Spatiotemporal patterns of maize drought stress and their effects on biomass in the Northeast and North China Plain from 2000 to 2019

2022 ◽  
Vol 315 ◽  
pp. 108821
Author(s):  
Wei Wan ◽  
Zhong Liu ◽  
Jiahui Li ◽  
Jianing Xu ◽  
Hanqing Wu ◽  
...  
Keyword(s):  
Drought Stress ◽  
North China Plain ◽  
North China ◽  
Spatiotemporal Patterns

scholarly journals Over-Optimistic Projected Future Wheat Yield Potential in the North China Plain: The Role of Future Climate Extremes

Agronomy ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 145
Author(s):  
Rui Yang ◽  
Panhong Dai ◽  
Bin Wang ◽  
Tao Jin ◽  
Ke Liu ◽  
...  
Keyword(s):  
Drought Stress ◽  
North China Plain ◽  
North China ◽  
Crop Yields ◽  
Wheat Yield ◽  
Yield Potential ◽  
Severe Drought ◽  
The North ◽  
The North China Plain ◽  
Stress Environments

Global warming and altered precipitation patterns pose a serious threat to crop production in the North China Plain (NCP). Quantifying the frequency of adverse climate events (e.g., frost, heat and drought) under future climates and assessing how those climatic extreme events would affect yield are important to effectively inform and make science-based adaptation options for agriculture in a changing climate. In this study, we evaluated the effects of heat and frost stress during sensitive phenological stages at four representative sites in the NCP using the APSIM-wheat model. climate data included historical and future climates, the latter being informed by projections from 22 Global Climate Models (GCMs) in the Coupled Model Inter-comparison Project phase 6 (CMIP6) for the period 2031–2060 (2050s). Our results show that current projections of future wheat yield potential in the North China Plain may be overestimated; after more accurately accounting for the effects of frost and heat stress in the model, yield projections for 2031-60 decreased from 31% to 9%. Clustering of common drought-stress seasonal patterns into key groups revealed that moderate drought stress environments are likely to be alleviated in the future, although the frequency of severe drought-stress environments would remain similar (25%) to that occurring under the current climate. We highlight the importance of mechanistically accounting for temperature stress on crop physiology, enabling more robust projections of crop yields under future the burgeoning climate crisis.

scholarly journals Contribution of Surface Solar Radiation and Precipitation to Spatiotemporal Patterns of Surface and Air Temperature Warming in China from 1960 to 2003

2016 ◽  
Author(s):  
Jizheng Du ◽  
Kaicun Wang ◽  
Jiankai Wang ◽  
Qian Ma
Keyword(s):  
Solar Radiation ◽  
Air Temperature ◽  
Energy Budget ◽  
South China ◽  
North China Plain ◽  
North China ◽  
Spatiotemporal Patterns ◽  
The North ◽  
The North China Plain ◽  
The Impact

Abstract. Although the global warming has been successfully attributed to the elevated atmospheric greenhouses gases, the reasons for spatiotemporal patterns the warming rates are still under debate. In this paper, we report surface and air warming based on observations collected at 1977 stations in China from 1960 to 2003. Our results show that the warming of daily maximum surface (Ts-max) and air (Ta-max) temperatures showed a significant spatial pattern, stronger in the northwest China and weaker in South China and the North China Plain. These warming spatial patterns are attributed to surface shortwave solar radiation (SSR) and precipitation, the key parameters of surface energy budget. During the study period, SSR decreased by −1.50 W m−2 10 yr−1 in China and caused the trends of Ts-max and Ta-max decreased by 0.139 and 0.053 °C 10 yr−1, respectively. More importantly, South China and the North China Plain had an extremely higher dimming rates than other regions. The spatial contrasts of trends of Ts-max and Ta-max in China are significantly reduced after adjusting for the impact of SSR and precipitation. For example, the difference in warming rates between North China Plain and Loess Plateau reduce by 97.8 % and 68.3 % for Ts-max and Ta-max respectively. After adjusting for the impact of SSR and precipitation, the seasonal contrast of Ts-max and Ta-max decreased by 45.0 % and 17.2 %, and the daily contrast of warming rates of surface and air temperature decreased by 33.0 % and 29.1 % over China. This study shows an essential role of land energy budget in determining regional warming.

scholarly journals Contributions of surface solar radiation and precipitation to the spatiotemporal patterns of surface and air warming in China from 1960 to 2003

2017 ◽  
Vol 17 (8) ◽  
pp. 4931-4944 ◽  
Author(s):  
Jizeng Du ◽  
Kaicun Wang ◽  
Jiankai Wang ◽  
Qian Ma
Keyword(s):  
Solar Radiation ◽  
Energy Budget ◽  
South China ◽  
North China Plain ◽  
North China ◽  
Surface Energy Budget ◽  
Spatiotemporal Patterns ◽  
Daily Maximum ◽  
The North ◽  
The North China Plain

Abstract. Although global warming has been attributed to increases in atmospheric greenhouses gases, the mechanisms underlying spatiotemporal patterns of warming trends remain under debate. Herein, we analyzed surface and air warming observations recorded at 1977 stations in China from 1960 to 2003. Our results showed a significant spatial pattern for the warming of the daily maximum surface (Ts-max) and air (Ta-max) temperatures, and the pattern was stronger in northwest and northeast China and weaker or negative in South China and the North China Plain. These warming spatial patterns were attributed to surface shortwave solar radiation (Rs) and precipitation (P), which play a key role in the surface energy budget. During the study period, Rs decreased by −1.50 ± 0.42 W m−2 10 yr−1 in China, which reduced the trends of Ts-max and Ta-max by about 0.139 and 0.053 °C 10 yr−1, respectively. More importantly, the decreasing rates in South China and the North China Plain were stronger than those in other parts of China. The spatial contrasts in the trends of Ts-max and Ta-max in China were significantly reduced after adjusting for the effect of Rs and P. For example, after adjusting for the effect of Rs and P, the difference in the Ts-max and Ta-max values between the North China Plain and the Loess Plateau was reduced by 97.8 and 68.3 %, respectively; the seasonal contrast in Ts-max and Ta-max decreased by 45.0 and 17.2 %, respectively; and the daily contrast in the warming rates of the surface and air temperature decreased by 33.0 and 29.1 %, respectively. This study shows that the land energy budget plays an essential role in the identification of regional warming patterns.

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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