-oasis transition zones, and low values concentrated in mountains. The results revealed that the changes in vegetation WUE in Xinjiang can be attributed to the influence of climatic factors such as precipitation, evapotranspiration and water vapor pressure. This study can be used as a reference for screening artificial and natural vegetation structure types with reasonable structure, high water conservation and productivity, and for achieving the sustainable development of vegetation construction in arid and semiarid regions, especially for the ecosystem security and sustainable development of agriculture and animal husbandry in Xinjiang.

Key words: water use efficiency, net primary productivity, evapotranspiration, climate change

GAO Xiaoyu, HAO Haichao, ZHANG Xueqi, CHEN Yaning. Responses of vegetation water use efficiency to meteorological factors in arid areas of northwest China: A case of Xinjiang[J].Arid Land Geography, 2023, 46(7): 1111-1120.

Figures/Tables 8

Fig. 1

Tab. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

References 29

[1]	Abd El-Mageed T A, Semida W M, Rady M M. Moringa leaf extract as biostimulant improves water use efficiency, physio-biochemical attributes of squash plants under deficit irrigation[J]. Agricultural Water Management, 2017, 193: 46-54. doi: 10.1016/j.agwat.2017.08.004
[2]	郝海超, 郝兴明, 花顶, 等. 2000—2018年中亚五国水分利用效率对气候变化的响应[J]. 干旱区地理, 2021, 44(1): 1-14.
	[Hao Haichao, Hao Xingming, Hua Ding, et al. Response of water use efficiency to climate change in five Central Asian countries from2000 to 2018[J]. Arid Land Geography, 2021, 44(1): 1-14.]
[3]	Jin N, Ren W, Tao B, et al. Effects of water stress on water use efficiency of irrigated and rainfed wheat in the Loess Plateau, China[J]. Science of the Total Environment, 2018, 642: 1-11. doi: 10.1016/j.scitotenv.2018.06.028
[4]	张桂玲, 李艳琴, 罗绪强, 等. 季节性干旱下喀斯特次生林不同树种水分利用效率变化[J]. 地球与环境, 2021, 49(1): 25-31.
	[Zhang Guiling, Li Yanqin, Luo Xuqiang, et al. Change of water use efficiency of different species in karst secondary forest under seasonal drought[J]. Earth and Environment, 2021, 49(1): 25-31.]
[5]	Lu X L, Zhuang Q L. Evaluating evapotranspiration and water-use efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data[J]. Remote Sensing of Environment, 2010, 114(9): 1924-1939. doi: 10.1016/j.rse.2010.04.001
[6]	Zhao M S, Heinsch F A, Nemani R R, et al. Improvements of the MODIS terrestrial gross and net primary production global data set[J]. Remote Sensing of Environment, 2005, 95(2): 164-176. doi: 10.1016/j.rse.2004.12.011
[7]	Mu Q Z, Zhao M S, Running S W. Improvements to a MODIS global terrestrial evapotranspiration algorithm[J]. Remote Sensing of Environment, 2011, 115(8): 1781-1800. doi: 10.1016/j.rse.2011.02.019
[8]	van Soest H L, den Elzen M G, van Vuuren D P. Net-zero emission targets for major emitting countries consistent with the Paris Agreement[J]. Nature Communications, 2021, 12: 2140, doi: 10.1038/s41467-021-22294-x. doi: 10.1038/s41467-021-22294-x pmid: 33837206
[9]	Cheng L, Zhang L, Wang Y P, et al. Recent increases in terrestrial carbon uptake at little cost to the water cycle[J]. Nature Communications, 2017, 8: 110, doi: 10.1038/s41467-017-00114-5. doi: 10.1038/s41467-017-00114-5 pmid: 28740122
[10]	Huang Y L, Chen L D, Fu B J, et al. The wheat yields and water-use efficiency in the Loess Plateau: Straw mulch and irrigation effects[J]. Agricultural Water Management, 2005, 72(3): 209-222. doi: 10.1016/j.agwat.2004.09.012
[11]	Hao H C, Li Z, Chen Y N, et al. Recent variations in soil moisture use efficiency (SMUE) and its influence factors in Asian drylands[J]. Journal of Cleaner Production, 2022, 373: 133860, doi: 10.1016/j.jclepro.2022.133860. doi: 10.1016/j.jclepro.2022.133860
[12]	刘伟, 姜逢清, 李小兰. 新疆气候变化的适应能力时空演化特征[J]. 干旱区研究, 2017, 34(3): 531-540.
	[Liu Wei, Jiang Fengqing, Li Xiaolan. Spatiotemporal evolution of adaptive capacity to climate change in Xinjiang[J]. Arid Zone Research, 2017, 34(3): 531-540.]
[13]	Williams J D, Long D S, Reardon C L. Productivity and water use efficiency of intensified dryland cropping systems under low precipitation in Pacific Northwest, USA[J]. Field Crops Research, 2020, 254: 107787, doi: 10.1016/j.fcr.2020.107787. doi: 10.1016/j.fcr.2020.107787
[14]	Gianluigi O, Matteo M. Precipitation seasonality promotes acquisitive and variable leaf water-economics traits in southwest Australian granite outcrop species[J]. Biological Journal of the Linnean Society, 2020, 133: 411-417. doi: 10.1093/biolinnean/blaa053
[15]	裴婷婷, 李小雁, 吴华武, 等. 黄土高原植被水分利用效率对气候和植被指数的敏感性研究[J]. 农业工程学报, 2019, 35(5): 119-125.
	[Pei Tingting, Li Xiaoyan, Wu Huawu, et al. Sensitivity of vegetation water use efficiency to climate and vegetation index in Loess Plateau, China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(5): 119-125.]
[16]	崔茜琳, 何云玲, 李宗善. 青藏高原植被水分利用效率时空变化及与气候因子的关系[J]. 应用生态学报, 2022, 33(6): 1525-1532. doi: 10.13287/j.1001-9332.202206.024
	[Cui Xilin, He Yunling, Li Zongshan. Spatial-temporal variation of vegetation water use efficiency and its relationship with climate factors over the Qinghai-Tibet Plateau, China[J]. Chinese Journal of Applied Ecology, 2022, 33(6): 1525-1532.] doi: 10.13287/j.1001-9332.202206.024
[17]	陈亚宁, 李稚, 方功焕, 等. 气候变化对中亚天山山区水资源影响研究[J]. 地理学报, 2017, 72(1): 18-26. doi: 10.11821/dlxb201701002
	[Chen Yaning, Li Zhi, Fang Gonghuan, et al. Impact of climate change on water resources in the Tianshan Mountains, Central Asia[J]. Acta Geographica Sinica, 2017, 72(1): 18-26.] doi: 10.11821/dlxb201701002
[18]	Zou J, Ding J L, Welp M, et al. Assessing the response of ecosystem water use efficiency to drought during and after drought events across Central Asia[J]. Sensors, 2020, 20(3): 581, doi: 10.3390/s20030581. doi: 10.3390/s20030581
[19]	Gilbert M E, Hernandez M I. How should crop water-use efficiency be analyzed? A warning about spurious correlations[J]. Field Crops Research, 2019, 235: 59-67. doi: 10.1016/j.fcr.2019.02.017
[20]	Liu S, Luo G P, Wang H. Temporal and spatial changes in crop water use efficiency in Central Asia from 1960 to 2016[J]. Sustainability, 2020, 12(2): 572, doi: 10.3390/su12020572. doi: 10.3390/su12020572
[21]	Abd El-Mageed T A, Semida W M, Rady M M. Moringa leaf extract as biostimulant improves water use efficiency, physio-biochemical attributes of squash plants under deficit irrigation[J]. Agricultural Water Management, 2017, 193: 46-54. doi: 10.1016/j.agwat.2017.08.004
[22]	刘海桂, 唐旭利, 周国逸, 等. 1981—2000年广东省净初级生产力的时空格局[J]. 生态学报, 2007, 27(10): 4065-4074.
	[Liu Haigui, Tang Xuli, Zhou Guoyi, et al. Spatial and temporal patterns of net primary productivity in the duration of 1981—2000 in Guangdong, China[J]. Acta Ecologica Sinica, 2007, 27(10): 4065-4074.]
[23]	原一荃, 薛力铭, 李秀珍. 基于CASA模型的长江口崇明东滩湿地植被净初级生产力与固碳潜力[J]. 生态学杂志, 2022, 41(2): 334-342.
	[Yuan Yiquan, Xue Liming, Li Xiuzhen. Net primary productivity and carbon sequestration potential of salt marsh vegetation in Chongming Dongtan of the Yangtze Estuary based on CASA model[J]. Chinese Journal of Ecology, 2022, 41(2): 334-342.]
[24]	Adams M A, Turnbull T L, Sprent J I, et al. Legumes are different: Leaf nitrogen, photosynthesis, and water use efficiency[J]. Proc Natl Acad Sci U S A, 2016, 113(15): 4098-4103. doi: 10.1073/pnas.1523936113
[25]	Yang J, Huang X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J]. Earth System Science Data, 2021, 13(8): 3907-3925. doi: 10.5194/essd-13-3907-2021
[26]	李稚, 李玉朋, 李鸿威, 等. 中亚地区干旱变化及其影响分析[J]. 地球科学进展, 2022, 37(1): 37-50. doi: 10.11867/j.issn.1001-8166.2021.124
	[Li Zhi, Li Yupeng, Li Hongwei, et al. Analysis of drought change and its impact in Central Asia[J]. Advances in Earth Science, 2022, 37(1): 37-50.] doi: 10.11867/j.issn.1001-8166.2021.124
[27]	Yang J L, Dong J W, Xiao X M, et al. Divergent shifts in peak photosynthesis timing of temperate and alpine grasslands in China[J]. Remote Sensing of Environment, 2019, 233: 111395, doi: 10.1016/j.rse.2019.111395. doi: 10.1016/j.rse.2019.111395
[28]	Liu X F, Feng X M, Fu B J. Changes in global terrestrial ecosystem water use efficiency are closely related to soil moisture[J]. Science of the Total Environment, 2020, 698: 134165, doi: 10.1016/j.scitotenv.2019.134165. doi: 10.1016/j.scitotenv.2019.134165
[29]	Kondratyev K Y, Varotsos C. Atmospheric greenhouse effect in the context of global climate change[J]. Il Nuovo Cimento C, 1995, 18(2): 123-151. doi: 10.1007/BF02512015

Metrics

Viewed

Full text

130

HTML			PDF

Just accepted	Online first	Issue	Just accepted	Online first	Issue
0	0	11	0	0	119

From	Others	local

Times	31	99
Rate	24%	76%

Abstract

274

Just accepted	Online first	Issue

0	0	274

From	Others	local

Times	227	47
Rate	83%	17%

Cited

Web of Science	Crossref	ScienceDirect	Search for Citations in Google Scholar >>


This page requires you have already subscribed to WoS.

Shared

产品名称	提供的地表特征参数	时间分辨率	空间分辨率
MCD15A3H	光合有效辐射吸收比例（FPAR）	4 a合成	500 m×500 m
MCD15A2H	光合有效辐射吸收比例（FPAR）	8 a合成	500 m×500 m
MOD13A1	归一化植被指数（NDVI）	16 a合成	500 m×500 m
Landsat5/7/8	归一化植被指数（NDVI）	16 a合成	30 m×30 m
MCD12Q1	土地利用类型（IGBP）	96 a合成	500 m×500 m
中科院数据产品	土地利用类型25类	2013年	30 m×30 m
T3H（GLDAS）	气温（TEM）	3 h	0.25°×0.25°
TerraClimate	太阳辐射（SOL）	月	1/24°（~4 km）
TerraClimate	饱和水汽压差（VPD）	月	1/24°（~4 km）
TerraClimate	实际水汽压（VAP）	月	1/24°（~4 km）
TerraClimate	潜在蒸散发（PET）	月	1/24°（~4 km）
TerraClimate	降水（PRE）	月	1/24°（~4 km）
TerraClimate	实际蒸散发（ET）	月	1/24°（~4 km）
ASTER GDEMV2	数字高程模型（DEM）	-	30 m

Responses of vegetation water use efficiency to meteorological factors in arid areas of northwest China: A case of Xinjiang

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 29

Related Articles 15

Metrics

Comments

Recommended 0

[1]	YAO Di, ZHANG Ziwen, HAN Weiwei. Distinguishing climate- and human-driven water storage anomalies in the Yellow River Basin [J]. Arid Land Geography, 2025, 48(2): 190-201.
[2]	LI Hongyang, CHEN Tianyu, WANG Shengjie, ZHANG Mingjun. Spatiotemporal variations of potential evapotranspiration on the northern slope of the Kunlun Mountains in Xinjiang from 1979 to 2021 [J]. Arid Land Geography, 2024, 47(9): 1443-1450.
[3]	KANG Limin, TENG Xinru, CHE Jiahang, HUAI Baojuan. Spatiotemporal variations of snow cover on the northern slope of Kunlun Mountains [J]. Arid Land Geography, 2024, 47(9): 1462-1471.
[4]	WANG Nan, LIU Zexuan, ZHENG Jianghua, ZHONG Tao, MENG Chengfeng. Spatiotemporal characteristics and driving forces of glacial lakes in Tianshan Mountains [J]. Arid Land Geography, 2024, 47(9): 1472-1481.
[5]	CHAO Bao, ZHAO Yuanyuan, WU Haiyan, LI Yuan, SU Ning. Ecosystem services and its response to climate factors in the Mongolian Plateau from 2000 to 2020 [J]. Arid Land Geography, 2024, 47(9): 1577-1586.
[6]	XIA Tingting, XUE Xuan, WANG Haowei, XU Wenzhe, SHENG Ziyi, WANG Yang. Changes in terrestrial water storage and its drivers on the north slope of Kunlun Mountains [J]. Arid Land Geography, 2024, 47(8): 1292-1303.
[7]	LIU Yu, MEI Hua, FAN Wenbo, REN Congzhe, WANG Shiwei, LI Shunshun. Temporal and spatial characteristics of drought in the Ta’e Basin from 1992 to 2022 based on the SPEI index [J]. Arid Land Geography, 2024, 47(8): 1338-1347.
[8]	ZHU Chenggang, CHEN Yaning, ZHANG Mingjun, CHE Yanjun, SUN Meiping, ZHAO Ruifeng, WANG Yang, LIU Yuting. Preliminary report on scientific investigation of water resources on the northern slope of Kunlun Mountains [J]. Arid Land Geography, 2024, 47(7): 1097-1105.
[9]	ZHANG Jing, MA Long, LIU Tingxi, SUN Bolin, QIAO Zixu. Reconstruction of the minimum temperature over the past 202 years based on tree rings of Picea crassifolia in the Helan Mountains [J]. Arid Land Geography, 2024, 47(6): 909-921.
[10]	FAN Jing, SHEN Yanbo, CHANG Rui. Impact of climate change on the selection of typical meteorological years in solar energy resource assessment [J]. Arid Land Geography, 2024, 47(6): 922-931.
[11]	LI Hui, LIU Tiejun, WANG Shaohui, LIU Dongwei. Spatial and temporal variation of water use efficiency and its influencing factors in desert steppe of Inner Mongolia from 2001 to 2021 [J]. Arid Land Geography, 2024, 47(6): 993-1003.
[12]	XIANG Yanyun, WANG Yi, CHEN Yaning, ZHANG Qifei, ZHANG Yujie. Prediction of future hydrological drought risk in the Yarkant River Basin based on CMIP6 models [J]. Arid Land Geography, 2024, 47(5): 798-809.
[13]	ZHAO Mingjie, WANG Ninglian, SHI Chenlie, HOU Jingqi. Temporal and spatial variations of lake ice phenology in large lakes of Central Asia from 2000 to 2020 [J]. Arid Land Geography, 2024, 47(4): 561-575.
[14]	MA Yali, NIU Zuirong, SUN Dongyuan. Relationship between changes in spatial and temporal patterns of potential evapotranspiration and meteorological factors in the Hexi Corridor [J]. Arid Land Geography, 2024, 47(2): 192-202.
[15]	WANG Shuzhi, WEN Deping. Attribution analysis of runoff change in the Datong River Basin, Qinghai-Tibet Plateau [J]. Arid Land Geography, 2024, 47(2): 203-213.