How can we use our current wealth of terrestrial data, encompassing biogenic and abiogenic systems, to determine the distinguishing properties of life? SCOBI (Statistical Classification of Biosignature Information) uses machine learning techniques to algorithmically identify combinations of measurements that are “indicative of life”. A set of ~1000 observations, comprising elemental abundance, isotopic fractionation, VNIR reflectance, and (in progress) Raman spectra, have been assembled from existing literature and databases. The observations cover systems classified as “indicative alive” (e.g., cells, vegetation), “indicative non-alive” (e.g., fossils, teeth), “mixed indicative” (e.g., soil, pond water), or “non-indicative” (e.g., rocks, meteorites). VNIR data was preprocessed by linear interpolation from 400-2100 nm and smoothed with a Savitzky-Golay filter. To limit the amount of Earth-biochemistry-specific (non-agnostic) information included, the first five spectral features extracted were number of peaks, number of troughs, mean reflectance, mean peak width, and broadest peak width. To help further emphasize agnostic biosignatures, Earth-specific features such as chlorophylls have been manually flagged so that feature importance with and without them can be compared. Classifiers including k-nearest neighbors (KNN), Gaussian Naïve Bayes (GNB), logistic regression (LR), random forest (RF), and support vector machine (SVM) were implemented, as was a combination voting classifier. Performance metrics included false positive rates, false negative rates, and AUC with 50-50 test/train splits (Monte Carlo simulations). Key takeaways from this stage, prior to the inclusion of Raman spectra, are (1) the overall success rate of 0.933 AUC was most heavily influenced by the elemental abundance data; and (2) VNIR reflectance had the lowest classification performance with 0.52 AUC (58% of objects correctly classified). The next steps are to complete integration of Raman spectral data and to improve the approach to pre-processing and feature extraction for both types of spectral data, such as automated baseline removal, whole spectrum matching, and dimensionality reduction.
In this study, a simple stochastic representation of the microscale spatial variability in thaw depth in permafrost regions was proposed. Thaw depth distribution measured in the two larch-type forests in eastern Siberia, Spasskaya Pad and Elgeeii, showed different spatial, seasonal, and interannual variability, respectively. Minor year-to-year variation in active-layer thickness was observed in Spasskaya Pad, where a transient layer may constrain further thawing. A gamma distribution accurately represented the thaw depth spatial variability in both sites as the cumulative probability. Thus, a simple model illustrating the spatiotemporal variation in thaw depth as a function of the mean thaw depth was developed using the gamma distribution. A hierarchy of models was introduced that sequentially considered the constant state, linearity, and non-linearity in the dependence of the rate parameter of the gamma distribution for the mean thaw depth. Although the requirements of the model levels differed between Spasskaya Pad and Elgeeii, the proposed model successfully represented the spatial variability in thaw depth at both sites during different thaw seasons.
Accurately tracing the sources and fate of excess PO43- in waterways is necessary for sustainable catchment management. The natural abundance isotopic composition of O in PO43- (δ18OP) is a promising tracer of point source pollution, but its ability to track diffuse agricultural pollution is unclear. We tested the hypothesis that δ18OP could distinguish between agricultural PO43- sources by measuring the integrated δ18OP composition and P speciation of contrasting inorganic fertilisers (compound v rock) and soil textures (sand, loam, clay). δ18OP composition differed between the three soil textures sampled across six working livestock farms: sandy soils had lower overall δ18OP values (21 ± 1 ‰) than the loams (23 ± 1 ‰), which corresponded with a smaller, but more readily leachable, PO43- pool. Fertilisers had greater δ18OP variability (~8‰) driven by both fertiliser type and manufacturing year. Upscaling these values showed that ‘agricultural soil leaching’ δ18OP signatures could span from 18 – 25 ‰, and are influenced by both fertiliser type and the time between application and leaching. These findings emphasise the potential of δ18OP to untangle soil-fertiliser P dynamics under controlled conditions, but that its use to trace catchment-scale agricultural PO43- losses is limited by uncertainties in soil biological P cycling and its associated isotopic fractionation.
We conducted dynamic viscoelastic measurements of three clay minerals in a solid–liquid two-phase state: kaolinite, illite, and smectite with water. These constituents of concentrated (dense) suspensions were investigated using a high-temperature and high-pressure rheometer, to understand tectonic and non-tectonic phenomena in the shallow part of a fault system, such as shallow slow slip events in subduction zones, and landslides on fault or bed planes. We observed shear strain rate dependencies of phase angles of both dynamic stress and strain waveforms on the rheometer at varying temperature and pressure. The pressure and temperature dependence of the viscoelastic properties of the system can be qualitatively understood by applying the Zwanzig–Mountain theory. The local packing fraction change owing to dynamic oscillations affects the changing viscoelastic properties in systems such as shallow fault systems.
Tree uprooting is an observable and consequential process that suddenly moves soil downslope, inverts the soil column, and roughens the surface with pit-mound topography. Quantifying fluxes due to tree throw is complicated by its stochastic nature and estimation requires averaging over a large area or long time. Here, we develop theory that leads to a dimensionless metric directly measurable from high resolution topographic data. The theory explains the flux and topographic roughness as a function of tree throw production and decay rate by creep-like processes. We then form a dimensionless variable that is the ratio of fluxes due to three throw versus creep-like processes. Applying the theory to hillslopes in Southern Indiana, we find that tree throw accounts for 10 to 20\% of the hillslope sediment flux. The theoretical and observational findings provide a framework and important constraints on quantifying Critical Zone function from topographic parameters such as roughness.
We observed temperature variations over 10 months within a Kuroko ore (hydrothermal sulfide) cultivation apparatus installed atop a 50-m-deep borehole drilled in the Noho hydrothermal system in the mid-Okinawa Trough, southwestern Japan, for monitoring of hydrothermal fluids and in situ mineral precipitation experiments. Temperature and pressure in the apparatus fluctuated with the tidal period immediately after its installation. Initially, the average temperature was 75–76 °C and the amplitude of the semi-diurnal tidal temperature modulation was ~0.3 °C. Four months later, the amplitude of tidal temperature modulation had gradually increased to 4 °C in synchrony with an average temperature decrease to ~40 °C. Numerical modeling showed that both the increase in tidal amplitude and the decrease in average temperature were attributable to a gradual decrease in inflow to the apparatus, which promoted conductive cooling through the pipe wall. The reduced inflow was probably caused by clogging inside the apparatus, but we cannot rule out a natural cause, because the drilling would have significantly decreased the volume of hot fluid in the reservoir. The temperature fluctuation phase lagged the pressure fluctuation phase by ~150°. Assuming that the fluctuations originated from inflow from the reservoir, we conducted 2-D numerical hydrothermal modeling for a poroelastic medium. To generate the 150° phase lag, the permeability in the reservoir needed to exceed that in the ambient formation by ~3 orders of magnitude. The tidal variation phase can be a useful tool for assessing the hydrological state and response of a hydrothermal system.
Solar energy development is land intensive and recent studies have demonstrated the negative impacts of large-scale solar deployment on vegetation and soil. Co-locating vegetation with managed grazing on utility scale solar PV sites could provide a sustainable solution to meeting the growing food and energy demands, along with providing several co-benefits. However, the impacts of introducing grazing on soil properties at vegetated solar PV sites are not well understood. To address this knowledge gap, we investigated the impacts of episodic sheep grazing on soil properties (micro and macro nutrients, carbon storage, soil grain size distribution) at six commercial solar PV sites (MN, USA) and compared that to undisturbed control sites. Results indicate that implementing managed sheep grazing significantly increased total carbon storage (10-80%) and available nutrients, and the magnitude of change correlated with the grazing frequency (1-5 years) at the study sites. Furthermore, it was found that sites that experienced consecutive annual grazing treatments benefitted more than intermittently grazed sites. The findings will help in designing resource conserving integrated solar energy and food/fodder systems, along with increasing soil quality and carbon sequestration.
Exotic plant invasions alter ecosystem properties and threaten ecosystem functions globally. Interannual climate variability (ICV) influences both plant community composition (PCC) and soil properties, and interactions between ICV and PCC may influence nitrogen (N) and carbon (C) pools. We asked how ICV and non-native annual grass invasion covary to influence soil and plant N and C in a semiarid shrubland undergoing widespread ecosystem transformation due to invasions and altered fire regimes. We sampled four progressive stages of annual grass invasion at 20 sites across a large (25,000 km2) landscape for plant community composition, plant tissue N and C, and soil total N and C in 2013 and 2016, which followed two years of dry and two years of wet conditions, respectively. Multivariate analyses and ANOVAs showed that in invasion stages where native shrub and perennial grass and forb communities were replaced by annual grass-dominated communities, the ecosystem lost more soil N and C in wet years. Path analysis showed that high water availability led to higher herbaceous cover in all invasion stages. In stages with native shrubs and perennial grasses, higher perennial grass cover was associated with increased soil C and N, while in annual-dominated stages, higher annual grass cover was associated with losses of soil C and N. Also, soil total C and C:N ratios were more homogenous in annual-dominated invasion stages as indicated by within-site standard deviations. Loss of native shrubs and perennial grasses and forbs coupled with annual grass invasion may lead to long-term declines in soil N and C and hamper restoration efforts. Restoration strategies that use innovative techniques and novel species to address increasing temperatures and ICV and emphasize maintaining plant community structure – shrubs, grasses, and forbs – will allow sagebrush ecosystems to maintain C sequestration, soil fertility and soil heterogeneity.
Use of cover cropping systems to improve soil health is still limited in Louisiana. This study aimed to examine the interaction between cover crops and nitrogen (N) fertilizers rates on crop yield, soil chemical and biological properties. Winter cover crops, including legumes, a grass & a brassica, and a fallow control, were combined with N fertilizer application at four rates (0, 90, 179, 269 kg N ha-1) in continuous corn production as part of a no-till system. Soil samples were collected at 0-8 cm before and after cover crop termination in 2017 and 2018. Soil nutrients, organic matter, inorganic N, microbial community composition, and soil enzymes were analyzed. Legumes increased corn grain yield overall and maximized yield at 90 kg N ha-1 compared to grass & brassica treatments which maximized corn grain yield at 179 kg N ha-1. Regardless of cover crop type, nitrogen fertilizer applications increased soil organic matter by 8% compared to no nitrogen applications. The concentrations of soil phosphorous from legume was 19% higher than the grass & brassica treatment, while grass & brassica had a greater soil potassium concentration than legume. Cover crops and N applications improved soil enzymes for carbon and N cycling. Nitrogen rates applied for the main crop promoted microbial biomass in spring soil sampling. Arbuscular mycorrhizal fungi were greatest in the grass & brassica treatment and when no N was applied. Overall, the incorporation of winter legumes could reduce N fertilizer input, sustain corn production, and benefit soil health.
Injecting manure and commercial fertilizer beneath the soil surface is an important nutrient management practice that conserves ammonia-nitrogen (N) but creates distinct bands of N below the soil surface. To date, no widely accepted soil nitrate sampling protocol has been developed to account for the extreme heterogeneity created by injection. To develop sampling recommendations for Pre-Sidedress Nitrate Test (PSNT), we quantified patterns of NO3–N concentrations in soil from of corn (Zea mays L) plots injected with liquid dairy cattle (Bos taurus L) manure at 76 cm spacing over two years. Soil monoliths were collected to allow precise sampling of 30 cm deep by 2.5 cm soil cores from which a mid-season PSNT was determined. Monte Carlo simulation was conducted to simulate the effects of alternative soil sampling protocols on bias and error. Results from the simulation support the following equispaced sampling protocol: five, 30-cm deep soil cores are spaced 15 cm apart and oriented in a line perpendicular to the injected manure bands, collected at four locations in the field, to produce a single composite of 20 samples for NO3- analysis. It is not necessary to know manure band location. As spatially discrete manure application patterns become more prevalent with the expansion of manure injection, we believe this PSNT sampling protocol balances risk of error with practical concerns needed to promote adoption.
Properly designed, calibrated, and operated weighing lysimeters are recognized as accurate tools for measuring changes of soil water storage (ΔS) in the soil profile contained in the lysimeter. The neutron probe, NP, also is recognized as an accurate tool for determining soil profile water storage, S, and ΔS over the depth range of the neutron probe readings, again when properly calibrated and operated. Both methods have been used to calculate evapotranspiration (ET) using the soil water balance equation (ET = ΔS + I + P + R + F) applied to a control volume, where I is irrigation, P is precipitation, R is the sum of runon and runoff, and F is flux into or out of the control volume. However, weighing lysimeters are expensive to install and operate and are not portable, and the neutron probe faces regulatory pressures and cannot be used unattended, limiting its use. Past attempts to use electromagnetic soil water sensors based on capacitance principles to accurately determine profile water content have not met with success. Recent advances in soil water senor technology have led to accurate, low power, and relatively inexpensive electronic soil water sensors based on time domain reflectometry (TDR) theory, which can be installed in situ. Assuming that accurate values or controls were available for I, P, R, and F, we concentrated on evaluating S and ΔS as determined by lysimeter, NP, and TDR sensors. Over a cropping season at Bushland, Texas, USA we compared S and ΔS in a 2.3-m deep profile of silty clay loam soil as assessed by a large, precision weighing lysimeter, the NP in two access tubes in the lysimeter, and three profiles of TDR soil water sensors installed in the lysimeter, each profile consisting of 15 sensors. Weighing lysimeter mass was recorded every 5 minutes and TDR sensors were read every 15 minutes, both automatically using dataloggers, while the neutron probe readings were done manually at approximately one-week intervals. Comparing TDR sensors with neutron probe, coefficients of determination for profile water content and for ΔS were 0.97 and 0.91, respectively, when one-week intervals were considered. Coefficients of determination for comparisons of TDR sensors to lysimeter were 0.95 for S and 0.92 for ΔS, while values for comparison of neutron probe to lysimeter were 0.91 for water storage and 0.83 for ΔS, again for one-week intervals. Poorer performance of the NP was likely due to the fact that it could not be read to depths greater than 1.90 m, which limited the profile sensed to the top 2.0 m of soil.
California’s Central Valley is responsible for $17 billion of annual agricultural output, producing 1/4 of the nation’s food. However, land in the Central Valley is sinking at a rapid rate (as much as 20 cm per year) due to continued groundwater pumping. Land subsidence has a significant impact on infrastructure resilience and groundwater sustainability. It is important to understand subsidence and groundwater depletion in a consistent framework using improved models capable of simulating in-situ well observations and observed subsidence. Currently, groundwater well data is sparse and sampled irregularly, compromising our understanding of groundwater changes. Moreover, groundwater pumping data is a major missing piece of the puzzle. Limited data availability and spatial/temporal uncertainty in the available data have hampered understanding the complex dynamics of groundwater and subsidence. To address this limitation, we first integrated multimodal data including InSAR, groundwater, precipitation, and soil composition by interpolating data with the same spatial and temporal resolutions. We then identified regions with different temporal dynamics of land displacement, groundwater depth, and precipitation. Some areas (e.g., Helm) with coarser grain soil compositions exhibited potentially reversible land transformations (elastic land compaction). Finally, we fed the integrated data into the deep neural network of a gated recurrent unit-based sequence-to-sequence generation model. We found that the combination of InSAR, groundwater depth, and precipitation data had predictive power for soil composition using deep neural networks (correlation coefficient R=0.83, normalized Nash-Sutcliffe model efficiency NNSE=0.84). A random forest model was tested as baseline (R=0.65, NNSE=0.69). We also achieved significant accuracy with only 40% of the training data (NNSE=0.8), suggesting that the model can be generalized to other regions for indirect estimation of soil composition. Our results indicate that soil composition can be estimated using InSAR, groundwater depth and precipitation data. In-situ measurements of soil composition can be expensive and time consuming and may be impractical in some areas. The generalizability of the model sheds light on high spatial resolution soil composition estimation utilizing existing measurements.
Microplastic (MP) contamination of freshwaters and soils has become one of the major challenges within the Anthropocene. MP is transported in large quantities through river systems from land to sea. However, the question is whether there is transport only or also deposition within the system? Floodplains and their soils as part of the river system are known for their sink function for sediments, nutrients, and pollutants. The present case study analyzes the spatial distribution of large (L-MP, 2,000–1,000 μm) and medium (M-MP, 1,000–500 μm) MP particles in floodplain soils of the Lahn River (Germany). Based on a geospatial sampling concept, the MP contents in floodplain soils are investigated down to a depth of 2 meters through a holistic method approach. The analysis of the plastic particles is carried out by density separation, visual fluorescence identification, and additional ATR-FTIR analysis. In addition, grain size analyses and 210Pb/137Cs dating was performed to reconstruct the MP deposition conditions in floodplains. The results prove a spatial frequent accumulation of MP in upper floodplain soils (0–50 cm) deposited by flood dynamics since the 1960s. MP detection over the entire soil column to a depth of 2 meters and below recent (>1960) sediment accumulation indicates MP relocation and in-situ vertical transfer of mobile MP particles through natural processes (e.g., preferential flow, bioturbation). Furthermore, the role of MP as a potential marker of the Anthropocene is assessed based on the findings. This study advances our understanding of the deposition and relocation of MP at the aquatic-terrestrial interface.
Remediation of contaminated soil sites is important to our environment and the growing population that interacts with these resources. Contamination of soil due to leaks, spills and seepage is a worldwide problem usually diagnosed by costly and time-consuming methods primarily using wet chemistry. Problems in remediation efforts involve finding technologies that are less time-consuming and more cost effective over time. Field portable spectrometers that cover key spectral ranges in the ultraviolet, visible and near infrared regions provide a solution for fast and easy identification of contaminants in soil. Using a field portable spectrometer to measure Petroleum Hydrocarbons (TPH) in soil is a fast and nondestructive method of analysis. Applying UV-VIS-NIR technology to these samples hydrocarbon spectra can potentially be characterized by four main absorption features at 1180nm, 1380, 1730nm, and 2310nm. This presentation aims to highlight the utility of field portable NIR technology for researchers in addressing potentially contaminated environments.
A portion of pore water is typically in a state of unfrozen condition in frozen soils due to the complex soil-water interactions. The variation of the amount of unfrozen water and ice has a significant influence on the physical and mechanical behaviors of the frozen soils. Several empirical, semi-empirical, physical and theoretical models are available in the literature to estimate the unfrozen water content (UWC) in frozen soils. However, these models have limitations due to the complex interactions of various influencing factors that are not well understood or fully established. For this reason, in the present study, an artificial neural network (ANN) modeling framework is proposed and the PyTorch package is used for predicting the UWC in soils. For achieving this objective, extensive UWC data of various types of soils tested under various conditions were collected through an extensive search of the literature. The developed ANN model showed good performance for the test dataset. In addition, the model performance was compared with two traditional statistical models for UWC prediction on four additional types of soils and found to outperform these traditional models. Detailed discussions on the developed ANN model, and its strengths and limitations in comparison to different other models are provided. The study demonstrates that the proposed ANN model is simple yet reliable for estimating the UWC of various soils. In addition, the summarized UWC data and the proposed machine learning modeling framework are valuable for future studies related to frozen soils.
High-school students tested soil, paint, and water for lead (Pb) in a total of 80 houses in their town of Pelham, New York, where blood-Pb data indicate relatively high levels of child exposure. All the samples were tested in the laboratory using established procedures but this was preceded by testing of soil and paint in the field with a kit by the students. The total Pb content of 32 of the 159 soil samples that were collected exceeded 400 ppm, the EPA standard for bare soil in areas where children play. Only 4 of the 118 tap water samples that were collected contained over 15 ppb Pb, with the data showing that flushing for 2 min clearly lowered Pb concentration further across the board. The highest risk of child exposure may be posed by old Pb-paint, however, which was detected in 9 of the 48 samples that were tested. Unfortunately, residents were also the least willing to let the students test or sample their paint. High-school students could help reduce exposure in the many towns where child blood-Pb levels remain high today while doing so learning about environmental science and measurement from this hands-on experience.
This article is composed of a commentary about the state of ICON principles (Goldman et al. 2021) in Volcanology, Geochemistry, Petrology (VGP) and discussion on the opportunities and challenges of adopting them. VGP encompasses a broad field that addresses volcanic, magmatic, hydrothermal, geomicrobial systems and process investigations that span the physical, geochemical and biological realms, and one that is extensively supported by state-of-the-art research facilities. We suggest that an open, inclusive, collaborative and evolving model of an international coordinated network is critical to answering the most pressing challenges in VGP. In this commentary piece, we begin to discuss the elements of, challenges to, and path forward in developing such a model. For this team, ICON means collaboration, equitable access to data for the entire scientific community, and forging of partnerships that potentially contribute to more innovative ways of coordinating and sharing research. It also means bringing more equity to science, by implementing effective measures which consider access to funding, analytical equipment, resources, and mentors. More importantly, ICON to us means having important conversations around what we value in the advancement of science, perhaps exploring outside the idea of meritocracy and evaluating what individual traits can contribute to science outside what has traditionally been considered the norm.
Land ecosystems offer an effective nature-based solution to climate change mitigation by absorbing approximately 30% of anthropogenically emitted carbon. This estimated absorption is primarily based on constraints from atmospheric and oceanic measurements while quantification from direct studies of the land carbon cycle themselves displays great uncertainty. The latter hinders prediction of the future fate of the land carbon sink. This talk will present a matrix approach, which will be shown to unify land carbon cycle models, help diagnose model performance with new analytics, accelerate computational efficiency for spin-up, enable data assimilation with complex models, and guide carbon cycle research with a new theoretical framework. The unified framework can be used to evaluate relative importance of various processes, identify sources of uncertainty in model predictions, and improve accuracy of quantification of land carbon sequestration.