The lack of a standardized database of eddy covariance observations has been an obstacle for data‐driven estimation of terrestrial CO2 fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data‐driven estimation of gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE). Data‐driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site‐level evaluation of the estimated CO2 fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8 days are reproduced (e.g., r2 = 0.73 and 0.42 for 8 day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor‐based Sun‐induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r2 = 1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere‐land CO2 fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land CO2 fluxes from SVR‐NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land CO2 fluxes. These data‐driven estimates can provide a new opportunity to assess CO2 fluxes in Asia and evaluate and constrain terrestrial ecosystem models.

Abstract. Larch forests are widely distributed across many cool-temperate and boreal regions, and they are expected to play an important role in global carbon and water cycles. Model parameterizations for larch forests still contain large uncertainties owing to a lack of validation. In this study, a process-based terrestrial biosphere model, BIOME-BGC, was tested for larch forests at six AsiaFlux sites and used to identify important environmental factors that affect the carbon and water cycles at both temporal and spatial scales.The model simulation performed with the default deciduous conifer parameters produced results that had large differences from the observed net ecosystem exchange (NEE), gross primary productivity (GPP), ecosystem respiration (RE), and evapotranspiration (ET). Therefore, we adjusted several model parameters in order to reproduce the observed rates of carbon and water cycle processes. This model calibration, performed using the AsiaFlux data, substantially improved the model performance. The simulated annual GPP, RE, NEE, and ET from the calibrated model were highly consistent with observed values.The observed and simulated GPP and RE across the six sites were positively correlated with the annual mean air temCorrespondence to: M. Ueyama (miyabi-flux@muh.biglobe.ne.jp) perature and annual total precipitation. On the other hand, the simulated carbon budget was partly explained by the stand disturbance history in addition to the climate. The sensitivity study indicated that spring warming enhanced the carbon sink, whereas summer warming decreased it across the larch forests. The summer radiation was the most important factor that controlled the carbon fluxes in the temperate site, but the VPD and water conditions were the limiting factors in the boreal sites. One model parameter, the allocation ratio of carbon between belowground and aboveground, was site-specific, and it was negatively correlated with the annual climate of annual mean air temperature and total precipitation. Although this study substantially improved the model performance, the uncertainties that remained in terms of the sensitivity to water conditions should be examined in ongoing and long-term observations.

[1] Thermal and hydrological conditions in the active layer were investigated at a mature larch forest and an experimental cutover, to clarify the characteristics of heat and water budget in the active layer and to assess the influence of clear-cutting on permafrost and active layer conditions. Clear-cutting enhanced ground thawing and the difference in the active layer thickness between the forest and the cutover after 1 year was 14 cm. The soil water content drastically decreased at the forest, while that at the cutover was retained during the first thaw season after clear-cutting. Although the ground heat flux continued to increase at the cutover (the difference in the total amounts between sites from May to August were 44 and 69 MJ/m 2 in 1 year and 2 years after the clear-cutting, respectively) marked changes in the active layer conditions were limited only to the first thaw season. The correspondent differences in the active layer thickness between the sites were 16 and 14 cm in 2 years and 3 years, respectively. Further increases in the maximum thaw depth at the cutover site were inhibited by the thermal inertial effect of the larger amount of ice in the second spring after disturbance. This suggests a self-retention mechanism of the active layer thickness after forest disturbance in this continuous permafrost zone.

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To promote Bio-Energy with Carbon dioxide Capture and Storage (BECCS), which aims to replace fossil fuels with bio energy and store carbon underground, and Reducing Emissions from Deforestation and forest Degradation (REDD+), which aims to reduce the carbon emissions produced by forest degradation, it is important to build forest management plans based on the scientific prediction of forest dynamics. For Measurement, Reporting and Verification (MRV) at an individual tree level, it is expected that techniques will be developed to support forest management via the effective monitoring of changes to individual trees. In this study, an end-to-end process was developed: (1) detecting individual trees from Unmanned Aerial Vehicle (UAV) derived digital images; (2) estimating the stand structure from crown images; (3) visualizing future carbon dynamics using a forest ecosystem process model. This process could detect 93.4% of individual trees, successfully classified two species using Convolutional Neural Network (CNN) with 83.6% accuracy and evaluated future ecosystem carbon dynamics and the source-sink balance using individual based model FORMIND. Further ideas for improving the sub-process of the end to end process were discussed. This process is expected to contribute to activities concerned with carbon management such as designing smart utilization for biomass resources and projecting scenarios for the sustainable use of ecosystem services.