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The continuous measurement of H2O fluxes using the eddy covariance (EC) technique is still challenging for forests because of large amounts of wet canopy evaporation (EWC/, which occur during and following rain events when the EC systems rarely work correctly. We propose a new gap-filling and partitioning technique for the H2O fluxes: a model-statistics hybrid (MSH) method. It enables the recovery of the missing EWC in the traditional gap-filling method and the partitioning of the evapotranspiration (ET) into transpiration and (wet canopy) evaporation. We tested and validated the new method using the data sets from two flux towers, which are located at forests in hilly and complex terrains. The MSH reasonably recovered the missing EWC of 16-41mmyr-1 and separated it from the ET (14-23% of the annual ET). Additionally, we illustrated certain advantages of the proposed technique which enable us to understand better how ET responds to environmental changes and how the water cycle is connected to the carbon cycle in a forest ecosystem. [ABSTRACT FROM AUTHOR]

• Eddy covariance system was used to investigate drought resilience of forest ecosystem. • Carbon fluxes were analyzed for evergreen Korean pine and deciduous oak forests. • Korean pine forest exhibited lower drought resistance, but greater drought recovery. • Drought resilience of forest carbon exchange was strongly affected by soil moisture. • Evergreen Korean pines will be more vulnerable to future drought than deciduous oaks. Climate change is expected to increase regional variations in extreme weather events including drought, which will significantly affect forest ecosystem carbon cycles. Although studies on the resilience of forests to drought have been increasing, the resilience of carbon flux components and its underlying biophysical mechanism during and after drought are still poorly understood. In the present study, changes in carbon exchange in adjacent evergreen Korean pine and deciduous oak forests were investigated to compare the resilience of the two forests to drought from 2016 to 2018 using an eddy covariance technique. A severe drought occurred in spring and early summer 2017 due to substantially low rainfall since August 2016. Although there were no major differences in other environmental factors between both sites during the drought, soil moisture content (θ) in the pine site was up to 8.7% lower than that in the oak site. The drought resulted in a temporary net carbon source in the pine stand with 38% increase in ecosystem respiration (R eco) and 45% reduction in net ecosystem production (NEP). These changes were more conspicuous than the changes in the oak stand (+30% of R eco , −17% of NEP). Although R eco and NEP recovered in 2018 when θ at both sites was replenished by large amounts of precipitation, they did not fully recover to their 2016 level due to drought legacies with reduced leaf area and enhanced R eco. We conclude that evergreen Korean pine stands could be more vulnerable than deciduous oak stands to intense and frequent droughts in the future, and this may accelerate succession from coniferous to broadleaved species in temperate forests. [ABSTRACT FROM AUTHOR]

Rice is a major crop that feeds more than half of global population. Rice paddy playscomplex roles in carbon cycle by emitting CH4 to the atmosphere while CO2 is sequesteredfrom or released to the atmosphere. Here, we present 3.5 years of eddy covariancemeasurements of CH4 and CO2 fluxes over an intermittently-irrigated rice paddy in Korea intandem with carbon stock measurements in leaf, stem, grain, and root. The interannualvariation of CO2 fluxes was little although the timings of transplantation and harvest weredifferent for each year. Rice paddy acted as a slight sink or neutral in CO2 (-47.3 ± 50.9 g Cm−2 y−1; mean ± 95% confidence interval) with 942.2 ± 129.4 g C m−2 y−1 and894.9 ± 106.5 g C m−2 y−1 for gross primary production (GPP) and ecosystemrespiration (Reco), respectively. Carbon stock in rice showed large interannualvariation (760.2 ± 183.7 g C m−2) with 70 ± 19.8 g C m−2, 232.1 ± 83.3 g Cm−2, 378.2 ± 75.5 g C m−2, and 79.8 ± 12.8 g C m−2 for leaf, stem, grain, androot, respectively. The rice paddy emitted 21.7 ± 2.5 g C m−2 y−1 of CH4. Whenconsidering the global warming potential of CH4 (197.3 g CO2-Ceq m−2 y−1),the paddy shift from carbon sink to source (150 g CO2-Ceq m−2 y−1). The CH4flux showed a bimodal seasonal pattern caused by the mid-season drainage whichturned the soils aerobic conditions so prohibited CH4 emissions. The irrigationpractices, which are different every year, resulted in the significant interannualvariation of CH4 emission (± 7%). These findings will be useful to understand carbonbudgets and cycles, and develop carbon and water managements in rice paddy. [ABSTRACT FROM AUTHOR]

• A machine learning method (NN SIF) is developed to partition NEE using SIF. • NN SIF effectively reduces structural errors of standard partitioning methods. • NN SIF better partitions NEE under high temperature and water stress. • NN SIF unravels physiological controls on ecosystem–scale GPP–SIF relationships. Accurately partitioning net ecosystem exchange (NEE) into ecosystem respiration (ER) and gross primary productivity (GPP) is critical for understanding the terrestrial carbon cycle. The standard partitioning methods rely on simplified empirical models, which have inherent structural errors. These structural errors lead to biased GPP and ER estimation, especially during extreme events (e.g., drought) and human disturbances (e.g., crop harvest). Recently, solar-induced chlorophyll fluorescence (SIF) has been shown to be well correlated to GPP, thus offering a path to improve the NEE partitioning by constraining GPP. However, the ecosystem-scale relationship between GPP and SIF remains limited. Here, we show that neural networks informed by SIF observations (NN SIF) can be successfully used to partition NEE, while simultaneously learning the ecosystem-scale GPP-SIF relationship. NN SIF was compared against standard partitioning methods and NN without SIF constraint (NN noSIF), using field data from different ecosystems and synthetic data generated by a coupled fluorescence-photosynthesis model (SCOPE). NN SIF showed superior performance as: (1) it effectively improves the ER estimation, especially at high temperature, (2) it better captures the moisture limitation on ER, (3) it more accurately estimates LUE variations to stress, and (4) it uniquely captures the rapid GPP drop after land management (harvest). Furthermore, NN SIF can retrieve the GPP-SIF relationship at the ecosystem scale, and elucidate how this relationship responds to environmental conditions. Overall, our algorithm provides the first direct and non-empirical estimate of the ecosystem-scale GPP-SIF relationship, without relying on any prior empirical assumptions on the relationships between CO 2 fluxes, climatic drivers, and SIF. The new knowledge learned by NN SIF can help better estimate global-scale GPP using satellite SIF, especially during extreme events and in the presence of land management. [ABSTRACT FROM AUTHOR]

• Maximum light use efficiency (LUE) and optimum temperatures were estimated based on phenology of paddy rice. • GPP estimates were strongly improved compared with the results of original VPM model and MODIS-based GPP. • Flux data of typical paddy rice sites in Japan, South Korea, the USA and China were used. Paddy rice is one of the main grain crops in the world. Accurate estimations of the gross primary production (GPP) of paddy rice are essential for assessing rice grain production and monitoring the carbon cycle in paddy fields with the aim of providing ideal conditions for crops throughout the growing season. Several studies have demonstrated the advantages of combining the eddy covariance technique with remotely sensed data to model GPP at CO 2 eddy flux tower sites. As paddy rice continuously changes during its growth and development, and important growth events frequently occur, it is critical to observe the growing conditions at various stages of the process. To better understand the variations in GPP at different growth stages, two key parameters that drive the vegetation photosynthesis model (VPM) are analyzed and estimated at various phenological phases. Specifically, general piecewise logistic functions are used to extract phenological transitions from data at four paddy rice flux tower sites. The maximum light-use efficiency (LUE) and optimum temperature are estimated from these phenological transitions, and these indicators are used to drive the VPM to simulate GPP over multiple years at the four sites. The simulation results show that GPP based on our phenological transition-based VPM (GPP PVPM) agrees reasonably well with the variations of GPP estimated from CO 2 flux data (GPP EC) (R2 > 0.9). In addition, a comparison indicates that GPP PVPM tracks the seasonal dynamics of GPP EC better than GPP estimated from the original VPM. Furthermore, GPP based on the improved maximum LUE is lower than GPP EC at most flux sites and GPP based on the improved optimum temperature is higher than GPP EC. These comparisons imply that the maximum LUE and optimum temperature estimated in the phenological transitions of paddy rice are beneficial to enhance the accuracy of GPP estimation. The improved estimation of GPP provides phenological insights into the temporal dynamics of vegetation photosynthesis in paddy fields. [ABSTRACT FROM AUTHOR]

• Non-growing season ecosystem respiration negated the CO 2 sink during the growing season. • Carbon allocation to rice components varied substantially over the seasons. • The ratio of net to gross primary productivity varied seasonally and interannually. • Mid-season drainage substantially reduced CH 4 emission. • The proportion of ebullitive CH 4 flux was 10 to 17% of the total during the cultivation period. Rice is a major crop that feeds more than half of the global population. Rice paddies play a complex role in the carbon cycle by emitting CH 4 to the atmosphere while either sequestering or releasing CO 2 to the atmosphere. Here, we present 3.5 years of eddy covariance measurements of CO 2 and CH 4 fluxes over an intermittently irrigated, single crop rice paddy in South Korea in tandem with carbon stock measurements for leaves, stems, grains, and roots. The rice paddy acted as a slight sink or neutral in CO 2 (−47 ± 51 g C m −2 y −1; mean ± 95% confidence interval) with 941 ± 130 g C m −2 y −1 and 894 ± 108 g C m −2 y −1 as the gross primary production (GPP) and ecosystem respiration (Reco), respectively. The mean annual net primary productivity (NPP) was 666 ± 31 g C m −2 y −1, which was partitioned to leaves (9%), stems (30%), grains (51%), and roots (10%), for a harvest index of 57%. The paddy emitted 20.6 ± 1.5 g C m −2 y −1 in the form of CH 4 , of which between 82 and 96% was emitted during the cultivation period (~132 days). The CH 4 flux showed a mid-season decline caused by mid-season drainage, which produced aerobic soil conditions that inhibited CH 4 emissions. The proportion of estimated ebullitive flux to total CH 4 flux ranged from 10 to 17% during the cultivation period. GPP, Reco, and CH 4 fluxes varied by 12%, 11%, and 7%, respectively, over the 3-year period. When considering the harvest and the global warming potential of CH 4 , the paddy shifted from a greenhouse gas sink to a strong source (2,741 ± 468 g CO 2 eq m −2 y −1). These findings will improve our understanding of carbon budgets and cycles, and help develop greenhouse gas and water management strategies for rice paddies. [ABSTRACT FROM AUTHOR]