GNSS2TWS水文大地测量开源软件平台

      陆地水资源是国家社会经济发展和生态环境保护的重要战略性和基础性自然资源。水文大地测量学作为学科交叉的新兴领域，重点利用多源大地测量技术（GNSS、InSAR、卫星重力和卫星测高等）定量分析不同时空尺度的陆地水储量变化及其影响，助力揭示陆地水文循环规律和水文气候交互机制，服务水资源优化配置和社会可持续发展。因此，发展基于大地测量技术的陆地水储量反演算法和开源软件具有重要的现实意义，有助于深化水文学领域和大地测量学领域的跨学科合作，服务国家水资源调查和科学管理。

      GNSS2TWS系列开源工具由中山大学大地测量与导航团队和西南交通大学卫星大地测量团队联合开发。基于弹性质量负荷理论，从空域和谱域实现基于GNSS/GRACE的独立和联合反演算法和软件。目前已开源了基于空域和谱域的GNSS独立反演软件，即gnss2tws_green和gnss2tws_slepian。后续将逐步开源基于GNSS/GRACE的联合反演软件(gg2tws_green和gg2tws_slepian)，实现两类大地测量数据在空域/谱域的深度融合。GNSS2TWS系列软件已被国内外多所高校和科研院所使用，广泛应用于水文大地测量研究，已在陆地水储量变化监测和区域气候变化研究等方面发挥作用。

GNSS2TWS系列开源软件框架和科学应用示意图

      GNSS2TWS系列开源软件由姜中山副教授和研究生汤苗、杨兴海、成帅等共同开发，并在袁林果教授、冯伟教授和钟敏教授的指导下完成。研发成员专注大地测量的水文学应用研究，开发了系列陆地水储量变化反演开源工具。相关成果已在《Remote Sensing of Environment》、《Earth and Planetary Science Letters》、《Journal of Geophysical Research: Solid Earth》、《Journal of Hydrology》和《GPS Solutions》等国际权威期刊上发表。GNSS2TWS系列软件的开源有望进一步推动GNSS技术在水文大地测量学中的广泛应用，为水资源监测、气候变化评估以及极端水文事件研究提供技术支持。团队将继续秉承开源精神，优化软件功能，打造模块化、多功能的研究平台，为国内外学者提供便捷易用的工具，共同助力水文大地测量学科发展。

      GNSS2TWS系列开源软件获得了国家自然科学基金（41904015, 42061134010、42074021、41674084）等资助。欢迎国内外同行使用并反馈意见。

软件源码下载地址：

gnss2tws_green: https://github.com/jzshhh/gnss2tws_green

gnss2tws_slepian: https://github.com/jzshhh/gnss2tws_slepian

中山大学大地测量与导航团队网址：

https://sges.sysu.edu.cn/geodesy

意见反馈联系方式：

姜中山，jiangzhsh@mail.sysu.edu.cn

汤苗，miaotang@my.swjtu.edu.cn

杨兴海，yxh@my.swjtu.edu.cn

已发表部分成果如下：

Jiang, Z., Hsu, Y. J., Yuan, L., Feng, W., Yang, X., & Tang, M. (2022). GNSS2TWS: an open-source MATLAB-based tool for inferring daily terrestrial water storage changes using GNSS vertical data. GPS Solutions, 26(4), 114. https://doi.org/10.1007/s10291-022-01301-8

Jiang, Z., Tang, M., Wen, H., Yuan, L., & Chang, M. (2025). GNSS2TWS_Slepian: A software to recover GNSS-inverted terrestrial water storage changes based on Slepian basis functions. Earth Science Informatics, 18(1), 1-14. https://doi.org/10.1007/s12145-024-01559-1

Jiang, Z., Hsu, Y. J., Yuan, L., & Huang, D. (2021). Monitoring time-varying terrestrial water storage changes using daily GNSS measurements in Yunnan, southwest China. Remote Sensing of Environment, 254, 112249. https://doi.org/10.1016/j.rse.2020.112249

Jiang, Z., Hsu, Y. J., Yuan, L., Cheng, S., Feng, W., Tang, M., & Yang, X. (2022). Insights into hydrological drought characteristics using GNSS-inferred large-scale terrestrial water storage deficits. Earth and Planetary Science Letters, 578, 117294. https://doi.org/10.1016/j.epsl.2021.117294

Jiang, Z., Tang, M., Yang, X., Wen, H., Yuan, L., Shen, Y., Feng, W., & Zhong, M. (2024). Characterizing multifarious hydroclimatic patterns using geodetic measurements in the Australian mainland. Journal of Hydrology, 642, 131792. https://doi.org/10.1016/j.jhydrol.2024.131792

Jiang, Z., Hsu, Y. J., Yuan, L., Yang, X., Ding, Y., Tang, M., & Chen, C. (2021). Characterizing spatiotemporal patterns of terrestrial water storage variations using GNSS vertical data in Sichuan, China. Journal of Geophysical Research: Solid Earth, 126(12), e2021JB022398. https://doi.org/10.1029/2021JB022398

Jiang, Z., Hsu, Y. J., Yuan, L., Cheng, S., Li, Q., & Li, M. (2021). Estimation of daily hydrological mass changes using continuous GNSS measurements in mainland of China. Journal of Hydrology, 598, 126349. https://doi.org/10.1016/j.jhydrol.2021.126349

Tang, M., Yuan, L., Jiang, Z., Yang, X., Li, C., & Liu, W. (2023). Characterization of hydrological droughts in Brazil using a novel multiscale index from GNSS. Journal of Hydrology, 617, 128934. https://doi.org/10.1016/j.jhydrol.2022.128934

Tang, M., Yuan, L., Yang, X., Jiang, Z., Han, S. C., & You, W. (2024). Insights into water mass change in the Yangtze River Basin from the spectral integration of GNSS and GRACE observations. Earth and Planetary Science Letters, 644, 118929. https://doi.org/10.1016/j.epsl.2024.118929

Yang, X., Yuan, L., Jiang, Z., Tang, M., Feng, X., & Li, C. (2023). Investigating terrestrial water storage changes in Southwest China by integrating GNSS and GRACE/GRACE-FO observations. Journal of Hydrology: Regional Studies, 48, 101457. https://doi.org/10.1016/j.ejrh.2023.101457

Yang, X., Yuan, L., Tang, M., & Jiang, Z. (2025). Assessing and attributing flood potential in Brazil using GPS 3D deformation. Remote Sensing of Environment, 318, 114535. https://doi.org/10.1016/j.rse.2024.114535