scholarly journals Simulation of nitrogen deposition in the North China Plain by the FRAME model

2011 ◽  
Vol 8 (11) ◽  
pp. 3319-3329 ◽  
Author(s):  
Y. Zhang ◽  
A. J. Dore ◽  
X. Liu ◽  
F. Zhang
Keyword(s):  
North China Plain ◽  
North China ◽  
Terrestrial Ecosystem ◽  
N Deposition ◽  
The Yellow Sea ◽  
Natural Ecosystems ◽  
Total N ◽  
Frame Model ◽  
The North ◽  
The North China Plain

Abstract. Simulation of atmospheric nitrogen (N) deposition in the North China Plain (NCP) at high resolution, 5 × 5 km2, was conducted for the first time by the Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model. The total N deposition budget was 1481 Gg in this region, with 77 % from reduced N and 23 % from oxidized N, and the annual deposition rate (47 kg N ha−1) was much higher than previously reported values for other parts of the world such as the UK (13 kg N ha−1), Poland (7.3 kg N ha−1) and EU27 (8.6 kg N ha−1). The exported N component (1981 Gg) was much higher than the imported N component (584 Gg), suggesting that the NCP is an important net emission source of N pollutants. Contributions of N deposition budgets from the seven provinces in this region were proportional to their area ratios. The calculated spatial distributions of N deposition displayed high rates of reduced N deposition in the south and of oxidized N deposition in the eastern part. The N deposition exceeded an upper limit of 30 kg N ha−1 for natural ecosystems over more than 90 % of the region, resulting in terrestrial ecosystem deterioration, impaired air quality and coastal eutrophication not only in the NCP itself but also in surrounding areas including the Bohai Sea and the Yellow Sea.

scholarly journals Simulation of nitrogen deposition in the North China Plain by the FRAME model

2011 ◽  
Vol 8 (4) ◽  
pp. 8161-8188 ◽  
Author(s):  
Y. Zhang ◽  
A. J. Dore ◽  
X. Liu ◽  
F. Zhang
Keyword(s):  
North China Plain ◽  
North China ◽  
N Deposition ◽  
The Yellow Sea ◽  
Natural Ecosystems ◽  
N Budget ◽  
Total N ◽  
Frame Model ◽  
The North ◽  
The North China Plain

Abstract. Simulation of atmospheric nitrogen (N) deposition in the North China Plain (NCP) at high resolution, 5 × 5 km2, was conducted for the first time by the Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model. The total N deposition budget was 1481 Gg in this region, with 77 % from reduced N and 23 % from oxidized N, and the annual deposition rate (47 kg ha−1) was much higher than previously reported values for other parts of the world such as the UK (13 kg ha−1), Poland (7.3 kg ha−1) and EU27 (8.6 kg ha−1). The exported N budget (1981 Gg) was much higher than the imported N budget (584 Gg), suggesting that the NCP is an important net emission source of N pollutants. Seven provinces in the region contributed N deposition budgets that were proportional to their area ratios. The calculated spatial distributions of N deposition displayed high rates of reduced N deposition in the south and of oxidized N deposition in the eastern part. The N deposition exceeded an upper limit of 30 kg N ha−1 for natural ecosystems over more than 90 % of the region, resulting in terrestrial ecosystem deterioration, impaired air quality and coastal eutrophication not only in the NCP itself but also in surrounding areas including the Bohai Sea and the Yellow Sea.

Effects of controlled traffic no-till system on soil chemical properties and crop yield in annual double-cropping area of the North China Plain

Soil Research ◽  
2016 ◽  
Vol 54 (6) ◽  
pp. 760
Author(s):  
Caiyun Lu ◽  
Hongwen Li ◽  
Jin He ◽  
Qingjie Wang ◽  
Khokan Kumer Sarker ◽  
...  
Keyword(s):  
Organic Matter ◽  
North China Plain ◽  
North China ◽  
Chemical Properties ◽  
Total N ◽  
The North ◽  
Double Cropping ◽  
The North China Plain ◽  
No Till ◽  
Controlled Traffic

A controlled traffic no-till system is a cropping system that has a significant potential to improve soil health, sustainability and crop yield. A pilot experiment was conducted to compare soil chemical properties and crop yields between controlled traffic no-till and random traffic in an annual double-cropping area of the North China Plain from 2005 to 2010. The experiment was performed using three treatments: (1) controlled traffic no-till (NTCT); (2) random traffic no-till (NTRT); and (3) conventional tillage (CT). The NTCT treatment significantly improved soil organic matter and total N compared with both NTRT and CT treatments and remarkably increased available P compared with CT treatment in the surface soil layer (0–10cm), but no significant differences were found in soil pH compared with both NTRT and CT treatments. However, in the 10–20- and 20–30-cm soil profiles, soil organic matter, total N and available P were reduced after NTCT treatment when compared with those obtained after CT treatment. At 0–10cm soil depths, soil bulk density under NTCT and NTRT was higher than in CT, whereas the opposite was true at soil depths of 10–30cm. Overall, it was found that the 6-year mean maize yield of NTCT and NTRT treatments was 10.9% and 1.1% higher respectively than the CT treatment, whereas the winter wheat yield was 1.1% and 3.0% higher respectively compared with the CT treatment. NTCT appears to be an improvement over current farming systems in an annual double-cropping area of the 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 The water-saving potential of using micro-sprinkling irrigation for winter wheat production on the North China Plain

2021 ◽  
Vol 20 (6) ◽  
pp. 1687-1700
Author(s):  
Li-chao ZHAI ◽  
Li-hua LÜ ◽  
Zhi-qiang DONG ◽  
Li-hua ZHANG ◽  
Jing-ting ZHANG ◽  
...  
Keyword(s):  
Winter Wheat ◽  
North China Plain ◽  
North China ◽  
Water Saving ◽  
Wheat Production ◽  
The North ◽  
The North China Plain
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