scholarly journals Decadal Climatic Variability, Trends, and Future Scenarios for the North China Plain

2009 ◽  
Vol 22 (8) ◽  
pp. 2111-2123 ◽  
Author(s):  
Guobin Fu ◽  
Stephen P. Charles ◽  
Jingjie Yu ◽  
Changming Liu
Keyword(s):  
North China Plain ◽  
North China ◽  
Climatic Variability ◽  
Future Climate ◽  
Intergovernmental Panel ◽  
Climate Elasticity ◽  
The North ◽  
The North China Plain ◽  
Agriculture Industry ◽  
Annual Means

Abstract Observed decadal climatic variability and trends for the north China plain (NCP) are assessed for significance with Kendall’s test and discussed in light of future climate scenarios from multi-GCM outputs from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). The results indicate that the NCP has become warmer and drier over the last four decades. The annual precipitation has declined by about 43.9 mm (6.7%, although not statistically significant), and the annual means of daily mean, maximum, and minimum temperatures have increased by 0.83°, 0.18°, and 1.46°C, respectively, during the past 40 yr. Both trends for annual means of daily mean and minimum temperatures are statistically significant. The future climate of the NCP is projected to be warmer and, with less confidence, wetter. However, streamflow could decline under these projections, based on the results of the two-parameter climate elasticity of streamflow index. This will produce serious challenges for water resources management and likely lead to exacerbated problems for agriculture, industry, urban communities, and the environment.

Effects of future climate warming on the morphology, structure, and gas exchange of maize leaves in the North China Plain

2016 ◽  
Vol 36 (6) ◽  
Author(s):  
郑云普 ZHENG YunPu ◽  
徐明 WANG JianShu ◽  
王建书 WANG HeXin ◽  
王贺新 XU Ming
Keyword(s):  
Gas Exchange ◽  
Climate Warming ◽  
North China Plain ◽  
North China ◽  
Future Climate ◽  
The North ◽  
The North China Plain ◽  
Maize Leaves

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

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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