Increase In Gross Primary Production Research Articles

Elevated atmospheric CO2 concentration and vegetation structural changes contributed to gross primary productivity increase more than climate and forest cover changes in subtropical forests of China

Abstract. The subtropical forests of China play a pivotal role in the global carbon cycle and in regulating the global climate. Quantifying the individual and combined effects of forest cover change (FCC), vegetation structural change (e.g. leaf area index (LAI)), CO2 fertilisation, and climate change (CC) on the annual gross primary productivity (GPP) dynamics of different subtropical forest types are essential for mitigating carbon emissions and predicting future climate changes, but these impacts remain unclear. In this study, we used a processed-based model to comprehensively investigate the impacts of these factors on GPP variations with a series of model experiments in China's subtropical forests from 2001 to 2018. Simulated GPP showed a significant increasing trend (20.67 gCm-2yr-1, p<0.001) under the interaction effects of FCC, LAI change, rising CO2, and CC. The CO2 fertilisation (6.84 gCm-2yr-1, p<0.001) and LAI change (3.79 gCm-2yr-1, p=0.004) were the two dominant drivers of total subtropical forest GPP increase, followed by the effects of FCC (0.52 gCm-2yr-1, p<0.001) and CC (0.92 gCm-2yr-1, p=0.080). We observed different responses to drivers depending on forest types. The evergreen broad-leaved forests showed the maximum carbon sequestration rate due to the positive effects of all drivers. Both the FCC (0.19 gCm-2yr-1, p<0.05) and CC (1.22 gCm-2yr-1, p<0.05) significantly decreased evergreen needle-leaved forest GPP, while their negative effects were almost offset by the positive impact of LAI changes. Our results indicated that LAI outweighed FCC in promoting GPP, which is an essential driver that needs to be accounted for in studies and ecological and management programmes. Overall, our study offers a novel perspective on different drivers of subtropical forest GPP changes and provides valuable information for policy makers to better manage subtropical forests to mitigate climate change risks.

Unraveling the effects of snow cover change on vegetation productivity: Insights from underlying surface types

AbstractChanges in snow cover have an important effect on vegetation gross primary productivity (GPP), but the role of underlying surface types in this effect still remains unclear. The underlying surface refers to the surface of the Earth and is located at the bottom of the snow cover during the snow season. Here, we focus on the influence of snow cover change on GPP in Northeast China from 1982 to 2015, revealing the various dependencies of the association between snow cover and GPP on forest, grassland, and dryland. The results show that the increases in GPP in dryland and grassland are primarily influenced by the increase in snow water equivalent, while the variations in GPP in forest are primarily influenced by snow phenological indicators, with snow cover end date and snow cover days dominating GPP in the early and peak growing seasons, respectively. Additionally, snow cover can have a rather strong time‐lagged effect on GPP by changing the soil temperature in dryland. Our findings emphasize the importance of considering underlying surface types in the snow–GPP relation and provide insight into how climate change may affect ecosystems in regions with comparable underlying surface types.

A spectral mixture analysis based framework for estimating and charactering water use efficiency in heterogeneous drylands

Climate change and irrational land utilization have significantly disrupted the function and structure of the dryland ecosystems, leading to tremendously potential effects on the carbon and water cycles of the global terrestrial ecosystems. Ecosystem-scale water use efficiency (WUE) is a crucial metric that defines the ratio of gross primary productivity (GPP) to evapotranspiration (ET), revealing the coupling relationship between such two ecological processes and playing a significant role in water resource management decision-making. However, quantifying WUE in drylands remains a challenge because of their heterogeneous land surface challenging to characterize model parameters using traditional remote sensing techniques. Here, we developed a process-based framework to estimate GPP and ET, as well as WUE based on physical endmember fractions from spectral mixture analysis (SMA) model. And such framework is used to quantify the spatial and temporal evolution of WUE by optimizing the light use efficiency and assisting in the construction of a GV-Cws model for the separation of transpiration and evaporation processes during computation by utilizing the physically fractional vegetation and soil obtained from SMA in the Hexi Corridor of China, a typical dryland ecosystem. The results demonstrate that our framework can accurately estimate GPP and ET in the Hexi Corridor, as indicated by favorable performance in R2 (0.669, 0.705), RMSE (0.383,0.885) and NSE (0.534, 0.690) respectively. Such results surpass the accuracy of both two Moderate Resolution Imaging Spectroradiometer (MODIS) products and Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2)’s ET product. These advantages highlight that our SMA-based framework can provide highly accurate and distinct characterization of surface carbon and water interaction processes. Meanwhile, the WUE of the Hexi Corridor ecosystem exhibited a positive trend from 2001 to 2017 (0.016 gC·m−2·mm−1·a-1), characterized by simultaneous increases in GPP and ET.

Exploring the Spatiotemporal Alterations in China’s GPP Based on the DTEC Model

Gross primary productivity (GPP) is a reliable measure of the carbon sink potential of terrestrial ecosystems and is an essential element of terrestrial carbon cycle research. This study employs the diffuse fraction-based two-leaf light-use efficiency (DTEC) model to imitate China’s monthly GPP from 2001 to 2020. We studied the trend of GPP, investigated its relationship with climatic factors, and separated the contributions of climate change and human activities. The findings showed that the DTEC model was widely applicable in China. During the study period, China’s average GPP increased significantly, by 9.77 g C m−2 yr−1 (p < 0.001). The detrimental effect of aerosol optical depth (AOD) on GPP was more widespread than that of total precipitation, temperature, and solar radiation. Areas that benefited from AOD, such as Northwest China, experienced significant increases in GPP. Climate change and human activities had a primary and positive influence on GPP during the study period, accounting for 28% and 72% of the increase, respectively. Human activities, particularly ecological restoration projects and the adoption of advanced agricultural technologies, played a significant role in China’s GPP growth. China’s afforestation plan was particularly notable, with the GPP increasing in afforestation areas at a rate greater than 10 g C m−2 yr−1. This research provides a theoretical foundation for the long-term management of China’s terrestrial ecosystems and helps develop adaptive ecological restoration tactics.

Assessing the spatiotemporal dynamics of water and carbon fluxes in subtropical forest of Xin’an River Basin using an improved Biome-BGC model

Extreme climate occurred frequently in subtropical region, which seriously affects carbon and water fluxes such as evapotranspiration (ET) and gross primary productivity (GPP) of terrestrial ecosystems. The process-based biome biogeochemical cycles (Biome-BGC) model is widely used for simulating carbon and water fluxes of forest ecosystems. However, the lack of the interaction information of climate, vegetation and soil, such as the hysteresis effect, canopy stratification on photosynthesis, impedes better simulations of the ecohydrological processes. Here, we tended to improve the simulation accuracy of Biome-BGC model at a subtropical forest on the Xin’an River in Southeastern China by reconstructing the precipitation series, modifying the ET and canopy multilayers modules, and optimizing the parameters. The spatiotemporal patterns of GPP, ET, water use efficiency (WUE) and their response to environmental factors across the Xin’an River Basin from 1982 to 2018 were further explored. The results showed that the improved model performed well, with the determination coefficient, root means square error and mean absolute error being 0.730, 1.522 gC/m2/d and 1.218 gC/m2/d for GPP, 0.857, 1.082 mm/d and 0.838 mm/d for ET, respectively. Basin-averaged GPP, ET and WUE increased during 1982–2018 and these increasing trends were more pronounced during 1999–2018. Significant positive trends of WUE occurred in the northeast corner. The increasing air temperature and precipitation respectively dominated the increase in GPP and ET, the increasing CO2 concentration and NDVI mitigated the negative effect of extreme precipitation on WUE. Given that human activities such as afforestation have effectively reduced the extent of damage to forest ecosystems from extreme precipitation, we highlight an urgent need to formulate adaptation strategies aimed at reducing the risk of extreme climate in humid regions.

How the enhanced East Asian summer monsoon regulates total gross primary production in eastern China

Recognizing the relationship between gross primary production (GPP) and precipitation in eastern China, the East Asian Summer Monsoon (EASM) plays a crucial role in shaping GPP. Despite confirmation of the strong link between EASM and GPP, there remains a notable research gap in understanding the specific impact of the EASM on GPP in different regions of eastern China. Here we used simulations from Trends in Net Land–Atmosphere Carbon Exchanges (TRENDY) models from 1951 to 2010 and divided eastern China into five subregions for the study. We also used the New East Asian Summer Monsoon Index (NEWI) as a quantitative metric to distinguish between periods of strong and weak EASM. Building on this, this study aims to investigate the response of GPP in different subregions of eastern China. Regionally, under strengthened EASM years (1954, 1957, 1965, 1969, 1977, 1980, 1983, 1987, 1993 and 1998), East China experienced the most pronounced increase in GPP at 12 ± 21 (mean ± 1 sigma) gC m−2 mon−1 compared to the weak EASM years (1958, 1961, 1972, 1973,1978, 1981, 1985, 1994, 1997 and 2004). In contrast, Southwest China showed a decline in GPP at −4 ± 10 gC m−2 mon−1. Moreover, GPP also increased in Northeast and North China when EASM strengthened, while South China showed a decline in GPP. This indicated that GPP changed with monsoon intensity. According to the mechanism analysis, during strong EASM, there was intense moisture convergence through alterations in the atmospheric circulation field over East China and abundant precipitation, which further contributed to the increase in GPP. Downward solar radiation in Southwest China decreased with EASM enhancement, which suppressed GPP and hindered vegetation growth. Overall, the results highlight the importance of accurately predicting the impact of different EASM intensities of regional carbon fluxes.

The Contributions of Climate and Human Activities to Water Use Efficiency in China’s Drylands

In the context of global warming, terrestrial ecosystems have undergone significant variations. China has implemented a variety of ecological engineering methods to enhance carbon stocks. However, understanding the spatial and temporal dynamics of carbon and water in drylands under climate change remains limited. Here, our research elucidates carbon and water dynamics in China’s drylands over the last two decades, with a focus on understanding spatial–temporal changes and the effects of ecological engineering on the carbon–water cycle. Furthermore, this study investigates the relationships among climate change, water use efficiency (WUE), and its components—Gross Primary Productivity (GPP) and Evapotranspiration (ET)—identifying key climatic drivers and assessing possible directions for enhancing WUE under changing climate conditions. Our research indicates that both GPP and ET have significantly increased over the past 20 years, with growth rates of 4.96 gC·m−2·yr−1 and 4.26 mm·yr−1, respectively. Meanwhile, WUE exhibited a slight declining trend, at a rate of −0.004 gC·mmH2O·yr−1. This confirms the positive impact of vegetation restoration efforts. We found that fluctuations in interannual WUE were influenced by human activities and climate change. Precipitation (Prec) was the key climatic factor driving the GPP increase. Both solar radiation (Solra) and Prec were crucial for the interannual variation of WUE. Interestingly, WUE was the main factor affecting GPP development. The decline in WUE in drylands is linked to interannual variability in WUE and increased Vapor Pressure Deficit (VPD) due to warming. Seasonal variations in how WUE responds to climatic factors were also observed. For instance, fall rainfall increased WUE, while spring rainfall decreased it. Fall WUE was highly sensitive to VPD. Spatially, we found higher WUE in China’s eastern and Xinjiang regions and lower in inland areas and the Tibetan Plateau. Geomorphologic factors and soil conditions were the main drivers of this spatial variability in WUE. Temperature (Tem), Solra, VPD, and relative humidity (Relah) also played significant roles. Our results show a generalized inverse persistence in WUE variability. This suggests a potential for increased WUE in the eastern regions and a risk of decreased WUE on the Tibetan Plateau. Addressing the threat of vegetation decline in arid regions, particularly within the Tibetan Plateau, is crucial. It is essential to adapt forestry practices to complement the carbon and water cycles in these landscapes.

Spatiotemporal patterns and driving factors of gross primary productivity over the Mongolian Plateau steppe in the past 20 years

The Eurasian steppe is the largest temperate grassland in the world. The grassland of the Mongolian Plateau (MP) represents an important part of the Eurasian steppe with high climatic sensitivity. Gross primary productivity (GPP) is a key indicator of the grassland's production, status and dynamic on the MP. In this study, we calibrated and evaluated the grassland-specific light use efficiency model (GRASS-LUE) against the observed GPP collected from nine eddy covariance flux sites on the MP, and compared the performance with other four GPP products (MOD17, VPM, GLASS and GOSIF). GRASS-LUE with higher R2 (0.91) and lower root mean square error (RMSE = 0.99 gC m−2 day−1) showed a better performance compared to the four GPP products in terms of model accuracy and dynamic consistency, especially in typical and desert steppe. The parameters of the GRASS-LUE are more suitable for water-limited grassland could be the reason for its outstanding performance in typical and desert steppe. Mean grassland GPP derived from GRASS-LUE was higher in the east and lower in the west of the MP. Grassland GPP was on average 205 gC m−2 over the MP between 2001 and 2020 with mean annual total GPP of 322 TgC yr−1. 30 % of the MP steppe showed a significant GPP increase. Growing season precipitation is the main factor affecting GPP of the MP steppe across regions. Anthropogenic factors (livestock density and population density) had greater effect on GPP than growing season temperature in pastoral counties in IM that take grazing as one of main industries. These findings can inform the status and trend of the productivity of MP steppe and help government and scientific research institutions to understand the drivers for spatial pattern of grassland GPP on the MP.

Carbon Allocation to Leaves and Its Controlling Factors and Impacts on Gross Primary Productivity in Forest Ecosystems of Northeast China

Carbon allocation in forest ecosystems is essential for the optimization of growth. However, remote-sensing-based research on the estimation of carbon allocation in forests is inadequate. This article considers forests in northeastern China as the research area and uses leaf area index (LAI) data combined with random forest and structural equation modelling methods to study the spatiotemporal distribution characteristics and driving factors of carbon allocation to leaves (ΔLAI) in deciduous broad-leaved forests (DBF), deciduous coniferous forests (DNF), and mixed forests (MF) during the green-up period (GUP) at a monthly scale during April, May, June, and July from 2001 to 2021, and clarifies the impact of leaf carbon allocation on gross primary productivity (GPP). The ΔLAI was the highest in DBF in April and in DNF and MF in May. The ΔLAI in April with an increasing trend year by year in DBF and MF, and the ΔLAI in May with an increasing trend in DNF. Among all the direct and indirect relationships that affect ΔLAI, temperature (TEM) has the highest path coefficient for DBF’s ΔLAI in April (−1.213) and the start of the season (SOS) has the highest path coefficient for DNF (−1.186) and MF (0.815). ΔLAI in the GUP has a significant positive impact on the GPP. In the MF, the higher ΔLAI in May was most conducive to an increase in GPP. During the critical period, that is April and May, carbon allocation to leaves effectively improves the carbon sequestration capacity of forestland. This information is of great value for the development and validation of terrestrial ecosystem models.

Changing Climate Threatens Irrigation Benefits of Maize Gross Primary Productivity in China

AbstractIntensive irrigation has been proven to profoundly impact climate through the surface energy budget. However, the impacts of irrigation and climate interactions on gross primary productivity (GPP) in maize cultivated areas remain uncertain. Here we quantified the irrigation effects on maize GPP (∆GPP) across China by combining a land surface model and a light‐use efficiency model and using satellite‐based irrigation water use. We show that irrigation significantly contributed to an increase in maize GPP by an average of 430 gC · m−2 · yr−1, equivalent to 28% of the irrigated maize GPP in China. These benefits (∆GPP) were attributed to irrigation effects (water supply, surface cooling) and climate interactions based on a machine learning framework (eXtreme Gradient Boosting model‐SHapley Additive exPlanations). Irrigation water supply and surface cooling explained 54% ± 19% and 23% ± 20% of ∆GPP respectively, the rest being due to strong climate interactions with irrigation through water and energy balance. Assuming business‐as‐usual irrigation levels, changing climate both increases and decreases ΔGPP over different regions, driven primarily by temperature changes. The irrigation benefits in those areas under heat stress are greatly threatened due to changing climate. The roles of climate change on ∆GPP are reversed from beneficial to detrimental in the North China Plain, dominated by different warming levels of future scenarios. Our analysis provides new insights into assessing irrigation potential with the climate interactions and future irrigation priority regions.

Solar radiation variation weakened the boost of gross primary production by vegetation restoration in China’s most forestry engineering areas during 2001–2020

Over the past decades, ecological restoration initiatives in China have made great progress in restoring degraded forests and increasing vegetation coverage, yet the carbon sequestration effects of these initiatives in the context of climate change are not clear. In this study, we assessed the effects of vegetation restoration on gross primary production (GPP) in China’s forestry engineering areas, where large-scale vegetation restoration programmes were launched, during 2001–2020 by disentangling the respective roles of land cover change (LCC), CO2 fertilization, and climate changes using a two-leaf light use efficiency model. We found that LCC attributed by the vegetation restoration dominantly accelerated the increase of GPP in seven out of the eight areas, and CO2 fertilization played a near-equivalent role in all areas. By contrast, the changes in different climate factors contributed to GPP variations diversely. The solar radiation variation greatly inhibited the vegetation GPP over time in seven out of these areas, and the changes in air temperature and vapor pressure deficit regulated GPP inter-annual variations without clear trends in all areas. This study advances our understanding of the contribution of China’s afforestation on its forest GPP in a changing climate, which may help to better manage forests to tackle the challenge of the climate crisis in the future.

The direct and indirect effects of the environmental factors on global terrestrial gross primary productivity over the past four decades

Gross primary productivity (GPP) is jointly controlled by the structural and physiological properties of the vegetation canopy and the changing environment. Recent studies showed notable changes in global GPP during recent decades and attributed it to dramatic environmental changes. Environmental changes can affect GPP by altering not only the biogeochemical characteristics of the photosynthesis system (direct effects) but also the structure of the vegetation canopy (indirect effects). However, comprehensively quantifying the multi-pathway effects of environmental change on GPP is currently challenging. We proposed a framework to analyse the changes in global GPP by combining a nested machine-learning model and a theoretical photosynthesis model. We quantified the direct and indirect effects of changes in key environmental factors (atmospheric CO2 concentration, temperature, solar radiation, vapour pressure deficit (VPD), and soil moisture (SM)) on global GPP from 1982 to 2020. The results showed that direct and indirect absolute contributions of environmental changes on global GPP were 0.2819 Pg C yr−2 and 0.1078 Pg C yr−2. Direct and indirect effects for single environmental factors accounted for 1.36%–51.96% and 0.56%–18.37% of the total environmental effect. Among the direct effects, the positive contribution of elevated CO2 concentration on GPP was the highest; and warming-induced GPP increase counteracted the negative effects. There was also a notable indirect effect, mainly through the influence of the leaf area index. In particular, the rising VPD and declining SM negatively impacted GPP more through the indirect pathway rather than the direct pathway, but not sufficient to offset the boost of warming over the past four decades. We provide new insights for understanding the effects of environmental changes on vegetation photosynthesis, which could help modelling and projection of the global carbon cycle in the context of dramatic global environmental change.

Management practices regulate the response of canopy and ecosystem water use efficiency in cropland ecosystems

ContextWith rising global temperature and frequent drought events, water resource supply is facing to great challenge. Understanding the effects of different management practices on cropland water use efficiency (WUE) is crucial to promote effective allocation of water resources and food security. ObjectiveThe objective of this study was to reveal the contrasting effects of different farm management practices including continuous and rotational, tillage and no-till, irrigated and rainfed on cropland water use efficiency from canopy (WUET) to ecosystem (WUEET) scales, respectively. MethodsOn the basis of 26 site-years eddy covariance-based carbon and water flux observations, this study used the underlying WUE (uWUE) model to partition evapotranspiration (ET) across maize and soybean ecosystems. Three control groups were used to compare the effects of different management practices on the coupling relationships between terrestrial carbon and water cycles, respectively. ResultsCompared with the rotational maize-soybean site, both WUET and WUEET at the continuous maize site were larger, which was mainly ascribed to stronger gross primary productivity (GPP) for the C4 crop. But over the maize-planting years, both WUET and WUEET at the rotational site were higher than the continuous site owing to the nitrogen fixation by rhizobia for preceding year, especially for WUET. Under conventional tillage and no-till management, multi-year mean WUEET and WUET were contrasting. WUEET under no-till was apparently larger than WUEET under till, because of similar GPP but relatively lower ET. Meanwhile, more water was consumed by crop transpiration (T) under no-till, which led to lower WUET in comparison with WUET under till. In comparison with the rainfed farming, GPP, ET and T all increased apparently after irrigation. However, both WUET and WUEET at the irrigated site were not improved owing to more water lost with relatively fewer GPP increase. ConclusionsThe study suggested that the crop rotation system can improve both WUEET and WUET apparently. Meanwhile, fewer water resource was consumed by cropland under no-till management. Finally, irrigation farming improved the cropland GPP but reduced WUEET and WUET. ImplicationsOur results provide valuable management implications for understanding the mechanisms that can enhance water use efficiency and result in significant water savings and high yield.

Non-Ignorable Differences in NIRv-Based Estimations of Gross Primary Productivity Considering Land Cover Change and Discrepancies in Multisource Products

The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001–2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm−2yr−1 across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP.

Maximum Gross Primary Productivity Dominates the Trend in Gross Primary Productivity in China’s Deciduous Forest Ecosystems

The terrestrial gross primary productivity (GPP) has increased over the past two decades. However, the climatic attribution and the physiological and phenological processes that control the trends in the GPP are still unclear. Here, we used remote-sensing-based vegetation GPP and phenology datasets, analyzed the spatial and temporal variation in the GPP, investigated the influence of the growing season length (GSL) and the maximum value of gross primary productivity (GPPmax) on the annual GPP, and quantified the effect of climate variables on the annual GPP. Our results identified a significant increase in the annual GPP (11.97 gC/m2/yr) during 2001–2020 in China’s deciduous forest. The GPPmax trend dominated the trends in the GPP, when compared with the GSL. Moreover, climate warming in summer contributes to the increase in the GPP and the GPPmax, while the extension of the GSL is primarily due to the temperature rise in spring. The annual GPP of the planted forest showed a higher increasing rate than the natural forest, due to the significant enhancement of the GPPmax and the high sensitivity of the GSL to climatic factors in the planted forest. Our findings provide a new perspective on the phenological and physiological causes of the trends in the GPP, and emphasize the importance of capturing the variability in the GPPmax when modeling the GPP.

Increased snow cover enhances gross primary productivity in cold and dry regions of the Tibetan Plateau

AbstractSnow cover is an important control element in the Tibetan Plateau (TP) ecosystem; however, the impact of snow cover changes on gross primary productivity (GPP) is largely unknown, particularly under complex geographical conditions. In this study, we investigated the impacts of snow cover changes on different seasonal GPP and their mechanisms in different geographical zones using multisource remote sensing data from 1982 to 2018. Snow cover significantly affected GPP by nearly 15% of the TP region, and snow cover days (SCDs) were the dominant snow cover indicators affecting GPP variations compared with snow water equivalent (SWE) and snow cover end date (SCED). In general, an increase in snow cover leads to a significant increase in GPP in regions with low precipitation and temperature, but limits the accumulation of GPP in humid and warm regions. Furthermore, from the humid to the arid zone, the “moisture effect” of snow cover (by altering soil moisture) plays an increasingly important role in regulating GPP variations with increasing drought levels. This study elucidates the importance of snow cover in regulating different seasonal GPP variations and significantly improves our insight into the response of vegetation carbon uptake to snow cover changes in the TP.

Urbanization expands the fluctuating difference in gross primary productivity between urban and rural areas from 2000 to 2018 in China

Urban and rural vegetation are affected by both climate change and human activities, but the role of urbanization in vegetation productivity is unclear given the dual impacts. Here, we delineated urban area (UA) and rural area (RA), quantified the relative impacts of climate change and human activities on gross primary production (GPP) in 34 major cities (MCs) in China from 2000 to 2018, and analyzed the intrinsic impacts of urbanization on GPP. First, we found that the total urban impervious surface coverage (ISC) of the 34 MCs increased by 13.25 % and the mean annual GPP increased by 211 gC m−2 during the study period. GPP increased significantly in urban core areas, but decreased significantly in urban expansion areas, which was mainly due to a large amount of vegetation loss due to land use conversion. Second, the variability of GPP in UA was generally lower than in RA. Both climate change and human activities had a positive impact on GPP in UA and RA in the 34 MCs, of which the contribution was 49 % and 51 % in UA, and 76 % and 24 % in RA, respectively. Third, under climate change and human activities, the increase in GPP offset 4.96 % and 12.35 % of the impact of land use conversion on GPP in 2000 and 2018, respectively, which indicated that the offset strengthened over time. These findings emphasize the role of human activities in promoting carbon sequestration in urban vegetation, which is crucial for better understanding the processes and mechanisms of urban carbon cycles. Decision-makers can manage urban vegetation based on vegetation carbon sequestration potential as regions urbanize, aiding comprehensive decision-making.

Understanding carbon sequestration trends using model and satellite data under different ecosystems in India

This study discusses carbon sequestration variability in different ecosystems of India. Four different biosphere regions, each over 0.5° × 0.5° area, have been selected considering the geospatial and climatic variability of these regions expanding from Central India (CI), the Northeast region (NER), the Western Ghats (WG), and the Western Himalayan region (WHNI). The climatic conditions of these four regions are different so are the biosphere constituents of these regions. We expect the Gross Primary Productivity (GPP) to enhance during the all India summer monsoon rainfall season but in varied magnitudes suggesting a role of climatic parameters and flora in these regions. The GPP from FLUXCOM for the duration of 2001 to 2019 (19 years) and satellite-derived vegetation indices like the Normalized Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI) are used in this study to understand the response of regional vegetation to this variability. EVI seems to be better related to GPP in comparison to NDVI in the preliminary analysis. Further analysis suggests LAI correlates better to GPP than EVI and NDVI in different seasons in these four regions. Also, meteorological parameters like surface temperature, rainfall, soil water, and other derived parameters like Vapor Pressure Deficit (VPD) are studied. It is also observed that the year-to-year variability in the climatic conditions could also have a role to play in the observed features. It is proven that the climate around the world is experiencing changes. Vegetation is one of the potent markers to monitor the impact of climate change. These long-term data and trends were studied to understand if there is any significant impact of the changing climatic conditions on the vegetation in these regions. Our study shows that there is an increasing (positive) trend in GPP at these locations though at different rates. WG and WHNI have shown a significant high rate of increase (6.44 and 5.36 gCm−2 y−1, respectively) in GPP over the last two decades.

Detection and attribution of an anomaly in terrestrial photosynthesis in Europe during the COVID-19 lockdown

Carbon dioxide (CO2) uptake by plant photosynthesis, referred to as gross primary production (GPP) at the ecosystem level, is sensitive to environmental factors, including pollutant exposure, pollutant uptake, and changes in the scattering of solar shortwave irradiance (SWin) − the energy source for photosynthesis. The 2020 spring lockdown due to COVID-19 resulted in improved air quality and atmospheric transparency, providing a unique opportunity to assess the impact of air pollutants on terrestrial ecosystem functioning. However, detecting these effects can be challenging as GPP is influenced by other meteorological drivers and management practices. Based on data collected from 44 European ecosystem-scale CO2 flux monitoring stations, we observed significant changes in spring GPP at 34 sites during 2020 compared to 2015–2019. Among these, 14 sites showed an increase in GPP associated with higher SWin, 10 sites had lower GPP linked to atmospheric and soil dryness, and seven sites were subjected to management practices. The remaining three sites exhibited varying dynamics, with one experiencing colder and rainier weather resulting in lower GPP, and two showing higher GPP associated with earlier spring melts. Analysis using the regional atmospheric chemical transport model (LOTOS-EUROS) indicated that the ozone (O3) concentration remained relatively unchanged at the research sites, making it unlikely that O3 exposure was the dominant factor driving the primary production anomaly. In contrast, SWin increased by 9.4 % at 36 sites, suggesting enhanced GPP possibly due to reduced aerosol optical depth and cloudiness. Our findings indicate that air pollution and cloudiness may weaken the terrestrial carbon sink by up to 16 %. Accurate and continuous ground-based observations are crucial for detecting and attributing subtle changes in terrestrial ecosystem functioning in response to environmental and anthropogenic drivers.

Quantifying effects of different types of droughts on gross primary production in China

AbstractThe increasing impact of drought stress on ecosystems with regard to climate change has received considerable attention, which has been demonstrated to have a time‐lag effect on ecosystems. However, the time‐lag effect on different types of droughts and gross primary production (GPP) at large regional scales remains unclear. Thus, using the GOSIF GPP product data of solar‐induced chlorophyll fluorescence, the meteorological drought index (standardized precipitation evapotranspiration index [SPEI]), and the agricultural drought index (soil moisture deficit index [SMDI]) based on the soil moisture data obtained from the Global Land Data Assimilation System, the time‐lag effect of regional drought and GPP and the contribution of drought to GPP variation were analysed in China from 2000 to 2018. Results showed that (1) China's annual GPP increased at an average rate of 9.26 g C m−2 y−1. With regard to the average rate for each climate zone, the largest increase of 30.44 g C m−2 y−1 was observed in the southern subtropical region, and the smallest increase (4.01 g C m−2 y−1) was observed in the Qinghai–Tibetan plateau, with about 53.11% of the regions showing a significant upward trend. In addition, the significantly declining regions were primarily concentrated in urban areas, accounting for about 2.26% of China. (2) SPEI and GPP had an average lag time of 6.28 months, whereas SMDI and GPP had an average lag time of 7.17 months. The comparison demonstrated that SMDI had a greater influence on GPP than SPEI. (3) The average contribution rate of meteorological drought and agricultural drought to the decrease of GPP was 79.37% and 87.06%, and their average contribution rate to the increase of GPP was 39.11% and 50.57%, respectively. This study will provide comprehensive understanding of the effects of drought on carbon and water cycles in terrestrial ecosystems with regard to climate change.