Net Primary Productivity In China Research Articles

NDVI joint process-based models drive a learning ensemble model for accurately estimating cropland net primary productivity (NPP)

The accurate estimation of cropland net primary productivity (NPP) remains a significant challenge. We hypothesized that incorporating prior information on NPP simulated by process-based models into normalized difference vegetation index (NDVI) data would improve the accuracy of cropland ecosystem NPP estimations. We used NDVI, MNPP (NPP of process-based model), and SNPP (statistic-based NPP) data estimated by nine process-based models and yield statistics to build a learning ensemble of the random forest model (LERFM). We used the new model to re-evaluate the cropland NPP in China from 1982 to 2010. Large spatial discrepancies among MNPPs, which indicate uncertainties in cropland NPP estimation using different methods, were observed when compared to SNPP. The LERFM model showed a slightly underestimation of only −0.37%, while the multi-model average process-based model (MMEM) strongly underestimated −15.46% of the SNPP. LERFM accurately estimated cropland NPP with a high simulation skill score. A consistent increasing trend in the LERFM and MMEM NPP during 1982–2010 and a significant positive correlation (r = 0.795, p < 0.001) between their total NPP indicate that the LERFM model can better describe spatiotemporal dynamic changes in cropland NPP. This study suggests that a learning ensemble method that combines the NDVI and process-based simulation results can effectively improve cropland NPP.

Spatiotemporal patterns of net primary productivity of subtropical forests in China and its response to drought

Net primary productivity (NPP) is an important indicator used to evaluate the carbon sequestration capacity of forest ecosystems. Subtropical forest ecosystems play an indispensable role in maintaining the global carbon balance, while frequently occurring drought events in recent years have seriously damaged their productivity. However, the spatiotemporal patterns of NPP, as well as its response to drought, remain uncertain. In this study, the multiscale drought characteristics in subtropical China during 1981–2015 were analyzed based on the standardized precipitation evapotranspiration index. Then, simulated and analyzed the spatiotemporal NPP of subtropical forests by the boreal ecosystem productivity simulator model. Finally, the response of NPP to drought was analyzed based on multiple statistical indices. The results show that most regions in subtropical China experienced mild and moderate drought during 1981–2015. In particular, the extent of drought severity has shown a noticeable increasing trend after 2000. The forest NPP ranged from 622.64 to 1323.82 gC·m−2·a−1, with an overall increase rate of 16.15 gC·m−2·a−1; in particular, the contribution of the western forest NPP became increasingly important. Drought stress has limited the growth of subtropical forest NPP in China, with summer and wet season time scales of drought having the greatest impact on forest NPP anomalies, followed by autumn time scales. The limitation is mostly because the drought duration continually increased, leading to differences in the impact of drought on forest NPP before and after 2000, with declines of 59.55 % and 82.45 %, respectively, mainly concentrated in southwestern regions, such as Yunnan, Guangxi, and Sichuan provinces. This study quantitatively analyzed the impact of drought on subtropical forest NPP, and provides scientific basis for subtropical forest response and adaptation to climate change.

Human activities significantly impact China’s net primary production variation from 2001 to 2020

Formulating ecological restoration strategies requires accurately quantifying how climate and anthropogenic factors influence net primary production (NPP). A Carnegie-Ames-Stanford approach (CASA) model was applied to estimate China’s terrestrial NPP from 2001 to 2020. We adopted a random forest (RF) method to identify the main driving forces for NPP change in China. Total NPP in China increased noticeably with a 24.91 Tg C/yr rate, as shown in our results. The significantly increased NPP was mainly attributed to human activities (64.29 ± 0.17%), chiefly due to human management and ecological projects (afforestation or other) fostered vegetation growth. The primary drivers of NPP variation varied in different geographic regions. Climate dominated the NPP dynamic in north China (52.38 ± 0.91%), where the main factor that restricted the increase of NPP was precipitation. Human activities strongly impacted the NPP variation in the remaining regions. Human management measures increased NPP in northwest and southwest China. In the northeast, east, and south-central China, the NPP change resulted from land use change, primarily grassland, cropland, and forest change. Collectively, our study expands the understanding of the driving forces of NPP change, informing different strategies for achieving ecological restoration and carbon neutrality.

Estimation and Climate Impact Analysis of Terrestrial Vegetation Net Primary Productivity in China from 2001 to 2020

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments.

Changes in Net Primary Productivity and Factor Detection in China’s Yellow River Basin from 2000 to 2019

Net primary productivity (NPP) is a main contributor to ecosystem carbon pools. It is crucial to monitor the spatial and temporal dynamics of NPP, as well as to assess the impacts of climate change and human activities to cope with global change. The dynamic of the NPP in China’s Yellow River Basin (YRB) from 2000 to 2019 and its influencing factors were analyzed by using trend and persistence tests and the GeoDetector method. The results show that the NPP had strong spatial heterogeneity, with a low NPP in the west and north, and a high NPP in the east and south. From 2000 to 2019, the NPP showed a statistically significant increase (at a mean of 5.5 g C m−2 yr−1, for a cumulative increase of 94.5 Tg C). A Hurst analysis showed that for the NPP in 76.3% of the YRB, the time series was anti-persistent. The spatial heterogeneity of the NPP in the YRB was mainly explained by precipitation and relative humidity (q value ranged from 0.24 to 0.44). However, the strength of the precipitation explained the decreased variation over time (q value decreased from 0.40 in 2000 to 0.26 in 2019). Interactions between the climate factors and human activities affected the NPP more strongly than individual factors. The results emphasize the importance of strengthening future research on the interaction between climate change and human activities. The results reveal the risk and optimal ranges of the driving factors and provide a quantification of the impacts of those factors regarding NPP. These findings can provide a scientific basis for vegetation restoration in the YRB.

Changes in dryland areas and net primary productivity in China from 1980 to 2020

Changes in dryland areas and net primary productivity in China from 1980 to 2020

Quantifying the contributions of climate factors and human activities to variations of net primary productivity in China from 2000 to 2020

Net primary productivity (NPP) plays a vital role in the globe carbon cycle. Quantitative assessment of the effects of climate changes and human activities on net primary productivity dynamics is vital for understanding the driving mechanisms of vegetation change and sustainable development of ecosystems. This study investigates the contributions of climatic factors and human activities to vegetation productivity changes in China from 2000 to 2020 based on the residual trend analysis (RESTREND) method. The results showed that the annual average net primary productivity in China was 325.11 g C/m2/year from 2000 to 2020 and net primary productivity showed a significantly increasing trend (p<0.05) at a rate of 2.32 g C/m2/year. Net primary productivity increased significantly (p<0.05) across 40.90% of China over the study period, while only 1.79% showed a significantly declining trend (p<0.05). The contributions of climatic factors and human activities to net primary productivity increase were 1.169 g C/m2/year and 1.142 g C/m2/year, respectively. Climate factors contributed positively mainly in Sichuan Basin, the Loess Plateau, the Mongolian Plateau, and Northeast China Plain. Positive contributions of human activities to net primary productivity mainly occurred in the Loess Plateau, Central China, and the Greater Khingan Mountains. The effects of climatic factors and human activities on net primary productivity changes varied among sub-regions. In Tropical Monsoon Climate Region and Subtropical Monsoon Climate Region, human activities had greater impacts on net primary productivity increase than climate factors, while climate factors were the dominant factor for net primary productivity recovery in other sub-regions. In addition, during 2000–2020, net primary productivity was dominated by both climate factors and human activities in 49.84% of China, while areas dominated solely by climate factors and human activities accounted for 13.67% and 10.92%, respectively. Compared to changed land cover types, the total net primary productivity as well as the increase of total net primary productivity in China was mostly contributed by unchanged land cover types, which contributed more than 90%.

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Stand carbon storage and net primary production in China’s subtropical secondary forests are predicted to increase by 2060

BackgroundForest ecosystems play an important role in carbon sequestration, climate change mitigation, and achieving China's target to become carbon (C) neutral by 2060. However, changes in C storage and net primary production (NPP) in natural secondary forests stemming from tree growth and future climate change have not yet been investigated in subtropical areas in China. Here, we used data from 290 inventory plots in four secondary forests [evergreen broad-leaved forest (EBF), deciduous and evergreen broad-leaved mixed forest (DEF), deciduous broad-leaved forest (DBF), and coniferous and broad-leaved mixed forest (CDF)] at different restoration stages and run a hybrid model (TRIPLEX 1.6) to predict changes in stand carbon storage and NPP under two future climate change scenarios (RCP4.5 and RCP8.5).ResultsThe runs of the hybrid model calibrated and validated by using the data from the inventory plots suggest significant increase in the carbon storage by 2060 under the current climate conditions, and even higher increase under the RCP4.5 and RCP8.5 climate change scenarios. In contrast to the carbon storage, the simulated EBF and DEF NPP declines slightly over the period from 2014 to 2060.ConclusionsThe obtained results lead to conclusion that proper management of China’s subtropical secondary forests could be considered as one of the steps towards achieving China’s target to become carbon neutral by 2060.

Change Trend and Restoration Potential of Vegetation Net Primary Productivity in China over the Past 20 Years

As an important vegetation parameter and ecological index, vegetation net primary productivity (NPP) can intuitively reflect changes in the ecological environment and the level of the carbon budget. However, the change trend of NPP and its recovery potential in China over the past 20 years remain unclear. Here, we used trend analysis, multiple regression analysis and residual analysis methods to analyse the change trend in the NPP of China’s terrestrial ecosystems from 2000 to 2019, as well as the climax background, restoration status and restoration potential of the NPP of forest, grassland and desert ecosystems. The results showed that (1) the change in vegetation NPP in China from 2000 to 2019 showed a continuous upward trend, with a change slope of 2.39 gC/m2/a2, and the area with a positive slope of change accounted for 68.10% of the country’s land area. The contribution rates of meteorological conditions and human activities to vegetation NPP changes were 85.41% and 14.59%, respectively. (2) The results obtained by the regression analysis method of meteorological conditions based on nature reserves could reflect the zonal climax vegetation status to a large extent, and the obtained values had a smooth transition within each ecogeographical division and between each ecogeographical division, which truly reflected the law of gradual change in climate, vegetation and natural conditions. The annual total NPP of the climax background vegetation in China’s forest, grassland and desert ecosystems was approximately 2.76 ± 0.28 PgC, and the annual total NPP of the three ecosystems was 1.90 ± 0.2 PgC, 0.80 ± 0.07 PgC and 0.009 ± 0.0005 PgC, respectively. (3) The annual total vegetation NPP of the restoration status of China’s forest, grassland and desert ecosystems was 2.24 PgC, and the annual total vegetation NPP of the three was 1.54 PgC, 0.65 PgC and 0.007 PgC, respectively. Benefiting from the effective implementation of climate warming and humidification and ecological engineering, the agro-pastoral zone, the Loess Plateau, the eastern Sichuan Basin and the Greater Khingan Range had the most significant increases in the past 20 years. (4) The annual total vegetation NPP of China’s forest, grassland and desert ecosystem restoration potential was approximately 0.52 ± 0.28 PgC, which accounted for approximately 19.05% of the annual total NPP of the climax background vegetation. The annual total vegetation NPP of forest, grassland and desert ecosystems restoration status was 0.36 ± 0.2 PgC, 0.16 ± 0.07 PgC and 0.002 ± 0.0005 PgC, respectively; the restoration potential accounted for 18.80%, 9.67% and 23.95% of the climax background vegetation NPP, respectively. The deployment of ecological projects should fully consider the restrictive climate conditions for decision makers and ecological scholars, and the benefits and costs of the projects should be considered comprehensively.

中国陆地植被净初级生产力估算模型优化与分析——基于中国生态系统研究网络数据

PDF HTML阅读 XML下载 导出引用 引用提醒 中国陆地植被净初级生产力估算模型优化与分析——基于中国生态系统研究网络数据 DOI: 10.5846/stxb202011263031 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划项目（2016YFC0500201，2016YFC0502102-02）和国家自然科学基金（41771464，31761143018-2） Optimization of net primary productivity estimation model for terrestrial vegetation in China based on CERN data 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:该研究基于中国生态系统研究网络（CERN）数据对传统CASA模型进行优化，对比两叶模型与优化CASA模型在站点尺度和像元尺度对于8个典型生态站点的植被净初级生产力（NPP）估算精度，选择在像元尺度表现更好的优化CASA模型，结合中国土地覆被数据（ChinaCover）开展2000-2019年中国陆地植被NPP监测与分析。研究结果表明：（1）基于FY2D PAR的优化方案能够有效避免空间插值导致的不确定性问题，显著提高了PAR估算精度；（2）在站点尺度上，两叶模型用于估算典型森林、草地生态系统的NPP表现更好，而在像元尺度上优化CASA模型估算精度更高；（3）在全国尺度上，优化了最大光能利用率、水分胁迫系数以及光合有效辐射计算方法的CASA模型能够较好地模拟中国陆地植被NPP，近20年中国陆地植被NPP变化范围为2.703-2.882 PgC/a，在空间上呈西北低东南高的格局，在时间上呈现波动中缓慢增加的趋势。 Abstract:In this study, we optimized the traditional Carnegie-Ames-Stanford Approach (CASA) model based on China Ecosystem Research Network (CERN) datasets, and compared the estimation accuracy of the two-leaf model and the optimized CASA model at the site scale and pixel scale at eight CERN sites covering major ecosystem types. The optimized CASA model with better performance at the pixel scale combined with China Land Cover Data (ChinaCover) were employed for mapping and monitoring the spatio-temporal changes of terrestrial vegetation net primary production (NPP) in China from 2000 to 2019. The results show that:(1) the optimization for model input parameter of photosynthetically active radiation based on FY2D PAR can effectively avoid the uncertainty caused by the spatial interpolation, and significantly improve the accuracy of PAR estimation; (2) The two-leaf model shows higher NPP estimation accuracy at the site scale, while the optimized CASA model performs better for the NPP estimation at the pixel scale; (3) At the national scale, the CASA model with optimized maximum light energy use efficiency, water stress coefficient and photosynthetically active radiation can better simulate China's terrestrial vegetation NPP. The estimated total NPP in Chinese terrestrial vegetation ranges from 2.703 PgC/a to 2.882 PgC/a in the past 20 years and indicates a fluctuated and slow increasing trend. The spatial distribution of the NPP in China shows a general pattern of gradually increasing from northwest to southeast. 参考文献 相似文献 引证文献

2000年以来中国潜在植被NPP的时空分布模拟

PDF HTML阅读 XML下载 导出引用 引用提醒 2000年以来中国潜在植被净初级生产力的时空分布模拟 DOI: 10.5846/stxb202111053112 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（42071216）；甘肃省自然科学基金项目（21JR7RA145） Estimation of temporal and spatial distribution of potential vegetation net primary productivity in China since 2000 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:潜在植被的净初级生产力（PNPP）是在无人类干扰情况下，立地所能发育形成最稳定成熟植被的净初级生产力，PNPP能够直接反映自然生态系统的质量状况，是分离人类活动对生态环境影响的重要指标。研究改进了CASA （Carnegie-Ames-Stanford Approach）模型，引入潜在光合有效辐射吸收比例（PFPAR）模拟了2000-2020年中国植被PNPP的时空分布格局。结果表明：中国PNPP空间分布差异较大，东南沿海和东北大、小兴安岭地区PNPP较高，新疆、西藏等西部干旱、高寒地区PNPP较低，呈现以400 mm等降水量线为界的空间分异格局。2000-2020年，中国PNPP总体上呈现递增趋势，年际波动小，PNPP减少的区域与增加的区域面积基本相等。PNPP波动较大的地区主要位于青藏高原、四川盆地和贵州高原。研究结果量化了气候变化背景下真实的生态状况和潜在生态状况的差异，可分离出人类活动对自然生态系统的直接影响，为制定针对性的生态修复对策提供了科学依据。 Abstract:Potential vegetation net primary productivity (PNPP) is the NPP of the most stable and mature vegetation type without human interference in the current climate conditions. PNPP can directly reflect the quality of natural ecosystems, which is an important index to separate the impact of human activities on ecological environment. This paper improves the CASA model and introduces the potential fraction of photosynthetically active radiation (PFPAR) to simulate the temporal and spatial distribution pattern of PNPP in China's vegetation from 2000 to 2020. The results show that the spatial distribution of China's PNPP is quite different. The PNPP is higher in the coastal areas of southeast China and the Greater and Lesser Khingan Range in northeast China, and lower in the arid and alpine regions of western China such as Xinjiang and Tibet, presenting a spatial differentiation pattern with 400 mm isoprecipitation line as the boundary. From 2000 to 2020, China's PNPP generally showed an increasing trend over time, but the interannual fluctuation was small. The area of PNPP decreased and increased was almost the same. The changes of PNPP in most regions are small, and the areas with large PNPP fluctuation are mainly located on Qinghai-Tibet Plateau, Sichuan Basin and Guizhou. The research results can separate the impact of human activities and climate change on natural ecosystem, and quantify the difference between real ecological status and potential ecological status under external pressure. It provides a scientific basis for formulating differentiated Ecological Restoration Countermeasures. 参考文献 相似文献 引证文献

Determinants of net primary productivity: Low-carbon development from the perspective of carbon sequestration

Net primary productivity, which reflects vegetation's carbon sequestration capacity, should not be ignored in the process of low-carbon development. To explore the paths of achieving low-carbon development goals from the perspective of carbon sequestration, this study takes net primary productivity as the study object and uses the logarithmic mean Divisia index decomposition model to analyze the influences of urbanization and land utilization change on net primary productivity in China. The results show that urbanization is the driving factor of the net primary productivity increase, and land utilization change is the main determinant of its decrease. This study argues that making reasonable land utilization planning is essential to reduce the net primary productivity loss caused by land utilization changes in the process of urbanization.

Regional contributions to interannual variability of net primary production and climatic attributions

Climate change profoundly affects the interannual variability (IAV) of net primary production (NPP) of terrestrial ecosystem from multiple aspects. However, the IAV of the nationwide annual NPP in China and the impacts of distinct climatic drivers are not well addressed. In this study, we investigated regional contributions to the IAV of the nationwide NPP and quantified the contributions of nine climatic drivers in different regions using the NPP estimated by the Carnegie-Ames-Stanford (CASA) model from 1982 to 2018. Our results showed that the simulated NPP exhibited an increasing trend of 15.2 Tg C yr-1 at the national scale. The nationwide NPP also showed large IAV ranging from ‍-0.29 to 0.22 Pg C with the mean absolute NPP IAV showing a descending gradient from southeastern to northwestern China. Our estimates and thirteen terrestrial biosphere models verified that humid region accounted for the largest contribution (62%) to this large IAV. Attribution analyses indicated that normal and high precipitation amount (nP, HP), as well as high temperature days (HT) and daily temperature range (DTR) exerted the largest contributions to the national NPP IAV. Regional analyses indicated that DTR and HP were the major climatic drivers to NPP IAV in humid region, whereas NPP IAV in water-limited regions (i.e., semi-humid, semi-arid, and arid regions) were tightly associated with nP, HP, and HT. DTR and nP exerted the largest contributions to NPP IAV in the Tibetan Plateau. However, more attention should be paid to the negative impacts of low temperature events and potential drought on NPP IAV in humid region and that of HT in water-limited regions. This study emphasized the dominant role of humid region in controlling the national NPP IAV and the different ecosystem responses to diverse climatic drivers, and therefore can be valuable for adaptive management of ecosystems when facing climate change.

Spatiotemporal Dynamics of Net Primary Productivity in China’s Urban Lands during 1982–2015

The rapid urbanization process has threatened the ecological environment. Net primary productivity (NPP) can effectively indicate vegetation growth status in an urban area. In this paper, we evaluated the change in NPP in China and China’s urban lands and assessed the impact of temperature, precipitation, the sunshine duration, and vegetation loss due to urban expansion on NPP in China’s three fast-growing urban agglomerations and their buffer zones (~5–20 km). The results indicated that the NPP in China exhibited an increasing trend. In contrast, the NPP in China’s urban lands showed a decreasing trend. However, after 1997, China’s increasing trend in NPP slowed (from 9.59 Tg C/yr to 8.71 Tg C/yr), while the decreasing trend in NPP in China’s urban lands weakened. Moreover, we found that the NPP in the Beijing–Tianjin–Hebei urban agglomeration (BTHUA), the Yangtze River Delta urban agglomeration (YRDUA), and the Pearl River Delta urban agglomeration (PRDUA) showed a decreasing trend. The NPP in the BTHUA showed an increasing trend in the buffer zones, which was positively affected by temperature and sunshine duration. Additionally, nonsignificant vegetation loss could promote the increase of NPP. In the YRDUA, the increasing temperature was the main factor that promoted the increase of NPP. The effect of temperature on NPP could almost offset the inhibition of vegetation reduction on the increase of NPP as the buffer zone expanded. In PRDUA, sunshine duration and vegetation loss were the main factors decreasing NPP. Our results will support future urban NPP prediction and government policymaking.

A new net primary productivity model and new management strategy of grassland classification based on CSCS in China

The discovery of grassland field, form and biomass in China was central to the sustainable development of grassland. In this study, the realistic spatial distribution patterns of grasslands were clarified through the combination of the International Geosphere-Biosphere Program (IGBP) and the Comprehensive and Sequential Classification System (CSCS). An optimal net primary productivity (NPP) model suitable for Chinese grasslands was introduced by integrating the classification indices-based model (CIM) with the Normalised Difference Vegetation Index (NDVI), and comparing it with the standard classical model (Miami, Schuur, CIM, CASA model). Using the optimal model as the algorithm basis, the net primary production spatial pattern of grassland in China was determined. The results showed that: (1) the total area of grassland was ~374.3 × 104 km2 in 2018, mainly distributed in north-western China. Among the grassland super-class groups, Tundra and alpine steppe were largest, and Warm desert smallest; (2) the optimal modified CIM had the highest prediction efficiency, and the overall accuracy was higher than the standard classical model (Miami, Schuur, CIM, CASA model). It achieved the accurate calculation of grassland NPP in China; (3) different grassland super-class groups had different carbon fixation efficiency per unit area, resulting in huge differences in total NPP. Among the various grassland super-class groups, the temperate humid grassland, steppe, tundra and alpine steppe had high conversion efficiency per unit area of NPP, whereas that for warm desert and the savanna was low. The total NPP was 388.04 × 1012 g C/year in the study area in 2018. The results provide a basis for the rational arrangement of grassland ecological and productive functions, and are significant for developing a new strategy of grassland classification management in China.

Spatio-Temporal Variation in AOD and Correlation Analysis with PAR and NPP in China from 2001 to 2017

Atmospheric aerosols can elicit variations in how much solar radiation reaches the ground surface due to scattering and absorption, which may affect plant photosynthesis and carbon uptake in terrestrial ecosystems. In this study, the spatio-temporal variations in aerosol optical depth (AOD) are compared with that in photosynthetically active radiation (PAR) and net primary productivity (NPP) during 2001–2017 in China using multiple remote sensing data. The correlations between them are analyzed at different scales. Overall, the AOD exhibited a northeast-to-southwest decreasing pattern in space. A national increasing trend of 0.004 year−1 and a declining trend of −0.007 year−1 of AOD are observed during 2001–2008 and 2009–2017. The direct PAR (PARdir) and diffuse PAR (PARdif) present consistent and opposite tendency with AOD during two periods, respectively. The total PAR (PARtotal) shows a similar variation pattern with PARdir. In terms of annual variation, the peaks of AOD coincide with the peaks of PARdif and the troughs of PARdir, indicating that aerosols have a significant positive impact on PARdir and a negative impact on PARdif. Furthermore, the PARdir has a stronger negative association with AOD than the positive correlation between PARdif and AOD at national and regional scales, indicating that PARdir is more sensitive to aerosol changes. The NPP has higher values in the east than in the west and exhibits a significant increasing trend of 0.035 gCm−2day−1 after 2008. The NPP has a negative correlation (−0.4–0) with AOD and PARdif and a positive correlation (0–0.4) with PARdir in most areas of China. The area covered by forests has the highest NPP-PAR correlation, indicating that NPP in forests is more sensitive to the PAR than is the NPP in grasslands and croplands. This study is beneficial for interpreting the aerosol-induced PAR impact on plant growth and for predicting plant production on haze days.

Spatiotemporal variations of carbon flux and nitrogen deposition flux linked with climate change at the centennial scale in China

The spatial and temporal variations of the vegetation carbon flux in China from 1901 to 2005 were studied using the vegetation net primary production (NPP) values from seven Earth-system models. In addition, the temporal and spatial changes in the nitrogen deposition flux in China were studied using the NH x and NO y fluxes from 1901 to 2005. The relationship between changes in the carbon flux, nitrogen flux and climate was analyzed. The results show that (1) over the past 100 years, NPP in China has shown an upward trend. The average trend coefficient is 0.88 and the NPP distribution trend is generally low in the north and high in the south, with a gradual increase from the northwest to the southeast. Temperature, precipitation and radiation are all conducive to plant growth in the direction of the gradient. The correlation coefficients between the ensemble model mean NPP and temperature, precipitation, longwave radiation and shortwave radiation are 0.88, 0.73, 0.91 and 0.67, respectively. (2) In the past 100 years, the NH x and NO y fluxes in China have shown an upward trend, with trend coefficients of 0.98 and 0.98, respectively, which pass the 99.9% confidence level of the t -test. NH x and NO y fluxes are also generally low in the north and high in the south, with a gradual increase from the northwest to the southeast in a step-like pattern. (3) The spatial distribution of the correlation coefficients between the NH x and NO y fluxes and air temperature is similar, with only slight differences in values. The spatial distribution of the correlation coefficients between the NH x and NO y fluxes and precipitation is similar in overall pattern, but the pattern is relatively complicated, with a positive-negative-positive-negative-positive pattern occurring across the monsoon region from north to south, and a negative-positive-negative-positive pattern occurring beyond the monsoon region from east to west. (4) The spatial distribution of the correlation coefficients between the NH x and NO y fluxes and NPP shows a generally consistent pattern, but the pattern is relatively uneven. The average distribution of the ensemble model mean is positive correlation in northeast China and southwest China, and alternating positive and negative correlation in other regions.

Afforestation promotes the enhancement of forest LAI and NPP in China

Recent national wide afforestation in China are playing an increasingly significant role in the global greening trend and CO2 mitigation. However, the long term knowledge of spatiotemporal pattern of plantation forests (PF) is essential to understand the continuability and sustainability of the PF contribution and its spatiotemporal pattern comparing to nature forests (NF). Here we combined an inventory based map of PF, three up-to-date remote sensing leaf area index (LAI) datasets, and a process-based terrestrial biogeochemical model to investigate the interannual variabilities (IAVs) and trends of LAI and net primary productivity (NPP) of natural and plantation forests in China, from 1982 to 2014. We found that PF possessed similar spatiotemporal patterns as those of NF, while they exhibited a greater enhancement of both LAI and NPP since 2000. The total proportions of LAI and NPP in the plantation areas increased from 22% to 23.4%, and 22% to 22.8% from 1982 to 2014, respectively. Although different datasets provided diverse rates, an increasing trend was in agreement. The spatially-explicit analysis also illustrated that the increasing proportions were primarily contributed by the Southern sub-regions, particularly the Southwest and South sub-regions, while the PF in Northern sub-regions (e.g., Inner Mongolia and Northeast) even showed a decreasing contribution trend. The quantitative outputs from this study supported that PF promoted the greening trend in China since the 21st century. However, we also argued that the swift increase in LAI and NPP by PF should mainly result from the growing number of tree plantations and the rapid growth of newly planted forests. The proper management of PF is, therefore, an essential task for the maintenance and long-term improvement of their ecosystem services.

Analysis of regional carbon allocation and carbon trading based on net primary productivity in China

Ecological balance and carbon sink economies have gained increased attention for tackling global warming. Based on an improved Carnegie–Ames–Stanford Approach model, this study demonstrated regional Net Primary Productivity (NPP) and analyzed regional carbon overdraft situations in China during 2005–2015. Regional carbon allowances were allocated according to carrying capacity of carbon sequestration and China's carbon intensity reduction goals in “13th Five-year plan”. Data Envelopment Analysis (DEA) technology with panel data was further employed to estimate potential benefits resulting from carbon trading and a carbon sink economy. Regional NPP decreased from south to north and from coast to inland, while regions with severe carbon overdrafts were gathered in North and East China. In order to maintain a regional carbon balance with lower abatement costs, regional cooperation of emission reduction within either North or East China is proposed in this study. It is concluded that the majority of provinces and cities in Eastern China and some provinces in the west would be the major purchasers of carbon credits under a national carbon emissions trading (CET) market. Following the introduction of emissions offset mechanisms, Yunnan, Sichuan, and Heilongjiang would be the major providers of carbon sinks in China.