Spatial and temporal dynamics of drought in Xinjiang and its response to climate change

doi:10.6046/zrzyyg.2021389

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Abstract

This study aims to achieve the dynamic and continuous monitoring of drought in Xinjiang. Based on the temperature vegetation dryness index (TVDI), as well as the Sen’s slope trend analysis, R/S, and partial correlation analysis, this study analyzed the spatial and temporal dynamics, changing trends, and future sustainable state of TVDI and the influences of seasonal precipitation and temperature on TVDI in Xinjiang during the period from 2001 to 2020. The results are as follows. ① The northern Tianshan Mountains and the Kunlun Mountains showed minimum TVDI values of less than 0.57, indicating light drought. The Tarim and Junggar basins showed TVDI values of greater than 0.86, indicating extraordinary drought. ② The TVDI values in spring decreased at a rate of 0.001 3/a. By contrast, the TVDI values in summer, autumn, and winter increased at a rate of 0.001 4/a, 0.002 0/a, and 0.000 8/a, respectively. Therefore, the increased amplitude of the TVDI values was the highest in autumn and the lowest in winter. ③ In the near future, the TVDI values in most regions of Xinjiang will increase in spring and winter, while the pixel quantity of most TVDI values will increase in summer and autumn. ④ The TVDI values were mainly negatively correlated with precipitation in spring and winter and were positively correlated with precipitation in summer and autumn. The TVDI values were mainly positively correlated with temperature in spring and were negatively correlated with temperature in autumn and winter. Moreover, the TVDI values in summer had a decreased correlation with temperature from west to east, with the correlation gradually changing from a negative to a positive correlation.

Keywords TVDI temperature precipitation Xinjiang

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TP79

Issue Date: 27 December 2022

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	Jun CHENG
	Yunzhen LI
	Yu ZOU

Cite this article:

Jun CHENG,Yunzhen LI,Yu ZOU. Spatial and temporal dynamics of drought in Xinjiang and its response to climate change[J]. Remote Sensing for Natural Resources, 2022, 34(4): 216-224.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021389 OR https://www.gtzyyg.com/EN/Y2022/V34/I4/216

Fig.1 Spatial distribution of land use, altitude and meteorological stations in the study area

Fig.2 Comparison between measured data of surface temperature station and LST results before and after correction

Fig.3 Interannual change trend of TVDI

Fig.4 Spatial distribution of TVDI

Fig.5 TVDI change trend

Fig.6 Statistics of different land use types of TVDI change trend

Fig.7 Spatial distribution of Hurst index of TVDI

Fig.8 Distribution of partial correlation coefficient between TVDI and temperature and precipitation

Fig.9 Distribution of mean correlation coefficient between TVDI and seasonal precipitation and temperature in different land use types

[1]	吴燕锋, 巴特尔·巴克, 罗那那. 1961—2012年北疆干旱时空变化[J]. 中国沙漠, 2017, 37(1):158-166.
[1]	Wu Y F, Bake B, Luo N N. Spatiotemporal pattern of drought in north Xinjiang,China in 1961—2012[J]. Journal of Desert Research, 2017, 37(1):158-166.
[2]	李江. 新形势下新疆重大水利工程科技需求与展望[J]. 水利水电技术, 2019, 50(12):1-9.
[2]	Li J. Scientific and technical demands and prospects of major water conservancy projects in Xinjiang under new circumstances[J]. Water Resources and Hydropower Engineering, 2019, 50(12):1-9.
[3]	魏林, 龙鸿艳. 新疆喀什干旱区实施新一轮退耕还草问题探讨[J]. 草食家畜, 2020(2):46-48.
[3]	Wei L, Long H Y. Discussion on the problems existing in implementation of the new round of returning farmland to grassland in arid region of Kashgar Xinjiang[J]. Grass-Feeding Livestock, 2020(2):46-48.
[4]	杨涛, 陆桂华, 李会会, 等. 气候变化下水文极端事件变化预测研究进展[J]. 水科学进展, 2011, 22(2):279-286.
[4]	Yang T, Lu G H, Li H H, et al. Advances in the study of projection of climate change impacts on hydrological extremes[J]. Advances in Water Science, 2011, 22(2):279-286.
[5]	秦大河, 丁一汇, 王绍武, 等. 中国西部生态环境变化与对策建议[J]. 地球科学进展, 2002, 17(3):314-319. doi: 10.11867/j.issn.1001-8166.2002.03.0314
[5]	Qin D H, Ding Y H, Wang S W, et al. Ecological and environmental change in West China and its response strategy[J]. Advance in Earth Sciences, 2002, 17(3):314-319.
[6]	姚玉璧, 张强, 李耀辉, 等. 干旱灾害风险评估技术及其科学问题与展望[J]. 资源科学, 2013, 35(9):1884-1897.
[6]	Yao Y B, Zhang Q, Li Y H, et al. Drought risk assessment technological progresses and problems[J]. Resources Science, 2013, 35(9):1884-1897.
[7]	谢培, 顾艳玲, 张玉虎, 等. 1961—2015年新疆降水及干旱特征分析[J]. 干旱区地理, 2017, 40(2):332-339.
[7]	Xie P, Gu Y L, Zhang Y H, et al. Precipitation and drought characteristics in Xinjiang during 1961—2015[J]. Arid Land Geography, 2017, 40(2):332-339.
[8]	高瑜莲, 柳锦宝, 柳维扬, 等. 近14 a新疆南疆绿洲地区地表蒸散与干旱的时空变化特征研究[J]. 干旱区地理, 2019, 42(4):830-837.
[8]	Gao Y L, Liu J B, Liu W Y, et al. Spatio-temporal variation characteristics of surface evapotranspiration and drought at the oasis area of the southern Xinjiang in recent 14 years[J]. Arid Land Geo-graphy, 2019, 42(4):830-837.
[9]	彭擎, 王让会, 蒋烨林, 等. 植被-地温指数(NDVI-LST)在新疆干旱监测中的适用性[J]. 生态学报, 2018, 38(13):4694-4703.
[9]	Peng Q, Wang R H, Jiang Y L, et al. Adaptability of drought situation monitor in Xinjiang with the NDVI-LST index[J]. Acta Ecologica Sinica, 2018, 38(13):4694-4703.
[10]	张喆, 丁建丽, 李鑫, 等. TVDI用于干旱区农业旱情监测的适宜性[J]. 中国沙漠, 2015, 35(1):220-227.
[10]	Zhang Z, Ding J L, Li X, et al. Suitability of TVDI used to monitor agricultural drought in arid area[J]. Journal of Desert Research, 2015, 35(1):220-227.
[11]	黄静, 张运, 汪明秀, 等. 近17年新疆干旱时空分布特征及影响因素[J]. 生态学报, 2020, 40(3):1077-1088.
[11]	Huang J, Zhang Y, Wang M X, et al. Spatial and temporal distribution characteristics of drought and its relationship with meteorological factors in Xinjiang in last 17 years[J]. Acta Ecologica Sinica, 2020, 40(3):1077-1088.
[12]	姚俊强, 毛炜峄, 胡文峰, 等. 1961—2015年新疆区域SPEI干旱指数数据集[J]. 中国科学数据(中英文网络版), 2019, 4(3):112-121.
[12]	Yao J Q, Mao W Y, Hu W F, et al. A dataset of drought indices based on the standardized precipitation evapotranspiration index (SPEI) over Xinjiang,China (1961—2015)[J]. China Scientific Data, 2019, 4(3):112-121.
[13]	马柱国, 符淙斌, 杨庆, 等. 关于我国北方干旱化及其转折性变化[J]. 大气科学, 2018, 42(4):248-258.
[13]	Ma Z G, Fu C B, Yang Q, et al. Drying trend in northern China and its shift during 1951—2016[J]. Chinese Journal of Atmospheric Sciences, 2018, 42(4):248-258.
[14]	Yao J, Zhao Y, Yu X. Spatial-temporal variation and impacts of drought in Xinjiang (Northwest China) during 1961—2015[J]. PeerJ, 2018, 6:e4926. doi: 10.7717/peerj.4926 url: https://peerj.com/articles/4926
[15]	Xie P, Lei X, Zhang Y, et al. Cluster analysis of drought variation and its mutation characteristics in Xinjiang Province,during 1961—2015[J]. Hydrology Research, 2018, 49(4):1016-1027. doi: 10.2166/nh.2018.105 url: https://iwaponline.com/hr/article/49/4/1016/38853/Cluster-analysis-of-drought-variation-and-its
[16]	Ma Y J, Shi F Z, Hu X, et al. Threshold vegetation greenness under water balance in different desert areas over the silk road economic belt[J]. Remote Sensing, 2020, 12(15):2452. doi: 10.3390/rs12152452 url: https://www.mdpi.com/2072-4292/12/15/2452
[17]	施雅风, 沈永平, 胡汝骥. 西北气候由暖干向暖湿转型的信号、影响和前景初步探讨[J]. 冰川冻土, 2002, 24(3):219-226.
[17]	Shi Y F, Shen Y P, Hu R J. Preliminary study on signal,impact and foreground of climatic shift from warm-dry to warm-humid in Northwest China[J]. Journal of Glaciology and Geocryology, 2002, 24(3):219-226.
[18]	鞠彬, 曾明, 徐国梁. 不同干旱指数在新疆哈巴河地区旱情评价中的应用研究[J]. 水资源与水工程学报, 2017, 28(2):237-243.
[18]	Ju B, Zeng M, Xu G L. Application research of different drought indexes for drought assessment in Habahe region of Xinjiang[J]. Journal of Water Resources and Water Engineering, 2017, 28(2):237-243.
[19]	轩俊伟, 郑江华, 刘志辉. 基于SPEI的新疆干旱时空变化特征[J]. 干旱区研究, 2016, 33(2):338-344.
[19]	Xuan J W, Zheng J H, Liu Z H. SPEI-based spatiotemporal variation of drought in Xinjiang[J]. Arid Zone Research, 2016, 33(2):338-344.
[20]	刘星含, 张佳华, 许晓宏. MODIS-TVDI指数监测新疆干旱动态[J]. 遥感信息, 2015(2):111-115.
[20]	Liu X H, Zhang J H, Xu X H. Drought monitoring in Xinjiang by using MODIS-TVDI[J]. Remote Sensing Information, 2015(2):111-115.
[21]	郭燕云, 胡琦, 傅玮东, 等. 基于SPEI指数的新疆天山草地近55 a干旱特征[J]. 干旱区研究, 2019, 36(3):670-676.
[21]	Guo Y Y, Hu Q, Fu W D, et al. Drought trend over the grasslands in the Tianshan Mountains,Xinjiang in recent 55 years based on SPEI[J]. Arid Zone Research, 2019, 36(3):670-676.
[22]	沙莎, 郭铌, 李耀辉, 等. 我国温度植被旱情指数TVDI的应用现状及问题简述[J]. 干旱气象, 2014, 32(1):128-134. doi: 10．11755/j．issn．1006－7639(2014)－01－0128
[22]	Sha S, Guo N, Li Y H, et al. Introduction of application of temperature vegetation dryness index in China[J]. Journal of Arid Meteoro-logy, 2014, 32(1):128-134.
[23]	Price J C. On the analysis of thermal infrared imagery:The limited utility of apparent inertia[J]. Remote Sensing of Environment, 1985, 18(1):59-47. doi: 10.1016/0034-4257(85)90038-0 url: https://linkinghub.elsevier.com/retrieve/pii/0034425785900380
[24]	Sandholt I, Rasmussen K, Andersen J. A simple interpretation of surface temperature / vegetation index space for assessment of surface moisture status[J]. Remote Sensing of Environment, 2002, 79(2-3):213-224. doi: 10.1016/S0034-4257(01)00274-7 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425701002747
[25]	Moran M S, Hymer D C, Qi J. Soil moisture evolution using multi-temporal synthetic aperture Radar SAR in semiarid rangeland[J]. Agricultural and Forest Meteorology, 2000, 105(1-3):69-80. doi: 10.1016/S0168-1923(00)00189-1 url: https://linkinghub.elsevier.com/retrieve/pii/S0168192300001891
[26]	汪攀, 刘毅敏. Sen’s斜率估计与Mann-Kendall法在设备运行趋势分析中的应用[J]. 武汉科技大学学报, 2014, 37(6):454-457,472.
[26]	Wang P, Liu Y M. Application of Sen’s slope estimation and Mann-Kendall method in the trend analysis of equipment operation[J]. Journal of Wuhan University of Science and Technology, 2014, 37(6):454-457,472.
[27]	姜丽霞, 王晾晾, 吕佳佳, 等. 基于Hurst指数的黑龙江省作物生长季降水趋势研究[J]. 气象与环境学报, 2020, 36(2):70-77.
[27]	Jiang L X, Wang L L, Lyu J J, et al. A study on rainfall trend during the crop growing season in Heilongjiang Province based on Hurst exponent method[J]. Journal of Meteorology and Environment, 2020, 36(2):70-77.
[28]	吴黎. 基于MODIS数据温度植被干旱指数干旱监测指标的等级划分[J]. 水土保持研究, 2017, 24(3):130-135.
[28]	Wu L. Classification of drought grades based on temperature vegetation drought index using the MODIS data[J]. Research of Soil and Water Conservation, 2017, 24(3):130-135.

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