Displays

Understanding the role of ecological communities in maintaining multiple ecosystem processes is a central challenge in ecology. Soil microbial communities perform vital ecosystem functions, such as the decomposition of organic matter to provide plant nutrition. However, despite the functional importance of soil microorganisms, attribution of ecosystem function to particular constituents of the microbial community has been impeded by a lack of information linking microbial processes to community structure.

Here, we propose a new conceptual framework to determine how microbial communities influence ecosystem processes, by applying a “top-down” approach. Looking from the “top”, we first view the microbial community associated with a specific function as a whole, and describe the dependence of microbial community processes on environmental factors (e.g. the intrinsic temperature dependence of bacterial growth rates), allowing us to define the aggregate functional response curve of the community. We then demonstrate that the whole community contribution to ecosystem function can be predicted, by parameterising the functional response curve with current environmental conditions. In a final step, we show how this functional information can be linked to the taxonomic community composition (amplicon assessments of microbial community composition) in order to identify “biomarker” taxa that capture microbial communities’ regulation of ecosystem processes and the susceptibility of microbial community structure and function to environmental change. Ultimately, these biomarkers may be used as a diagnostic tool, enabling predictions of ecosystem function from community composition information combined with environmental metadata.

How to cite: Rousk, J. and Hicks, L.: Viewing soil systems from the top-down can make microbial ecology predictive for ecosystem functions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3414, https://doi.org/10.5194/egusphere-egu2020-3414, 2020.

Trait-based models have improved the understanding and prediction of soil organic matter dynamics in terrestrial ecosystems. Microscopic observations and pore scale models are now increasingly used to quantify and elucidate the effects of soil heterogeneity on microbial processes. Combining both approaches provides a promising way to accurately capture spatial microbial-physicochemical interactions and to predict overall system behavior. The present study aims to quantify controls on carbon (C) turnover in soil due to the mm-scale spatial distribution of microbial decomposer communities in soil. A new spatially explicit trait-based model (SpatC) has been developed that captures the combined dynamics of microbes and soil organic matter (SOM) by taking into account microbial life-history traits and SOM accessibility. Samples of spatial distributions of microbes at µm-scale resolution were generated using a spatial statistical model based on Log Gaussian Cox Processes which was originally used to analyze distributions of bacterial cells in soil thin sections. These µm-scale distribution patterns were then aggregated to derive distributions of microorganisms at mm-scale. We performed Monte-Carlo simulations with microbial distributions that differ in mm-scale spatial heterogeneity and functional community composition (oligotrophs, copiotrophs and copiotrophic cheaters). Our modelling approach revealed that the spatial distribution of soil microorganisms triggers spatiotemporal patterns of C utilization and microbial succession. Only strong spatial clustering of decomposer communities induces a diffusion limitation of the substrate supply on the microhabitat scale, which significantly reduces the total decomposition of C compounds and the overall microbial growth. However, decomposer communities act as functionally redundant microbial guilds with only slight changes in C utilization. The combined statistical and process-based modelling approach derives distribution patterns of microorganisms at the mm-scale from microbial biogeography at microhabitat scale (µm) and quantifies the emergent macroscopic (cm) microbial and C dynamics. Thus, it effectively links observable process dynamics to the spatial control by microbial communities. Our study highlights a powerful approach that can provide further insights into the biological control of soil organic matter turnover.

How to cite: Pagel, H., Kriesche, B., Uksa, M., Poll, C., Kandeler, E., Schmidt, V., and Streck, T.: Spatial control of carbon dynamics in soil by microbial decomposer communities , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13107, https://doi.org/10.5194/egusphere-egu2020-13107, 2020.

The organic carbon reservoir of soils is a key component of climate change, calling for an accurate knowledge of the residence time of soil organic carbon (SOC). Existing proxies of the labile SOC pool such as particulate organic carbon or basal respiration tests are time consuming and unable to consistently predict SOC mineralization over years to decades. Similarly, models of SOC dynamics often yield unrealistic values of the size of SOC kinetic pools. Rock-Eval® 6 (RE6) thermal analysis of bulk soil samples has recently been shown to provide useful and cost-effective information regarding the long-term in-situ decomposition of SOC. The objective of this study was to design a method based on RE6 indicators to assess for a given soil, the proportion of SOC that will be mineralized in the coming 20 years.

To do so, we needed samples ready to be analyzed using RE6 with a known proportion of SOC mineralized in 20 years. We used archived soil samples from 4 long-term bare fallows and 8 C₃/C₄ chronosequences. For each sample, the value of bi-decadal SOC mineralization was obtained from the observed SOC dynamics of its long-term bare fallow plot or the calculated C₃-derived SOC decline following the conversion to C₄ plants. Those values ranged from 0.3 to 14.3 gC·kg⁻¹ (concentration data), representing 8.6 to 52.6% of total SOC (proportion data). All samples were analyzed using RE6 and simple linear regression models were used to predict bi-decadal SOC loss (concentration and proportion data) from 4 RE6 parameters: 1) HI (the amount of hydrogen-rich effluents formed during the pyrolysis phase of RE6; mgCH.g^-1 SOC), 2) OI_RE6 (the O recovered as CO and CO₂ during the pyrolysis phase of RE6; mgO₂.g^-1 SOC), 3) PC/SOC (the amount of organic C evolved during the pyrolysis phase of RE6; % of total SOC) and 4) T50 CO₂ oxidation (the temperature at which 50% of the residual organic C was oxidized to CO₂ during the RE6 oxidation phase; °C).

The RE6 HI parameter yielded the best predictions of bi-decadal SOC mineralization, for both concentration and proportion data. PC/SOC and T50 CO₂ oxidation parameters also yielded significant regression models. The OI_RE6 parameter was not a good predictor of bi-decadal SOC loss, with non-significant regression models. The results showed that SOC chemical composition (HI is a proxy for SOC H/C ratio), and to a lesser degree SOC thermal stability, are related to bi-decadal SOC dynamics. The RE6 thermal analysis method can therefore provide a quantitative and accurate estimate of SOC biogeochemical stability.

How to cite: Barré, P., Soucémarianadin, L., François, B., Claire, C., Christensen, B., Don, A., Girardin, C., Houot, S., Kätterer, T., Macdonald, A., van Oort, F., Poeplau, C., and Cécillon, L.: Predicting bi-decadal soil organic carbon mineralization with Rock-Eval® thermal analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13545, https://doi.org/10.5194/egusphere-egu2020-13545, 2020.

In most soil organic carbon (SOC) dynamics models, SOC is divided into pools to which different mineralization rates are ascribed. The lack of a reliable, operational and fully validated method to initialize the size of the different SOC kinetic pools is a limitation for the accuracy of predictions of SOC stocks evolution provided by these models. AMG is a simple, well established French model, successfully used to simulate the evolution of C stocks for a large network of long-term monitored sites with agricultural experiments (LTEs). Initial conditions, namely the size of the stable C pool (C_S) at the onset of the simulation, have been shown to be important for the accuracy of the model. Recently, Rock-Eval 6® (RE) thermal analysis has been proposed as a new method for direct determination of SOM stability. Based on this technique, a random forests model (RE model) was developed, calibrated on Long Term Bare Fallow data, which allows the estimation of the size of the centennially persistent SOC fraction (CP_SOC) in a sample. Here, we first aimed at evaluating the performance of the RE model on fully independent soil samples. For this purpose, we compared the CP_SOC values of 73 samples from 7 LTEs calculated with the RE model with the corresponding C_S values optimized from AMG ex-post simulations. Then, we used the CP_SOC values given by the RE model to define the size of the stable C pool of the AMG model (C_S) at the onset of AMG simulations for the 7 sites. We show that the CP_SOC (RE model) and optimized C_S (ex-post AMG simulations) fractions are in good agreement (slope b=1.01, intercept a=0.04 / spearman ρ=0.88). This observation serves as a successful independent validation of the RE model. Finally, we show that the use of the RE based model improves the accuracy of the AMG model compared to default initialization (mean RMSE decreased by 13.5%), especially for sites with complex land-use history and long-term organic matter amendment. Our study therefore provides an operational method suitable to initialize the AMG model that can be expanded to other SOC dynamics models.

How to cite: Kanari, E., Cécillon, L., Baudin, F., Clivot, H., Ferchaud, F., Mary, B., Soucémarianadin, L., Chenu, C., and Barré, P.: Improving the accuracy of soil carbon models using a Rock-Eval-based initialization method, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10442, https://doi.org/10.5194/egusphere-egu2020-10442, 2020.

With advances in the understanding of the mechanisms leading to the persistence or vulnerability of soil organic carbon (SOC) at the profile scale, it is essential to develop infrastructure to integrate this knowledge with landscape-scale mapping and models. To address this need, we are developing a soil functional unit framework, intended to better scale mechanistic soil knowledge by merging geospatial datasets with targeted sample collection and analyses. Here we provide a proof of concept of this approach for SOC stocks (the soil function of interest) in the East River study area located near Gothic, Colorado, USA. We first generate a map estimating SOC stocks based only on available geospatial datasets, including factors such as topography, vegetation, geology, and basic soil maps. We then compare the mapped functional units against an independent SOC dataset of 450 soil profiles (~1700 samples) collected from the study region and refine the soil functional map to best capture the spatial variability observed in the dataset.  With the calibrated soil functional unit mapping algorithm, we can then calculate SOC stocks at landscape scales and better constrain the mechanisms that drive the observed heterogeneity.  The resulting data-driven soil functional maps can then be merged with regional scale SOC models to enhance forecasts of SOC change in response to disturbances.

How to cite: Lawrence, C.: Soil Functional Mapping: Using Data-Model Integration to Improve Regional-Scale SOC Forecasts , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10532, https://doi.org/10.5194/egusphere-egu2020-10532, 2020.

The complexity and interplay of soil processes are still investigated extensively. Continuous focus on this field of research is important since soil properties such as nitrate reductive capacity has a great influence on groundwater quality. Here, we try to give insight into the dynamics of a vadose zone soil under agricultural management.

A field of study was selected in Darum in Southwestern Jutland, Denmark. The site is situated in an old periglacial terrain on meltwater-deposited sand. The field has been under maize (Zea mays) monoculture for the past 20 years. Prior to this period it had been kept with C3 plants only.  Soil sampling was accomplished in three replicates of 1.6 m.

The bulk soil samples were analyzed for total C and N, δ¹³C and δ¹⁵N. Dissolved organic matter (DOM) and NO₃^- were recovered from cold-water extractions of the soil samples. Extractions were analyzed for their UV-Vis absorption spectra.

Incubation experiments were performed on bulk soil portions in order to assay the activity and isotopic imprint CO₂ respiration. The soil were also incubated under anoxic conditions with substrate amendments (KNO₃ and C additions). The resulting N₂O releases were assigned to biologically driven nitrate reduction. Ultimately, principal component analyses (PCA) were carried out on the results.

The C and N concentrations were highest in the Ap horizon and decreased with soil depth. The respiratory and nitrate reductive capacity also declined with depth, but were evident in all of the analyzed soil depths. All individual depths responded statistically significant to substrate addition by increase in the N₂O production.

The isotopic results showed that the main pool of maize-derived C were also found in the plough layer. However, the respiratory isotopic results evidenced the presence of C4 plant derived C throughout the soil profile, after 20 years of monoculture.

The UV-Vis absorption spectra gave insight into the quality of the DOM pools. The parameter E253/E203 is associated with functional groups on aromatic rings and increases with composting time. The soil had an overall increase in this parameter with depth. The integrated magnitude of distinct wavelengths (270-300 nm, 300-380 nm and 380-500 nm) is an index of protein-, fulvic-, and humic like substances. Surprisingly, no substantial discrepancies in the distribution between these pools was found with depth. However, the overall pattern was declining steeply with soil depth, emphasizing the importance of dilution when assessing DOM availability and quality.

The PCA could explain >55 % of the variance by the first principal component. The PCA showed that the C and N concentrations were positively correlated. Alongside were the ambient N₂O activity to the indexes of protein-, fulvic and humic like substances. The inherent NO₃^- concentration, the N₂O activity (KNO₃ amended) and the respiratory CO₂ production were also positively correlated – however negatively correlated with the E253/E203 parameter.
Therefore, respiratory and nitrate reductive capacities of the Darum soil, depends notably on the presence of less degraded DOM, on the concentration of protein-, fulvic and humic like substances, and finally on the inherent soil NO₃^- concentration.

How to cite: Pedersen, A. C. K., Ernstsen, V., Breuning-Madsen, H., and Ambus, P. L.: An integration of soil parameters characterizing a Danish agricultural soil, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20117, https://doi.org/10.5194/egusphere-egu2020-20117, 2020.

While the demographic pressure for food demand is continuously rising, global environmental changes are threatening the productivity of agroecosystems. Climatic events like floods or droughts, and long-term decrease in soil organic matter stocks due to intensive agriculture are examples pointing to the necessity to find solutions for sustainable performance of agroecosystems.

Significant amounts of water and nutrients are stored in deep soil horizons, and thus subsoil management is being considered as an alternative to sustain high demand in crop productivity.

We used samples from an ongoing field experiment in Germany where the agricultural management was adapted to investigate the potential benefits of deep ploughing with OM incorporation. We recorded hyperspectral images of soil cores (depth = 1 m) using Vis-NIR reflectance spectroscopy and the C distribution within the soil was modeled at a very high spatial resolution (53×53 μm). The SOC mapping revealed an increase in SOC stocks resulting from deep ploughing, and the high resolution images generated allows the observation of OM distribution in the subsoil and the response in SOM stocks to different types of organic matter incorporation (compost vs green manure). The same imaging technique was also combined with solid-state ¹³C NMR measurements to track the molecular composition of the organic amendment during decomposition.

Hyperspectral imaging of soil cores allows the quantification of OM stocks and changes at the pedon scale, and fine scale resolution of heterogeneity in the spatial distribution of soil organic matter is helping to understand and quantify the processes related to changes in soil C stocks in subsoils.

How to cite: Guigue, J., Just, C., Luo, S., Hobley, E., and Kögel-Knabner, I.: C stocks and subsoil management in agroecosystems: application of hyperspectral imaging to study organic matter dynamics in the top one metre of soil, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20653, https://doi.org/10.5194/egusphere-egu2020-20653, 2020.

Crop yields of rainfed cereal are highly dependent of the soil-plant-atmosphere system, especially referred to the weather conditions and soil properties. The study of this interaction is feasible through the earth observations of historical data. Remote sensing data and agricultural survey work together identifying and analyzing plots with monocrop cereal sequences. In this research, we investigate the relation of the Normalized Difference Vegetation Index (NDVI) residual time series behavior relative to soil classes from Self-Organizing Maps (SOM) and the precipitation residual time series.

The midlands of Eresma-Adaja watershed (Dueros’ River basin, Spain) is historically depicted to rainfed cereal agriculture, some evidence of monocropping sequences are worrisome the water availability in the area. Within this area, two contrasting soil properties sites were selected to assess plots with at least 20 years of rainfed monocropping sequences but under similar weather regime. This allows analyzing the effect and relationships of this practice by soil type in time. For this, we treat the NDVI and precipitation time residual series as signals. The use of the Generalized Structure Function applied to these residual time series and the Hurst exponent, serve to confirm the soil properties differences from SOM and to reinforce the scaling properties of soil-climate interaction in semiarid regions for cereals in monocrop. As a result, the NDVI and precipitation series present an antipersistence behavior supporting that precipitation regime is influencing as the same manner the NDVI residual time series among complimentary factors.

ACKNOWLEDGEMENTS

Finding for this work was partially provided by Boosting agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020. The authors also acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain. The data provided by ITACyL and AEMET is greatly appreciated.

How to cite: Rivas-Tabares, D., Martín-Sotoca, J. J., Saa-Requejo, A., and Tarquis, A. M.: Improving the analysis of the soil-plant-atmosphere system thought earth observations in large monocrop cereal sequences, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9770, https://doi.org/10.5194/egusphere-egu2020-9770, 2020.

Sediment, nutrient deprivation and saltwater intrusion, among other factors, are driving widespread organic soil collapse and marsh loss in the Mississippi River Delta. Freshwater wetland diversions were designed to reintroduce Mississippi River water and sediment into the adjacent basins to manage salinity and mitigate land loss. However, there is concern that loading of excess nutrients from the Mississippi River into Barataria Basin wetlands can potentially lead to increased soil OM decomposition, less soil strength or increasing buoyancy and decreased belowground biomass. A baseline study was effected of a 3,145 km² area of wetlands and estuaries within Barataria Basin in 2007, in which the spatial variation in plant and soils were described at 140 stations before full scale diversion operations began in 2009. A subsequent spatial survey was conducted in 2018 after 11 years of diversion influence. By resampling the top 20 cm, separated into 0-10 cm and 10-20 cm layers, in 2018 provides an assessment of the status of those soils produced since 2007 and provides context for changing soil conditions. For the 2018 sampling, the soil

How to cite: Corstanje, R., Spera, A., and White, J.: Mitigating for coastal erosion and C loss in the Mississippi Delta; do river water diversion do more harm than good? , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17467, https://doi.org/10.5194/egusphere-egu2020-17467, 2020.

The aim of the study is to perform a regional scale (Russia) soil type-specific soil C models calibration with ESMs using Russian National Soil database. Particularly, to obtain temperature  and moisture dependencies of soil C cycle reaction rates, the model can be fitted to soil profiles of the same soil type in different climatic conditions. For this aim historical climate data, carbon concentration profiles and soil profile descriptions (soil type,  texture and other properties that can provide additional information to constrain model parameter values) are needed in different spatial locations. 

The regional soil database consists of standardized detailed descriptions of soil profiles for each soil type encountered in Russia, including soil carbon content and organic matter composition proxies, profile distribution of soil texture, nitrogen and pH.

We introduced a data collection system with a tool for field soil description according to the standard as well as a tool for soil determination which includes Kohonen algorithm to predict soil type based on the determined soil profile sequence of horizon indexes. Further the system allows automated RunaWFE based request for soil sample analysis (national method standards) which is obligatory accompanied with standard soil profile and samples descriptions. The system collects all data for expansion of regional soil database.

How to cite: Vasiliev, T., Vladimirov, A., Pashkov, A., and Vasilyeva, N.: Standardized automated data collection for soil C model parametrization at the regional scale of Russia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21414, https://doi.org/10.5194/egusphere-egu2020-21414, 2020.

The complex dynamics of changes in land use and land cover at different scales cause changes in the composition and configuration of the landscape. Deforestation, mainly caused by the transformation of forest to agricultural land, has been one of the most representative changes in recent years worldwide. In Ecuador, this transformation has occurred in different areas of the country, even in those areas declared by UNESCO as biosphere reserves (BRs), endangering the diversity of ecosystems and species existing within each of them. In this context, the identification of patterns, trajectories and magnitudes associated with the deforestation process in the BR areas is essential for the management, conservation and even evaluation of the protection effectiveness of these areas. We analyze the changes in land cover produced between 1990 and 2016 in the Sumaco and Bosque Seco BRs belonging to continental Ecuador, as well as the patterns associated with the deforestation process that occurred in that period. The quantification of land cover changes was performed using a cross-tabulation-table and their spatial location was done through a cross-classification image. The patterns were characterized using landscape ecology metrics and their nature described through multifractal analysis. In addition, the scales at which self-similarity characteristics are detected were identified by lacunarity analysis. The results show that there are three patterns associated with deforestation processes, (1) regrowth of preexisting patches without fusion of the adjacent patches, (2) Regrowth and fusion of preexisting patches and (3) the appearance of new deforested patches. In addition, the multifractal nature of the deforested structure was verified and characteristics of self-similarity at parroquia scale were identified.

ACKNOWLEDGEMENTS

This work was supported by the Program of University of Zaragoza-Santander for Ibero633 Americans in Doctorate studies. Second author acknowledges support from Project No. 634 PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and to the Comunidad de Madrid (Spain) and Structural Funds 2014–2020 (ERDF and ESF) project AGRISOST-CM S2018/BAA-4330. We are also grateful to University of Azuay and Ministry of Environment of Ecuador for providing data and resources for the development of this academic work.

How to cite: Urgilez-Clavijo, A., Tarquis, A., Rivas-Tabares, D., and de la Riva, J.: Soil-landscape patterns and fractal parameters of deforestation: a case study of Continental Ecuador Biosphere Reserves., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9561, https://doi.org/10.5194/egusphere-egu2020-9561, 2020.

Earth observation (EO) has a large potential for mapping of soil functional properties such as soil organic carbon, soil pH or acidity, soil fertility parameters and soil texture. Recent advances in the application of EO data in combination with systematic field data sampling, standardized soil data reference analysis and the use of soil spectroscopy have shown these approaches to be both robust and scalable. We present a case study from Rwanda where we apply EO data in combination with field and laboratory data collected using the Land Degradation Surveillance Framework (LDSF) to map functional soil properties, soil erosion prevalence and land cover at fine spatial resolution. Digital soil maps were produced at a spatial resolution of 30m with an accuracy of 85 to 90%, while soil erosion prevalence was mapped with an accuracy of 86% using Landsat satellite imagery and machine learning models. 

We also assess interactions between spatial assessments of soil organic carbon, soil erosion prevalence and land cover at a spatial resolution of 30m in order to identify land degradation hotspots and better target interventions to restore degraded land across four districts in Rwanda. We further explore the effects of soil erosion, root-depth restrictions and soil organic carbon content on saturated hydraulic conductivity in three LDSF sites in Nyagatare, Kayonza and Bugesera districts, respectively. Saturated hydraulic conductivity was modeled based on single-ring measurements of infiltration capacity using a modified Reynolds & Elrick steady-state single ring model for 48 LDSF plots per site. The results show significant spatial variation in infiltrability within sites.

The results of the study show the importance of rigorous protocols for sampling and analyses of soil properties and indicators of land health across landscapes. By simultaneously assessing soil properties, indicators of land degradation and soil infiltrability we demonstrate the utility of these approaches in understanding drivers of land degradation across multiple spatial scales for targeting of options for land restoration and monitoring of the effectiveness of these interventions over time across multiple dimensions of land health.

How to cite: Vågen, T.-G., Winowiecki, L. A., and Bargues-Tobella, A.: Earth Observation for Accurate Mapping of Soil Functional Properties, Land Health and Soil Infiltrability in Rwanda, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19380, https://doi.org/10.5194/egusphere-egu2020-19380, 2020.

Soil is the largest terrestrial carbon (C) reservoir and is an important component of climate-carbon feedbacks, potentially sequestering or releasing large amounts CO₂ from or to the atmosphere. In global land models soil C dynamics is determined by the long-term balance between C inputs and turnover rates, and the latter are usually a function of soil texture, temperature, and soil moisture, which represents environmental limitation of microbial soil organic carbon (SOC) mineralization. Hydrologic C export is often overlooked in the terrestrial C cycle models, likely because proportionally soils contain a very small amount of C that can be exported with runoff, contributing around 2.9 Pg C yr^-1 to aquatic systems globally. However, ignoring hydrologic C export in areas, where it has substantial effect on SOC turnover rate, could result in systematic overestimation of SOC stocks and inaccurate simulation of SOC responses to changing environmental conditions. We combined water quality data from the United States Geological Survey with hydrologic and soil chemistry data products to estimate the relative contribution of hydrologic export to bulk soil turnover rates across the continental USA. The catchment area weighted average of hydrologic export effect on SOC turnover was 5.2%. Hydrologic export accounted for 0-2% of the bulk SOC turnover in arid regions, 2-15% - in forests, and 20-40% - in wetland-rich areas. The SOC stocks generated for the continental U.S. using microbe-mediated turnover alone amounted to 88.3 Pg C and were 15.4% higher than the amount reported in the Harmonized World Soil Database (76.5 Pg C), thus illustrating the importance of accounting for hydrologic C export when simulating SOC dynamics.

How to cite: Hararuk, O., Jones, S., and Solomon, C.: Effect of Hydrologic Export on Soil Carbon Turnover Rates, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5514, https://doi.org/10.5194/egusphere-egu2020-5514, 2020.

Small changes in soil organic carbon (SOC) may have great influences on the climate-carbon cycling feedback. However, there are large uncertainties in predicting the dynamics of SOC in soil profile at the global scale, especially the role of soil microbial biomass in regulating the vertical distribution of SOC. Here, we developed a global database of soil microbial biomass carbon (SMBC), soil microbial quotient (SMQ, the ratio of SMBC to SOC), and SOC from 312 soil profiles, as well as climate, ecosystem type, and edaphic factors associated with these soil profiles. We assessed the global pattern of vertical distributions of SMBC and SMQ and the contributions of climate, ecosystem type, and edaphic factors to their vertical patterns. Our results showed that SMBC and SMQ decreased exponentially with depth, especially in the top 0-40 cm soil. SOC also decreased exponentially with depth but in different magnitudes compared to SMBC and SMQ. Edaphic factors (e.g., soil clay content and C/N ratio) were the most important controls for the vertical distributions of SMBC and SMQ, probably by mediating the preservation of substrates and nutrient supply for microbial growth in soils. Mean annual temperature and ecosystem types (i.e. forests, grasslands, and croplands) exerted weak influences on SMBC and SMQ. Overall, our data synthesis provides quantitative information of how SMBC, SMQ, and SOC changed along soil profiles globally and identifies important factors that influence their vertical distributions. The findings can help improve the prediction of C cycling in the terrestrial ecosystem by integrating the contributions of soil microbial roles in Earth system carbon models.

How to cite: Feng, W., Sun, T., Wang, Y., and Jing, X.: Global patterns of vertical distribution of soil microbial biomass carbon, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8132, https://doi.org/10.5194/egusphere-egu2020-8132, 2020.