Dynamic change of groundwater storage is one of the most important topics in the sustainable management of groundwater resources. Groundwater storage variations are firstly isolated from the terrestrial water storage change using the Global Land Data Assimilation System (GLDAS). Two datasets are used: (1) annual groundwater resources and (2) groundwater storage changes estimated from point-based groundwater level data in observation wells. Results show that the match between the GRACE-derived groundwater storage variations and annual water resources variation is not good in six river basins of Northern China. However, it is relatively good between yearly GRACE-derived groundwater storage data and groundwater storage change dataset in Huang-Huai-Hai Plain and the Song-Liao Plain. The mean annual depletion rate of groundwater storage in the Northern China was approximately 1.70 billion m³ yr⁻¹ from 2003 to 2012. In terms of provinces, the yearly depletion rate is higher in Jing-Jin-Ji (Beijing, Tianjin, and Hebei province) and lowest in Henan province from 2003 to 2012, with the rate of 0.70 and 0.21 cm yr⁻¹ Equivalent Water Height (EWH), respectively. Different land surface models suggest that the patterns from different models almost remain the same, and soil moisture variations are generally bigger than snow water equivalent variations.

This paper first presents CycleDance, a novel dance style transfer system that transforms an existing motion clip in one dance style into a motion clip in another dance style while attempting to preserve the motion context of the dance. CycleDance extends existing CycleGAN architectures with multimodal transformer encoders to account for the music context. We adopt a sequence length-based curriculum learning strategy to stabilize training. Our approach captures rich and long-term intra-relations between motion frames, which is a common challenge in motion transfer and synthesis work. Building upon CycleDance, we further propose StarDance, which enables many-to-many mappings across different styles using a single generator network. Additionally, we introduce new metrics for gauging transfer strength and content preservation in the context of dance movements. To evaluate the performance of our approach, we perform an extensive ablation study and a human study with 30 participants, each with 5 or more years of dance experience. Our experimental results show that our approach can generate realistic movements with the target style, outperforming the baseline CycleGAN and its variants on naturalness, transfer strength, and content preservation. Our proposed approach has potential applications in choreography, gaming, animation, and tool development for artistic and scientific innovations in the field of dance.

GRACE observations and land subsidence data derived from InSAR both assess groundwater storage changes. However, GRACE data at local scales are restricted by the coarser spatial resolution of satellite systems, and inversion of Groundwater Storage Anomalies (GWSA) by InSAR requires extensive and unavailable lithological data. Here, we propose a New Subsidence Feature Weighted Combination (NSFWC) scheme to enhance the spatial resolution of GRACE-derived GWSA from 0.5° to 0.05°. This method can not only retain the spatial distribution of groundwater changes but also reflect local details related to surface subsidence. A case study was executed to evaluate the performance of the NSFWC scheme in the Beijing Plain, which has seriously overexploited groundwater. Results showed that the simulated GWSA were consistent with in situ measurements in most regions, with a correlation coefficient of 0.85 and an RMSE of 4.41 mm/year. Additionally, there were 22 overexploited wells in the Beijing Plain, although groundwater levels generally recovered after the South to North Water Diversion Project. Simultaneously, four cones of depression were detected by the InSAR technology, where the maximum cumulative subsidence and subsidence rate achieved −198.52 mm and −53.09 mm/year, respectively. This paper provides data support and technical guarantees for small-scale groundwater resources management.

Obtaining high-resolution products that can accurately estimate the spatiotemporal changes in regional water resources is essential for the rational utilization of water resources. The temporal gravity field model derived from the Gravity Recovery and Climate Experiment (GRACE) satellite can effectively monitor the regional terrestrial water storage (TWS) changes. Nevertheless, the main shortcoming of GRACE products is the coarse spatial resolution which limits its applicability to small-scale regions. Herein, we propose a geographically weighted regression downscaled model (GWRDM) to improve the spatial resolution of TWS from 0.5° to 0.1° and validate it against the downscaled results provided by the spatial global regression downscaled model (SGRDM) and temporal grid regression downscaled model (TGRDM). The results show that the downscaled products based on GWRDM outperform SGRDM and TGRDM. The GWRDM not only effectively improves the spatial resolution of GRACE products but also maintains the intensity of the original signal. Similarly, the GWRDM is further used to downscale the groundwater storage (GWS) and validated against the in-situ observations. The downscaled GWS based on GWRDM agrees well with in-situ observations. On the monthly scale, the average Correlation Coefficient ( CC ) for both is 0.53 and above 0.70 for some in-situ observations. On the annual scale, 81.25% and 37.50% of the in-situ observations have CC values larger than 0.60 and 0.90, respectively, which further verifies the reliability of GWRDM. Consequently, the GWRDM provides a practical algorithm for obtaining high-resolution water storage estimates, which is expected to provide a reference for small-scale regional water resources management.

Overexploitation of groundwater resources has caused groundwater-related problems all over the world. Effective groundwater governance is a favorable guarantee for its protection and sustainable utilization. Accurate prediction of groundwater level (GWL) or depth to groundwater (GWD) plays an important role in groundwater resource management. Due to the limitations and complexity of numerical models, this study aims to develop surrogate models that can dually control the GWL (or GWD) and groundwater quantity (GWQ) in each district of the Beijing Plain, China, using the methods of multiple linear regression (MLR) and back propagation artificial neural network (BP-ANN). This study used 180 monthly GWD data records, including the first 168 data records for model development (training) and the remaining 12 data records for model verification. The results indicate that the Nash–Sutcliffe efficiency coefficient (NSE) and correlation coefficient (R) for both the MLR and BP-ANN models are high in most districts and that the MLR models are more appropriate in this study. Fifteen scenarios under different conditions of groundwater use and precipitation are designed to demonstrate the applicability of the developed model in groundwater management. The surrogate models are effective tools that can be used by decision-makers for groundwater management.