The terrestrial water storage anomaly (TWSA) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite and its successor GRACE Follow-On (GRACE-FO) provides a new means for monitoring floods. However, due to the coarse temporal resolution of GRACE/GRACE-FO, the understanding of flood occurrence mechanisms and the monitoring of short-term floods are limited. This study utilizes a statistical model to reconstruct daily TWS by combining monthly GRACE observations with daily temperature and precipitation data. The reconstructed daily TWSA is utilized to monitor the catastrophic flood event that occurred in the middle and lower reaches of the Yangtze River basin in 2020. Furthermore, the study compares the reconstructed daily TWSA with the vertical displacements of eight Global Navigation Satellite System (GNSS) stations at grid scale. A modified wetness index (MWI) and a normalized daily flood potential index (NDFPI) are introduced and compared with in situ daily streamflow to assess their potential for flood monitoring and early warning. The results show that terrestrial water storage (TWS) in the study area increases from early June, reaching a peak on 19 July, and then receding till September. The reconstructed TWSA better captures the changes in water storage on a daily scale compared to monthly GRACE data. The MWI and NDFPI based on the reconstructed daily TWSA both exceed the 90th percentile 7 days earlier than the in situ streamflow, demonstrating their potential for daily flood monitoring. Collectively, these findings suggest that the reconstructed TWSA can serve as an effective tool for flood monitoring and early warning.

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.

Dynamic deflection deformation is a crucial index which can reflect the healthy operation of bridges; due to their limitations, monitoring technologies (e.g., sensors, automatic total stations, and GPS) cannot perform high-frequency and whole-process non-contact dynamic deformation monitoring. Therefore, taking the Jinan Fenghuangshan Road Bridge as an example, this paper developed key low-cost and high-precision close-range photogrammetry technology and combined this with GB-RAR technology to remotely monitor bridge dynamic deformation. The results indicate that the measurement accuracy pertaining to the technology, which is based on an isometric virtual surface, is approximately 0.5 mm when the monitoring distance is 250 m, which can meet the accuracy requirements of bridge deformation monitoring. A vehicle’s dynamic load is transmitted in the form of a stress wave inside the bridge, and with regard to span, the wave is most significant in the 1/2 to 1/4 range. This study observes that the maximum deflection deformation of the bridge is 47.2 mm, which is within the allowable deformation range of the General Specifications for Design of Highway Bridges and Culverts. The research results herein provide technical support and data reference for safety monitoring, and for the reinforcement and maintenance of bridges such as the Fenghuangshan Road Bridge.

The deep learning method based on multi-channel convolutional neural network(MCCNN) can be used for gravity recovery from altimetry and bathymetry data. Compared with the traditional inverse Vening Meinesz (IVM) method, the MCCNN method enhances gravity accuracy but introduces long-wavelength gravity errors in training data-scarce areas. The Enhanced MCCNN method (EMCCNN) is proposed, which treats geo-locations in the input layer as nodes rather than channels. Evaluated by independent ship-borne data, the EMCCNN method demonstrates a gravity accuracy improvement of 0.05-0.24 mGal compared to the IVM method, maintaining consistent gravity accuracy compared to the MCCNN method. Additionally, assessed by a recognized global marine gravity model, the EMCCNN method achieves the highest accuracy. Particularly in regions with limited training data, the EMCCNN method outperforms the MCCNN method in minimizing gravity differences relative to assessment data. In the wavelengths larger than 100 km, the gravity noise for EMCCNN method can be reduced by up to about 50% compared to MCCNN method. These findings highlight EMCCNN’s effectiveness in improving gravity recovery in training data-scarce areas.

Satellite altimetry technology has unparalleled advantages in the monitoring of hydrological resources. After decades of development, satellite altimetry technology has achieved a perfect integration from the geometric research of geodesy to the natural resource monitoring research. Satellite altimetry technology has shown great potential, whether solid or liquid. In general, this paper systematically reviews the development of satellite altimetry technology, especially in terms of data availability and program practicability, and proposes a multi-source altimetry data fusion method based on deep learning. Secondly, in view of the development prospects of satellite altimetry technology, the challenges and opportunities in the monitoring application and expansion of surface water changes are sorted out. Among them, the limitations of the data and the redundancy of the program are emphasized. Finally, the fusion scheme of altimetry technology and deep learning proposed in this paper is presented. It is hoped that it can provide effective technical support for the monitoring and application research of hydrological resources.

The redistribution of surface mass (e.g., atmosphere, soil water, oceans, and groundwater) can cause load responses, resulting in vertical deformations of the crust. Indeed, the global navigation satellite system (GNSS)-based continuously operating reference stations (CORS) are able to accurately measure the vertical deformation caused by surface mass loads. In this study, the CORS was used to invert groundwater storage anomalies (GWSA), represented by the equivalent water height (EWH), after removing the effect of the non-groundwater surface mass load (atmospheric, groundwater, and non-tidal oceanic loads) from the vertical deformation monitored by CORS. In addition, the global and regional high-resolution surface mass models were combined to calculate the high-precision load deformation field in in western Yunnan using the remove–restore method, thereby obtaining more accurate surface mass load data and improving the accuracy of the inverted GWSA results. In order to assess the feasibility of the CORS inversion for the GWSA used, 66 CORS stations in western Yunnan Province were considered, presenting weekly GWSA data from 10 January 2018 to 31 December 2020. The results revealed significant seasonal variation in GWSA in the study area, showing an amplitude range of −200–200 mm. This approach is based on the already-established CORS network without requiring additional set-up costs. In addition, the reliability of CORS inverse results was assessed using Gravity Recovery and Climate Experiment (GRACE) inverse results and actual groundwater monitoring data. According to the obtained results, GWSA can be monitored by both CORS and GRACE data; however, CORS provided a more effective spatiotemporal resolution of GWSA. Therefore, the CORS network combined with surface mass load data is able to effectively monitor the spatiotemporal dynamics of GWSA in small-scale areas and provides important references for the study of hydrology.