Evaluating the carbon budget pattern of Chinese terrestrial ecosystem from 1960 to 2006 using Integrated Biosphere Simulator

Yanzheng, 杨延征 Yang; Yuandan, 马元丹 Ma; Hong, 江洪 Jiang; Qiu'an, 朱求安 Zhu; Jinxun, 刘金勋 Liu; Changhui, 彭长辉 Peng

doi:10.5846/stxb201410262092

Cited by 11 publications

(9 citation statements)

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“…Previous studies have shown that water use efficiency at different levels in typical grasslands of northern China has a specific response to daytime and night warming. Warming during the day reduced the water use efficiency of the ecosystem by 5.66% [69]. Therefore, the increasing of T max modifies the way that water is utilized in meadows and herbaceous swamp vegetation, further restraining the growth of vegetation.…”

Section: Response Of the Yrb's Nep To Diurnal Asymmetric Warmingmentioning

confidence: 99%

Spatial Responses of Net Ecosystem Productivity of the Yellow River Basin under Diurnal Asymmetric Warming

Zhang

Jing

et al. 2018

Sustainability

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The net ecosystem productivity (NEP) of drainage basins plays an important role in maintaining the carbon balance of those ecosystems. In this study, the modified CASA (Carnegie Ames Stanford Approach) model and a soil microbial respiration model were used to estimate net primary productivity (NPP) and NEP of the Yellow River Basin’s (YRB) vegetation in the terrestrial ecosystem (excluding rivers, floodplain lakes and other freshwater ecosystems) from 1982 to 2015. After analyzing the spatiotemporal variations in the NEP using slope analysis, the coefficient of variation, and the Hurst exponent, precipitation was identified as the main factor limiting vegetation growth in the YRB. Hence, precipitation was treated as the control variable and a second-order partial correlation method was used to determine the correlation between diurnal asymmetric warming and the YRB’s NEP. The results indicate that: (i) diurnal asymmetric warming occurred in the YRB from 1982 to 2015, with nighttime warming (Tmin) being 1.50 times that of daytime warming (Tmax). There is a significant correlation between variations in NPP and diurnal warming; (ii) the YRB’s NEP are characterized by upward fluctuations in terms of temporal variations, large differences between the various vegetation types, high values in the western and southeastern regions but low values in the northern region in terms of spatial distribution, overall relative stability in the YRB’s vegetation cover, and changes in the same direction being more dominant than those in the opposite direction (although the former is not sustained); and (iii) positive correlations between the NEP and nighttime and daytime warming are approximately 48.37% and 67.51% for the YRB, respectively, with variations in nighttime temperatures having more extensive impacts on vegetation cover.

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Section: Response Of the Yrb's Nep To Diurnal Asymmetric Warmingmentioning

confidence: 99%

Spatial Responses of Net Ecosystem Productivity of the Yellow River Basin under Diurnal Asymmetric Warming

Zhang

Jing

et al. 2018

Sustainability

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“…For example, Chinese scholars used the CEVSA, GOSAT, IBIS, CEVSA2, BEPS, TEC, and other models to simulate the average annual NEP of the terrestrial ecosystems in China on different time scales. From 1960 to 2010, the average annual total NEP in different study periods ranged from 0.07 PgC/ y to 1.89 PgC/y (Cao et al, 2003;Zhang et al, 2014;Wang et al, 2015;Yang et al, 2016;Yao et al, 2018;He et al, 2019;Zhang et al, 2020;Wang et al, 2021). Nayak et al (2015) used the CASA model to simulate the NEP of the Indian terrestrial ecosystem from 1981 to 2006 and concluded that the average annual NEP was 10 TgC.…”

Section: Introductionmentioning

confidence: 99%

Changes and net ecosystem productivity of terrestrial ecosystems and their influencing factors in China from 2000 to 2019

et al. 2023

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Changes in net ecosystem productivity (NEP) in terrestrial ecosystems in response to climate warming and land cover changes have been of great concern. In this study, we applied the normalized difference vegetation index (NDVI), average temperature, and sunshine hours to drive the C-FIX model and to simulate the regional NEP in China from 2000 to 2019. We also analyzed the spatial patterns and the spatiotemporal variation characteristics of the NEP of terrestrial ecosystems and discussed their main influencing factors. The results showed that (1) the annual average NEP of terrestrial ecosystems in China from 2000 to 2019 was 1.08 PgC, exhibiting a highly significant increasing trend with a rate of change of 0.83 PgC/10 y. The terrestrial ecosystems in China remained as carbon sinks from 2000 to 2019, and the carbon sink capacity increased significantly. The NEP of the terrestrial ecosystem increased by 65% during 2015–2019 compared to 2000–2004 (2) There was spatial differences in the NEP distribution of the terrestrial ecosystems in China from 2000–2019. Taking the line along the Daxinganling-Yin Mountains-Helan Mountains-Transverse Range as the boundary, the NEP was significantly higher in the eastern part than in the western part. Among them, the NEP was positive (carbon sink) in northeastern, central, and southern China, and negative (carbon source) in parts of northwestern China and the Tibet Autonomous Region. The spatial variation of NEP in terrestrial ecosystems increased from 2000 to 2009. The areas with a significant increase accounted for 45.85% and were mainly located in the central and southwestern regions. (3) The simulation results revealed that vegetation changes and CO2 concentration changes both contributed to the increase in the NEP in China, contributing 85.96% and 36.84%, respectively. The vegetation changes were the main factor causing the increase in the NEP. The main contribution of this study is to further quantify the NEP of terrestrial ecosystems in China and identify the influencing factors that caused these changes.

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“…Notably, while closeness centrality shows a weak positive correlation, other metrics exhibit a significant positive correlation with carbon sequestration. In terms of carbon sequestration capacity, research by Fang et al and Yang et al demonstrates that the carbon sequestration capacity of terrestrial vegetation systems in China exhibits an increasing trend on a national scale [88,89].…”

Section: Correlation Analysis Between Network Topological Characteris...mentioning

confidence: 99%

Research on the Carbon Sequestration Capacity of Forest Ecological Network Topological Features and Network Optimization Based on Modification Recognition in the Yellow River Basin Mining Area: A Case Study of Jincheng City

Li,

Yu,

et al. 2024

Remote Sensing

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Forests are vital for terrestrial ecosystems, providing crucial functions like carbon sequestration and water conservation. In the Yellow River Basin, where 70% of forest coverage is concentrated in the middle reaches encompassing Sichuan, Shaanxi, and Shanxi provinces, there exists significant potential for coal production, with nine planned coal bases. This study centered on Jincheng City, Shanxi Province, a representative coal mining area in the Yellow River Basin, and combined the MSPA analysis method and MCR model to generate the five-period forest ecological network of Jincheng City from 1985 to 2022 under the background of coal mining and calculate the degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality; the correlation between the four centralities and carbon sequestration ability is further explored. Simultaneously, employing the RAND-ESU algorithm for motif identification within forest ecological networks, this study integrates the ecological policies of the research area with the specific conditions of the coal mining region to optimize the forest ecological network in Jincheng City. Findings reveal the following. (1) Forest ecological spatial networks: Forest ecological networks exhibit robust overall ecological connectivity in the study area, with potential ecological corridors spanning the region. However, certain areas with high ecological resistance hinder connectivity between key forest ecological nodes under the background of coal mining. (2) Correlation between topological indices and carbon sequestration ecological services: From 1985 to 2022, the carbon sequestration capacity of Jincheng City’s forest source areas increased year by year, and significant positive correlations were observed between degree centrality, betweenness centrality, eigenvector centrality with carbon sequestration ecological services, indicating a strengthening trend over time. (3) Motif Recognition and Ecological Network Optimization: During the study, four types of motifs were identified in the forest ecological network of Jincheng City based on the number of nodes and their connections using the RAND-ESU network motif algorithm. These motifs are 3a, 4a, 4b, and 4d (where the number represents the number of nodes and the letter represents the connection type). Among these, motifs 3a and 4b play a crucial role. Based on these motifs and practical considerations, network optimization was performed on the existing ecological source areas to enhance the robustness of the forest ecological network.

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Evaluating the carbon budget pattern of Chinese terrestrial ecosystem from 1960 to 2006 using Integrated Biosphere Simulator

Cited by 11 publications

References 0 publications

Spatial Responses of Net Ecosystem Productivity of the Yellow River Basin under Diurnal Asymmetric Warming

Spatial Responses of Net Ecosystem Productivity of the Yellow River Basin under Diurnal Asymmetric Warming

Changes and net ecosystem productivity of terrestrial ecosystems and their influencing factors in China from 2000 to 2019

Research on the Carbon Sequestration Capacity of Forest Ecological Network Topological Features and Network Optimization Based on Modification Recognition in the Yellow River Basin Mining Area: A Case Study of Jincheng City

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