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With large area of primary tropical rainforest converted into rubber (Hevea brasiliensis) plantation in Southeast Asia, it is necessary to examine the change in soil CO2 and CH4 emissions, and their underlying drivers in tropical rainforest (TRF) and rubber plantation. In TRF and RP in Xishuangbanna Southwest China, we measured the soil CO2 , CH4 , temperature, and water content once each week from 2003 to 2008, and twice weeks in 2013 and 2014. Additionally, the concentrations of soil carbon (C) and nitrogen (N) fractions from 2013 to 2014 were observed. Inputs of litter and live, dead, decomposed fine roots dynamics were also included. TRF transplanted to RP did not change significantly the annual soil CO2 emissions (TRF, 359 ± 91 and RP 352 ± 41 mg CO2 m−2 h−1) but decreased soil CH4 uptake significantly (TRF, −0.11 ± 0.18 mg CH4 m−2 h−1) RP, −0.020 ± 0.087 mg CH4 m−2 h−1). The most important influence on soil CO2 and CH4 emissions in the RP was the leaf area index and soil water content, respectively, whereas the soil water content, soil temperature, and dead fine roots were the most important factors in the TRF. Variations in the soil CO2 and CH4 caused by land-use transition were individually explained by soil temperature and fine root growth and decomposition, respectively. The results show that land-use change varied the soil CH4 and CO2 emission dynamics and drivers by the variation of soil environmental and plant's factors.

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How aquatic primary productivity influences the carbon (C) sequestering capacity of wetlands is uncertain. We evaluated the magnitude and variability in aquatic C dynamics and compared them to net ecosystem CO2 exchange (NEE) and ecosystem respiration (Reco) rates within calcareous freshwater wetlands in Everglades National Park. We continuously recorded 30-min measurements of dissolved oxygen (DO), water level, water temperature (Twater), and photosynthetically active radiation (PAR). These measurements were coupled with ecosystem CO2 fluxes over 5 years (2012–2016) in a long-hydroperiod peat-rich, freshwater marsh and a short-hydroperiod, freshwater marl prairie. Daily net aquatic primary productivity (NAPP) rates indicated both wetlands were generally net heterotrophic. Gross aquatic primary productivity (GAPP) ranged from 0 to − 6.3 g C m−2 day−1 and aquatic respiration (RAq) from 0 to 6.13 g C m−2 day−1. Nonlinear interactions between water level, Twater, and GAPP and RAq resulted in high variability in NAPP that contributed to NEE. Net aquatic primary productivity accounted for 4–5% of the deviance explained in NEE rates. With respect to the flux magnitude, daily NAPP was a greater proportion of daily NEE at the long-hydroperiod site (mean = 95%) compared to the short-hydroperiod site (mean = 64%). Although we have confirmed the significant contribution of NAPP to NEE in both long- and short-hydroperiod freshwater wetlands, the decoupling of the aquatic and ecosystem fluxes could largely depend on emergent vegetation, the carbonate cycle, and the lateral C flux.

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Forests have climate change mitigation potential since they sequester carbon. However, their carbon sink strength might depend on management. As a result of the balance between CO2 uptake and emission, forest net ecosystem exchange (NEE) reaches optimal values (maximum sink strength) at young stand ages, followed by a gradual NEE decline over many years. Traditionally, this peak of NEE is believed to be concurrent with the peak of primary production (e.g., gross primary production, GPP); however, in theory, this concurrence may potentially vary depending on tree species, site conditions and the patterns of ecosystem respiration (Reco). In this study, we used eddy-covariance (EC)-based CO2 flux measurements from 8 forest sites that are dominated by Norway spruce (Picea abies L.) and built machine learning models to find the optimal age of ecosystem productivity and that of CO2 sequestration. We found that the net CO2 uptake of Norway spruce forests peaked at ages of 30-40 yrs. Surprisingly, this NEE peak did not overlap with the peak of GPP, which appeared later at ages of 60-90 yrs. The mismatch between NEE and GPP was a result of the Reco increase that lagged behind the GPP increase associated with the tree growth at early age. Moreover, we also found that newly planted Norway spruce stands had a high probability (up to 90%) of being a C source in the first year, while, at an age as young as 5 yrs, they were likely to be a sink already. Further, using common climate change scenarios, our model results suggest that net CO2 uptake of Norway spruce forests will increase under the future climate with young stands in the high latitude areas being more beneficial. Overall, the results suggest that forest management practices should consider NEE and forest productivity separately and harvests should be performed only after the optimal ages of both the CO2 sequestration and productivity to gain full ecological and economic benefits. How to cite: Zhao, J., Lange, H., and Meissner, H.: Mismatch between the optimal ages for ecosystem productivity and net CO2 sequestration in Norway spruce forests, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4257, https://doi.org/10.5194/egusphere-egu21-4257, 2021.

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In a young Norway spruce stand (planted in 2012) at Hoxmark, Southeast Norway, Net Ecosystem Exchange (NEE) was measured using Eddy Covariance. The data were carefully processed with time-dependent stand parameters (i.e. canopy height), a detailed footprint analysis and calculated at 30 min temporal resolution. Photosynthetic Active Radiation (PAR) as the primary driver for carbon uptake was also available at the site. Despite its young age, the plantation already acted as a net carbon sink according to the annual NEE budget, e.g. by ca. 300 g C m-2 in 2019. However, the response of the system depended strongly on hydrometeorological conditions. We demonstrate this by investigating the relationship between NEE and PAR for this system in a temporally local fashion (30 days moving windows), using a Michaelis-Menten approach involving three parameters. Although the regression captured up to ca. 80% of the variance, the parameter estimates differed substantially throughout the season, and were contrasting between the very dry year 2018 and the close to normal year 2019. Comparison with other EC-equipped sites in a future study will clarify whether this variable sensitivity is due to the young age or is a pattern pertaining also to mature spruce stands. https://doi.org/10.5194/egusphere-egu21-5028

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In order to predict the effects of climate change on the global carbon cycle, it is crucial to understand the environmental factors that affect soil carbon storage in grasslands. In the present study, we attempted to explain the relationships between the distribution of soil carbon storage with climate, soil types, soil properties and topographical factors across different types of grasslands with different grazing regimes. We measured soil organic carbon in 92 locations at different soil depth increments, from 0 to 100 cm in southwestern China. Among soil types, brown earth soils (Luvisols) had the highest carbon storage with 19.5 ± 2.5 kg m−2, while chernozem soils had the lowest with 6.8 ± 1.2 kg m−2. Mean annual temperature and precipitation, exerted a significant, but, contrasting effects on soil carbon storage. Soil carbon storage increased as mean annual temperature decreased and as mean annual precipitation increased. Across different grassland types, the mean carbon storage for the top 100 cm varied from 7.6 ± 1.3 kg m−2 for temperate desert to 17.3 ± 2.9 kg m−2 for alpine meadow. Grazing/cutting regimes significantly affected soil carbon storage with lowest value (7.9 ± 1.5 kg m−2) recorded for cutting grass, while seasonal (11.4 ± 1.3 kg m−2) and year-long (12.2 ± 1.9 kg m−2) grazing increased carbon storage. The highest carbon storage was found in the completely ungrazed areas (16.7 ± 2.9 kg m−2). Climatic factors, along with soil types and topographical factors, controlled soil carbon density along a soil depth in grasslands. Environmental factors alone explained about 60% of the total variation in soil carbon storage. The actual depth-wise distribution of soil carbon contents was significantly influenced by the grazing intensity and topographical factors. Overall, policy-makers should focus on reducing the grazing intensity and land conversion for the sustainable management of grasslands and C sequestration.

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Soil respiration is an important ecosystem process that releases carbon dioxide into the atmosphere. While soil respiration can be measured continuously at high temporal resolutions, gaps in the dataset are inevitable, leading to uncertainties in carbon budget estimations. Therefore, robust methods used to fill the gaps are needed. The process-based non-linear least squares (NLS) regression is the most widely used gap-filling method, which utilizes the established relationship between the soil respiration and temperature. In addition to NLS, we also implemented three other methods based on: 1) artificial neural networks (ANN), driven by temperature and moisture measurements, 2) singular spectrum analysis (SSA), relying only on the time series itself, and 3) the expectation-maximization (EM) approach, referencing to parallel flux measurements in the spatial vicinity. Six soil respiration datasets (2017–2019) from two boreal forests were used for benchmarking. Artificial gaps were randomly introduced into the datasets and then filled using the four methods. The time-series-based methods, SSA and EM, showed higher accuracies than NLS and ANN in small gaps (<1 day). In larger gaps (15 days), the performance was similar among NLS, SSA and EM; however, ANN showed large errors in gaps that coincided with precipitation events. Compared to the observations, gap-filled data by SSA showed similar degree of variances and those filled by EM were associated with similar first-order autocorrelation coefficients. In contrast, data filled by both NLS and ANN exhibited lower variance and higher autocorrelation than the observations. For estimations of the annual soil respiration budget, NLS, SSA and EM resulted in errors between −3.7% and 5.8% given the budgets ranged from 463 to 1152 g C m−2 year−1, while ANN exhibited larger errors from −11.3 to 16.0%. Our study highlights the two time-series-based methods which showed great potential in gap-filling carbon flux data, especially when environmental variables are unavailable.

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The measurement network Integrated Carbon Observation System (ICOS) is dedicated to the quantification of fluxes of CO2, H2O, N2O and CH4 at the boundary between vegetation surfaces and the lower atmosphere. The implementation of observations sites follows strict protocols and a challenging labelling process to ensure standardized intercomparable observations. We report on our experiences in attempting to establish the only Norwegian ICOS Ecosystem site thus far, NO-Hur, located in an old-growth spruce forest at Hurdal in Southeast Norway. NOHur is planned as a class 2 site, with the option to an upgrade to class 1 later. The instrumentation and sensors needed, the requirements for spatial homogeneity and a detailed analysis of a digital terrain model are presented. The current status of the tower construction, the preliminary measurements obtained with the existing ICOScertified equipment at a test site, and the plans for integrating the measurements operationally into the network are shown

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The measurement network Integrated Carbon Observation System (ICOS) is dedicated to the quantification of fluxes of CO2, H2O, N2O and CH4 at the boundary between vegetation surfaces and the lower atmosphere. The implementation of observations sites follows strict protocols and a challenging labelling process to ensure standardized intercomparable observations. We report on our experiences in attempting to establish the only Norwegian ICOS Ecosystem site thus far, NO-Hur, located in an old-growth spruce forest at Hurdal in Southeast Norway. NOHur is planned as a class 2 site, with the option to an upgrade to class 1 later. The instrumentation and sensors needed, the requirements for spatial homogeneity and a detailed analysis of a digital terrain model are presented. The current status of the tower construction, the preliminary measurements obtained with the existing ICOScertified equipment at a test site, and the plans for integrating the measurements operationally into the network are shown

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As the main drivers of climate change, greenhouse gas (e.g., CO2 and CH4) emissions have been monitored intensively across the globe. The static chamber is one of the most commonly used approaches for measuring greenhouse gas fluxes from ecosystems (e.g., stem/soil respiration, CH4 emission, etc.) because of its easy implementation, high accuracy and low cost (Pumpanen et al., 2004). To perform the measurements, a gas analyzer is usually used to measure the changes of greenhouse gas concentrations within a closed chamber that covers an area of interest (e.g., soil surface) over a certain period of time (usually several minutes). The flux rates (F) are then calculated from the recorded gas concentrations assuming that the changing rate is linear: F = vol/(R · T a · area) · dG/dt where vol is the volume of the chamber (l), R is the universal gas constant (l atm K-1 mol-1), Ta is the ambient temperature (K), area is the area of the chamber base (m2 ), and dG/dt is the rate of the measured gas concentration change over time t (ppm s-1) (i.e., the slope of the linear regression).

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Climate change has altered global precipitation patterns and has led to greater variation in hydrological conditions. Wetlands are important globally for their soil carbon storage. Given that wetland carbon processes are primarily driven by hydrology, a comprehensive understanding of the effect of inundation is needed. In this study, we evaluated the effect of water level (WL) and inundation duration (ID) on carbon dioxide (CO2) fluxes by analysing a 10‐year (2008–2017) eddy covariance dataset from a seasonally inundated freshwater marl prairie in the Everglades National Park. Both gross primary production (GPP) and ecosystem respiration (ER) rates showed declines under inundation. While GPP rates decreased almost linearly as WL and ID increased, ER rates were less responsive to WL increase beyond 30 cm and extended inundation periods. The unequal responses between GPP and ER caused a weaker net ecosystem CO2 sink strength as inundation intensity increased. Eventually, the ecosystem tended to become a net CO2 source on a daily basis when either WL exceeded 46 cm or inundation lasted longer than 7 months. Particularly, with an extended period of high‐WLs in 2016 (i.e., WL remained >40 cm for >9 months), the ecosystem became a CO2 source, as opposed to being a sink or neutral for CO2 in other years. Furthermore, the extreme inundation in 2016 was followed by a 4‐month postinundation period with lower net ecosystem CO2 uptake compared to other years. Given that inundation plays a key role in controlling ecosystem CO2 balance, we suggest that a future with more intensive inundation caused by climate change or water management activities can weaken the CO2 sink strength of the Everglades freshwater marl prairies and similar wetlands globally, creating a positive feedback to climate change.

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Many nonlinear methods of time series analysis require a minimal number of observations in the hundreds to thousands, which is not always easy to achieve for observations of environmental systems. Eddy Covariance (EC) measurements of the carbon exchange between the atmosphere and vegetation provide a noticeable exception. They are taken at high temporal resolution, typically at 20 Hz. This generates very long time series (many millions of data points) even for short measurement periods, rendering finite size effects unimportant. In this presentation, we investigate high-resolution raw data of 3D wind speed, CO2 concentrations, water vapor and temperature measured at a young forest plantation in Southeast Norway since July 2018. Guiding for the analysis is the gain or added value of the high resolution compared to more aggregated data, i.e. the scaling behavior of nonlinear properties of the time series. We present results of complexity analysis, Tarnopolski diagrams, q-Entropy, Hurst analysis, Empirical Mode Decomposition and Singular System Analysis. This provides detailed insights into the nature of dynamics of carbon fluxes across this system boundary at different temporal scales.