Greenhouse gas fluxes in a managed floodplain forest in the Amazon estuary

Evaluating temporal controls on greenhouse gas (GHG) fluxes in an Arctic tundra environment: An entropy-based approach

Freshwater wetlands can contribute significantly to the global carbon budget as a net source or sink of the major greenhouse gas (GHG) fluxes such as carbon dioxide (FCO2) and methane (FCH4). The amount of GHG fluxes in the freshwater wetlands is highly variable and depends on a range of environmental drivers. These wetlands are commonly hypothesized to be net sinks (i.e., burial) of FCO2 and net sources (emission) of FCH4 at the monthly to annual scales. Understanding the environmental controls on the wetland GHG fluxes is essential for an accurate estimation of the global GHG budget, which is often used as a pivotal measure to reduce GHG emissions and enhance carbon sequestration. In this study, we analyzed FLUXNET data from 38 freshwater wetlands located across the globe to investigate the relationships of monthly-scale GHG fluxes with various climatic and ecohydrological drivers. Data analytics with multivariate pattern recognition techniques—including principal component analysis, factor analysis, and partial least squares regression— were performed to identify and quantify the dominant controls of wetland FCO2 and FCH4 fluxes. The environmental controls on the GHG fluxes in freshwater wetlands were found to highly vary based on the climatic zones. In the tropical (i.e., mega thermal) zone, the GHG fluxes were overall primarily controlled by photosynthetically active radiation (PAR), soil temperature (TS), wind speed (WS), friction velocity (USTAR), and vapor pressure deficit (VPD). However, the latent heat flux (LE) and VPD, alongside PAR, TS, and USTAR, exhibited the dominant controls on the GHG fluxes in the dry (or arid) zone wetlands. Both GHG fluxes in wetlands of the temperate (or mesothermal) zone were mainly controlled by water table depth (WL), TS, and LE. Surprisingly, PAR did not appear to be a strong driver of the monthly averaged fluxes in the temperate wetlands. In contrast, PAR, LE, TS, WS, and USTAR were the primary controlling factors of the GHG fluxes in wetlands representing continental (or microthermal) climates. However, in wetlands of the polar (alpine) region, sensible heat flux (H) had a strong linkage with the GHG fluxes, alongside the controls of PAR, TS, WS, VPD and USTAR. These findings and new knowledge can help inform wetland management and conservation strategies, particularly in the context of climate and land cover changes. Effective management and conservation of wetlands can help reduce GHG emissions, thereby contributing to the mitigation efforts on global warming.

Global urbanization has significantly affected land use, with former agricultural or forested land being used for human settlements and urban green spaces. How this urbanization may have affected the spatial and temporal patterns of soil greenhouse gas (GHG) fluxes, especially those of nitrous oxide (N2O), remains largely unexplored, although a recent study indicated that urbanization accelerates GHG fluxes from soils. In this study, we investigated soil GHG fluxes at Aarhus University Park (AU Park), a public park located in a hilly landscape with different use intensities. Soil GHG fluxes were measured 2-3 times per week over a period of 7 months using a fast chamber approach at about 55 sampling points with different management, vegetation, and landscape position (uphill, slope, foothill, ponds). Specifically, we focused on the identification of GHG flux hot and cold spots, and thereby investigated the temporal persistence of such spatial emission patterns. Our results show that GHG fluxes were highly variable over the observation period, but that major GHG flux hotspots, such as those near a pond, were hotspots at all observation times. In addition, we were able to relate the spatio-temporal variations in soil GHG fluxes to landscape parameters such as slope and exposition, and to soil parameters such as soil organic carbon concentration, pH, and texture. Our measurements show that there are significant spatio-temporal variations in GHG fluxes in urban parks and that these variations are strongly influenced by environmental and landscape parameters. This observation may allow a better scaling of GHG fluxes of urban green spaces and thus a better assessment of how urbanization changes landscape fluxes.

Understory management and fertilization affected soil greenhouse gas emissions and labile organic carbon pools in a Chinese chestnut plantation

Response of soil greenhouse gas fluxes to warming: A global meta‐analysis of field studies

The study of greenhouse gas (GHG) fluxes in terrestrial ecosystems is becoming increasingly important as the observed rise in global temperature and increased frequency of extreme weather events are attributed by the majority of climate experts to increased atmospheric GHG concentrations. Adequate and comprehensive knowledge of surface GHG fluxes is important for obtaining reliable information on CO2 and other GHG fluxes at regional and global scales, as well as for preparing reports on national GHG emissions and removals. The need to obtain accurate estimates of GHG fluxes at regional and global scales has led to the development of innovative mathematical models of varying complexity. These models can be divided into forward and inverse models. Forward algorithms provide the ability to estimate GHG fluxes when sufficient information on the structure of GHG sources and sinks is available. Inverse algorithms allow the retrieval of surface fluxes using remote sensing data. The most promising way to study high resolution fluxes over areas with complex topography and mosaic vegetation patterns is the use of unmanned aerial vehicles (UAVs).In our study, we proposed and tested a forward and inverse model for estimating GHG fluxes over an inhomogeneous underlying surface. The forward model is based on the RANS hydrodynamic model to calculate the wind velocity and turbulence coefficient, and the solution of the advection-diffusion equation to find a three-dimensional distribution of GHG concentrations. The GHG fluxes at the specified height above the ground surface are then calculated using the obtained concentration distribution and turbulence coefficient. The inverse algorithm is based on minimizing a cost functional, defined as the root mean square deviation of the modeled concentration field from the measured data. Concentration measurements at multiple (at least two) levels can be performed using UAV-based gas analyzers.Three experimental sites selected for our modeling study differ in geographic location, topography, and vegetation heterogeneity. These sites are: i) swampy and forested areas of the "Mukhrino" carbon supersite (Khanty-Mansiysk Autonomous Okrug, Russia, 60°53'20" N, 68°42'10" E), ii) the Roshni-Chu mountain forest site, which is part of the "Way Carbon" supersite (Chechen Republic, Russia, 43°2'59" N, 45°25'32" E), iii) the mixed forest experimental site "Lyali" (Komi Republic, Russia, 62°16'28" N, 50°39'54" E). For our numerical experiments we used measured data on surface topography, LAI, soil respiration, air temperature, prevailing wind direction, vertical canopy CO2 concentration profile and CO2 fluxes measured by eddy covariance technique.The model results show a rather good agreement with the measured data and could help to interpret the experimentally observed dependence of CO2 fluxes on wind direction in areas with an inhomogeneous underlying surface.

Coastal salt marshes play an important role in mitigating global warming by removing atmospheric carbon at a high rate. We investigated the environmental controls and emergent scaling of major greenhouse gas (GHG) fluxes such as carbon dioxide (CO2) and methane (CH4) in coastal salt marshes by conducting data analytics and empirical modeling. The underlying hypothesis is that the salt marsh GHG fluxes follow emergent scaling relationships with their environmental drivers, leading to parsimonious predictive models. CO2 and CH4 fluxes, photosynthetically active radiation (PAR), air and soil temperatures, well water level, soil moisture, and porewater pH and salinity were measured during May–October 2013 from four marshes in Waquoit Bay and adjacent estuaries, MA, USA. The salt marshes exhibited high CO2 uptake and low CH4 emission, which did not significantly vary with the nitrogen loading gradient (5–126 kg · ha−1 · year−1) among the salt marshes. Soil temperature was the strongest driver of both fluxes, representing 2 and 4–5 times higher influence than PAR and salinity, respectively. Well water level, soil moisture, and pH did not have a predictive control on the GHG fluxes, although both fluxes were significantly higher during high tides than low tides. The results were leveraged to develop emergent power law‐based parsimonious scaling models to accurately predict the salt marsh GHG fluxes from PAR, soil temperature, and salinity (Nash‐Sutcliffe Efficiency = 0.80–0.91). The scaling models are available as a user‐friendly Excel spreadsheet named Coastal Wetland GHG Model to explore scenarios of GHG fluxes in tidal marshes under a changing climate and environment.

Quantifying the role of soils in nature-based solutions require accurate estimates of soil greenhouse gas (GHG) fluxes. Technological advances allow to simultaneously measure multiple GHGs and now is possible to provide complete GHG budgets from soils (i.e., CO2, CH4 and N2O fluxes). We propose that there is a conflict between the convenience of simultaneously measuring multiple soil GHG fluxes at fixed time intervals (e.g., once, or twice per month) and the intrinsic temporal variability and patterns of different GHG fluxes. Information derived from fixed time intervals -as is commonly done during manual field campaigns- had limitations to reproduce statistical properties, temporal dependence, annual budgets, and associated uncertainty, when compared with information derived from continuous measurements (i.e., automated hourly measurements) for all soil GHG fluxes. We present a novel approach (i.e., temporal univariate Latin Hypercube sampling) that can be applied to optimize monitoring efforts of GHG fluxes across time. We suggest that multiple GHG fluxes should not be simultaneously measured at few fixed time intervals (especially once a month), but an optimized sampling approach can be used to reduce bias and uncertainty. These results have implications for assessing GHG fluxes from soils and consequently reduce uncertainty on the role of soils in nature-based solutions.

Quantifying the role of soils in nature-based solutions require accurate estimates of soil greenhouse gas (GHG) fluxes. Technological advances allow to simultaneously measure multiple GHGs and now is possible to provide complete GHG budgets from soils (i.e., CO2, CH4 and N2O fluxes). We propose that there is a conflict between the convenience of simultaneously measuring multiple soil GHG fluxes at fixed time intervals (e.g., once, or twice per month) and the intrinsic temporal variability and patterns of different GHG fluxes. Information derived from fixed time intervals -as is commonly done during manual field campaigns- had limitations to reproduce statistical properties, temporal dependence, annual budgets, and associated uncertainty, when compared with information derived from continuous measurements (i.e., automated hourly measurements) for all soil GHG fluxes. We present a novel approach (i.e., temporal univariate Latin Hypercube sampling) that can be applied to optimize monitoring efforts of GHG fluxes across time. We suggest that multiple GHG fluxes should not be simultaneously measured at few fixed time intervals (especially once a month), but an optimized sampling approach can be used to reduce bias and uncertainty. These results have implications for assessing GHG fluxes from soils and consequently reduce uncertainty on the role of soils in nature-based solutions.

Abstract. Quantifying the role of soils in nature-based solutions requires accurate estimates of soil greenhouse gas (GHG) fluxes. Technological advances allow us to measure multiple GHGs simultaneously, and now it is possible to provide complete GHG budgets from soils (i.e., CO2, CH4, and N2O fluxes). We propose that there is a conflict between the convenience of simultaneously measuring multiple soil GHG fluxes at fixed time intervals (e.g., once or twice per month) and the intrinsic temporal variability in and patterns of different GHG fluxes. Information derived from fixed time intervals – commonly done during manual field campaigns – had limitations to reproducing statistical properties, temporal dependence, annual budgets, and associated uncertainty when compared with information derived from continuous measurements (i.e., automated hourly measurements) for all soil GHG fluxes. We present a novel approach (i.e., temporal univariate Latin hypercube sampling) that can be applied to provide insights and optimize monitoring efforts of GHG fluxes across time. We suggest that multiple GHG fluxes should not be simultaneously measured at a few fixed time intervals (mainly when measurements are limited to once per month), but an optimized sampling approach can be used to reduce bias and uncertainty. These results have implications for assessing GHG fluxes from soils and consequently reduce uncertainty in the role of soils in nature-based solutions.

Quantifying the role of soils in nature-based solutions require accurate estimates of soil greenhouse gas (GHG) fluxes. Technological advances allow to simultaneously measure multiple GHGs and now is possible to provide complete GHG budgets from soils (i.e., CO2, CH4 and N2O fluxes). We propose that there is a conflict between the convenience of simultaneously measuring multiple soil GHG fluxes at fixed time intervals (e.g., once, or twice per month) and the intrinsic temporal variability and patterns of different GHG fluxes. Information derived from fixed time intervals -as is commonly done during manual field campaigns- had limitations to reproduce statistical properties, temporal dependence, annual budgets, and associated uncertainty, when compared with information derived from continuous measurements (i.e., automated hourly measurements) for all soil GHG fluxes. We present a novel approach (i.e., temporal univariate Latin Hypercube sampling) that can be applied to optimize monitoring efforts of GHG fluxes across time. We suggest that multiple GHG fluxes should not be simultaneously measured at few fixed time intervals (especially once a month), but an optimized sampling approach can be used to reduce bias and uncertainty. These results have implications for assessing GHG fluxes from soils and consequently reduce uncertainty on the role of soils in nature-based solutions.

Quantifying the role of soils in nature-based solutions require accurate estimates of soil greenhouse gas (GHG) fluxes. Technological advances allow to simultaneously measure multiple GHGs and now is possible to provide complete GHG budgets from soils (i.e., CO2, CH4 and N2O fluxes). We propose that there is a conflict between the convenience of simultaneously measuring multiple soil GHG fluxes at fixed time intervals (e.g., once, or twice per month) and the intrinsic temporal variability and patterns of different GHG fluxes. Information derived from fixed time intervals -as is commonly done during manual field campaigns- had limitations to reproduce statistical properties, temporal dependence, annual budgets, and associated uncertainty, when compared with information derived from continuous measurements (i.e., automated hourly measurements) for all soil GHG fluxes. We present a novel approach (i.e., temporal univariate Latin Hypercube sampling) that can be applied to optimize monitoring efforts of GHG fluxes across time. We suggest that multiple GHG fluxes should not be simultaneously measured at few fixed time intervals (especially once a month), but an optimized sampling approach can be used to reduce bias and uncertainty. These results have implications for assessing GHG fluxes from soils and consequently reduce uncertainty on the role of soils in nature-based solutions.

Interactions between climate warming and land management regulate greenhouse gas fluxes in a temperate grassland ecosystem

Background and Aims Plant-soil interactions are critical in governing soil carbon (C) stocks and greenhouse gas (GHG) fluxes, but they vary significantly across land uses, soil types, and soil management practices. Finding a potential intervention that could enhance soil C and GHG fluxes relies on reliable baseline data that can capture these variations. Complex estates, characterised by such heterogeneous conditions, require standardised protocols to ensure reproducibility and comparability across sites. Methods This study introduces a five-stage protocol for systematically measuring and potentially monitoring soil C stocks (including organic and inorganic forms) and GHG fluxes. The protocol is exclusively designed for "Time-Zero" (T = 0) baseline assessments and the strategic selection of sampling sites. However, it also offers a consistent and robust adjustment of the protocol for long-term soil sampling and GHG flux measurements (i.e. monitoring purposes). The approach was tested at RAF Leeming, a Royal Air Force base (500 ha) located in Yorkshire, UK, with varied land uses, soil types, and management practices. Results The protocol provided a rigorous, reproducible and adaptable framework for obtaining robust baseline data. It also facilitated the quantification of soil C and GHG fluxes, demonstrating the value of a standardised approach to avoid potential under- or overestimation. Additionally, the proposed protocol proved to be useful to guide site-specific interventions by ensuring that relevant factors, such as plant and soil interactions and environmental covariates, are integrated to enhance comparability across space and time. The results also reinforce the scalability of the protocol, with potential applications across a range of complex estates, including urban areas, military installations, airports, and other managed landscapes. Conclusions The proposed protocol enables standardised, transparent soil C and GHG flux monitoring to meet internationally accepted standards. We advocate for its broad implementation across estates with varying land uses and soil characteristics to support sustainable soil management and climate mitigation efforts.

Soil water potential (Ψ) controls the dynamics of water in soils and can therefore affect greenhouse gas fluxes. We examined the relationship between soil moisture content (θ) at five different levels of water potential (Ψ = 0, −0.05, −0.1, −0.33 and −15 bar) and greenhouse gas (carbon dioxide, CO2; nitrous oxide, N2O and methane, CH4) fluxes. The study was conducted in 2011 in a silt loam soil at Freeman farm of Lincoln University. Soil samples were collected at two depths: 0–10 and 10–20 cm and their bulk densities were measured. Samples were later saturated then brought into a pressure plate for measurements of Ψ and θ. Soil air samples for greenhouse gas flux analyses were collected using static and vented chambers, 30 cm in height and 20 cm in diameter. Determination of CO2, CH4 and N2O concentrations from soil air samples were done using a Shimadzu Gas Chromatograph (GC-14). Results showed that there were significant correlations between greenhouse gas fluxes and θ held at various Ψ in the 0–10 cm depth of soil group. For instance, θ at Ψ = 0 positively correlated with measured CO2 (p = 0.0043, r = 0.49), N2O (p = 0.0020, r = 0.64) and negatively correlated with CH4 (p = 0.0125, r = −0.44) fluxes. Regression analysis showed that 24%, 41% and 19% of changes in CO2, N2O and CH4 fluxes, respectively, were due to θ at Ψ = 0 (p < 0.05). This study stresses the need to monitor soil water potential when monitoring greenhouse gas fluxes.