Precipitation and Anthropogenic Activities Jointly Green the China–Mongolia–Russia Economic Corridor

Climate change and anthropogenic activities are widely considered the main factors affecting vegetation growth. However, their relative contributions are under debate. Within the non-climatic impact, detailed human activities, particularly government policy adjustments, are less investigated. In this study, we develop a fractional vegetation coverage (FVC) extraction method based on MODIS-EVI satellite data to analyze the spatiotemporal variation of vegetation and its attributions in the China–Mongolia–Russia Economic Corridor (CMREC). The average FVC has improved, with a general increase of 0.02/10a from 2000 to 2020. We construct a driving factor identification system for FVC change, based on partial and multiple correlation coefficients, and we divide the driving forces of FVC changes into seven climate-driven types and one non-climate-driven type. The results reveal that FVC changes caused by climatic factors account for 28.2% of CMREC. The most prominent greening (19.5%) is precipitation-driven, and is extensively distributed in Khentii Aimag, Mongolia; southeast Inner Mongolia; west Jilin Province; and southwest Heilongjiang Province, China. Moreover, we quantify the relative contribution of climatic and non-climatic factors to significant FVC change using the first-difference multivariate regression method. The results indicate that the effects of non-climatic factors on vegetation change outweigh those of climatic factors in most areas. According to the land cover change and regional policy adjustment, anthropogenic activities such as afforestation, reclamation, and planting structure adjustment explain most vegetation improvement in the Northeast Plain; eastern Inner Mongolia; and the Hetao Irrigation District, China. Meanwhile, both vegetation improvement and degradation disperse concurrently in the Mongolian and Russian parts of CMREC, where climate change and anthropogenic activities positively and negatively affect vegetation change, respectively. Despite the greening in most CMREC, it must be noted that human-induced greening is unsustainable to some degree. The overdevelopment of black soil area and sandy land, adverse effects of afforestation projects, and natural hazards related to weather and climate extremes altogether threaten the local ecological security in the long run. Therefore, governments should develop new desertification countermeasures in accordance with the laws of nature, and enhance international cooperation to guarantee the ecological safety of CMREC.

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The Coincident Relationships between the Qehan Lake Area Fluctuation and the Climate Change in the Nearly 40 Years

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.137.286 ◽

2011 ◽

Vol 137 ◽

pp. 286-290 ◽

Cited By ~ 1

Author(s):

Xi Chun ◽

Mei Jie Zhang ◽

Mei Ping Liu

Keyword(s):

Climate Change ◽

Inner Mongolia ◽

Climatic Factors ◽

Annual Precipitation ◽

Lake Area ◽

Annual Average ◽

Average Temperature ◽

Changing Patterns ◽

Annual Evaporation ◽

Annual Average Temperature

The objective of this study is to analyse the climate changing patterns chronologically for exposing the coincident relationships between the lake area fluctuation and the climate change in Qehan lake of Abaga county of Inner Mongolia. The results show that there’s highly interrelation between the changes of the lake area and the climatic factors here, the annual average temperature and annual evaporation are negatively interrelate to the lake area fluctuation, and the annual precipitation interrelate to it is positive. The lake area has descended about 75 km2 during the nearly past 40 years. There were about two considerable lake expansions in 1973, 1998 through the generally lake area descending process.

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NDVI-Based Vegetation Change in Inner Mongolia from 1982 to 2006 and Its Relationship to Climate at the Biome Scale

Advances in Meteorology ◽

10.1155/2014/692068 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 35

Author(s):

Linghui Guo ◽

Shaohong Wu ◽

Dongsheng Zhao ◽

Yunhe Yin ◽

Guoyong Leng ◽

...

Keyword(s):

Climate Change ◽

Land Use ◽

Land Use Change ◽

Inner Mongolia ◽

Vegetation Change ◽

Vegetation Index ◽

Potential Evapotranspiration ◽

Vegetation Type ◽

Growing Season ◽

Desert Steppe

Based on the normalized difference vegetation index (NDVI), we analyzed vegetation change of the six major biomes across Inner Mongolia at the growing season and the monthly timescales and estimated their responses to climate change between 1982 and 2006. To reduce disturbance associated with land use change, those pixels affected by land use change from the 1980s to 2000s were excluded. At the growing season scale, the NDVI increased weakly in the natural ecosystems, but strongly in cropland. Interannual variations in the growing season NDVI for forest was positively linked with potential evapotranspiration and temperature, but negatively correlated with precipitation. In contrast, it was positively correlated with precipitation, but negatively related to potential evapotranspiration for other natural biomes, particularly for desert steppe. Although monthly NDVI trends were characterized as heterogeneous, corresponding to monthly variations in climate change among biome types, warming-related NDVI at the beginning of the growing season was the main contributor to the NDVI increase during the growing season for forest, meadow steppe, and typical steppe, but it constrained the NDVI increase for desert steppe, desert, and crop. Significant one-month lagged correlations between monthly NDVI and climate variables were found, but the correlation characteristics varied greatly depending on vegetation type.

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Grassland dynamics in response to climate change and human activities in Inner Mongolia, China between 1985 and 2009

The Rangeland Journal ◽

10.1071/rj12042 ◽

2013 ◽

Vol 35 (3) ◽

pp. 315 ◽

Cited By ~ 31

Author(s):

S. J. Mu ◽

Y. Z. Chen ◽

J. L. Li ◽

W. M. Ju ◽

I. O. A. Odeh ◽

...

Keyword(s):

Climate Change ◽

Land Use ◽

Ecological Restoration ◽

Human Activities ◽

Inner Mongolia ◽

Management Practices ◽

Net Primary Productivity ◽

Temporal Dynamics ◽

Driving Forces ◽

Landscape Patterns

China’s grassland has been undergoing rapid changes in the recent past owing to increased climate variability and a shift in grassland management strategy driven by a series of ecological restoration projects. This study investigated the spatio-temporal dynamics of Inner Mongolia grassland, the main grassland region in China and part of the Eurasia Steppe, to detect the interactive nature of climate, ecosystems and society. Land-use and landscape patterns for the period from 1985 to 2009 were analysed based on TM- and MODIS-derived land-use data. Net Primary Productivity (NPP) estimated by using the Carnegie-Ames-Stanford Approach model was used to assess the growth status of grassland. Furthermore, the factors related to the dynamics of grassland were analysed from the perspectives of two driving factors, climate change and human activities. The results indicated that higher temperatures and lower precipitation may generally have contributed to grassland desertification, particularly in arid regions. During the period from 1985 to 2000, a higher human population and an increase in livestock numbers were the major driving forces responsible for the consistent decrease in NPP and a relatively fragmented landscape. From 2000 to 2009, the implementation of effective ecological restoration projects has arrested the grassland deterioration in some ecologically fragile regions. However, a rapid growth of livestock numbers has sparked new degradation onnon-degraded or lightly degraded grassland, which was initially neglected by these projects. In spite of some achievement in grassland restoration, China should take further steps to develop sustainable management practices for climate adaptation and economic development to bring lasting benefits.

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Inconsistency of Global Vegetation Dynamics Driven by Climate Change: Evidences from Spatial Regression

Remote Sensing ◽

10.3390/rs13173442 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3442

Author(s):

Dou Zhang ◽

Xiaolei Geng ◽

Wanxu Chen ◽

Lei Fang ◽

Rui Yao ◽

...

Keyword(s):

Climate Change ◽

Vegetation Index ◽

Climatic Factors ◽

Potential Evapotranspiration ◽

Anthropogenic Activities ◽

Test Method ◽

Spatial Error ◽

Vegetation Growth ◽

Annual Peak ◽

Global Vegetation

Global greening over the past 30 years since 1980s has been confirmed by numerous studies. However, a single-dimensional indicator and non-spatial modelling approaches might exacerbate uncertainties in our understanding of global change. Thus, comprehensive monitoring for vegetation’s various properties and spatially explicit models are required. In this study, we used the newest enhanced vegetation index (EVI) products of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 to detect the inconsistency trend of annual peak and average global vegetation growth using the Mann–Kendall test method. We explored the climatic factors that affect vegetation growth change from 2001 to 2018 using the spatial lag model (SLM), spatial error model (SEM) and geographically weighted regression model (GWR). The results showed that EVImax and EVImean in global vegetated areas consistently showed linear increasing trends during 2001–2018, with the global averaged trend of 0.0022 yr−1 (p < 0.05) and 0.0030 yr−1 (p < 0.05). Greening mainly occurred in the croplands and forests of China, India, North America and Europe, while browning was almost in the grasslands of Brazil and Africa (18.16% vs. 3.08% and 40.73% vs. 2.45%). In addition, 32.47% of the global vegetated area experienced inconsistent trends in EVImax and EVImean. Overall, precipitation and mean temperature had positive impacts on vegetation variation, while potential evapotranspiration and vapour pressure had negative impacts. The GWR revealed that the responses of EVI to climate change were inconsistent in an arid or humid area, in cropland or grassland. Climate change could affect vegetation characteristics by changing plant phenology, consequently rendering the inconsistency between peak and mean greening. In addition, anthropogenic activities, including land cover change and land use management, also could lead to the differences between annual peak and mean vegetation variations.

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Variation of Net Primary Production and Its Correlation with Climate Change and Anthropogenic Activities over the Tibetan Plateau

Remote Sensing ◽

10.3390/rs10091352 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1352 ◽

Cited By ~ 8

Author(s):

Zhaohui Luo ◽

Wenchen Wu ◽

Xijun Yu ◽

Qingmei Song ◽

Jian Yang ◽

...

Keyword(s):

Climate Change ◽

Tibetan Plateau ◽

Climatic Factors ◽

Anthropogenic Activities ◽

Mean Value ◽

Mean Annual Temperature ◽

The Tibetan Plateau ◽

Casa Model ◽

Negative Effect ◽

Cumulative Precipitation

Grasslands in the Tibetan Plateau are claimed to be sensitive and vulnerable to climate change and anthropogenic activities. Quantifying the impacts of climate change and anthropogenic activities on grassland growth is an essential step for developing sustainable grassland ecosystem management strategies under the background of climate change and increasing anthropogenic activities occurring in the plateau. Net primary productivity (NPP) is one of the key components in the carbon cycle of terrestrial ecosystems, and can serve an important role in the assessment of vegetation growth. In this study, a modified Carnegie–Ames–Stanford Approach (CASA) model, which considers remote sensing information for the estimation of the water stress coefficient and time-lag effects of climatic factors on NPP simulation, was applied to simulate NPP in the Tibetan Plateau from 2001 to 2015. Then, the spatiotemporal variations of NPP and its correlation with climatic factors and anthropogenic activities were analyzed. The results showed that the mean values of NPP were 0.18 kg∙C∙m−2∙a−1 and 0.16 kg∙C∙m−2∙a−1 for the original CASA model and modified CASA model, respectively. The modified CASA model performed well in estimating NPP compared with field-observed data, with root mean square error (RMSE) and mean absolute error (MAE) of 0.13 kg∙C∙m−2∙a−1 and 0.10 kg∙C∙m−2∙a−1, respectively. Relative RMSE and MAE decreased by 45.8% and 44.4%, respectively, compared to the original CASA model. The variation of NPP showed gradients decreasing from southeast to northwest spatially, and displayed an overall decreasing trend for the study area temporally, with a mean value of −0.02 × 10−2 kg∙C∙m−2∙a−1 due to climate change and increasing anthropogenic activities (i.e., land use and land cover change). Generally, 54% and 89% of the total pixels displayed a negative relationship between NPP and mean annual temperature, as well as annual cumulative precipitation, respectively, with average values of –0.0003 (kg∙C∙m−2 a−1)/°C and −0.254 (g∙C∙m−2∙a−1)/mm for mean annual temperature and annual cumulative precipitation, respectively. Additionally, about 68% of the total pixels displayed a positive relationship between annual cumulative solar radiation and NPP, with a mean value of 0.038 (g∙C∙m−2·a−1)/(MJ m−2). Anthropogenic activities had a negative effect on NPP variation, and it was larger than that of climate change, implying that human intervention plays a critical role in mitigating the degenerating ecosystem. In terms of human intervention, ecological destruction has a significantly negative effect on the NPP trend, and the absolute value was larger than that of ecological restoration, which has a significantly positive effect on NPP the trend. Our results indicate that ecological destruction should be paid more attention, and ecological restoration should be conducted to mitigate the overall decreasing trend of NPP in the plateau.

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Impacts of climate change and anthropogenic activities on vegetation change: Evidence from typical areas in China

Ecological Indicators ◽

10.1016/j.ecolind.2021.107648 ◽

2021 ◽

Vol 126 ◽

pp. 107648

Author(s):

Kaiyuan Zheng ◽

Linshan Tan ◽

Yanwei Sun ◽

Yanjuan Wu ◽

Zheng Duan ◽

...

Keyword(s):

Climate Change ◽

Vegetation Change ◽

Anthropogenic Activities ◽

Impacts Of Climate Change

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Using Budyko-Type Equations for Separating the Impacts of Climate and Vegetation Change on Runoff in the Source Area of the Yellow River

Water ◽

10.3390/w12123418 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3418

Author(s):

Dan Yan ◽

Zhizhu Lai ◽

Guangxing Ji

Keyword(s):

Climate Change ◽

Human Activities ◽

Vegetation Change ◽

Yellow River ◽

Climatic Factors ◽

Source Area ◽

The Yellow River ◽

Vegetation Changes ◽

Potential Evaporation ◽

Runoff Change

Assessing the contribution rates of climate change and human activities to the runoff change in the source area of the Yellow River can provide support for water management in the Yellow River Basin. This paper firstly uses a multiple linear regression method to evaluate the contribution rates of climate change and human activities to the vegetation change in the source area of the Yellow River. Next, the paper uses the Budyko hypothesis method to calculate the contribution rates of climatic factors (including precipitation, potential evaporation, and subsequent vegetation changes) and vegetation changes caused by human activities to the runoff change of the Tangnaihai Hydrometric Station. The results showed that: (1) the annual runoff and precipitation in the source area of the Yellow River have a downward trend, while the annual potential evaporation and NDVI (Normalized Difference Vegetation Index) show an increasing trend; (2) The contribution rates of climate change and human activities to the vegetation change in the source area of the Yellow River is 62.79% and 37.21%, respectively; (3) The runoff change became more and more sensitive to changes in climate and underlying surface characteristic parameters; (4) The contribution rates of climatic factors (including precipitation, potential evaporation, and subsequent vegetation changes) and vegetation changes caused by human activities to the runoff change at Tangnaihai Hydrological Station are 75.33% and 24.67%, respectively; (5) The impact of precipitation on runoff reduction is more substantial than that of potential evaporation.

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Attribution Analysis of Climate and Anthropic Factors on Runoff and Vegetation Changes in the Source Area of the Yangtze River from 1982 to 2016

Land ◽

10.3390/land10060612 ◽

2021 ◽

Vol 10 (6) ◽

pp. 612

Author(s):

Guangxing Ji ◽

Huiyun Song ◽

Hejie Wei ◽

Leying Wu

Keyword(s):

Climate Change ◽

Yangtze River ◽

Vegetation Change ◽

Climatic Factors ◽

Source Area ◽

Linear Regression Method ◽

The Yangtze River ◽

Potential Evaporation ◽

Runoff Change ◽

The Impact

Analyzing the temporal variation of runoff and vegetation and quantifying the impact of anthropic factors and climate change on vegetation and runoff variation in the source area of the Yangtze River (SAYR), is of great significance for the scientific response to the ecological protection of the region. Therefore, the Budyko hypothesis method and multiple linear regression method were used to quantitatively calculate the contribution rates of climate change and anthropic factors to runoff and vegetation change in the SAYR. It was found that: (1) The runoff, NDVI, precipitation, and potential evaporation in the SAYR from 1982 to 2016 all showed an increasing trend. (2) The mutation year of runoff data from 1982 to 2016 in the SAYR is 2004, and the mutation year of NDVI data from 1982 to 2016 in the SAYR is 1998. (3) The contribution rates of precipitation, potential evaporation and anthropic factors to runoff change of the SAYR are 75.98%, −9.35%, and 33.37%, respectively. (4) The contribution rates of climatic factors and anthropic factors to vegetation change of the SAYR are 38.56% and 61.44%, respectively.

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An Improved Conceptual Model Quantifying the Effect of Climate Change and Anthropogenic Activities on Vegetation Change in Arid Regions

Remote Sensing ◽

10.3390/rs11182110 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2110

Author(s):

Yu ◽

Yang ◽

Li ◽

Yang

Keyword(s):

Climate Change ◽

Conceptual Model ◽

Arid Regions ◽

Vegetation Change ◽

Hydrological Cycle ◽

Anthropogenic Activities ◽

Regional Scale ◽

Anthropogenic Activity ◽

Area Index ◽

The Impact

Vegetation shows a greening trend on the global scale in the past decades, which has an important effect on the hydrological cycle, and thus quantitative interpretation of the causes for vegetation change is of great benefit to understanding changes in ecology, climate, and hydrology. Although the Donohue13 model, a simple conceptual model based on gas exchange theory, provides an effective tool to interpret the greening trend, it cannot be used to evaluate the impact from land use and land cover change (LULCC) on the regional scale, whose importance to vegetation change has been demonstrated in a large number of studies. Hence, we have improved the Donohue13 model by taking into account the change in vegetation cover ratio due to LULCC, and applied this model to the Yarkand Oasis in the arid region of northwest China. The estimated change trend in leaf area index (LAI) is 1.20%/year from 2001 to 2017, which accounts for approximately half of the observed (2.31%/year) by the moderate resolution imaging spectroradiometer (MODIS). Regarding the causes for vegetation greening, the contributions of: (1) LULCC; (2) atmospheric CO2 concentration; and (3) vapor pressure deficit were: (1) 88.3%; (2) 40.0%; and (3) −28.3%, respectively, which reveals that the largest contribution was from LULCC, which is probably driven by increased total water availability in whole oasis with a constant transpiration in vegetation area. The improved Donohue13 model, a simple but physics-based model, can partially explain the impact of factors related to climate change and anthropogenic activity on vegetation change in arid regions. It can be further combined with the Budyko hypothesis to establish a framework for quantifying the changes in coupled response of vegetation and hydrological processes to environment changes.

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Determinizing the contributions of human activities and climate change on greening in the Beijing–Tianjin–Hebei Region, China

Scientific Reports ◽

10.1038/s41598-021-00788-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Wei Cao ◽

Dan Wu ◽

Lin Huang ◽

Mei Pan ◽

Taoli Huhe

Keyword(s):

Climate Change ◽

Human Activities ◽

Driving Forces ◽

Vegetation Coverage ◽

Agricultural Activities ◽

Positive Effects ◽

Nonlinear Responses ◽

Spatial Differences ◽

Temporal And Spatial ◽

Region Scale

AbstractChina accounts for 25% of the global greening. There are temporal and spatial differences of China’s greening and intrinsic driving forces. Thus, it is crucial to determinize the contributions of human activities and climate change on greening at region scale. The Beijing–Tianjin–Hebei Region (BTHR) is one of the most active areas with human activities in China. It is necessary to explore negative or positive impacts of human activities on the regional greening or browning under climate change. A time series of annual vegetation coverage from satellite data was selected to quantify regional greening in the BTHR from 2000 to 2019 and their responses to climate change and human activities. Results showed generally widespread greening over the last 20 years at an average increased rate of 0.036 decade−1 in vegetation coverage (P < 0.01). Overall warmer and wetter climate across the BTHR were positively correlated with regional greening. The positive effects of human activities on greening accounted for 48.4% of the BTHR, especially the benefits of ecological restoration projects and the agricultural activities. Increases in vegetation coverage had resulted from the combined effects of climate change and human activities. Climate change had a stronger influence on vegetation coverage than human activities. Contributions of climate change to greening and browning was about 74.1% and < 20%, respectively. The decrease in vegetation coverage was mainly the results of the inhibition of human activities. More detailed socioeconomic and anthropogenic datasets are required for further analysis. Further research consideration would focus on the nonlinear responses of vegetation to climate change.

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