Spatial variability of shallow groundwater level, electrical conductivity and nitrate concentration, and risk assessment of nitrate contamination in North China Plain

Agricultural water management is a vital component of realizing the United Nation’s Sustainable Development Goals because of water shortages worldwide leading to a severe threat to ecological environments and global food security. As an agro-intensified irrigation area, the North China Plain (NCP) is the most important grain basket in China, which produces 30%–40% of the maize and 60%–80% of the wheat for China. However, this area has already been one of the largest groundwater funnels in the world due to long-term over-exploitation of groundwater. Due to the low precipitation during the growing period, winter wheat requires a large amount of groundwater to be pumped for irrigation, which consumes 70% of the groundwater irrigation. To alleviate the overexploitation of groundwater, the Chinese government implemented the Winter Wheat Fallow Policy (WWFP) in 2014. The evaluation and summarization of the WWFP will be beneficial for improving the groundwater overexploitation areas under high-intensity irrigation over all the world. So far, there have been few attempts at estimating the effectiveness of this policy. To fill this gap, we assessed the planting area of field crops and calculated the evapotranspiration of crops based on remote-sensed and meteorological data in the key area—Hengshui. We compared the agricultural water consumption before and after the implementation of this policy, and we analyzed the relationship between changes in crop planting structure and groundwater variations based on geographically weighted regression. Our results showed the overall classification accuracies for 2013 and 2015 were 85.56% and 82.22%, respectively. The planting area of winter wheat, as the most reduced crop, decreased from 35.71% (314,053 ha) in 2013 to 32.98% (289,986 ha) in 2015. The actual reduction in area of winter wheat reached 84% of the target (26 thousand ha) of the WWFP. The water consumption of major crops decreased from 2.98 billion m3 of water in 2013 to 2.83 billion m3 in 2015, a total reduction of 146 million m3, and 88.43% of reduced target of the WWFP (166 million m3). The planting changes of winter wheat did not directly affect the change of shallow groundwater level, but ET was positively related to shallow groundwater level and precipitation was negatively related to shallow groundwater levels. This study can provide a basis for the WWFP’s improvement and the development of sustainable agriculture in high-intensity irrigation areas.

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Human Health Risk Assessment of Heavy Metals in Groundwater in the Luan River Catchment within the North China Plain

Geofluids ◽

10.1155/2020/8391793 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Yu Liu ◽

Rong Ma

Keyword(s):

Heavy Metals ◽

Risk Assessment ◽

Heavy Metal ◽

North China Plain ◽

North China ◽

Human Health Risk ◽

Shallow Groundwater ◽

River Catchment ◽

The North ◽

The North China Plain

The Luan River catchment within the North China plain has been famous for the development of its iron mineral resources since the 1950s. At the same time, it is also the main grain-producing area, known as the granary of eastern Hebei Province. Groundwater plays an important role in this region, and thus, it is imperative for us to improve our understanding of the heavy metal groundwater contamination in this catchment. Therefore, a total of 144 groundwater samples were collected for chemical analysis from 16 operational private wells of local residents in the study area, over eight consecutive periods from December 2016 to May 2017. Each shallow groundwater sample was analyzed for 39 heavy metals including among others, As, B, Ba, Be, Cd, Cr, Cu, Fe, Li, Mn, Mo, Ni, Sb, Se, Sn, Sr, V, and Zn. However, subsequent analyses only focused on three heavy metals (Cd, Cr, and Ni) that exceeded the Groundwater Quality Standard III. Spatial and temporal variations of Cd, Cr, and Ni in the shallow groundwater were analyzed. Cr was found to have the highest concentrations, followed by Ni and Cd. A human health risk assessment was conducted where carcinogenic risks and Hazard Quotients (HQs) were evaluated separately. The results indicate that both the carcinogenic risks and HQs of Ni and Cr are higher than the recommended standard value. Therefore, the prevention and control of heavy metal contamination in the Luan River catchment should focus on Ni and Cr.

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Artificial neural network approach for quantifying climate change and human activities impacts on shallow groundwater level: A case study of Wuqiao in North China Plain

2010 18th International Conference on Geoinformatics ◽

10.1109/geoinformatics.2010.5567678 ◽

2010 ◽

Cited By ~ 1

Author(s):

Haorui Chen ◽

Shaoli Wang ◽

Zhanyi Gao ◽

Yaqiong Hu

Keyword(s):

Neural Network ◽

Climate Change ◽

North China Plain ◽

North China ◽

Shallow Groundwater ◽

Network Approach ◽

Neural Network Approach ◽

Artificial Neural Network Approach ◽

Shallow Groundwater Level

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Variations of Groundwater Quality in the Multi-Layered Aquifer System near the Luanhe River, China

Sustainability ◽

10.3390/su11040994 ◽

2019 ◽

Vol 11 (4) ◽

pp. 994 ◽

Cited By ~ 4

Author(s):

Qiaona Guo ◽

Zhifang Zhou ◽

Guojiao Huang ◽

Zhi Dou

Keyword(s):

River Water ◽

Groundwater Level ◽

North China Plain ◽

North China ◽

Nitrate Concentration ◽

Time Lag ◽

Alluvial Fan ◽

The North ◽

Nitrate Concentrations ◽

Depression Cone

Nitrate pollution is an environmental problem in the North China Plain. This paper investigates the variation of groundwater levels and nitrate concentrations in an alluvial fan of the Luanhe river, northeast of the North China Plain. Three transects perpendicular to the riverbank were selected to investigate the exchange between river water and groundwater, and nitrate concentration with its isotopic composition (δ15N-NO3 and δ18O-NO3). The results showed that the groundwater level decreased slightly during the dry season, and increased regularly during the period of river stage rise. The groundwater is recharged by the river over 10 months each year. The nitrate concentration in the groundwater and river water varied with seasons. The nitrate concentration of groundwater in wells near the river is affected by the river water, which varied in basically the same way as the river. The nitrate concentrations in the zone of groundwater level depression cone were lower than those in the wells near the river, due to the long-term pumping of groundwater. However, the nitrate concentrations of river water have little influence on those of groundwater in wells far from the river. The values of δ15N-NO3 and the relationship between the two isotopes (δ15N-NO3 and δ18O-NO3) suggested that NO3-N was mainly attributable to sewage, livestock manure and natural soil organic matter. Due to the existence of a groundwater depression cone near the river, nitrate contamination can be transported into the aquifer with the flow. The average time lag of nitrate migration from the river to the zone of groundwater level depression cone is different in different sections, which shows an increasing trend from the upstream to downstream along the river, with an average of two to six months. It is mainly related to the stratigraphic structure, the migration distance, the hydraulic conductivities of the aquifer and the riverbed sediment. Compared with the case of considering the silt layer, the time lag of nitrate migration is greater than that of the case of ignoring the silt layer. The results will provide useful information for detecting nitrate concentrations in the alluvial fan area of the Luanhe river, northeast of the NCP (North China Plain).

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Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain

Sensors ◽

10.3390/s21010046 ◽

2020 ◽

Vol 21 (1) ◽

pp. 46

Author(s):

Gangqiang Zhang ◽

Wei Zheng ◽

Wenjie Yin ◽

Weiwei Lei

Keyword(s):

Machine Learning ◽

Data Fusion ◽

Groundwater Level ◽

North China Plain ◽

North China ◽

Gradient Boosting ◽

Fusion Model ◽

The North ◽

The North China Plain ◽

Prediction Module

The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.

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An Integrated Flood Risk Assessment Model for Cities Located in the Transitional Zone between Taihang Mountains and North China Plain: A Case Study in Shijiazhuang, Hebei, China

Atmosphere ◽

10.3390/atmos10030104 ◽

2019 ◽

Vol 10 (3) ◽

pp. 104 ◽

Cited By ~ 2

Author(s):

Qiang Liu ◽

Hongmao Yang ◽

Min Liu ◽

Rui Sun ◽

Junhai Zhang

Keyword(s):

Risk Assessment ◽

Flood Risk ◽

North China Plain ◽

North China ◽

Transitional Zone ◽

Assessment Model ◽

Analysis Tool ◽

Risk Assessment Model ◽

Flood Risk Assessment ◽

Risk Map

Cities located in the transitional zone between Taihang Mountains and North China plain run high flood risk in recent years, especially urban waterlogging risk. In this paper, we take Shijiazhuang, which is located in this transitional zone, as the study area and proposed a new flood risk assessment model for this specific geographical environment. Flood risk assessment indicator factors are established by using the digital elevation model (DEM), along with land cover, economic, population, and precipitation data. A min-max normalization method is used to normalize the indices. An analytic hierarchy process (AHP) method is used to determine the weight of each normalized index and the geographic information system (GIS) spatial analysis tool is adopted for calculating the risk map of flood disaster in Shijiazhuang. This risk map is consistent with the reports released by Hebei Provincial Water Conservancy Bureau and can provide reference for flood risk management.

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