融合GNSS和GRACE/GFO数据反演西南地区陆地水储量变化

doi:10.13474/j.cnki.11-2246.2024.1007.

测绘通报 ›› 2024, Vol. 0 ›› Issue (10): 39-45.doi: 10.13474/j.cnki.11-2246.2024.1007.

• 学术研究 • 上一篇

融合GNSS和GRACE/GFO数据反演西南地区陆地水储量变化

刘鹏¹, 段虎荣^1,2, 张成浩¹, 王金驰¹, 梁文康¹

1. 西安科技大学测绘科学与技术学院, 陕西西安 710054;
2. 中国科学院精密测量科学与技术创新研究院大地测量与地球动力学国家重点实验室, 湖北武汉 430077

收稿日期:2024-01-25 发布日期:2024-11-02
通讯作者: 段虎荣,E-mail:duanhurong@126.com
作者简介:刘鹏(1997—),男,硕士生,研究方向为卫星大地测量。E-mail:pliu126@126.com
基金资助:
大地测量与地球动力学国家重点实验室开放基金(SKLGED2022-5-2);陕西省自然科学基础研究计划(2021JQ562)

Integrating GNSS and GRACE data to estimate terrestrial water storage changes in the southwestern region

LIU Peng¹, DUAN Hurong^1,2, ZHANG Chenghao¹, WANG Jinchi¹, LIANG Wenkang¹

1. College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China;
2. State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China

Received:2024-01-25 Published:2024-11-02

摘要/Abstract

摘要： 为解决单一使用GNSS或GRACE/GFO等技术反演区域陆地储水量变化存在的不足,本文借鉴分解-重构法的思想,提出了一种融合GNSS和GRACE/ GFO反演区域陆地储水量变化的方法。利用来自中国陆态网西南地区的90个GNSS测站时间序列数据,融合GNSS和GRACE/GFO反演了中国西南地区2012—2022年的陆地水储量变化,并通过空间分布分析对比其他融合方法及降水数据,验证了本文融合方法的性能。结果表明,融合反演结果在空间分布上综合了GNSS和GRACE/GFO的优点;在时间尺度上,GNSS、GRACE/GFO的反演结果均滞后于降水2个月,而融合反演结果仅滞后于降水1个月,且相应的相关系数也高于GNSS、GRACE/GFO反演结果。因此,本文方法为融合多源数据以获取更可靠的区域陆地水储量变化提供了一种参考。

关键词: GNSS, GRACE/GFO, 融合反演, 陆地水储量变化, 中国西南地区

Abstract: In order to address the limitations of using global navigation satellite system (GNSS) or Gravity Recovery and Climate Experiment (GRACE)/GRACE follow-on(GFO) technology alone for retrieving regional terrestrial water storage changes. A method for integrating GNSS and GRACE/GFO data to estimate changes in regional terrestrial water storage is provided in this paper, drawing inspiration from the “decomposition-reconstruction method.” Changes in terrestrial water storage in southwestern China from 2012 to 2022 are estimated by integrating GNSS and GRACE/GFO data using time series data from 90 GNSS stations in southwestern China. The performance of our fusion method is verified through spatial distribution analysis and comparison with other fusion methods and precipitation data. The results demonstrate that the fusion inversion combines the advantages of GNSS and GRACE/GFO in spatial distribution. On the time scale, the inversion results from GNSS and GRACE/GFO both lag behind precipitation by 2 months, whereas the fusion inversion results lag behind precipitation by only 1 month, and the corresponding correlation coefficients are also higher than those of GNSS or GRACE/GFO inversion results. Therefore, the fusion method provided in this paper offers a reference for combining multiple data sources to obtain more reliable regional terrestrial water storage changes.

Key words: GNSS, GRACE/GFO, fusion inversion, terrestrial water storage change, southwest China

中图分类号:

P237

刘鹏, 段虎荣, 张成浩, 王金驰, 梁文康. 融合GNSS和GRACE/GFO数据反演西南地区陆地水储量变化[J]. 测绘通报, 2024, 0(10): 39-45.

LIU Peng, DUAN Hurong, ZHANG Chenghao, WANG Jinchi, LIANG Wenkang. Integrating GNSS and GRACE data to estimate terrestrial water storage changes in the southwestern region[J]. Bulletin of Surveying and Mapping, 2024, 0(10): 39-45.

参考文献

[1] JIN Shuanggen,ZHANG Tengyu.Terrestrial water storage anomalies associated with drought in southwestern USA from GPS observations[J].Surveys in Geophysics,2016,37(6): 1139-1156.
[2] 冯伟,王长青,穆大鹏,等.基于GRACE的空间约束方法监测华北平原地下水储量变化[J].地球物理学报,2017,60(5): 1630-1642.
[3] CHEN Jianli,CAZENAVE A,DAHLE C,et al.Applications and challenges of GRACE and GRACE follow-on satellite gravimetry[J].Surveys in Geophysics,2022,43(1): 305-345.
[4] 汪秋昱,饶维龙,张岚,等.GRACE时变重力信号反演方法研究的进展和展望[J].华中科技大学学报(自然科学版),2022,50(9): 104-116.
[5] SWENSON S,CHAMBERS D,WAHR J.Estimating geocenter variations from a combination of GRACE and ocean model output[J].Journal of Geophysical Research: Solid Earth,2008,113(B8):2007JB005338.
[6] ARGUS D F,FU Y,LANDERER F W.Seasonal variation in total water storage in California inferred from GPS observations of vertical land motion[J].Geophysical Research Letters,2014,41(6):1971-1980.
[7] 肖家豪,张双喜,韦瑜,等.基于GPS垂向位移测定云南地区陆地水储量及其对累积降雨的响应机制[J].测绘通报,2022(1): 61-65.
[8] JIANG Zhongshan,HSU Y J,YUAN Linguo,et al.Characterizing spatiotemporal patterns of terrestrial water storage variations using GNSS vertical data in Sichuan,China[J].Journal of Geophysical Research: Solid Earth,2021,126(12): e2021JB022398.
[9] RIETBROEK R,FRITSCHE M,DAHLE C,et al.Can GPS-derived surface loading bridge a GRACE mission gap？[J].Surveys in Geophysics,2014,35:1267-1283.
[10] LI Wanqiu,ZHANG Chuanyin,WANG Wei,et al.Inversion of regional groundwater storage changes based on the fusion of GNSS and GRACE data: a case study of Shaanxi-Gansu-Ningxia[J].Remote Sensing,2023,15(2): 520.
[11] LI Xianpao,ZHONG Bo,LI Jiancheng,et al.Joint inversion of GNSS and GRACE/GFO data for terrestrial water storage changes in the Yangtze River Basin[J].Geophysical Journal International,2023,233(3): 1596-1616.
[12] JIANG Zhongshan,HSU Y J,YUAN Linguo,et al.Monitoring time-varying terrestrial water storage changes using daily GNSS measurements in Yunnan,southwest China[J].Remote Sensing of Environment,2021,254: 112249.
[13] DILL R,DOBSLAW H.Numerical simulations of global-scale high-resolution hydrological crustal deformations[J].Journal of Geophysical Research: Solid Earth,2013,118(9): 5008-5017.
[14] 姜卫平,王锴华,李昭,等.GNSS坐标时间序列分析理论与方法及展望[J].武汉大学学报(信息科学版),2018,43(12): 2112-2123.
[15] LIU Ning,DAI Wujiao,SANTERRE R,et al.A MATLAB-based Kriged Kalman Filter software for interpolating missing data in GNSS coordinate time series[J].Gps Solutions,2018,22: 1-8.
[16] LOOMIS B D,RACHLIN K E,WIESE D N,et al.Replacing GRACE/GRACE-FO with satellite laser ranging: impacts on Antarctic Ice Sheet mass change[J].Geophysical Research Letters,2020,47(3): e2019GL085488.
[17] RODELL M,FAMIGLIETTI J S,WIESE D N,et al.Emerging trends in global freshwater availability[J].Nature,2018,557(7707): 651-659.
[18] FARRELL W E.Deformation of the Earth by surface loads[J].Reviews of Geophysics,1972,10(3): 761-797.
[19] MATTHEWS M V,SEGALL P.Estimation of depth‐dependent fault slip from measured surface deformation with application to the 1906 San Francisco earthquake[J].Journal of Geophysical Research: Solid Earth,1993,98(B7): 12153-12163.
[20] 姚朝龙,李琼,罗志才,等.利用广义三角帽方法评估GRACE反演中国大陆地区水储量变化的不确定性[J].地球物理学报,2019,62(3): 883-897.
[21] YANG Xinghai,YUAN Linguo,JIANG Zhongshan,et al.Investigating terrestrial water storage changes in Southwest China by integrating GNSS and GRACE/GRACE-FO observations[J].Journal of Hydrology: Regional Studies,2023,48: 101457.

融合GNSS和GRACE/GFO数据反演西南地区陆地水储量变化

Integrating GNSS and GRACE data to estimate terrestrial water storage changes in the southwestern region

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	邓南山, 刘洋, 李小伟, 斯小华, 雷磊. 基于北斗/GNSS PWV数据构建的短临强降雨模型及其应用[J]. 测绘通报, 2024, 0(9): 96-100.
[2]	胡祥祥, 宋宝, 石亚亚, 庞栋栋, 吴成永, 张利利, 李一蜚. 基于自适应优化选择-抗差自适应卡尔曼滤波混合模型的GNSS+5G组合定位[J]. 测绘通报, 2024, 0(7): 24-29.
[3]	李鲲鹏, 李麒麟, 杨雪峰, 孙洪斌, 渠述锋. 基于GNSS的五塔连续斜拉桥主梁上CPⅢ平面网快速测量方法试验研究及应用[J]. 测绘通报, 2024, 0(7): 83-87.
[4]	杨晓峰, 魏浩翰, 张强, 向云飞. 融合孤立森林和深度学习的GNSS-IR土壤湿度反演[J]. 测绘通报, 2024, 0(7): 88-94.
[5]	陶诗量, 王陈哲, 吴文坛, 陈永立. 单频低成本GNSS接收机单历元姿态解算新方法[J]. 测绘通报, 2024, 0(5): 71-76,126.
[6]	杨春宇, 尹航, 郭爽, 罗振华, 马凤富. 北斗/GNSS和精密水准测量综合方法在老采空塌陷区地表形变监测中的应用[J]. 测绘通报, 2024, 0(5): 142-146.
[7]	简灵活, 王新鹏, 陶庭叶. 基于VMD算法的GNSS-MR潮位反演方法[J]. 测绘通报, 2024, 0(4): 18-22,34.
[8]	唐卫明, 戚克培, 邓辰龙, 邹璇, 李洋洋, 胡泽奇. 因子图框架下里程计辅助GNSS/INS组合导航算法[J]. 测绘通报, 2024, 0(3): 63-68.
[9]	王式太, 杨可心, 殷敏, 马岳, 蒋威, 刘续, 魏嘉林. GNSS-IR多星融合的稳健回归土壤湿度反演方法[J]. 测绘通报, 2024, 0(3): 69-74.
[10]	钟昌海, 林自乐, 黄昕, 孔建. 长距离跨海高程基准传递方法研究及广西北部湾工程验证[J]. 测绘通报, 2024, 0(2): 161-164,182.
[11]	魏德宏, 禤键豪, 杨嘉伟, 张兴福, 余旭. 基于深度BP/ELMAN神经网络的山区GNSS高程转换精度分析[J]. 测绘通报, 2023, 0(9): 113-116,143.
[12]	李昂, 钟若飞, 刘正军, 谢东海, 吴伟, 张琰. GNSS失锁下融合描述子与粒子滤波的LiDAR定位方法研究[J]. 测绘通报, 2023, 0(8): 84-90.
[13]	靳鑫洋, 王斌, 刘晓云, 郭鑫伟, 田婕. 省级似大地水准面再精化可行性分析[J]. 测绘通报, 2023, 0(8): 151-154.
[14]	谭明明, 樊亚灵, 吕柯欣, 蒿赏, 徐涛, 蔡国田, 张杰, 李志才. 不同卫星导航系统组合监测的玛多地震同震形变与精度分析[J]. 测绘通报, 2023, 0(7): 80-84.
[15]	黄宁, 付世洋, 张露露, 熊玲, 黄良珂, 刘立龙. 顾及分段表达的中国区域对流层天顶湿延迟模型[J]. 测绘通报, 2023, 0(5): 96-100.