Vegetation Carbon Sink Research Articles

Decoupling the Impacts of Climate Change and Human Activities on Terrestrial Vegetation Carbon Sink

Net ecosystem productivity (NEP) plays a vital role in quantifying the carbon exchange between the atmosphere and terrestrial ecosystems. Understanding the effects of dominant driving forces and their respective contribution rates on NEP can aid in the effective management of terrestrial carbon sinks, especially in rapidly urbanizing coastal areas where climate change (CC) and human activities (HA) occur frequently. Combining MODIS NPP products and meteorological data from 2000 to 2020, this paper established a Modis NPP-Soil heterotrophic respiration (Rh) model to estimate the magnitude of NEP in China’s coastal zone (CCZ). Hotspot analysis, variation trend, partial correlation, and residual analysis were applied to explore the spatiotemporal patterns of NEP and the contributions of CC and HA to the dynamics of NEP. We also explored the changes in NEP in different land use types. It was found that there is a clear north–south difference in the spatial pattern of NEP in CCZ, with Zhejiang Province serving as the main watershed for this difference. In addition, NEP in most regions showed an improvement trend, especially in the Beijing–Tianjin–Hebei region and Shandong Province, but the pixel values of NEP here were generally not as high as that in most southern provinces. According to the types of driving forces, the improvement of NEP in these regions primarily results from the synergistic effects of CC and HA. NEP changes in provinces south of Zhejiang are mainly dominated by single-factor-driven degradation. The area where HA contributes to the increase in NEP is much larger than that of CC. From the perspective of land use types, forests and farmland are the dominant contributors to the magnitude of NEP in CCZ.

Estimation of carbon emissions in various clustered regions of China based on OCO-2 satellite XCO2 data and random forest modelling

Atmospheric carbon dioxide (CO2) stands as one of the most important greenhouse gasses, with steadily increasing concentrations attributable to human activities. In the pursuit of reaching peak carbon and carbon neutrality goals, it is essential to quantify carbon emissions and evaluate carbon reduction strategies. To establish a high-precision observation with full time series and spatial coverage, a spatio-temporal interpolation method was developed to obtain XCO2 data over mainland China at a resolution of 0.5° × 0.5° for the years 2015–2021. An east-west gradient, higher levels in the east and lower levels in the west, was observed, exhibiting a seasonal pattern of elevation in spring and reduction in summer. Subsequently, the research area is classified into seven clusters based on time-series XCO2 anomalies (ΔXCO2) and ODIAC (Open Source Data Inventory of Anthropogenic Carbon Dioxide) carbon emission data. This classification aims to emphasize the differentiation of spatial heterogeneity in carbon emissions and the results highlight that regions with high ΔXCO2 reflect higher carbon emission. Finally, the carbon emissions of each cluster were estimated by using a random forest model individually yielding an R2 of approximately 0.6. For assessing the variables influencing carbon emission predictions, the importance of each variable was calculated. Specifically, NightTime Lighting data (NTL), representing human production activities, emerged as a crucial variable influencing carbon emission predictions in most clusters. In comparison, Gross Primary Productivity (GPP) is considered a more critical variable in Southwest China (SWC), primarily owing to the intricate vegetation carbon sink system in this region. Temperature (T) emerges as a key variable influencing the estimation of carbon emissions in certain developed cities in Eastern China (EC), driven by the urban heat island effect which amplifies energy consumption, modifies land use, and impacts urban systems, influencing the spatial patterns of carbon emissions. Carbon emissions in different characteristic regions was quantified by establishing machine learning models with remote sensing data, which can provide new insights and support for refined carbon monitoring and management strategy.

Investigating the Dynamic Change and Driving Force of Vegetation Carbon Sink in Taihang Mountain, China

Vegetation plays an important role in absorbing carbon dioxide and accelerating the achievement of carbon neutrality. As the ecological barrier of North China, the Taihang Mountains are pivotal to the ecological construction project of China. Nevertheless, the dynamic development of the vegetation carbon sink in the region and the impact factors on the sink have not been systematically evaluated. This study employed a comprehensive approach, utilising remote sensing technology and meteorological and topographic data, in conjunction with the net ecosystem productivity (NEP) estimation model to reveal the characteristics of vegetation carbon sinks in the Taihang Mountain, and then revealed the dynamics evolution of the NEP and the inter-annual trend by using Theil–Sen Median slope estimation, the Mann–Kendall test, and the coefficient of dissociation and analysed the driving roles of the influencing factors by using the parameter optimal geographic detector. Our findings suggest that the NEP in the Taihang Mountain area has a clear growth trend in time, the average value of NEP in the Taihang Mountain area is 289 gC-m−2-a−1 from 2000 to 2022, and the spatial distribution shows the characteristics of high in the northeast and low in the middle and west, with a gradual increase from the northeast to the southwest; the areas with high fluctuation of NEP are mainly distributed in the areas around some cities that are susceptible to the interference of natural or anthropogenic factors. The vegetation carbon sinks in the Taihang Mountains are influenced by a variety of natural factors, among which the explanatory power of each natural factor is as follows: DEM (0.174) > temperature (0.148) > precipitation (0.026) > slope (0.017) > slope direction (0.003). The natural factor DEM had the strongest explanatory power for NEP changes, and the two-by-two effects of the natural factors on vegetation carbon sinks were all significantly stronger than the effects of a single factor, in which the interaction between DEM and precipitation had the strongest explanatory power; distinguishing from climate change factors, the contribution of anthropogenic activities to NEP changes in more than 90% of the area of the Taihang Mountainous Region was more than 60%, and the driving force of anthropogenic factors on NEP changes in the Taihang Mountainous Region was significantly stronger than that of natural climate change. The contribution of anthropogenic factors to NEP changes in the Taihang Mountains was significantly stronger than that of natural climate change. The results of this study can not only provide a reference for carbon reduction and sink increase and ecological restoration projects in the Taihang Mountains but also benefit the research paradigm of vegetation carbon sequestration in other regions.

Characteristics and influencing factors of carbon source/sink variations in the Zoige grassland wetland ecological function zone on the eastern slope of the Tibetan Plateau

Net ecosystem productivity (NEP) refers to the portion of net primary productivity (NPP) that is available for carbon cycling in terrestrial ecosystems after subtracting photosynthetic carbon consumed by heterotrophic respiration. The amount of the NEP reflects the size of carbon sinks/sources in terrestrial ecosystems, holding great significance for the research of climate change and global carbon cycle. In this study, the NEP of the Zoige grassland wetland ecological function zone (ZGW) on the eastern slope of the Tibetan Plateau from 2001 to 2020 is estimated by using the improved Carnegie-Ames-Stanford Approach model for NPP and a statistical model for soil heterotrophic respiration, based on the meteorological data, vegetation data and socioeconomic data. Additionally, the spatio-temporal variations of the NEP are analyzed, and the influences of natural factors and anthropogenic activities on the NEP are investigated. The results indicate that the ZGW overall plays a role as a carbon sink, and the carbon sink area accounts for approximately 99.3% of the whole ZGW. The annual average NEP in the study area is 447.9 g·m−2, showing a gradual increase at a rate of 5.0 g·m−2·a−1, although the increasing trend is not significant. The carbon sink capacity increased in 93.5% of the ZGW, remained relatively stable in 5.9% of the ZGW, and decreased and significantly decreased in 0.6% of the ZGW. Climate warming and humidifying promote the enhancement of carbon sink capacity in the ecosystem of the ZGW, and precipitation is the dominant climatic factor influencing NEP variations. Natural factors are the determinants of NEP variations, while anthropogenic activities play a secondary role. The implementation of ecological restoration and management projects in the areas along the Yellow River, around the main roads and the core area of wetlands, as well as the continuation of green and coordinated development policies of orderly developing grassland resources, is conducive to enhancing vegetation carbon sink capacity of the ZGW.

Terrestrial vegetation carbon sink analysis and driving mechanism identification in the Qinghai-Tibet Plateau

The estimation of terrestrial carbon sinks in the Qinghai-Tibet Plateau (QTP) still faces significant uncertainties, and the spatiotemporal dynamics of terrestrial carbon sinks along altitudinal gradients remain unexplored. Moreover, the driving mechanisms of terrestrial carbon sinks at the watershed scale in the QTP continue to be lacking. To address these research gaps, based on multi-source remote sensing data and meteorological data, this study calculated the Net Ecosystem Productivity (NEP) in the QTP from 2000 to 2020 using the Modis NPP-soil respiration model. Through the coefficient of variation (CV), the Mann-Kendall test (MK), and the spatial autocorrelation methods, the spatial distribution pattern and spatiotemporal trends of NEP were investigated. Employing a pixel accumulation method, the variation of NEP along altitudinal gradients was explored. Grey relation analysis, Pearson correlation analysis, and Geographical detector (GD) were used to investigate the driving mechanisms of NEP at the watershed scale. Results showed that: (1) the terrestrial ecosystem in the QTP served as a carbon sink, which produced a total of 2.04 Pg C from 2000 to 2020, and the multi-year average of total carbon sinks was 96.92 Tg C; (2) the spatial distribution of NEP shows a decreasing change from southeast to northwest, and the clustering characteristic of NEP is significant at the watershed scale; (3) the elevation of 4507 m we proposed is likely to be a key threshold for biophysical processes of the terrestrial ecosystems in the QTP; (4) the fluctuation and change trend of carbon sources and carbon sinks show significant differences between the East and West; (5) at the watershed scale, precipitation and temperature play a dominant role in the variation of NEP, while the impact of human activities on NEP variation is weak. Our study aims to address the existing knowledge gaps and provide valuable insights into the management of terrestrial carbon sinks in QTP.

Study on the Synergistic Effects of Karst Carbon Sink and Vegetation Carbon Sink in Watersheds under Different Geological Backgrounds—A Case Study of Darongjiang and Lingqu Watersheds

In this study, the hydrochemistry-runoff method and remote sensing estimation method were used to calculate the karst carbon sink flux (KCSF) and the forest vegetation carbon sequestration flux (FVCSF) in Darongjiang (DRJ) and Lingqu (LQ) watersheds. The results show the following: (1) The KCSF in DRJ and LQ watersheds is 238.43 × 105 t·y−1 and 353.44 × 105 t·y−1, respectively. Influenced by changes in flow rate, the two watersheds both show that their KCSF is higher in the rainy season than in the dry season. (2) The FVCSF in DRJ and LQ watersheds was 680.78 × 107 t·y−1 and 229.63 × 107 t·y−1, respectively. Through comparison, it can be seen that the FVCSF is much higher than the KCSF in both watersheds, but the FVCSF and the KCSF are at the same order of magnitude. (3) Through further analysis of the influence factors on the FVCSF and the KCSF, we found that the highest values of FVCSF are mainly distributed around Kitten Mountain on the upper reach and along the LQ watershed. That may be because most of the upper reach areas are mountainous forestland, and the strong weathering of rocks in the LQ watershed promotes the development of forest vegetation carbon sequestration. Therefore, it can be inferred that there is a certain synergistic effect between the karst carbon sink (KCS) and the forest vegetation carbon sequestration (FVCS) in the study area, and such a synergistic effect is caused by rock weathering.

How hydrothermal factors and CO2 concentration affect vegetation carbon sink over time and elevation gradient

How hydrothermal factors and CO2 concentration affect the vegetation carbon sink over time and across elevation gradients is crucial for carbon sequestration and enhancement, which remains unclear. Here, the temporal changes of the interannual relationship of net primary productivity (NPP) with hydrothermal factors and CO2 concentration over elevation were revealed using ensemble empirical mode decomposition (EEMD) method and moving window-based multiple regression analysis during 1981–2018, in a case study of six provinces along the ‘Maritime Silk Road’ of China. We found that: (1) except for low-elevation farmland, the interannual relationship of vegetation NPP with CO2 (RNPP-CO2), precipitation accumulation (RNPP-PA), soil moisture (RNPP-SM), downward surface shortwave radiation (RNPP-SR) and minimum temperature (RNPP-TMN) is mainly positive, while the relationship with maximum temperature (TMAX, RNPP-TMX) and vapor pressure deficit (VPD, RNPP-VPD) is mainly negative, indicating that most hydrothermal factors can increase NPP except TMAX and VPD. Most of the interannual relationship is strengthening, especially for thermal factor, indicating the strengthened impacts of hydrothermal factors and CO2, while the weakening relationship with hydrological factors accounted for large areas, which cannot be ignored. (2) With increasing elevation, the positive RNPP-CO2 diminished, while the positive RNPP-SM and the negative RNPP-VPD amplified, indicating the positive impacts from CO2 decreased, while those from SM and VPD increased. Meanwhile, RNPP-SR, RNPP-TMN, and RNPP-PA reversed from positive to negative, while RNPP-TMX reversals from negative to positive, indicating reversed impacts. (3) With increasing elevation, the temporal changes of positive RNPP-CO2 and RNPP-SM varied from weakening to strengthening and from strengthening to weakening, respectively, while those of RNPP-SR and RNPP-TMN together with RNPP-PA varied from strengthening positive to strengthening negative and weakening negative, respectively. Meanwhile, the strengthening negative of RNPP-TMX, and RNPP-VPD, changed to weakening positive and weakening negative, respectively. Our study highlights the importance of the divergent trends of the interannual relationship of NPP with hydrothermal factors and CO2 concentration across elevation for accurate prediction of future carbon sink potential under global change.

Monitoring long‐term vegetation dynamics over the Yangtze River Basin, China, using multi‐temporal remote sensing data

AbstractVegetation plays a crucial role in nature, with intricate interactions between it and the geographical environment. The Yangtze River Basin (YRB) refers to the third largest river basin globally and an essential ecological security barrier in China. Monitoring vegetation dynamics in the basin is of profound significance for addressing climate change, soil erosion, and biodiversity loss in the basin's ecosystems. Here, we investigate the spatiotemporal variations of vegetation at both the basin and land cover scales in the YRB from 2000 to 2020. We elucidate the determinants driving the changes and explore future normalized difference vegetation index (NDVI) trends. The results indicate that NDVI in the YRB increased at a rate of 0.0032 year−1 (p < 0.01) over the past 21 years, and it is anticipated to maintain an upward trend in the future. Regions in the upper and middle reaches of the YRB demonstrated higher NDVI, whereas regions in the headwater area and the lower reaches showed lower NDVI. Significant vegetation improvement was primarily concentrated in the central part of the basin, while noticeable vegetation degradation was observed in the eastern region. Temperature and wind speed were identified as the primary controlling factors affecting vegetation greenness. Global‐scale climate oscillations played a significant role in driving periodic variations in NDVI, with La Niña events tending to increase NDVI, while El Niño events hindered its rise. Land cover types were influenced by long‐term interactions between natural factors and human activities, although short‐term vegetation variations might be more affected by the latter. Our findings provide valuable insights into the mechanisms behind vegetation variability driven by multiple variables, and the strong vegetation carbon sink capacity advances the conservation and development of ecosystems.

Sensitivities of Vegetation Gross Primary Production to Precipitation Frequency in the Northern Hemisphere from 1982 to 2015

Vegetation of the Northern Hemisphere plays a vital role in global ecosystems and the carbon cycle. Variations in precipitation profoundly affect vegetation productivity, plant growth, and species communities. Precipitation frequency directly controls soil moisture availability, which has an impact on the vegetation carbon sink. However, it is unclear how precipitation frequency affects the vegetation productivity of different land cover types in different seasons. In this study, the sensitivities of the gross primary production (GPP) of six vegetation types (forest, cropland, grassland, shrubland, tundra and barren land) in response to the frequency of five categories of precipitation (trace: 0.1–5 mm/day, small: 5–10 mm/day, moderate: 10–15 mm/day, heavy: 15–20 mm/day, and very heavy: >20 mm/day) were analyzed based on the XGBoost model. The results showed that, between 1982 and 2015, precipitation frequency declined in most land cover types but increased significantly in the pan-Arctic. Differences in the sensitivity to precipitation frequency were observed between seasons and precipitation categories in northern latitudes. The GPP values of forest and barren land vegetation were less sensitive to precipitation frequency than grassland, shrubland and tundra. This may be related to different vegetation community structures and underlying surfaces and gradually increasing drought resistance capability. The sensitivity to precipitation frequency declined for moderate and heavy precipitation in cropland, but it increased in winter. As the frequency of trace precipitation diminishes in winter, the sensitivity of each vegetation type reduces by an average of 0.03%/decade. Conversely, the sensitivities to small and moderate rain increase by 0.01%/decade and 0.02%/decade, respectively, for ecosystems such as cultivated land, forests, and shrubs. However, shrubs and tundra exhibit distinct behaviors, where shifts in precipitation frequency align directly with trends in sensitivity. These results show that the frequency of precipitation significantly affects vegetation productivity and has different sensitivities, and vegetation shows different feedback mechanisms in the face of environmental changes.

Quantitative assessment of spatiotemporal dynamics in vegetation NPP, NEP and carbon sink capacity in the Weihe River Basin from 2001 to 2020

Vegetation carbon storage is crucial role in terrestrial ecosystems, impacting material and energy conversion processes. The combined influence of climate change and anthropogenic activities underscores their significance in achieving carbon neutrality. Understanding regional climate change necessitates the examination of spatiotemporal variations in net primary productivity (NPP) and net ecosystem productivity (NEP) of vegetation, as well as quantifying the relative contributions of climate change and anthropogenic activities to vegetation dynamics. This study conducts a comprehensive analysis of NPP and NEP changes and influencing factors in the Weihe River Basin (WHRB) employing various analytical techniques, including the Mann-Kendall (M-K) test, partial correlation analysis, and a land use and cover change (LUCC) matrix. The relative contributions of anthropogenic activities and climate change to NPP are investigated through a two-step linear regression approach. Results indicate a significant increase in NPP and NEP within the WHRB from 2001 to 2020, with both climate change and anthropogenic activities exerting positive impacts on vegetation growth. Conversion from grassland to forest results in the greatest net increase in NEP, while conversion to cropland leads to the largest net decrease. Climate change and anthropogenic activities contribute 4.6 gC·m−2·a−1 (64.16%) and 2.57 gC·m−2·a−1 (35.84%) to NPP, respectively. The capacity for vegetation carbon sink demonstrates an overall increase from 2001 to 2020, with higher values observed in the southern region and lower values in the northern region. The western region and areas along the river have transitioned from carbon sinks to carbon sources, while the potential for source-to-sink transformation has gradually intensified from the central to eastern, northern, and southeast regions. This study enhances the understanding of drivers and patterns of vegetation dynamics in response to climate change and anthropogenic activities in the WHRB. The findings provide valuable scientific insights for improving NPP and carbon sink capacities and advancing sustainable development objectives.

Characteristics of vegetation carbon sink carrying capacity and restoration potential of China in recent 40 years

Improving vegetation carbon sink is a key measure for China to achieve the goal of carbon neutrality. However, the gradual saturation of vegetation carbon fixation under the constraints of climate change and human activities has limited the long-term and sustainable growth of vegetation carbon sinks, resulting in significant uncertainty in understanding future carbon sink capacity and recovery potential. In this study, NPP has been selected as a carbon sink indicator to quantitatively evaluated the carbon sink carrying capacity (CSCC) and the carbon sink resilience (CSR) of China. Results demonstrated that (1) the NPP of vegetation cover areas in China exhibited an increasing trend from 1981 to 2018, with an average growth rate of 1.66 g C m−2 yr.−1 and a total net NPP increase of 0.32 P g C. (2) The average CSCC in China was 658.59 g C m−2 yr.−1, with a total amount of 4.13 Pg C yr.−1. The NPP exceeded 60% of CSCC in various years, and the total NPP in 2018 was 3.26 Pg C yr.−1, which reached 78.74% of CSCC. In the future, carbon sink that can be increased is 0.88 Pg C yr.−1, with an increase proportion of 21.26%. (3) From 2000 to 2018, the NPP of vegetation-covered area (VCA) in China had generally maintained a positive succession trend with the stronger CSR. Only four provinces (HUN, CQ, AH, and JS) in China showed the extremely strong levels of CSR. The study has revealed the huge and sustainable CSCC and restoration potential in China over the past 40 years, and has provided spatial positioning and theoretical guidance for future ecological carbon sink restoration and benefit assessment.

Maximum potential of vegetation carbon sink in Chinese forests

Forest vegetation is essential in sequestering carbon dioxide (CO2) from the atmosphere and mediating global warming. The carbon (C) sink potential of forest vegetation in different provinces is vital for policymakers to develop C-neutral technical routes and regional priorities in China; however, the mechanism remains unclear. In this study, we compiled the public data on forest vegetation biomass or storage along forest succession series between 2003 and 2022 and obtained the spatial variation of the maximum C storage(BCmax) of forest vegetation using classic logistic equation and nonlinear fitting. Furthermore, the C sink potential (∆Cpot) of the Chinese forest vegetation was calculated based on the differences between the BCmax and intensive field-investigated data in the 2010s. The results showed that the BCmax in the Chinese forest vegetation was approximately 19.03 Pg. The BCmax in southwest and northeast China were higher than those in other regions. The ∆Cpot was estimated as 8.83 Pg. Moreover, 1 km × 1 km spatial raster data for ∆Cpot were produced using the spatial raster calculation. Similarly, the per capita ∆Cpot of regions with low economic development (southwest, central, and southern Chinese provinces) were five to ten times higher than those of regions with a higher economic level. The ∆Cpot correlated negatively with gross domestic product (GDP)across all Chinese provinces. Our findings provide new insights into the ∆Cpot of the Chinese forest vegetation under natural restoration and emphasize that some differences in financial and political support among different provinces facilitate achieving a large ∆Cpot for C neutrality.

NPP and Vegetation Carbon Sink Capacity Estimation of Urban Green Space Using the Optimized CASA Model: A Case Study of Five Chinese Cities

Urban area is a major source of CO2 and other greenhouse gases. Urban green space (UGS) is an essential element to increase carbon sequestration directly and reduce emission indirectly. In this study, the net primary production (NPP) and net ecosystem productivity (NEP) was monitored in order to enhance the carbon sequestration function of UGS and promote urban low-carbon development. Based on the Sentinel-2 L2A satellite images, meteorological data, and vegetation type data in 2019, we used the optimized Carnegie Ames Stanford Approach (CASA) model to estimate the NPP values of UGS types including attached green space, park green space, protective green space, and regional space in Beijing, Guangzhou, Shanghai, Shenyang, and Xi’an. The NEP values were evaluated based on NPP and soil heterotrophic respiration (RH) to quantify the vegetation carbon sink capacity. The accuracy test shows that the estimated NPP values based on the optimized CASA model are effective. The results indicate that the average NPP values (1008.5 gC·m−2·a−1) and vegetation carbon sink capacity (771.49 gC·m−2·a−1) of UGS in Beijing rank first among the cities, which is followed by the values in Guangzhou. The regional green space and park green space in five cities function as carbon sinks with high NPP values and have vegetation carbon sink capacity, whereas the attached green space in Shanghai and Xi’an as well as the protective green space in Guangzhou and Xi’an function as carbon sources. Moreover, the NEP distribution shows obvious spatial aggregation characteristics, that is, the high NEP values of UGS are clustered in mountainous forest areas in the west and north of Beijing, Northeast Guangzhou, and South Xi’an whereas the low NEP values are mostly concentrated in the urban built-up areas under strong influences of human activities. This research provides a new method for NPP and NEP estimation of UGS at the city scale and the scientific basis for the improvement of the vegetation carbon sink capacity of UGS.

Whether the enhanced terrestrial vegetation carbon sink affect the water resources in the middle-low latitude karst areas of China?

To meet the carbon peak and carbon neutrality objectives, the middle-low latitude karst areas of Southwest China have become popular research areas due to their rich terrestrial carbon sink storage capability. However, whether the enhanced terrestrial vegetation carbon sink will consume more water resources in this area is still unclear. In this research, three mid-small basins that represent different levels of carbon sink and human activity were selected in the middle-low latitude karst area of Southwest China. Net ecosystem productivity (NEP) was used to indicate the variation in the carbon sink, and the remote sensing-driven distributed time-variant gain model (RS-DTVGM) and a new runoff monitoring technology named the remote sensing hydrological station (RSHS) were integrated to quantitatively analyze the correlation between the carbon sink and water resources from 2000 to 2020. The results showed that 1) in the past 20 years, the NEP of the three basins all showed a trend of first increasing and then decreasing, while the runoff of all basins showed a trend of first decreasing and then increasing. The opposite trends of NEP and runoff supported the negative correlation between NEP and runoff. 2) The enhanced carbon sink will consume more water, but on the premise of similar climatic and geographical conditions, the significant strong negative correlation between the carbon sink and water resources will gradually weaken and become insignificant as the land use/cover change (LUCC) occurs and the forest background conditions improve from approximately 35% to more than 80%. 3) The serious drought event had a large influence on the water yield and carbon sink, and the basin with higher forest coverage could resist extreme drought by consuming more runoff to retain more carbon. This research quantitatively answered the question of whether the enhanced carbon sink will consume more water resources in the middle-low latitude humid karst areas, which provide valuable implications to managers who want to increase carbon sinks but worry about lost water resources, and the results can be used to promote the co-evolution of the water-carbon system, thereby achieving the carbon neutrality objective.

Influence of the ecosystem conversion process on the carbon and water cycles in different regions of China

Ecosystem conversion processes in different regions of China have different impacts on carbon and water cycle changes. Water use efficiency (WUE) is a key factor in coupling ecosystem carbon and water cycles, and it is important to quantify the effects of ecosystem conversional processes on the WUE in different regions of China to improve our understanding of the effects of ecological restoration projects and ecosystem conversional processes on carbon and water cycles in different regions. In this study, we proposed a dual–type extraction algorithm for ecosystem conversion processes (DTEA–ECP) to study the impact of ecosystem conversion processes on the WUE in different regions of China from 2001 to 2020. Our results showed that in the 2001–2020 period, the area of barren ecosystem conversion to grassland ecosystem increased at the fastest rate (4.29*103 km2 y-1). In the snow climate zone with dry winters and warm summers, projects returning cropland to forest were beneficial for improving WUE. In the northwestern part of the semiarid climate zone, returning cropland to grassland was beneficial for increasing the WUE of the ecosystem, while in northeastern China, the change in WUE after the conversion of grassland ecosystem to cropland ecosystem was not significant. WUE decreased after the conversion of forest ecosystems to grassland ecosystems, while WUE increased after the conversion of grassland ecosystems to forest ecosystems. This study identified where it was most suitable to plant each vegetation type and can help with the implementation of ecological projects such as afforestation, returning farmland to forest and grass, grassland protection and wind and sand management. The results provide information support for the in-depth development of national greening and low-carbon land restoration, improve vegetation carbon sink capacity, enhance carbon emission reduction and carbon sequestration, and help to achieve the goal of peak carbon and carbon neutrality.

Dynamic Change Analysis of Vegetation Carbon Sinks in Agricultural Land

The method of ecological economics was applied to calculate the vegetation carbon sink from farmland and its dynamic change characteristics in Shaanxi Province from 2009 to 2018. Results showed that the vegetation of agricultural land increased by 3.3×1010 t over the period studied, with an average rise of 36.5%. In Shaanxi Province, the vegetation carbon sinks of cultivated land, forest land, and garden plots all showed an increasing trend, while grassland carbon sinks showed a decreasing trend. The vegetation carbon sinks of cultivated land mainly came from the three crops of wheat, corn and rice, and the carbon sinks were all above 10 t/hm2. The carbon sink of rice is the highest among the main crops, with an average value of 54.75 t/hm2. The change trend of vegetation carbon sink of forest land and garden land was consistent with the area. The decline was 0.09%. From 2009 to 2010, the garden area increased by 140,000 hm2, an increase of 19.83%. Since 2010, the garden area began to decrease, with a mean annual change rate of 3.85%. Vegetation carbon sink from grassland was the highest in 2009, at 6801475.3 t, the lowest in 2010, 4883565.8 t, with an average annual decrease of 0.64%.

黄河流域生态功能区植被碳汇估算及其气候影响要素

PDF HTML阅读 XML下载 导出引用 引用提醒 黄河流域生态功能区植被碳汇估算及其气候影响要素 DOI: 10.5846/stxb202203110591 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 水沙科学与水利水电工程国家重点实验室(清华大学)2021年对外开放基金(sklhse-2021-A-06);辽宁省兴辽英才项目资助;国家自然科学基金项目(52079060) Estimation of vegetation carbon sink in the Yellow River Basin ecological function area and analysis of its main meteorological elements Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:基于改进的CASA模型和土壤呼吸模型,综合遥感数据、气象数据、植被数据等多源数据类型,以黄河流域为研究对象,估算生态功能区植被碳源/汇,探讨其时空变化及主要气候影响要素。结果显示:(1)黄河流域植被净初级生产力(NPP)在2000-2020年整体呈波动上升趋势,多年平均NPP值为317.18 gC m-2 a-1;林地＞耕地＞沼泽＞草地,水体、戈壁、风蚀劣地和裸沙地的NPP总体处于较低值。空间尺度上,黄河流域NPP呈现出南高北低的分布特点,高值区集中在黄土高原农业与草原生态区与汾渭盆地农业生态区交界处,空间差异显著。(2)土壤微生物呼吸量极大值出现2018年,为15.82 gC m-2 a-1,最小值出现在2004年为14.34 gC m-2 a-1;空间上呈现从东南往西北、从东往西依次递减的空间分布格局。(3)黄河流域总体呈碳汇属性(碳汇区域超过60%),年均固碳量约为111.02 MgC/a,年均排碳量为-9.96 MgC/a,年均净碳汇总量约为111.41 MgC/a。(4)黄河流域植被碳汇的形成与降水和太阳辐射的相关性最大,气温的影响面积仅占12.44%;当气温为30℃,降水150 mm,太阳辐射550 MJ/m2时,植被固碳能力效果最强。研究结果为区域全面准确地评估陆地生态系统碳源/汇,实现黄河流域高质量发展,达到碳中和目标提供参考依据。 Abstract:Based on the improved CASA (Carnegie-Ames-Stanford Approach) model and soil respiration model, this paper integrates multi-source data, such as remote sensing data, meteorological data and vegetation data, to estimate vegetation carbon sources/sinks in ecological function areas, and explore their spatio-temporal variations and main climate influencing elements in the Yellow River Basin. The results show that (1) the overall Net Primary Productivity (NPP) of vegetation in the Yellow River Basin presented a fluctuating upward trend from 2000 to 2020, with a multi-year average NPP value of 317.18 gC m-2 a-1. The NPP of various vegetation types showed that: forest land>cropland>swamp>grassland, and water bodies, Gobi, wind eroded poor land and bare sand land were at lower values overall. On the spatial scale, the NPP in the Yellow River Basin shows the distribution characteristics of high in the south and low in the north, and the high value areas are concentrated at the junction of the Loess Plateau agricultural and grassland ecological zone and the Fenwei Basin agroecological zone, with significant spatial differences. (2) The maximum value of soil microbial respiration appeared in 2018 at 15.82 gC m-2 a-1, and the minimum value appeared in 2004 at 14.34 gC m-2 a-1; spatially it showed a spatial distribution pattern decreasing from southeast to northwest and from east to west. (3) The Yellow River Basin has a carbon sink attribute in general (the carbon sink area exceeds 60%) The average annual carbon sequestration is about 111.02 MgC/a, the average annual carbon emission is -9.96 MgC/a, and the total annual net carbon sink is about 111.41 MgC/a. (4) The formation of vegetation carbon sink in the Yellow River Basin is mostly correlated with precipitation and solar radiation, and the influence area of air temperature only accounts for 12.44%; the effect of vegetation carbon sequestration capacity is the strongest when the temperature is 30℃, precipitation is 150 mm, and solar radiation is 550 MJ/m2. The results of the study provide a reference basis for a comprehensive and accurate regional assessment of carbon sources/sinks in terrestrial ecosystems to achieve high-quality development in the Yellow River Basin and meet the carbon neutrality target. 参考文献 相似文献 引证文献

Simulation of Vegetation Carbon Sink of Arbor Forest and Carbon Mitigation of Forestry Bioenergy in China.

Mitigating carbon emissions through forest carbon sinks is one of the nature-based solutions to global warming. Forest ecosystems play a role as a carbon sink and an important source of bioenergy. China’s forest ecosystems have significantly contributed to mitigating carbon emissions. However, there are relatively limited quantitative studies on the carbon mitigation effects of forestry bioenergy in China, so this paper simulated the carbon sequestration of Chinese arbor forest vegetation from 2018 to 2060 based on the CO2FIX model and accounted for the carbon emission reduction brought about by substituting forestry bioenergy for fossil energy, which is important for the formulation of policies to tackle climate change in the Chinese forestry sector. The simulation results showed that the carbon storage of all arbor forest vegetation in China increased year by year from 2018 to 2060, and, overall, it behaved as a carbon sink, with the annual carbon sink fluctuating in the region of 250 MtC/a. For commercial forests that already existed in 2018, the emission reduction effected by substituting forestry bioenergy for fossil energy was significant. The average annual carbon reduction in terms of bioenergy by using traditional and improved stoves reached 36.1 and 69.3 MtC/a, respectively. Overall, for China’s existing arbor forests, especially commercial forests, forestry bioenergy should be utilized more efficiently to further exploit its emission reduction potential. For future newly planted forests in China, new afforestation should focus on ecological public welfare forests, which are more beneficial as carbon sinks.

Can the Establishment of National Key Ecological Function Areas Enhance Vegetation Carbon Sink? A Quasi-Natural Experiment Evidence from China.

The National Key Ecological Functional Areas (NKEFAs) of China rely on the main functional area planning, with the core goal of enhancing the supply of ecological products. Carbon sink is an important ecological product, and it is necessary to understand whether the establishment of NKEFAs has enhanced vegetation carbon sink (CS). Considering the establishment of NKEFAs as a quasi-natural experiment, based on the panel data of prefecture-level cities in China from 2001 to 2019, a time-varying difference-in-differences (DID) model is used to systematically examine the impact of NKEFAs on CS. The study found that the establishment of NKEFAs has significantly enhanced the CS, and compared to the non-NKEFAs, NKEFAs has increased CS in the covered areas by an average treatment effect (ATE) of 2.1625. The establishment of NKEFAs can enhance CS through the optimization of territory spatial structure, the upgrading of industrial structure and the inter-industrial mobility of labor. The enhancement roles of NKEFAs on CS are heterogeneous across different functional area types, geospatial locations, and quantile levels, with higher enhancement of CS at windbreak–sand fixation type, northwestern region and high quantiles, respectively. In addition, NKEFAs not only have a significant positive ecological spillover effect, but also balanced with local economic growth, they achieve the goals of “lucid waters and lush mountains are invaluable assets”.

Tracking 21st century anthropogenic and natural carbon fluxes through model-data integration

Monitoring the implementation of emission commitments under the Paris agreement relies on accurate estimates of terrestrial carbon fluxes. Here, we assimilate a 21st century observation-based time series of woody vegetation carbon densities into a bookkeeping model (BKM). This approach allows us to disentangle the observation-based carbon fluxes by terrestrial woody vegetation into anthropogenic and environmental contributions. Estimated emissions (from land-use and land cover changes) between 2000 and 2019 amount to 1.4 PgC yr−1, reducing the difference to other carbon cycle model estimates by up to 88% compared to previous estimates with the BKM (without the data assimilation). Our estimates suggest that the global woody vegetation carbon sink due to environmental processes (1.5 PgC yr−1) is weaker and more susceptible to interannual variations and extreme events than estimated by state-of-the-art process-based carbon cycle models. These findings highlight the need to advance model-data integration to improve estimates of the terrestrial carbon cycle under the Global Stocktake.