Whether climatic factors influence the frequency of punctual on-demand deliveries of groundwater for irrigation? Empirical study in the North China Plain

2020 ◽  
Vol 159 (2) ◽  
pp. 269-287 ◽  
Author(s):  
Lijuan Zhang ◽  
Jinxia Wang ◽  
Guangsheng Zhang ◽  
Qiuqiong Huang
Keyword(s):  
Empirical Study ◽  
North China Plain ◽  
North China ◽  
Climatic Factors ◽  
On Demand ◽  
The North ◽  
The North China Plain

scholarly journals Study on the Change Characteristics of and Population Exposure to Heatwave Events on the North China Plain

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Long-Fei Zhan ◽  
Yanjun Wang ◽  
Hemin Sun ◽  
Jianqing Zhai ◽  
Mingjin Zhan
Keyword(s):  
North China Plain ◽  
North China ◽  
Climatic Factors ◽  
Demographic Data ◽  
Population Exposure ◽  
Central Position ◽  
Daily Maximum ◽  
The North ◽  
The North China Plain ◽  
The Impact

In accordance with the China Meteorological Administration definition, this study considered a weather process with a maximum surface temperature of ≥35°C for more than three consecutive days as a heatwave event. Based on a dataset of daily maximum temperatures from meteorological stations on the North China Plain, including ordinary and national basic/reference surface stations, the intensity-area-duration method was used to analyze the spatiotemporal distribution characteristics of heatwave events on the North China Plain (1961–2017). Moreover, based on demographic data from the Statistical Yearbook and Greenhouse Gas Initiative (GGI) Population Scenario Database of the Austrian Institute for International Applied Systems Analysis (IIASA), population exposure to heatwave events was also studied. The results showed that the frequency, intensity, and area of impact of heatwave events on the North China Plain initially decreased (becoming weaker and less extensive) and then increased (becoming stronger and more extensive). Similarly, the trend of population exposure to heatwave events initially decreased and then increased, and the central position of exposure initially moved southward and then returned northward. Population exposure in the eastern Taihang Mountains was found significantly higher than in the western Taihang Mountains. In relation to the change of population exposure to heatwave events on the North China Plain, the influence of climatic factors was found dominant with an absolute contribution rate of >75%. Except for 2011–2017, increase in population also increased the exposure to heatwaves, particularly in the first half of the study period. Interaction between climatic and population factors generally had less impact on population exposure than either climatic factors or population factors alone. This study demonstrated a method for assessing the impact of heatwave events on population exposure, which could form a scientific basis for the development of government policy regarding adaption to climate change.

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.

scholarly journals Responses of Winter Wheat Yield to Drought in the North China Plain: Spatial–Temporal Patterns and Climatic Drivers

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3094
Author(s):  
Jianhua Yang ◽  
Jianjun Wu ◽  
Leizhen Liu ◽  
Hongkui Zhou ◽  
Adu Gong ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Time Scale ◽  
North China Plain ◽  
North China ◽  
Climatic Factors ◽  
Wheat Yield ◽  
Response Patterns ◽  
The North ◽  
The North China Plain ◽  
Winter Wheat Yield

Understanding the winter wheat yield responses to drought are the keys to minimizing drought-related winter wheat yield losses under climate change. The research goal of our study is to explore the response patterns of winter wheat yield to drought in the North China Plain (NCP) and then further to study which climatic factors drive the response patterns. For this purpose, winter wheat yield was simulated by the Environmental Policy Integrated Climate (EPIC) crop model. Drought was quantified by standardized precipitation evapotranspiration index (SPEI), and the contributions of the various climatic factors were evaluated using predictive discriminant analysis (PDA) method. The results showed that the responses of winter wheat yield to different time-scale droughts have obvious spatial differences from the north part to the south part in the NCP. Winter wheat yield is more sensitive to the medium (6–9 months) and long (9–12 months) time-scale droughts that occurred in the key growth periods (April and May). The different response patterns of winter wheat yield to the different time-scale droughts are mainly controlled by temperature and water balance (precipitation minus potential evapotranspiration) in winter in the NCP. Compared with the water balance, temperature plays a more important role in driving the response pattern characteristics. These findings can provide a reference on how to reduce drought influences on winter wheat yield in the NCP.

scholarly journals Interactions among the Phenological Events of Winter Wheat in the North China Plain-Based on Field Data and Improved MODIS Estimation

2019 ◽  
Vol 11 (24) ◽  
pp. 2976 ◽  
Author(s):  
Xifang Wu ◽  
Wei Yang ◽  
Chunyang Wang ◽  
Yanjun Shen ◽  
Akihiko Kondoh
Keyword(s):  
Winter Wheat ◽  
Field Data ◽  
North China Plain ◽  
North China ◽  
Prediction Models ◽  
Climatic Factors ◽  
Human Beings ◽  
Positive Correlation ◽  
The North ◽  
The North China Plain

Identification of complete drivers for phenology changes is crucial for developing prediction models of plant phenology. In addition to climatic factors, the interaction among phenological events has recently been reported as an important driver for the phenology changes of forests, savannas, and grasslands. However, open questions remain as to whether the phenological interaction exists in agricultural ecosystems, among which winter wheat plays a vital role in feeding human beings. In this study, we investigated the interaction among the phenological events of winter wheat in the North China Plain (NCP) using both field and satellite data. Considering the large discrepancies between the existing satellite estimation and field measurements of winter wheat phenology, we first improved the MODIS-based estimation of green-up date (GUD), heading date (HD), and maturity date (MD) through a re-calibrated relative threshold method (RTM) in the NCP. The GUD, HD, and MD were accurately estimated with the mean absolute errors (MAE) and root mean squared errors (RMSE) lower than 7.5 days, compared with the RMSEs ranging from 12.0 to 36.1 days in previous studies. Then, the relationships among the GUD, HD, and MD were analyzed using the field data collected at agricultural meteorological stations. The GUD (HD) showed a significantly positive correlation with the HD (MD). Quantitatively, a one-day earlier GUD (HD) would result in an earlier HD (MD) of 0.57 days (0.60 days). Furthermore, we applied the partial correlation analysis to the improved MODIS estimation of GUD, HD, and MD to investigate their interactions by considering the simultaneous influences from climatic factors. The results showed that the HD (MD) with 85.2% (94.5%) of all winter wheat pixels presented a significantly positive correlation with the GUD (HD). Meanwhile, the GUD (HD) with 84.2% (33.3%) of the entire winter wheat area presented a significantly negative correlation with pre-season temperature. These results suggest that both the climatic factors and phenological interactions should be included in the future development of winter wheat phenology models to improve the prediction accuracies.

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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