Effects of Climate Factors and Human Activities on the Ecosystem Water Use Efficiency throughout Northern China

Du, Xiangdong; Zhao, Xiang; Zhou, Tao; Jiang, Bo; Xu, Peipei; Wu, Deming; Tang, Bijian

doi:10.3390/rs11232766

Cited by 34 publications

(22 citation statements)

References 77 publications

(129 reference statements)

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“…where k is the trend and b is the intercept term. A positive k value represents an increasing Y trend, while a negative k value represents a decreasing Y trend [54].…”

Section: Data Preprocessing and Trend Analysismentioning

confidence: 99%

Quantitatively Assessing the Impact of Driving Factors on Vegetation Cover Change in China’s 32 Major Cities

Zhao

et al. 2022

Remote Sensing

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After 2000, China’s vegetation underwent great changes associated with climate change and urbanization. Although many studies have been conducted to quantify the contributions of climate and human activities to vegetation, few studies have quantitatively examined the comprehensive contributions of climate, urbanization, and CO2 to vegetation in China’s 32 major cities. In this study, using Global Land Surface Satellite (GLASS) fractional vegetation cover (FVC) between 2001 and 2018, we investigated the trend of FVC in China’s 32 major cities and quantified the effects of CO2, urbanization, and climate by using generalized linear models (GLMs). We found the following: (1) From 2001 to 2018, the FVC in China generally illustrated an increasing trend, although it decreased in 23 and 21 cities in the core area and expansion area, respectively. (2) Night light data showed that the urban expansion increased to varying degrees, with an average increasing ratio of approximately 168%. The artificial surface area increased significantly, mainly from cropland, forest, grassland, and tundra. (3) Climate factors and CO2 were the major factors that affected FVC change. The average contributions of climate factors, CO2, and urbanization were 40.6%, 39.2%, and 10.6%, respectively. This study enriched the understanding of vegetation cover change and its influencing factors, helped to explain the complex biophysical mechanism between vegetation and environment, and guided sustainable urban development.

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“…where k is the trend and b is the intercept term. A positive k value represents an increasing Y trend, while a negative k value represents a decreasing Y trend [54].…”

Section: Data Preprocessing and Trend Analysismentioning

confidence: 99%

Quantitatively Assessing the Impact of Driving Factors on Vegetation Cover Change in China’s 32 Major Cities

Zhao

et al. 2022

Remote Sensing

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“…where k is the FVC change trend, representing the interannual variability of FVC, and b is the intercept term [51]. A positive value of k indicated an increasing trend in FVC, while a negative value indicated a decreasing trend in FVC.…”

Section: Data Preprocessing and Trend Analysismentioning

confidence: 99%

Section: Data Preprocessing and Trend Analysismentioning

confidence: 99%

“…The p-values of two-sided Student's t-tests were computed to test the significance of the trend (k). The p-value of 0.05 was the threshold to distinguish whether the pixel had a significant temporal change [51]. According to the p-value and k value, we divided the change of FVC into five situations: (1) k > 0 and p < 0.01: significant increase; (2) k > 0 and 0.01 < p <0.05: slight increase; (3) k < 0 and p < 0.01: significant decrease; (4) k < 0 and 0.01 < p <0.05: slight decrease; (5) p > 0.05: not significant.…”

Section: Data Preprocessing and Trend Analysismentioning

confidence: 99%

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Vegetation Cover Change and Its Attribution in China from 2001 to 2018

Zhao

et al. 2021

Remote Sensing

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It is confirmed that China has been greening over the last two decades. Such greening and its driving factors are therefore significant for understanding the relationship between vegetation and environments. However, studies on vegetation changes and attribution analyses at the national scale are limited in China after 2000. In this study, fractional vegetation cover (FVC) data from Global Land Surface Satellite (GLASS) was used to detect vegetation change trends from 2001 to 2018, and the effects of CO2, temperature, shortwave radiation, precipitation, and land cover change (LCC) on FVC changes were quantified using generalized linear models (GLM). The results showed that (1) FVC in China increased by 14% from 2001 to 2018 with a greening rate of approximately 0.0019/year (p < 0.01), which showed an apparent greening trend. (2) On the whole, CO2, climate-related factors, and LCC accounted for 88% of FVC changes in China, and the drivers explained 82%, 89%, 90%, and 89% of the FVC changes in the Qinghai–Tibet region, northwest region, northern region, and southern region, respectively. CO2 was the major driving factor for FVC changes, accounting for 31% of FVC changes in China, indicating that CO2 was an essential factor in vegetation growth research. (3) The statistical results of pixels with land cover changes showed that LCC explained 12% of FVC changes, LCC has played a relatively important role and this phenomenon may be related to the ecological restoration projects. This study enriches the study of vegetation changes and its driving factors, and quantitatively describes the response relationship between vegetation and its driving factors. The results have an important significance for adjusting terrestrial ecosystem services.

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“…In addition to precipitation and temperature, leaf area index, soil moisture, water vapor and human activities are also potential influencing factors affecting WUE [30]. As discovered by Du, et al [31], climate factors contribute to 84% of WUE variations, while human activities contribute 16%. WUE, as defined by the ratio of GPP and ET, will be influenced first by GPP.…”

Section: Introductionmentioning

confidence: 96%

Remote Sensing of Ecosystem Water Use Efficiency in Different Ecozones of the North China Plain

Chen

2022

Sustainability

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Water use efficiency (WUE), as an environmental factor of metabolism in different ecosystem functional areas, is a key indicator of the ecosystem carbon-water cycle. WUE is defined as the ratio of carbon absorbed by ecosystems to water evaporated. Exploring the spatiotemporal variation in carbon and water cycles in different ecological zones of the North China Plain and their driving factors is important for the ecological management and sustainable development of the different ecological zones in the North China Plain. Based on remote sensing data products, this paper studies the spatiotemporal variations of WUE and their driving factors in different ecological functional areas of the North China Plain from 2001 to 2017. This study found that: (1) The spatial distribution of WUE and gross primary production (GPP) in the North China Plain is similar, with the multiyear average of WUE at 0.74 g C m−2 y−1. The variation trend of WUE is mainly affected by the variation trend of GPP (44.38% of the area of the North China Plain). (2) The change trend of WUE mainly showed a mild decrease and a mild increase, accounting for 73.22% of the area of the North China Plain; the area with medium-low fluctuation of WUE accounted for the largest proportion, accounting for 59.90% of the area of the North China Plain. In addition, the multiyear average values of WUE in the ecological functional area are Qin Ling Mountains deciduous forests > Central China loess plateau mixed forests > Mongolian-Manchurian grassland > Ordos Plateau steppe > Changjiang Plain evergreen forests > Huang He Plain mixed forests > Bohai Sea saline meadow, in the order from high to low. (3) The influence of precipitation on WUE was higher than that of temperature. The area of WUE that increased with the increase of precipitation accounted for 23.74% of the area of the North China Plain and was mainly distributed in the Qin Ling Mountains deciduous forests, Changjiang Plain evergreen forests, and Huang He Plain mixed forests’ ecological functional areas. The results of the study can provide a reference and theoretical basis for the conservation and management of carbon and water cycles in the functional areas of North China’s ecosystems.

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Effects of Climate Factors and Human Activities on the Ecosystem Water Use Efficiency throughout Northern China

Cited by 34 publications

References 77 publications

Quantitatively Assessing the Impact of Driving Factors on Vegetation Cover Change in China’s 32 Major Cities

Quantitatively Assessing the Impact of Driving Factors on Vegetation Cover Change in China’s 32 Major Cities

Vegetation Cover Change and Its Attribution in China from 2001 to 2018

Remote Sensing of Ecosystem Water Use Efficiency in Different Ecozones of the North China Plain

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