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

2019 ◽  
Vol 11 (4) ◽  
pp. 994 ◽  
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).

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

Sensors ◽  
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.

scholarly journals Winter Wheat Green-up Date Variation and its Diverse Response on the Hydrothermal Conditions over the North China Plain, Using MODIS Time-Series Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1593 ◽  
Author(s):  
Linghui Guo ◽  
Jiangbo Gao ◽  
Chengyuan Hao ◽  
Linlin Zhang ◽  
Shaohong Wu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
North China Plain ◽  
North China ◽  
Time Series Data ◽  
Climatic Factors ◽  
Time Lag ◽  
Series Data ◽  
Lag Effects ◽  
The North ◽  
The North China Plain

Vegetation phenology plays a critical role in the dynamic response of terrestrial ecosystems to climate change. However, the relationship between the phenology of winter wheat and hydrothermal factors is inadequate, especially in typical agricultural areas. In this study, the possible effects of preseason climate changes on the green-up date (GUD) of winter wheat over the North China Plain (NCP) was investigated, using the MODIS EVI 8-day time-series data from 2000 to 2015, as well as the concurrent monthly mean temperature (Tm), mean maximum (Tmax) and minimum temperature (Tmin) and total precipitation (TP) data. Firstly, we quantitatively identified the time lag effects of winter wheat GUD responses to different climatic factors; then, the major driving factors for winter wheat GUD were further explored by applying multiple linear regression models. The results showed that the time lag effects of winter wheat GUD response to climatic factors were site- and climatic parameters-dependent. Negative temperature effects with about a 3-month time lag dominated in most of the NCP, whereas positive temperature effects with a zero-month lag were most common in some of the southern parts. In comparison, total precipitation had a negative zero-month lag effect in the northern region, but two lagged months occurred in the south. Regarding the time lag effects, the explanation power of climatic factors improved relatively by up to 77%, and the explanation area increased by 41.20%. Additionally, change in winter wheat GUD was primarily determined by temperature rather than by TP, with a marked spatial heterogeneity of the Tmax and Tmin effect. Our results confirmed different time lag effects from different climatic factors on phenological processes in spring, and further suggested that both Tmax and Tmin should be considered to improve the performance of spring phenology models.

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