Simulated Gross Primary Production Research Articles

Evaluating the spatiotemporal patterns of GPP and tree growth for their response to CO2 fertilization effects in mid-latitude forests of China

ABSTRACT The investigation of the spatiotemporal variation trend of the atmospheric CO2 fertilization effect ( β ) has emerged as a prominent topic of interest on a global scale in recent times. Nevertheless, the spatiotemporal patterns of β remain unclear. Herein, we selected the mid-latitude forests of China as the designated study region. Accordingly, remote sensing Gross Primary Productivity (GPP) products were used along with model-based GPP simulation results and tree-ring data in this study. This was combined with the random forest algorithm and a moving window approach to assess the spatiotemporal patterns of vegetation productivity and tree growth responses to atmospheric CO2 variations between 1982 and 2015. Our findings suggest that from 1982 to 2015, the estimated β derived from the two remote sensing GPP products demonstrated a declining trend. In particular, the EC-LUE GPP exhibited a decrease rate of −0.46%.100 ppm−1yr−1, while the NIRv GPP showed a decrease rate of −0.04%.100ppm−1yr−1. Similarly, the findings from the estimation based on models also indicated a decline in β , with an average decrease rate of −0.08%.100 ppm−1yr−1 across a total of 18 models. Based on the analysis of tree rings from 16 sites, it was observed that the radial growth response of vegetation to atmospheric CO2 exhibited a decline with an average decrease rate of −0.81%.100 ppm−1yr−1. We speculated that the observed trend in β is primarily driven by LAI and forest age.

Estimation of radiation scalar using deep learning for improved gross primary productivity estimation based on a light-use efficiency model

ABSTRACT Accurately quantifying terrestrial gross primary productivity (GPP) can provide insights into the dynamics of global ecosystems. Light Use Efficiency (LUE) models with simple and effective structures have been widely used to estimate GPP. However, GPP estimation using LUE models was biased due to inaccurately representing the nonlinear effect of solar radiation on photosynthesis. To address this issue, we developed a hybrid LUE model, termed LUE-MLP, which integrates a multilayer perceptron (MLP) neural network simulating the relationship between GPP and solar radiation with a big-leaf LUE model. LUE-MLP was optimized and tested for GPP simulation on data from 166 flux sites from the FLUXNET2015 dataset, which covers 10 biome types. Results show that LUE-MLP exhibited a reliable performance (R2 = 0.76 and RMSE = 4.17 μmol m−2 s−1) in estimating (half-)hourly GPP on test data. Compared to the original big-leaf and two-leaf LUE models, LUE-MLP's R² (RMSE) for (half-)hourly GPP simulation across biomes rose (fell) by – 0.01-0.13 (−7.6%−16.8%) and 0.01-0.15 (0.39%−29.1%), respectively. Additionally, LUE-MLP well captured the seasonal variations in tower-based GPP. The proposed model can improve GPP estimation on a global scale, enhancing our understanding of global ecosystem dynamics.

Potential of Solar-Induced Chlorophyll Fluorescence for Monitoring Gross Primary Productivity and Evapotranspiration in Tidally-Influenced Coastal Salt Marshes

Solar-induced chlorophyll fluorescence (SIF) offers significant potential as a novel approach for quantifying carbon and water cycling in coastal wetland ecosystems across multiple spatial scales. However, the mechanisms governing these biogeochemical processes remain insufficiently understood, largely due to the periodic influence of tidal inundation. In this study, we investigated the effects and underlying mechanisms of meteorological and tidal factors on the relationships between canopy-level solar-induced chlorophyll fluorescence at 760 nm (SIF760) and key ecosystem processes, including gross primary productivity (GPP) and evapotranspiration (ET), in coastal wetlands. These processes are critical components of the ecosystem carbon and water cycles. Our approach involved a comparative analysis of simulations from the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model with field measurements. The results showed that: (1) simulations of SIF760 improved following observation-based calibration of the fluorescence photosynthesis module in the SCOPE model; (2) under optimal moisture and temperature conditions (VPD 1.2–1.4 kPa and temperatures of 20–23 °C for air, soil, and water), the simulations of GPP, ET, and SIF760 were most accurate, although salinity stress reduced performance. GPP simulations tended to overestimate under drought stress but improved at higher air temperatures (30–32 °C); (3) during tidal inundation, the SIF760-GPP relationship weakened while the SIF760-ET strengthened. The range of significant correlations between SIF760, water levels, and temperature narrowed, with both relationships becoming more complex due to salinity stress. These findings suggest that tidal inundation can alleviate temperature stress on photosynthesis and transpiration; however, it also decreases photosynthetic efficiency and alters radiative transfer processes due to elevated salinity and water levels. These factors are critical considerations when using SIF to monitor GPP and ET dynamics in coastal wetlands. This study demonstrated that the tidal dynamics significantly affected the SIF760-GPP and SIF760-ET relationships, underscoring the necessity of incorporating tidal influences in the application of SIF remote sensing for monitoring GPP and ET dynamics. The results of this study not only contribute to a deeper understanding of the mechanisms linking SIF760 with GPP and ET but also provide new insights into the development and refinement of SIF-based remote sensing for carbon quantification in coastal blue-carbon ecosystems on a large-scale domain.

Simulating the Vegetation Gross Primary Productivity by the Biome-BGC Model in the Yellow River Basin of China

In terrestrial ecosystems, the quantification of carbon absorption is primarily represented by the gross primary productivity (GPP), which signifies the initial substances and energy acquired by the ecosystem. The GPP also serves as the foundation for the carbon cycle within the entire terrestrial ecosystem. The Biome-BGC model is a widely used biogeochemical process model for simulating the stocks and fluxes of water, carbon, and nitrogen between ecosystems and the atmosphere. However, it is the abundance of eco-physiological parameters that lead to challenges in calibrating the model. The parameter optimization method of coupling the differential evolution algorithm (DE) with the Biome-BGC model was used to calibrate and validate the eco-physiological parameters of the seven typical vegetation types in the Yellow River Basin (YRB). And then we used the calibrated parameters to simulate the GPP by way of grid-based simulation. Finally, we conducted model adaptability testing and spatiotemporal analysis of GPP variations in the YRB. The results of the validation (R2, RMSE) were: temperate grasses (0.94, 24.33 g C m−2), alpine meadows (0.94, 18.13 g C m−2), shrubs (0.94, 29.20 g C m−2), evergreen needle leaf forests (0.96, 27.88 g C m−2), deciduous broad leaf forests (0.94, 32.09 g C m−2), one crop a year (0.96, 16.19 g C m−2), and two crops a year (0.90, 38.15 g C m−2). After adaptability testing, the average R2 value between the simulated GPP values and the GPP product values in the YRB was 0.85, and the average RMSE value was as low as 50.92 g C m−2. Overall, the model exhibited strong simulation accuracy. Therefore, after calibrating the model with the DE algorithm, the Biome-BGC model could effectively adapt to the ecologically complex YRB. Moreover, it was able to accurately estimate the GPP, which establishes a foundation for analyzing the spatiotemporal trends of the GPP in the YRB. This study provides a reference for optimizing Biome-BGC model parameters and simulating diverse vegetation types on a large scale.

Optimal parameter schemes for global and regional gross primary productivity estimation: a comparative analysis

ABSTRACT The spatiotemporal heterogeneity of the parameters greatly impacts the estimation of Gross Primary Productivity (GPP) by Light Use Efficiency (LUE) models. Vegetation type-specific parameters are commonly set at present; however, the various environmental heterogeneities at global and regional scales can also induce disparate optimal parameter schemes, which has not been further explored. Therefore, in this study, we comparatively explored the parameter differences and GPP estimation accuracy under different parameter schemes, including Vegetation type Parameters (VPs), Fixed Parameters (FPs), Vegetation type Monthly Parameters (VMPs), and Fixed Monthly Parameters (FMPs), at both global and regional scales. Two different strategies were applied to validate the ability of the temporal prediction and spatial expansion for the four parameter schemes. The results indicate that the VP scheme shows only a limited superiority over the FP scheme for GPP estimation at the global scale (ΔR2 = 0.01–0.02), which indicates that the VP scheme is not necessary for the global application of LUE models, due to the considerable spatiotemporal heterogeneity of vegetation. However, for regional applications, such as the Mediterranean region, the VP scheme is preferable to the FP scheme, with an improved ΔR2 of more than 0.05, which is because the vegetation within the same type is much more similar than at a global scale. Furthermore, it is found that the time-varying parameters (VMPs and FMPs) contribute little to the GPP simulation at both global and regional scales, which is possibly due to the limited amount of available data. Overall, the results of this study will not only offer guidance and optimal parameter schemes for the global and regional estimation of GPP, but also highlight the importance of considering spatiotemporal heterogeneity for parameters.

Stronger effects of accumulated soil moisture deficit on gross primary productivity and light use efficiency than lagged soil moisture deficit for cropland and forest

Many studies have underscored the impacts of drought on ecosystems, and some researchers reported the effects of accumulated soil moisture deficit (ASMD) on light use efficiency (LUE) in grassland. However, the potential effects of ASMD on gross primary productivity (GPP) and LUE for both cropland and forest ecosystems are still not understood. This study elucidated the effects of accumulated and lagged soil moisture deficit (ASMD and LSMD, respectively) on GPP and LUE in these two ecosystems by using observations from 10 cropland and 25 forest flux sites during drought years. The results showed that the effects of ASMD and LSMD on LUE/GPP for both cropland and forests obviously surpass the concurrent effects (CSMD). For cropland, the mean R2 between CSMD/LSMD/ASMD with LUE were 0.22, 0.47, 0.56, respectively, and were 0.29, 0.54, 0.74 with GPP, respectively. For forest, the mean R2 between CSMD/LSMD/ASMD with LUE were 0.21, 0.36, 0.46, respectively, and were 0.34, 0.63, and 0.65 with GPP, respectively. Additionally, the effects of ASMD and LSMD on LUE are more pronounced for cropland than for forests, and for both cropland and forest, the effect of ASMD is stronger than that of LSMD. This study underscores the crucial role of ASMD in influencing LUE and GPP for cropland and forests, thereby offering a theoretical foundation for incorporating ASMD into LUE models to enhance the accuracy of GPP simulations, especially during drought periods.

Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset

Remote sensing and process-coupled ecological models are widely used for the simulation of GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models do not differentiate the C3 and C4 photosynthetic pathways and neglect the effect of nitrogen content on Vmax and Jmax, leading to considerable bias in the estimation of gross primary productivity (GPP). Here, we developed a model driven by the leaf area index, climate, and atmospheric CO2 concentration to estimate global GPP with a spatial resolution of 0.1° and a temporal interval of 1 day from 2000 to 2022. We validated our model with ground-based GPP measurements at 128 flux tower sites, which yielded an accuracy of 72.3%. We found that the global GPP ranged from 116.4 PgCyear−1 to 133.94 PgCyear−1 from 2000 to 2022, with an average of 125.93 PgCyear−1. We also found that the global GPP showed an increasing trend of 0.548 PgCyear−1 during the study period. Further analyses using the structure equation model showed that atmospheric CO2 concentration and air temperature were the main drivers of the global GPP changes, total associations of 0.853 and 0.75, respectively, while precipitation represented a minor but negative contribution to global GPP.

GPP of a Chinese Savanna Ecosystem during Different Phenological Phases Simulated from Harmonized Landsat and Sentinel-2 Data

Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand the long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary productivity (GPP) estimation remains challenging because of the high spatial and seasonal variations in savanna GPP. China’s savanna ecosystems constitute only a small part of the world’s savanna ecosystems and are ecologically fragile. However, studies on GPP and phenological changes, while closely related to climate change, remain scarce. Therefore, we simulated savanna ecosystem GPP via a satellite-based vegetation photosynthesis model (VPM) with fine-resolution harmonized Landsat and Sentinel-2 (HLS) imagery and derived savanna phenophases from phenocam images. From 2015 to 2018, we compared the GPP from HLS VPM (GPPHLS-VPM) simulations and that from Moderate-Resolution Imaging Spectroradiometer (MODIS) VPM simulations (GPPMODIS-VPM) with GPP estimates from an eddy covariance (EC) flux tower (GPPEC) in Yuanjiang, China. Moreover, the consistency of the savanna ecosystem GPP was validated for a conventional MODIS product (MOD17A2). This study clearly revealed the potential of the HLS VPM for estimating savanna GPP. Compared with the MODIS VPM, the HLS VPM yielded more accurate GPP estimates with lower root-mean-square errors (RMSEs) and slopes closer to 1:1. Specifically, the annual RMSE values for the HLS VPM were 1.54 (2015), 2.65 (2016), 2.64 (2017), and 1.80 (2018), whereas those for the MODIS VPM were 3.04, 3.10, 2.62, and 2.49, respectively. The HLS VPM slopes were 1.12, 1.80, 1.65, and 1.27, indicating better agreement with the EC data than the MODIS VPM slopes of 2.04, 2.51, 2.14, and 1.54, respectively. Moreover, HLS VPM suitably indicated GPP dynamics during all phenophases, especially during the autumn green-down period. As the first study that simulates GPP involving HLS VPM and compares satellite-based and EC flux observations of the GPP in Chinese savanna ecosystems, our study enables better exploration of the Chinese savanna ecosystem GPP during different phenophases and more effective savanna management and conservation worldwide.

Optimizing the terrestrial ecosystem gross primary productivity using carbonyl sulfide (COS) within a two-leaf modeling framework

Abstract. Accurately modeling gross primary productivity (GPP) is of great importance for diagnosing terrestrial carbon–climate feedbacks. Process-based terrestrial ecosystem models are often subject to substantial uncertainties, primarily attributed to inadequately calibrated parameters. Recent research has identified carbonyl sulfide (COS) as a promising proxy of GPP due to the close linkage between leaf exchange of COS and carbon dioxide (CO2) through their shared pathway of stomatal diffusion. However, most of the current modeling approaches for COS and CO2 do not explicitly consider the vegetation structural impacts, i.e., the differences between the sunlit and shaded leaves in COS uptake. This study used ecosystem COS fluxes from seven sites to optimize GPP estimation across various ecosystems with the Biosphere-atmosphere Exchange Process Simulator (BEPS), which was further developed to simulate the canopy COS uptake under its state-of-the-art two-leaf framework. Our results demonstrated substantial improvement in GPP simulation across various ecosystems through the data assimilation of COS flux into the two-leaf model, with the ensemble mean of the root mean square error (RMSE) for simulated GPP reduced by 20.16 % to 64.12 %. Notably, we also shed light on the remarkable identifiability of key parameters within the BEPS model, including the maximum carboxylation rate of RuBisCO at 25 °C (Vcmax25), minimum stomatal conductance (bH2O), and leaf nitrogen content (Nleaf), despite intricate interactions among COS-related parameters. Furthermore, our global sensitivity analysis delineated both shared and disparate sensitivities of COS and GPP to model parameters and suggested the unique treatment of parameters for each site in COS and GPP modeling. In summary, our study deepened insights into the sensitivity, identifiability, and interactions of parameters related to COS and showcased the efficacy of COS in reducing uncertainty in GPP simulations.

Integration of prognostic sowing and harvesting schemes to enhance crop dynamic growth simulation in Noah-MP-Crop model

Detailed sowing and harvesting (S&H) information is crucial for climate-coupled crop models to accurately simulate dynamic crop growth. The timing of intra-annual crop growth not only reflects adaptation to climate change but also significantly influences terrestrial biophysical and biochemical processes, as well as the local climate. However, accurately providing sowing and harvesting dates in crop models is challenging due to the limited availability of S&H observations worldwide. In this study, we integrated a prognostic S&H scheme into the Noah-MP-Crop model to eliminate the need for prescribed S&H dates and optimised key crop-related parameters to better reproduce dynamic maize and soybean growth in the U.S. Corn Belt. Results indicated that the bias in estimating site-level S&H dates was within one week. The prognostic S&H schemes, along with optimised crop-related parameters, effectively captured maize and soybean growth at the site scale, as evidenced by leaf area index (LAI) and gross primary production (GPP) simulations. The determination coefficient (R2) for GPP ranged from 0.88 to 0.91 for maize and from 0.70 to 0.82 for soybean at two flux stations. Moreover, the prognostic schemes exhibited better regional LAI and GPP simulations at the beginning and end of the growing season compared to those using state-level median S&H dates, with significant improvements in correlation coefficients ranging from 0 to 0.6, particularly in maize-dominated regions. However, the accuracy in reproducing latent heat flux and sensible heat flux was less satisfactory and showed little association with crop growth status. This work provides an alternative approach to obtaining crop sowing and harvesting information in the Noah-MP-Crop model and facilitates studies on interactions between dynamic crop growth and the climate system, particularly when coupled with the widely used Weather Research and Forecasting (WRF) model.

Effects of slow temperature acclimation of photosynthesis on gross primary production estimation

The slow temperature acclimation of photosynthesis has been confirmed through early field experiments and studies. However, this effect is difficult to characterize and quantify with some simple and easily accessible indicators. As a result, the impact of slow temperature acclimation of photosynthesis on gross primary production (GPP) estimation has often been overlooked or not integrated into most GPP models. In this study, we used a theorical variable-state of acclimation (S), to characterize the slow temperature acclimation. This variable represents the temperature to which the photosynthetic machinery adapts and is defined as a function of air temperature (Ta) and time constant (τ) required for vegetation to respond to temperature, to discuss its impact on GPP simulation. We used FLUXNET2015 dataset to calculate S and established a GPP model using S and shortwave radiation (SW) based on random forest algorithm (S model). As a comparison, we directly used Ta and SW to build the other GPP model (Ta model). Moreover, the divergent temperature acclimation capacities of plants are crucial to predict and make preparations for likely temperature stress in the future. Therefore, the spatial distribution of τ values was also mapped using satellite sun induced chlorophyll fluorescence (SIF) and Ta datasets. The results indicated that: (1) taking into account the slow temperature acclimation of photosynthesis led to a more precise estimation of GPP which mainly reflected in reduction of excessive fluctuations in GPP predictions; (2) considering the slow temperature acclimation of photosynthesis can reduce the sensitivity of vegetation to temperature; (3) the improvement of S model in GPP estimations was different in different vegetation growth stages which was more significant in the springtime recovery stage; (4) τ values had significant spatial distribution which was strongly affected by the determinants of vegetation growth and seasonal variations in temperature.

An improved canopy interception scheme into biogeochemical model for precise simulation of carbon and water fluxes in subtropical coniferous forest

The process-based Biome-BGC (Biome Biogeochemical Cycles) model is widely used to simulate the fluxes of carbon and water of terrestrial ecosystems. While exhibiting excellent performance in simulating carbon cycle processes, this model provides a relatively simple simulation of the water cycle processes, particularly in canopy interception, which may result in significant errors in evapotranspiration (ET) and soil moisture estimations. This study initially refined the temporal scale of the original Biome-BGC model from a daily scale to an hourly scale, and then incorporated a theoretical interception module into the hourly-scale model for correcting canopy interception algorithm, to enhance the accuracy of hydrological processes simulations. The modified model was tested using the half-hourly observed meteorological and eddy covariance flux data at the Qianyanzhou subtropical coniferous forest site. In comparison with the original daily-scale model, simulations from the hourly-scale model showed better agreement with measurements at Qianyanzhou forest site, with 30.61 % and 40.92 % lower annual average gross primary productivity (GPP) and ET simulations. Although the accuracy of GPP and ET simulations did not show a significant improvement after canopy interception correction, it notably enhanced the accuracy of canopy interception and soil moisture simulations. The canopy interception rates were 55.8 % and 44.1 % for the original daily-scale and hourly-scale model, obviously overstimated compared with canopy correction simulation (31.1 %), leading to a significant underestimation of runoff. The canopy interception and runoff simulations after interception correction were proven relatively more reasonable. Additionally, the coefficient of determination (R2) for measured vs simulated soil water content (SWC) were 0.38, 0.51 and 0.60 for the original daily-scale, hourly-scale, and canopy interception correction simulations respectively, with a notable improvement in accuracy. This study suggested that improvements in temporal scale and canopy interception for process-based biogeochemical model can provide a better understanding of the carbon and water exchanges in response to instantaneous meteorological conditions and support improved water resource management.

Data-constrained modeling of terrestrial gross primary production over the Tibetan Plateau for 2003–2019

The gross primary productivity (GPP) of terrestrial ecosystem is the largest carbon flux between atmosphere and land surface. However, accurately simulating ecosystem GPP remains a great challenge for most land surface models (LSMs) due to the biased leaf area index (LAI) simulated by the models. In this study, we use remotely sensed LAI dataset for the period 2003–2019 to drive the Farquhar model and simulate the spatial-temporal changes of GPP for the alpine ecosystem over Tibetan Plateau. The annual GPP over the Tibetan Plateau is estimated to be 540.8 ± 27.3 Tg C yr−1, which is consistent with the benchmark datasets derived from flux tower measurements and remote sensing estimations. The GPP for the past two decades has been increasing at a rate of 2.6 Tg C yr−2. The canopy greening featured by increasing LAI contributed to 27.2 % of the increasing trend in GPP, while the direct impacts from warming and wetting climate contributed to 65.2 % of GPP changes. The contribution from the direct impact of elevated atmosphere CO2 concentration is only 6.7 %. In this study we show that constraining Farquhar model with remotely sensed LAI datasets could provide reliable simulation of GPP. Given the great challenge in modeling LAI and the considerable overestimation of LAI in current LSMs, our results highlight the importance of improving LAI modeling in LSMs and the necessity of constraining LAI when tuning the parameters for photosynthesis module.

Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0

Abstract. Land ecosystems are important sources and sinks of atmospheric components. In turn, air pollutants affect the exchange rates of carbon and water fluxes between ecosystems and the atmosphere. However, these biogeochemical processes are usually not well presented in Earth system models, limiting the explorations of interactions between land ecosystems and air pollutants from regional to global scales. Here, we develop and validate the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) by upgrading the Yale Interactive Terrestrial Biosphere Model with process-based water cycles, fire emissions, wetland methane (CH4) emissions, and trait-based ozone (O3) damage. Within iMAPLE, soil moisture and temperature are dynamically calculated based on the water and energy balance in soil layers. Fire emissions are dependent on dryness, lightning, population, and fuel load. Wetland CH4 is produced but consumed through oxidation, ebullition, diffusion, and plant-mediated transport. The trait-based scheme unifies O3 sensitivity of different plant functional types (PFTs) with the leaf mass per area. Validations show correlation coefficients (R) of 0.59–0.86 for gross primary productivity (GPP) and 0.57–0.84 for evapotranspiration (ET) across the six PFTs at 201 flux tower sites and yield an average R of 0.68 for CH4 emissions at 44 sites. Simulated soil moisture and temperature match reanalysis data with high R above 0.86 and low normalized mean biases (NMBs) within 7 %, leading to reasonable simulations of global GPP (R=0.92, NMB=1.3 %) and ET (R=0.93, NMB=-10.4 %) against satellite-based observations for 2001–2013. The model predicts an annual global area burned of 507.1 Mha, close to the observations of 475.4 Mha with a spatial R of 0.66 for 1997–2016. The wetland CH4 emissions are estimated to be 153.45 Tg [CH4] yr−1 during 2000–2014, close to the multi-model mean of 148 Tg [CH4] yr−1. The model also shows reasonable responses of GPP and ET to the changes in diffuse radiation and yields mean O3 damage of 2.9 % to global GPP. iMAPLE provides an advanced tool for studying the interactions between land ecosystems and air pollutants.

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.

Optimizing seasonally variable photosynthetic parameters based on joint carbon and water flux constraints

Terrestrial biosphere models (TBMs) often adopt the Farquhar biochemical model coupled with the Ball-Berry stomatal conductance (gs) model to simulate ecosystem carbon and water fluxes. The parameters m, representing the sensitivity of gs to the photosynthetic rate, and Vcmax25, representing the leaf photosynthetic capacity, are two pivotal parameters but the two main sources of uncertainties in TBM simulations. The temporal variations of m in TBMs are still elusive, due to the lack of direct observations. It also remains unclear how accurate estimates of m and Vcmax25 can improve the simulations of carbon and water fluxes. In this study, we used a Bayesian parameter optimization approach to estimate seasonally varying m and Vcmax25 from eddy covariance observations in a mixed forest stand at the Borden Forest Research Station located in southern Ontario, Canada and used in-situ observations of m and Vcmax25 for validation. Three strategies were tested for optimizing m and Vcmax25, including the carbon, water, and carbon-water coupling scenarios. m and Vcmax25 optimized from carbon-water coupling constraints shows best correlations with the measured m (R2 = 0.70) and Vcmax25 (R2 = 0.70). By incorporating optimized m and Vcmax25with seasonal variations, we found considerable improvements in the estimated gross primary productivity (GPP) and evapotranspiration (ET) compared with constant m and Vcmax25, with R2 increasing from 0.78 to 0.85 for GPP, from 0.65 to 0.71 for ET and RMSE reducing from 2.579 g C m−2d−1 to 2.038 g C m−2d−1 for GPP, from 1.151 mm d−1 to 0.137 mm d−1 for ET. This study proposes an effective approach to retrieve m and Vcmax25 for TBMs and demonstrates the efficacy of incorporating seasonally variable m and Vcmax25 for reducing the uncertainties in GPP and ET simulations, which supports accurate quantifications of land-atmosphere exchanges.

Modelling changes in vegetation productivity and carbon balance under future climate scenarios in southeastern Australia

Australia, characterized by extensive and heterogeneous terrestrial ecosystems, plays a critical role in the global carbon cycle and in efforts to mitigate climate change. Prior research has quantified vegetation productivity and carbon balance within the Australian context over preceding decades. Nonetheless, the responses of vegetation and carbon dynamics to the evolving phenomena of climate change and escalating concentrations of atmospheric carbon dioxide remain ambiguous within the Australian landscape. Here, we used LPJ-GUESS model to assess the impacts of climate change on Gross Primary Productivity (GPP) and Net Biome Productivity (NBP) of carbon for the state of New South Wales (NSW) in southeastern Australia. LPJ-GUESS simulations were driven by an ensemble of 27 global climate models under different emission scenarios. We investigated the change of GPP for different vegetation types and whether NSW ecosystems will be a net sink or source of carbon under climate change. We found that LPJ-GUESS successfully simulated GPP for the period 2003–2021, demonstrating a comparative performance with GPP derived from upscaled eddy covariance fluxes (R2 = 0.58, nRMSE = 14.2 %). The simulated NBP showed a larger interannual variation compared with flux data and other inversion products but could capture the timing of rainfall-driven carbon sink and source variations in 2015–2020. GPP would increase by 10.3–19.5 % under a medium emission scenario and 19.7–46.8 % under a high emission scenario. The mean probability of NSW acting as a carbon sink in the future showed a small decrease with a large uncertainty with >8 of the 27 climate models indicating an increased potential for carbon sink. These findings emphasize the significance of emission scenarios in shaping future carbon dynamics but also highlight considerable uncertainties stemming from different climate projections. Our study represents a baseline for understanding natural ecosystem dynamics and their key role in governing land carbon uptake and storage in Australia.

Simulation of gross primary productivity and impact of drought in Liulin watershed of Taihang mountains over 2000–2020

As the largest flux in the global terrestrial carbon cycle, Gross Primary Productivity (GPP) is a key factor for the terrestrial carbon budget. Based on the eddy flux tower data, remote sensing data and the meteorological precipitation data, we calibrated three parameters of maximum light use efficiency, optimum temperature, and maximum temperature in the Vegetation Photosynthesis Model (VPM) to reproduce the GPP spatiotemporal characteristic in Liulin watershed of Taihang Mountains. We further analyzed the time lag effect and cumulative effect of the standard precipitation index (SPI) on GPP by the Pearson correlation coefficients. Results show that VPM captures the GPP characteristics of eddy flux tower observation in Liulin watershed. During the calibration period (R2 = 0.94, RMSE = 0.47 g C m-2·d-1) and validation period (R2 = 0.91, RMSE = 0.60 g C m-2 d-1), the adjusted VPM model successfully reproduced the temporal dynamics of site-observed GPP. From 2000 to 2020, VPM model can effectively reproduce the long-term variations in GPP (R2 = 0.69 vs. NIRv_GPP), with the GPP ranging from 446.4 to 828.1 g C m-2 y-1 and an average value of 545.6 g C m-2 y-1 in Liulin watershed. The GPP exhibited an increasing trend from 2000 to 2020. A mutation in the GPP occurred in 2010, with an average annual increase of 13.9 g C m-2 y-1 from 2000 to 2010 (p < 0.05), followed by an average annual increase of 10.22 g C m-2 y-1 from 2011 to 2020 (p > 0.05). The SPI effectively monitored drought events in the Liulin watershed. With a cumulative effect of 9 months and a lag effect of 6–7 months, the time lag and cumulative effects of drought had a long-term effect on GPP. It is imperative to have long-term eddy flux tower data and meteorological data to better evaluate the spatiotemporal dynamics of GPP and the impact of drought.

Joint improvement on absorbed photosynthetically active radiation and intrinsic quantum yield efficiency algorithms in the P model betters the estimate of terrestrial gross primary productivity

Terrestrial gross primary productivity (GPP) plays a crucial role in global carbon cycle and budget. A range of light use efficiency (LUE) models have been developed to estimate GPP at different spatial scales. However, large uncertainties still remain in GPP output from such models, mainly owing to the difficulty in the proper determination of maximum light use efficiency (LUEmax). The recently developed P model directly quantifies actual LUE based on environmental and physiological factors, reducing the uncertainty in GPP estimation caused by the ambiguous LUEmax. However, the existing P models still suffer from a potential underestimation in global validation, which might be related to the calculation of absorbed photosynthetically active radiation (APAR) and intrinsic quantum yield efficiency (φ0) in the P model. In this study, we improved the P model by differentiating sunlit and shaded leaves for APAR calculation and by considering the spatial variability of the optimal temperature for φ0 (Topt_phi0). The roles of modified APAR and φ0 calculations in reducing the uncertainty of simulated GPP were assessed through model simulation experiments with monthly tower-based GPP from 120 flux sites globally distributed as the benchmark. The validation indicates that simultaneous improvements on APAR and φ0 algorithms are able to better the performance of the P model. Monthly GPP simulated with this improved P model (i.e., IP model) is in good agreement with tower-based values, with a linear R2, root mean squared error, mean error, mean absolute error, and DISO of 0.76, 1.88 g C m−2d−1, -0.14 g C m−2d−1, 1.77 g C m−2d−1 and 0.60. The total global terrestrial GPP output from the IP model (IPGPP) averaged 134.3 Pg C yr−1 during 1981—2021, in the upper range of existing GPP products.

A new global time-series GPP production: DFRF-GPP

Understanding the global ecosystem carbon cycle requires a quantitative estimation of terrestrial gross primary production (GPP). Although most existing GPP estimates can accurately reflect the spatial distribution, interannual trends and even seasonal cycles of GPP under average conditions to some extent, the GPP estimates of different models under extreme climate conditions are still unsatisfactory. In this study, based on the random forest algorithm, we integrated the multimodel GPP simulation results published by the Multiscale Synthesis and Terrestrial Model Intercomparison Project, the FLUXNET flux-site-observed GPP, the standardized precipitation index (SPI) and the standardized temperature index (STI) to generate a set of global GPP time-series data products from 2001 to 2010. The new GPP product was named DFRF-GPP, referring to the GPP generated by data fusion based on random forest. The quality of DFRF-GPP was evaluated by comparison with current commonly used GPP datasets and GPP observations at FLUXNET flux sites. The results showed that DFRF-GPP is in great agreement with other GPP products and can reproduce the spatial and temporal patterns and interannual trends of global GPP, with optimal performance in regions such as those north of 30° in the Northern Hemisphere where there are more FLUXNET flux sites. DFRF-GPP has higher accuracy and excellent performance on drought and high-temperature samples, with higher sensitivity to drought and high temperature. This study not only provides a set of time-series products of global gross primary production but also provides a convenient and practical solution for enhancing the applicability of GPP estimation products in large-scale extreme climate studies.