Spatial heterogeneity of changes in cropland ecosystem water use efficiency and responses to drought in China

Zhao, Anzhou; Yu, Qiuyan; Cheng, Dayu; Zhang, Anbing

doi:10.1007/s11356-021-16829-4

Cited by 2 publications

(2 citation statements)

References 66 publications

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“…These non-parametric methods were free from data distribution or missing data. They have been widely used to analyze trends in vegetation, precipitation, and temperature [47,48]. The Theil-Sen median trend is calculated using the following equation:…”

Section: Analysis Methods 231 Trend Analysismentioning

confidence: 99%

Air Quality Improvement in China: Evidence from PM2.5 Concentrations in Five Urban Agglomerations, 2000–2021

et al. 2022

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Air pollution endangers human health and sustainable socio-economic development, especially in urban agglomeration (UA). The Chinese government has implemented a series of policies and standards to improve air quality. However, few studies have compared variations in PM2.5 concentrations across multiple UAs, and current research often lacks analysis relative to the clean air policies implemented by the government. In this study, we used econometric and geostatistical methods to assess the distribution and spatial evolution of PM2.5 concentrations in five UAs (the Beijing–Tianjin–Hebei UA (BTHUA), middle reaches of the Yangtze River UA (MYRUA), Chengdu–Chongqing UA (CCUA), Harbin Changchun UA (HCUA), and Beibu Gulf UA (BGUA)) in China from 2000 to 2021 to explore the effectiveness of the clean air policies implemented by the government on air pollution control, to analyze the ambient air quality of UAs, and to make recommendations for public outdoor activities. The results indicated that the clean air policy implemented by the Chinese government in 2013 achieved significant treatment results. PM2.5 concentrations were plotted as an inverted U-shaped curve based on time, which showed an upward trend before 2013 and a downward trend after 2013. PM2.5 concentrations showed a similar seasonal pattern, with a single-valley “V” shape. PM2.5 concentration was the highest in winter and the lowest in summer. The PM2.5 concentration of HCUA and BGUA was lower than that of CCUA, MYRUA, and BTHUA. The increase in PM2.5 concentration mainly occurred in autumn and winter, while the decrease mainly occurred in spring. In 2021, the PM2.5 air quality compliance rates (<35 µg/m3) in BTHUA, MYRUA, CCUA, HCUA, and BGUA were 44.57%, 80.00%, 82.04%, 99.74%, and 100%, respectively. However, in 2021, 19.19% of the five UAs still had an ambient air quality of Grade II (i.e., 50 < AQIPM2.5 < 100). People with abnormally sensitive breathing in these areas should reduce their outdoor activities. These results contribute to epidemiological studies on human health and disease prevention and suggest reasonable pathways by which governments can improve air quality through sustainable urban planning.

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Section: Analysis Methods 231 Trend Analysismentioning

confidence: 99%

Air Quality Improvement in China: Evidence from PM2.5 Concentrations in Five Urban Agglomerations, 2000–2021

et al. 2022

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“…It may be associated with the fact that cultivated vegetation predominates in HC [60]. Compared with Central Asia, the WUE in HC was more impacted by human activities, such as fertilization and irrigation [34,61]. The multi-year average WUE in QM was 0.5 ± 0.327 gC kg −1 H 2 O, lower than the average WUE (1.14 gC kg −1 H 2 O) calculated by Ji et al [62] using MODIS data for grassland across the Tibetan Plateau and closer to the WUE of 0.4-1.38 gC kg −1 H 2 O using data from three flux towers in the Tibetan Plateau during 2003-2006 [63].…”

Section: Impact Of Bio-meteorological Factors On the Spatial Variatio...mentioning

confidence: 99%

Quantifying the Spatio-Temporal Variations and Impacts of Factors on Vegetation Water Use Efficiency Using STL Decomposition and Geodetector Method

Wang

Zhao

et al. 2022

Remote Sensing

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Water use efficiency of vegetation (WUE), the ratio of carbon gain to water loss, is a valid indicator to describe the photosynthetic carbon–water coupling relationship. Understanding how and why WUE changes are essential for regional ecological conservation. However, the impacts of various factors and their interactions on the spatial variation of WUE remain uncertain in the arid land of Northwest China. Here, we selected the Qilian Mountains (QM) and Hexi Corridor (HC) as the study areas. Supported by the Google Earth Engine, we explored the spatio-temporal variations of WUE in QM and HC for 2002–2021 using STL decomposition (a seasonal-trend decomposition procedure), trend analysis, and the Hurst index. Then, the Geodetector method was applied to quantify impacts of geographical and eco-meteorological factors on the spatial variation of WUE. The WUE in HC was higher than that in QM. Interestingly, the opposite longitude zonality characteristics are shown in the QM and HC. In QM, the WUE showed an upward trend with longitude increasing, while a downward trend with longitude increasing in the oases of HC. The WUE of cropland was the highest (1.15 ± 0.35 gC kg−1 H2O), and that of alpine vegetation was the lowest (0.2 ± 0.15 gC kg−1 H2O). WUE showed a decreasing trend across the study area, almost certainly due to a drop from May to July during 2002–2021. The air temperature is the dominant factor influencing the spatial variation of WUE. In addition, the interaction of any two factors is greater than the independent influence of either factor alone. The Geodetector method proved to be effective for quantifying the impact of complex multi-factors on the spatial variation of WUE. This study provides a new technical scheme to analyze the spatio-temporal pattern and quantify the impact of multi-factors on the spatial variation of WUE. These findings aid in understanding underlying mechanisms of WUE variation and thereby will be beneficial for clarifying the response of vegetation to climate change.

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Spatial heterogeneity of changes in cropland ecosystem water use efficiency and responses to drought in China

Cited by 2 publications

References 66 publications

Air Quality Improvement in China: Evidence from PM2.5 Concentrations in Five Urban Agglomerations, 2000–2021

Air Quality Improvement in China: Evidence from PM2.5 Concentrations in Five Urban Agglomerations, 2000–2021

Quantifying the Spatio-Temporal Variations and Impacts of Factors on Vegetation Water Use Efficiency Using STL Decomposition and Geodetector Method

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