Despite nearly complete coverage of the Martian surface with thermal infrared datasets, uncertainty remains over a wide range of observed thermal trends. Combinations of grain sizes, packing geometry, cementation, volatile abundances, subsurface heterogeneity, and sub-pixel horizontal mixing lead to multiple scenarios that would produce a given thermal response at the surface. Sedimentary environments on Earth provide a useful natural laboratory for studying how the interplay of these traits control diurnal temperature curves and identifying the depositional contexts those traits appear in, which can be difficult to model or simulate indoors. However, thermophysical studies at Mars-analog sites are challenged by distinct controls present on Earth, such as soil moisture and atmospheric density. In this work, as part of a broader thermophysical analog study, we developed a model for determining thermal properties of in-place sediments on Earth from thermal imagery that considers those additional controls. The model uses Monte Carlo simulations to fit calibrated surface temperatures and identify the most probable dry thermal conductivity as well as any potential subsurface layering. The program iterates through a one-dimensional surface energy balance on the upper boundary of a soil column and calculates subsurface heat transfer with temperature-dependent parameters. The greatest sources of uncertainty stem from the complexity of how thermal conductivity scales with water abundance and from surface-atmosphere heat exchange, or sensible heat. Using data from a 72-hr campaign at a basaltic eolian site in the San Francisco Volcanic Field, we tested multiple models for how dry soil components and water contribute to thermal conductivity and multiple approaches to estimating sensible heat from field measurements. Field measurements include: upwelling and downwelling radiation, air temperature, relative humidity, wind speed, and soil moisture, all collected from a ground station, as well as UAV-derived surface geometries. By mitigating Earth-specific uncertainty and isolating the controls that are most relevant to Martian sediments, we can then validate those controls with in situ thermophysical probe measurements and ultimately improve interpretations of thermal data for the Martian surface.
Soil organic matter (SOM), the accumulated, decaying debris of biota living on or in the soil, represents the largest of the active terrestrial C pools, holding about 1500 Pg C to a depth of 1 m. In aquatic ecosystems, SOM is a storehouse of inorganic nutrients which, after mineralization, are released to the stream and used by planktonic and benthic microorganisms. Here we present the results of a study designed to elucidate the controls on the spatial and temporal variations of the SOM distribution along the Clear Fork River, which drains a mixed urban-agricultural landscape in north-central Ohio. Fluvial bed sediments were sampled monthly (March to October) in eight stations along the river. Organic matter (OM) and carbonate content were determined by loss-on-ignition (LOI). Sediments from all stations were analyzed in triplicate to account for intrasample variation and to provide a measure of precision. Textural analysis was also performed in all samples. Results show OM content varying between 14 and 109 g kg-1, with highest values observed during spring, and lower values during summer. Sediments from stations where the stream flow is high generally presented lower OM concentration. In addition, stations located within urban landscapes presented the highest OM concentrations.
The development of several large-, ‘continental’-scale ecosystem research infrastructures over recent decades has provided a unique opportunity in the history of ecological science. The Global Ecosystem Research Infrastructure (GERI) is an integrated network of analogous, but independent, site-based ecosystem research infrastructures (ERI) dedicated to better understand the function and change of indicator ecosystems across global biomes. Bringing together these ERIs, harmonizing their respective data and reducing uncertainties enables broader cross-continental ecological research. It will also enhances the research community capabilities to anticipate and address future global scale ecological challenges to the planet. Moreover, increasing the international capabilities of these ERIs goes beyond their original design intent, and is an unexpected added value of these large national investments. Here, we identify specific global grand challenge areas and research trends to advance the ecological frontiers across continents that can be addressed through the federation of these cross-continental-scale ERIs.
Abiotic efflux of CO2 from soil is often attributed to dissolution of carbonates, and therefore not expected to occur in soils with a low pH. However, another abiotic source of CO2, less constrained by pH, may arise from reactions that oxidize natural soil organic matter and reduce metal oxides. Studies of redox reactions between phenolic compounds and Fe and Mn oxides in soil have been focused mainly on the environmental fate of both oxidants and reductants and formation of organic matter. We measured CO2 formed during 3-hour, room temperature (22±2 oC), incubations of samples of archived soils and from an ongoing crop diversity study. Subsamples (8 g. ODE) of each soil, were treated (5 ml) with water, or solutions of glucose (0.029 M), or gallic acid (0.025 M). For each soil, subsamples amended with H2O or with the glucose solution produced little CO2 and were nearly identical to each other, while CO2 quickly formed after treatment with gallic acid regardless of pH. The net increase in CO2 due to gallic acid, observed from the 18 archived soils, ranged from less than 0.5 to more than 80 mg CO2-C kg-1 soil. Significant treatment effects were observed in samples from the crop diversity study with more (Tukey’s P≤0.05) net CO2 from a small grain-fallow treatment compared a 5-year rotation treatment, 19.04 and 15.77 mg CO2-C kg-1 soil, respectively. This study suggests abiotic reactions capable of rapidly producing a burst of CO2 can occur in a wide range of soils following inputs of simple phenolic compounds and be impacted by management regimes. We suggest these are redox reactions in soil linked to Mn or Fe metal oxides and when considered together with fluctuations of carbon inputs to soil and redox cycling, might be a larger contributor to C emissions than previously accounted for.
Soil carbon is intimately related to the living part of the organic matter, as represented by the soil microbial biomass, which mediates the decomposition, mineralization, and immobilization of organic carbon available in soils under different land-use systems. Forest-to-agriculture conversion and land-use change often lead to a loss in microbial biomass carbon (MBC) and shifts in microbial activity, directly influencing the soil carbon dynamics. The main aim of this study was to evaluate the effects of land-use change and geographical distribution on the microbial and environmental patterns related to soil C-dynamics. We evaluated MBC and microbial respiration in soils under five different land-use systems and two contrasting seasons, at a regional scale in Santa Catarina State, Southern Brazil. At the west mesoregion, changes in the MBC were correlated to sampling season in forest and grassland systems. Yet at the plateau mesoregion, we observed a land-use effect, as MBC decreased in no-till and crop-livestock integration systems. At the two mesoregions, forest and grassland had presented the highest values of MBC and microbial activity, as represented by microbial respiration. The grassland sites have presented lower values of the metabolic quotient (qCO2) and higher values of the microbial quotient (qMic). The qCO2 was lower in winter for all land-use systems. The forest sites have shown the highest total and particulate organic carbon values. The chemical-physical characteristics have shown correlations with microbiological variables related to the soil microbial C-dynamics. The land-use intensity, season, and geographic location were the main drivers of changes in microbial C-dynamics.
The U.S. Climate Reference Network (USCRN) has been engaged in ground-based soil water and soil temperature measurements since 2009. As a nationwide climate network, the network stations are distributed across vast complex terrains. Due to the expansive distribution of the network and the related variability in soil properties, obtaining site-specific calibrations for sensors is a significant and costly endeavor. Presented here are three commercial-grade electromagnetic sensors, with built-in thermistors to measure both soil water and soil temperature, including the SoilVUE10 Time Domain Reflectometry (TDR) probe (hereafter called SP, for SoilVUE Probe) (Campbell Scientific, Inc., Logan, UT), the 50 MHz coaxial impedance dielectric sensor (model HydraProbe (hereafter called HP), Stevens Water Monitoring Systems, Inc., Portland, OR), and the TDR-315L Acclima Probe (hereafter called AP) sensor (model TDR-315L, Acclima, Inc., Meridian, ID), which were evaluated in a nonconductive loam soil in Oak Ridge, Tennessee, USA from 2021 to 2022. The manufacturer-supplied calibration equation for loam soils was successfully used in this study. Measurements of volumetric water content by SP were much lower than gravimetric measurements in the top 20-cm soil horizon, where soil water showed relatively large spatial variability. Study results highlight that the SP may be an important alternative to reduce soil disturbances that usually ensue when HP and AP sensors are installed; however, in-situ calibrations are essential for the SP for xeric soil water conditions.
The regional to global responses of the Standardized Precipitation-Evapotranspiration Index, Palmer Drought Severity Index, and Aridity Index to future global warming tend to be much more pervasively and strongly negative than the responses of comprehensive land model runoff and bulk soil-moisture outputs to the same warming. We term these systematic differences “index-impact gaps.” Some studies have assumed that these gaps arise because land-surface models include water-saving CO2-plant effects that the dryness indices do not, but recently published work makes clear that the gaps largely persist even in model simulations in which these effects are switched off. Thus, the main reason(s) for the index-impact gaps are still unclear, making it difficult to trust either the common dryness indices or the comprehensive land-surface models under climate change. In this study, we are investigating several postulated causes of these index-impact gaps using sensitivity experiments with the state-of-the-art Community Land Model version 5.0. In addition to CO2-plant effects, we are testing the roles of stomatal closure driven by high vapor-pressure deficits, short-term runoff enhancement due to sharper concentration of rain in time with warming, and annual-scale runoff enhancement due to changes in the seasonality of precipitation and/or infiltration with warming. If CLM5.0’s runoff and bulk soil-moisture responses start to agree with the dryness-index responses much more after eliminating these pathways, it will imply that the dryness indices are in fact a useful theoretical baseline for understanding the comprehensive model responses. However, if the index-impact gaps still remain wide, it will imply either that dryness-index responses are fundamentally different from runoff and soil-moisture responses to climate change, or else that CLM5.0’s evapotranspiration is not sensitive enough to rising temperatures. Further experiments will be required in that case.
Sustainable aviation fuels (SAF) produced from lipid feedstocks are an increasingly mature and low-cost option for aviation sector decarbonization. Ethiopian mustard (Brassica carinata) is a non-food oilseed crop that can be grown on winter fallow land in the southeastern US and used as a feedstock for SAF, with a high-protein livestock feed co-product. Integrating carinata into existing annual crop rotations produces an additional revenue stream for landowners, with potential co-benefits for soil carbon and other ecosystem services. The Southeast Partnership for Advanced Renewables from Carinata (SPARC) is a USDA-funded research consortium to advance carinata production and associated SAF and bioproduct supply chains in the region. A SPARC research team used the DayCent ecosystem model to estimate the potential production of carinata across the tri-state region of Alabama, Florida, and Georgia, and assess associated changes in soil carbon storage and emissions of nitrous oxide (N2O), the main biogenic greenhouse gas (GHG) emissions from agriculture. First, we calibrated DayCent to reproduce the phenology, harvest index, productivity response to nitrogen application, root-to-shoot biomass ratio, and tissue nitrogen content data observed for a set of carinata field trials in the region. Next, we simulated the integration of carinata into a typical cotton/peanut rotation across the 2.3 million hectares of annual cropland within the climate suitability range for this crop, grown once every third winter. We show an annual production potential of greater than 1 billion liters of SAF from this feedstock in the region. Our base carinata management case is approximately neutral in biogenic GHG emissions, with modest soil carbon sequestration that offsets the associated small increase in N2O emissions. However, adopting conservation management practices such as no-till establishment or poultry litter soil amendments results in a more substantial net soil carbon sink, reducing the GHG footprint of carinata-derived SAF by up to 20 grams of CO2-equivalent per megajoule of fuel. This work supports SPARC’s ongoing efforts to develop improved crop varieties and management practices that simultaneously improve the economics and ecosystem service value of carinata production.
Knowing the centimeter- to meter-scale distribution of sand in clayey deposits is important for determining the dominating water flow pathways. Borehole information has a high vertical resolution, on the millimeter- to centimeter-scale, but provides poor lateral coverage. For highly heterogeneous deposits, such as glacial diamicts, this detailed borehole information may not be sufficient for creating reliable geological models. Crosshole ground-penetrating radar (GPR) can provide information on the decimeter- to meter-scale variation between boreholes, as the GPR response depends on the dielectric permittivity, electric conductivity, and the magnetic permeability of the subsurface. In this study, we investigate whether crosshole GPR can provide information on the material properties of diamicts, such as water content, bulk density, and clay content, as well as their structural relationships. To achieve ground truth, we compare the crosshole GPR data with geological information from both boreholes and excavation at the field site. The GPR data were analyzed comprehensively using several radar wave attributes in both time- and frequency domain, describing the signal velocity, strength, and shape. We found small variations in signal velocity (between 0.06-0.07 m/ns) but large variations in both amplitude and shape (either order of magnitude variation or doubling/tripling of attribute values). We see that the GPR response from wetter and more clayey diamicts have both lower amplitudes and lower centroid frequencies than the response from their drier and sandier counterparts. Furthermore, we find that the variation in amplitude and shape attributes are better correlated to the diamicts’ material properties than the signal velocity is.
Nitrous oxide (N2O) is one of the important greenhouse gases (GHGs), with its global warming potential 265 times greater than that of carbon dioxide (CO2). About 60% of the anthropogenic N2O emission is from agriculture production. To date, estimating N2O emissions from cropland remains a challenging task because the related microbial origin processes (e.g. incomplete nitrification and denitrification) are controlled by a diverse factors of climate, soil, plant and human activities. In this study, we developed a ML model with physical/biogeochemical domain knowledge, namely knowledge guided machine learning (KGML), for simulating daily N2O fluxes from the agriculture ecosystem. The Gated Recurrent Unit (GRU) was used as the basis to build the model structure. A range of ideas have been implemented to optimize the model performance, including 1) hierarchical structure based on variable causal relations, 2) intermediate variable (IMV) prediction and transfer, 3) inputting IMV initials for constraints, 4) model pretrain/retrain, and 5) multitask learning. The developed KGML was pre-trained by millions of synthetic data generated by an advanced PB model, ecosys, and then re-trained by observations from six mesocosm chambers during three growing seasons. Six other pure ML models were developed using the same data from mesocosm chambers to serve as the benchmark for the KGML model. The results show that KGML can always outperform the PB model in efficiency and ML models in prediction accuracy of capturing N2O flux magnitude and dynamics. Besides, the reasonable predictions of IMVs increase the interpretability of KGML. We believe the footprint of KGML development in this study will stimulate a new body of research on interpretable machine learning for biogeochemistry and other related geoscience processes.
Improving the estimation of CO2 exchange between the atmosphere and terrestrial ecosystems is critical to reducing the large uncertainty in the global carbon budget. Large amounts of the atmospheric CO2 assimilated by plants return to the atmosphere by ecosystem respiration (Reco), including plant autotrophic respiration (Ra) and soil microbial heterotrophic respiration (Rh). However, Ra and Rh are challenging to be estimated at large regional scales because of the limited understanding of the complex interactions among physical, chemical, and biological processes and the resulting high spatio-temporal dynamics. Traditional approaches for estimating Reco including process-based (PB) models are limited by human knowledge resulting in limited accuracy and efficiency. Accumulation of the in situ observation of net ecosystem exchange (NEE), weather, and soil, and satellite data of GPP, LAI and soil moisture make it possible for applying data driven machine learning (ML) approaches. But the ML model approach has disadvantages of omission of domain knowledge and lack of interpretability. Here we propose a novel knowledge guided machine learning (KGML) method for predicting daily Ra and Rh in the US crop fields. With Gated Recurrent Unit (GRU) as the basis, we develop the KGML models constructing the hierarchical structure of ML with a mass balance constraint. The KGML models were pre-trained using synthetic data generated by an advanced agroecosystem model, ecosys, and re-trained with real-world FLUXNET observation data. We extrapolate the best KGML model to crop fields over the US with the help of satellite data, reanalysis climate forcings, and soil database to reveal the spatio-temporal variations and key controlling factors. We believe this study advances the interpretable machine learning concept for carbon cycle estimation and will shed light on many other process-based biogeochemistry research.
With population surge, industrialization and urbanization; use of surfactants has increased manifold. After their use in households and industries, surfactants either end up in sewerage systems or are directly discharged into surface waters. They can hence be found dispersed in water phase or adsorbed onto aquatic sediments and sewage sludge. Due to limited metabolic pathways, most of the common surfactants are not degradable in anaerobic conditions that generally prevail in sewage ingressed water bodies so sludge accumulated in these water bodies after the anaerobic digestion process is rich in surfactants (Ying, 2006, Environ. Int.). Sediment is a complex mixture of organic (bacteria, proteins, humic and fulvic acids, humin, etc.) and inorganic (silica, minerals, metal oxides and hydroxides, aluminosilicates) components. Absorptivity of surfactant depends on the sorbent composition and type of surfactant (Ishiguro & Koopal, 2016, Adv. Colloid Interface Sci.). A crucial question related to surfactant absorption is its reversibility- as to whether the surfactant can desorb. The knowledge of this can be beneficial in understanding the environmental fate of surfactants. The presence of surfactants in water bodies can form foams. Generally, foaming in surface water is a result of a mixture of surfactants from various sources (Schilling & Zessner, 2011, Water Res.). However, contribution of sediment as a source of surfactant in a foaming water body has not been studied adequately. Also, existence of surfactants in water, beyond certain concentrations not only induces unpleasant taste and odor, but also causes undesirable changes in the ecosystem. Thus, it becomes imperative to study the ratio of the surfactant associated with water, to the extent of surfactant associated the sorbent such as sediment/sludge to understand the environmental risk associated with surfactant. This study aims to understand a foaming urban lake which foams only after heavy rainfalls. This study tests the hypothesis that surfactants accumulate in the lake sediment in significant proportions and desorbs upon dilution occurring due to addition of rainwater into the lake. This when churned by heavy runoff causes large quantities of stable foam. This study aims at analyzing the role of lake sediment in foaming of a lake.
Freshwater lakes and reservoirs play a disproportionate role in the global organic carbon (OC) budget, as active sites for carbon processing and burial. Associations between OC and iron (Fe) are hypothesized to contribute substantially to the stabilization of OC in sediment, but the magnitude of freshwater Fe-OC complexation remains unresolved. Moreover, global declines in bottom-water oxygen concentrations have the potential to alter OC and Fe cycles in multiple ways, and the net effects of low-oxygen (hypoxic) conditions on OC and Fe are poorly characterized. Here, we measured the pool of Fe-bound OC (Fe-OC) in surficial sediments from two eutrophic reservoirs, and we paired whole-ecosystem experiments with sediment incubations to determine the effects of hypoxia on OC and Fe cycling over multiple timescales. Our experiments demonstrated that short (2–4 week) periods of hypoxia can increase aqueous Fe and OC concentrations while decreasing OC and Fe-OC in surficial sediment by 30%. However, exposure to seasonal hypoxia over multiple years was associated with a 57% increase in sediment OC and no change in sediment Fe-OC. These results suggest that the large sediment Fe-OC pool (~30% of sediment OC in both reservoirs) contains both oxygen-sensitive and oxygen-insensitive fractions, and over multiannual timescales OC respiration rates may play a more important role in in determining the effect of hypoxia on sediment OC than Fe-OC dissociation. Consequently, we anticipate that global declines in oxygen concentrations will alter OC and Fe cycling, with the direction and magnitude of effects dependent upon the duration of hypoxia.
Pedogenic carbonates document a wealth of environmental information, but their seasonal variations may obscure long-term trends. Here we report evidence of changing seasonality of pedogenic carbonate growth from the Chinese Loess Plateau during the Quaternary glacial cycles, using the d18O of pedogenic carbonates (d18Oc). The glacial and interglacial d18Oc show negative and positive correlations with proxy-inferred rainfall amount, respectively. We explain this pattern using modern observations and modeling results, which show opposite correlations between d18Oc and rainfall amount in growing versus non-growing seasons. In glacial episodes under weak monsoon, pedogenic carbonate growth occurred within the growing season, inheriting a negative d18Oc-rainfall correlation. Conversely, pedogenic carbonate growth likely extended into the non-growing season during interglacials due to intensified monsoonal rainfall, incorporating a positive d18Oc-rainfall correlation. Our work links seasonal fluctuations of pedogenic carbonates with their long-term records, shedding new light on interpreting this paleoarchive.
In organic soils the availability of electron acceptors determines the ratio of CO2 to CH4 formation under anoxic conditions. While typically only inorganic electron acceptors are considered, the importance of electron accepting capacities of organic matter is increasingly acknowledged. Redox properties of organic matter are yet only investigated for a limited set of peat and reference materials. Therefore, we incubated 60 peat samples of 15 sites located in five major peatland regions covering a variety of both bog and fen samples and characterized their capacities to serve as electron acceptor for anaerobic CO2 production. We quantified CO2 and CH4 formation, and changes in available EAC in anoxic incubations of 56 days. On the time scale of our experiment, on average 36.5 % of CO2 could be attributed to CH4 formation, assuming an CO2/CH4 ratio for methanogenesis of 1:1. Regarding the remaining CO2 formed, for which a corresponding electron acceptor would be needed, we could on average explain 70.8 % by corresponding consumption of EAC from both organic and inorganic electron acceptors, the latter contributing typically less than 0.1 %. When the initial EAC was high, CO2 formation from apparent consumption of EAC was high and outweighed CO2 formation from methanogenesis. A rapid depletion of available EAC resulted in a higher share of CO2 from CH4 formation. Our study demonstrates that EAC provides the most important redox buffer for competitive suppression of CH4 formation in peat soils. Moreover, electron budgets including EAC of organic matter could largely explain anaerobic CO2 production.
The analysis of drought onset and their potential relationship to drought severity (deficit volume) are critical for providing timely information for agricultural operations, such as cultivation planning and crop productivity monitoring. A coupling between drought timing and deficit volume can be used as a proxy for drought-related damage estimation and associated risks. Despite its high importance, so far little attention was paid to determine the timing of drought and its linkage with deficit volume for hydrological droughts. This study utilizes quality-controlled streamflow observations from 1965 to 2018 to unveil regional patterns of hydrological drought onset, trends in event-specific deficit volume, and nonlinear relationships between onset timing and deficit volume across 97 rain-dominated catchments in Peninsular India (8-24o N, 72-87o E). Our results show a shift towards earlier hydrological drought onset in conjunction with a decrease in deficit volume during the Indian monsoon (June-September) season, which is contrasted by a delayed onset in the pre-monsoon (March-May) and post-monsoon (October-February) seasons. Further, approximately one-third of the catchments show a significant nonlinear dependency between drought deficit volume and onset time. We find environmental controls, such as soil organic carbon, vertical distance to channel network, and soil wetness are dominant factors in influencing droughts. Our analysis provides new insights into the causal chain and physical processes linking climatic and physiographic controls on streamflow drought mechanisms, which can support drought forecasting and climate impact assessment studies.
Flash droughts have recently gained significant attention due to their severe economic and ecological impacts. Despite extensive and growing research on flash drought processes, predictability, and trends, there is still no standard quantitative definition that encompasses all flash drought characteristics and pathways. This has motivated efforts to define, inventory, monitor, and forecast flash drought events. In our recent studies of flash droughts over the United States, we have introduced the Soil Moisture Volatility Index definition (SMVI) to inventory the onset dates and severity of flash across the Contiguous United States (CONUS) for the period 1979-2018. Post to an extended evaluation and comparison to other flash drought definitions and independent vegetation and crop datasets for seminal flash drought events, the SMVI has proved effectiveness in capturing flash drought onset in both humid and semi-arid regions. Using our SMVI inventory of flash droughts, we examine and classify flash droughts events based on multiple land surface and atmospheric conditions that may represent predictable drivers using a K-means-based clustering methodology. We found that there are three distinct classes of flash drought that can be diagnosed in our inventory. The first defined class of events are the “dry and demanding” droughts, showing high anomalies of evaporative demand and low soil moisture levels; The second are “evaporative” events, which develop under conditions of high demand and when elevated evapotranspiration accelerates soil drying, and a third class that we refer to as “stealth” events, which may be challenging to predict based on precursor atmospheric conditions due to the lack of a clear atmospheric signal with the observed modest anomalies. The contrasting meteorological and surface process signatures of the three classes do, however, indicate that events identified as “flash drought” using a reasonable definition, including events that have been widely reported as seminal flash droughts, represent a diversity of onset and intensification processes. Our results suggest that recognizing this diversity is critical to advance our understanding and ability to predict these events.
Robust estimation of average soil water content with spatial resolution of a few tens to a few hundreds of meters is essential for evaluating models or data assimilation products. Due to the high spatial variability of soil moisture at the point scale, sufficient coverage of spatial observations is required to estimate a robust field average. If sensors fail over time, averaging the remaining measurements risks the introduction of artificial shifts in the resulting time series. Here, we explore the problem of using incomplete soil moisture observations to estimate spatial averages and propose a correction accounting for temporal persistence of spatial patterns. By transforming, i.e. upscaling, each sensor measurement to the field scale using information from time periods with sufficient coverage, the dependence on full spatial coverage can be decreased. The transformed values allow to build a more robust approximation to the spatial mean, even when spatial coverage becomes sparse. We found that high temporal stability of the sensors does not necessarily guarantee that the transformed time series will provide a good estimate of the mean and therefore recommend the use of robust statistics to derive the field mean, which requires at least three estimates per observation time. The proposed protocol is applicable for observational time series with varying sample size across a given spatial extent, and it can be adopted for other variables exhibiting a temporally stable bias between the individual point observations and field scale average.