Boreal Ecosystem Productivity Simulator Model Research Articles

Spatiotemporal patterns of net primary productivity of subtropical forests in China and its response to drought

Net primary productivity (NPP) is an important indicator used to evaluate the carbon sequestration capacity of forest ecosystems. Subtropical forest ecosystems play an indispensable role in maintaining the global carbon balance, while frequently occurring drought events in recent years have seriously damaged their productivity. However, the spatiotemporal patterns of NPP, as well as its response to drought, remain uncertain. In this study, the multiscale drought characteristics in subtropical China during 1981–2015 were analyzed based on the standardized precipitation evapotranspiration index. Then, simulated and analyzed the spatiotemporal NPP of subtropical forests by the boreal ecosystem productivity simulator model. Finally, the response of NPP to drought was analyzed based on multiple statistical indices. The results show that most regions in subtropical China experienced mild and moderate drought during 1981–2015. In particular, the extent of drought severity has shown a noticeable increasing trend after 2000. The forest NPP ranged from 622.64 to 1323.82 gC·m−2·a−1, with an overall increase rate of 16.15 gC·m−2·a−1; in particular, the contribution of the western forest NPP became increasingly important. Drought stress has limited the growth of subtropical forest NPP in China, with summer and wet season time scales of drought having the greatest impact on forest NPP anomalies, followed by autumn time scales. The limitation is mostly because the drought duration continually increased, leading to differences in the impact of drought on forest NPP before and after 2000, with declines of 59.55 % and 82.45 %, respectively, mainly concentrated in southwestern regions, such as Yunnan, Guangxi, and Sichuan provinces. This study quantitatively analyzed the impact of drought on subtropical forest NPP, and provides scientific basis for subtropical forest response and adaptation to climate change.

Quantitative Assessment of Factors Influencing the Spatiotemporal Variation in Carbon Dioxide Fluxes Simulated by Multi-Source Remote Sensing Data in Tropical Vegetation

Vegetation plays a vital role in the global carbon cycle, a function of particular significance in regulating carbon dioxide fluxes within tropical ecosystems. Therefore, it is crucial to enhance the precision of carbon dioxide flux estimates for tropical vegetation and to explore the determinants influencing carbon sequestration. In this study, Landsat series images and Sentinel-2 Multispectral Instrument satellite data were used to invert vegetation biophysical parameters, thereby improving the timeliness and resolution of state variables from the boreal ecosystem productivity simulator (BEPS). The BEPS model at a 30 m resolution was developed to accurately capture tropical vegetation carbon dioxide fluxes across Hainan Island (HN) over the preceding two decades. The impacts of climate variations and anthropogenic activities on the carbon dioxide fluxes of tropical vegetation were further quantified using quantile regression models and a land-use transfer matrix. Results indicate significant increases in both net primary productivity (NPP) and net ecosystem productivity (NEP) in HN during the period 2000–2020, by 5.81 and 4.29 g C/m2 year, respectively. Spatial trends in vegetation carbon dioxide fluxes exhibited a consistent decline from inland regions to coastal zones. Anthropogenic activities were the dominant factor in the reduced stability of coastal NPP, while the post-2005 vegetation restoration promoted the southward expansion of high NPP (>1200 g C/m2) in the central part of HN. NPP in this tropical island was more sensitive to temperature than to precipitation, with a 1 °C temperature increase resulting in 4.1 g C/m2 reduction in dry-season NPP compared to wet-season NPP. Upgrades of cropland quality and grassland restoration have improved NPP yields, and land use transfers have resulted in a 0.301 Tg C net increase in NPP. This study provides new insight into the improvement of the carbon dioxide flux model at a finer scale for tropical vegetation and highlights ecological construction as an adaptation strategy to enhance the carbon sinks of tropical vegetation under negative climate change conditions.

Modeling China's terrestrial ecosystem gross primary productivity with BEPS model: Parameter sensitivity analysis and model calibration

Terrestrial ecosystems are the largest sink for carbon, and their ecosystem gross primary productivity (GPP) regulates variations in atmospheric carbon dioxide (CO2) concentrations. Current process-based ecosystem models used for estimating GPP are subject to large uncertainties due to poorly constrained parameter values. In this study, we implemented a global sensitivity analysis (GSA) on parameters in the Boreal Ecosystem Productivity Simulator (BEPS) considering the parameters’ second-order impacts. We also applied the generalized likelihood estimation (GLUE) method, which is flexible for a multi-parameter calibration, to optimize the GPP simulation by BEPS for 10 sites covering 7 plant functional types (PFT) over China. Our optimized results significantly reduced the uncertainty of the simulated GPP over all the sites by 17 % to 82 % and showed that the GPP is sensitive to not only the photosynthesis-related parameters but also the parameters related to the soil water uptake as well as to the energy balance. The optimized GPP across South China showed that the mix forest, shrub, and grass have a higher GPP and are more controlled by the soil water availability. This study showed that the GLUE method together with the GSA scheme could constrain the ecosystem model well when simulating GPP across multiple ecosystems and provide a reasonable estimate of the spatial and temporal distribution of the ecosystem GPP over China. We call for more observations from more sites, as well as data on plant traits, to be collected in China in order to better constrain ecosystem carbon cycle modeling and understand its response to climate change.

Simulating canopy carbonyl sulfide uptake of two forest stands through an improved ecosystem model and parameter optimization using an ensemble Kalman filter

The uptake of carbonyl sulfide (COS) by plant canopy closely correlated with photosynthetic CO2 uptake as they share the same diffusion pathway. Process-based ecosystem models have been used to simulate the CO2, H2O and COS fluxes between terrestrial ecosystems and atmosphere. Some model parameters often vary seasonally and interannually. In this study, the hourly Boreal Ecosystem Productivity Simulator (BEPS) was first improved to simulate the canopy COS uptake rate. Then, an ensemble Kalman filter was designed to optimize the key parameters of the hourly BEPS model, considering the errors in the input, parameters and measurements. The optimized parameters include leaf maximum carboxylation rate at 25 °C at top of canopy (Vcmax250) and a calibration term (α) used to scale the apparent conductance for COS uptake from the intercellular airspace (gcos) to Vcmax. The fluxes of gross primary productivity (GPP), latent heat (LE) and COS uptake by canopy (Fcos_veg) measured using eddy covariance method at a temperate deciduous forest during 2012–2013 and a boreal conifer forest during 2013–2017 were used for data assimilation. Parameters were optimized daily and presented as 10-day averages. It was found that the parameters showed considerable seasonally and interannually variations. With parameter optimization using EnKF, the improved hourly BEPS model could explain 86%, 68% and 55% of variations in measured daily GPP, LE and Fcos_veg at US-Ha1 site during 2012 and 2013 and captured 90%, 74% and 69% of variations in the measured GPP, LE and Fcos_veg at FI-Hyy site during 2013–2017, respectively. The simulated daily Fcos_veg with the parameters optimized using EnKF could capture the day-to-day variations of Fcos_veg at the two studied sites and performed better than the simulations with calibrated parameter values. Further efforts were required to investigate the impacts of model structure and different parameter optimization methods on the optimized parameter values.

Spatiotemporal Evolution of the Carbon Fluxes from Bamboo Forests and their Response to Climate Change Based on a BEPS Model in China

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.

Evaluation and Validation of the Net Primary Productivity of the Zoigê Wetland Based on Grazing Coupled Remote Sensing Process Model

Zoigê wetland is located in the southeastern edge of the Qinghai–Tibet Plateau, in recent years, the wetland local area grassland productivity decline, land sanding, and other serious degradation phenomenon. Using remote sensing and hydrology data combined with the basic principles of vegetation ecology, we applied the Carnegie–Ames–Stanford Approach (CASA) model and Boreal Ecosystem Productivity Simulator (BEPS) model to invert the net primary productivity (NPP) of the Zoigê wetland ecosystem and then identified the deficiencies of the two models. In this article, we tested the factors that influence the light use efficiency of vegetation and considered the effects of livestock grazing on grassland ecosystems and wetland moisture content on vegetation growth. A new grazing coupled remote sensing process (GCRSP) model suitable for the productivity of the Zoigê wetland was proposed. Field survey data were combined with the CASA model, BEPS model, and Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product, and the accuracy of the proposed GCRSP model was analyzed at regional level. The results show that after fitting the field measurements to the GCRSP model, the fitted variance between the model and the measured data is 0.84, which is larger than the variance between the other two methods and the measured data. In addition, the variance of the linear fit between the GCRSP model and the MODIS NPP product was 0.11, which was relatively small, indicating that the regional-scale GCRSP model was relatively accurate for the Zoigê wetland NPP. Therefore, this model was suitable for the inversion of regional-scale NPP. The results indicated that the average grassland NPP of the Zoigê wetland was 487.57 gC/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and larger NPP values were distributed near the rivers and wetland and smaller NPP values were distributed in the mountain glacier and desertified land areas. These results were consistent with the regional characteristics. This article provided technical and theoretical support for the desertification management of the Zoigê grassland and a new method for studying vegetation NPP, and the findings could help improve the accuracy of regional-scale vegetation NPP estimates.

Suitability of the boreal ecosystem simulator (BEPS) model for estimating gross primary productivity in hemi-boreal upland pine forest

Abstract Gross Primary Productivity (GPP) is the core component of the terrestrial and global carbon cycle and Earth’s climate research. In this study, GPP estimation was performed with the Boreal Ecosystem Productivity Simulator (BEPS) model to check its performance for hemi-boreal forests on the example of the Soontaga area in Estonia. The model was run by using a combination of remote sensing (leaf area index (LAI), clumping index) and meteorological data inputs (air temperature, global radiation, air humidity, precipitation and wind speed). The results were validated against GPP derived from the available flux tower measurements. The spatial representativeness of the site was evaluated using multiple spatial thresholds (500 m–2 km), as well. We found that the BEPS model can track the GPP changes with the season and inter-annual variation very well in a coniferous hemi-boreal forest, given that good quality input data are provided.

Incorporating water availability into autumn phenological model improved China’s terrestrial gross primary productivity (GPP) simulation

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.

The impacts of vegetation on the soil surface freezing-thawing processes at permafrost southern edge simulated by an improved process-based ecosystem model

Permafrost degradation due to climate warming would potentially increase the release of previously frozen soil carbon and change the carbon budget of the cold region ecosystem. The underlying permafrost degradation would be effectively mediated by soil surface freezing-thawing (FT) processes. Aboveground vegetation can regulate soil FT processes, however its effects on ground thermal transfer have not been well represented by ecosystem models. In this study, we improved the hydrothermal module of the Boreal Ecosystem Productivity Simulator (BEPS) through more careful parameterization of snowpack density, puddled water, soil organic matter and super-cooled soil water. The impacts of vegetation on the soil surface FT processes have also been investigated using the improved BEPS model and the measured soil temperature data at forest and grassland sites on the southern edge of permafrost region in Mongolia and northeastern China. The improved BEPS model performs better than the original model in simulations of soil temperature and soil FT processes. Smaller amplitudes of soil diurnal FT cycles were found in forest sites compared to grassland sites. Forest sites have delayed soil thaw timing and similar soil freezing time compared to grassland sites. Differences in snow depths and soil organic matter content due to distinct vegetation community structures have considerable influences on the disparity in soil FT processes. Thus, it is important to improve the simulation of the impacts of vegetation on soil surface FT processes for better forecasting the permafrost degradation.

Drought Risk of Global Terrestrial Gross Primary Productivity Over the Last 40 Years Detected by a Remote Sensing‐Driven Process Model

AbstractGross primary productivity (GPP) is the largest flux in the global terrestrial carbon cycle. Drought has significantly impacted global terrestrial GPP in recent decades, and has been projected to occur with increasing frequency and intensity. However, the drought risk of global terrestrial GPP has not been well investigated. In this study, global terrestrial GPP during 1981–2016 was simulated with the process‐based Boreal Ecosystem Productivity Simulator model. Then, the drought risk of GPP was quantified as the product of drought probability and reduction of GPP caused by drought, which was determined using the standardized precipitation evapotranspiration index. During the study period, the drought risk of GPP was high in the southeastern United States, most of South America, southern Europe, central and eastern Africa, eastern and southeastern Asia, and eastern Australia. It was low at some high latitudes of the Northern Hemisphere and in part of tropical South America, where terrestrial GPP increased slightly in drought years. The drought risk of terrestrial GPP was greater during 2000–2016 than during 1981–1999 in 21 out of 24 climatic zones. The global mean drought risk of GPP increased from 13.6 g C m−2 yr−1 during 1981–1999 to 19.3 g C m−2 yr−1 during 2000–2016. The increase in drought risk of GPP was mainly caused by the increase in drought vulnerability. Simulation experiments indicated that the drought vulnerability of GPP was mainly induced by climatic variability. This study advances our understanding on the impact of drought on GPP over the globe.

Analyzing NPP Response of Different Rangeland Types to Climatic Parameters over Mongolia

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.

Global positive gross primary productivity extremes and climate contributions during 1982–2016

Gross primary production (GPP) quantifies the photosynthetic uptake of carbon by the terrestrial ecosystem. Positive GPP extremes represent the potential capacity of the terrestrial ecosystem to uptake carbon dioxide. Studying the positive GPP extreme is vital for the global carbon cycle and mitigation of global warming. With increasing climate extreme events, many kinds of research focus on studying negative GPP and the negative impact of climatic extremes on GPP. There is still a lack of research on positive GPP extremes and whether climatic extremes could be beneficial to global carbon uptake. In this study, we used daily Boreal Ecosystem Productivity Simulator (BEPS) to simulate GPP of the global terrestrial ecosystem during 1982–2016 and combined TRENDY models to detect positive GPP extremes and investigate the effects of climate extremes on GPP. We found the results of the TRENDY models have large differences in some areas of the globe, and the BEPS model driven by remote sensing data could be more suitable for simulating the long-term time series of global terrestrial GPP. Compared to other plant functional types, grasslands contributed the most to positive GPP extremes, accounting for approximately 41.6% (TRENDY) and 34.8% (BEPS) of the global positive GPP extremes. The probabilities of positive GPP extremes caused by positive precipitation extremes were significantly higher than those caused by temperature and radiation in most areas of the globe, indicating that sufficient precipitation (not a flood) would boost the carbon uptake ability of the global terrestrial ecosystem to form positive GPP extremes. On the contrary, the partial correlation coefficients between temperature and GPP were negative in most areas of globe, suggesting that global warming will not be conducive to carbon uptake of the terrestrial ecosystem. This study may provide new knowledge on the global positive GPP extremes.

Analysis of spatiotemporal dynamics of forest Net Primary Productivity of Nepal during 2000–2015

ABSTRACTProper estimation and spatiotemporal trend analysis of net primary productivity (NPP) are very important for forest ecosystem monitoring and management. In this article, we investigate the spatiotemporal trend and variability of the Boreal Ecosystem Productivity Simulator (BEPS)-derived forest NPP over Nepal during the period 2000–2015. This study is the first attempt to simulate nation-wide NPP at 500 m spatial resolution at the daily step for the recent 16-year period. In our work, we use a process-based model BEPS, to estimate the NPP in the forest of Nepal. All the meteorological, forest cover, soil data, leaf area index (LAI), tree cover, biomass, and biophysical parameters, required for the model, were downloaded from different sources or prepared, pre-processed and used in the BEPS model to come up with the daily NPP. In spite of some fluctuations in NPP in some years, there is an overall positive change in the NPP throughout the study period. The annual average national NPP has been raised from 1.34 g m−2 day−1 to 1.74 g m−2 day−1 from 2000 to –2015. Examining the inter-annual variation, we found almost 87% of the total forested area has a positive inter-annual variation and 13% negative. The overall average daily NPP per m2 over the entire forest in the entire period ranges from 0.1 to 4.7 g m−2 day−1 with an average of 1.65 g m−2 day−1. Similarly, by physiographic regions, the average NPP in the plain was 2.43 g m−2 day−1, hills 1.83 g m−2 day−1, and mountain 1.41 g m−2 day−1. The total daily NPP for the entire forest area ranges from 3 to 257 tons with an average of 96 tons over the study period. NPP was found to be highly influenced by rainfall, solar radiation, and temperature. While in case of the variation of NPP on different forest types, broad-leaved forest was found to have almost 1.7 times more (NPP 1.97 g m−2 day−1) than that of needle-leaved forest (NPP 1.18 g m−2 day−1). The slope per cent of <15% is favourable (NPP 2.15 g m−2 day−1) for higher NPP among different slope per cent. We found that this method could be more convenient for estimation and studies of the NPP in Nepal. The result from this study gives us important information on intra and inter-annual spatiotemporal trends and variability of NPP in the forest of overall Nepal of different physiographic regions. It also gives us information on the variation in trends of NPP with the change in various climatic and vegetation parameters, which can be important for the proper forest ecosystem monitoring, management, and planning operations.

Assimilating spatiotemporal MODIS LAI data with a particle filter algorithm for improving carbon cycle simulations for bamboo forest ecosystems

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.

Derivation of temporally continuous leaf maximum carboxylation rate (Vcmax) from the sunlit leaf gross photosynthesis productivity through combining BEPS model with light response curve at tower flux sites

Photosynthesis plays an important role in terrestrial carbon cycle, and simulation of photosynthesis through terrestrial biosphere models usually requires a specification of maximum carboxylation rate (Vcmax). However, estimating Vcmax by gas-exchange experiments is laborious and tedious, resulting in a general paucity of Vcmax data. In this study, a reliable assimilation scheme to derive temporally continuous Vcmax from measured sunlit gross photosynthesis productivity (GPP) was proposed and validated at three Canadian tower flux sites. To fulfill this purpose, the Boreal Ecosystem Productivity Simulator (BEPS) and light response curve were combined to separate the eddy covariance GPP data into sunlit GPP and shaded GPP, due to the lack of measured sunlit GPP. The reliability of Vcmax was validated through the correlation analysis between the normalized difference vegetation index (NDVI) and Vcmax. To illustrate the advantages of the proposed scheme in our study, BEPS simulated GPP using the derived Vcmax from sunlit GPP in our study, the derived Vcmax from canopy GPP and the reference Vcmax as constants were respectively compared with corresponding eddy covariance GPP measurements. Results showed that (1) the proposed assimilation scheme based on sunlit GPP can be used to derive reliable temporally continuous Vcmax. The Vcmax obtained in our study was significantly correlated with NDVI (R2 = 0.77); (2) the seasonal variations in Vcmax could significantly improve the accuracy of photosynthesis simulation. The GPP difference between eddy covariance GPP and BEPS simulated GPP using derived Vcmax from sunlit GPP (mean = 0.12, std = 0.92) were significantly smaller than those between eddy covariance GPP and BEPS simulated GPP using reference Vcmax as constants (mean = 1.37, std = 1.37); (3) the sunlit GPP is able to obtain the relatively accurate Vcmax that can improve the simulation of photosynthesis. GPP difference between eddy covariance GPP and BEPS simulated GPP using derived Vcmax from sunlit GPP (mean = 0.12, std = 0.92) showed lower mean values and standard deviations than those between eddy covariance GPP and BEPS simulated GPP using derived Vcmax from canopy GPP (mean = 0.67, std = 1.00). Our study illustrated the reliability that sunlit GPP can be used to derive relatively accurate temporally continuous Vcmax, and the assimilation scheme gives new potential to improve the simulation of photosynthesis through terrestrial biosphere models.

Estimating Diurnal Courses of Gross Primary Production for Maize: A Comparison of Sun-Induced Chlorophyll Fluorescence, Light-Use Efficiency and Process-Based Models

Accurately quantifying gross primary production (GPP) is of vital importance to understanding the global carbon cycle. Light-use efficiency (LUE) models and process-based models have been widely used to estimate GPP at different spatial and temporal scales. However, large uncertainties remain in quantifying GPP, especially for croplands. Recently, remote measurements of solar-induced chlorophyll fluorescence (SIF) have provided a new perspective to assess actual levels of plant photosynthesis. In the presented study, we evaluated the performance of three approaches, including the LUE-based multi-source data synergized quantitative (MuSyQ) GPP algorithm, the process-based boreal ecosystem productivity simulator (BEPS) model, and the SIF-based statistical model, in estimating the diurnal courses of GPP at a maize site in Zhangye, China. A field campaign was conducted to acquire synchronous far-red SIF (SIF760) observations and flux tower-based GPP measurements. Our results showed that both SIF760 and GPP were linearly correlated with APAR, and the SIF760-GPP relationship was adequately characterized using a linear function. The evaluation of the modeled GPP against the GPP measured from the tower demonstrated that all three approaches provided reasonable estimates, with R2 values of 0.702, 0.867, and 0.667 and RMSE values of 0.247, 0.153, and 0.236 mg m−2 s−1 for the MuSyQ-GPP, BEPS and SIF models, respectively. This study indicated that the BEPS model simulated the GPP best due to its efficiency in describing the underlying physiological processes of sunlit and shaded leaves. The MuSyQ-GPP model was limited by its simplification of some critical ecological processes and its weakness in characterizing the contribution of shaded leaves. The SIF760-based model demonstrated a relatively limited accuracy but showed its potential in modeling GPP without dependency on climate inputs in short-term studies.

Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating.

Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% (n = 35, p < 0.05, RMSE = 2.20 kg/m2) and 85% (n = 100, p < 0.01, RMSE = 1.71 kg/m2) of variation in field- and ALS-based forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB.

Coupled LAI assimilation and BEPS model for analyzing the spatiotemporal pattern and heterogeneity of carbon fluxes of the bamboo forest in Zhejiang Province, China

Bamboo forests are widely distributed in the tropical and sub-tropical regions, and have a great carbon sequestration capacity. However, the carbon dynamics of bamboo forest are still poorly understood. This study developed an algorithm for assimilating LAI dynamics based on an integrated ensemble Kalman filter using MODIS LAI data, MODIS reflectance data, and canopy reflectance data modeled by PROSAIL for two typical bamboo forests. The assimilated LAI was highly consistent with the observed LAI, yielding correlation coefficient (r) of 0.84 and 0.94 for Moso bamboo forest and Lei bamboo forest, respectively. Then, the spatiotemporal pattern of the CO2 fluxes within bamboo forest were modeled for Zhejiang Province during 2011–2014 using the Boreal Ecosystem Productivity Simulator model driven by assimilated LAI. The modeled CO2 fluxes were in agreement with the CO2 fluxes measured by eddy covariance technology at two selected sites within the study region. Average r, absolute deviation, and root mean square error values were 0.77, 0.65gCm−2day−1, and 0.82gCm−2day−1, respectively. Bamboo stands with high Net Ecosystem Exchange (NEE) values were found to be distributed throughout mountainous areas in the northwest, southwest, and northeast of Zhejiang, while low NEE stands were located in the mountain basin areas, high altitude areas, and the majority of coastal areas. Among the eleven cities in Zhejiang Province, Hangzhou was the highest annual total NEE city (0.34±0.06TgCyr−1), and Huzhou was the highest NEE density city (7.65±1.02MgChm−2yr−1). The carbon sequestrated by bamboo forest was 1.74TgCyr−1, accounted 6.36Tg CO2 yr−1, which indicated the bamboo forested area in Zhejiang Province was a carbon sink. The spatial heterogeneity of CO2 fluxes of bamboo forest can be revealed by an exponential semivariance model. The analysis of the model structure indicated that the spatial heterogeneity of CO2 fluxes of bamboo forest was characterized by significant autocorrelation, with an average autocorrelation range of 3.00km, however the effect of random factors (management levels, management measures, etc.) cannot be ignored. This is the first time that the CO2 fluxes of bamboo forest in Zhejiang Province have been mapped and this study provides a new estimate of CO2 fluxes in this region.

Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

Sunshine duration is widely used to estimate solar radiation, but this estimated inherently contains some uncertainties, limiting its applications. This study investigated the impacts of the estimated solar radiation on simulated gross primary productivity (GPP), which were obtained using ecosystem models – light use efficiency model (LUE) and process-based model – Boreal Ecosystem Productivity Simulator (BEPS) at an evergreen coniferous forest ecosystem in southeast China. The models for solar radiation and diffuse radiation estimation were calibrated through observation data from nearby meteorological stations. The results showed that the established model could be successfully used to estimate solar radiation with high coefficient of determination (0.92) and low root mean square error (2.18MJm−2day−1), but the solar radiation was overestimated when the clearness index was less than 0.15 and underestimated when it was within the range of 0.2–0.35 or greater than 0.6. The estimated solar radiation has significant influence on the diffuse radiation estimation and GPP simulation comparing with using observations. The two ecosystem models reacted differently to the errors of estimated solar radiation. For the LUE model, the estimated solar radiation led to the underestimated GPP in growing season (May–October), and overestimated GPP during non-growing season (November–April) with the bias ranged from −11% to 10% depending on the month of a year. For the BEPS model, estimated solar radiation resulted in overestimated GPP in most months with the bias ranged from −6% to 20%. The difference between the simulated GPP based on these two sources of solar radiation could be counteracted to some extent at the annual scale, especially for LUE model.

Evapotranspiration and water yield over China's landmass from 2000 to 2010

Abstract. Terrestrial carbon and water cycles are interactively linked at various spatial and temporal scales. Evapotranspiration (ET) plays a key role in the terrestrial water cycle, altering carbon sequestration of terrestrial ecosystems. The study of ET and its response to climate and vegetation changes is critical in China because water availability is a limiting factor for the functioning of terrestrial ecosystems in vast arid and semiarid regions. To constrain uncertainties in ET estimation, the process-based Boreal Ecosystem Productivity Simulator (BEPS) model was employed in conjunction with a newly developed leaf area index (LAI) data set, MODIS land cover, meteorological, and soil data to simulate daily ET and water yield at a spatial resolution of 500 m over China for the period from 2000 to 2010. The spatial and temporal variations of ET and water yield were analyzed. The influences of climatic factors (temperature and precipitation) and vegetation (land cover types and LAI) on these variations were assessed. Validations against ET measured at five ChinaFLUX sites showed that the BEPS model was able to simulate daily and annual ET well at site scales. Simulated annual ET exhibited a distinguishable southeast to northwest decreasing gradient, corresponding to climate conditions and vegetation types. It increased with the increase of LAI in 74% of China's landmass and was positively correlated with temperature in most areas of southwest, south, east, and central China. The correlation between annual ET and precipitation was positive in the arid and semiarid areas of northwest and north China, but negative in the Tibetan Plateau and humid southeast China. The national annual ET varied from 345.5 mm in 2001 to 387.8 mm in 2005, with an average of 369.8 mm during the study period. The overall rate of increase, 1.7 mm yr−1 (R2 = 0.18, p = 0.19), was mainly driven by the increase of total ET in forests. During 2006–2009, precipitation and LAI decreased widely and consequently caused a detectable decrease in national total ET. Annual ET increased over 62.2% of China's landmass, especially in the cropland areas of the southern Haihe River basin, most of the Huaihe River basin, and the southeastern Yangtze River basin. It decreased in parts of northeast, north, northwest, south China, especially in eastern Qinghai-Tibetan Plateau, the south of Yunnan Province, and Hainan Province. Reduction in precipitation and increase in ET caused vast regions in China, especially the regions south of Yangtze River, to experience significant decreases in water yield, while some sporadically distributed areas experienced increases in water yield. This study shows that the terrestrial water cycles in China's terrestrial ecosystems appear to have been intensified by recent climatic variability and human induced vegetation changes.