Model Of Carbon Dynamics Research Articles

Rainfall and Seasonality Drive Pyrogenic Carbon Stocks in Coarse‐Textured Mineral Soils

AbstractPyrogenic carbon (PyC) from biomass burning is a large, but poorly quantified, slow‐cycling component of the soil organic carbon pool. Modeling of soil carbon dynamics can be improved by including the processes governing the input and cycling of PyC in the soil. The carbon isotope composition of PyC (δ13CPyC) provides a tracer for the partitioning of PyC into the soil from biomass. We report the stocks and δ13C values for PyC and organic carbon (OC) for 41 regions dominated by savannas and seasonally wet to arid regions of Australia and Africa. Stocks of PyC in the 0–5 cm interval ranged from 0 to 1.17 MgC ha−1 (mean 0.43 ± 0.25 MgC ha−1) and in the 0–30 cm interval ranged from 0.25 to 3.89 MgC ha−1 (mean 1.65 ± 0.77 MgC ha−1). PyC stocks averaged 8% (but were up to 25%) of total organic carbon (TOC) stocks. Stocks tended to highest in relatively wet, but seasonally dry, regions such as tropical savannas. PyC abundance could be predicted (r = 0.8 to 0.95) from environmental variables only. δ13CPyC values varied widely between regions, but with no systematic differences within regions related to current vegetation or sample depth, likely due to the long residence time of PyC in the soil. δ13CPyC values were strongly correlated with δ13COC values but were systematically 1–2‰ higher even in C3 only regions.

Improved estimation of non-photosynthetic vegetation cover using a novel multispectral slope difference index with soil information, Sentinel-1 data, and machine learning

Non-photosynthetic vegetation (NPV) plays a crucial role in vegetation–soil ecosystems by influencing the dynamic uptake of carbon, water, and nutrients. The accurate estimation of the fractional cover of NPV (FNPV) is vital for managing natural resources, modeling carbon dynamics, and monitoring vegetation systems. NPV indices (NPVIs) are widely utilized for estimating FNPV over large areas. However, distinguishing NPV from bare soil (BS) remains challenging owing to subtle differences in their multispectral reflectance and the interference of the soil background. To address this, we propose a novel NPV spectral slope difference index (NSSDI) based on the distinct spectral curve shapes of NPV and BS. The NSSDI incorporates three spectral slopes: two between the visible and near-infrared bands and one between the shortwave infrared 1 (SWIR1) and SWIR2 bands from Sentinel-2 (S2) and Landsat-8 (L8). Additionally, we investigate the integration of soil information and radar data to improve the FNPV estimation using three machine learning algorithms. Validation against in situ measurements reveals that the NSSDI from S2 is more sensitive to FNPV than the recently published NPV–soil separation index (NSSI), whereas the NSSDI from L8 outperforms traditional NPVIs. Incorporating soil properties such as soil organic matter and soil moisture improves the FNPV estimation accuracy compared with using S2 or L8 NPVIs alone. The combination of Sentinel-1 (S1) synthetic aperture radar (SAR) data with either optical satellite also enhances the retrieval accuracy. The Gaussian process regression (GPR) model, which integrates S2 NSSDI, soil data, and S1 SAR data, achieves an R2 of 0.78 and the lowest RMSE of 0.109. Similarly, the GPR model based L8 NPVIs, soil data, and SAR data attains an R2 of 0.71 and an RMSE of 0.128. Both GPR models outperform the random forest and XGBoost models. Monthly FNPV estimates from April to August 2019 in the Northern Territory, Australia demonstrates strong spatial consistency across both satellites using the GPR models in the Google Earth Engine. These results suggest that combining NPVIs with soil and SAR data can facilitate accurate large-scale FNPV estimations.

Modeling carbon dynamics from a heterogeneous watershed in the mid-Atlantic USA: A distributed-calibration and independent verification (DCIV) approach

The terrestrial ecosystem plays a vital role in regulating regional and global carbon budgets. Ecosystem models are extensively employed to estimate carbon fluxes across different spatial scales. However, there remains a need to reduce the uncertainties associated with model parameterization and input data. To address these limitations, we assessed a distributed-calibration and independent-verification (DCIV) approach that uses (1) remotely sensed net primary production (NPP) and evapotranspiration (ET) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), (2) multi-site eddy covariance net ecosystem exchange (NEE) data; and (3) field sampling of soil organic carbon (SOC) and aboveground biomass (ABG) data to improve the overall predictability of carbon fluxes for the different land use and land cover (LULC) types at a watershed scale. The DCIV approach was applied to an advanced version of the Soil and Water Assessment Tool (SWAT)-Carbon (or SWAT-C), equipped with Century-based SOC algorithms to simulate carbon dynamics for watersheds with heterogeneous vegetation. The objective of the modeling effort was to assess carbon stocks and fluxes under different land management scenarios for a 3000-acre experimental farm and forest preserve in the northeastern United States. Our study showed that a large SOC stock of at least 100 tons ha−1 is stored under mixed forest, deciduous, shrubland, and floodplain (grass). Our study also showed that converting floodplain (grass) to deciduous forest has the potential to increase CO2 uptake (-NEE) by an order of three magnitude and ABG by 77 %, leading to an increased SOC stock of 23 % after twenty years. Similarly, we found that converting ungrazed grassland to grazed pasture leads to a non-statistically decreasing trend of SOC, especially in the 0–30 cm soil layer. Thus, the methodology used in this study can be applied to improve carbon dynamic prediction from a heterogeneous watershed at a regional scale.

Spatial Pattern of Forest Age in China Estimated by the Fusion of Multiscale Information

Forest age is one of most important biological factors that determines the magnitude of vegetation carbon sequestration. A spatially explicit forest age dataset is crucial for forest carbon dynamics modeling at the regional scale. However, owing to the high spatial heterogeneity in forest age, accurate high-resolution forest age data are still lacking, which causes uncertainty in carbon sink potential prediction. In this study, we obtained a 1 km resolution forest map based on the fusion of multiscale age information, i.e., the ninth (2014–2018) forest inventory statistics of China, with high accuracy at the province scale, and a field-observed dataset covering 6779 sites, with high accuracy at the site scale. Specifically, we first constructed a random forest (RF) model based on field-observed data. Utilizing this model, we then generated a spatially explicit forest age map with a 1 km resolution (random forest age map, RF map) using remotely sensed data such as tree height, elevation, meteorology, and forest distribution. This was then used as the basis for downscaling the provincial-scale forest inventory statistics of the forest ages and retrieving constrained maps of forest age (forest inventory constrained age maps, FIC map), which exhibit high statistical accuracy at both the province scale and site scale. The main results included the following: (1) RF can be used to estimate the site-scale forest age accurately (R2 = 0.89) and has the potential to predict the spatial pattern of forest age. However, (2) owing to the impacts of sampling error (e.g., field-observed sites are usually located in areas exhibiting relatively favorable environmental conditions) and the spatial mismatch among different datasets, the regional-scale forest age predicted by the RF model could be overestimated by 71.6%. (3) The results of the downscaling of the inventory statistics indicate that the average age of forests in China is 35.1 years (standard deviation of 21.9 years), with high spatial heterogeneity. Specifically, forests are older in mountainous and hilly areas, such as northeast, southwest, and northwest China, than in southern China. The spatially explicit dataset of the forest age retrieved in this study encompasses synthesized multiscale forest age information and is valuable for the research community in assessing the carbon sink potential and modeling carbon dynamics.

Evaluating MONICA's capability to simulate water, carbon and nitrogen fluxes in a wet grassland at contrasting water tables

Wet grasslands, which are vital for water and nutrient regulation, are characterised by distinct water, carbon (C) and nitrogen (N) dynamics, and their interactions. Due to their shallow groundwater table, wet grasslands promote a strong interconnection between diverse vegetation and soil water. Researchers have investigated how wet grasslands respond to environmental changes, using various simulation models to understand how these sites contribute to water, C and N dynamics. However, a comprehensive, simultaneous study of all three of these dynamics is still lacking. This study makes use of a grassland lysimeter study with differently managed groundwater levels and employs the process-based MOdel for NItrogen and Carbon dynamics in Agroecosystems (MONICA) to simulate these dynamics. By using SPOTPY (Statistical Parameter Optimization Tool) to optimise the relevant parameters, we find that MONICA performs well in simulating vegetation growth (aboveground biomass), and elements of the water (evapotranspiration), C (gross primary productivity, ecosystem respiration) and N (N in aboveground biomass, nitrate in soil solution, Nitrous oxide emissions) balance, with Willmott's Refined Index of Agreement always larger than 0.35. This level of accuracy demonstrates that MONICA is ready to be applied for scenario simulations of groundwater management and climate change to evaluate their impact on greenhouse gas emissions and long-term carbon storage, as well as water and nitrogen losses in wet grasslands.

Forest products circular economy in an export-focused jurisdiction: Can it fill the emission reduction gap?

The use of post-consumer wood in cascades offers several potential carbon-related benefits, including longer carbon retention, less landfilling, deferred tree harvesting, and increased substitution of emissions-intensive materials. However, the establishment of well-regulated wood cascading systems is limited in many jurisdictions. In this study, we utilized a wood products carbon dynamics model (MitigAna), and data from British Columbia (BC), Canada, to estimate the mitigation potential of cascading uses of wood.We designed three scenarios: a baseline scenario without cascading, a reuse scenario assuming an 85 % recycling ratio (as the upper bound), and an achievable cascading scenario of different commodity pathways with an average 30 % recycling ratio.The achievable cascading scenario resulted in a biogenic emission reduction of 1.16 MtCO2e yr-1 (2.4 %) compared to the baseline. Further mitigation benefits may be achieved by reducing harvest, potentially leading to an additional 1.1 MtCO2e yr-1 reduction. However, as a major wood exporter, the circular economy policies of BC have limited influence, as many of the end-of-life events occur outside of BC and Canada's jurisdiction. If the cascading system were to be implemented exclusively in Canada, the emission reduction would only be 0.2 MtCO2e yr-1.Increasing the recycling ratio from 30 % to 85 % only yielded a further 2.2 % biogenic emission reduction, which suggests a diminishing rate of return for the cascading uses of wood. Jurisdictions therefore may prioritize implementing a simple and cost-effective system initially.There are economic and environmental challenges associated with collection, sorting, treatment, transportation, remanufacturing and commercialization of the post-consumer wood in cascade. Continued research is necessary to address these challenges and inform the development of effective strategies in this area.

Forest Carbon Modeling in Poplar and Black Locust Short Rotation Coppice Plantation in Hungary

Forest carbon dynamic modeling for estimating the carbon stock in short rotation coppice bioenergy plantation in Hungary will be vital for better comprehending the role of black locust (Robinia pseudoacacia) and poplar (Populus sp.) in carbon dioxide sequestration from the atmosphere. The research aims were to estimate the potential carbon stock and describe the carbon distribution of the short rotation coppice bioenergy plantation above and below ground. Various sources were used to acquire parameterization data for developing forest carbon dynamic models. CO2FIX modeling V.3.2 was utilized in the data analysis to estimate the total carbon stock in biomass, soil, harvested wood products, and bioenergy compartments. Modeling has been around for 45 years. In this research, the total carbon stock of black locust and poplar at the end of the simulation period was 64.13 and 131.08 MgC.ha-1, respectively. The average carbon allocation above and below ground for black locust and poplar was 0.76, 19.76, 1.80, and 21.67 MgC.ha-1, respectively. In conclusion, poplar outperformed black locust regarding carbon storage in the short rotation coppice bioenergy plantation. Below ground carbon allocation was much higher than above ground. Therefore, more attention should be paid on below ground allocation through environmentally friendly soil management. Keywords: bioenergy plantation, carbon dynamics, climate change mitigation, CO2FIX model, fast growing species

Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)

Abstract. Simulation models are tools commonly used to predict changes in soil carbon stocks. Prior validation is essential, however, for determining the reliability and applicability of model results. In this study, the process-based biogeochemical model MONICA (Model of Nitrogen and Carbon dynamics on Agro-ecosystems) was evaluated with respect to soil organic carbon (SOC), using long-term monitoring data from 46 German agricultural sites. A revision and parameterisation of equations, encompassing crop- and fertiliser-specific C contents and the abiotic factors of soil temperature, soil water and clay content, were undertaken and included in the model. The modified version was also used for a Morris elementary effects screening method, which confirmed the importance of environmental and management factors to the model's performance. The model was then calibrated by means of Bayesian inference, using the Metropolis–Hastings algorithm. The performance of the MONICA model was compared with that of five established carbon turnover models (CCB, CENTURY, C-TOOL, ICBM and RothC). The original MONICA model systematically overestimated SOC decomposition rates and produced on average a ∼17 % greater mean absolute error (MAE) than the other models. The modification and calibration significantly improved its performance, reducing the MAE by ∼30 %. Consequently, MONICA outperformed CENTURY, CCB and C-TOOL, and produced results comparable with ICBM and RothC. Use of the modified model allowed mostly adequate reproduction of site-specific SOC stocks, while the availability of a nitrogen, plant growth and water submodel enhanced its applicability when compared with models that only describe carbon dynamics.

Metabolite diversity among representatives of divergent Prochlorococcus ecotypes.

Approximately half of the annual carbon fixation on Earth occurs in the surface ocean through the photosynthetic activities of phytoplankton such as the ubiquitous picocyanobacterium Prochlorococcus. Ecologically distinct subpopulations (or ecotypes) of Prochlorococcus are central conduits of organic substrates into the ocean microbiome, thus playing important roles in surface ocean production. We measured the chemical profile of three cultured ecotype strains, observing striking differences among them that have implications for the likely chemical impact of Prochlorococcus subpopulations on their surroundings in the wild. Subpopulations differ in abundance along gradients of temperature, light, and nutrient concentrations, suggesting that these chemical differences could affect carbon cycling in different ocean strata and should be considered in models of Prochlorococcus physiology and marine carbon dynamics.

Carbon dioxide release from retrogressive thaw slumps in Siberia

Thawing of ice-rich permafrost soils in sloped terrain can lead to activation of retrogressive thaw slumps (RTSs) which make organic matter available for decomposition that has been frozen for centuries to millennia. Recent studies show that the area affected by RTSs increased in the last two decades across the pan-Arctic. Combining a model of soil carbon dynamics with remotely sensed spatial details of thaw slump area and a soil carbon database, we show that RTSs in Siberia turned a previous quasi-neutral ecosystem into a strong source of carbon dioxide of 367 ± 213 gC m-1 a-1. On a global scale, recent CO2 emissions from Siberian thaw slumps of 0.42 ± 0.22 Tg carbon per year are negligible so far. However, depending on the future evolution of permafrost thaw and hence thaw slump-affected area, such hillslope processes can transition permafrost landscapes to become a major source of additional CO2 release into the atmosphere.

Nonlinear modeling of carbon dynamics in soil treated with tannery sludge

In 2020, the Brazilian commercial cattle herd was the largest in the world, representing 14.3% of the worldwide herd, with201.7 million heads. Although this activity yields significant profits, contributing to the economic and social development ofBrazil, it has been the target of concerns, mainly due to the large production of waste/effluents associated to bovine leatherprocessing. In this scenario, mineralization is important because nutrients essential for plant growth are released duringthe process of organic waste decomposition, and the dynamics of carbon release can be described by nonlinear regressionmodels. Thus, this study aimed to model the mineralization of organic carbon in the soil for 6, 12, 24, and 36 megagramsdoses per hectare (Mg ha−1) of tannery sludge using the Stanford & Smith, Cabrera, and Juma nonlinear models. Very clayeysoil samples were used: eutroferric red nitosol (NVef). Mineralized carbon was measured in 21 observations over time untilthe 105th day of incubation. Parameters were estimated using the least squares method. Adjustments were compared usingthe corrected Akaike information criterion (AICc) and the adjusted coefficient of determination (R2aj) as selection criteria. The Cabrera model had the best adjustments for doses 6, 12, and 24 Mg ha−1 and Juma for dose 36 Mg ha−1, based on the selection criteria used. Although the Stanford & Smith model is the most widely used in the literature to model soil carbon dynamics, its use was not the most appropriate for any of the doses evaluated in this study. The higher the dose of tannerysludge, the greater the amount of potentially mineralizable carbon.

The Effect of Forest Growth Rate on Climate Change Impacts of Logging Residue Utilization

Biofuel is encouraged because of its low impact on climate change. A new framework was developed to accurately assess the climate change impacts (CCI) of biofuel by integrating the atmospheric carbon cycle model and vegetation carbon dynamic models. Forests with different growth rates (fast, medium, slow) and three collection intensities (71%, 52%, 32%) of logging residues were presumed to test the performance of this framework. The CCI of biofuel was analyzed under two functional units: 1 GJ of biofuels and 1 ha of forests to supply biofuels. According to this study, increasing the forest growth rate could decrease the CCI in both functional units. Increasing the collection intensity could decrease the CCI of 1 GJ of biofuel but increase the CCI of 1 ha of forest land (unless the impacts were negative in fast-growth forests with high and medium collection intensities). Producing bioethanol resulted in a lower CCI (−3.1–67.7 kg CO2 eq/GJ) compared to bio-diesel (29.3–94.7 kg CO2 eq/GJ). Hence, collecting all available logging residues (without inhibiting forest regrowth) to produce low CCI biofuels such as bioethanol was found to be the optimal option for achieving high mitigation effects.

Ecosystem-scale modelling of soil carbon dynamics: Time for a radical shift of perspective?

Over the last few years, several researchers working on the development of “biogeochemical” or “ecosystem-scale” models of soil carbon dynamics have reported struggling with a number of difficult challenges. At the same time, work in this area has focused exclusively on microbial activity described at a macro-ecological level, and has entirely bypassed the abundant literature produced in the last two decades on the study of soil processes at the microscale. Juxtaposition of these different observations suggests that a radical shift of perspective is in order. In this general context, the present article carries out an in-depth analysis of several of the key limitations of current ecosystem-scale models and recommends a number of steps to shift the perspective to one that is argued to have a better chance of success in the relatively short time we have to address several pressing soil-related environmental problems. These steps, in particular, require the development of large-spatial-scale models of soil carbon dynamics to be far more interdisciplinary than it has been till now, and to adopt a “bottom-up” approach, building on what the research at the microscale reveals about soil processes. Nevertheless, because it may assist in upscaling efforts, it is argued that some room should be preserved for work to continue on the search for empirical models applicable at large spatial scales.

Linking Soil Structure, Hydraulic Properties, and Organic Carbon Dynamics: A Holistic Framework to Study the Impact of Climate Change and Land Management

AbstractClimate change and unsustainable land management practices have resulted in extensive soil degradation, including alteration of soil structure (i.e., aggregate and pore size distributions), loss of soil organic carbon, and reduction of water and nutrient holding capacities. Although soil structure, hydrologic processes, and biogeochemical fluxes are tightly linked, their interaction is often unaccounted for in current ecohydrological, hydrological and terrestrial biosphere models. For more holistic predictions of soil hydrological and biogeochemical cycles, models need to incorporate soil structure and macroporosity dynamics, whether in a natural or agricultural ecosystem. Here, we present a theoretical framework that couples soil hydrologic processes and soil microbial activity to soil organic carbon dynamics through the dynamics of soil structure. In particular, we link the Millennial model for soil carbon dynamics, which explicitly models the formation and breakdown of soil aggregates, to a recent parameterization of the soil water retention and hydraulic conductivity curves and to solute and O2 diffusivities to soil microsites based on soil macroporosity. To illustrate the significance of incorporating the dynamics of soil structure, we apply the framework to a case study in which soil and vegetation recover over time from agricultural practices. The new framework enables more holistic predictions of the effects of climate change and land management practices on coupled soil hydrological and biogeochemical cycles.

Bacterial functional redundancy and carbon metabolism potentials in soil, sediment, and water of thermokarst landscapes across the Qinghai-Tibet Plateau: Implications for the fate of permafrost carbon

Permafrost thaw create widespread thermokarst landscapes. As a result, distinct habitats are provided to harbor different bacterial communities in degraded permafrost soil (PBCs), thermokarst lake sediment (SBCs), and lake water (WBCs), driving carbon metabolism differentially. In this study, we investigated functional diversity and redundancy, and carbon metabolism potentials of PBCs, SBCs, and WBCs in thermokarst landscapes across the Qinghai-Tibet Plateau. The results showed that PBCs and SBCs had higher taxonomic and functional alpha diversity than WBCs, while WBCs had lower functional redundancy. WBCs had the highest beta diversity followed by SBCs and PBCs, suggesting strong determination of taxonomic variations on functional differences. Community assembly processes also had significant influences on beta diversity, especially for SBCs. Metabolism pathways of carbohydrate metabolism, methane metabolism, and carbon fixation were enriched differentially in PBCs, SBCs, and WBCs, suggesting different C fate in distinct habitats. Carbohydrate metabolism data suggested that PBCs might have stronger potentials to mineralize a greater diversity of organic carbon substrate than SBCs and WBCs, promoting degradation of organic carbon stocks in degraded permafrost soils. Methane metabolism data showed that SBCs had a stronger methanogenesis potential followed by PBCs and WBCs, while PBCs had a stronger methane oxidation potential. High abundance of genes involving in formaldehyde assimilation might suggested that a large proportion of produced methane might be assimilated by methanotrophs in the thermokarst landscapes. Both aerobic and anaerobic carbon fixation pathways were enriched in PBCs. The results added our understanding of functional properties and biogeochemical carbon cycles in thermokarst landscapes, improving our abilities in accurate modeling of carbon dynamics and the ultimate fate of permafrost carbon in a warming world.

Ideas and perspectives: Allocation of carbon from net primary production in models is inconsistent with observations of the age of respired carbon

Abstract. Carbon allocation in vegetation is an important process in the terrestrial carbon cycle; it determines the fate of photoassimilates, and it has an impact on the time carbon spends in the terrestrial biosphere. Although previous studies have highlighted important conceptual issues in the definition and metrics used to assess carbon allocation, very little emphasis has been placed on the distinction between the allocation of carbon from gross primary production (GPP) and the allocation from net primary production (NPP). An important number of simulation models and conceptual frameworks are based on the concept that C is allocated from NPP, which implies that C is respired immediately after photosynthetic assimilation. However, empirical work that estimates the age of respired CO2 from vegetation tissue (foliage, stems, roots) shows that it may take from years to decades to respire previously produced photosynthates. The transit time distribution of carbon in vegetation and ecosystems, a metric that provides an estimate of the age of respired carbon, indicates that vegetation pools respire carbon of a wide range of ages, on timescales that are in conflict with the assumption that autotrophic respiration only consumes recently fixed carbon. In this contribution, we attempt to provide compelling evidence based on recent research on the age of respired carbon and the theory of timescales of carbon in ecosystems, with the aim to promote a change in the predominant paradigm implemented in ecosystem models where carbon allocation is based on NPP. In addition, we highlight some implications for understanding and modeling carbon dynamics in terrestrial ecosystems.

Modeling of total and active organic carbon dynamics in agricultural soil using digital soil mapping: a case study from Central Nova Scotia

Monitoring the changes in soil organic carbon (SOC) pools is critical for sustainable soil and agricultural management. This case study models total and active organic carbon dynamics (2015/2016 to 2019/2020) using digital soil mapping (DSM) techniques. Model predictors include topographic variables generated from light detection and ranging data; soil and vegetation indices derived from Landsat satellite images; and soil and crop inventory information from Agriculture and Agri-Food Canada to predict total organic carbon (TOC) and permanganate oxidizable carbon (POXc) at the 0–15 cm depth increment for a 37 km2 study area in Truro, Nova Scotia. Quantile Regression Forest and stochastic Gradient Boosting Model were utilized for prediction. Although both models performed equally well for predicting TOC and POXc, the accuracy of TOC predictions (e.g., concordance correlation coefficient (CCC) = 0.67) was better than POXc predictions (e.g., CCC = 0.53). The Landsat variables and crop inventory were dominant predictors, while topographic variables across the relatively homogeneous terrain had relatively little influence. During the study period, changes in POXc were predicted across 98% of the study area, with a mean absolute loss of 5.77 (±11.48) mg/kg/year, and in TOC on 27% of the area, with a mean absolute loss of 0.15 (±0.09) g/kg/year. While the annual crop fields observed the highest loss of TOC and POXc, the decline in pasture–grassland–forage fields was relatively low. The study reinforced the effectiveness of DSM for modeling multiple SOC pools at the farm to landscape scales.

A Conterminous USA-Scale Map of Relative Tidal Marsh Elevation

Tidal wetlands provide myriad ecosystem services across local to global scales. With their uncertain vulnerability or resilience to rising sea levels, there is a need for mapping flooding drivers and vulnerability proxies for these ecosystems at a national scale. However, tidal wetlands in the conterminous USA are diverse with differing elevation gradients, and tidal amplitudes, making broad geographic comparisons difficult. To address this, a national-scale map of relative tidal elevation (Z*MHW), a physical metric that normalizes elevation to tidal amplitude at mean high water (MHW), was constructed for the first time at 30 × 30-m resolution spanning the conterminous USA. Contrary to two study hypotheses, watershed-level median Z*MHW and its variability generally increased from north to south as a function of tidal amplitude and relative sea-level rise. These trends were also observed in a reanalysis of ground elevation data from the Pacific Coast by Janousek et al. (Estuaries and Coasts 42 (1): 85–98, 2019). Supporting a third hypothesis, propagated uncertainty in Z*MHW increased from north to south as light detection and ranging (LiDAR) errors had an outsized effect under narrowing tidal amplitudes. The drivers of Z*MHW and its variability are difficult to determine because several potential causal variables are correlated with latitude, but future studies could investigate highest astronomical tide and diurnal high tide inequality as drivers of median Z*MHW and Z*MHW variability, respectively. Watersheds of the Gulf Coast often had propagated Z*MHW uncertainty greater than the tidal amplitude itself emphasizing the diminished practicality of applying Z*MHW as a flooding proxy to microtidal wetlands. Future studies could focus on validating and improving these physical map products and using them for synoptic modeling of tidal wetland carbon dynamics and sea-level rise vulnerability analyses.

A strong mitigation scenario maintains climate neutrality of northern peatlands

A strong mitigation scenario maintains climate neutrality of northern peatlands

Storage or loss of soil active carbon in cropland soils: The effect of agricultural practices and hydrology

Few studies have simultaneously addressed the issue of the short- and long-term hydrological control of organic carbon (OC) export from soils and the role of the leaching process in the long-term dynamics of the soil OC pool. We combined short- and long-term approaches by investigating dissolved organic carbon (DOC) at the outlet of a small drainage catchment and establishing a relationship between DOC concentrations (3.5 ± 1.8 mgC L-1 on average) and subsurface runoff (175 mm yr−1 on average). We then calculated the annual DOC export as a function of average annual water runoff for a 9-year period. We assumed that the annual flux of leaching is proportional to the active soil OC stock, which we compared with data from the literature. We added a leaching function to the AMG two-compartment model of soil carbon dynamics. The innovative use of the Rock–Eval method for agricultural soils made it possible to determine the stable and active carbon fractions (OCp and OCA, respectively), necessary to characterize the system in the model, for 52 plots in organic and conventional agricultural farms in the Seine Basin. No significant difference was found in OC for the two agricultural systems (11.4 ± 2.5 gC kg−1 vs. 12.3 ± 4.2 gC kg−1, respectively, for the 0 to 30 cm layer).Using the AMG model with its leaching function, we calculated the equilibrium value of OCA, representing the size of the OCA pool that would be reached in the long term under constant farming practices and hydrological conditions in a given plot. Deviation from this equilibrium indicates whether carbon storage or loss occurs. Overall, for the plots sampled in the Seine Basin, an annual carbon loss of ∼−0.24 % yr−1 of the total OC pool was found. This may increase by 15% (i.e., to ∼−0.28% yr−1) under higher subsurface runoff, which is plausible under ongoing climate change (e.g., 600 mm yr−1 vs. 175 mm yr−1 currently observed).