Annual Gross Primary Production Research Articles

Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes

Abstract. The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version 1.0 is described here. This version of the model simulates plant ecophysiological (e.g., photosynthetic and stomatal) responses to varying meteorological conditions and concentrations of CO2 and ground-level ozone (O3) based on prescribed meteorological and atmospheric chemical inputs from various sources. Driven by the same meteorological data used in the GEOS-Chem chemical transport model, this allows asynchronously coupled experiments with GEOS-Chem simulations with unique coherency for investigating biosphere–atmosphere chemical interactions. TEMIR agrees well with FLUXNET site-level gross primary productivity (GPP) in terms of both the diurnal and monthly cycles (correlation coefficients R2>0.85 and R2>0.8, respectively) for most plant functional types (PFTs). Grass and shrub PFTs have larger biases due to generic model representations. The model performs best when driven by local site-level meteorology rather than reanalyzed gridded meteorology. Simulation using gridded meteorology agrees well for annual GPP in seasonality and spatial distribution with a global average of 134 Pg C yr−1. Application of Monin–Obukhov similarity theory to infer canopy conditions from gridded meteorology does not improve model performance, predicting an increase of +7 % in global GPP. Present-day O3 concentrations simulated by GEOS-Chem and an O3 damage scheme at high sensitivity show a 2 % reduction in global GPP with prominent reductions of up to 15 % in eastern China and the eastern USA. Regional correlations are generally unchanged when O3 is present and biases are reduced, especially for regions with high O3 damage. An increase in atmospheric CO2 concentration of 20 ppmv from the level in 2000 to the level in 2010 modestly decreases O3 damage due to reduced stomatal uptake, consistent with ecophysiological understanding. Our work showcases the utility of this version of TEMIR for evaluating biogeophysical responses of vegetation to changes in atmospheric composition and meteorological conditions.

Beyond the visible: Accounting for ultraviolet and far-red radiation in vegetation productivity and surface energy budgets.

Photosynthetically active radiation (PAR) is typically defined as light with a wavelength within 400-700 nm. However, ultra-violet (UV) radiation within 280-400 nm and far-red (FR) radiation within 700-750 nm can also excite photosystems, though not as efficiently as PAR. Vegetation and land surface models (LSMs) typically do not explicitly account for UV's contribution to energy budgets or photosynthesis, nor FR's contribution to photosynthesis. However, whether neglecting UV and FR has significant impacts remains unknown. We explored how canopy radiative transfer (RT) and photosynthesis are impacted when explicitly implementing UV in the canopy RT model and accounting for UV and FR in the photosynthesis models within a next-generation LSM that can simulate hyperspectral canopy RT. We validated our improvements using photosynthesis measurements from plants under different light sources and intensities and surface reflection from an eddy-covariance tower. Our model simulations suggested that at the whole plant level, after accounting for UV and FR explicitly, chlorophyll content, leaf area index (LAI), clumping index, and solar radiation all impact the modeling of gross primary productivity (GPP). At the global scale, mean annual GPP within a grid would increase by up to 7.3% and the increase is proportional to LAI; globally integrated GPP increases by 4.6 PgC year-1 (3.8% of the GPP without accounting for UV + FR). Further, using PAR to proxy UV could overestimate surface albedo by more than 0.1, particularly in the boreal forests. Our results highlight the importance of improving UV and FR in canopy RT and photosynthesis modeling and the necessity to implement hyperspectral or multispectral canopy RT schemes in future vegetation and LSMs.

Dynamics of Carbon and Water Fluxes over Cropland and Agroforest Ecosystems on the Southern Chinese Loess Plateau

Studies on the spatiotemporal dynamics in ecosystem carbon and water exchanges are essential in predicting the effects of climate change on regional carbon and energy budgets. Using the eddy covariance technique, carbon and water fluxes were observed in a typical winter wheat ecosystem (WWE) and an agroforest ecosystem (AFE) in the southern Loess Plateau from 2004 to 2010. The seasonal and inter-annual variability in gross primary productivity (GPP), net ecosystem exchange (NEE), evapotranspiration (ET), and water use efficiency (WUE) were examined and the main influencing factors were identified using the Pearson correlation. The results indicate that the seasonal GPP and NEE showed a bimodal distribution in WWE, while this was unimodal in AFE. The sinusoidal function did well in the characterization of seasonal ET dynamics for both ecosystems, with the determination coefficients being 0.85 and 0.94, respectively. In WWE and AFE, the annual mean GPP were 724.33 and 723.08 g C m−2 a−1, respectively, and the corresponding ET were 392.22 and 410.02 mm a−1. However, the difference in NEE between the two ecosystems was obvious, NEE were −446.28 and −549.08 g C m−2 a−1, respectively, showing a stronger carbon sink in AFE. There were strong coupling relationships between the GPP and ET of both ecosystems; the overall slopes were 1.71 and 1.69, respectively. The seasonal trend of WUE was bimodal in WWE, with peak values of 3.94 and 3.65 g C kg−1 H2O, occurring in November and April, respectively. However, the monthly WUE in AFE had one single peak of 4.07 g C kg−1 H2O in January. Photosynthetically active radiation (PAR) and soil temperature (Ts) were most positively correlated with GPP, net radiation (Rn) and Ts were the major factors influencing ET, while vapor pressure deficit (VPD) and soil water content (SWC) were the major influencing factors for WUE. These results provide observational support for regional carbon neutrality simulations.

A study on the simulation of carbon and water fluxes of Dangxiong alpine meadow and its response to climate change

The alpine meadow ecosystem accounts for 27 % of the total area of the Tibetan Plateau and is also one of the most important vegetation types. The Dangxiong alpine meadow ecosystem, located in the south-central part of the Tibetan Plateau, is a typical example. To understand the carbon and water fluxes, water use efficiency (WUE), and their responses to future climate change for the alpine meadow ecosystem in the Dangxiong area, two parameter estimation methods, the Model-independent Parameter Estimation (PEST) and the Dynamic Dimensions Search (DDS), were used to optimize the Biome-BGC model. Then, the gross primary productivity (GPP) and evapotranspiration (ET) were simulated. The results show that the DDS parameter calibration method has a better performance. The annual GPP and ET show an increasing trend, while the WUE shows a decreasing trend. Meanwhile, ET and GPP reach their peaks in July and August, respectively, and WUE shows a “dual-peak” pattern, reaching peaks in May and November. Furthermore, according to the simulation results for the next nearly 100 years, the ensemble average GPP and ET exhibit a significant increasing trend, and the growth rate under the SSP5–8.5 scenario is greater than that under the SSP2–4.5 scenario. WUE shows an increasing trend under the SSP2–4.5 scenario and a significant increasing trend under the SSP5–8.5 scenario. This study has important scientific significance for carbon and water cycle prediction and vegetation ecological protection on the Tibetan Plateau.摘要全球气候变化对青藏高原生态系统产生了深远影响, 暖湿化背景下青藏高原植被碳, 水通量变化趋势值得关注. 高寒草甸是青藏高原最主要的植被类型之一, 为理解青藏高原当雄地区高寒草甸生态系统碳, 水通量, 水分利用效率及其对未来气候变化的响应, 本研究利用PEST和DDS两种参数率定方法优化Biome-BGC模型, 进而模拟2000–2019年当雄站的总初级生产力 (GPP) 和蒸散量 (ET) . 研究结果表明: DDS参数率定方法具有更优的性能. GPP和ET在研究时段内呈上升趋势, 而水分利用效率 (WUE) 则呈下降趋势. 同时, ET和GPP分别在7月和8月达到峰值, 而WUE则呈“双峰”变化, 分别于5月和11月达到峰值. 此外, 未来近百年的模拟表明GPP和ET的集合平均结果呈显著增加趋势, 其中在SSP5–8.5情景下的增速大于SSP2–4.5情景. WUE在SSP2–4.5情景下呈增加趋势, 而在SSP5–8.5情景下呈显著增加趋势. 本研究结果可为青藏高原碳, 水循环预测研究和植被生态保护的应用研究提供参考和借鉴.

Comparative assessment of leaf photosynthetic capacity datasets for estimating terrestrial gross primary productivity

The maximum Rubisco carboxylation rate normalized to 25 °C (Vcmax25) is a key parameter in terrestrial biosphere models for simulating carbon cycling. Recently, global distributions of Vcmax25 have been derived through various methods and different data, including field measurements, ecological optimality theory (EOT), leaf chlorophyll content (LCC), and solar-induced chlorophyll fluorescence (SIF). However, direct validation poses challenges due to high uncertainty arising from limited ground-based observations. This study conducted an indirect evaluation of four Vcmax25 datasets by assessing the accuracy of gross primary productivity (GPP) simulated using the Biosphere-atmosphere Exchange Process Simulator (BEPS) at both site and global scales. Results indicate that, compared to utilizing Vcmax25 fixed by plant functional types (PFT) derived from field measurements, incorporating Vcmax25 derived from SIF and LCC (SIF + LCC), or solely LCC, into BEPS significantly reduces simulated errors in the annual total GPP, with a 23.2 %–25.1 % decrease in the average absolute bias across 196 FLUXNET2015 sites. Daily GPP for evergreen needleleaf forests, deciduous broadleaf forests, shrublands, grasslands, and croplands shows a 7.8 %–27.6 % decrease in absolute bias, primarily attributed to reduced simulation errors during off-peak seasons of vegetation growth. Conversely, the annual total GPP error simulated using EOT-derived Vcmax25 increases slightly (2.2 %) compared to that simulated using PFT-fixed Vcmax25. This is primarily due to a significant overestimation in evergreen broadleaf forests and underestimation in croplands, despite slight increased accuracy for other PFTs. The global annual GPP simulated using Vcmax25 with seasonal variations (i.e., LCC Vcmax25 and SIF + LCC Vcmax25) yields a 4.3 %–7.3 % decrease compared to that simulated using PFT-fixed Vcmax25. Compared to FLUXCOM and GOSIF GPP products, the GPP simulated based on SIF + LCC Vcmax25 and LCC Vcmax25 demonstrates better consistency (R2 = 0.91–0.93, RMSE = 314.2–376.6 g C m−2 yr−1). This study underscores the importance of accurately characterizing the spatiotemporal variations in Vcmax25 for the accurate simulation of global vegetation productivity.

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.

Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin

In addition to serving as North China’s water supply and ecological barrier, the mountainous area of the Haihe River basin (MHRB) is a crucial location for the application of ecological engineering. Vegetation is an important component in the ecological conservation and eco-hydrological progress of the MHRB. A better understanding of regional vegetation growth can be achieved by a thorough investigation of vegetation indicators. In this research, the leaf area index (LAI) and gross primary productivity (GPP) were chosen as vegetation indicators. The characteristics and driving forces of the spatiotemporal variations of LAI and GPP in the MHRB were explored through Sen’s slope, the Mann–Kendall test, the optimal parameter-based geographical detector model, and correlation analysis. From 2001 to 2018, the annual LAI and GPP increased significantly on the regional scale. The areas with significantly increased vegetation accounted for more than 81% of the MHRB. Land use was the most influential element for the spatial heterogeneity of LAI and GPP, and the humidity index was the most crucial one among climate indicators. Non-linear enhancement or bivariate enhancement was discovered between any two factors, and the strongest interaction was from land use and humidity index. The lowest vegetation cover was found in dry regions with annual precipitation below 407 mm and the humidity index under 0.41; while in both forests and large undulating mountains, higher LAI and GPP were observed. About 87% of the significantly increased vegetation was found in areas with unaltered land use. The increase in vegetation in the MHRB from 2001 to 2018 was promoted by the increased precipitation and humidity index and the reduced vapor pressure deficit. The sensitivity of GPP to climate change was stronger than that of LAI. These findings can serve as a theoretical guide for the application of ecological engineering and ecological preservation in the MHRB.

Dry-season length affects the annual ecosystem carbon balance of a temperate semi-arid shrubland

Semi-arid ecosystems have been shown to dominate over tropical forests in determining the trend and interannual variability of land carbon (C) sink. However, the magnitude and variability of ecosystem C balance remain largely uncertain for temperate semi-arid shrublands at the decadal scale. Using eddy-covariance and micro-meteorological measurements, we quantified the interannual variation in net ecosystem production (NEP) and its components, gross primary production (GPP) and ecosystem respiration (Reco, i.e., the sum of autotrophic and heterotrophic respiration), in a semi-arid shrubland of the Mu Us Desert, northern China during 2012–2022. This shrubland was an overall weak C sink over the 11 years (NEP = 12 ± 46 g C m−2 yr−1, mean ± SD). Annual NEP ranged from −66 to 77 g C m−2 yr−1, with the ecosystem frequently switching between being an annual C sink and a C source. GPP was twice as sensitive as Reco to prolonged dry seasons, leading to a close negative relationship between annual NEP and dry-season length (R2 = 0.80, P < 0.01). Annual GPP (R2 = 0.51, P = 0.01) and NEP (R2 = 0.58, P < 0.01) were positively correlated with annual rainfall. Negative annual NEP (the ecosystem being a C source) tended to occur when the dry season exceeded 50 d yr−1 or rainfall dropped below 280 mm yr−1. Increases in dry-season length strengthened the effects of low soil moisture relative to high vapor pressure deficit in constraining NEP. Both GPP and NEP were more closely correlated with C uptake amplitude (annual maximum daily values) than with C uptake period. These findings indicate that dry-season extension under climate change may reduce the long-term C sequestration in semi-arid shrublands. Plant species adapted to prolonged dry seasons should be used in ecosystem restoration in the studied area to enhance ecosystem functions.

A High-Resolution Simulation of Forest Ecosystem Carbon Budget Change with Complex Terrain

Forest ecosystems represent important carbon sinks within terrestrial ecosystems. Simulating carbon budgets in forest ecosystems located within complex terrains forms the foundation for understanding global carbon cycle dynamics. In this study, employing the FORCCHN model and utilizing mountain climate data as forcing parameters, the impact of the terrain factors of the Changbai Mountain Nature Reserve on carbon budget change in 1998–2017 was studied. The dynamics and distribution patterns of carbon fluxes at different slope aspects and elevations were discussed. Findings demonstrated that between 1998 and 2017, the per-unit area gross primary production (GPP), net primary production (NPP), and net ecosystem production (NEP) for the Changbai Mountain Reserve were 839.35, 462.03, and 192.72 gC m −2 a −1 , respectively. The carbon fluxes varied significantly with elevation: the GPP of the north, south, and west and the NPP of the east, south, and west decreased, and the NEP of the south and west increased. The total amount in GPP and NPP of the Changbai Mountain Nature Reserve fluctuated slightly year by year, but the carbon sequestration capacity decreased from year to year. The reserve had an average annual total GPP, NPP, and NEP of 1.62, 0.89, and 0.37 TgC a −1 , respectively. The north was the strongest carbon sink in the study area, contributing 35.98%, 35.88%, and 36.80% to the total amount of GPP, NPP, and NEP, respectively. This study emphasized the significance of precise mountain meteorological data to accurately quantify the carbon fluxes of mountain forest ecosystems.

Evaluating the effectiveness of different variable retention harvesting treatments on forest carbon uptake using remote sensing

Variable retention harvesting (VRH) is an ecologically based forest management practice applied to emulate natural post-disturbance residual canopy structure and increase the biodiversity and resilience of a regenerating stand. The pattern and density of canopy retention also influences the productivity, mortality, and carbon (C) sequestration rates of residual trees. In this study, we used high-resolution satellite and drone remote sensing data to evaluate the effect of five different VRH treatments on growth and C uptake of a 90-year-old red pine (Pinus resinosa Ait.) plantation forest in southern Ontario, Canada. The treatments included four different combinations of harvesting intensity residual tree distribution including 33% aggregate retention (33A), 55% aggregate retention (55A), 33% dispersed retention (33D), and 55% dispersed retention (55D) and unharvested control (CN) each replicated four times in 1 ha plots. Satellite- and drone-derived normal difference vegetation index (NDVI) and gross primary productivity (GPP) were estimated for each treatment during the growing seasons from 2010 to 2020. Over this period, observed mean daily NDVI values ranged from 0.25 to 0.86 among treatments, where the 55D treatment consistently showed the highest NDVI values. Overall, the highest mean daily GPP values were observed in the CN treatment, followed by the 55D, 55A, 33D, and 33A treatments. Remote sensing-derived mean annual GPP for the entire 20 ha study site was 1651 ± 89 g C m−2 year−1, with a range of 1407–1864 g C m−2 year−1 from 2010 to 2020. Satellite-derived annual GPP values were linearly related with observed annual GPP (R2 = 0.88, p = 0.032) measured using the eddy covariance technique in an adjacent white pine plantation of similar age and height over the study period. Study results suggested that VRH treatments that create a uniformly dispersed residual canopy retaining more than half of the initial basal area (e.g. 55D) is a viable forest management practice where optimizing forest growth and C uptake is a primary management objective. Our study will help forest managers and researchers to develop methodologies to evaluate the effectiveness of forest management practices, tailor them to achieve climate mitigation and adaptation goals and to develop forest management pathways for nature-based climate solutions.

Global prediction of gross primary productivity under future climate change

The ecosystem gross primary productivity (GPP) is crucial to land-atmosphere carbon exchanges, and changes in global GPP as well as its influencing factors have been well studied in recent years. However, identifying the spatio-temporal variations of global GPP under future climate changes is still a challenging issue. This study aims to develop data-driven approach for predicting the global GPP as well as its monthly and annual variations up to the year 2100 under changing climate. Specifically, Catboost was employed to examine the potential relationship between the GPP and environmental factors, with climate variables, CO2 concentration and terrain attributes being selected as environmental factors. The predicted monthly and annual GPP from Coupled Model Intercomparison Project phase 6 (CMIP6) under future SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5 scenarios were analyzed. The results indicate that the global GPP is predicted to increase under the future climate change in the 21st century. The annual GPP is expected to be 115.122 Pg C, 116.537 Pg C, 117.626 Pg C, and 120.097 Pg C in 2100 under four future scenarios, and the predicted monthly GPP shows seasonal difference. Meanwhile, GPP tends to increase in the northern mid–high latitude regions and decrease in the equatorial regions. For the climate zones form Köppen-Geiger classification, the arid, cold, and polar zones present increased GPP, while GPP in the tropical zone will decrease in the future. Moreover, the high importance of climate variables in GPP prediction illustrates that the future climate change is the main driver of the global GPP dynamics. This study provides a basis for predicting how global GPP responds to future climate change in the coming decades, which contribute to understanding the interactions between vegetation and climate.

Mango‐GPP: A Process‐Based Model for Simulating Gross Primary Productivity of Mangrove Ecosystems

AbstractMangrove ecosystems are becoming increasingly important in global climate mitigation. However, large gaps still exist in evaluating mangroves' gross primary productivity (GPP) due to reasons such as the specific influences, for example, temperature and salt stresses are poorly described in Earth System Models (ESMs). This study developed a process‐based biogeochemical model (Mango‐GPP) to improve the GPP simulation in natural and restored mangroves. The model integrates mangrove‐specific physiological processes, including the response to salt and temperature stresses, as well as the light‐use efficiency at different growing stages. Eddy covariance flux measurements at two natural sites and one restored site in China were used to calibrate and validate Mango‐GPP. The model was calibrated by inverse analysis approach based on two cases and independently validated against the other cases. The validation results showed that it was generally capable of simulating the seasonal and interannual GPP variations at different sites. The simulated daily and annual GPPs agreed well with the observations and yielded R2 of 0.67 and 0.96, with model efficiency of 0.64 and 0.93, respectively. In comparison, Mango‐GPP showed better performances than many current satellite‐based GPP products and ESMs. The model was more sensitive to solar radiation, carbon dioxide concentration, and leaf traits. Future improvements should focus on enhancing Mango‐GPP's descriptive power of key processes, and further simulating other carbon fluxes at regional scales. This work provides a model foundation for further simulating carbon exchanges between the atmosphere, mangrove, and ocean for studying the coastal wetland restoration on regional carbon neutrality.

Drought limits vegetation carbon sequestration by affecting photosynthetic capacity of semi-arid ecosystems on the Loess Plateau

Drought is the driver for ecosystem production in semi-arid areas. However, the response mechanism of ecosystem productivity to drought remains largely unknown. In particular, it is still unclear whether drought limits the production via photosynthetic capacity or phenological process. Herein, we assess the effects of maximum seasonal photosynthesis, growing season length, and climate on the annual gross primary productivity (GPP) in vegetation areas of the Loess Plateau using multi-source remote sensing and climate data from 2001 to 2021. We found that maximum seasonal photosynthesis rather than growing season length dominates annual GPP, with above 90 % of the study area showing significant and positive correlation. GPP and maximum seasonal photosynthesis were positively correlated with self-calibrating Palmer Drought Severity Index (scPDSI), standardized precipitation and evapotranspiration index (SPEI) in >95 % of the study area. Structural equation model demonstrated that both drought indices contributed to the annual GPP by promoting the maximum seasonal photosynthesis. Total annual precipitation had a positive and significant effect on two drought indices, whereas the effects of temperature and radiation were not significant. Evidence from wood formation data also confirmed that low precipitation inhibited long-term carbon sequestration by decreasing the maximum growth rate in forests. Our findings suggest that drought limits ecosystem carbon sequestration by inhibiting vegetation photosynthetic capacity rather than phenology, providing a support for assessing the future dynamics of the terrestrial carbon cycle and guiding landscape management in semi-arid ecosystems.

Exploring the spatio-temporal patterns of global mangrove gross primary production and quantifying the factors affecting its estimation, 1996–2020

Mangrove ecosystems, as an important component of “Blue Carbon”, play a curial role on global carbon cycling; however, the lack of the global estimates of mangrove ecosystem gross primary production (GPP) and the underlying environmental controls on its estimation remain a gap in knowledge. In this study, we utilized global mangrove eddy covariance data and applied Gaussian Process Regression (GPR) to estimate GPP for global mangrove ecosystems, aiming to elucidate the factors influencing these estimates. The optimal GPR achieved favorable estimation performance through cross-validation (R2 = 0.90, RMSE = 0.92 gC/m2/day, WI = 0.86). Over the study period, the globally annual averaged GPP was 2054.53 ± 38.51 gC/m2/yr (comparable to that of evergreen broadleaf forests and exceeds the GPP of most other plant function types), amounting to a total of 304.82 ± 7.71TgC/yr, hotspots exceeding 3000 gC/m2/yr observed near the equator. The analysis revealed a decline in global mangrove GPP during 1996–2020 of −0.89 TgC/yr. Human activities (changes in mangrove cover area) played a relatively consistent role in contributing to this decrease. Conversely, variations in external environmental conditions showed distinct inter-annual differences in their impact. The spatio-temporal distribution patterns of mangrove ecosystems GPP (e.g., the bimodal annual pattern, latitudinal gradients, etc.) demonstrated the regulatory influence of external environmental conditions on GPP estimates. The model ensemble attribution analysis indicated that the fraction of absorbed photosynthetically active radiation exerted the dominant control on GPP estimations, while temperature, salinity, and humidity acted as secondary constraints. The findings of this study provide valuable insights for monitoring, modeling, and managing mangrove ecosystems GPP; and underscore the critical role of mangroves in global carbon sequestration. By quantifying the influences of environmental factors, we enhance our understanding of mangrove carbon cycling estimates, thereby helping sustain of these disproportionately productive ecosystems.

Ecosystem responses dominate the trends of annual gross primary productivity over terrestrial ecosystems of China during 2000–2020

The annual gross primary productivity (AGPP) forms the foundation for terrestrial carbon sink. AGPP trends stem from the combined impacts of environmental factors and ecosystem responses to those factors. These responses include model structure modifications altering both the structural properties (represented by leaf area index) and the functional properties (represented by per leaf AGPP). Understanding the contributions of ecosystem responses to AGPP trends is crucial for accurately assessing spatiotemporal variations in AGPP, which aids in effective carbon management. Based on eddy covariance measurements of AGPP in China, we generated AGPP during 2000–2020 by constructing yearly AGPP mapping schemes from site measurements and analyzed AGPP trends. The roles of ecosystem responses and their pathways were further elucidated by employing analysis of variance. Results showed that the total AGPP in China was 7.39 ± 0.62 PgC yr−1, with an increasing trend of 0.095 PgC yr−2. This increase primarily sourced from the total AGPP in Subtropical Evergreen Needleleaf Forest. AGPP trends spatially decreased from east to west, with most regions showing positive values. Structural and functional properties contributed similarly to AGPP trends, with varying regional contributions. The structural properties played a dominant role in north regions while the functional properties were more important in south and west regions. Ecosystem responses similarly determined the trends of ecosystem properties, which decreased from west to east. Ecosystem responses thus dominated AGPP trends through the structural properties in north regions but through the functional properties in south and west regions. Therefore, Chinese AGPP exhibited a spatially varied increasing trend during 2000–2020. Ecosystem responses dominated AGPP trends through divergent properties across regions. Our results highlight the significanc of ecosystem responses in AGPP trends and their divergent pathways across regions. This also provides independent data support for quantifying regional carbon budgets.

Coupled primary production and respiration in a large river contrasts with smaller rivers and streams

AbstractAlthough time series in ecosystem metabolism are well characterized in small and medium rivers, patterns in the world's largest rivers are almost unknown. Large rivers present technical difficulties, including depth measurements, gas exchange (, ) estimates, and the presence of large dams, which can supersaturate gases. We estimated reach‐scale metabolism for the Hanford Reach of the Columbia River (Washington state, USA), a free‐flowing stretch with an average discharge of 3173 . We calculated from semi‐empirical models and directly estimated it from tracer measurements. We fixed at the median value from these calculations (0.5 ), and used maximum likelihood to estimate reach‐scale, open‐channel metabolism. Both gross primary production (GPP) and ecosystem respiration (ER) were high (GPP range: 0.3–30.8 g , ER range: 0.8–30.6 g ), with peak GPP and ER occurring in the late summer or early fall. GPP increased exponentially with temperature, consistent with metabolic theory, while light was seasonally saturating. Annual average GPP, estimated at 1500 g carbon , was in the top 2% of estimates for other rivers. GPP and ER were tightly coupled and 90% of GPP was immediately respired, resulting in net ecosystem production near 0. Patterns in the Hanford Reach contrast with those in small‐medium rivers, suggesting that metabolism magnitudes and patterns in large rivers may not be simply scaled from knowledge of smaller rivers.

Assessing carbon and water fluxes in a mixed Mediterranean protected forest under climate change: An integrated bottom – up and top – down approach

This paper proposes a framework based on the integration of bottom-up and top-down approaches to assess and quantify the effect of local climate change on carbon and water fluxes in a protected Mediterranean mixed forest. The forest, with an extent of 50 ha, is in a coastal zone and is in decline because of severe summer drought stress. Since 2018, it has been involved in the LIFE PRIMED ecological restoration project.The bottom-up approach used gas exchange measurements and the MOCA model (MOdelling for Carbon Assessment) to quantify photosynthesis and transpiration of the main species in the forest (P. latifolia, P. lentiscus, Q. cerris, and F. angustifolia sub. oxycarpa). The top-down approach used the satellite models C-fix and NDVI-CWS, to quantify and spatialise gross primary production (GPP), net primary production (NPP), and evapotranspiration (ETA) at the forest stand scale. The up-scaling process from leaf to forest stand was made possible by calibrating two key parameters: light use efficiency (ε) and vegetation transpiration coefficient (Kcveg).The results indicate a decline in GPP, NPP, and ETA during the study period (2018–2022) due to the summer drought conditions caused by rising temperatures (16.60 °C in 2018 vs. 16.98 °C in 2022), increased vapor pressure deficit, reduced soil water content, and rainfall (1328 mm in 2018 vs. 468 mm in 2022). In 2018, the annual GPP was 1186.83 ± 351.40 gC/m2y, which decreased to 1128.67 ± 331.21 gC/m2y in 2022. During the dry summer period, GPP ranged from 401.76 ± 119.54 gC/m2y in 2018 to 347.39 ± 101.62 gC/m2y in 2022. Annual ETA rates varied between 1184.86 ± 351.86 mmH₂O/m2y (2018) and 1304.92 ± 384.07 mmH₂O/m2y (2022). The summer ETA rates were significantly influenced by drought conditions, decreasing from 638.73 ± 190.67 mmH₂O/m2y in 2018 to 390.35 ± 108.83 mmH₂O/m2y in 2022.Based on the observed trend in other Mediterranean areas, species-specific responsiveness to local climate change suggests that P. latifolia and P. lentiscus, which are better adapted to water-limited environments, could gradually replace Q. cerris and F. angustifolia subs. oxycarpa.This study provides useful quantitative indications for the management and restoration goals of the area and suggests a methodology that can be applicable in several management practices, particularly for small heterogeneous and fragmented forested areas such as the Mediterranean ones.

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.

Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data

While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (> 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake.

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.