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.

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 continuing impact between the Eurasia Plate and India results in the thickening and shortening of the N-S Tibetan Plateau. There has been strong tectonic movement along the boundary of the zones of deformation of the NE corner of the Tibetan plateau (NET) since the new tectonic period, with its dynamic mechanisms remaining controversial. Here, we use observations of 39 Continuous Global Positioning System (CGPS) gauges and 451 Crustal Movement Observation Network of China (CMONOC) campaign-mode stations to detect the three-dimensional deformation of the crust in the NET. Improved processing procedures are implemented to strengthen the patterns of strain throughout the NET. The principal component analysis (PCA) technique is introduced to decompose the time series into spatial eigenvectors and principal components (PCs), and the first three PCs are used to estimate and rectify common mode errors (CMEs). In addition, GRACE observations are used to detect deformation changes that account for non-tidal oceanic mass loading, hydrological loading, and surface pressure. The rectified deformation of the crust indicates the anisotropic nature of both the subsidence and uplift, and that the highest uplift rate of the Longmen Shan fault uplift reaches 7.13 ± 0.53 mm/yr. Finally, the horizontal velocity is further used to enumerate the strain rates throughout the NET. The results show that the shear band retained property in line with the strike-slip fault along the Altyn Tagh fault, the Qilian Shan faults, the Haiyuan fault, the West Qinling fault, the East Kunlun fault, and the Longmen Shan fault. In addition, the results further indicate that the whole NET shows a strong relationship with the mean principal rates of horizontal shortening strain. Extension and compression of the crust reasonably describe its sinking and uplifting.

On 21 May 2021, an Ms 6.4 earthquake struck Yangbi County, Dali Prefecture, Yunnan Province, China, which is the largest earthquake to hit this area since 1976. In this paper, we obtained the coseismic deformation of the Yangbi earthquake in Yunnan Province based on the interferometric synthetic aperture radar (InSAR) observation. After that, we obtained the fault geometry and slip distribution model of this earthquake via the two-step inversion method. The maximum deformation in the ascending orbit along the line of sight (LOS) direction was 7.3 cm, and the maximum deformation in the descending orbit along the LOS direction was 8.9 cm; the slip distribution model showed that the slip distribution of this earthquake was concentrated at a depth of 1–14 km, and the maximum slip was 0.6 m at a depth of 5 km. Based on the modeling result, it was inferred that the seismogenic fault of this earthquake was a dextral strike-slip fault on the west side of the Weixi-Qiaohou–Weishan fault. Combining the existing geological data and the changes in Coulomb stress caused by this earthquake, the seismic hazard and seismogenic structure in the area near the epicenter were analyzed and discussed, and the results showed that, in the northwest of the Weixi-Qiaohou fault zone, there will be an increased hazard of a future earthquake in the NW trend; thus, we should pay attention to this area.

Because of the characteristics of the strong suddenness of disasters, many secondary disasters, and high requirements for response timeliness, the development of integrated disaster monitoring with space-air-ground (SAG) multi-source data is a key scientific issue to deal with natural disasters and ensure the safety of people's lives and properties. However, the existing technical methods of emergency response information extraction and collaborative monitoring and evaluation are slightly weak, and the application service capability needs to be improved. Based on the formation mechanism and evolution law of earthquake disasters, this paper studies the SAG multi-source collaborative monitoring method. Using multi-source data, we not constructed the algorithm model of disaster evolution process simulation and risk dynamic assessment, but realized rapid disaster emergency assessment and intelligent emergency decision-making. At the same time, it has developed an integrated platform for earthquake disaster monitoring and smart spatial information services, which realizes the integrated expression of elements such as rapid access to multi-source spatiotemporal information, smart service models, and disaster monitoring information. We provide a task-driven software-hardware cooperative service application. In this way, the emergency response capability of regional earthquake disasters can be improved, the development of disaster prevention, mitigation, and relief emergency industries can be accelerated, and scientific and technological support can be provided, which ensures national development.

This chapter systematically explores the theoretical foundation of computational gravimetry and its application in the analysis of earthquake disasters. Firstly, the chapter introduces the historical development of computational gravimetry, the critical role of gravity in the earthquake process, and the significance of computational gravimetry in earthquake disaster research, as well as the limitations of traditional seismological methods. Subsequently, it outlines the characteristics and challenges of earthquake disasters and elaborates on the fundamental principles of computational gravimetry and the physical constraints in earthquake disaster research. Building on this foundation, the chapter analyzes the earthquake source mechanisms, seismic wave propagation characteristics, and their integration in model construction; examines the impact of crustal lithological structures on earthquake disasters; and discusses the relationship between temporal and spatial variations of the gravity field and earthquake disasters, as well as how to use gravity field changes for earthquake disaster prediction. The chapter also proposes methods for constructing and simulating earthquake disaster models and validates these models through case studies. The research indicates that computational gravimetry, as an emerging research method, has significant advantages in revealing the earthquake process, analyzing the causes of earthquake disasters, and formulating prevention and mitigation measures. However, to fully utilize its role in earthquake disaster research, continuous improvements in model construction, computational methods, and technological innovation are necessary. This chapter not only provides a theoretical basis and practical guidance for the prediction, prevention, and emergency response of earthquake disasters but also offers a new perspective and method for earthquake disaster prevention.