Evidence shows large CO2 efflux from rewetted dry soil surfaces peaking within seconds since wetting, too fast to be attributed to the biological “Birch effect”. We conducted experiments with long-term archived soils with different textures and soil organic carbon (SOC) to quantify CO2 release rates during controlled water imbibition. Total soil CO2 efflux varied considerably across textures (that also affected water imbibition rates) with cumulative efflux ranging from 5 (loamy sand) to 35 (loam) mmol/m2 during water imbibition times of 400 s (loamy sand) to 800s (loam). The measured CO2-efflux was reproduced using a physically based model for wetting front displacement, gas diffusion, and wetting-induced CO2 desorption. Repeated rewetting following oven drying of the soil samples resulted in different CO2 release behavior, suggesting kinetic effects of CO2 re-adsorption rates (especially for SOC) and potential bypassing of CO2 bearing surfaces during imbibition. In other words, surface accessibility to water, wettability and nano-porosity play a role in CO2 adsorption and desorption rates. While the measurements suggest only a minor role of this temporary carbon sink in the global carbon balance, there is a potential for a persistent measurement bias by eddy covariance flux towers (missing CO2 release during rainfall) that may lead to a bias in carbon balance of the order of 0.1-0.5 Gton/year globally. Additionally, it highlights a potential role of physical CO2 efflux, which operates at a much faster time scale than biological fluxes.
How to cite: Or, D., Lehmann, P., and Schymanski, S.: Rapid soil CO2 release following wetting governed by physical desorption not biology , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20499, https://doi.org/10.5194/egusphere-egu25-20499, 2025.
Soil respiration (RS) is the primary source of atmospheric carbon dioxide from terrestrial ecosystems. RS has been shown to respond exponentially to temperature, a relationship summarized by the Q10 parameter, which quantifies the increase in RS with a 10°C rise in temperature. Q10 depends on both temperature and soil water content, but the latter’s effect on RS and Q10 remains unclear, especially in temperate forests. Unlike soil temperature, whose influence on RS is widely accepted, soil water content’s effect is more site-due to multiple factors such as land use, soil and climate characteristics. Continuous, high-frequency field data are needed to improve understanding, but such data are challenging to collect in forest environments.
Here, we question the effect of soil water content on Q10 along the annual cycle in a temperate deciduous beech forest. Do soil water content levels have an impact on Q10 values? Is the relationship between soil water content and Q10 the same throughout the annual cycle? The experimental site is located in the Châtillonnais National Forest Park in the North-Eastern part of France. There, RS and environmental parameters are measured hourly using 4 automated chambers (LI-8100, LI-COR) since 2020 (4-year dataset). The forest has been protected from harvesting for over 30 years and is classified as an integral biological reserve.
The dataset includes over 145,000 RS measurements with about 6.1% missing data. After quality control and outlier removal, 92.7% of the data are used for analysis. For each hour, the mean RS is calculated by fitting exponential and linear models, with the best model selected based on AIC (ResChamberProc package in R developed by Wutzler T.). RS values are considered reliable when the coefficient of determination exceeds 0.9. Soil water content, measured near each chamber, shows high temporal consistency but high magnitude spread due to heterogenous soil conditions. To standardize, values are normalized by dividing each by the maximum recorded value, creating bins from 0 (dry) to 1 (wet).
We found that Q10 can only be calculated for spring and autumn (2.71 and 3.12 respectively) in our study site. During winter, low temperatures prevent meaningful Q10 calculation, while in summer, dry soil conditions limit results. A threshold analysis revealed that soil water content positively affects Q10 when it exceeds 40% of the maximum value. This indicates that RS is much more temperature-sensitive in wet than dry soils.
How to cite: Capelle, J., Bonnefoy-Claudet, C., Cognard, E., Lévêque, J., Thevenot, M., Crétat, J., Amiotte-Suchet, P., and Mathieu, O.: Effect of soil water content on soil respiration sensitivity to temperature (Q10) in a temperate beech forest: overview of data processing from four years of observation with automatic chambers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4318, https://doi.org/10.5194/egusphere-egu25-4318, 2025.
Resolving the role of arid and hyper-arid regions in the global carbon cycle and their influence on atmospheric CO2 concentrations remains a significant challenge, both theoretically and practically. Theoretical challenges stem from the widespread underestimation of these seemingly lifeless regions, while practical barriers arise from the logistical and financial limitations of conducting studies in hyper-arid soils. Although a few studies have quantified surface flux and net carbon sink in these areas, most lack continuous, long-term monitoring of soil CO2 concentrations and flux. As a result, our understanding of these regions is largely based on "snapshot" assessments, which may fail to capture their true role. Furthermore, even with knowledge of total flux, it remains a "black box" unless the contributions of biotic and abiotic mechanisms to CO2 fluxes are disentangled. In our study, CO2 sensors and automated flux chambers installed at subsurface and surface respectively, have been used to continuously monitor soil CO2 concentration and flux at arid and hyper-arid sites in the Negev Desert. Based on this data, we will conduct a CO2 source-sink assessment for each site. Biotic-abiotic partitioning analyses, utilizing modeling and isotopic tools will be used to further elucidate the sources driving CO2 fluxes in arid and hyper-arid environments. By identifying the distinct contributions of biotic and abiotic processes to CO2 flux, we can refine our understanding of the role arid and hyper-arid ecosystems play in the global carbon cycle and improve our predictions of how these ecosystems may respond to future climate change.
How to cite: Nguyen, T. T., Bekin, N., Agam, N., and Levintal, E.: Abiotic/biotic partitioning of soil CO2 flux in arid and hyper-arid soils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6839, https://doi.org/10.5194/egusphere-egu25-6839, 2025.
Liming is a common agricultural practice used to enhance soil productivity by correcting soil acidity and this change of soil chemical properties is often considered a potential strategy to mitigate nitrous oxide (N2O) emissions from soils. However, its overall impact on greenhouse gas (GHG) dynamics remains uncertain due to the dual influence of liming product on carbon dioxide (CO2) emissions: direct release from calcium carbonate (CaCO3) and potential changes in soil organic carbon (SOC) dynamics. Existing studies, based on limited field data, report contrasting effects of liming on both inorganic and organic CO2 emissions, raising concerns about whether reductions in N2O emissions are counterbalanced by increased CO2 fluxes.
This study investigated the impact of liming on soil GHG emissions by monitoring in situ N2O and CO2 fluxes following the application of two liming products: synthetic CaCO3 (SC) and marine CaCO3₃ (MC), in an acidic soil cultivated with winter rye. Using the static chamber method, we measured soil gas fluxes throughout the growing season alongside key variables, including soil pH, mineral nitrogen concentrations, moisture, and temperature. Biomass yield and SOC (stocks and composition) were also assessed at harvest.
Liming application increased soil pH from 5.7 to around 7.0 and enhanced kernel yield from 320 to over 400 g m-2. Notably, both liming treatments reduced soil CO2 emissions by about 40%, contrary to IPCC predictions of increased CO2 from lime-derived carbon. While N2O emissions rose slightly, they remained very low during the study period and did not impact the overall GHG budget of the crop. SOC stocks showed no significant change at harvest, though dissolved organic and inorganic carbon concentrations increased.
Our results suggest that current IPCC guidelines for estimating CO2 emissions from liming may require revision, as liming could offer dual benefits for soil pH management and CO2 emission mitigation under certain conditions. This session will be an excellent opportunity to discuss the hypothesis regarding pH influence on the CO2 and N2O emissions balance, to discuss underlying mechanisms of observed mitigation and explore potential pathways for optimising liming practices to enhance climate-smart soil management.
How to cite: Rousset, C., Bréfort, H., Fonseca, R. F., Guyerdet, G., Bizouard, F., Arkoun, M., and Hénault, C.: Surprising minimisation of CO2 emissions from a sandy loam soil over a rye growing period achieved by liming (CaCO3), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17613, https://doi.org/10.5194/egusphere-egu25-17613, 2025.
Non-steady state chambers are widely used for measuring soil and ecosystem greenhouse gas (GHG) fluxes, but causes non-steady state diffusion between the soil air and headspace, leading to non-linear behavior of gas accumulation over time during enclosure. In turn, linear regression (LM), commonly used to estimate GHG fluxes, may underestimate the pre-deployment flux (f0). Many alternatives to LM have been developed to provide a more accurate estimation of f0, for instance the method of Hutchinson and Mosier (HM), which accounts for non-linearity in gas accumulation during enclosure. However, non-linear models may overestimate f0, due to exaggerated curvature at time zero. Users therefore need to make subjective choices between LM and HM, often based on visual inspection or unsuited statistical metrics, such as R2, which can have profound impacts on end results. High-precision greenhouse gas analyzers, often combined with automatic chamber systems, promise to broaden our understanding of soil-atmosphere feedback in time and space. On the other hand, such systems produce enormous amounts of data that need to be processed automatically and consistently for reliable outputs: for example, by automatically selecting the best flux estimate based on either linear or non-linear models. At the same time, the number of researchers measuring soil-atmosphere fluxes from chambers is increasing and there is a need to develop GHG flux analysis tools that transcends prior user experience and produce the highest-quality data.
We here present the goFlux R package designed as an all-inclusive flux calculation tool to calculate chamber GHG fluxes. goFlux allows for an easy import of raw data directly into R from a variety of instruments (LI-COR, LGR, GAIA2TECH, Gasmet, Picarro, Aeris and PP-Systems); simplifies identification of start and end times of individual flux measurements; quality checks the results based on objective criteria that goes beyond simply using R2; and provides the user with a recommendation for the best flux estimate. In summary, goFlux is meant to be “student proof”, meaning that no extensive knowledge or experience is needed for data import and pre-processing in R, and selecting the best flux estimate (LM or HM).
In goFlux, a central element is to constrain the maximal curvature allowed due to non-linearity, by using the parameter of kappa-max (k), first introduced in Hüppi et al. (2018). The advantage of the k parameter is that it is based on objective metrics of instrument precision and chamber specific dimensions and applying k essentially avoids excessive flux overestimation, especially for noisy or small fluxes which often appears in chamber-based applications. Furthermore, following flux calculation, the best flux estimate is selected based on a user selection of multiple statistical criteria, such as the g-factor (ratio between LM and HM flux estimates) and indices of model fit (e.g. MAE, RMSE, AICc). In addition, poor quality measurements may be flagged based on minimal detectable flux (MDF), an intercept out of bounds, or due to insufficient number of observations.
For more information, visit our webpage: https://qepanna.quarto.pub/goflux/
How to cite: Rheault, K., Riis Christiansen, J., and Steenberg Larsen, K.: goFlux: A user-friendly tool for calculating GHG fluxes regardless of experience, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3658, https://doi.org/10.5194/egusphere-egu25-3658, 2025.
We present a software package for the analysis of data from automated flux chamber systems. It was developed for the Skyline chamber system, in which a chamber is moved robotically to close on a series of chamber bases along a transect. The system generates 1-Hz data over several months, so requires software to automate the processing of the multiple data streams. We describe the software system here, which is extensible to other automated chamber systems. The software merges data from the different data streams, including greenhouse gas mole fractions and chamber position data, environmental variables, and meta-data on experimental treatments. Algorithms performs identification of deadbands/chamber enclosure time, quality control based on the CO2 response, and calculation of fluxes with linear and non-linear equations. With appropriate internet connectivity, the software can be scheduled to update on a near-real-time basis. Graphical output and hierarchical statistical analysis of treatment effects is included. We illustrate applications with examining diurnal patterns in greenhouse gas fluxes and experiments on agricultural emission factors for N2O.
How to cite: Levy, P. E. and Appleton, E.: A software package for the analysis of Skyline data: an automated flux chamber system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21695, https://doi.org/10.5194/egusphere-egu25-21695, 2025.
Gas fluxes measurements are widely used when assessing the impact of global change drivers on key aspects of ecosystem dynamics. In particular, gas fluxes help estimate the carbon balance of an ecosystem and the impacts of global changes. Ecosystem gas fluxes are typically calculated from field-measured gas concentrations over time using a linear, quadratic or exponential model and manually selecting good quality data. This approach is time consuming and prone to bias that can be amplified in further analyses, as well as presenting major reproducibility issues. The lack of a reproducible and bias-free approach creates challenges when combining global change studies to make biome and landscape scale comparisons.
The Fluxible R package aims to fill this critical gap with a workflow that removes individual evaluation of each flux, reduces risk of bias, and makes the process reproducible. Users set data quality standards and selection parameters as function arguments that are applied to the entire dataset. The current version of Fluxible provides flux calculation for a closed loop chamber system using linear, quadratic or exponential models. The latest update also includes an automated segmentation tool to process data from a leaky setup such as with flux tents, where leakage cannot be assumed negligible. This automated and fully reproducible segmentation tool is a major upgrade as it allows the use of the Fluxible workflow in setups that are prone to leaks or other disturbances that previously had to be taken care of manually.
The package runs the calculations automatically without prompting the user to make decisions, and provides plots for visual check and a quality summary of the dataset at the end of the process. These outputs make it easier to process large flux datasets and to integrate the package into a reproducible workflow. Using the Fluxible R package makes the workflow reproducible, increases compatibility across studies, and is more time efficient.
How to cite: Gaudard, J., Trepel, J., Dawson, H. R., Enquist, B., Halbritter, A. H., Mustri, M., Niittynen, P., Santos-Andrade, P. E., Topper, J. P., Vandvik, V., and Telford, R. J.: Fluxible: an R package to calculate ecosystem gas fluxes from closed loop chamber systems in a reproducible and automated workflow, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12409, https://doi.org/10.5194/egusphere-egu25-12409, 2025.
Static chambers are the predominating method for measuring N2O emissions from agricultural ecosystems and thus provide the basis for deriving corresponding emission factors. In order to obtain representative N2O emission values, long-term measurements (one to several years) are necessary that cover relevant short-term variations including pulse-like emissions after fertilizer application or rainfalls. This is often a problem for the widely used manual chamber measurements that are typically applied in weekly or fortnightly intervals. Fully automated chamber systems, on the other hand, can provide continuous measurements over longer time periods, but are cost-extensive as they require online (on-site) gas analysis.
To overcome this problem, we constructed an (non-steady-state) automated time integrating chamber system (ATIC) that can sample at intervals of a few hours but accumulates the flux signal over many chamber closure cycles. During each 15-min chamber closure phase, small air samples are collected every 3.5 min and accumulated in four different gas bags. After typically one week the gas bags are brought to the lab for analysis. The accumulated samples in the four bags represent a time-averaged concentration increase that is used to calculate weekly time-integrated gas emission fluxes. In addition, the system can be used for analyzing the isotopic composition of the emitted N2O in order to determine the underlying source process.
The ATIC systems, that can run on battery power, were successfully applied in two long-term field experiments (> 2 years) for N2O emission monitoring on grassland, as well as studying changes in emission fluxes and isotopic composition of urine patches over a few months. We will show the setup of the system, the quality control of the data and discuss the resulting N2O emission data and major contributing processes.
How to cite: Ammann, C., Barczyk, L., Jocher, M., Havsteen, J., Mohn, J., and Wang, Y.: An automated time-integrating chamber system with offline gas analysis to monitor N2O fluxes and isotopic composition in agricultural ecosystems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12003, https://doi.org/10.5194/egusphere-egu25-12003, 2025.
Monitoring soil gas fluxes, particularly greenhouse gases like CO2 and CH4, often relies on a portable chamber-based approach that integrates data from multiple sensors and an accumulation chamber. While increasing the spatiotemporal resolution of soil gas flux measurements for a given study site is critical to disentangling coupled hydro-bio-geochemical processes, budget constraints and complex data processing can pose significant challenges. Automated, low-cost soil gas flux systems offer promising alternatives, enabling scalability and site-specific customization. However, these continuous low-cost systems generate large volumes of data that require automated quality-check routines and adapted flux calculation schema.
Here, we present the developments of an open-source, low-cost CO2 soil gas flux system complemented by a laboratory advection-based soil gas flux experiment that allowed us to assess the performance of the sensor and chamber design. Furthermore, we propose a novel flux calculation schema that avoids arbitrary assumptions, such as a fixed measuring time for calculating the flux. Instead, our approach employs an expanding time window to estimate the soil gas flux and some metrics, such as the Akaike information criterion, to identify the optimal interval considered to calculate the soil gas flux and estimate uncertainties.
Considering that data quality can only be assured given an adequately designed chamber, our laboratory methodology addressed the low-cost sensor’s accuracy and the low-cost system’s capacity to accumulate and determine the rate of change of CO2. Additionally, the proposed approach for calculating the soil gas flux provides users with a flexible and objective framework that adapts the total measurement time used for the calculations, enhancing the reliability of the soil gas flux estimates independently of the field conditions. This works highlights the potential of low-cost soil gas flux systems for enabling high spatiotemporal greenhouse gas monitoring capabilities while maintaining data quality standards.
How to cite: Asato Kobayashi, A. N., Roques, C., Hunkeler, D., and Brunner, P.: Data quality and flux calculation of low-cost soil gas flux systems: insights from laboratory experiments and novel raw data processing schema, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15472, https://doi.org/10.5194/egusphere-egu25-15472, 2025.
An estimated 80% of terrestrial carbon is stored below-ground but a large proportion of our soils are carbon depleted. There are broadly two ways to increase organic carbon stored below-ground, either protect and expand carbon-rich ecosystems or manage ecosystems to enhance carbon sequestration. Identifying carbon-rich ecosystems and understanding the key drivers of carbon losses is essential to design ecosystem management. However, the key processes which drive carbon losses are multifactorial and can vary between ecosystems and locations. We present a two-year study of four contrasting ecosystems situated in proximity. Our focal ecosystems were (a) an unimproved species-rich grassland, (b) managed hazel coppice woodland, (c) broadleaved woodland, and (d) coniferous woodland. We monitored temporal variability in soil respiration (SR) and net ecosystem exchange (NEE) using automated chambers and we measured spatial variability in SR and NEE across the ecosystems on a quarterly basis using survey chambers, also measuring a suite of biogeochemical, and plant and soil biodiversity metrics. We found that mean soil carbon was greatest in the broadleaved woodland, then the coniferous woodland, followed by the hazel coppice and finally the grassland site. Interestingly, soil carbon and many of the other biogeochemical parameters varied throughout the year and across the ecosystems. Soil moisture and soil temperature were important drivers of changes in SR and NEE, but the magnitude of these effects varied between the ecosystems and over time. Spatial variability in SR and NEE was also substantial with many of the biogeochemical and biodiversity metrics each explaining some of the variation in our data. Overall, high-resolution temporal datasets from automated chambers combined with spatial data using survey chambers in different ecosystems located closely together gives us a greater understanding of the key drivers of changes in SR and NEE. Using these data could inform land management decisions aimed at increased soil carbon sequestration. This could make an important contribution to achieving net zero.
How to cite: Lee, M. and Egan, G.: Combining automated chambers with surveys to measure spatial and temporal variation in soil carbon, soil respiration and net ecosystem exchange in four contrasting ecosystems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20183, https://doi.org/10.5194/egusphere-egu25-20183, 2025.
Plant mediated fluxes of trace gases, such as methane and nitrous oxide, are potentially important components of the greenhouse gas budget in many ecosystems. Measurement of these fluxes is possible through a variety of methods that operate over a range of spatial scales. For measurements constrained to the canopy scale, use of large (one to a few cubic meters in volume) closed-transient chambers has been common. Lack of a single source manufacture for all components necessary for this measurement, however, has meant that historically canopy scale trace gas flux measurements have required engineering effort on the part of the researcher to achieve. This has ranged from researcher-built measurement systems where some to all components, beyond the gas analyzer(s), were custom designed and built by the researcher. While this has spurred innovation advancing this area of research, the lack of standardized tools and a data processing platform has been a key source of uncertainty in these measurements. It has also meant that these measurements have been limited to only research programs with adequate engineering resources to construct and operate such a measurement system. Here we describe integration of two different commercially available systems to provide a complete off-the-shelf solution for canopy scale trace gas flux measurement. The combined system integrates a large canopy chamber (Eosense, eosAC-LT) with a gas sampling/analysis/data processing system (LI-COR, LI-8250, LI-78xx and SoilFluxPro). We demonstrate both hardware and software integration, including data processing, and performance of the combined measurement system.
How to cite: Hupp, J., Nickerson, N., Creelman, C., and Vath, R.: Moving towards commonality: An integrated chamber, gas sampling and data processing system for canopy scale measurements of trace gas fluxes using only commercially available components., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2177, https://doi.org/10.5194/egusphere-egu25-2177, 2025.
Measurements of soil trace gas fluxes are crucial for understanding their atmospheric budgets and assessing the impacts of land use and management practices on these budgets. Automatic chambers coupled with sensitive gas analyzers offer valuable insights into the temporal and spatial variability of trace gas fluxes, advancing our understanding of the mechanisms governing these emissions.
Recent developments in “plug-and-play” automatic chambers, equipped with built-in programs for flux calculation, have simplified soil flux measurements. However, before these new instruments can be effectively utilized for flux analysis, it is essential to fully understand the data they produce.
In this study, we used an array of seven automatic chambers coupled to two infrared gas analyzers (IRGA) to measure soil fluxes of three major greenhouse gases: carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) in a Negev Desert of Israel. Measuring soil fluxes in this environment poses significant challenges, including extreme temperatures (reaching ~60 °C during dry summers and freezing during winters), dust and rainstorms, and very low fluxes approaching the detection limits of state-of-the-art instruments.
To calculate fluxes, we developed an in-house program for flux analysis capable of handling data from multiple sensors connected to automatic static chambers. We compared this program's performance with the manufacturer-supplied software. Additionally, we created an empirical method to assess the limit of detection (LOD) for measured fluxes and compared these empirical LODs with calculated values.
We found that the measured LOD was ~1000 times larger than the calculated LOD, with the discrepancy primarily stemming from minor pressure fluctuations near the soil surface. After applying appropriate corrections for LOD, we observed the temporal variability of CH₄, N₂O, and CO₂ fluxes from desert soils with varying carbon (C) content.
Surprisingly, while N₂O fluxes were effectively zero and CO₂ emissions exhibited a diurnal cycle peaking around noon, CH₄ fluxes were consistently positive (indicating net emissions to the atmosphere). These CH₄ emissions correlated with soil C content but not with soil moisture. Furthermore, emissions were notably higher during the dry summer compared to the wetter winter season. We attribute these unexpected CH₄ emissions to the photodegradation of soil C, driven by high soil temperatures and intense solar radiation during summer months.
How to cite: Gelfand, I. and Grabovsky, V. I.: Understanding Soil Trace Gas Flux Measurements with Automatic Chambers: System Design, Analyzers, Detection Limits, and Automated Data Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2796, https://doi.org/10.5194/egusphere-egu25-2796, 2025.
Soil gas fluxes are an important signal for ecosystems and various soil functions, as soils can be both sources and sinks of greenhouse gases (GHG). Because of this critical role, soil gas research has focused mainly on GHGs, while other important gas species have received much less attention. Soil O2 concentration is key to many soil processes, such as ammonification, nitrification and denitrification, and root growth. Studying the relationship between of CO2 and O2 exchange at the soil-atmosphere interface or within the soil profile would be key to better understanding these processes. Gas fluxes between terrestrial ecosystems or the soil surface and the atmosphere are typically measured using the eddy covariance approach (or related micrometeorological approaches), chamber-based measurements, or the flux gradient method. Knowing the precision of the individual sensors used in the methods, but even more so the overall uncertainty of a measurement method, including all steps from sensor to calculation is essential (e.g. the minimum detectable flux). Yet, it is a requirement that is rarely adequately assessed. While there are a variety of suitable CO2 sensors and setups commonly used for measuring CO2 fluxes in soil, the measurement of O2 fluxes in soil is still in its beginning.
Our aim was 1) to develop a chamber method for online CO2 and O2 measurements and 2) to improve a soil gas profile probe for online CO2 and O2 measurements (Maier et al. 2024) to calculate the apparent respiratory coefficient (CO2 efflux divided by O2 influx). We used a multilevel O2-CO2profile probe with built-in online sensors based on the previously published design of an online CO2 sampler (Osterholt et al., 2023). For the chamber measurements, we used an automatic LI-COR chamber equipped with the same sensors and an additional AAB CO2 laser spectrometer. Extensive laboratory tests with a large sand column and controlled injections of CO2 and O2 were performed to test the effects of temperature and air pressure on the chamber system and the soil profile sampler. We present the results of these laboratory experiments, focusing on the technical performance of the measurement system and its impact on the uncertainty of the estimates of CO2 and O2 fluxes and the respiration coefficient.
Acknowledgements
This research was supported by the German Research Foundation (DFG, MA 5826/4-1 project number: 535470615)
Maier, M.; Osterholt, L.; Levintal: Development of an online O2-CO2 soil profile probe for flux estimations, EGU General Assembly 2024, EGU24-6777; https://doi.org/10.5194/egusphere-egu24-6777, 2024.
Osterholt, L.; Kolbe, S.; Maier, M. (2022): A differential CO2 profile probe approach for field measurements of soil gas transport and soil respiration #. In J. Plant Nutr. Soil Sci. 185 (2), pp. 282–296. DOI: 10.1002/jpln.202100155.
How to cite: Maier, M., Schirrmeister, F., and Levintal, E.: How and how accurately can we measure soil O2-CO2 fluxes?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5232, https://doi.org/10.5194/egusphere-egu25-5232, 2025.
Direct measurements of greenhouse gas exchange between the land surface and the atmosphere are critical in developing our understanding of the underlying biogeochemical processes and in parameterizing global climate models. Gas accumulation chambers are regularly used to make localized soil gas flux measurements and there are a variety of chamber implementations with a range of operational characteristics. One developing niche of applications is the use of tall transparent chambers with large surface areas, which enclose vegetation while allowing natural light in, facilitating the measurement of soil and vegetation net gas flux (i.e. NEE) in a way that many chamber designs don't allow. This direct measurement can be valuable in characterizing the flow of gases in an ecosystem without needing to resort to indirect measurements.
Despite the benefits of these types of measurements, chambers with a large volume require additional considerations to ensure high quality data is produced. The volume creates difficulties with adequately mixing gas within the chamber headspace to draw an accurate sample. Chamber transparency allows light in but acts as a greenhouse while the chamber is closed thereby increasing the internal temperature. Depending on the implementation, tall soil collars or chamber bases can additionally alter the natural turbulence and heat exchange in the boundary layer causing a buildup of gases and excess heat, which can lead to high humidity and condensation in the chamber headspace. In order to ensure accurate chamber-based measurements, these impacts must be considered and mitigated where appropriate.
Here, we use a variety of commercially available chamber configurations in addition to peripheral environmental measurements in order to understand their impact on flux measurements. We deployed three large footprint (52 cm diameter) chambers, with overall heights of 45 cm, 75cm, and 115cm, each on identical terrain. We present accumulation curves and flux data from these chambers, contextualized with soil and meteorological measurements, including an in-chamber temperature profile within the tallest chamber. Finally, we discuss how prominent the preceding factors are, the effect they have on chamber measurements, and how they can be accounted for to produce high quality data.
How to cite: Nickerson, N., Taylor, M., Coleman, M., Creelman, C., and McCormick, L.: Measuring Gas Exchanges of Soil and Vegetation with Large, Transparent Gas Accumulation Chambers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6082, https://doi.org/10.5194/egusphere-egu25-6082, 2025.
Volatile organic compounds (VOCs) are important to atmospheric chemistry as they readily react with ambient oxidants, such as ozone (O3) and hydroxy radicals (OH) to produce particles, thereby influencing air quality, trace gas lifetimes and climate. The Amazon rainforest is the largest natural source of VOCs in the atmosphere, with net emissions resulting from a complex balance between sources and sinks across the different ecosystem compartments, such as the canopy and soil. The aim of this study was to characterize the seasonal dynamics of VOC fluxes from Amazon rainforest soil. The experiments were conducted at the Amazon Tall Tower Observatory research facility during four seasons: the dry-to-wet transition and dry season of 2023 ( with the latter being influenced by El Niño) and the wet and dry seasons of 2024.
Soil VOC samples were collected on sorption tubes from three manual steady-state soil chambers. The samples were analysed using thermal desorption-gas chromatography - time of flight mass spectrometry with a chiral column to further separate chiral terpenoids into their separate mirror image forms known as enantiomers. We focused on terpenoid species including isoprene, monoterpenes and sesquiterpenes.
Results showed that in all seasons the Amazon rainforest soil was a net sink for both isoprene (average flux of -12 nmol m-2 h-1) and its oxidation products methacrolein (average fluxes of -4 nmol m-2 h-1) and methyl vinyl ketone (-5 nmol m-2 h-1). In contrast, the Amazon rainforest soil was a source of sesquiterpenes with an average flux of 2 nmol m-2 h-1. The most abundant sesquiterpenes were β-Caryophyllene and α-Copaene. Monoterpene behaviour varied among species, time of day, season and rainfall events.
The highest VOC emissions from soil were observed during the El Niño-influenced dry season of 2023, likely driven by intense heat and drought stress, which significantly reduced soil microbial VOC uptake and increased VOC emissions from abiotic degradation processes. During the wet season and dry-to-wet transition season, the magnitude of the observed soil fluxes was smaller, indicating a more balanced state between uptake and emission processes, likely attributed to the restoration of the microbial uptake in the more humid soil.
How to cite: Schüttler, J., Pugliese, G., Byron, J., Quaresma Dias-Júnior, C., and Williams, J.: Seasonal dynamics of Volatile Organic Compound Fluxes from Soil in the Amazon rainforest , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6566, https://doi.org/10.5194/egusphere-egu25-6566, 2025.
Climate change is expected to impact the methane budget of boreal peatlands, highlighting the need to understand the factors that influence methane cycling, including plant community structure. In northern peatlands, the majority of methane is transported through plants, and the magnitude of this process is strongly linked to plant community composition. This presentation explores the causes of spatial variability in plot-scale methane fluxes in a northern boreal rich fen. Methane fluxes were measured using the manual chamber technique in a context of fine-scale biomass variations in plant community compositions from 36 study plots over 232 days throughout a full year. We found a significant correlation between methane fluxes and a vascular plant cluster statistically dominated by the sedge Carex rostrata in year-round, snow-free and snow season. The biomass of vascular plants, as well as the ratio of vascular plant to bryophyte biomass, also significantly correlated with methane fluxes in year-round and snow-free season. By identifying vegetation-driven emission hotspots, these results can enhance efforts to upscale emission predictions and improve ecosystem-scale methane modelling initiatives. Thus, our findings provide valuable insights for predicting realistic future changes in peatland methane emissions throughout the year.
How to cite: Järvi-Laturi, E.: Plant community composition controls spatial variation in year-round methane fluxes in a boreal rich fen, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8186, https://doi.org/10.5194/egusphere-egu25-8186, 2025.
Quantifying spatial heterogeneity is important for the accurate measurement of broader scale greenhouse gas fluxes and can be done with relatively low construction costs using manual chambers. Existing guidelines on chamber measurements promote more standardized measurement and processing techniques but the extent to which they are implemented within the flux community is unknown. We aimed to identify major differences between the approaches for chamber methane fluxes used by different researchers. We conducted an expert survey to collect information on chamber-based methane flux measurements, including field sites, research questions, measurement setups and routines as well as data processing and quality control of data. We received 36 responses from researchers in North America, Europe, and Asia which indicated that most, but not all, of the respondents use recommended chamber designs, such as airtight sealing, fans, and a pressure vent. In addition, we asked about data processing and quality control of chamber flux data, presented a standardized set of methane concentrations from observed flux measurements and used this information for flux calculations. The responses showed broad disagreement among the experts on the processes resulting in nonlinear methane concentration increases and how to treat many non-linear and low fluxes. Based on the expert responses, we estimated an uncertainty of 17 to 28% across flux measurements due to researcher-based differences: different researchers deciding differently on discarding vs. accepting a measurement and choosing different time periods within the same measurement for flux calculation. This highlights the need to understand drivers of the concentration patterns visible from high-resolution analyzers and to develop standardized procedures and guidelines for future chamber methane flux measurements.
How to cite: Treat, C. C., Jentzsch, K., van Delden, L., and Fuchs, M.: Expert survey shows needs for standardized data processing and process-level understanding of chamber flux data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8726, https://doi.org/10.5194/egusphere-egu25-8726, 2025.
Rice paddies are among the most important cultivated areas globally both in terms of food security and greenhouse gas (GHG) emissions. Rice paddies are estimated to contribute approximately 10% of anthropogenic CH4 emissions and are also potential hot spots for N2O emissions. However, emission estimates are highly uncertain as observational data consist primarily of manual, static closed chamber measurements combined with gas chromatography. This methodology is highly time consuming and sets limitations on the number of observations over time, which is problematic due to the highly variable temporal nature of both CH4 and N2O fluxes with respect to water table fluctuations and fertilisation. The temporal dynamics of CH4 and N2O emissions from rice paddies are therefore not well understood.
Here we present the methodology and initial results from a novel automatic chamber setup with continuous GHG measurements on rice paddies under varying agricultural practices, including water regimes. The concentrations of all three major GHGs, i.e. CO2, CH4 and N2O, are continuously measured using state-of-the-art automatic chambers operated in both light and dark mode. The automatic chamber setup is designed to accommodate changes in both water table, plant height and crop types. GHG fluxes are estimated using both linear and non-linear (Hutchinson and Mosier model) regression.
This setup will provide the most detailed GHG measurements on rice paddies to date and improve our understanding of the GHG dynamics on rice paddies under varying agricultural practices and water regimes.
How to cite: Hansen, H. F. E., Larsen, K. S., Olesen, A., Christensen, H., Frietzsche, L. M., Quynh, V. D., Trinh, M. V., and Elberling, B.: Continuous monitoring of greenhouse gas emissions from rice paddies using automatic flux chambers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9854, https://doi.org/10.5194/egusphere-egu25-9854, 2025.
Non-steady-state chambers are commonly used to measure soil and manure emissions of CO2, CH4, and N2O. When paired with online gas analyzers, automated non-steady-state (a-NSS) chambers enable high-frequency monitoring of greenhouse gas (GHG) fluxes. Despite their advantages in capturing detailed emission patterns, these systems pose challenges in handling large datasets, performing complex flux calculations, and scaling results over time. This study introduces a computationally efficient algorithm designed to process continuous, high-resolution data from a-NSS chambers, providing instantaneous flux calculations and diel emission estimates. The algorithm was validated using field dataset, capturing simultaneous flux measurements for CO2, CH4, and N2O. High-frequency data collection allowed for the identification of episodic flux events. By employing shape-constrained additive models, the algorithm achieved median percentage deviations (bias) of -1.03% for CO2 and -4.34 % for N2O. Temporal upscaling from instantaneous to diel fluxes was performed using Simpson’s rule, ensuring accurate integration over time. This tool offers a rapid, reliable method for real-time flux computation, significantly improving GHG flux measurement accuracy and enhancing insights into the temporal variability of soil emissions.
How to cite: Omirou, M., Themistokleous, G., Savvides, A., and Philippou, K.: A Computational Tool for High-Frequency GHG Flux Analysis: Instantaneous and Diel Estimates from Automated Non-Steady-State Soil Chambers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12472, https://doi.org/10.5194/egusphere-egu25-12472, 2025.
Soil trace gas flux rates derived from closed-transient chamber-based techniques rely on estimating the rate of change in gas concentrations prior to disturbance by the chamber. Several regression methods are available for estimating this rate of change, all of which require selecting a fitting window over which to apply the regression. Window selection has historically been subjective and reliant on expert knowledge, using information from only small subset of measurements to fit to a larger dataset. This somewhat arbitrary approach overlooks the influence of individual chamber and sampling location properties on the development of turbulence, as well as the unique transport characteristics of different trace gases. Here, we describe an empirical method for selecting the fitting window for an exponential regression model, based upon iterative analysis of within-chamber gas mixing dynamics. The method operates at a batch scale, incorporating data from every observation to determine the appropriate start and stop times for the fitting window of the larger dataset, using SoilFluxPro 5.2’s JavaScript console.
Chamber-based estimates of the stable carbon isotope ratio (δ13C) of soil-respired CO2, which utilize the Keeling mixing model, are also sensitive to the chosen fitting window; however no clear empirical approach for window optimization has been proposed. We also present a potential model-based approach to reduce uncertainty in Keeling model-derived δ13C estimates, offering a comprehensive analysis using low-magnitude soil gas flux data from a desert ecosystem. Together these fitting window optimization strategies enhance the robustness of soil gas flux and δ13C estimates.
How to cite: He, S., Hupp, J., Lauria, C., Lingol, T., Reed, S., and Vath, R.: Recent advancements in empirical and model-based approaches for optimizing fit windows for closed-transient chamber-based soil trace gas flux and δ13C data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13457, https://doi.org/10.5194/egusphere-egu25-13457, 2025.
Peatland rewetting can reduce CO2 emissions but unintendedly increase CH4 emissions from drained peatlands. The magnitude of these emissions depends on factors such as time since rewetting, nutrient availability, vegetation type and water table fluctuations. In this study, we used automatic chambers to measure CO2 and CH4 emissions in a rewetted fen peatland under (a) grass-dominated and (b) Juncus sp. dominated vegetation plots. Our objectives were to (1) quantify CO2 and CH4 emissions, (2) identify how emissions were affected by vegetation type, and (3) relate emissions to changes in water table.
The study was conducted in the Nørreå valley in central Denmark. Autochambers (ECOFlux; DMR A/S) measuring gas fluxes first in transparent and then in dark mode in the same plot, were placed in a small area with mixed vegetation and connected to a multiplexer system which allowed simultaneous CO2 and CH4 flux measurements. We used a LGR-ICOSTM GLA131-GGA microportable gas analyzer (ABB Ltd.), and a LI-7810 trace gas analyzer (LI-COR, Inc.). Three autochambers were placed in grass-dominated plots, while three others were placed in Juncus sp. dominated plots. Axillary data on soil temperature, soil moisture, water table depth (WTD), and photosynthetic active radiation (PAR) were obtained within each chamber. Gas flux measurements were performed five to six times per day per chamber from 1st of May to 10th of November 2024. Biomass development was estimated with biweekly light reflectance measurements and the RVI vegetation index was calculated using a RapidSCAN CS-45 (Holland Scientific Inc., Lincoln, NE, USA). At the end of the study period, the aboveground vegetation was harvested and analyzed for dry matter content, total C (TC) and total N (TN). Additionally, peat cores were collected at six depth increments (0-10, 10-20, 20-40, 40-60, 60-80, and 80-100 cm), and the soil samples were analyzed for TC, TN, field bulk density, pH, Fe, and microbial composition. Preliminary findings suggest that CH4 emissions is higher from the Juncus than from the grass-dominated vegetation, but the effect of vegetation type on both CO2 and CH4 emissions depends on WTD, soil temperature, peat physicochemical characteristics and microbial composition. Findings from this study will provide valuable information on how high frequency WTD along with automatic chamber measurements can contribute to the understanding of peatland rewetting and management strategies in order to minimize greenhouse gas emissions during the rewetting process.
How to cite: Rodriguez Grisales, A. F., Pullens, J. W. M., Christiansen, J. R., Larsen, K. S., Elsgaard, L., and Lærke, P. E.: Using Automatic Chambers to Disentangle the Role of Vegetation in CO2 and CH4 Emissions From a Rewetted Fen Peatland , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15885, https://doi.org/10.5194/egusphere-egu25-15885, 2025.
The physical parameters of soils strongly influence the gas exchange between the soil and the atmosphere. Soil texture may control greenhouse gas (GHG) emission or absorption through different mechanisms. On the one hand, soils with different textures create various conditions for soil microorganisms activity and growth. Higher content of clay in soils improves nutrients storage due to larger cation exchange capacity often resulting in higher microbial biomass and enhanced enzymatic activity. In addition, through the distribution of micropores, soil texture regulates water storage capacity, gases diffusion and their availability for microorganisms. In consequence, silty soils often are richer in nutrients, while sandy soil are better aerated and with gas diffusion occurring more easily, controlling soil processes differently. By regulating gases diffusion, including O2 availability, and creating anaerobic parts, texture strongly determines the soil processes responsible for GHG emissions or uptake.
Field studies on GHG exchange were carried out in agricultural soils of different textures under grasslands and under different crops cultivation. The widely accepted chamber method was used to assess GHG emissions or consumption over two growing seasons. The results showed that among the three key GHGs (CO2, CH4, N2O), soil texture particularly controlled the uptake of atmospheric CH4. Seasonally, sandy soils consumed several times more CH4 than silty soils due to higher gases diffusion, which was also regulated by soil moisture. The studies carried out provide valuable quantitative results on key GHG emissions, allowing their balance to be estimated in soils of different textures.
Funding
This work was funded by the ReLive project (CIRCULARITY/61/ReLive/2022) financed by the Polish National Centre for Research and Development within the Joint Call of the Co-fund ERA-Net Programme.
References:
Hamarashid, N.H. et al., 2010. Effect of soil texture on chemical compositions, microbial populations and carbon mineralization in soil. Egypt. J. Exp. Biol. 6, 59–64; Costa, K.H., Groffman, P.M., 2013. Factors regulating net methane flux by soils in urban forests and grasslands. Soil Sci. Soc. Am. J. 77 (3), 850–855
How to cite: Walkiewicz, A.: Soil texture as a strong regulator of greenhouse gas emissions from agricultural soils , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16065, https://doi.org/10.5194/egusphere-egu25-16065, 2025.
To study the drivers of volatile organic compound (VOC) emissions from boreal forest soil emissions, a study was conducted at two long-term and well-documented experiments in Finland. The first experiment, located in Karkkila (60.577°N, 24.261°E), is a spruce dominated stand with a nitrogen (N) fertilization experiment. The second experiment is in Taivalkoski (65.316°N, 28.161°E), where a tree species experiment is taking place with individual plots on which either silver birch, Scots pine, or Norway spruce were grown on originally similar soil.
Between May and October 2023, we collected soil VOC emissions on multi-bed adsorbent tubes at three locations in each plot (control and N-fertilized in Karkkila, and different tree species in Taivalkoski) about once a month, using an enclosure put on metallic frames placed at each sampling location at least one month before the first sampling. The collected emission samples were analysed with a thermal desorption gas chromatograph coupled to mass spectrometry (TD-GC-MS). The conditions both inside and outside the enclosure were recorded during sampling. Furthermore, the soil moisture was measured, and vegetation was visually assessed after each sampling. In addition, 15 to 20 soil cores were taken by a soil auger around 1-2m of each VOC sampling location during the early growing season, and the organic layer separated. Organic layer samples were combined to make one composite sample for each sampling location. Pretreatments and analysis were performed as described in Soronen et al. (2024) to derive soil parameters, including microbial properties. We also used a microdialysis sampling technique to determine induced diffusive fluxes of plant-available N compounds in the organic layer (Soronen et al., 2024). Fluxes were measured twice during the growing season at each site (early and late season).
For a given plot, emissions collected on the same day show similar composition with varying quantities. However, seasonal variations influenced the emissions’ composition, reflecting various direct and indirect underlying processes. Monoterpenes usually dominated the emissions, but chloroform was also emitted, especially from the N-fertilized plot. The relationships between stand properties, soil properties, environmental conditions, and VOC emissions were explored using multiple linear regression. We found that individual compounds are affected differently, emphasizing the importance of speciation. In general, the dominant tree species, moss cover, and shrub cover appear to be the vegetation factors most influencing VOC emissions. From soil properties, increasing dissolved organic carbon, organic matter, and the microbial biomass C-to-N ratio increased BVOC emissions.
Rising global temperatures lead to increased biomass in boreal areas (including litter and organic soil matter), an extended growing season, and heat stress. These alterations can affect vegetation, soil microbes, and forest floor plants, potentially causing changes in VOC emissions. The analysis presented here could be used in models of biogenic VOC emissions from boreal forest soils to investigate future scenarios.
Reference:
Soronen, P., Henttonen, H.M. and Smolander, A. (2024). Grey alder at the regeneration stage: Long-term effects on soil nitrogen and carbon pools and Norway spruce growth. Forest Ecology and Management, 554, 121686. doi:10.1016/j.foreco.2023.121686.
How to cite: Praplan, A. P., Soronen, P., Pyysalo, E.-L., Tykkä, T., Thomas, S. J., Díez-Palet, I., Luoto, M., Hellén, H., and Smolander, A.: Drivers of volatile reactive soil emissions in the boreal forest, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3204, https://doi.org/10.5194/egusphere-egu25-3204, 2025.
Soil microbial communities often get sequenced using general primers that allow the detection of a wide range of microbial species. This sometimes leads to unexpected finds in sequencing data. We noticed the presence of sequences attributed to aerobic methanotrophs in multiple field sites. These were all carbon rich, water saturated, anoxic soils that were known for their high methane emission potential. Using qPCR, we found pmoA to be abundant deep in the anoxic layer of these soils. Although aerobic methanotrophs have been found in anoxic locations before, they are often dismissed as inactive or as purely a remnant of past oxic conditions. We aimed to discover whether these methane oxidizing bacteria could play a significant role in in situ methane oxidation and in mediating methane emissions.
Using samples from these locations, we created oxic enrichment cultures that rapidly became dominated by methanotrophic bacteria, the same that were detected in the initial anoxic soil sample sequencing results. These enrichment cultures were dominated by methanotrophs of the genera Methylobacter (thawed permafrost soil), Methylotetracoccus (rice paddy) and Methylovulum (alpine peatland). Although some species of these genera have been detected in anoxic locations before, and certain species are known to have the genetic capability to perform fermentation, it is unknown which electron acceptor these methanotrophs rely on in anoxic or hypoxic conditions. We perform culture experiments with iron, nitrate and organic compounds as electron acceptors, to elucidate the pathways used by these methanotrophs. We also added enriched methanotrophs to natural soil samples of the same locations, to test whether these methanotrophs were able to enhance methane removal from the original sampling locations.
How to cite: van Grinsven, S., Kappler, A., and Voggenreiter, E.: Aerobic methanotrophs in anoxic soils: comparing peatland, rice paddy and thawing permafrost methanotrophs and their respective metabolisms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11759, https://doi.org/10.5194/egusphere-egu25-11759, 2025.
Chamber measurements are a common method for assessing greenhouse gas (GHG) exchange between ecosystems and the atmosphere. They are particularly useful for measurements at homogeneous sites and for partitioning fluxes and processes. Automated chamber measurements provide significantly improved temporal coverage compared to manual chambers. This factor is especially important for temporally variable soil N2O emissions. Although automated chambers are less labor-intensive, they typically rely on an electric grid. In contrast, manual chambers are relatively simple to operate and can function using batteries.
The test-operational MOBile MEasurement STAtion (MOBMESTA) offers a solution for automated chamber measurements without access to an electric grid. This is achieved by: 1) minimizing power use and implementing power-saving features, 2) utilizing compressed air to operate (open and close) the chambers, 3) employing bifacial solar panels to provide power, and 4) incorporating large interchangeable lithium iron phosphate (LiFePO4) batteries.
The system is designed for year-round operation, even in snowy, cold, and dark conditions. The system is designed for a maintenance cycle of few weeks. The design stems from decades of experience at both the Hyytiälä Forest Station and the INAR Ecosystems Research Group at the University of Helsinki.
While the primary focus of MOBMESTA is on soil GHG exchange measurements, it is also capable of operating shoot or stem chambers. Although the initial design is based on pneumatic operation of the chambers, electric motors are also supported. The system features 16 sample channels capable of both closed-loop and flow-through operation. The station will provide meteorological and environmental background measurements, along with optional site-specific ecological measurements.
The system is compact enough to fit in a van. Each individual component has a maximum weight of 22 kg, allowing for transport by a single person. The system itself is constructed on a two-wheeled trolley with a flat loading surface (load area 1 x 0.6 m, maximum load 400 kg). As of spring 2025, the system is undergoing field testing.
How to cite: Korhonen, J., Polvinen, T., Koskinen, M., Pihlatie, M., Lohila, A., Bäck, J., and Haapanala, S.: MOBMESTA – Automated GHG chamber measurements without electrical grid , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15438, https://doi.org/10.5194/egusphere-egu25-15438, 2025.
Forests soils are characterized by a pronounced spatial variability of chemical, biological and physical soil parameters, and therefore pose a great challenge for the monitoring of greenhouse gas fluxes (GHG). Disturbances such as compaction caused by forest machinery, affecting up to 40% of the area of an actively managed forest stand, additionally contribute to the variation of soil conditions over short distances and lead to large spatial differences in GHG fluxes.
To address spatial variability, current approaches often rely on spatially distributed manual measurements, which provide good spatial coverage but typically miss episodic extreme events, leading to an underestimation of GHG fluxes. In contrast, automated measurements provide high temporal resolution but are usually limited to small areas and, due to a lack of spatial replication, cannot capture the forest stand heterogeneity properly. Resolving this trade-off is crucial for improving the accuracy of GHG budgets. Therefore, our study aims not only to provide high-resolution temporal data on soil GHG fluxes, but also to combine automatic and manual measurements through means of modeling to overcome these limitations.
The long-term monitoring site Klausen-Leopoldsdorf, Austria, established in 1990 as part of the Austrian ICP Forests Program, offers the opportunity to address this challenge. The beech forest on Stagnic Cambisol developed from Flysch sediments, is representative for the highly productive, forested parts of the Wienerwald. In 2016, part of the forest stand was thinned using a fully mechanized harvesting system (single-grip harvester, forwarder), creating a plot of a thinned beech stand (BS) with skid trails (ST). Within this area, an automated GHG measurement system was installed to measure CO2 soil fluxes at 5-minute intervals at 2 subplots with 6 chambers each (LI-840A, LI-COR Inc., USA). In 2022, the measurement equipment system was replaced by 2 LI-COR trace gas analyzers (LI-7810 and LI-7820) to facilitate the detection of CH4 and N2O soil fluxes. This setup allows for high-resolution measurements of GHG fluxes from disturbed and undisturbed soils. From 2022 to 2024, supplemental manual measurements were carried out at 3-week intervals on collars (n = 24) along a transect within the 19-ha forest stand using a soil respiration chamber (8200-01S LI-COR Smart Chamber). Stratified regression modeling of the automated and manual measurements is used to calculate GHG fluxes at the forest stand scale.
Preliminary results highlight substantial differences in GHG flux rates between control (BS) and compacted (ST) areas. Disturbed areas exhibit elevated and prolonged emission peaks following precipitation events. These findings underscore the huge impact of soil compaction on heavy clayey soils and altered soil structure on GHG dynamics.
By combining high-frequency soil flux measurements with comprehensive environmental monitoring, this study improves the understanding of factors driving GHG flux variability. These findings contribute to more accurate stand-scale GHG budgets for managed temperate beech forests and provide a robust dataset for upscaling to national GHG budgets and improving biogeochemical models.
How to cite: Malli, A., Behringer, M., Gartner, K., Gollobich, G., Katzensteiner, K., and Kitzler, B.: The Challenge: Estimating Greenhouse Gas Budgets from Heterogeneous Forest Soils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17377, https://doi.org/10.5194/egusphere-egu25-17377, 2025.
Non-steady-state chambers are widely used for measuring the exchange of greenhouse gases (GHGs) between soils or ecosystems and the atmosphere. It is known that non-steady-state chambers induce a non-linear concentration development inside the chamber after closure, even across short chamber closure periods, and that both linear and non-linear flux estimates are impacted by the chamber closure period itself. However, despite the existence of recommendations on how long to keep the chamber closed, it has been little explored to what extent the length of the chamber closure period affects the estimated flux rates, and which closure periods may provide the most accurate linear and non-linear flux estimates.
In the current study, we analyzed how linear regression and Hutchinson and Mosier (1981) modeled flux estimates were affected by the length of the chamber closure period by increasing it in increments of 30 s, with a minimum and maximum chamber closure period of 60 and 300 s, respectively. Across 3,159 individual soil CO2 and CH4 flux measurements, the effect of chamber closure period length varied between 1.4–8.0% for linear regression estimates and between 0.4–17.8% for Hutchinson–Mosier estimates and the largest effect sizes were observed when the measured fluxes were high.
Both linear regression and Hutchinson–Mosier based flux estimates decreased as the chamber closure period increased. This effect has been observed previously when using linear regression models, but the observed effect on Hutchinson-Mosier modeled estimates is a novel finding. We observed a clear convergence between the short-period linear regression estimates and the long-period Hutchinson–Mosier estimates, showing that closure periods as short as possible should be used for linear regression flux estimation, while ensuring long-enough closure periods to observe a stabilization of flux estimates over time when using the Hutchinson-Mosier model. Our analysis was based on soil flux measurements, but because the perturbation of the concentration gradient is related to the non-steady-state chamber technique rather than the measured ecosystem component, our results have implications for all flux measurements conducted with non-steady-state chambers. However, optimal chamber closure times may depend on individual chamber designs and analyzer setups, which suggests testing individual chamber/system designs for optimal measurement periods prior to field application
How to cite: Larsen, K. S., Johannesson, C.-F., Nordén, J., Lange, H., and Silvennoinen, H.: Optimizing the closure period for improved accuracy of chamber-based greenhouse gas flux estimates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21702, https://doi.org/10.5194/egusphere-egu25-21702, 2025.
Gas fluxes between soil and atmosphere play a significant role as global greenhouse gas sinks or sources. Accurate estimation of these gas fluxes is challenging due to the heterogeneous nature of soil properties, dynamic environmental factors, and the complexity of measurement in soil systems. The flux-gradient method is a reliable approach for estimating CO2 gas fluxes. This method utilises Fick's first law of diffusion to calculate gas fluxes by analysing gas concentration profiles. The gas concentration gradient is multiplied with the apparent gas diffusion coefficient of the gas species in the soil. The estimation of the apparent gas diffusion coefficient is dependent on numerous parameters, including soil pore space and soil water content, which must be carefully measured or derived. These parameters typically cannot be measured at the exactly same location as not to interfere with the gas measurements. All these factors are subject to different uncertainties depending on the parameters and the measured soil location.
In order to address and comprehend the extent of these uncertainties, Bayesian inference was employed, as this methodology enables uncertainty to be measured through probability distributions with credible intervals as opposed to point estimates. Furthermore, Bayesian inference functions effectively with small datasets and permits the incorporation of prior knowledge, a factor which also benefits soil gas modelling.
We used a previously published dataset (Wordell-Dietrich et al. 2020) to estimate CO2 fluxes by using the flux-gradient method. The gas diffusion coefficient was derived through the use of a Bayesian inference model. The resulting data were then compared with chamber measurements and other modelling approaches.
Acknowledgment
Wordell-Dietrich, P.; Wotte, A.; Rethemeyer, J.; Bachmann, J.; Helfrich, M.; Kirfel, K.; Leuschner, C.; Don, A. (2020): Vertical partitioning of CO2 production in a forest soil. Biogeosciences, 17, 6341-6356. https://doi.org/10.5194/bg-17-6341-2020
How to cite: Schirrmeister, F., Maier, M., and Kneib, T.: Using a Bayesian inference approach to assess the uncertainty in flux-gradient derived soil CO2 flux estimates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9268, https://doi.org/10.5194/egusphere-egu25-9268, 2025.
