Excessive exploitation and massive coal mine closures have brought about extensive goafs in Shanxi where 8780 coal mines have been abandoned in the last 20 years. Acid mine drainage (AMD) poses severe environmental impact and has become a prominent problem in Shanxi abandoned coal mine areas, which has aggravated the shortage of water resources and threatened the safety of the local drinking water supply. The purpose of this review is to protect the precious water resources and maintain sustainable use in Shanxi coal mines and downstream. By retrieving and analyzing about 90 domestic and international publications, a critical review of the AMD research results in Shanxi abandoned coal mines is conducted from the perspective of the formation mechanism, migration and transformation, prediction, treatment and management. The results shows that pyrite is the prerequisite for the formation of AMD, oxygen is the inducement, water is the carrier, and Fe³⁺ and microorganisms are the catalyst. The roadway system and geological structure are the dominant control factors. Finally, current difficulties and future research are pointed out. It is necessary to further strengthen the systematic research on the geological and hydrogeological conditions of abandoned coal mines, and explore an efficient, cheap, environmental technology, and construct the pollution risk assessment model for the AMD treatment. This study provides a scientific basis for the comprehensive treatment and management of AMD in abandoned coal mines in Shanxi.

Accurate forecasting of annual runoff is necessary for water resources management. However, a runoff series consists of complex nonlinear and non-stationary characteristics, which makes forecasting difficult. To contribute towards improved prediction accuracy, a novel hybrid model based on the empirical mode decomposition (EMD) for annual runoff forecasting is proposed and applied in this paper. Firstly, the original annual runoff series is decomposed into a limited number of intrinsic mode functions (IMFs) and one trend term based on the EMD, which makes the series stationary. Secondly, it will be forecasted by a least squares support vector machine (LSSVM) when the IMF component possesses chaotic characteristics, and simulated by a polynomial method when it does not. In addition, the reserved trend term is predicted by a Gray Model. Finally, the ensemble forecast for the original runoff series is formulated by combining the prediction results of the modeled IMFs and the trend term. Qualified rate (QR), root mean square errors (RMSE), mean absolute relative errors (MARE), and mean absolute errors (MAE) are used as the comparison criteria. The results reveal that the EMD-based chaotic LSSVM (EMD-CLSSVM) hybrid model is a superior alternative to the CLSSVM hybrid model for forecasting annual runoff at Shangjingyou station, reducing the RMSE, MARE, and MAE by 39%, 28.6%, and 25.6%, respectively. To further illustrate the stability and representativeness of the EMD-CLSSVM hybrid model, runoff data at three additional sites, Zhaishang, Fenhe reservoir, and Lancun stations, were applied to verify the model. The results show that the EMD-CLSSVM hybrid model proved its applicability with high prediction precision. This approach may be used in similar hydrological conditions.

Due to climate change and human activities over the last fifty years, the spring flow volume of karst groundwater has sharply diminished in China. Climate change is one of the critical factors that initiates a series of karst hydrogeologic and water ecological environmental problems, because the precipitation shows a decreasing trend while the temperature shows an increasing trend. The Jinci Spring is one of the largest, most famous springs in northern China. This study employed data from the Taiyuan Meteorological Station and ten precipitation stations in and around the Jinci Spring region as well as the runoff data gathered from two hydrological monitoring stations during 1960–2012. The sliding average method and the Mann-Kendall test were used to analyze the variation tendency of precipitation, temperature, and land evaporation in this area. Finally, the following were calculated: the varying pattern of the karst groundwater recharge amount and the response of the recharge amount to precipitation, land evaporation, and river runoff by quantitative analysis. The results indicated that the precipitation and land evaporation amount decreased at first and then subsequently increased. Likewise, the variation trend of the karst groundwater recharge amount in the spring region was roughly consistent with the precipitation variation pattern. In contrast, the temperature displayed an increasing trend. The climate change resulted in a reduction of the karst groundwater recharge amount, and it had the greatest influence in the 1990s, which caused the karst groundwater recharge amount to decrease 26.75 mm as compared to that of the 1960s (about 39.68% lower than that of the 1960s). The Jinci Spring had zero flow during this period. The reduction in precipitation was one of main factors that caused the cutoff of the Jinci Spring.