利用GPS垂直位移反演云南省陆地水储量变化

doi:10.11947/j.AGCS.2018.20170255

测绘学报 ›› 2018, Vol. 47 ›› Issue (3): 332-340.doi: 10.11947/j.AGCS.2018.20170255

利用GPS垂直位移反演云南省陆地水储量变化

何思源^1,2, 谷延超¹, 范东明¹, 赵鸿彬¹, 郑蓉³

1. 西南交通大学地球科学与环境工程学院, 四川成都 611756;
2. 中铁第一勘察设计院集团有限公司, 陕西西安 710043;
3. 中国电力工程顾问集团西北电力设计院有限公司, 陕西西安 710075

收稿日期:2017-05-17 修回日期:2017-12-04 出版日期:2018-03-20 发布日期:2018-03-29
通讯作者: 范东明 E-mail:dmfan@swjtu.edu.cn
作者简介:何思源(1990-),男,硕士生,助理工程师,研究方向为卫星大地测量。E-mail:570394332@qq.com
基金资助:
国家自然科学基金（41574018；41404018）

Seasonal Variation of Terrestrial Water Storage in Yunnan Province Inferred from GPS Vertical Observations

HE Siyuan^1,2, GU Yanchao¹, FAN Dongming¹, ZHAO Hongbin¹, ZHENG Rong³

1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China;
2. China Railway First Survey & Design Institute Group, Xi'an 710043, China;
3. Northwest Electric Power Design Institute Co. Ltd. of China Power Engineering Consulting Group, Xi'an 710075, China

Received:2017-05-17 Revised:2017-12-04 Online:2018-03-20 Published:2018-03-29
Supported by:
The National Natural Science Foundation of China(Nos. 41574018;41404018)

摘要/Abstract

摘要： 地表质量的重分布会引起固体地球的弹性形变，GPS连续运行观测站能够精确测定地表负荷引起的地壳形变。本文通过模拟数据对利用云南省及其周边47个中国大陆构造环境监测网（陆态网）台站反演云南地区陆地水储量的可行性进行分析：以水文模型周年振幅为真值，计算47个台站点的负荷形变，同时加入随机误差构成模拟观测数据，最后采用模型反演陆地水储量变化；1000次的随机模拟试验表明利用当前GPS台站数据可有效地反演云南地区陆地水储量变化。基于上述结论，笔者反演了云南省2010—2014年陆地水储量变化，GPS反演结果表明：云南省陆地水变化呈现明显的地域分布特征，西南部高山地区的水储量周年变化高于东部平原地区；在时间尺度上，云南省大部分地区水储量在10月（夏季末）达到最大值，在4月（冬季末）达到最小值；云南省2010—2014年陆地水呈缓慢增长趋势，约为20 mm/a。通过GPS陆地水储量反演结果与GRACE、GLDAS以及TRMM数据综合对比分析，表明利用云南地区当前GPS台站可以作为独立观测量用于GRACE与GRACE Follow-on衔接期间的陆地水储量变化监测。

关键词: GPS, 陆地水储量, GRACE, 负荷形变

Abstract: In response to the mass redistribution on the surface of the earth,crustal vertical displacements are deformed in solid elastic earth,which can be recorded by continuous GPS.With GPS vertical observations at 47 stations from crustal movement observation network of China (CMONOC) in and around Yunnan province,we explore the feasibility analysis of using the data to infer the variation of water storage.Simulation experiments are conducted by using GLDAS (global land data assimilation system) Noah hydrological model,whose annual amplitude is referred as real signals.We used the GLDAS annual amplitude to calculate the vertical displacements,and combined the displacements with random errors to constitute simulated vertical displacements.The simulated vertical displacements are then converted to variation of water storage,which is compared with the original GLDAS water storage.We performed 1000 simulated experiments to evaluate robustness of the method,and it is evident that the variation of water storage could be effectively obtained by means of the vertical observations at the 47 CMONOC stations.Therefore,we investigated the variation of water storage over a period from 2010 to 2014.Results inferred from GPS data show obvious spatial and temporal distribution:the seasonal water in the southwestern mountain is much larger than that in eastern plain;the water storage reaches the maximum in October (after summer) and the minimum in April (after winter);a rate of 20 mm/a in total water storage in Yunnan province is presented during 2010-2014.The comparison between GPS-inferred water storage and GRACE,GLDAS and TRMM results showed good consistency in spatial and temporal distribution,indicating that CMONNOC GPS stations could be used as independent measurement to estimate terrestrial water storage changes during the gap between GRACE and future GRACE Follow-on missions.

Key words: GPS, terrestrial water storage, GRACE, loading deformation

中图分类号:

P223

何思源, 谷延超, 范东明, 赵鸿彬, 郑蓉. 利用GPS垂直位移反演云南省陆地水储量变化[J]. 测绘学报, 2018, 47(3): 332-340.

HE Siyuan, GU Yanchao, FAN Dongming, ZHAO Hongbin, ZHENG Rong. Seasonal Variation of Terrestrial Water Storage in Yunnan Province Inferred from GPS Vertical Observations[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(3): 332-340.

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利用GPS垂直位移反演云南省陆地水储量变化

Seasonal Variation of Terrestrial Water Storage in Yunnan Province Inferred from GPS Vertical Observations

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