袁林果,,
姜中山,
刘中冠,
张迪,
徐小凤
西南交通大学地球科学与环境工程学院, 成都 611756
基金项目: 国家自然科学基金(42074021)，四川省科技计划项目(2015JQ0046)资助
详细信息
作者简介: 成帅, 男, 1996年生, 硕士生, 主要研究方向为卫星大地测量.E-mail: 1301951516@qq.com
通讯作者: 袁林果, 男, 1980年生, 教授, 主要研究方向为大地测量学.E-mail: lgyuan@swjtu.edu.cn
中图分类号: P228收稿日期:2020-05-26
修回日期:2021-01-06
上线日期:2021-04-10
Investigating terrestrial water storage change in Sichuan, Yunnan and Chongqing using Slepian basis functions
CHENG Shuai,YUAN LinGuo,,
JIANG ZhongShan,
LIU ZhongGuan,
ZHANG Di,
XU XiaoFeng
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
More Information
Corresponding author: YUAN LinGuo,E-mail:lgyuan@swjtu.edu.cn
MSC: P228--> Received Date: 26 May 2020
Revised Date: 06 January 2021
Available Online: 10 April 2021
摘要
摘要:局部Slepian函数是将局部区域内的地球物理信号转化为空间谱的一种方法，其可以保证在球面上局部范围内获得最优谱平滑解，非常适用于局部范围地球物理信号的研究.本文利用中国陆态网西南地区72个测站的连续GPS观测资料分析川云渝地区陆地水负荷形变特征，并基于Slepian函数方法解算60阶的空间谱基函数，结合弹性质量负荷理论研究了川云渝地区2011年至2015年陆地水储量变化的时空分布模式.针对Slepian函数的边界效应问题，本文使用GLDAS格网数据计算得到站点处垂直负荷位移时间序列，然后利用该位移数据来进行水储量变化恢复实验，结果表明当边界扩充为3°时能较好地恢复GLDAS模型输出的陆地水储量变化.通过对比区域内GPS、GRACE、GLDAS得到的等效水高以及降雨数据，发现季节性降水是陆地水变化的一个重要驱动因子，GPS反演结果与GRACE和GLDAS数据具有较强的空间一致性.云南地区周年变化要强于川渝地区，其中云南西部的山区陆地水变化最大，约为30 cm，最小为川北以及重庆地区仅为7 cm.相较于GPS反演结果，GRACE与GLDAS明显低估了陆地水储量的季节性变化，分别达到24%和47%.比较分析地区内平均等效水高时间序列的相位发现，GPS得到的陆地水变化与降雨数据一致性较好，而GRACE与GLDAS存在一到两个月左右的时延.同时GPS能较好的探测出2015年1月左右南方地区大范围的强降水，而GRACE与GLDAS并没有体现出该现象，说明GPS能更为灵敏地探测到局部地区陆地水的变化.在站点等效水高时间序列上，GPS与GRACE的相关性总体上要优于GPS与GLDAS，陆地水周年变化较大的云南和四川西部地区站点三种数据间相关性较好，而其他季节性信号不明显的地区则相关性较差.本文的研究表明运用GPS-Slepian方法能够独立地监测高时空分辨率的陆地水储量变化，是作为当前补充GRACE观测资料空缺期的有益尝试.
关键词: Slepian基函数/
GPS垂向位移/
陆地水储量变化/
负荷形变
Abstract:The Slepian basis function is the approach that is capable of transforming the geophysical signals into spatial spectrum information and obtaining the optimal spectral smoothing solution on the spherical surface, which is suitable to study the geophysical signal at a regional spatial scale. In this study, the characteristics of the hydrological loading deformation in Sichuan, Yunnan, and Chongqing were analyzed by using long-time GPS coordinate time series at 72 Crustal Movement Observing Network of China (CMONOC) stations. Hereafter, based on the elastic mass loading theory and the Slepian basis functions up to 60 order, we further investigated the spatiotemporal patterns of terrestrial water storage change in Southwest China throughout the period of 2011-2015. For testing the effect of different expanded boundary on the quality of terrestrial water storage, the gridded GLDAS data were employed to calculate the vertical loading displacement time series at each site, then they were used to recover the water storage change. Results show that the terrestrial water storage change can be well reproduced by GLDAS data when the boundary is expanded up to 3 degrees. Comparison of the equivalent water heights (EWH) determined by GPS, GRACE, and GLDAS data indicates that the GPS-derived EWH is consistent with those from GRACE and GLDAS in terms of spatial pattern and that seasonal precipitation is one of the major driving factors for terrestrial water storage change. The annual variation of terrestrial water storage is large in Yunnan in comparison with that in Sichuan and Chongqing, which peaks at the amplitude of about 30 cm in Western Yunnan. Whereas it presents small fluctuations of 7 cm in northern Sichuan and Chongqing. Compared with the GPS-derived values, the GRACE and GLDAS significantly underestimate the seasonal amplitude of terrestrial water storage change by 24% and 47%, respectively. The average EWH time series inferred by GPS data show a good correlation with precipitation data, while temporal variations between precipitation and GRACE- and GLDAS-derived EWH present a time delay of one or two months. In contrast to the GRACE and GLDAS, GPS-derived terrestrial water storage product has a good capability of tracing the regional-scale extreme rainfall as illustrated in the strong rainfall event occurred in South China on January 2015. In general, GNSS-inverted time-varying water height has a better correlation with GRACE results than that with GLDAS. We could obtain good consistency between these three datasets where there are large annual water variations, in contrast to poor correlation in regions without conspicuous seasonal water changes. Our results imply that the GPS inversion for water changes based on Slepian basis function succeeds in tracking the high spatial-temporal variations of terrestrial water storage, and it is a useful tool to fill the data gap when GRACE measurements are not available.
Key words:Slepian basis function/
GPS vertical displacement/
Terrestrial water storage variation/
Loading deformation
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