基于GRACE卫星数据的中国陆地水储量变化和影响因素分析

doi:10.12118/j.issn.1000-6060.2022.629

摘要/Abstract

摘要：

掌握陆地水储量变化的空间分布特点和变化情况，了解其变化的原因有助于水资源的可持续性综合管理。以中国陆地为研究区域，基于重力恢复和气候实验（GRACE）卫星反演得到的陆地水储量变化数据，使用曼-肯德尔法（Mann-Kendall，M-K）趋势检验和经验正交函数（EOF）分析了陆地水储量变化的趋势以及时空变化特征，并选择了包括气象、下垫面和社会经济3个方面的10个影响因子，即气温、降水、标准化降水蒸散发指数（SPEI）、不透水层占比、水体占比、归一化植被指数（NDVI）、高程、坡度、国内生产总值和人口，利用地理探测器、皮尔逊（Pearson）相关分析和随机森林3种方法综合分析其与陆地水储量变化的关系。结果表明：松花江、嫩江和松嫩平原附近以及柴达木盆地-长江-东南沿海条带上陆地水储量变化显著增加，中国西南以及新疆-黄土高原-华北平原条带上陆地水储量变化显著减少，从高纬度到低纬度呈现出“高-低-高-低”的变化特点。整体而言，气象因素对陆地水储量变化的解释力最强，其次是社会经济因素和下垫面因素，其中降水量是引起陆地水储量变化的主要因素。月陆地水储量变化对降水量、温度、SPEI和NDVI响应存在滞后性，且陆地水储量变化对降水量和SPEI以1个月滞后为主，对温度和NDVI的响应以无滞后（即0个月滞后）为主。

关键词: 陆地水储量变化, GRACE卫星, 影响分析, 滞后效应

Abstract:

Determining the spatial distribution characteristics and changes in terrestrial water storage and understanding the reasons behind these terrestrial water storage changes (TWSC) are necessary for the sustainable and comprehensive management of water resources. Based on the data of the TWSC obtained by the gravity recovery and climate experiment satellite retrieval, this study first analyzes the trend and spatiotemporal variation characteristics of the TWSC in China using the Mann-Kendall trend test and empirical orthogonal function (EOF) analysis. Subsequently, 10 influencing factors were selected to comprehensively analyze their relationship with the TWSC by employing the following three methods: geographic detector, Pearson correlation analysis, and random forest. The 10 influencing factors were temperature, precipitation, standardized precipitation evapotranspiration index (SPEI), area proportion of impervious layer, area proportion of water body, normalized difference vegetation index (NDVI), elevation, slope, gross domestic product (GDP), and population. The results showed that areas with a significant increase in terrestrial water storage were mainly distributed in the areas near the Songhua River, Nenjiang River, and Songnen Plain, and the belt of the Qaidam Basin-Yangtze River-southeast coastal region, while areas with a significant decrease in terrestrial water storage were mainly distributed in southwest China and the belt of the Xinjiang-Loess Plateau-North China Plain. From high to low latitudes, the terrestrial water storage showed an alternating change pattern of high-low-high-low. Overall, meteorological factors had the strongest explanatory power for the TWSC, followed by socioeconomic factors and geomorphologic and geologic factors. Lag-correlation analyses showed that the monthly TWSC had a time lag response to precipitation, temperature, SPEI, and NDVI. The time lag of the monthly TWSC for each factor was different in the different regions. The response of TWSC to precipitation and SPEI mainly showed one-month lag, and the response of TWSC to temperature and NDVI mainly showed no lag (i.e. 0-month lag).

Key words: terrestrial water storage change, GRACE satellite, impact analysis, time-lag effect

石振君, 朱秀芳, 唐谊娟. 基于GRACE卫星数据的中国陆地水储量变化和影响因素分析[J]. 干旱区地理, 2023, 46(9): 1397-1406.

SHI Zhenjun, ZHU Xiufang, TANG Yijuan. Changes and influencing factors of terrestrial water storage in China based on GRACE satellite data[J]. Arid Land Geography, 2023, 46(9): 1397-1406.

图/表 10

图1

图2

图3

表1

表2

表3

表4

表5

图4

表6

参考文献 27

[1]	Long D, Pan Y, Zhou J, et al. Global analysis of spatiotemporal variability in merged total water storage changes using multiple GRACE products and global hydrological models[J]. Remote Sensing of Environment, 2017, 192(4): 198-216. doi: 10.1016/j.rse.2017.02.011
[2]	Jia X X, Shao M A, Zhu Y J, et al. Soil moisture decline due to afforestation across the Loess Plateau, China[J]. Journal of Hydrology, 2017, 546: 113-122. doi: 10.1016/j.jhydrol.2017.01.011
[3]	Chen Z, Jiang W G, Wu J J, et al. Detection of the spatial patterns of water storage variation over China in recent 70 years[J]. Scientific Reports, 2017, 7: 6423, doi: 0.1038/s41598-017-06558-5. pmid: 28743953
[4]	Jia Y, Lei H M, Yang H B, et al. Terrestrial water storage change retrieved by GRACE and its implication in the Tibetan Plateau: Estimating areal precipitation in ungauged region[J]. Remote Sensing, 2020, 12(19): 3129, doi: 10.3390/rs12193129.
[5]	李武东, 郭金运, 常晓涛, 等. 利用GRACE重力卫星反演2003—2013年新疆天山地区陆地水储量时空变化[J]. 武汉大学学报(信息科学版), 2017, 42(7): 1021-1026.
	[Li Wudong, Guo Jinyun, Chang Xiaotao, et al. Terrestrial water storage changes in the Tianshan Mountains of Xinjiang measured by GRACE during 2003—2013[J]. Geomatics and Information Science of Wuhan University, 2017, 42(7): 1021-1026.]
[6]	徐子君, 尹立河, 胡伏生, 等. 2002—2015年西北地区陆地水储量时空变化特征[J]. 中国水利水电科学研究院学报, 2018, 16(4): 314-320.
	[Xu Zijun, Yi Lihe, Hu Fusheng, et al. Spatial and temporal variations of terrestrial water storage in northwest China during 2002—2015[J]. Journal of China Institute of Water Resources and Hydropower Research, 2018, 16(4): 314-320.]
[7]	Han Z M, Huang S Z, Huang Q, et al. Assessing GRACE-based terrestrial water storage anomalies dynamics at multi-timescales and their correlations with teleconnection factors in Yunnan Province, China[J]. Journal of Hydrology, 2019, 574(7): 836-850. doi: 10.1016/j.jhydrol.2019.04.093
[8]	Yang P, Xia J, Zhan C S, et al. Monitoring the spatio-temporal changes of terrestrial water storage using GRACE data in the Tarim River Basin between 2002 and 2015[J]. Science of the Total Environment, 2017, 595(10): 218-228. doi: 10.1016/j.scitotenv.2017.03.268
[9]	Wei L Y, Jiang S H, Ren L L, et al. Spatiotemporal changes of terrestrial water storage and possible causes in the closed Qaidam Basin, China using GRACE and GRACE Follow-On data[J]. Journal of Hydrology, 2021, 598(7): 126274, doi: 10.1016/j.jhydrol.2021.126274.
[10]	李晓英, 叶根苗, 蔡晨凯, 等. 基于GRACE和MODIS数据的长江流域陆地水储量变化分析及预测[J]. 长江科学院院报, 2018, 35(5): 130-135. doi: 10.11988/ckyyb.20161195
	[Li Xiaoying, Ye Genmiao, Cai Chenkai, et al. Analysis and prediction of the anomaly of terrestrail water storage in the Yantze River Basin based on MODIS and GRACE[J]. Journal of Yangtze River Scientific Research Institute, 2018, 35(5): 130-135.] doi: 10.11988/ckyyb.20161195
[11]	Meng F C, Su F G, Li Y, et al. Changes in terrestrial water storage during 2003—2014 and possible causes in Tibetan Plateau[J]. Journal of Geophysical Research-Atmospheres, 2019, 124(6): 2909-2931. doi: 10.1029/2018JD029552
[12]	Zhong Y L, Feng W, Humphrey V, et al. Human-induced and climate-driven contributions to water storage variations in the Haihe River Basin, China[J]. Remote Sensing, 2019, 11(24): 3050, doi: 10.3390/rs11243050.
[13]	钟玉龙, 冯伟, 钟敏, 等. 中国区域基于降水重构陆地水储量变化数据集(2002—2019)[EB/OL]. [2022-04-18]. https://doi.org/10.11888/Hydro.tpdc.270990.
	[Zhong Yulong, Feng Wei, Zhong Min, et al. Dataset of reconstructed terrestrial water storage in China based on precipitation (2002—2019)[EB/OL]. [2022-04-18]. https://doi.org/10.11888/Hydro.tpdc.270990.]
[14]	Sun Z L, Zhu X F, Pan Y Z, et al. Drought evaluation using the GRACE terrestrial water storage deficit over the Yangtze River Basin, China[J]. Science of the Total Environment, 2018, 634(9): 727-738. doi: 10.1016/j.scitotenv.2018.03.292
[15]	Yue S, Pilon P, Cavadias G. Power of the Mann-Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series[J]. Journal of Hydrology, 2002, 259(1-4): 254-271. doi: 10.1016/S0022-1694(01)00594-7
[16]	Wang F, Wang Z M, Yang H B, et al. Utilizing GRACE-based groundwater drought index for drought characterization and teleconnection factors analysis in the North China Plain[J]. Journal of Hydrology, 2020, 585(3): 124849, doi: 10.1016/j.jhydrol.2020.124849.
[17]	吕叶, 杨涵, 黄粤, 等. 咸海流域陆地水储量时空变化研究[J]. 干旱区地理, 2021, 44(4): 943-952.
	[Lü Ye, Yang Han, Huang Yue, et al. Spatiotemporal variation of terrestrial water storage in Aral Sea Basin[J]. Arid Land Geography, 2021, 44(4): 943-952.]
[18]	Zhang H, Zhang L L, Li J, et al. Monitoring the spatiotemporal terrestrial water storage changes in the Yarlung Zangbo River Basin by applying the P-LSA and EOF methods to GRACE data[J]. Science of the Total Environment, 2020, 713(4): 136274, doi: 10.1016/j.scitotenv.2019.136274.
[19]	束秋妍, 潘云, 宫辉力, 等. 基于GRACE的华北平原地下水储量时空变化分析[J]. 国土资源遥感, 2018, 30(2): 132-137.
	[Shu Qiuyan, Pan Yun, Gong Huili, et al. Spatiotemporal analysis of GRACE-based groundwater storage vairation in North China Plain[J]. Remote Sensing for Natural Resources, 2018, 30(2): 132-137.]
[20]	Xie Z, Huete A, Cleverly J, et al. Multi-climate mode interactions drive hydrological and vegetation responses to hydroclimatic extremes in Australia[J]. Remote Sensing of Environment, 2019, 231(9): 111270, doi: 10.1016/j.rse.2019.111270.
[21]	刘莹, 朱秀芳, 徐昆. 用于灌溉耕地制图的特征变量优选[J]. 农业工程学报, 2022, 38(3): 119-127.
	[Liu Ying, Zhu Xiufang, Xu Kun. Optimizing the feature variables for irrigated farmland mapping[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(3): 119-127.]
[22]	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
[23]	孙倩. 基于GRACE和GLDAS的新疆水资源时空变化研究[D]. 乌鲁木齐: 新疆大学, 2015.
	[Sun Qian. GRACE and GLDAS data-based estimation of spatial variations in terrestrial water variations over Xinjiang[D]. Urumqi: Xinjiang University, 2015.]
[24]	周志博, 刘杰, 杨超, 等. GRACE重力卫星探究我国华北地区陆地水储量变化[J]. 南水北调与水利科技(中英文), 2020, 18(5): 66-73.
	[Zhou Zhibo, Liu Jie, Yang Chao, et al. The variation of terrestrial water storage in north China based on GRACE gravity satellite[J]. South-to-North Water Transfers and Water Science & Technology, 2020, 18(5): 66-73.]
[25]	吴奇凡. 黄土高原陆地水储量变化归因分析及区域尺度地下水补给[D]. 咸阳: 西北农林科技大学, 2019.
	[Wu Qifan. Attribution analysis of terrestrial water storage and estimating groundwater recharge at regional scale on the Loess Plateau[D]. Xianyang: Northwest A & F University, 2019.]
[26]	刘璐, 陈亚鹏, 李肖杨. 生态输水对孔雀河地下水埋深及植被的影响[J]. 干旱区研究, 2021, 38(4): 901-909.
	[Liu Lu, Chen Yapeng, Li Xiaoyang. Effect of ecological water conveyance on groundwater depth and vegetation in the Kongque River[J]. Arid Zone Research, 2021, 38(4): 901-909.]
[27]	李晓格, 张颖, 单永娟. 基于能值生态足迹模型的榆林市水资源可持续利用研究[J]. 干旱区研究, 2022, 39(4): 1066-1075.
	[Li Xiaoge, Zhang Ying, Shan Yongjuan. Suatainable utilization of water resources in Yulin City based on an emergy ecological footprint model[J]. Arid Zone Research, 2022, 39(4): 1066-1075.]

影响因子	q值	排序	影响因子	q值	排序
降水量	0.276	1	DEM	0.042	6
温度	0.149	2	人口	0.035	7
SPEI	0.064	3	GDP	0.028	8
NDVI	0.050	4	坡度	0.017	9
不透水层占比	0.044	5	水体占比	0.001	10

影响因子	DEM	GDP	NDVI	人口	降水量	SPEI	温度	不透水层占比	水体占比	坡度
DEM	0.042	-	-	-	-	-	-	-	-	-
GDP	0.085	0.028	-	-	-	-	-	-	-	-
NDVI	0.116	0.092	0.050	-	-	-	-	-	-	-
人口	0.096	0.049	0.102	0.035	-	-	-	-	-	-
降水量	0.377	0.343	0.349	0.353	0.276	-	-	-	-	-
SPEI	0.184	0.170	0.146	0.172	0.373	0.064	-	-	-	-
温度	0.308	0.218	0.220	0.233	0.461	0.341	0.149	-	-	-
不透水层占比	0.140	0.082	0.117	0.093	0.337	0.143	0.248	0.044	-	-
水体占比	0.069	0.043	0.059	0.053	0.308	0.082	0.186	0.075	0.001	-
坡度	0.093	0.056	0.070	0.063	0.293	0.087	0.190	0.068	0.037	0.017

影响因子	重要性值	排序	影响因子	重要性值	排序
降水量	0.335	1	DEM	0.072	6
温度	0.160	2	NDVI	0.059	7
SPEI	0.110	3	坡度	0.039	8
人口	0.082	4	不透水层占比	0.038	9
GDP	0.073	5	水体占比	0.032	10

影响因子	不同滞后时长下的栅格数目占比
影响因子	0个月滞后	1个月滞后	2个月滞后	3个月滞后
NDVI	42.0	40.0	9.2	8.8
降水量	24.3	42.1	23.0	10.6
温度	43.7	9.0	18.2	29.2
SPEI	25.3	30.8	15.4	28.5