High‑precision greenhouse gas (GHG) measurements are essential for accurately assessing soil carbon and nitrogen cycling. Traditionally, gas chromatography (GC) with autosamplers has been the standard for soil incubation studies due to its accuracy and high‑throughput capability. Recent advances in laser‑based systems, such as Picarro’s G2508 Cavity Ring‑Down Spectroscopy (CRDS) analyzer, now enable continuous, real‑time monitoring of CO₂, CH₄, N₂O, and other gases. When paired with Picarro’s new Sage gas autosampler, the system supports automated, high‑throughput measurements of discrete, small‑volume gas samples using only zero air (or N₂) for flushing and requiring minimal maintenance.

To evaluate performance relative to GC, we conducted an intercomparison study using 60 mL samples split into two equal aliquots—one measured with the G2508–Sage system and one with GC. Certified reference gases (9.9 ppm N₂O, 10 ppm CH₄, 1008 ppm CO₂) and their 10–80% dilutions were analyzed. Both systems achieved coefficients of variation (CVs) below 5%, with the G2508–Sage consistently showing lower CVs when sample concentrations were within the analyzer’s dynamic range. Strong linear correlations (R² > 0.99) were observed across all gases.

For soil incubation headspace samples containing elevated GHG concentrations, the two systems generally agreed within CVs <5%. The G2508–Sage delivered more precise CH₄ measurements, while CO₂ and N₂O occasionally showed higher CVs—likely due to sample carryover, concentrations exceeding the CRDS dynamic range, or insufficient flush times between variable samples. We present best-practice recommendations to ensure the highest accuracy and precision with the G2508-Sage system.

Overall, the Picarro G2508–Sage autosampler system provides a robust, cost‑effective, and user‑friendly alternative to GC for soil GHG analysis, especially for concentrations within the CRDS optimal range. Its low consumable requirements, reduced installation and maintenance demands, and suitability for both headspace and high‑throughput flux studies expand instrumentation options for soil biogeochemistry research.

How to cite: Hofmann, M. E. G., Woźniak, J., Bhattacharya, J., Perez, T., and Silver, W. L.: Rethinking soil incubation greenhouse gas monitoring: How the Picarro G2508 coupled with the Sage gas autosampler compares against gas chromatography analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9304, https://doi.org/10.5194/egusphere-egu26-9304, 2026.

Northern wetlands are an important sink of atmospheric CO₂ and source of methane (CH₄) to the atmosphere but many uncertainties remain in the magnitude of fluxes due to high spatial, temporal, and methodological variability. Chamber measurements are an important method to link CO₂ and CH₄ fluxes to underlying soil processes. While high-frequency laser gas analyzers have been crucial for improving the number and quality of flux measurements, costs for purchase and maintenance of these systems are still cost-prohibitive for widespread applications of this method for quantification of fluxes.

In the MISO project, we test a low-cost NDIR-based portable sensor for flux measurements at a wetland site in Finland. We deployed the new MISO sensor in a two existing automated chambers (one transparent, one opaque) and evaluated the performance of the low-cost sensor for quantifying fluxes of CO₂ and CH₄ during a three-week period in July 2025. The results indicate that CO₂ fluxes can be measured well with the sensor setup. Methane fluxes show strong variability in the raw signal; calibrated values are highly dependent on methods used to correct for interference from water vapor and temperature. These findings indicate that this method is promising for applications in wetlands and would provide an important step forward in enabling widespread flux monitoring networks.

How to cite: Treat, C. C., Gehlmann, M., Dallo, F., Wastine, B., Gaynullin, B., Gia, H. D., and Cao, T.-V.: Evaluation of low-cost sensors for measurement of CO2 and CH4 fluxes at a Finnish wetland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17029, https://doi.org/10.5194/egusphere-egu26-17029, 2026.

Chamber-based in situ measurements of greenhouse gas (GHG) fluxes have been widely applied in numerous studies. However, with the increasing applications of online optical GHG analyzers, the chamber system designs and measurement protocols are quite diverse and also uncertain.

Here, we compared various measurement approaches for soil CO₂, CH₄, and N₂O fluxes, by optimizing the chamber base collar, chamber volume, within-chamber fan, fertilizer treatments and integration time for flux calculation. Additionally, we conducted a flux monitoring campaign over 80 hours with both automated multi-chamber system (M16 multiplexer + LI-COR 7810&7820) and discrete sampling with offline measurement by gas chromatography (GC; Agilent 8890). Fluxes were computed from concentration time series and evaluated using goodness-of-fit and error diagnostics (e.g., R², CV, RMSE, MAE) and by systematically varying the regression time window to quantify window dependence. In parallel, soil water status (WFPS) and rainfall were monitored, and soil inorganic N (NH₄⁺ and NO₃^-) was measured at selected rounds to interpret event-driven responses. 

The results showed that base collar installation and internal fan is highly necessary in assuring good linearity of concentration measurement along time and minimizing disturbance upon chamber closure. Regarding the integration time for flux calculation, the length of datasets markedly affected fitting performance and flux magnitude, which are mostly critical for CH₄ and N₂O. During the 80 h automated deployment, fertilized plots exhibited pronounced, pulse peaks of N₂O emissions (hot moments) superimposed on chamber-cycle oscillations, while CH₄ displayed stronger shared background variability across chambers. These N₂O pulses coincided with wetting-related dynamics indicated by WFPS or rainfall patterns and concurrent shifts in inorganic N. Cross-validation between online (LI-COR) and offline (GC) fluxes showed a general agreement for N₂O but a weaker association for CH₄, indicating systematic bias and limited explanatory power of sparse offline point sampling under rapidly changing conditions. Together, our results provide practical, species-specific guidance for chamber-based flux measurements and highlight the need for harmonized synchronization and computation protocols when integrating online and/or offline approaches, especially during event-driven flux dynamics.   

Keywords: Static Chamber; Greenhouse Gas Flux; Online–offline Comparison; Hot Moments

How to cite: Wu, L. and Yu, L.: Method optimization and comparison for high-frequency static-chamber measurements of CO2, CH4 and N2O fluxes from soil, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10028, https://doi.org/10.5194/egusphere-egu26-10028, 2026.

How to cite: Duong Gia, H., Dallo, F., Heltne, T., Gehlmann, M., Wastine, B., Gaynullin, B., Treat, C., Matthew Platt, S., and Cao, T.-V.: Low-cost, high-frequency greenhouse-gas flux observations in wetlands using automated and portable chamber systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17636, https://doi.org/10.5194/egusphere-egu26-17636, 2026.

Carbonic anhydrases (CAs) are ubiquitous metalloenzymes found in plants and soil microbes. They play an important role in the cycling of carbon within terrestrial ecosystems by catalyzing the rapid conversion of CO₂ into bicarbonate. CAs also catalyse the irreversible hydrolysis of carbonyl sulfide (COS) and the oxygen isotope exchange between atmospheric CO₂ and terrestrial water pools (CO¹⁸O + H₂O <-> CO₂ + H₂¹⁸O). Soil CA activity and its drivers can therefore be studied by using gas exchange systems that measure soil-air fluxes of COS and CO¹⁸O.

Soil CA activity is commonly quantified using such gas exchange systems that allow the retrieval of macroscopic rates of COS hydrolysis (k_h) and ¹⁸O exchange between CO₂ and soil water (k_iso). These macroscopic rates can be related to soil properties such as soil moisture and temperature, as well as average enzyme kinetic parameters at the soil microbial community level. These ‘community-level’ enzymatic parameters (k_max/K_m) are expected to vary across contrasted ecosystems and soil microbial communities. However, microbial communities could also vary at a finer scale, between tree species, and particularly between different types of mycorrhizal symbioses, which reflect contrasting nutrient acquisition strategies and metabolic pathways.

In this study, we tested this hypothesis by measuring COS and CO¹⁸O fluxes in intact soil monoliths collected in a common garden under different tree species growing within the same climate and pedological context. This approach allowed us to characterize how soil CA activity (k_h and k_iso) vary between tree species and mycorrhizal types.

Contrary to our initial hypothesis, we found no difference between ‘community-level’ enzymatic parameters from different tree species or types of mycorrhizal associations. By measuring the monoliths at different soil temperature and soil moisture levels, we were also able to validate for the first time how the macroscopic rates kh and kiso can be related to these two abiotic factors, and estimate ‘community-level’ kmax/Km for this particular ecosystem.

This improved understanding of soil CA activity can help refine the representation of soil processes in large-scale models and better constrain the contribution of soils to the global CO₂ and COS mass balance.

How to cite: Demullier, E., Ogée, J., Arette-Hourquet, P., Devert, N., Dong, Y., Millan-Navarro, D., Milin, S., and Wingate, L.: Assessing soil carbonic anhydrase activity using CO₂ and COS exchange across tree–mycorrhizal associations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17931, https://doi.org/10.5194/egusphere-egu26-17931, 2026.

Measuring trace gas exchanges from structural sources and surfaces with complex topography remains a challenge. The inverse dispersion method provides a suitable option that allows long-term monitoring without interfering with the system. In the case of ammonia emissions such as from animal housings and slurry storage tanks the inverse dispersion method is beneficial compared to other methods which can interfere with daily operations over longer time periods. Ammonia emissions from agriculture can constitute up to 80-90% of reactive N inputs in nearby ecosystems, especially in areas with high livestock densities such as Switzerland. Almost half of these originate from animal housings, which can be difficult to quantify in order to investigate and test mitigation options. However, when measuring emissions from housing and slurry storage facilities, it is necessary to install the instruments at some distance downwind of the structures to avoid turbulence interferences. Since ammonia strongly adsorbs to surfaces immediately following emission, this can lead to a considerable loss before the point of measurement.

Deposition models can be used to correct for this loss. Most models consist of a series of resistances to deposition that must be overcome to estimate the total loss. However, the calculation of the bulk canopy resistance requires significant additional site information, which even with its incorporation results in a relatively high uncertainty. Here we present measurements of ammonia emissions from animal housings using the inverse dispersion method which includes a simplified deposition correction. Based on multiple measurement campaigns we show this method to provide a reasonable estimate without the need for additional data while remaining within the same uncertainty range. We further discuss the importance of small-scale deposition from large point sources using controlled release experiments and highlight future development opportunities.

How to cite: Valach, A., Qin, W., Häni, C., Bowald, S., and Kupper, T.: Measuring complex structural emissions with inverse dispersion method and correcting for deposition in the case of ammonia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21102, https://doi.org/10.5194/egusphere-egu26-21102, 2026.

The use of in-situ high precision gas analyzers for soil flux chamber measurements has dramatically improved the quality of collected data compared to manual sampling. When deployed in the field long-term, these systems can provide high resolution monitoring of both baseline and event-driven emissions. These environments pose challenges to maintaining complex instrumentation however, especially in humid or varying temperature conditions. These physical factors can accelerate calibration drift, and thus require more frequent intervention to correct this effect.

We present a methodology for automating this recalibration in-situ as applied to a Gasmet GT5000 Terra FTIR Gas Analyzer coupled with an Eosense autochamber system. Through the use of a simple hardware module, nitrogen gas based background calibrations can be scheduled alongside soil flux chamber measurements without the need for manual intervention such as changing fittings or opening tanks. This combined system allows for reference gas corrections at pre-planned intervals, or reactively in response to changes in ambient temperature or sampled water content.

How to cite: Nickerson, N., Creelman, C., Taylor, M., Maxner, H., Ketonen, S., Bradey, A., Marshall, A., and Covey, G.: Automating Analyzer Background Calibration for a Long-Term Flux Chamber System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14340, https://doi.org/10.5194/egusphere-egu26-14340, 2026.

The goFlux R package was 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; 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 the last year, goFlux has been improved with new functionalities and additional ongoing improvements, including auto.ID for an automatic selection of the observation window (no more clicking!), iso.comp for isotopic composition determination of different isotope ratios (¹³C, ¹⁵N or ¹⁸O), crop.meas for additional pre-processing of data after import (e.g., adding a deadband or cropping measurements), and auto.deadband for an automatic detection of the best deadband per measurement. Furthermore, goFlux is now compatible with a larger selection of instruments from LI-COR, LGR, GAIA2TECH, Gasmet, Picarro, Aeris, PP-Systems, Earthbound Scientific Ltd, Healthy Photon, Eosense, and PRI-ECO. Finally, goFlux now integrates new functionalities for reproducible calculation of GHG fluxes from static or floating chamber measurements in aquatic ecosystems, also accounting for ebullition events and separating total fluxes into diffusive and ebullitive components.

Further improvements are being made to the automatic selection of the best flux estimate using the function best.flux. 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 appear in chamber-based applications. Similar to the kappa-max parameter, we are developing the kappa-min parameter, which indicates a minimum threshold under which the best flux estimate will automatically default to the non-linear model.

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 (linear or non-linear models). This poster presentation will highlight new functionalities and improvements made to the goFlux R package in the last year, as well as ongoing developments, that have been made to improve the "user-friendliness" of the package. Come see us at our poster session to discuss new features you would like to see added in the future!

For more information, visit our webpage: https://qepanna.quarto.pub/goflux/

How to cite: Rheault, K., Minaudo, C., Riis Christiansen, J., and Steenberg Larsen, K.: Latest updates to the goFlux R package: new functionalities and improvements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8947, https://doi.org/10.5194/egusphere-egu26-8947, 2026.

Natural attenuation process occurring at hydrocarbon-impacted sites are driven by biogeochemical interactions between the subsurface, microbial communities, and environmental conditions. Beyond direct volatile organic compounds (VOCs) volatilization from the contamination source, aerobic biodegradation pathways lead to the consumption of hydrocarbons and the production of gaseous emission from the subsurface, including CO₂. Current monitoring campaigns for evaluating gas fluxes are generally conducted periodically, relying on either soil-gas sampling and subsequent laboratory analysis or the use of high-cost instrumentation for rapid and expedited concentration measurements. These methods, while providing representative results of average values over specific and narrow time intervals, do not allow for an accurate description of the temporal dynamics of VOC biodegradation and the consequent CO₂ emissions, which are known to exhibit significant fluctuations on both daily and seasonal scales. To overcome this limitation, there has been growing interest in recent years in developing low-cost systems that allow for continuous monitoring of gas emissions.

This work, conducted as part of a research project funded by INAIL (BRiC ID21-2022),  presents the development and application of a self-designed automated static flux chamber for real-time and continuous monitoring of biodegradation-related gas emissions from the subsurface. The system integrates low-cost Non-Dispersive Infrared (NDIR) sensors for CO₂ measurement, together with a Photoionization Detector sensor (PID) for VOC concentration measurements, and is additionally equipped with sensors for environmental parameters (i.e. temperature, relative humidity and atmospheric pressure). The chamber is equipped with two air pumps dedicated to periodic automatic air exchange, ensuring operational continuity and allowing the acquisition of one flux measurement every 20 minutes. The electronic hardware is managed by an ESP32 microcontroller and is completed with an SD card for raw data storage and with a LoRaWAN transmission module for real-time data visualization and management in remote IoT clouds. Furthermore, the system is externally powered by an AGM lead-acid battery, connected to a photovoltaic panel, enabling energy self-sufficiency during field deployments.

The system was calibrated with a commercial multi-gas analyzer through a series of laboratory tests, with results comparable to those of commercially available instruments. Furthermore, experimental tests were conducted using the developed flux chamber prototype to investigate the biodegradation dynamics of soils artificially contaminated with two different fuel types. Continuous monitoring over a two-month period enabled the observation of biodegradation-related processes and the associated emissions of VOCs and CO₂. Subsequently, the automated chamber was employed in a two-week monitoring campaign at a contaminated site, to evaluate its efficiency in real contamination scenarios.

The system developed in this work represents a promising step toward an economical and scalable solution for a deeper understanding of soil biodegradation processes and the resulting gas emissions at contaminated sites, accounting for correlations with environmental parameters as temperature, humidity and atmospheric pressure. Furthermore, the integration with IoT environments, together with full system automation and energy self-sufficiency, provides a significant contribution to the digitalization and automation of subsurface monitoring techniques.

How to cite: Tonolo, N., Teggi, S., Berardi, S., Bogliolo, M. P., and Verginelli, I.: An Innovative IoT-Based Automated Flux Chamber for Continuous Monitoring of CO2 and VOC Emissions from the Subsurface, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12634, https://doi.org/10.5194/egusphere-egu26-12634, 2026.

Continuous automated measurements are essential for quantifying the spatial and temporal variability in soil greenhouse gas fluxes and for resolving “hot spot” and “hot moments” that contribute disproportionately to ecosystem-scale emissions. This is especially important for methane (CH₄) and nitrous oxide (N₂O), where short-lived emission pulses can account for a substantial fraction of the annual mean and are readily missed by low-frequency sampling.

We found that hot moments accounted for <5% of the measurements but contributed ~71% of the annual N₂O flux from a wheat cropland soil in a high-emission year, and CH₄ hot moments ranged from ~100% to >700% of seasonal means. Similarly, in peatland maize system, hot spots and hot moments accounted for <2% of the measurements but were 45 ± 1% of mean annual N₂O fluxes and up to 140 ± 9% of mean annual CH₄ fluxes. Automated chamber systems can provide multiple flux estimates per hour and dense concentration time series within each enclosure, increasing the likelihood of capturing short-lived pulses, diurnal dynamics, and event-driven responses.

Quantifying extreme flux events is not without challenges, however. Deriving fluxes from automated chamber time series requires careful and transparent processing. Non-linear concentration trajectories may arise from diffusion and certain chamber-soil geometries, in which case linear regressions can underestimate fluxes. Conversely, apparent non-linearity can result from artifacts (e.g., inadequate mixing, leaks, or collar effects), and thus non-linear models may yield good statistical fits but biased flux estimates. High flux events can cause carryover of gases between consecutive chamber measurements and generate substantial artifacts, often appearing as inflated concentrations at the start of the next measurement and, in some cases, as subsequent apparent negative fluxes when the system “recovers.

Relating fluxes to drivers presents additional challenges. Continuous measurements of key environmental variables, such as soil oxygen, moisture, and temperature, are required at the same temporal and spatial scale as greenhouse gas fluxes to accurately capture relationships. Data on soil pH and mineral nitrogen also significantly improve model prediction, although few studies sample with sufficient intensity to provide strong inference.

In this work, we provide a roadmap for improving the utility of automated measurements at ecosystem scales, assessing transparent, physics-based selection of linear vs. nonlinear models, the adoption of standardized, community-aligned flux processing workflows, quality control diagnostics, and recommended sensor suites to improve comparability across studies and strengthen inference about hot moments and hot spots in soil greenhouse emissions.

How to cite: Silver, W., Perez, T., and Kwong, C.: Hot spots, hot moments, or hot mess: determining the patterns and drivers of greenhouse gas emissions using continuous automated measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13287, https://doi.org/10.5194/egusphere-egu26-13287, 2026.

High-latitude ecosystems are already experiencing accelerated warming, and predictions indicate that those areas will warm more than the global average in the coming decades. There is a lack of long-term manipulation experiments in Arctic and subarctic grasslands that can help with predictions on how these changes will affect those keystone ecosystems.

Here, we investigate the effects of medium-term (16 years) and long-term (>60 years) soil warming on leaf-level gas exchange measured in situ on Ranunculus acris L. (R. acris) growing at unmanaged grasslands at the ForHot soil warming infrastructure in southern Iceland. Measurements were done in plots having no additional warming (ambient) or with an increase in mean annual soil temperature of +8°C. Clamp-on measurements as well as response curves for both intracellular CO₂ (A/C_i) and light (A/I) were done, and non-linear modelling and the Farquhar model were used to estimate different physiological or photosynthetic traits. Finally, chemical analyses on the measured leaves were executed to gain further insights into apparent changes.

Our results showed little to no significant effect of prolonged soil warming on the characteristics of the A/C_i or A/I curve parameters, indicating a conservative response in C uptake per unit leaf area. Also, no significant effects were found for stomatal conductance (g_s), stable isotope ratio (δ¹³C) and leaf-N between the soil warming treatments, indicating that the expected indirect effects of the prolonged soil warming were not apparent. However, across the entire experiment (i.e., across all soil temperature plots), R. acris showed a strong positive response to leaf-N concentrations across almost all estimated traits. Indicating that variability in plant N status was still the primary indirect driver of photosynthetic capacity in these ambient and warmed subarctic grasslands, irrespective of soil warming or duration of warming.

Our findings suggest that R. acris already had high photosynthetic capacity in soils at ambient conditions, and it may therefore have allocated additional nutrients acquired in warmer soils to other growth-related processes rather than to enhancing the photosynthetic system at a leaf level.

How to cite: Tchana Wandji, R., Callebaut, T., Filella, I., Lootens, P., and D. Sigurdsson, B.: Effects of Medium- and Long-Term Soil Warming on Plant Photosynthesis in a Subarctic Grassland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11210, https://doi.org/10.5194/egusphere-egu26-11210, 2026.

Climate change due to anthropogenic factors is of global concern. Monitoring and reporting all potential carbon sinks and sources are therefore important. Iceland adopted the Paris Agreement in 2016 and decided in 2019 to participate jointly with the EU in meeting the Paris Agreement commitments. This requires compiling and reporting emission data for the whole country. Land use, land use change and forestry (LULUCF) is the main contributing factor of GHG emissions in Iceland. However, heathlands are the spatially largest of the LULUCF grassland category although not the largest contributor to CO₂ emissions, as that is the wetland converted to grassland subcategory. Emission from the heathlands subcategory is currently uncertain and still rely on the IPCC default Tier 1 emission factors. In this ongoing research the aim is to improve the understanding of the carbon processes in Icelandic heathlands and to push the national accounting for grasslands to a higher tier. Since 2022 CO₂ emissions have been measured weekly at different heathland sites during the summer months; May to September. Currently, around 50 plots are active. Each plot is comprised of 9 to 12 hollow 12 cm cylinders driven roughly 10 cm into the ground in clusters of 3. EGM-5 portable CO₂ gas analyzer with a CPY-5 transparent chamber is used for field measurements, first with the chamber uncovered then covered. Other measurements include soil temperature, soil moisture, air temperature and PAR. Preliminary results show a seasonal trend of sequestration increase later in the summer. There is a high variability in the data but an overall higher sequestration rate than emission rate is detected. Cloud cover and weather conditions have a clear effect on the carbon flux. This variability underlines the importance of long-term series for such datasets.

How to cite: Gudmundsdottir, S. B., Sanchez, S., Benediktsdottir, E. B., Fridriksdottir, H. V., and Thorsson, J.: Carbon flux in Icelandic heathlands; a contribution to LULUCF Tier 2 emission factors, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12931, https://doi.org/10.5194/egusphere-egu26-12931, 2026.

It is widely known that the increasing presence of invasive annual grasses (IAG), such as Bromus tectorum (cheatgrass), is reshaping landscapes through positive fire-IAG feedback loops across the ~520,000 km² sagebrush ecosystems of the western US. However, the short-term effects of invasive annuals and fire cycles on net ecosystem CO₂ exchange (NEE) and their controlling mechanisms remain poorly quantified, partly due to limited observations able to resolve heterogeneity in plant community composition, fire effects, and soil-vegetation interactions. The objectives of this study were (1) to quantify how fire, plant community species composition, IAG presence, plant canopy greenness (NDVI), and environmental drivers influence late season NEE; and (2) to compare 4x3 meter plot-level NEE values measured with an automated mobile transparent ecosystem gas exchange chamber with simultaneously collected NEE measured across the entire study site using eddy covariance.  We measured diel NEE using the automated chamber system on 20 4x3 m experimental plots, which were categorized into pairs according to their plant compositions, namely the varying amounts of B. tectorum, perennial native herbaceous species, and native perennial grasses. In late autumn 2025, one plot within each pair was experimentally burned, allowing for comparison of burned and unburned plots with similar pre-fire vegetation.  Repeated pre- and post-burn CO₂ flux measurements were collected and analyzed in relation to key NEE drivers, including photosynthetically active radiation (PAR), air temperature, and vegetation composition. Nighttime NEE ranged from 0.62 to 1.48 µmol CO₂ m⁻² s⁻¹, consistent with net CO₂ release via ecosystem respiration, while daytime NEE ranged from 0.32 to −4.36 µmol CO₂ m⁻² s⁻¹, indicating net CO₂ uptake. Negative daytime NEE was observed on all measurement dates for most plots, including post-fire burned plots, coincident with late-season germination of B. tectorum. Daytime NEE measured in burned plots actually became more negative relative to values measured in these plots before they were burned, whereas nighttime values remained unchanged. These patterns highlight the importance of IAGs in mediating short-term carbon exchange in recently disturbed sagebrush ecosystems. This work aims to provide new insight into the short-term carbon dynamics of fire-prone sagebrush systems and improve mechanistic understanding of disturbance-driven changes in ecosystem CO₂ exchange. 

How to cite: Bohlmann, N. V., Beres, N. D., Arnone III, J. (. A., Jansoni, R. L., and Andrade, A. J.: Short-term impacts of prescribed fire and invasive grass on net ecosystem CO2 exchange (NEE) in sagebrush ecosystems: quantifying NEE drivers through automated plot-scale chamber measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14871, https://doi.org/10.5194/egusphere-egu26-14871, 2026.

Climate change has increased the frequency and intensity of extreme weather events, including droughts and heavy rainfall, across large parts of Central Europe. Forest ecosystems in this region remain exposed to elevated nitrogen inputs from agricultural and industrial sources, despite declining atmospheric N deposition. The combined effects of altered precipitation regimes and N deposition are expected to modify key soil biogeochemical processes and greenhouse gas (GHG) fluxes, yet their interactive impacts remain poorly constrained.

We investigated soil GHG responses to combined drying–rewetting (DRW) cycles and N addition across three representative Austrian broadleaf forest sites. DRW treatments excluded natural rainfall during the growing season, thereby inducing soil drought, and redistributed long-term mean precipitation into three extreme rainfall events, thereby rewetting the dry soil. Nitrogen addition was applied at a rate of 40 kg N ha⁻¹ yr⁻¹. Soil CO₂, N₂O, and CH₄ fluxes were measured at high temporal resolution using automated chamber systems, alongside continuous soil moisture and temperature measurements, during the years 2021, 2022, and 2025. Ancillary soil chemical and biological data were collected to support field observations.

Soil GHG fluxes were analysed using empirical modelling and Bayesian inference. Drying–rewetting treatments led to reduced soil CO₂ emissions and strongly suppressed N₂O fluxes, as drought-induced reductions in these fluxes outweighed the rewetting pulses, while CH₄ uptake was enhanced compared to ambient conditions. During naturally dry periods, N₂O emissions converged between DRW and control plots. Nitrogen addition exerted only modest effects on GHG fluxes across sites. Modelling results revealed site-specific differences in the relative importance of soil moisture and temperature as drivers of GHG fluxes, linked to soil type and hydrological context.

Our findings highlight the importance of high-frequency automated chamber measurements combined with empirical modelling approaches for assessing the relative importance of extreme precipitation regimes and N deposition on forest soil GHG budgets, with implications for understanding ecosystem–climate feedbacks under future climate scenarios.

How to cite: Goff, D., Thomas, D., Ika, D., Markus, G., Barbara, K., Johannes, K., Mathias, S., and Eugenio, D.-P.: Relative importance of soil temperature and soil moisture on GHG fluxes is site specific: Results from three drying-rewetting experiments in Austrian forests, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17474, https://doi.org/10.5194/egusphere-egu26-17474, 2026.

Greenhouse gases (GHGs) are responsible for global warming, which has intensified due to human activities over the past few decades. Coastal wetlands—mangroves and salt marshes—represent an alternative for mitigating the negative effects of GHGs due to the ecosystem services they provide, especially carbon storage. However, they can act as sources or sinks of GHGs, but studies in Mexico are scarce and limited to the tropical and subtropical climates of the country, while the arid ecosystems of the northwest are understudied. This study was conducted in mangroves and salt marshes of Laguna La Cruz, Gulf of California, to understand the temporal and spatial dynamics, as well as the relationship between biogenic structures (crab burrows, and pneumatophores) and abiotic factors that influence GHG fluxes. Monthly measurements were taken during June, October, and November (2024) using static chambers coupled to an infrared spectrophotometer (FTIR, Gasmet DX4015) to simultaneously measure CO₂, CH₄, and N₂O. Eight chambers per site were used under three treatments: the presence of burrows, and pneumatophores, and the absence of these structures. Fluxes were estimated using the Rfluxes package, and data were analyzed using REML and PCA (RStudio 2024). CO₂, CH₄, and N₂O fluxes ranged from -53.53 to 437.13, -0.0899 to 0.0413, and -0.0325 to 0.0213 mg m^-2 h^-1, respectively. The most relevant factors for CO₂ and CH₄ were month, soil temperature, and biogenic structures. Biogenic structures facilitate the interaction between the soil and the atmosphere, while the month and soil temperature affect the metabolic activity of GHG-producing microorganisms. N₂O did not show a clear relationship with any of the studied variables. GHG fluxes showed temporal variation and seem to be influenced by the presence of biogenic structures. However, more samples are needed to understand the annual variation of GHG, as well as to consider other variables that better explain the N₂O variation.

How to cite: Gutiérrez Rongel, J. M., Molina Freaner, F. E., and González Méndez, B.: Effect of biogenic structures on greenhouse gases emissions in wetlands of La Cruz Lagoon, Gulf of California, Sonora, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22189, https://doi.org/10.5194/egusphere-egu26-22189, 2026.