Underground mining severely lowers the water table and worsens the ecological environment. To determine the mechanism influencing drawdown in unconsolidated aquifers induced by deep mining combined with overburden movement, with a view to environmental protection, physical simulations for three different depths were designed to investigate and contrast deformation and fracture distribution characteristics at diverse depths after mining. The impacts on overlying unconsolidated aquifers were then analyzed by monitoring overburden movement and ground water level data, and the deformation limit was estimated. Modeling results indicated that coal mining had different impacts on the overlying unconsolidated aquifer via various mechanisms of groundwater level reduction. The aquifer outside the fractured water-conducting zone could be affected by coal mining, and the water tables dropped rapidly. The failure and the deformation features of the aquitard floor were the key to revealing the mechanism of groundwater depletion in deep mining. The effects on aquitard decreased as the distance from the working face increased. A relationship between the strata deformation and the drawdown rate was established; that is, the rate of decline of the groundwater level did not accelerate until the maximum subsidence of the aquitard floor exceeded approximately 0.9 m, or the subsidence-bending area of the aquitard floor exceeded approximately 156 m².

Straw mulching is an effective agricultural technology to reduce soil water loss in arid and semi-arid areas. Herein, the soil temperature and soil water content of bare land (LD) and 5 cm (JG5), 10 cm (JG10), 15 cm (JG15), 20 cm (JG20) and 30 cm (JG30) straw mulch thicknesses were measured through field experiments performed to assess the soil water evaporation using the simultaneous heat and water model during a freeze–thaw period. The results showed that the inhibiting effect of straw mulching on soil water evaporation during the freeze-thaw period reached 24–56.7%, and straw mulch reduced the range of daily soil water evaporation by 2.02–2.48 mm, the effects of random factors on the daily soil water evaporation were significantly decreased. The highest soil water evaporation rate occurs during the unstable freezing stage, and the lowest occurs during the stable freezing stage. When the straw mulch thickness exceeded 10 cm, the effect of increasing straw mulch thickness on daily soil water evaporation was reduced. The straw mulch layer could not completely inhibit the effect of the external environment on soil water evaporation even when the straw mulch thickness was increased to 30 cm. This research results can provide a basis for the scientific evaluation and prevention of soil water evaporation in arid and semi-arid areas.

Accurate groundwater level (GWL) prediction is crucial in groundwater resource management. Currently, it relies mainly on physics-based models for prediction and quantitative analysis. However, physics-based models used for prediction often have errors in structure, parameters, and data, resulting in inaccurate GWL predictions. In this study, machine learning algorithms were used to correct the prediction errors of physics-based models. First, a MODFLOW groundwater flow model was created for the Hutuo River alluvial fan in the North China Plain. Then, using the observed GWLs from 10 monitoring wells located in the upper, middle, and lower parts of the alluvial fan as the test standard, three algorithms—random forest (RF), extreme gradient boosting (XGBoost), and long short-term memory (LSTM)—were compared for their abilities to correct MODFLOW’s predicted GWLs of these 10 wells under two sets of feature variables. The results show that the RF and XGBoost algorithms are not suitable for correcting predicted GWLs that exhibit continuous rising or falling trends, but the LSTM algorithm has the ability to correct them. During the prediction period, the LSTM2 model, which incorporates additional source–sink feature variables based on MODFLOW’s predicted GWLs, can improve the Pearson correlation coefficient (PR) for 80% of wells, with a maximum increase of 1.26 and a minimum increase of 0.02, and can reduce the root mean square error (RMSE) for 100% of the wells with a maximum decrease of 1.59 m and a minimum decrease of 0.17 m. And it also outperforms the MODFLOW model in capturing the long-term trends and short-term seasonal fluctuations of GWLs. However, the correction effect of the LSTM1 model (using only MODFLOW’s predicted GWLs as a feature variable) is inferior to that of the LSTM2 model, indicating that multiple feature variables are superior to a single feature variable. Temporally and spatially, the greater the prediction error of the MODFLOW model, the larger the correction magnitude of the LSTM2 model.

In this study, we conducted a comprehensive analysis of surface water area changes in 9235 small and medium-sized reservoirs across China from 1985 to 2021. Using Landsat and Sentinel-2 satellite data, our investigation delved into the spatiotemporal changes in these reservoirs and explored reservoir-based droughts. Using a robust algorithm, we examined the spatial and temporal patterns of surface water area (SWA) change on a national scale. While cumulative SWA remained stable at the national level, our analysis revealed diverse variations in individual catchments. To improve our understanding of reservoir-based hydrological drought, we introduced the Standardized Area Index (SAI). This index serves as a metric for quantifying drought severity and reveals a distinct north–south divide in China. The study shows that water-scarce northern regions experience prolonged and severe droughts, necessitating increased conservation efforts. Conversely, the water-rich southern region prioritizes increasing reservoir capacity. Our findings underscore the importance of small and medium-sized reservoirs in shaping China’s water resources landscape. Furthermore, this study provides valuable insights into the nuanced characteristics of droughts, facilitating the development of regionally tailored water management strategies.