Land subsidence is an environmental geological phenomenon mainly caused by groundwater overexploitation. Long-term overexploitation of groundwater not only causes compaction of aquifer thickness and surface deformation but also leads to the loss of aquifer water storage capacity. The skeleton water storage coefficient (S-k) is an important parameter for evaluating the water storage capacity of aquifer groups. This article proposes a new research framework for obtaining the S-k of different aquifer groups: combining permanent scatter for SAR interferometry technology and a multiscale geographic weighted regression model to obtain subsidence information for different aquifer groups, inverting the S-k of different aquifer groups from the spatial scale, and discussing the deformation characteristics of soil layers under different water head change modes to evaluate the deformation and water storage characteristics of different aquifer groups. This framework is applied to the land subsidence region of the Beijing Plain. We calculated that the settlement proportions of different compression layer groups were 14.75%, 23.65%, 33.44%, and 28.16%. Due to the different lithological compositions and groundwater exploitation of different aquifers, the S-k values exhibit different spatial distribution characteristics. With the continuous development of subsidence, the water storage performance of the aquifer group is continuously declining. These findings contribute to managing the sustainable use of groundwater resources and controlling subsidence. It is demonstrated that the research framework proposed in this article can serve as an effective tool for obtaining settlement information and the S-k of different aquifer groups.

Permafrost regions store a large amount soil organic carbon (SOC), and the decomposition of these carbon pools can release greenhouse gases and further strength climate warming. An explicit spatial distribution of SOC is one of the basic databases for Earth System Models. However, efficient approaches for obtaining the spatial distribution of SOC remain challenging, especially in mountainous areas which are characterized by complex terrains. Here, we modeled the spatial SOC distribution using the geographically weighted regression (GWR) approach in an area on the eastern part of the Qinghai-Tibetan Plateau (QTP). We analyzed multiple environmental variables and soil profile data (n = 73) to find the best prediction models for the SOC density (SOCD) for the 0-50 cm layers. The results showed that normalized difference vegetation index (NDVI), elevation, and slope gradient are the significant predictors for the SOCD. For the upper 50 cm soil layers, the SOCD ranged from 1.08 to 18.32 kgm(-2), with higher values in mountain slopes but lower values in mountain valleys and basins. The GWR model had a higher prediction accuracy in the modeling SOCD in comparison with other models such as ordinary kriging (OK) interpolation, multiple linear regression (MLR) model. Our results showed that GWR model is a useful tool for modeling of SOC distribution and potentially can be integrated into Earth system models in areas of complex terrains.