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Leaf area index (LAI) is the key driving data of BEPS model, and it is important to obtain high accuracy LAI for regional forest ecosystem carbon cycle, however, the MODIS LAI products used in most current studies lack credibility. To this end, this study constructed a data assimilation system based on LAI dynamic model, PROSAIL radiative transfer model and Hierarchical Bayesian Network (HBN) to obtain LAI data with a spatial resolution of 20 m to drive the BEPS model and simulate the vegetation net primary productivity (NPP) of Saihanba Mechanical Forest during 2011—2021, and the spatial and temporal variation of NPP and the influencing factors of NPP were analyzed. The results showed that the high-resolution LAI data obtained based on Bayesian assimilation method greatly improved the accuracy of MODIS LAI products; the correlation between the simulated forest NPP obtained from BEPS model driven by assimilated LAI data and the NPP calculated from the sample plots was high (R² = 0. 77); the mean value of vegetation NPP in Saihanba Mechanical Forest during 2011—2021 was 307. 4 g / (m²·a), and the NPP of forest showed a steady growth trend; the simulated NPP of different vegetation types were different, and the simulated NPP of coniferous, deciduous and mixed forests were 484. 9, 402. 4, 287. 9 g / (m²·a); the correlation between vegetation NPP and temperature factor was high, and the bias correlation coefficient was 0. 2-0. 8, while the correlation between vegetation NPP and precipitation was relatively low in general, with bias correlation coefficients of -0. 3-0. 4. The influence of precipitation on vegetation NPP was low in this region, and temperature was the dominant factor of NPP variation in this region. In this study, high spatial resolution LAI data were obtained to provide a basis for accurate spatial and temporal simulation of the carbon cycle in forest ecosystems [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is the key driving data of BEPS model, and it is important to obtain high accuracy LAI for regional forest ecosystem carbon cycle, however, the MODIS LAI products used in most current studies lack credibility. To this end, this study constructed a data assimilation system based on LAI dynamic model, PROSAIL radiative transfer model and Hierarchical Bayesian Network (HBN) to obtain LAI data with a spatial resolution of 20 m to drive the BEPS model and simulate the vegetation net primary productivity (NPP) of Saihanba Mechanical Forest during 2011—2021, and the spatial and temporal variation of NPP and the influencing factors of NPP were analyzed. The results showed that the high-resolution LAI data obtained based on Bayesian assimilation method greatly improved the accuracy of MODIS LAI products; the correlation between the simulated forest NPP obtained from BEPS model driven by assimilated LAI data and the NPP calculated from the sample plots was high (R 2 = 0. 77); the mean value of vegetation NPP in Saihanba Mechanical Forest during 2011—2021 was 307. 4 g / (m 2·a), and the NPP of forest showed a steady growth trend; the simulated NPP of different vegetation types were different, and the simulated NPP of coniferous, deciduous and mixed forests were 484. 9, 402. 4, 287. 9 g / (m 2·a); the correlation between vegetation NPP and temperature factor was high, and the bias correlation coefficient was 0. 2-0. 8, while the correlation between vegetation NPP and precipitation was relatively low in general, with bias correlation coefficients of -0. 3-0. 4. The influence of precipitation on vegetation NPP was low in this region, and temperature was the dominant factor of NPP variation in this region. In this study, high spatial resolution LAI data were obtained to provide a basis for accurate spatial and temporal simulation of the carbon cycle in forest ecosystems. [ABSTRACT FROM AUTHOR]

Seasonal variations of photosynthetic capacity parameters, notably the maximum carboxylation rate, Vcmax, play an important role in accurate estimation of CO2 assimilation in gas-exchange models. Satellite-derived normalised difference vegetation index (NDVI), enhanced vegetation index (EVI) and model-data fusion can provide means to predict seasonal variation in Vcmax. In this study, Vcmax was obtained from a process-based model inversion, based on an ensemble Kalman filter (EnKF), and gross primary productivity, and sensible and latent heat fluxes measured using eddy covariance technique at two deciduous broadleaf forest sites and a mixed forest site. Optimised Vcmax showed considerable seasonal and inter-annual variations in both mixed and deciduous forest ecosystems. There was noticeable seasonal hysteresis in Vcmax in relation to EVI and NDVI from 8 d composites of satellite data during the growing period. When the growing period was phenologically divided into two phases (increasing VIs and decreasing VIs phases), significant seasonal correlations were found between Vcmax and VIs, mostly showing R²<0.95. Vcmax varied exponentially with increasing VIs during the first phase (increasing VIs), but second and third-order polynomials provided the best fits of Vcmax to VIs in the second phase (decreasing VIs). The relationships between NDVI and EVI with Vcmax were different. Further efforts are needed to investigate Vcmax-VIs relationships at more ecosystem sites to the use of satellite-based VIs for estimating Vcmax. [ABSTRACT FROM AUTHOR]

Due to the heterogeneity and complexity of terrestrial ecosystems of East Asia, a better understanding of relationships between climate change and net primary productivity (NPP) distribution is important to predict future carbon dynamics. The objective of this study is to analyze the temporal-spatial patterns of NPP in East Asia (10°S - 55°N, 60 - 155°E) from 1982 to 2006 using the process-based Boreal Ecosystem Productivity Simulator (BEPS) model. Prior to the regional simulation, the annual simulated NPP was validated using field observed NPP demonstrating the ability of BEPS to simulate NPP in different ecosystems of East Asia. Simulated NPP exhibited a southeast-northwest decreasing distribution, above 1000 g Cm-2 yr-1 in most areas of Southeast Asia and below 50 g Cm-2 yr-1 in northwestern China. The annual total of simulated NPP was 11.27 Pg C yr-1 (˜18.78% of the global total) averaged over 25 years from 1982 to 2006. Total NPP increased by 7.33%, mainly in the northwestern part of the study area. The change of NPP was determined by precipitation in northern and southwestern regions, vapor pressure deficit and precipitation in North and Northeast Plains of China, radiation in South China, and temperature on the Tibetan Plateau. Climate was the main factor regulating the NPP trends in the interior and northern parts of the study area while the change in leaf area index was the factor dominating the trends of NPP in the eastern part of the study area. [ABSTRACT FROM AUTHOR]

Affected by natural and anthropogenic disturbances such as forest fires, insect-induced mortality and harvesting, forest stand age plays an important role in determining the distribution of carbon pools and fluxes in a variety of forest ecosystems. An improved understanding of the relationship between net primary productivity (NPP) and stand age (i.e., age-related increase and decline in forest productivity) is essential for the simulation and prediction of the global carbon cycle at annual, decadal, centurial, or even longer temporal scales. In this paper, we developed functions describing the relationship between national mean NPP and stand age using stand age information derived from forest inventory data and NPP simulated by the BEPS (Boreal Ecosystem Productivity Simulator) model in 2001. Due to differences in ecobiophysical characteristics of different forest types, NPP-age equations were developed for five typical forest ecosystems in China (deciduous needleleaf forest (DNF), evergreen needleleaf forest in tropic and subtropical zones (ENF-S), deciduous broadleaf forest (DBF), evergreen broadleaf forest (EBF), and mixed broadleaf forest (MBF)). For DNF, ENF-S, EBF, and MBF, changes in NPP with age were well fitted with a common non-linear function, with R2 values equal to 0.90, 0.75, 0.66, and 0.67, respectively. In contrast, a second order polynomial was best suitable for simulating the change of NPP for DBF, with an R2 value of 0.79. The timing and magnitude of the maximum NPP varied with forest types. DNF, EBF, and MBF reached the peak NPP at the age of 54, 40, and 32 years, respectively, while the NPP of ENF-S maximizes at the age of 13 years. The highest NPP of DBF appeared at 122 years. NPP was generally lower in older stands with the exception of DBF, and this particular finding runs counter to the paradigm of age-related decline in forest growth. Evaluation based on measurements of NPP and stand age at the plot-level demonstrates the reliability and applicability of the fitted NPP-age relationships. These relationships were used to replace the normalized NPP-age relationship used in the original InTEC (Integrated Terrestrial Ecosystem Carbon) model, to improve the accuracy of estimated carbon balance for China’s forest ecosystems. With the revised NPP-age relationship, the InTEC model simulated a larger carbon source from 1950–1980 and a larger carbon sink from 1985–2001 for China’s forests than the original InTEC model did because of the modification to the age-related carbon dynamics in forests. This finding confirms the importance of considering the dynamics of NPP related to forest age in estimating regional and global terrestrial carbon budgets. [Copyright &y& Elsevier]

Carbon flux is the main basis for judging the carbon source/sink of forest ecosystems. Bamboo forests have gained much attention because of their high carbon sequestration capacity. In this study, we used a boreal ecosystem productivity simulator (BEPS) model to simulate the gross primary productivity (GPP) and net primary productivity (NPP) of bamboo forests in China during 2001–2018, and then explored the spatiotemporal evolution of the carbon fluxes and their response to climatic factors. The results showed that: (1) The simulated and observed GPP values exhibited a good correlation with the determination coefficient (R2), root mean square error (RMSE), and absolute bias (aBIAS) of 0.58, 1.43 g C m−2 day−1, and 1.21 g C m−2 day−1, respectively. (2) During 2001–2018, GPP and NPP showed fluctuating increasing trends with growth rates of 5.20 g C m−2 yr−1 and 3.88 g C m−2 yr−1, respectively. The spatial distribution characteristics of GPP and NPP were stronger in the south and east than in the north and west. Additionally, the trend slope results showed that GPP and NPP mainly increased, and approximately 30% of the area showed a significant increasing trend. (3) Our study showed that more than half of the area exhibited the fact that the influence of the average annual precipitation had positive effects on GPP and NPP, while the average annual minimum and maximum temperatures had negative effects on GPP and NPP. On a monthly scale, our study also demonstrated that the influence of precipitation on GPP and NPP was higher than that of the influence of temperature on them. [ABSTRACT FROM AUTHOR]

Abstract: Process-based terrestrial ecosystem models have been widely used to simulate carbon cycle, climate and ecosystem interactions. Some parameters used in biological functions often change seasonally and inter-annually. In this study, sequential data assimilation with an ensemble Kalman filter is designed to optimize the key parameters of the Boreal Ecosystem Producitivity Simulator (BEPS) model, taking into account the errors in the input, parameters and observation. The parameters adjusted through data assimilation include foliage clumping index (C f), slope of stomatal conductance to the net photosynthetic rate (m), maximum photosynthetic carboxylation rate (V cmax) and electron transport rate (J max) at reference temperature of 25°C, multiplier to the soil organic matter decomposition rates (K r). The fluxes of CO2 (separated into gross primary production (GPP) and ecosystem respiration (R E)) and water vapor measured using the eddy covariance technique at the BOREAS/BERMS Old Aspen site, Canada during 1997–2004 are used for the optimization. Parameters are optimized at a daily time step and presented as 10-day averages. The results show that the parameters varied significantly at seasonal and inter-annual scales. Photosynthetic capacity (V cmax, J max) usually increased rapidly at the leaf expansion stage and reached a plateau in the early summer, then followed an abrupt decrease when foliage senescence occurred. The multiplier K r to soil respiration coefficients were reduced to 0.5 in wintertime; however it increased rapidly in the spring and reached about 1.0 in summertime. The intensity of soil respiration may be related to the metabolic responses of the microbial communities and the availability of labile substances in summer and winter. From leaf expanding in the spring to senescing in the autumn, C f presented declining trend from 0.88 to 0.78 with slight variation; m increased from 5 and approached to an approximately stable value of 8 since early summer. With optimized parameters, the estimates of GPP, R E, net ecosystem production and water vapor fluxes were significantly improved compared with the measurements at daily and annual time steps. With eddy covariance fluxes, data assimilation with an ensemble Kalman filter can successfully retrieve the seasonal and inter-annual variations of parameters related to photosynthesis and respiration of this boreal ecosystem site. [Copyright &y& Elsevier]

Global warming threatens ecosystem functions, biodiversity, and rangeland productivity in Mongolia. The study analyzes the spatial and temporal distributions of the Net Primary Production (NPP) and its response to climatic parameters. The study also highlights how various land cover types respond to climatic fluctuations from 2003 to 2018. The Boreal Ecosystem Productivity Simulator (BEPS) model was used to simulate the rangeland NPP of the last 16 years. Satellite remote sensing data products were mainly used as input for the model, where ground-based and MODIS NPP were used to validate the model result. The results indicated that the BEPS model was moderately effective (R2 = 0.59, the Root Mean Square Error (RMSE) = 13.22 g C m−2) to estimate NPP for Mongolian rangelands (e.g., grassland and sparse vegetation). The validation results also showed good agreement between the BEPS and MODIS estimates for all vegetation types, including forest, shrubland, and wetland (R2 = 0.65). The annual total NPP of Mongolia showed a slight increment with an annual increase of 0.0007 Pg (0.68 g C per meter square) from 2003 to 2018 (p = 0.82) due to the changes in climatic parameters and land cover change. Likewise, high increments per unit area found in forest NPP, while decreased NPP trend was observed in the shrubland. In conclusion, among the three climatic parameters, temperature was the factor with the largest influence on NPP variations (r = 0.917) followed precipitation (r = 0.825), and net radiation (r = 0.787). Forest and wetland NPP had a low response to precipitation, while inter-annual NPP variation shows grassland, shrubland, and sparse vegetation were highly sensitive rangeland types to climate fluctuations. [ABSTRACT FROM AUTHOR]

Bamboo forests are an important part of the forest ecosystem, which has strong carbon sequestration potential and plays an important role in the global carbon cycle. As a key parameter for simulating the carbon cycle using forest ecosystem models, the quality of leaf area index (LAI) data has a direct influence on the accuracy of modelling results. Here, we used the particle filter (PF) algorithm and PROSAIL model to assimilate MODIS LAI products, which were then used to drive a boreal ecosystem productivity simulator model to simulate the bamboo forest carbon cycle. The results showed that the relationship between the assimilated and observed LAI values was very significant, with an R2 of 0.95 and an RMSE of 0.28, greatly improving the precision of MODIS LAI products. The R2 values for the gross primary productivity (GPP), net ecosystem exchange (NEE), and total ecosystem respiration (TER) simulated by the assimilated LAI values and observed carbon fluxes were 0.65, 0.45 and 0.70, respectively, and the RMSE values were 1.10 g C m−2 day−1, 1.00 g C m−2 day−1 and 0.35 g C m−2 day−1, respectively. Compared with the results of the carbon cycle simulated by non-assimilated LAI, the R2 values of the GPP, NEE and TER values that were simulated by assimilated LAI increased by 27.5%, 45.2% and 6.1%, and the RMSE values decreased by 29.9%, 23.7% and 22.2%, respectively. Therefore, coupling the PF and PROSAIL models can greatly improve the simulation precision for the large-scale bamboo forest carbon cycle. This study laid the foundation for simulating the carbon cycle over a large-scale bamboo forest based on low-resolution data in the future. Unlabelled Image • The accuracy of MODIS LAI was improved by coupling particle filter and PROSAIL model. • Carbon cycle was simulated by coupling data assimilation algorithm and BEPS model. • Carbon fluxes simulated using assimilated LAI was more accurate than using MODIS LAI. [ABSTRACT FROM AUTHOR]