Hydraulic redistribution affects modeled carbon cycling via soil microbial activity and suppressed fire
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Accurately capturing dynamic soil response to disturbance effects in agroecosystem models remains elusive, thereby limiting projections of climate change mitigation potential. Perennial grasses cultivated in zero‐tillage management systems hold promise as sustainable agroecosystems. High‐yielding tropical C 4 grasses often have extensive rooting systems, and the belowground processes of root turnover, aggregate formation, and mineral stabilization drove rapid C accumulation after cultivation in a recent study. We sought (i) to understand and constrain the size and responsiveness of dynamic, intermediate‐cycling C pools contributing to the observed C accrual rates, and (ii) to simulate C stocks over time under the disturbance of elevated temperature using soil incubation at multiple temperatures and physical fractionation via density and sonication. Three‐pool transfer modeling of soil incubations revealed small pools of readily available (i.e., days to months) microbial substrate that were responsive to temperature, time since cultivation, and inputs. Larger, kinetically slow‐cycling pools were more indicative of long‐term (i.e., years to decades) changes in C stock and strongly connected to measured changes in physical fractions. Combining the sensitivity of readily available microbial substrate with three‐pool transfer modeling of the physical fractions over time since cultivation revealed that dynamic transfers of inputs occurred between the free organic and aggregate‐protected fractions, and from these fractions to the mineral‐associated dense fraction. Under 5°C temperature elevation, increased transfer rates outweighed elevated decomposition losses to sustain soil C accrual into the future. To effectively plan managed landscapes and monitor sustainable agroecosystems for climate change mitigation, tools must incorporate the complexity of soil response to change. Core Ideas Dynamic aspects of soil C are inadequately represented in most models. Intermediate‐cycling pools are more responsive to disturbances than thought. Root inputs are rapidly moved from particulate debris to mineral‐bound stable pools. Temperature increased the transfer rate to stable pools and sustained C accumulation Climate change mitigation projections must capture dynamic responses in agroecosystems.
In situ soil flushing is being using at the Hanford 100 K-East (100 KE) area to transport mobile chromium contamination in the vadose zone to the water table, where it can be collected and treat through pump and treat operations. The efficacy of soil flushing is directly related to the volume of clean water that infiltrates through contaminated soils. In practice, it is infeasible to comprehensively monitor which regions of the vadose zone are being infiltrated through direct sampling of pore water. Consequently, there can be significant uncertainty about which regions of the subsurface have been treated, especially if hydrogeologic conditions are favorable for the development of unstable flows and preferred flow pathways through the vadose zone (Jarvis, Koestel, and Larsbo 2016). Current approaches for quantitative monitoring of soil flushing performance rely on contaminant concentration measurements collected from extractions wells. There is no quantitative information on the volume of flush water delivered to targeted regions of the vadose zone at the Hanford Site, leading to significant uncertainty regarding source term removal and long-term impacts to groundwater. If the subsurface hydrogeologic properties at the 100 KE Area were adequately known, qualitative metrics of soil flushing performance could be simulated, thereby negating expenses required to obtain quantitative performance information through borehole drilling/sampling. However, estimating in situ hydrogeologic properties has long proven elusive, due primarily to a lack of sufficient information to constrain heterogeneous property estimates to a useful degree of certainty. Estimating vadose zone hydrogeologic properties is particularly challenging due the dependence of hydraulic conductivity on saturation. This report describes progress toward a first-of-its-kind demonstration using surface time-lapse 3D electrical resistivity tomography (ERT) monitoring data to estimate the hydrogeologic properties that control flush water transport at the 100 KE soil flushing site. The ultimate objective is (1) to verify sufficient information exists in the ERT monitoring data to adequately resolve vadose zone hydraulic properties, and (2) generate a “digital twin” (i.e., a numerical simulator) that can be used to simulate the amount of flush water that has been delivered to each targeted region of the vadose zone, and thereby assess the efficacy of flush water delivery. Resulting performance estimates can be used in leu of comprehensive borehole drilling and direct sampling (or wellbore logging) that would otherwise be required to obtain the same information.
Abstract. Rapid warming of Arctic ecosystems exposes soil organic matter (SOM) to accelerated microbial decomposition, potentially leading to increased emissions of carbon dioxide ( CO 2 ) and methane ( CH 4 ) that have a positive feedback on global warming. Current estimates of the magnitude and form of carbon emissions from Earth system models include significant uncertainties, partially due to the oversimplified representation of geochemical constraints on microbial decomposition. Here, we coupled modeling principles developed in different disciplines, including a thermodynamically based microbial growth model for methanogenesis and iron reduction, a pool-based model to represent upstream carbon transformations, and a humic ion-binding model for dynamic pH simulation to build a more versatile carbon decomposition model framework that can be applied to soils under varying redox conditions. This new model framework was parameterized and validated using synthesized anaerobic incubation data from permafrost-affected soils along a gradient of fine-scale thermal and hydrological variabilities across Arctic polygonal tundra. The model accurately simulated anaerobic CO 2 production and its temperature sensitivity using data on labile carbon pools and fermentation rates as model constraints. CH 4 production is strongly influenced by water content, pH, methanogen biomass, and presence of competing electron acceptors, resulting in high variability in its temperature sensitivity. This work provides new insights into the interactions of SOM pools, temperature increase, soil geochemical feedbacks, and resulting CO 2 and CH 4 production. The proposed anaerobic carbon decomposition framework presented here builds a mechanistic link between soil geochemistry and carbon mineralization, making it applicable over a wide range of soils under different environmental settings.
Research efforts were concentrated on attempts to obtain data concerning the dry heat resistance of particle microflora in Kennedy Space Center soil samples. The in situ dry heat resistance profiles at selected temperatures for the aggregate microflora on soil particles of certain size ranges were determined. Viability profiles of older soil samples were compared with more recently stored soil samples. The effect of increased particle numbers on viability profiles after dry heat treatment was investigated. These soil particle viability data for various temperatures and times provide information on the soil microflora response to heat treatment and are useful in making selections for spacecraft sterilization cycles.
The effects of organic matter, free iron oxides, texture, moisture content, and cation exchange capacity on the spectral reflectance of soils were investigated along with techniques for differentiating soil orders by computer analysis of multispectral data. By collecting soil samples of benchmark soils from the different climatic regions within the United States and using the extended wavelength field spectroradiometer to obtain reflectance values and curves for each sample, average curves were constructed for each soil order. Results indicate that multispectral analysis may be a valuable tool for delineating and quantifying differences between soils.
Six Luna 24 soils (24077, 24109, 24149, 24174, 24182, 24210), their grain-size separates, and twenty-five greater than 1 mm soil particles were analyzed by ferromagnetic resonance (FMR) and static magnetic techniques. The values of the FMR maturity index for the soils ranged from 19.0 to 39.0 units; soils 24077 and 24109 are submature and soils 24149, 24174, 24182, and 24210 are immature. The metallic iron contents of the soils ranged from 0.30 to 0.51 wt % and are commensurate with their maturities. The 'best estimate' value for the concentration of metallic iron of Luna 24 mare basalts is 0.038 wt %.
Predictive procedures for developing soil hydrologic properties (i.e., relationships of soil water pressure and hydraulic conductivity to soil water content) are presented. Three models of the soil water pressure-water content relationship and one model of the hydraulic conductivity-water content relationship are discussed. Input requirements for the models are indicated, and computational procedures are outlined. Computed hydrologic properties for Keith silt loam, a soil typer near Colby, Kansas, on which the 1978 Agricultural Soil Moisture Experiment was conducted, are presented. A comparison of computed results with experimental data in the dry range shows that analytical models utilizing a few basic hydrophysical parameters can produce satisfactory data for large-scale applications.
Soil samples were fractionated and analyzed in order to assess the physical and chemical interactions of entrained soil with solid-rocket exhaust clouds. The sandy soil consisted primarily of quartz (silica) particles, 30 to 500 microns in diameter, and also contained seashell fragments. Differential and cumulative soil-mass size distributions are presented along with mineralogy, elemental compositions, and solution pH histories. About 90 percent of the soil mass consisted of particles 165 microns in diameter. Characteristic reaction times in aqueous HC1 slurries varied from a few minutes to several days, and capacities for reaction under acidic conditions varied from 10 to 40 g HCl/kg soil, depending on particle size. Airborne lifetimes of particles 165 microns are conservatively 30 min, and this major grouping is predicted to represent a small short-term chemical sink for up to 5% of the total HC1. The smaller and more minor fractions, below a 165 micron diameter, may act as giant cloud condensation nuclei over much longer airborne lifetimes. Finally, the demonstrated time dependency of neutralization is a complicating factor; it can influence the ability to deduce in-cloud HCl scavenging with reaction and can affect the accuracy of measured chemical compositions of near-field wet deposition.
An experiment on remote sensing of soil moisture content was conducted over bare fields with microwave radiometers at the frequencies of 1.4, 5, and 10.7 GHz, during July-September of 1981. Three bare fields with different surface roughnesses and soil textures were prepared for the experiment. The experimental results show that the effect of surface roughness is to increase the soil's brightness temperature and to reduce the slope of regression between brightness temperature and moisture content. The slopes of regression for soils with different textures are found to be comparable and the effect of soil texture is reflected in the difference of regression line intercepts at brightness-temperature axis. Measurements on wet smooth bare fields give lower brightness temperatures at 5 than at 1.4 GHz. This phenomenon is not expected from current radiative transfer theory, using laboratory measurements of the relationship between permittivity and moisture content for different soil-water mixtures at frequencies of not greater than 5 GHz.
Field experiments to (1) study the biomass and geometrical structure properties of vegetation canopies to determine their impact on microwave emission data, and (2) to verify whether time series microwave data can be related to soil hydrologic properties for use in soil type classification. Truck mounted radiometers at 1.4 GHz and 5 GHz were used to obtain microwave brightness temperatures of bare vegetated test plots under different conditions of soil wetness, plant water content and canopy structure. Observations of soil moisture, soil temperature, vegetation biomass and other soil and canopy parameters were made concurrently with the microwave measurements. The experimental design and data collection procedures for both experiments are documented and the reduced data are presented in tabular form.
New compositional data (by INAA) for 29 samples of Apollo 15 soil fractions are presented along with data for six depth intervals of the bottom half of the double drive tube at station 2 on the Apennine Front. The compositional trends in the Apollo 15 soils are discussed in terms of four techniques used to determine the important components of the regolith: graphical techniques, factor analysis, chemical analysis of individual soil particles, and mixing models. The concept of 'mixing levels' is introduced, which states that a sample of lunar regolith can be considered as a mixture on several different compositional levels (I, igneous rock types; II, local rock types; III, soils), and it is shown that most attempts at modeling lunar soils as mixtures have been directed at level II. It is argued that only four components are required to account for the variation in concentrations of lithophile elements in the Apollo 15 samples: mare basalt, KREEP basalt, green glass, and Apennine front soil (best represented by the station 2 samples).
The file structure and coding of four soils data sets derived from the Zobler (1986) world soil file is described. The data were digitized on a one-degree square grid. They are suitable for large-area studies such as climate research with general circulation models, as well as in forestry, agriculture, soils, and hydrology. The first file is a data set of codes for soil unit, land-ice, or water, for all the one-degree square cells on Earth. The second file is a data set of codes for texture, land-ice, or water, for the same soil units. The third file is a data set of codes for slope, land-ice, or water for the same units. The fourth file is the SOILWRLD data set, containing information on soil properties of land cells of both Matthews' and Food and Agriculture Organization (FAO) sources. The fourth file reconciles land-classification differences between the two and has missing data filled in.
In order to determine the significance of organic matter content on the microwave emissivity of soils when estimating soil moisture, field experiments were conducted in which 1.4 GHz microwave emissivity data were collected over test plots of sandy loam soil with different organic matter levels (1.8, 4.0, and 6.1 percent) for a range of soil moisture values. Analyses of the observed data show only minor variation in microwave emissivity due to a change in organic matter content at a given moisture level for soils with similar texture and structure. Predictions of microwave emissivity made using a dielectric model for aggregated soils exhibit the same trends and type of response as the measured data when appropriate values for the input parameters were utilized.
In order to determine the significance of organic matter content on the microwave emissivity of soils when estimating soil moisture, field experiments were conducted in which 1.4 GHz microwave emissivity data were collected over test plots of sandy loam soil with different organic matter levels (1.8, 4.0, and 6.1 percent) for a range of soil moisture values. Analyses of the observed data show only minor variation in microwave emissivity due to a change in organic matter content at a given moisture level for soils with similar texture and structure. Predictions of microwave emissivity made using a dielectric model for aggregated soils exhibit the same trends and type of response as the measured data when appropriate values for the input parameters were utilized.
The main objective of these NASA-funded projects is to improve our understanding of land-use impacts on soil carbon dynamics in the Amazon Basin. Soil contains approximately one half of tropical forest carbon stocks, yet the fate of this carbon following forest impoverishment is poorly studied. Our mechanistics approach draws on numerous techniques for measuring soil carbon outputs, inputs, and turnover time in the soils of adjacent forest and pasture ecosystems at our research site in Paragominas, state of Para, Brazil. We are scaling up from this site-specific work by analyzing Basin-wide patterns in rooting depth and rainfall seasonality, the two factors that we believe should explain much of the variation in tropical soil carbons dynamics. In this report, we summarize ongoing measurements at our Paragominas study site, progress in employing new field data to understand soil C dynamics, and some surprising results from our regional, scale-up work.
Soil organic carbon (SOC) regulates terrestrial ecosystem functioning, provides diverse energy sources for soil microorganisms, governs soil structure, and regulates the availability of organically bound nutrients. Investigators in increasingly diverse disciplines recognize how quantifying SOC attributes can provide insight about ecological states and processes. Today, multiple research networks collect and provide SOC data, and robust, new technologies are available for managing, sharing, and analyzing large data sets. Here, we advocate that the scientific community capitalize on these developments to augment SOC data sets via standardized protocols. We describe why such efforts are important and the breadth of disciplines for which it will be helpful, and outline a tiered approach for standardized sampling of SOC and ancillary variables that ranges from simple to more complex. We target scientists ranging from those with little to no background in soil science to those with more soil-related expertise, and offer examples of the ways in which the resulting data can be organized, shared, and discoverable.
Abstract Changes in soil organic carbon (SOC) storage have the potential to affect global climate; hence identifying environments with a high capacity to gain or lose SOC is of broad interest. Many cross‐site studies have found that SOC‐poor soils tend to gain or retain carbon more readily than SOC‐rich soils. While this pattern may partly reflect reality, here we argue that it can also be created by a pair of statistical artifacts. First, soils that appear SOC‐poor purely due to random variation will tend to yield more moderate SOC estimates upon resampling and hence will appear to accrue or retain more SOC than SOC‐rich soils. This phenomenon is an example of regression to the mean. Second, normalized metrics of SOC change—such as relative rates and response ratios—will by definition show larger changes in SOC at lower initial SOC levels, even when the absolute change in SOC does not depend on initial SOC. These two artifacts create an exaggerated impression that initial SOC stocks are a major control on SOC dynamics. To address this problem, we recommend applying statistical corrections to eliminate the effect of regression to the mean, and avoiding normalized metrics when testing relationships between SOC change and initial SOC. Careful consideration of these issues in future cross‐site studies will support clearer scientific inference that can better inform environmental management.