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The quantification of emissions of greenhouse gases and air pollutants through the inversion of plumes in satellite images remains a complex problem that current methods can only assess with significant uncertainties. The anticipated launch of the CO2M (Copernicus Anthropogenic Carbon Dioxide Monitoring) satellite constellation in 2026 is expected to provide high-resolution images of CO2 (carbon dioxide) column-averaged mole fractions (XCO2), opening up new possibilities. However, the inversion of future CO2 plumes from CO2M will encounter various obstacles. A challenge is the low CO2 plume signal-to-noise ratio due to the variability in the background and instrumental errors in satellite measurements. Moreover, uncertainties in the transport and dispersion processes further complicate the inversion task. To address these challenges, deep learning techniques, such as neural networks, offer promising solutions for retrieving emissions from plumes in XCO2 images. Deep learning models can be trained to identify emissions from plume dynamics simulated using a transport model. It then becomes possible to extract relevant information from new plumes and predict their emissions. In this paper, we develop a strategy employing convolutional neural networks (CNNs) to estimate the emission fluxes from a plume in a pseudo- XCO2 image. Our dataset used to train and test such methods includes pseudo-images based on simulations of hourly XCO2 , NO2 (nitrogen dioxide), and wind fields near various power plants in eastern Germany, tracing plumes from anthropogenic and biogenic sources. CNN models are trained to predict emissions from three power plants that exhibit diverse characteristics. The power plants used to assess the deep learning model's performance are not used to train the model. We find that the CNN model outperforms state-of-the-art plume inversion approaches, achieving highly accurate results with an absolute error about half of that of the cross-sectional flux method and an absolute relative error of ∼ 20 % when only the XCO2 and wind fields are used as inputs. Furthermore, we show that our estimations are only slightly affected by the absence of NO2 fields or a detection mechanism as additional information. Finally, interpretability techniques applied to our models confirm that the CNN automatically learns to identify the XCO2 plume and to assess emissions from the plume concentrations. These promising results suggest a high potential of CNNs in estimating local CO2 emissions from satellite images. [ABSTRACT FROM AUTHOR]

The assessment of emission factors for many peatlands is difficult, and reliable data on the exchange of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) between soil and atmosphere of these areas is particularly scarce. Reasons for this are the multitude of soil and land use combinations that control greenhouse gas exchange and the high effort associated with data acquisition. We investigated the greenhouse gas exchange of a peat bog restoration sequence over a period of 2 yr (July 2007-June 2009) in an Atlantic raised bog in Northwest Germany. We set up three sites representing different land use intensities: intensive grassland (mineral fertilizer, cattle manure and 4-5 cuts per year); extensive grassland (no fertilizer or manure, maximal 1 cutting per year); near-natural peat bog (almost no anthropogenic influence). We obtained seasonal and annual estimates of greenhouse gas exchange based on closed chamber measurements. CH4 and N2O fluxes were recorded bi-weekly, CO2 NEE determinations were carried out 3-4 weekly. To get annual sums the CH4 and N2O fluxes were interpolated linearly while NEE was modelled. The intensive grassland site emitted 548±169gCO2-Cm-2 in the first and 817±140gCO2-Cm-2 in the second year. The extensive grassland site showed a slight uptake in the first year (-148 ± 143gCO2-Cm-2), and a small emission of 88±146gCO2-Cm-2 in the second year. In contrast to these agriculturally used sites, the near-natural site took up CO2-C in both years(-8±68gCO2-Cm-2 and -127±53gCO2-Cm-2). Under consideration of N2O and CH4 exchange, the total average greenhouse warming potential (GWP) for 2008 amounts to 441 ±157gm-2,14±152gm-2 and 31 ±68gm-2 CO2-C-equivalent for the intensive grassland, the extensive grassland and the near-natural site, respectively. Despite inter-annual variability, rewetting contributes considerably to mitigating GHG emission from formerly drained peatlands. Already extensively used grassland on moderately drained peat approaches the carbon sequestration potential of near-natural sites, albeit it may oscillate between being a small sink and being a small source depending on interannual climatic variability. [ABSTRACT FROM AUTHOR]

Energy supply in Germany is subject to a profound change. The present paper addresses the German potential of storing excess energy from renewable power sources in the geological subsurface. Wind and solar electricity can be transformed into hydrogen, and with carbon dioxide subsequently into methane. When needed, electricity is regained in a gas turbine power plant combusting the methane. Here, we are taking into account the actual German storage capacity for natural gas and show that the outlined technology is ready for operation and economically competitive. The current potential for combined storage of methane and carbon dioxide allows to store around 80 TWh renewable excess energy. This is far more than required to date and estimated to provide the entire coverage in 2050. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions from rivers are globally relevant, but quantification of these emissions comes with considerable uncertainty. Quantification of ecosystem-scale emissions is challenged by both spatial and short-term temporal variability. We measured spatio-temporal variability of CO 2 and CH 4 fluxes from a 1 km long reach of the lowland river Elbe in Germany over 3 d to establish which factor is more relevant to be taken into consideration: small-scale spatial variability or short-term temporal variability of CO 2 and CH 4 fluxes. GHG emissions from the river reach studied were dominated by CO 2 , and 90 % of total emissions were from the water surface, while 10 % of emissions were from dry fallen sediment at the side of the river. Aquatic CO 2 fluxes were similar at different habitats, while aquatic CH 4 fluxes were higher at the side of the river. Artificial structures to improve navigability (groynes) created still water areas with elevated CH 4 fluxes and lower CO 2 fluxes. CO 2 fluxes exhibited a clear diurnal pattern, but the exact shape and timing of this pattern differed between habitats. By contrast, CH 4 fluxes did not change diurnally. Our data confirm our hypothesis that spatial variability is especially important for CH 4 , while diurnal variability is more relevant for CO 2 emissions from our study reach of the Elbe in summer. Continuous measurements or at least sampling at different times of the day is most likely necessary for reliable quantification of river GHG emissions. [ABSTRACT FROM AUTHOR]

The O 2 : CO 2 exchange ratio (ER) between terrestrial ecosystems and the atmosphere is a key parameter for partitioning global ocean and land carbon fluxes. The long-term terrestrial ER is considered to be close to 1.10 mol of O 2 consumed per mole of CO 2 produced. Due to the technical challenge in measuring directly the ER of entire terrestrial ecosystems (ER eco), little is known about variations in ER at hourly and seasonal scales, as well as how different components contribute to ER eco. In this modeling study, we explored the variability in and drivers of ER eco and evaluated the hypothetical uncertainty in determining ecosystem O 2 fluxes based on current instrument precision. We adapted the one-dimensional, multilayer atmosphere–biosphere gas exchange model "CANVEG" to simulate hourly ER eco from modeled O 2 and CO 2 fluxes in a temperate beech forest in Germany. We found that the modeled annual mean ER eco ranged from 1.06 to 1.12 mol mol -1 within the 5-year study period. Hourly ER eco showed strong variations over diel and seasonal cycles and within the vertical canopy profile. The determination of ER from O 2 and CO 2 mole fractions in air above and within the canopy (ER conc) varied between 1.115 and 1.15 mol mol -1. CANVEG simulations also indicated that ecosystem O 2 fluxes could be derived with the flux-gradient method using measured vertical gradients in scalar properties, as well as fluxes of CO 2 , sensible heat and latent energy derived from eddy covariance measurements. Owing to measurement uncertainties, however, the uncertainty in estimated O 2 fluxes derived with the flux-gradient approach could be as high as 15 µ mol m -2 s -1 , which represented the 90 % quantile of the uncertainty in hourly data with a high-accuracy instrument. We also demonstrated that O 2 fluxes can be used to partition net CO 2 exchange fluxes into their component fluxes of photosynthesis and respiration if ER eco is known. The uncertainty of the partitioned gross assimilation ranged from 1.43 to 4.88 µ mol m -2 s -1 assuming a measurement uncertainty of 0.1 or 2.5 µ mol m -2 s -1 for net ecosystem CO 2 exchange and from 0.1 to 15 µ mol m -2 s -1 for net ecosystem O 2 exchange, respectively. Our analysis suggests that O 2 measurements at ecosystem scale have the potential to partition net CO 2 fluxes into their component fluxes, but further improvement in instrument precision is needed. [ABSTRACT FROM AUTHOR]

Anthropogenic activities increase the contributions of inland waters to global greenhouse gas (GHG; CO 2 , CH 4 , and N 2 O) budgets, yet the mechanisms driving these increases are still not well constrained. In this study, we quantified year-long GHG concentrations, fluxes, and water physico-chemical variables from 28 sites contrasted by land use across five headwater catchments in Germany. Based on linear mixed-effects models, we showed that land use was more significant than seasonality in controlling the intra-annual variability of the GHGs. Streams in agriculture-dominated catchments or with wastewater inflows had up to 10 times higher daily CO 2 , CH 4 , and N 2 O emissions and were also more temporally variable (CV > 55 %) than forested streams. Our findings also suggested that nutrient, labile carbon, and dissolved GHG inputs from the agricultural and settlement areas may have supported these hotspots and hot-moments of fluvial GHG emissions. Overall, the annual emission from anthropogenic-influenced streams in CO 2 equivalents was up to 20 times higher (∼ 71 kg CO 2 m -2 yr -1) than from natural streams (∼ 3 kg CO 2 m -2 yr -1), with CO 2 accounting for up to 81 % of these annual emissions, while N 2 O and CH 4 accounted for up to 18 % and 7 %, respectively. The positive influence of anthropogenic activities on fluvial GHG emissions also resulted in a breakdown of the expected declining trends of fluvial GHG emissions with stream size. Therefore, future studies should focus on anthropogenically perturbed streams, as their GHG emissions are much more variable in space and time and can potentially introduce the largest uncertainties to fluvial GHG estimates. [ABSTRACT FROM AUTHOR]

Globally, soil temperature to 1 m depth is predicted to be up to 4 ∘C warmer by the end of this century, with pronounced effects expected in temperate forest regions. Increased soil temperatures will potentially increase the release of carbon dioxide (CO2) from temperate forest soils, resulting in important positive feedback on climate change. Dark CO2 fixation by microbes can recycle some of the released soil CO2 , and CO2 fixation rates are reported to increase under higher temperatures. However, research on the influence of temperature on dark CO2 fixation rates, particularly in comparison to the temperature sensitivity of respiration in soils of temperate forest regions, is missing. To determine the temperature sensitivity (Q10) of dark CO2 fixation and respiration rates, we investigated soil profiles to 1 m depth from beech (deciduous) and spruce (coniferous) forest plots of the Hummelshain forest, Germany. We used 13C - CO2 labelling and incubations of soils at 4 and 14 ∘C to determine CO2 fixation and net soil respiration rates and derived the Q10 values for both processes with depth. The average Q10 for dark CO2 fixation rates normalized to soil dry weight was 2.07 for beech and spruce profiles, and this was lower than the measured average Q10 of net soil respiration rates with ∼2.98. Assuming these Q10 values, we extrapolated that net soil respiration might increase 1.16 times more than CO2 fixation under a projected 4 ∘C warming. In the beech soil, a proportionally larger fraction of the label CO2 was fixed into soil organic carbon than into microbial biomass compared to the spruce soil. This suggests a primarily higher rate of microbial residue formation (i.e. turnover as necromass or release of extracellular products). Despite a similar abundance of the total bacterial community in the beech and spruce soils, the beech soil also had a lower abundance of autotrophs, implying a higher proportion of heterotrophs when compared to the spruce soil; hence this might partly explain the higher rate of microbial residue formation in the beech soil. Furthermore, higher temperatures in general lead to higher microbial residues formed in both soils. Our findings suggest that in temperate forest soils, CO2 fixation might be less responsive to future warming than net soil respiration and could likely recycle less CO2 respired from temperate forest soils in the future than it does now. [ABSTRACT FROM AUTHOR]

Inland waters, particularly small and shallow lakes, are significant sources of carbon dioxide (CO2) and methane (CH4) to the atmosphere. However, the spatial in-lake heterogeneity of CO2 and CH4 production processes and their drivers in the sediment remain poorly studied. We measured potential CO2 and CH4 production in slurry incubations from 12 sites within the small and shallow crater lake Windsborn in Germany, as well as fluxes at the water–atmosphere interface of intact sediment core incubations from four sites. Production rates were highly variable and ranged from 7.2 to 38.5 µ mol CO2 gC -1 d -1 and from 5.4 to 33.5 µ mol CH4 gC -1 d -1. Fluxes ranged from 4.5 to 26.9 mmol CO2 m -2 d -1 and from 0 to 9.8 mmol CH4 m -2 d -1. Both CO2 and CH4 production rates and the CH4 fluxes exhibited a significant and negative correlation (p<0.05 , ρ<-0.6) with a prevalence of recalcitrant organic matter (OM) compounds in the sediment as identified by Fourier-transformed infrared spectroscopy. The carbon / nitrogen ratio exhibited a significant negative correlation (p<0.01 , ρ=-0.88) with CH4 fluxes but not with production rates or CO2 fluxes. The availability of inorganic (nitrate, sulfate, ferric iron) and organic (humic acids) electron acceptors failed to explain differences in CH4 production rates, assuming a competitive suppression, but observed non-methanogenic CO2 production could be explained up to 91 % by prevalent electron acceptors. We did not find clear relationships between OM quality, the thermodynamics of methanogenic pathways (acetoclastic vs. hydrogenotrophic) and electron-accepting capacity of the OM. Differences in CH4 fluxes were interestingly to a large part explained by grain size distribution (p<0.05 , ρ=±0.65). Surprisingly though, sediment gas storage, potential production rates and water–atmosphere fluxes were decoupled from each other and did not show any correlations. Our results show that within a small lake, sediment CO2 and CH4 production shows significant spatial variability which is mainly driven by spatial differences in the degradability of the sediment OM. We highlight that studies on production rates and sediment quality need to be interpreted with care, though, in terms of deducing emission rates and patterns as approaches based on production rates only neglect physical sediment properties and production and oxidation processes in the water column as major controls on actual emissions. [ABSTRACT FROM AUTHOR]

Stomatal regulation and whole plant hydraulic signaling affect water fluxes and stress in plants. Land surface models and crop models use a coupled photosynthesis–stomatal conductance modeling approach. Those models estimate the effect of soil water stress on stomatal conductance directly from soil water content or soil hydraulic potential without explicit representation of hydraulic signals between the soil and stomata. In order to explicitly represent stomatal regulation by soil water status as a function of the hydraulic signal and its relation to the whole plant hydraulic conductance, we coupled the crop model LINTULCC2 and the root growth model SLIMROOT with Couvreur's root water uptake model (RWU) and the HILLFLOW soil water balance model. Since plant hydraulic conductance depends on the plant development, this model coupling represents a two-way coupling between growth and plant hydraulics. To evaluate the advantage of considering plant hydraulic conductance and hydraulic signaling, we compared the performance of this newly coupled model with another commonly used approach that relates root water uptake and plant stress directly to the root zone water hydraulic potential (HILLFLOW with Feddes' RWU model). Simulations were compared with gas flux measurements and crop growth data from a wheat crop grown under three water supply regimes (sheltered, rainfed, and irrigated) and two soil types (stony and silty) in western Germany in 2016. The two models showed a relatively similar performance in the simulation of dry matter, leaf area index (LAI), root growth, RWU, gross assimilation rate, and soil water content. The Feddes model predicts more stress and less growth in the silty soil than in the stony soil, which is opposite to the observed growth. The Couvreur model better represents the difference in growth between the two soils and the different treatments. The newly coupled model (HILLFLOW–Couvreur's RWU–SLIMROOT–LINTULCC2) was also able to simulate the dynamics and magnitude of whole plant hydraulic conductance over the growing season. This demonstrates the importance of two-way feedbacks between growth and root water uptake for predicting the crop response to different soil water conditions in different soils. Our results suggest that a better representation of the effects of soil characteristics on root growth is needed for reliable estimations of root hydraulic conductance and gas fluxes, particularly in heterogeneous fields. The newly coupled soil–plant model marks a promising approach but requires further testing for other scenarios regarding crops, soil, and climate. [ABSTRACT FROM AUTHOR]