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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]

The maximum carboxylation rate (Vcmax) is an important parameter affecting the photosynthesis rate of plant leaves. In terrestrial ecosystem models (TEMs), Vcmax at 25°C (Vm25°) is often assigned as constants according to plant functional types (PFTs), while its variations with leaf temperature and nutrient contents are described using empirical functions. However, Vm25° could itself vary seasonally due to changes in the leaf physiological state that cannot be described by the empirical functions, potentially causing large uncertainties in simulated water and carbon fluxes. So far, the seasonal variation in Vm25° has not been systematically studied. Here, we generated a Vm25° data set of eight main biomes from 2000 to 2020 using eddy covariance (EC) measurements at globally distributed 176 sites. The boreal ecosystem productivity simulator was combined with a light response curve model (BEPS‐LRC) to invert Vm25° from EC data. We investigated seasonal variations of Vm25° and analyzed how different environmental and physiological factors, such as physiological (leaf chlorophyll content, LCC and Rubisco or RuBP) and climatic environment factors, including air temperature (Ta), solar shortwave radiation, CO2 concentration (CO2), and soil water content (SWC), influence this parameter. Vm25° values derived from flux data using BEPS‐LRC correlate well with Vm25° of the reference data set (R2 = 0.74, slope = 0.77, and root mean square error = 24.45 μmol m−2 s−1, p < 0.001). Leaf Vm25° has strong seasonal variations in all PFTs except for in evergreen broadleaf forests, but its seasonal variation patterns differ greatly among eight biomes. Air temperature (Ta) is the most important determinant of Vm25°, followed by SWC. The interactive effects of Ta and SWC on Vm25° vary among different biomes. The seasonal variation of Vm25° was strongly dependent on LCC. After the correction of temperature effect, the contribution of LCC to the seasonal variation of Vm25° averaged 26% among eight biomes. These findings provide useful information for better parameterization of Vm25° in TEMs. Plain Language Summary: In terrestrial ecosystem models, Vcmax at 25°C (Vm25°) is often assigned as constants according to plant functional types, while its variations with leaf temperature and nutrient contents are described using empirical functions. However, Vm25° could itself vary seasonally due to changes in the leaf physiological state that cannot be described by the empirical functions, potentially causing large uncertainties in simulated water and carbon fluxes. In this study, we developed the boreal ecosystem productivity simulator with a light response curve model based on the method of Zheng et al. (2017, https://doi.org/10.1016/j.agrformet.2017.01.008) to derive seasonal variations of Vm25° over the 2000–2020 period from flux‐derived gross primary productivity at 176 sites in eight biomes over the globe. The Vm25° data derived from flux data using the BEPS‐LRC model agree well with a data set assembled from different sources. Our study confirmed the seasonal variation of Vm25°, possibly caused by the seasonal variations in carboxylation enzyme Rubisco and RuBP. Therefore, in modeling global water and carbon cycles, it is necessary to describe the temporal and spatial variability of Vm25°, and the data derived from this study would be useful for this purpose. Key Points: Vm25° is derived from flux data at globally distributed 176 sites using the boreal ecosystem productivity simulator with a light response curve (BEPS‐LRC)Vm25° has strong seasonal variations except in evergreen broadleaf forestAir temperature significantly 33% of the flux sites showed significant seasonal variations in Vm25° [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

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.

Ecosystem models provide an effective approach to quantify the terrestrial carbon cycle, but the lack of accurate phenological information prevents them from better simulations of the physical processes. Compared with spring phenology (i.e. the start of the growing season, SOS), the vegetation phenology in autumn (the end of the growing season, EOS) is not well-simulated and it is challenging to incorporate vegetation phenology into ecosystem models. The simulation of EOS based on temperature and photoperiod was widely accepted, such as Delpierre et al (2009 Agric. For. Meteorol. 149 938–48)’s model (DM), yet its accuracy has not been fully discussed at a regional scale. Here, we developed a regional autumn phenological model (DMS) with inputs of temperature, photoperiod, and water availability for China’s terrestrial ecosystems. The new DMS model significantly improved the representation of EOS in terms of the lower root mean square error (RMSE), higher model efficiency, and a higher percentage of significant correlation with the referenced EOS. We observed widespread delaying trends of EOS with an average rate of 0.1 d yr ^−1 for vegetated areas over 2001–2018. We further incorporated the improved EOS into the boreal ecosystem productivity simulator (BEPS) and found that the phenology-modified BEPS model had better performances in predicting annual gross primary productivity (GPP) with ∼28% lower RMSE than the original model when testing against GPP measurements from flux tower sites. From 2001 to 2017, the interannual GPP simulated by the modified BEPS model showed an increasing trend with a rate of 6.0 g C m ^−2 yr ^−2 . In conclusion, our study proves that water availability is of great significance for modeling autumn phenology, and the incorporation of phenological dates into an ecosystem model is helpful for productivity simulation.

Grassland, as an important part of land cover, plays an important role in the global carbon cycle and carbon balance. Net ecosystem productivity (NEP) is a key indicator of the carbon cycle process and an important factor in assessing ecosystem security and maintaining ecosystem balance. In this paper, Boreal Ecosystem Productivity Simulator (BEPS) combining meteorological data, leaf area index, and land cover type data were used to simulate the grassland NEP of China from 1979 to 2008. This model was also used to analyze the responses to changes in climate factors, interannual variation in carbon conversion efficiency, drought stress coefficient, and water use efficiency of grassland in China. Results showed that from 1979 to 2008, the mean annual grassland NEP was 13.6 g C/m2 with weak carbon sinks. The grassland NEP distribution increased from northwest to southeast across China. Regions with NEP of > 0 (C sink) accounted for 73.1% of the total grassland area of China. The total C sequestration reached 26.6 Tg yearly, and grassland NEP was positive from 1979 to 2008. The annual changing characteristics were analyzed. Grassland NEP was positive with carbon sink from June to September, which was negative with carbon source in the remaining months. The carbon conversion efficiency and water use efficiency of the grassland increased significantly within 30 years. NEP showed positive correlation with precipitation (accounting for 74.2% of the total grassland area was positively correlated) but weakly positive correlation with temperature (50.2% of the case). Furthermore, significant positive correlation was found between grassland NEP and precipitation, especially in northeastern and central Inner Mongolia, northern Tianshan of Xinjiang, southwestern Tibet, and southern Qinghai Lake. [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.

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Topography has remarkable effects on the local climate, especially in mountainous areas. The accuracy of climate data is pivotal to the estimation of net primary productivity (NPP). Unrealistic simulations of climate data without considering the topography would lead to biased estimation of NPP. In this work, we aim to evaluate quantitatively the NPP difference with and without considering the topographical effects of climate data, and furthermore, to explore the spatio-temporal characteristics of NPP difference and the primary contributing variables to the difference. For these purposes, two different climate datasets were first built and compared with the station observations, one of which considered topographical effects (terrain-based climate dataset) while the other one did not (ordinary climate dataset). We quantified topographical effects of climate data on NPP estimation by inputting two different climate datasets to the same ecosystem model, the Boreal Ecosystem Productivity Simulator (BEPS), to evaluate the importance of considering topography during NPP calculations. Then, spatio-temporal characteristics of the NPP difference were explored, and the primary contributing variables were determined through a series of simulation experiments. Results showed that, on average, ordinary climate dataset underestimated NPP by 12.5% compared with terrain-based climate dataset over the whole Wuling mountainous area. Topographical effects of climate data had larger impacts on the NPP estimation in summer time than in winter months. In space, differences between ordinary NPP and terrain-based NPP were negligible below 200 m, and above 200 m, the differences increased first and then steadily decreased; discrepancies between NPPs continually augmented with the slope increasing; and NPP was more likely to be affected in the north and northwest than in the south and southeast. The primary climate variables contributing to the NPP difference in Wuling mountainous area were temperatures, followed by global solar radiation. The research methods developed in this case study can also be applied to other study areas and other ecosystem models. [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]

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]

Net primary productivity (NPP) is an important component of the terrestrial carbon cycle. Accurately mapping the spatial-temporal variations of NPP in China is crucial for global carbon cycling study. In this study the process-based Boreal Ecosystem Productivity Simulator (BEPS) was employed to study the changes of NPP in China's ecosystems for the period from 2000 to 2010. The BEPS model was first validated using gross primary productivity (GPP) measured at typical flux sites and forest NPP measured at different regions. Then it was driven with leaf area index (LAI) inversed from the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and land cover products and meteorological data interpolated from observations at 753 national basic meteorological stations to simulate NPP at daily time steps and a spatial resolution of 500 m from January 1, 2000 to December 31, 2010. Validations show that BEPS is able to capture the seasonal variations of tower-based GPP and the spatial variability of forest NPP in different regions of China. Estimated national total of annual NPP varied from 2.63 to 2.84Pg C·yr, averaging 2.74 Pg C·yr during the study period. Simulated terrestrial NPP shows spatial patterns decreasing from the east to the west and from the south to the north, in association with land cover types and climate. South-west China makes the largest contribution to the national total of NPP while NPP in the North-west account for only 3.97% of the national total. During the recent 11 years, the temporal changes of NPP were heterogamous. NPP increased in 63.8% of China's landmass, mainly in areas north of the Yangtze River and decreased in most areas of southern China, owing to the low temperature freezing in early 2008 and the severe drought in late 2009. [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]

By using an ensemble Kalman filter (EnKF) to assimilate the observed soil moisture data, the modified boreal ecosystem productivity simulator (BEPS) model was adopted to simulate the dynamics of soil moisture in winter wheat root zones at Xuzhou Agro-meteorological Station, Jiangsu Province of China during the growth seasons in 2000-2004. After the assimilation of observed data,the determination coefficient, root mean square error, and average absolute error of simulated soil moisture were in the ranges of 0.626-0.943, 0.018-0.042, and 0.021-0.041, respectively, with the simulation precision improved significantly, as compared with that before assimilation, indicating the applicability of data assimilation in improving the simulation of soil moisture. The experimental results at single point showed that the errors in the forcing data and observations and the frequency and soil depth of the assimilation of observed data all had obvious effects on the simulated soil moisture. [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]

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]

LAI is one of the most important observation data in the research of carbon cycle of forest ecosystem, and it is also an important parameter to drive process-based ecosystem model. The Moso bamboo forest (MBF) and Lei bamboo forest (LBF) were selected as the study targets. Firstly, the MODIS LAI time series data during 2014-2015 was assimilated with Dual Ensemble Kalman Filter method. Secondly, the high quality assimilated MBF LAI and LBF LAI were used as input dataset to drive BEPS model for simulating the gross primary productivity (GPP), net ecosystem exchange (NEE) and total ecosystem respiration (TER) of the two types of bamboo forest ecosystem, respectively. The modeled carbon fluxes were evaluated by the observed carbon fluxes data, and the effects of different quality LAI inputs on carbon cycle simulation were also studied. The LAI assimilated using Dual Ensemble Kalman Filter of MBF and LBF were significantly correlated with the observed LAI, with high R 2 of 0.81 and 0.91 respectively, and lower RMSE and absolute bias, which represented the great improvement of the accuracy of MODIS LAI products. With the driving of assimilated LAI, the modeled GPP, NEE, and TER were also highly correlated with the flux observation data, with the R 2 of 0.66, 0.47, and 0.64 for MBF, respectively, and 0.66, 0.45, and 0.73 for LBF, respectively. The accuracy of carbon fluxes modeled with assimilated LAI was higher than that acquired by the locally adjusted cubic-spline capping method, in which, the accuracy of mo-deled NEE for MBF and LBF increased by 11.2% and 11.8% at the most degrees, respectively.

Keeping other parameters as empirical constants, different numerical combinations of the main photosynthetic parameters V c max and J max were conducted to estimate daily GPP by using the iteration method in this paper. To optimize V c max and J max in BEPSHourly model at hourly time steps, simulated daily GPP using different numerical combinations of the parameters were compared with the flux tower data obtained from the temperate deciduous broad-leaved forest of the Maoershan Forest Farm in Northeast China. Comparing the simulated daily GPP with the observed flux data in 2011, the results showed that optimal V c max and J max for the deciduous broad-leaved forest in Northeast China were 41.1 μmol·m -2 ·s -1 and 82.8 μmol·m -2 ·s -1 respectively with the minimal RMSE and the maximum R 2 of 1.10 g C·m -2 ·d -1 and 0.95. After V c max and J max optimization, BEPSHourly model simulated the seasonal variation of GPP better.