Spatial geochemical and isotopic characteristics associated with groundwater flow in the North China Plain

2004 ◽  
Vol 18 (16) ◽  
pp. 3133-3146 ◽  
Author(s):  
Jianyao Chen ◽  
Changyuan Tang ◽  
Yasuo Sakura ◽  
Akihiko Kondoh ◽  
Jingjie Yu ◽  
...  
Keyword(s):  
Groundwater Flow ◽  
North China Plain ◽  
North China ◽  
The North ◽  
The North China Plain

Parallel simulation of groundwater flow in the North China Plain

2014 ◽  
Vol 25 (6) ◽  
pp. 1059-1066 ◽  
Author(s):  
Tangpei Cheng ◽  
Jingli Shao ◽  
Yali Cui ◽  
Zeyao Mo ◽  
Zhong Han ◽  
...  
Keyword(s):  
Groundwater Flow ◽  
North China Plain ◽  
North China ◽  
Parallel Simulation ◽  
The North ◽  
The North China Plain

ROx Budgets and O3 Formation during Summertime at Xianghe Suburban Site in the North China Plain

2021 ◽  
Author(s):  
Min Xue ◽  
Jianzhong Ma ◽  
Guiqian Tang ◽  
Shengrui Tong ◽  
Bo Hu ◽  
...  
Keyword(s):  
North China Plain ◽  
North China ◽  
The North ◽  
The North China Plain ◽  
Suburban Site

Winter Wheat and Summer Maize Roots in Agro‐Ecosystems on the North China Plain

2021 ◽  
pp. 271-288
Author(s):  
Fadong Li ◽  
Zhao Li ◽  
Yunfeng Qiao ◽  
Nong Zhu ◽  
Kun Du ◽  
...  
Keyword(s):  
Winter Wheat ◽  
North China Plain ◽  
North China ◽  
Summer Maize ◽  
The North ◽  
The North China Plain ◽  
Maize Roots

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.

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