In recent years, the groundwater level (GWL) and its dynamic changes in the Hebei Plain have gained increasing interest. The GWL serves as a crucial indicator of the health of groundwater resources, and accurately predicting the GWL is vital to prevent its overexploitation and the loss of water quality and land subsidence. Here, we utilized data-driven models, such as the support vector machine, long-short term memory, multi-layer perceptron, and gated recurrent unit models, to predict GWL. Additionally, data from six GWL monitoring stations from 2018 to 2020, covering dynamical fluctuations, increases, and decreases in GWL, were used. Further, the first 70% and remaining 30% of the time-series data were used to train and test the model, respectively. Each model was quantitatively evaluated using the root mean square error (RMSE), coefficient of determination (R²), and Nash–Sutcliffe efficiency (NSE), and they were qualitatively evaluated using time-series line plots, scatter plots, and Taylor diagrams. A comparison of the models revealed that the RMSE, R², and NSE of the GRU model in the training and testing periods were better than those of the other models at most groundwater monitoring stations. In conclusion, the GRU model performed best and could support dynamic predictions of GWL in the Hebei Plain.

Water resources are scarce in arid or semiarid areas; groundwater is an important water source to maintain residents’ lives and the social economy; and identifying the hydrogeochemical characteristics of groundwater and its seasonal changes is a prerequisite for sustainable use and protection of groundwater. This study takes the Hongjiannao Basin as an example, and the Piper diagram, the Gibbs diagram, the Gaillardet diagram, the Chlor-alkali index, the saturation index, and the ion ratio were used to analyze the hydrogeochemical characteristics of groundwater. Meanwhile, based on self-organizing maps (SOM), quantification error (QE), topological error (TE), and the K-means algorithm, groundwater chemical data analysis was carried out to explore its seasonal variability. The results show that (1) the formation of groundwater chemistry in the study area was controlled by water–rock interactions and cation exchange, and the hydrochemical facies were HCO₃-Ca type, HCO₃-Na type, and Cl-Na type. (2) Groundwater chemical composition was mainly controlled by silicate weathering and carbonate dissolution, and the dissolution of halite, gypsum, and fluorite dominated the contribution of ions, while most dolomite and calcite were in a precipitated state or were reactive minerals. (3) All groundwater samples in wet and dry seasons were divided into five clusters, and the hydrochemical facies of clusters 1, 2, and 3 were HCO₃-Ca type; cluster 4 was HCO₃-Na type; and cluster 5 was Cl-Na type. (4) Thirty samples changed in the same clusters, and the groundwater chemistry characteristics of nine samples showed obvious seasonal variability, while the seasonal changes of groundwater hydrogeochemical characteristics were not significant.

In addition to implementing active water resource allocation measures to solve the serious groundwater overexploitation problem caused by large-scale rice planting in the Sanjiang Plain, the reasonable adjustment of rice planting areas is another method of doing so. From the perspective of groundwater recharge and discharge balance, this paper carries out a novel assessment of suitable rice planting areas in the Sanjiang Plain, which is expected to provide a new method for the implementation of land exploitation according to water resource conditions. The technical scheme is as follows: by adjusting the water resource allocation data and rice spatial distribution data in the surface water–groundwater coupled model (baseline model with dynamic land use) in the Sanjiang Plain, static land-use models under different rice planting scales were established. Through simulation and comparison, the rice area that could achieve the balance of groundwater recharge and discharge was considered the suitable rice planting scale in the Sanjiang Plain. The results showed that the average annual change in groundwater storage from 2000 to 2014 simulated by the baseline model was −0.313 billion m³, indicating that there was space for further optimization and adjustment of the rice planting scale in the Sanjiang Plain. By comparing the static land-use models of each year under the current water resource allocation pattern, the rice area of 1.021 million hm² in 2005 could effectively realize the balance of groundwater recharge and discharge. Under the new water resource allocation pattern of 2035, the water resource conditions in the Sanjiang Plain will be greatly improved, which can support a rice planting scale of 3.058 million hm² on the basis of ensuring the balance of groundwater recharge and discharge. Our research results can provide a reference for water resource allocation and land-use optimization regulation in the Sanjiang Plain.

It is reasonable to simulate the hydrologic cycle in regions with drastic land use change using a distributed hydrologic model in the dynamic land use mode (dynamic mode). A new dynamic mode is introduced into an object-oriented modularized model for basin-scale water cycle simulation (MODCYCLE), a distributed hydrologic model based on sub-watersheds, and the hydrological response unit (HRU). The new mode can linearly interpolate data for the years without land use data and consistently transfer HRU water storage between two adjacent years after a land use data update. The hydrologic cycle simulation of the Sanjiang Plain in China was carried out from 2000 to 2014 in the dynamic mode using land use maps of 2000, 2005, 2010, and 2014. Through calibration and validation, the performance of the model reached a satisfactory level. Replacing the land use data of the calibrated model using that of the year 2000, a comparison model in the static land use mode (static mode) was built (i.e., land use unchanged since 2000). The hydrologic effects of land use change were analyzed using the two models. If the land use pattern remained unchanged from 2000, despite the average annual runoff increasing by 4% and the average annual evapotranspiration decreasing by 4% in this region only, the groundwater storage of the plain areas in 2014 would increase by 4.6 bil. m³ compared to that in 2000, rather than the actual decrease of 4.7 bil. m³. The results show that the fluxes associated with groundwater are obviously more disturbed by land use change in the Sanjiang Plain. This study suggests that the dynamic mode should be used to simulate the hydrologic cycle in regions with drastic land use change, and the consistent transfer of HRU water storage may be considered in the dynamic mode.

This study aims to improve the accuracy and interpretability of deep groundwater level forecasting in Cangzhou, a typical overexploitation area in the North China Plain. To address the limitations of traditional models and existing machine learning approaches, we develop a Stacking ensemble learning framework that integrates meteorological, spatial, and anthropogenic variables, including lagged groundwater levels to reflect aquifer memory. The model combines six heterogeneous base learners with a meta-model to enhance prediction robustness. Performance evaluation shows that the ensemble model consistently outperforms individual models in accuracy, generalization, and spatial adaptability. Scenario-based simulations are further conducted to assess the effects of the South-to-North Water Diversion Project. Results indicate that the diversion project significantly mitigates groundwater depletion, with the most overexploited zones showing water level recovery of up to 17 m compared to the no-diversion scenario. Feature importance analysis confirms that lagged water levels and pumping volumes are dominant predictors, aligning with groundwater system dynamics. These findings demonstrate the effectiveness of ensemble learning in modeling complex groundwater behavior and provide a practical tool for water resource regulation. The proposed framework is adaptable to other groundwater-stressed regions and supports dynamic policy design for sustainable groundwater management.