青海湖流域地表温度时空变化特征研究

doi:10.12118/j.issn.1000-6060.2022.525

干旱区地理 ›› 2023, Vol. 46 ›› Issue (7): 1084-1097.doi: 10.12118/j.issn.1000-6060.2022.525

青海湖流域地表温度时空变化特征研究

康利刚^1,²(),曹生奎^1,^2,³(),曹广超^1,^2,³,严莉^1,²,陈链璇^1,²,李文斌^1,²,赵浩然^1,²

1.青海师范大学地理科学学院青海省自然地理与环境过程重点实验室，青海西宁 810008
2.青海师范大学青藏高原地表过程与生态保育教育部重点实验室，青海西宁 810008
3.青海省人民政府-北京师范大学高原科学与可持续发展研究院，青海西宁 810008

收稿日期:2022-10-15 修回日期:2023-01-03 出版日期:2023-07-25 发布日期:2023-08-03
通讯作者: 曹生奎（1979-），男，博士，教授，主要从事生态水文与水资源学方面的研究. E-mail: caoshengkui@163.com
作者简介:康利刚（1998-），男，硕士研究生，主要从事生态水文与水资源学方面的研究. E-mail: 2369564480@qq.com
基金资助:
国家自然科学基金项目(42061008)

Spatiotemporal variation of land surface temperature in Qinghai Lake Basin

KANG Ligang^1,²(),CAO Shengkui^1,^2,³(),CAO Guangchao^1,^2,³,YAN Li^1,²,CHEN Lianxuan^1,²,LI Wenbin^1,²,ZHAO Haoran^1,²

1. Qinghai Province Key Laboratory of Physical Geography and Environmental Process, College of Geographical Science, Qinghai Normal University, Xining 810008, Qinghai, China
2. Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation (Ministry of Education), Qinghai Normal University, Xining 810008, Qinghai, China
3. Qinghai Provincial People’s Government-Institute of Plateau Science and Sustainable Development, Beijing Normal University, Xining 810008, Qinghai, China

Received:2022-10-15 Revised:2023-01-03 Online:2023-07-25 Published:2023-08-03

摘要/Abstract

摘要：

地表温度是陆面过程研究的重要参数之一。基于MOD11A2地表温度数据，结合数字高程模型（DEM）、土地利用类型、气象和归一化植被指数（NDVI）等数据，采用ArcGIS空间分析与数理统计方法，对2002—2021年青海湖流域的年度、季节、月份及昼夜等时间尺度，以及不同土地利用类型、不同海拔高度的地表温度变化特征进行了分析，并利用地理探测器模型对地表温度与环境因子之间的关系进行了研究。结果表明：（1）青海湖流域年均地表温度总体随年份增加呈现上升趋势，平均地表温度为2.20 ℃。空间分布呈现比较规律的由流域东南向西北逐渐下降的特征。青海湖北岸和东岸地表温度上升趋势最显著，呈上升趋势的区域占流域主体。（2）地表温度在不同季节表现为：夏季>春季>秋季>冬季。除春季随年份增加呈下降趋势外，夏季、秋季、冬季均呈逐年上升趋势。（3）月间地表温度表现为规律的先增加后减小，以7月为“对称轴”呈显著相似的变化趋势。（4）不同土地利用类型的地表温度从高到低排序为：耕地>林地>灌木>草地>荒地>冰川。（5）年均地表温度与海拔为显著负相关关系（P<0.05），海拔每升高100 m，年均地表温度下降约0.8 ℃。（6）根据单因子探测结果，环境因子对地表温度的解释力存在差异，其中海拔高度和年均气温对地表温度的解释力较高，而土地利用类型对其解释力最低。在所有因子的交互作用中，年均气温和海拔高度交互，q值的解释力最大（0.90），表明年均气温和海拔高度的耦合与青海湖流域地表温度关系密切。

关键词: 青海湖流域, 地表温度, 时空变化, 地理探测器, 环境因子

Abstract:

The land surface temperature considerably influences land surface processes. Based on MOD11A2 land surface temperature data, the annual, seasonal, monthly, and diurnal land surface temperature changes in the Qinghai Lake Basin of China during 2002—2021 were analyzed using the digital elevation model, land use type, meteorology and normalized difference vegetation index, ArcGIS spatial analysis, and mathematical statistics. The results revealed that: (1) The average annual land surface temperature of the Qinghai Lake Basin increased over the years, and the average land surface temperature was 2.20 ℃. The spatial distribution exhibited a regular characteristic of gradually decreasing from the southeast to the northwest of the basin. The north and east shores of Qinghai Lake exhibited the most significant increasing trend of the land surface temperature, and the area with increasing trend accounted for the main part of the basin. (2) The land surface temperature values in various seasons were in the sequence summer>spring>autumn>winter. With the exception of spring, in which a decreasing trend was observed over the year, an increasing trend was observed annually for summer, autumn, and winter. (3) The land surface temperature between months exhibited a regular increase and subsequent decrease, with July as the “symmetry axis” showing a significantly similar trend. (4) The land surface temperatures of various land use types from high to low followed the order arable land>forest land>shrubs>grassland>wasteland>glacier. (5) The annual average land surface temperature was significantly negatively correlated with elevation (P<0.05), and the annual average land surface temperature decreased by approximately 0.8 ℃ for every 100 m increase in the elevation. (6) Single-factor detection results revealed differences in the explanatory power of environmental factors on the land surface temperature. Among these factors, altitude and the mean annual temperature exhibited high explanatory power on the land surface temperature, whereas the land use type exhibited the lowest explanatory power. Among the interactions of all factors, the annual mean temperature and altitude interacted with the highest explanatory power of q value (0.90). This result indicated that the coupling of the annual mean temperature and altitude was closely related to the land surface temperature of Qinghai Lake Basin.

Key words: Qinghai Lake Basin, land surface temperature, spatiotemporal variation, geographic detector, environmental factors

康利刚, 曹生奎, 曹广超, 严莉, 陈链璇, 李文斌, 赵浩然. 青海湖流域地表温度时空变化特征研究[J]. 干旱区地理, 2023, 46(7): 1084-1097.

KANG Ligang, CAO Shengkui, CAO Guangchao, YAN Li, CHEN Lianxuan, LI Wenbin, ZHAO Haoran. Spatiotemporal variation of land surface temperature in Qinghai Lake Basin[J]. Arid Land Geography, 2023, 46(7): 1084-1097.

图/表 14

图1

图2

图3

图4

图5

图6

图7

表1

图8

图9

图10

表2

图11

图12

参考文献 37

[1]	张祎. 长江流域土地利用/覆被变化对地表温度的影响[D]. 武汉: 中国地质大学, 2018.
	[Zhang Yi. The influence of LUCC on the land surface temperature in Yangtze River Basin[D]. Wuhan: China University of Geosciences, 2018.]
[2]	刘俊杰, 潘自武, 秦奋, 等. 基于MODIS的秦巴山地气温估算与山体效应分析[J]. 地理研究, 2020, 39(3): 735-748. doi: 10.11821/dlyj020190164
	[Liu Junjie, Pan Ziwu, Qin Fen, et al. Estimation of air temperature based on MODIS and analysis of mass elevation effect in the Qinling-Daba Mountains[J]. Geographical Research, 2020, 39(3): 735-748.] doi: 10.11821/dlyj020190164
[3]	安彬, 肖薇薇, 张淑兰, 等. 1960—2017年黄土高原地表温度时空变化特征[J]. 干旱区地理, 2021, 44(3): 778-785.
	[An Bin, Xiao Weiwei, Zhang Shulan, et al. Spatial and temporal characteristics of surface temperature in the Loess Plateau during 1960—2017[J]. Arid Land Geography, 2021, 44(3): 778-785.]
[4]	Delnat V, Verborgt J, Janssens L, et al. Daily temperature variation lowers the lethal and sublethal impact of a pesticide pulse due to a higher degradation rate[J]. Chemosphere, 2021, 263: 128114, doi: 10.1016/j.chemosphere.2020.128114. doi: 10.1016/j.chemosphere.2020.128114
[5]	IPCC. Summary for policy makers of the synthesis report of the IPCC fifth assessment report[M]. Cambridge: Cambridge University Press, 2013.
[6]	Ethan H, Kirsten M B, Braden C O, et al. Evaluation of the MODIS collections 5 and 6 for change analysis of vegetation and land surface temperature dynamics in north and south America[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 156: 121-134. doi: 10.1016/j.isprsjprs.2019.07.011
[7]	姬霖, 段克勤. 1960—2017年渭河流域极端气温变化及其对区域增暖的响应[J]. 地理科学, 2020, 40(3): 466-477. doi: 10.13249/j.cnki.sgs.2020.03.015
	[Ji Lin, Duan Keqin. Variations of extreme temperature and its response on regional warming in the Weihe River Basin during 1960—2017[J]. Scientia Geographica Sinica, 2020, 40(3): 466-477.] doi: 10.13249/j.cnki.sgs.2020.03.015
[8]	丁一汇, 王会军. 近百年中国气候变化科学问题的新认识[J]. 科学通报, 2016, 61(10): 1029-1041.
	[Ding Yihui, Wang Huijun. Newly acquired knowledge on the scientific issues related to climate change over the recent 100 years in China[J]. Chinese Science Bulletin, 2016, 61(10): 1029-1041.]
[9]	热伊莱·卡得尔, 玉苏甫·买买提, 玉素甫江·如素力, 等. 伊犁河谷2001—2014年地表温度时空分异特征[J]. 中国沙漠, 2018, 38(3): 637-644. doi: 10.7522/j.issn.1000-694X.2017.00012
	[Kadeer Reyilai, Maimaiti Yusufu, Rusuli Yusufujiang, et al. Temporal and spatial differentiation characteristics of surface temperature in Yili Valley from 2001 to 2014[J]. Journal of Desert Research, 2018, 38(3): 637-644.] doi: 10.7522/j.issn.1000-694X.2017.00012
[10]	张威, 纪然. 辽宁省地表温度时空变化及影响因素[J]. 生态学报, 2019, 39(18): 6772-6784.
	[Zhang Wei, Ji Ran. Analysis of spatiotemporal variation and factors influencing surface temperature in Liaoning Province[J]. Acta Ecologica Sinica, 2019, 39(18): 6772-6784.]
[11]	叶钰, 秦建新, 胡顺石. 长沙市热岛效应时空特征变化研究[J]. 地球信息科学学报, 2017, 19(4): 518-527. doi: 10.3724/SP.J.1047.2017.00518
	[Ye Yu, Qin Jianxin, Hu Shunshi. Spatial-temporal evolution of urban heat island effects in Changsha City[J]. Journal of Geo-information Science, 2017, 19(4): 518-527.] doi: 10.3724/SP.J.1047.2017.00518
[12]	刘劲宏, 姚宜斌, 桑吉章. PM_2.5对地表温度的影响及其应用[J]. 大地测量与地球动力学, 2019, 39(5): 502-505.
	[Liu Jinhong, Yao Yibin, Sang Jizhang. Influence of PM_2.5concentrations on surface temperature and its application[J]. Journal of Geodesy and Geodynamics, 2019, 39(5): 502-505.]
[13]	王万同, 王卷乐, 杜佳. 基于ETM+与MODIS数据融合的伊洛河流域地表蒸散估算[J]. 地理研究, 2013, 32(5): 817-827.
	[Wang Wantong, Wang Juanle, Du Jia. Land surface evapotranspiration estimation of Yiluo River Basin based on fusion of ETM+ and MODIS data[J]. Geographical Research, 2013, 32(5): 817-827.]
[14]	刘宝康. 气候变化背景下青海湖流域草地与湖泊时空变化特征研究[D]. 兰州: 兰州大学, 2016.
	[Liu Baokang. Spatial and temporal variation characteristics of grassland and lake in Qinghai Lake Basin under climate change[D]. Lanzhou: Lanzhou University, 2016.]
[15]	李广泳, 姜翠红, 程滔, 等. 青海湖流域植被物候格局时空动态变化及其与植被退化的关系[J]. 草业学报, 2016, 25(1): 22-32. doi: 10.11686/cyxb2015210
	[Li Guangyong, Jiang Cuihong, Cheng Tao, et al. Spatial-temporal variation of vegetation phenology and their relationships with vegetation degradation in a Qinghai Lake watershed[J]. Acta Prataculturae Sinica, 2016, 25(1): 22-32.] doi: 10.11686/cyxb2015210
[16]	赵美亮, 曹广超, 曹生奎, 等. 1980—2017年青海省地表温度时空变化特征[J]. 干旱区研究, 2021, 38(1): 178-187.
	[Zhao Meiliang, Cao Guangchao, Cao Shengkui, et al. Spatial-temporal variation characteristics of land surface temperature in Qinghai Province from 1980 to 2017[J]. Arid Zone Research, 2021, 38(1): 178-187.]
[17]	韩有香, 刘金青. 1963—2016年久治地区日照时数变化特征分析[J]. 现代农业科技, 2017(21): 233-236.
	[Han Youxiang, Liu Jinqing. Analysis on the change characteristics of sunshine hours in Jiuzhi from 1963 to 2016[J]. Modern Agricultural Science and Technology, 2017(21): 233-236.]
[18]	杨羽帆, 曹生奎, 冯起, 等. 青海湖沙柳河流域浅层地下水氢氧稳定同位素分布特征[J]. 中国沙漠, 2019, 39(5): 45-53. doi: 10.7522/j.issn.1000-694X.2018.00101
	[Yang Yufan, Cao Shengkui, Feng Qi, et al. Spatial distribution characteristics of composition of stable hydrogen and oxygen isotopes of shallow groundwater in Shaliu River Basin of Qinghai Lake[J]. Journal of Desert Research, 2019, 39(5): 45-53.] doi: 10.7522/j.issn.1000-694X.2018.00101
[19]	曹生奎, 曹广超, 陈克龙, 等. 青海湖高寒湿地生态系统服务价值动态[J]. 中国沙漠, 2014, 34(5): 1402-1409. doi: 10.7522/j.issn.1000-694X.2013.00441
	[Cao Shengkui, Cao Guangchao, Chen Kelong, et al. Dynamics of service value of alpine wetland ecosystem in Qinghai Lake[J]. Journal of Desert Research, 2014, 34(5): 1402-1409.] doi: 10.7522/j.issn.1000-694X.2013.00441
[20]	李惠梅, 张安录, 高泽兵, 等. 青海湖地区生态系统服务价值变化分析[J]. 地理科学进展, 2012, 31(12): 1747-1754. doi: 10.11820/dlkxjz.2012.12.022
	[Li Huimei, Zhang Anlu, Gao Zebing, et al. Quantitative analysis of the impacts of climate and socio-economic driving factors of land use change on the ecosystem services value in the Qinghai Lake area[J]. Progress in Geography, 2012, 31(12): 1747-1754.] doi: 10.11820/dlkxjz.2012.12.022
[21]	潘虹, 顾海敏, 史建桥, 等. 基于RS和GIS的青海湖流域植被覆盖度变化与驱动因子研究[J]. 资源开发与市场, 2016, 32(7): 827-831.
	[Pan Hong, Gu Haimin, Shi Jianqiao, et al. Study on changes of vegetation fraction and lts driving factors in Qinghai Lake Basin based on RS and GIS[J]. Resource Development & Market, 2016, 32(7): 827-831.]
[22]	苏芬, 刘宝康, 张翠花, 等. 青海湖流域牧草物候期对气候变化的响应[J]. 青海草业, 2018, 27(4): 12-18.
	[Su Fen, Liu Baokang, Zhang Cuihua, et al. Response of pasture phenological period to climate change in Qinghai Lake Basin[J]. Qinghai Prataculture, 2018, 27(4): 12-18.]
[23]	肖雄, 李小雁, 吴华武, 等. 青海湖流域高寒草甸壤中流水分来源研究[J]. 水土保持学报, 2016, 30(2): 230-236.
	[Xiao Xiong, Li Xiaoyan, Wu Huawu, et al. Study on the water sources of subsurface flow in alpine meadow of the Qinghai Lake Basin[J]. Journal of Soil and Water Conservation, 2016, 30(2): 230-236.]
[24]	王志刚, 曹生奎, 曹广超. 近15年来青海湖流域气温、降水变化对植被物候驱动分析[J]. 水土保持研究, 2022, 29(1): 249-255.
	[Wang Zhigang, Cao Shengkui, Cao Guangchao. Analysis on the drive of temperature and precipitation changes to vegetation phenology of the Qinghai Lake Basin in the past 15 years[J]. Research of Soil and Water Conservation, 2022, 29(1): 249-255.]
[25]	Zhao W, Duan S B. Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS/Terra land products and MSG geostationary satellite data[J]. Remote Sensing of Environment, 2020, 247: 111931, doi: 10.1016/j.rse.2020.111931. doi: 10.1016/j.rse.2020.111931
[26]	程丹妮, 王颖琪, 程勇翔, 等. 新疆典型沙漠和绿洲植被-水汽-地表温度相关性分析[J]. 干旱区地理, 2022, 45(2): 456-466.
	[Cheng Danni, Wang Yingqi, Cheng Yongxiang, et al. Vegetation-water vapor-land surface temperature correlation analysis of typical deserts and oases in Xinjiang[J]. Arid Land Geocraphy, 2022, 45(2): 456-466.]
[27]	彭璐璐, 李楠, 郑智远, 等. 中国居民消费碳排放影响因素的时空异质性[J]. 中国环境科学, 2021, 41(1): 463-472.
	[Peng Lulu, Li Nan, Zheng Zhiyuan, et al. Spatial-temporal heterogeneity of carbon emissions and influencing factors on household consumption of China[J]. China Environmental Science, 2021, 41(1): 463-472.]
[28]	王劲峰, 徐成东. 地理探测器: 原理与展望[J]. 地理学报, 2017, 72(1): 116-134. doi: 10.11821/dlxb201701010
	[Wang Jinfeng, Xu Chengdong. Geodetector: Principle and prospective[J]. Acta Geographica Sinica, 2017, 72(1): 116-134.] doi: 10.11821/dlxb201701010
[29]	李佳洺, 陆大道, 徐成东, 等. 胡焕庸线两侧人口的空间分异性及其变化[J]. 地理学报, 2017, 72(1): 148-160. doi: 10.11821/dlxb201701012
	[Li Jiaming, Lu Dadao, Xu Chengdong, et al. Spatial heterogeneity and its changes of population on the two sides of Hu Line[J]. Acta Geographica Sinica, 2017, 72(1): 148-160.] doi: 10.11821/dlxb201701012
[30]	吕晨, 蓝修婷, 孙威. 地理探测器方法下北京市人口空间格局变化与自然因素的关系研究[J]. 自然资源学报, 2017, 32(8): 1385-1397. doi: 10.11849/zrzyxb.20160707
	[Lü Chen, Lan Xiuting, Sun Wei. A study on the relationship between natural factors and population distribution in Beijing using geographical detector[J]. Journal of Natural Resources, 2017, 32(8): 1385-1397.] doi: 10.11849/zrzyxb.20160707
[31]	王少剑, 王洋, 蔺雪芹, 等. 中国县域住宅价格的空间差异特征与影响机制[J]. 地理学报, 2016, 71(8): 1329-1342. doi: 10.11821/dlxb201608004
	[Wang Shaojian, Wang Yang, Lin Xueqin, et al. Spatial differentiation patterns and influencing mechanism of housing prices in China: Based on data of 2872 counties[J]. Acta Geographica Sinica, 2016, 71(8): 1329-1342.] doi: 10.11821/dlxb201608004
[32]	Li Q X, Sun W B, Huang B Y, et al. Consistency of global warming trends strengthened since 1880s[J]. Science Bulletin, 2020, 65(20): 1709-1712. doi: 10.1016/j.scib.2020.06.009 pmid: 36659242
[33]	杨涵洧, 封国林. 2013年盛夏中国持续性高温事件诊断分析[J]. 高原气象, 2016, 35(2): 484-494. doi: 10.7522/j.issn.1000-0534.2014.00130
	[Yang Hanwei, Feng Guolin. Diagnostic analyses of characteristics and causes of regional and persistent high temperature event in China[J]. Plateau Meteorology, 2016, 35(2): 484-494.] doi: 10.7522/j.issn.1000-0534.2014.00130
[34]	张百平, 姚永慧. 山体效应对生态垂直格局的影响[J]. 地理学报, 2016, 26(7): 871-877.
	[Zhang Baiping, Yao Yonghui. Implications of mass elevation effect for the altitudinal patterns of global ecology[J]. Acta Geographica Sinica, 2016, 26(7): 871-877.]
[35]	朱伊, 范广洲, 华维, 等. 1981—2015年青藏高原地表温度的时空变化特征分析[J]. 西南大学学报(自然科学版), 2018, 40(11): 127-140.
	[Zhu Yi, Fan Guangzhou, Hua Wei, et al. Analysis of the temporal and spatial variation in land surface temperature over the Qinghai-Tibet Plateau from 1981 to 2015[J]. Journal of Southwest University (Natural Science Edition), 2018, 40(11): 127-140.]
[36]	胡盼盼. 基于MODIS数据的青藏高原2003—2020年地表温度时空变化分析[D]. 北京: 中国地质大学, 2021.
	[Hu Panpan. Analyzing the spatiotemporal variations of the land surface temperature on the Tibetan Plateau from 2003—2020 based on MODIS data[D]. Beijing: China University of Geosciences, 2021.]
[37]	姚宜斌, 雷祥旭, 张良, 等. 青藏高原地区 1979—2014 年大气可降水量和地表温度时空变化特征分析[J]. 科学通报, 2016, 61(13): 1462-1477.
	[Yao Yibin, Lei Xiangxu, Zhang Liang, et al. Analysis of precipitable water vapor and surface temperature variation over Qinghai-Tibetan Plateau from 1979 to 2014[J]. Chinese Science Bulletin, 2016, 61(13): 1462-1477.]

地表温度	2002年	2007年	2012年	2017年	2020年	年均	年均白天	年均夜晚
耕地	4.52	4.97	4.5	5.05	5.22	5.19	17.43	-7.02
林地	3.43	3.38	2.13	3.47	3.96	3.63	13.2	-7.17
灌木	3.21	3.16	2.01	3.28	2.89	3.31	14.27	-7.29
草地	2.48	2.65	1.44	2.61	2.21	2.89	15.26	-9.68
冰川	-6.72	-7.43	-8.05	-7.12	-7.17	-7.63	0.23	-15.59
荒地	-1.24	-0.99	-2.05	-0.81	-1.25	-0.11	11.58	-12.03

环境因子	Moran’s I指数	Z得分
年均地表温度	0.70	67.83
年均气温	0.80	77.46
年均降水量	0.89	86.26
年均NDVI	0.57	54.79

青海湖流域地表温度时空变化特征研究

Spatiotemporal variation of land surface temperature in Qinghai Lake Basin

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 37

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	杨雨, 宋福铁, 张杰. 基于地理探测器的中国金融网络空间结构特征及影响因素研究[J]. 干旱区地理, 2023, 46(9): 1524-1535.
[2]	李建辉, 陈琳, 党争. 黄河流域爱国主义教育基地空间格局及影响因素研究[J]. 干旱区地理, 2023, 46(9): 1536-1544.
[3]	唐太斌, 周保, 金晓媚, 魏赛拉加, 马涛, 张永艳. 黄河源区夏季地表温度变化研究[J]. 干旱区地理, 2023, 46(8): 1250-1259.
[4]	张昊, 韩增林, 乔国荣, 王辉, 王宏业, 段冶. 黄河流域城市间旅游经济联系格局及影响因素研究[J]. 干旱区地理, 2023, 46(8): 1344-1354.
[5]	夏文浩, 王铭扬, 姜磊. 新疆农业碳排放强度时空变化趋势与收敛分析[J]. 干旱区地理, 2023, 46(7): 1145-1154.
[6]	陈跃萍, 武胜利, 赵昕, 张艺加. 近60 a哈密市极端气温时空变化特征[J]. 干旱区地理, 2023, 46(6): 868-879.
[7]	穆佳薇, 乔保荣, 余国新. 新疆塔里木河流域县域农业低碳生产率时空格局及影响效应研究[J]. 干旱区地理, 2023, 46(6): 968-981.
[8]	孔德明, 郝丽莎, 夏四友, 李红波. 粮食单产视角下中国北方农牧交错带粮食安全研究[J]. 干旱区地理, 2023, 46(5): 782-792.
[9]	张勇军, 杨余辉, 胡义成, 冯先成, 杨景燕. 新疆喀什河流域水化学时空变化特征及灌溉适应性评价[J]. 干旱区地理, 2023, 46(4): 583-594.
[10]	罗嘉艳, 张靖, 徐梦冉, 莫宇, 同丽嘎. 浑善达克沙地植被变化定量归因及多情景预测[J]. 干旱区地理, 2023, 46(4): 614-624.
[11]	朱磊, 李燕楠, 胡静, 田小波, 徐佳慧, 卿琪. 中国研学实践基地空间格局的多尺度特征及影响机理研究[J]. 干旱区地理, 2023, 46(4): 625-635.
[12]	李娜,武永利,赵桂香,钱锦霞,李芬,赵海英,韩普. 近60 a山西省极端气温事件的年际变化及其对区域增暖的响应[J]. 干旱区地理, 2023, 46(3): 337-348.
[13]	任涛涛,李双双,段克勤,何锦屏. 黄土高原热浪型和缺水型骤旱时空变化特征及其影响因素[J]. 干旱区地理, 2023, 46(3): 360-370.
[14]	杨玉欢,贺建雄,张新红,芮旸. 中国农文旅耦合协调发展空间分异特征及影响肌理[J]. 干旱区地理, 2023, 46(3): 448-459.
[15]	程静,王鹏,陈红翔,韩永贵. 渭河流域生境质量时空演变及其地形梯度效应与影响因素[J]. 干旱区地理, 2023, 46(3): 481-491.