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[1] Southeastern Asian tropical rain forests are among the most important biomes in terms of annual productivity and water cycling. How their hydrologic budgets are altered by projected shifts in precipitation is examined using a combination of field measurements, global climate model (GCM) simulation output, and a simplified hydrologic model. The simplified hydrologic model is developed with its primary forcing term being rainfall statistics. A main novelty in this analysis is that the effects of increased (or decreased) precipitation on increased (or decreased) cloud cover and hence evapotranspiration is explicitly considered. The model is validated against field measurements conducted in a tropical rain forest in Sarawak, Malaysia. It is demonstrated that the model reproduces the probability density function of soil moisture content (s), transpiration (T r ), interception (I c ), and leakage loss (Q). On the basis of this model and projected shifts in precipitation statistics by GCM the probability distribution of I c , Q and, to a lesser extent, s varied appreciably at seasonal timescales. The probability distribution of T r was least impacted by projected shifts in precipitation.

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.

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Revealing the seasonal and interannual variations in forest canopy photosynthesis is a critical issue in understanding the ecological mechanisms underlying the dynamics of carbon dioxide exchange between the atmosphere and deciduous forests. This study examined the effects of temporal variations of canopy leaf area index (LAI) and leaf photosynthetic capacity [the maximum velocity of carboxylation (V (cmax))] on gross primary production (GPP) of a cool-temperate deciduous broadleaf forest for 5 years in Takayama AsiaFlux site, central Japan. We made two estimations to examine the effects of canopy properties on GPP; one is to incorporate the in situ observation of V (cmax) and LAI throughout the growing season, and another considers seasonality of LAI but constantly high V (cmax). The simulations indicated that variation in V (cmax) and LAI, especially in the leaf expansion period, had remarkable effects on GPP, and if V (cmax) was assumed constant GPP will be overestimated by 15%. Monthly examination of air temperature, radiation, LAI and GPP suggested that spring temperature could affect canopy phenology, and also that GPP in summer was determined mainly by incoming radiation. However, the consequences among these factors responsible for interannual changes of GPP are not straightforward since leaf expansion and senescence patterns and summer meteorological conditions influence GPP independently. This simulation based on in situ ecophysiological research suggests the importance of intensive consideration and understanding of the phenology of leaf photosynthetic capacity and LAI to analyze and predict carbon fixation in forest ecosystems.

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