Inversion of Regional Groundwater Storage Changes Based on the Fusion of GNSS and GRACE Data: A Case Study of Shaanxi–Gansu–Ningxia

Abstract

This paper aims to address the limitations of the distribution number and uniformity of Continuously Operating Reference Stations (CORS) and their impact on the reliability of inverting regional groundwater storage (GWS) based on Green’s function method and using global navigation satellite system (GNSS) data. A fusion method on the inversion of regional GWS changes from GNSS and the Gravity Recovery and Climate Experiment (GRACE) was proposed in this paper. Taking the Shaanxi–Gansu–Ningxia (SGN) region as an example, the in situ groundwater level data from ten CORS stations and eight wells were used for test analyses. In this paper, an atmospheric pressure model from the European Centre for Medium-Range Weather Forecasts (ECMWF), a global land data assimilation system (GLDAS), a WaterGAP global hydrology model (WGHM), and mean sea level anomaly (MSLA) data were used to quantitatively monitor the influence of vertical deformation caused by non-tidal environmental load. After deducing these loading deformations from the filtered time series of non-linear monthly geodetic height from the GNSS, the GWS changes in the SGN region from 2011 to 2014 were inverted. Meanwhile, the change in surface water storage from the GLDAS and WGHM models were removed from the terrestrial water storage (TWS) changes derived from GRACE. On this basis, the remove–restore theory in the Earth’s gravity field was introduced to both fuse the inversion results and obtain the regional GWS changes based on the fusion method. The results showed the following: (1) The local characteristics from the fusion results were more prominent than those of GRACE on the spatial scale, such as in the southwest and northeast in the study area. In addition, the fusion results were more uniform than those from GNSS, especially for the sparse and missing areas in which CORS stations were located, and the local effect was weakened. (2) On the time scale, compared with GRACE, the trends in GWS changes obtained from the fusion method and from GNSS inversion were roughly the same as the in situ groundwater level changes. (3) For the in situ groundwater wells “6105010031” and “6101260010”, the correlation coefficients of the fusion result were 0.53 and 0.56, respectively. The accuracy of the fusion method was slightly higher than that from GNSS, which indicates that the fusion method may be more effective for areas where CORS stations are missing or sparsely distributed. The methods in this paper can provide significant reference material for hydrodynamic research, sustainable management of water resources, and the dynamic maintenance of height data.

The analysis on groundwater storage variations from GRACE/GRACE-FO in recent 20 years driven by influencing factors and prediction in Shandong Province, China

“…The could be obtained from GRACE data. Because the GLDAS model is widely used to estimate the SnWS and SWS 4 – 6 . Therefore, the GLDAS with 2.1 version hydrological model of the same period can be adopted to obtain , , .…”

“…Additionally, accurately predicting the GWS change can provide an important reference for water resources planning and management. However, the instability and mutability for the time series of GWS changes, as well as the small-sample characteristics of GRACE data, which can bring some difficulties to accurately prediction 6 . The prediction of time series is one of the leading research topics in artificial intelligence 7 – 10 .…”

Changes of Terrestrial Water Storage in the Yellow River Basin Under Global Warming

“…Previous studies have indicated that the latest version of the WaterGAP 2.2d model exhibits reasonable performance in simulating key variables such as TWS and human water abstraction (Müller et al [51] ). Therefore, it is used for studying water storage and human water usage (An et al [52] ; Li et al [53] ). We selected data on potential water withdrawals and potential consumptive water use for four sectors: irrigation, industrial, domestic, and livestock, spanning from 2003 to 2016.…”

Height Variations in the Mississippi River Basin From Daily Time-Varying Satellite Gravity Data and Hydrological Model

“…Since GRACE and GNSS-observed surface vertical deformation are independent of each other, the results of GRACE calculations can be validated by comparison [17]. Li et al [18] combined GNSS and GRACE data to estimate changes in groundwater storage in the Shaanxi-Gansu-Ningxia region.…”