Artificial underground reservoirs have changed the hydrological cycle from its natural condition. This modification may trigger a series of negative environmental effects both at the local and regional levels. This study investigated the impact of the Wanghe artificial underground reservoir on groundwater flow and quality in the reservoir and its downstream area. Wanghe is a typical artificial underground reservoir scheme in China, which assumes the dual function of fresh-water preservation and control of seawater intrusion. The groundwater flow pattern has changed after the reservoir construction, and the water level in the reservoir rose rapidly. Evaluation of long-term groundwater level fluctuation suggested that the reservoir deprived the downstream aquifer of the runoff, which it received under the natural flow regime. A preliminary isotopic evaluation using 3H was developed to understand the groundwater flow and renewal rates in the study area. The uniform distribution of tritium levels in the reservoir indicated that the stored water was well-mixed in both horizontal and vertical directions. The intervention on groundwater circulation also made differences in groundwater renewal rates between stored and downstream water. Field investigations on groundwater nitrogen pollution showed that the construction of the artificial underground reservoir resulted in nitrate accumulation in the stored water. Agriculturally derived nitrate was the largest contributor, and NO 3 − concentration varied considerably over time due to fertilization and irrigation activities, rainfall, and denitrification. NO 3 − -N distributed homogeneously in the reservoir, which was attributed to the construction of the subsurface dam, land use pattern and artificial groundwater flow.
In China, where some regions are over-reliant on groundwater, groundwater consumption is faster than replenishment, which results in a continuous decrease in the groundwater level. Here, we applied spatial and temporal methods to analyze the spatiotemporal variations in groundwater in China from GRACE, GRACE-FO, and GLDAS data. From a national perspective, groundwater storage showed a decreasing trend in northern China and an increasing trend in southern China. The results showed that the rates of groundwater depletion in North China, the Loess Plateau, and Northwest China were −10.09 ± 0.94, −10.05 ± 1.05, and –4.91 ± 0.28 mm y−1 equivalent height of water from 2003 to 2019, respectively. Furthermore, the groundwater in South China, the middle-lower Yangtze River, and the Ch-Yu region had a positive trend, with rates of 7.26 ± 1.51, 7.73 ± 1.35, and 3.61 ± 0.53 mm y−1 equivalent height of water, respectively. We also found that groundwater storage fluctuated slightly before 2016 on the Qinhai-Tibet Plateau and in Northeast China and decreased significantly after 2016. The Yun-Gui Plateau had a fluctuating trend. Investigating the spatiotemporal variation in groundwater storage in China can provide data for initiating regional ecological and environmental protection.
Water resource has become a key constraint for implementing the “Belt and Road” initiative which was raised by the Chinese government. Besides the study of spatial and temporal variability of precipitation, this study created a water hazard risk map along the “Belt and Road” zone through combined flood and drought data from 1985. Our results showed that South-Eastern Asia, southern China and eastern Southern Asia are areas with the most abundant precipitations, while floods in these areas are also the most serious. Northwest China, Western Asia, Northern Africa and Southern Asia are areas highly vulnerable to drought. Furthermore, the potential influence of flood and drought were also analyzed by associating with population distribution and corridor map. It reveals that China, South-Eastern Asia, Southern Asia, Western Asia and Northern Africa have the largest population number facing potential high water hazard risk. China–India–Burma Corridor and China–Indo-China Peninsula Corridor have the largest areas facing potential high water hazard risk.
Aiming to prevent from the imbalance between supply and demand of energy in which the share of emerging type is rapidly increasing, to predict the supply of emerging energy reliably is significant. However, the expected distribution uncertain and high-noise characteristics of emerging energy supply impede the reliable prediction. The Dual-LSTM (Long Short-Term Memory) model was constructed for the characteristic extracting and effective prediction of the expected distribution uncertain high-noise emerging energy supply time series. A case study on coal bed gas supply in China was conducted. Results showed that the Dual-LSTM model effectively solved the the problem of superfluous and non-quantifiable variables in the prediction of coal bed gas supply and extracted the statistical characteristics of expected distribution uncertain and high-noise data samples effectively with a relative error major less than 5% in short-term. Besides, the Dual-LSTM model has a significantly higher prediction accuracy while comparing with ARIMA model and original LSTM model. Ultimately, it is predicted that the year-on-year growth rates of coal bed gas supply of China from January to September, 2021, approximately maintains 75% in average based on the Dual-LSTM model. The Dual-LSTM model provides a reliable statistical model for policy decision to maintain national sustainability and stability. INDEX TERMSImproved deep learning model; Uncertain expected distribution high-noise sample; Emerging energy; Supply-demand stability; Reliable prediction; Time series analysis
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.