Cosmic ray neutron sensors (CRNS) allow to determine field-scale soil moisture content non-invasively due to the dependence of aboveground measured epithermal neutrons on the amount of hydrogen. Because other pools besides soil contain hydrogen (e.g. biomass), it is necessary to consider these for accurate soil moisture content measurements, especially when they are changing dynamically (e.g., arable crops, de- and reforestation). In this study, we compare four approaches for the correction of biomass effects on soil moisture content measurements with CRNS using experiments with three crops (sugar beet, winter wheat and maize) on similar soils: I) site-specific functions based on in-situ measured biomass, II) a generic approach, III) the thermal-to-epithermal neutron ratio (Nr) and IV) the thermal neutron intensity. Calibration of the CRNS during bare soil conditions resulted in root mean square errors (RMSE) of 0.097, 0.041 and 0.019 m3/m3 between estimated and reference soil moisture content of the cropped soils, respectively. Considering in-situ measured biomass for correction reduced the RMSE to 0.015, 0.018 and 0.009 m3/m3. When thermal neutron intensity was considered for correction, similarly accurate results were obtained. Corrections based on Nr and the generic approach were less accurate. We also explored the use of CRNS for biomass estimation. The use of Nr only provided accurate biomass estimates for sugar beet. However, significant site-specific relationships between biomass and thermal neutron intensity were obtained for all three crops. It was concluded that thermal neutron intensity can be used to correct soil moisture content estimates from CRNS and to estimate biomass.
Important progress has been made in recent years in characterizing surface soil moisture (SSM) at regional scales, through remote sensing estimates and the implementation of new in situ networks. Each of these sources of information has intrinsic features, such as the dynamic range of the SSM and the temporal frequency of acquisition. Another relevant factor is the period of data availability. Improving the knowledge of the limitations and biases of these features is crucial to increase the potential and the consistency of data sources validations. As a case of study we considered an agricultural area in the Argentinean Pampas, characterized by a sub-humid climate with a marked seasonal dynamic. It also holds a synchronized cropping rhythm and is subject to flooding and waterlogging that can last from days to months. The features mentioned above and considering that the region is almost devoid of irrigation, offer a natural laboratory that is distinguished by a wide dynamic range of SSM conditions. In this context, we analyze and expose different sources of SSM data gaps over long periods of time, using information from in situ stations and from the SMOS and SMAP satellite systems, during 2015-2019. We found SMAP data gaps resulting from the filtering of high SSM signals that are not spurious but typical for this flood-prone region. Reports from national institutions and comparison with other data sources allowed us to identify that high soil water content in the same period in which the data gaps occurred. In a different way, the SMOS register has a low-frequency range of data due to radio frequency interference over the study area. This data gap occurs during a long-anomalously wet period and it is relevant to take it into account when analyzing SMOS data for the full period. Our study shows the importance of using multiple sources of information and the relevance of examining the availability of data.
Amending soils with sewage sludge biochar is a promising waste management strategy and value-added approach to reuse the waste while minimizing environmental contamination risks. Soil pot experiment was conducted to examine the effect of a 300°C sludge-biochar in soil health and crop productivity using a strongly acidic soil. Three treatments of the soil pots were included: 1% biochar– (10 g kg-1 biochar/soil ratio), 2% biochar– (20 g kg-1 biochar/soil ratio), and control (soil without biochar). Winter wheat (Triticum aestivum L.), spinach (Spinacia oleracea), and mung bean (Vigna radiata) were grown sequentially in the soil pots over 9 months under greenhouse and field conditions. Plant biomass and soil health parameters were assessed. Soils amended with 2% biochar demonstrated higher biomass in winter wheat, spinach, and mung bean compared to unamended control treatments. The effect of sludge biochar was not observed in soil bulk density; however, soil aggregates stability was higher in soils amended with 2% biochar (24.17%) compared to control (21.38%). Soil acidity was corrected in soils amended with 2% biochar (pH value 6.5) compared to control (5.8), electric conductivity (EC) was higher in 1% biochar (0.25 dS m-1) compared to control (0.20 dS m-1). Respiration rate was higher in 1% biochar (0.52 mg CO2 g-1 dry soil) compared to control (0.43 mg CO2 g-1), and total organic carbon (TOC) was lower in soils amended with biochar compared to control. Sewage sludge derived biochar improved crop production and soil health in strongly acidic soils and should be adopted in commercial agriculture.
The fate of organic carbon (C) in permafrost soils is important to the climate system due to the large global stocks of permafrost C. Thawing permafrost can be subject to dynamic hydrology, making redox processes an important factor controlling soil organic matter (SOM) decomposition rates and greenhouse gas production. In iron (Fe)-rich permafrost soils, Fe(III) can serve as a terminal electron acceptor, suppressing methane (CH4) production and increasing carbon dioxide (CO2) production. Current large-scale models of Arctic C cycling do not include Fe cycling or pH interactions. Here, we coupled Fe redox reactions and C cycling in a geochemical reaction model to simulate the interactions of SOM decomposition, Fe(III) reduction, pH dynamics, and greenhouse gas production in permafrost soils subject to dynamic hydrology. We evaluated the model using measured CO2 and CH4 fluxes as well as changes in pH, Fe(II), and dissolved organic C concentrations from oxic and anoxic incubations of permafrost soils from polygonal permafrost sites in northern Alaska, United States. In simulations of higher frequency oxic-anoxic cycles, rapid oxidation of Fe(II) to Fe(III) during oxic periods and gradual Fe(III) reduction during anoxic periods reduced cumulative CH4 fluxes and increased cumulative CO2 fluxes. Lower pH suppressed CH4 fluxes through its direct impact on methanogenesis and by increasing Fe(III) bioavailability. Our results suggest that models that do not include Fe-redox reactions and its pH dependence could overestimate CH4 production and underestimate CO2 emissions and SOM decomposition rates in Fe-rich, frequently waterlogged Arctic soils.
Radon is a natural radioactive gas accounting for approximately one in ten lung cancer deaths, with substantially higher death rates in sub-Arctic communities. Radon transport is significantly reduced in permafrost, but permafrost is now thawing due to climate change. The effect of permafrost thawing on domestic radon exposure is unknown. Here we present results from radon transport modeling through soil, permafrost and model buildings either with basements or built on piles. We find that permafrost acts as an effective radon barrier, reducing radiation exposure to a tenth of the background level, while producing a ten-fold increase in the radon activity behind the barrier. When we model thawing of the permafrost barrier, we find no increase in radon to the background level for buildings on piles. However, for buildings with basements the radon increases to over one hundred times its initial value and can remain above the 200 Bq/m3 threshold for up to seven years depending on the depth of the permafrost and the speed of thawing. When thawing speed is taken into account, radiations remains higher than the threshold for all scenarios where 40% thawing occurs within 15 years. This new information suggests that a significant sub-Arctic population could be exposed to radon levels dangerous to health as a result of climate change thawing of permafrost, with implications for health provision, building codes and ventilation advice.
Terrestrial ecosystems of Canada store a large amount of organic carbon (C) in soils, peats and plant materials, yet little is known about the C stock size and distributions, both spatially and in various C pools. As temperature rises, C is becoming available for disturbance, decomposition and eventual release into the atmosphere, which makes the quantification of C stocks in terrestrial ecosystems of Canada of high interest for the assessment of climate change impacts and conservation efforts. We used a large number of field measurements, multisource satellite, climate and topographic data and a machine learning algorithm to produce the first wall-to-wall estimates of C stocks and uncertainties in plants and soils of Canada at 250 m spatial resolution. Our findings show that above and belowground live biomass and detritus store a total of 21.1 Pg C. Whereas the Canadian soils store 384 (±214, 90% confidence interval) Pg organic C in the top 1 m, 92 Pg C of which are stored in peatlands, confirming that the soil organic C dominates terrestrial carbon stocks in Canada. We also find previously under-reported large soil organic C stocks in forested peatlands on the boreal shields of Canada. Given that Canada is warming twice the global average rate and Canadian soils store approximately 25% of world soil C stocks in top 1 m, initiatives to understand their vulnerabilities to climate change and disturbance are indispensable not only for Canada but also for the global C budget and cycle.
The ferrimagnetic (FM) and antiferromagnetic (AFM) particles of iron oxides are considered pedogenic and climatic indicators due to their enrichment with comparable increasing in rainfall and temperature. However, the opposite changes in rainfall and temperature result in rapid change of relative humidity (RH), which could lead to their competition and transformation. We examined two soil sequences undergone contrary climate development on the eastern edge of the Tibetan Plateau. The dry and warm climate with low RH favors the coordinative enrichment of AFM hematite and FM particles, while the wet and cool climate with high RH mainly produces goethite but leads to competition between low content AFM hematite and FM particles. The outcome well interprets the changing relationship between color and magnetism in soils and sediments, and suggests that temperature is as important as precipitation in paleoclimate reconstruction based on iron oxides, especially during strong dry-wet cycles and climate pattern shifts.
In the United States, voluntary and compliance carbon markets are being created there is an effort to match producers of carbon credits with those that would like to purchase credits. Each market has unique obligations and requirements. Farmers and those advising farmers are confused about the marketplace. The purpose of this document is to provide useful information about the voluntary and compliance markets. The target audience is farmers, crop consultants, and scientists. The document is organized into four sections, general information about the markets, answers to questions from certified crop consultants, market glossary, and requirements about specific markets. These marketplaces are rapidly evolving and will likely change as the markets mature.
Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL), have created tremendous excitement and opportunities in the earth and environmental sciences communities. To leverage these new ‘data-driven’ technologies, however, one needs to understand the fundamental concepts that give rise to DL and how they differ from ‘process-based’, mechanistic modelling. This paper revisits those fundamentals and addresses 10 questions often posed by earth and environmental scientists with the aid of a real-world modelling experiment. The overarching objective is to contribute to a future of AI-assisted earth and environmental sciences where DL models can (1) embrace the typically ignored knowledge base available, (2) function credibly in ‘true’ out-of-sample prediction, and (3) handle non-stationarity in earth and environmental systems. Comparing and contrasting earth and environmental problems with prominent AI applications, such as playing chess and trading in stock markets, provides critical insights for better directing future research in this field.
Fossil fuel combustion, land use change and other human activities have increased the atmospheric carbon dioxide (CO2) abundance by about 50% since the beginning of the industrial age. The atmospheric CO2 growth rates would have been much larger if natural sinks in the land biosphere and ocean had not removed over half of this anthropogenic CO2. As these CO2 emissions grew, uptake by the ocean increased in response to increases in atmospheric CO2 partial pressure (pCO2). On land, gross primary production (GPP) also increased, but the dynamics of other key aspects of the land carbon cycle varied regionally. Over the past three decades, CO2 uptake by intact tropical humid forests declined, but these changes are offset by increased uptake across mid- and high-latitudes. While there have been substantial improvements in our ability to study the carbon cycle, measurement and modeling gaps still limit our understanding of the processes driving its evolution. Continued ship-based observations combined with expanded deployments of autonomous platforms are needed to quantify ocean-atmosphere fluxes and interior ocean carbon storage on policy-relevant spatial and temporal scales. There is also an urgent need for more comprehensive measurements of stocks, fluxes and atmospheric CO2 in humid tropical forests and across the Arctic and boreal regions, which are experiencing rapid change. Here, we review our understanding of the atmosphere, ocean, and land carbon cycles and their interactions, identify emerging measurement and modeling capabilities and gaps and the need for a sustainable, operational framework to ensure a scientific basis for carbon management.
In 2018–2019, Central Europe experienced an unprecedented multi-year drought with severe impacts on society and ecosystems. In this study, we analyzed the impact of this drought on water quality by comparing long-term (1997-2017) nitrate export with 2018–2019 export in a heterogeneous mesoscale catchment. We combined data-driven analysis with process-based modelling to analyze nitrogen retention and the underlying mechanisms in the soils and during subsurface transport. We found a drought-induced shift in concentration-discharge relationships, reflecting exceptionally low riverine nitrate concentrations during dry periods and exceptionally high concentrations during subsequent wet periods. Nitrate loads were up to 70% higher compared to the long-term load-discharge relationship. Model simulations confirmed that this increase was driven by decreased denitrification and plant uptake and subsequent flushing of accumulated nitrogen during rewetting. Fast transit times (<2 months) during wet periods in the upstream sub-catchments enabled a fast water quality response to drought. In contrast, longer transit times downstream (>20 years) inhibited a fast response but potentially contribute to a long-term drought legacy. Overall, our study reveals that severe multi-year droughts, which are predicted to become more frequent across Europe, can reduce the nitrogen retention capacity of catchments, thereby intensifying nitrate pollution and threatening water quality.
Methane (CH4) hydrate dissociation and CH4 release are potential geohazards currently investigated using X-ray computed tomography (XCT) imaging in laboratory experiments. Image segmentation constitutes an important data processing step for this type of research, but it is often time consuming, computing resource-intensive and operator-dependent. Furthermore, segmentation procedures are frequently tailored for each XCT dataset due to differences in image characteristics, such as greyscale contrast variations. To address these issues, an investigation has been carried out using U-Nets, a class of Convolutional Neural Network, to segment synchrotron radiation XCT (SRXCT) images of CH4-bearing sand during hydrate formation. Emphasis was given to CH4 gas bubbles, due to their paucity and low contrast. Three U-Net deployments previously untried for this task were assessed: (1) a bespoke 3D hierarchical method, (2) a 2D multi-label, multi-axis method and (3) RootPainter, an application that combines a 2D U-Net with interactive corrections. U-Nets were trained using very small hand-annotated datasets to reduce operator time. Results show high segmentation accuracy and consistency, with RootPainter slightly outperforming the alternative approaches and all three methods surpassing mainstream watershed and thresholding techniques. Greyscale contrast between material phases was found to affect segmentation performance, with the lowest metrics corresponding to data exhibiting the lowest contrast. Segmentation accuracy affected derived parameters such as CH4-saturation and porosity, but errors were small compared with gravimetric methods. It was also found that U-Net models trained on low greyscale contrast images could be used to segment higher-contrast datasets and also data collected at a different facility, thereby demonstrating model portability. Such portability is anticipated to be advantageous when the segmentation of large XCT datasets needs to be delivered over short timespans.
The facultative, chemolithotrophic bacteria Hydrogenophilus thermoluteolus is an understudied thermophilic, hydrogen- and thiosulfate- oxidizing microorganism that has been found globally in hot spring environments. It was identified in a series of four soil samples collected around the Polloquere hot spring of Lauca National Park, Chile, in 10m intervals from the hot spring water line. Metagenome-assembled genomes (MAGs) of H. thermoluteolus were reconstructed from each sample, exhibiting high completion and a 98% average nucleotide identity with the reference genome of the cultured H. thermoluteolus isolate. In this study, we collected and analyzed publicly available genomes of H. thermoluteolus and other members of the Hydrogenophilceae family derived from cultures and metagenomes from a diverse set of geothermal environments for pangenomic comparison with the Polloquere MAGs. The Polloquere soils are characterized by distinct changes to the environmental chemistry and biology across the 30m distance from the hot spring. In particular, increased aridity and pH, as well as lower temperatures and biomass, coincided with a shift from a characteristic geothermal microbial population, to that of an arid desert community. Notably, however, the presence and relative abundance of H. thermoluteolus remained stable over the same distance (~0.1% of the total community). Using pangenomics, we were able to deduce several genomic differences between soil samples closest (0m) and furthest (30m) from the hot spring, as well as between the Polloquere MAGs and the cultured reference. Functionally, the 30m MAG lacked carbon fixation capabilities, while all of the soil MAGs showed added genomic capacity for denitrification not present in the reference genome. These results contribute significantly to the pool of genomic data for H. thermoluteolus, adding to our understanding of the organism’s high metabolic flexibility. The Polloquere MAGs also represent a rare example of this organism appearing in a dry, colder, soil environment, presumably transported from the local hot spring. This study investigates how the genomes and metabolisms of H. thermoluteolus vary between environments from a biogeographical perspective, both globally and across a small spatial distance defined by a steep environmental gradient.
Previous studies of wind-blown sand have considered either fully erodible or non-erodible soils, but the transport over sparsely sand-covered soils is still poorly understood. The quantitative modeling of this transport is important for the parametrization of Aeolian processes under low sand availability. Here we show, by means of particle-based numerical simulations, that the Aeolian sand transport rate Q scales with the wind shear velocity u∗ as Q = a.[1 + b . (u∗/u∗t − 1)] .√(d/g) . ρf. (u∗² − u∗t²), where u∗t is the minimal threshold u∗ for sustained transport, d is particle size, g is gravity and ρf is air density, while u∗t and the empirical parameters a and b depend on the sand cover thickness. Our model explains the transition from the quadratic to cubic scaling of Q with u∗ as soil conditions change from fully erodible to rigid and provides constraints for modeling Aeolian transport under low sand availability.
Landfalling atmospheric rivers (ARs) frequently trigger heavy and sometimes prolonged precipitation, especially in regions with favored orographic enhancement. The presence and strength of ARs are often described using the integrated water vapor (IWV) and the integrated vapor transport (IVT). However, the associated precipitation is not directly correlated with these two variables. Instead, the intensity of precipitation is mainly determined by the net convergence of moisture flux and the initial degree of saturation of the air column. In this study, a simple algorithm is proposed for estimating the heavy precipitation attributable to the IVT convergence. Bearing a strong resemblance to the Kuo-Anthes parameterization scheme for cumulus convection, the proposed algorithm calculates the large-scale primary condensation rate (PCR) as a proportion of the IVT convergence, with a reduction to account for the general moistening in the atmosphere. The amount of reduction is determined by the column relative humidity (CRH), which is defined as the ratio of IWV to its saturation counterpart. Our analysis indicates that the diagnosable PCR compares well to the forecast precipitation rate given by a numerical weather prediction model. It is also shown that the PCR in an air column with CRH < 0.50 is negligibly small. The usefulness of CRH and PCR as two complements to standard AR analysis is illustrated in three case studies. The potential application of PCR to storm classification is also explored.
Earthworms play a critical role in soil ecosystems. Analyzing the spatial structure of earthworm burrows is important to understand their impact on water flow and solute transport. Existing in-situ extraction methods for earthworm burrows are time-consuming, labor-intensive and inaccurate, while CT scanning imaging is complex and expensive. The aim of this study was to quantitatively characterize structural characteristics (cross-sectional area (A), circularity (C), diameter (D), actual length (Lt), tortuosity (τ)) of anecic earthworm burrows that were open and connected at the soil surface at two sites of different tillage treatments (no-till at Lu Yuan (LY) and rotary tillage at Shang Zhuang (SZ)) by combining a new in-situ tin casting method with three-dimensional (3D) laser scanning technology. The cross-sections of anecic earthworm burrows were almost circular, and the C values were significantly negatively correlated with D and A. Statistically, there were no significant differences in the τ values (1.143 ± 0.082 vs 1.133 ± 0.108) of anecic earthworm burrows at LY and SZ, but D (6.456 ± 1.585 mm) and A (36.929 ± 21.656 mm2) of anecic earthworm burrows at LY were significantly larger than D (3.449 ± 0.531 mm) and A (9.786 ± 2.885 mm2) at SZ. Our study showed that burrow structures at two different sites differed from each other. Soil tillage methods, soil texture and soil organic matter content at the two sites could have impacted earthworm species composition, variation of earthworm size and the morphology of burrows. The method used in this research enabled us to adequately assess the spatial structure of anecic earthworm burrows in the field with a limited budget.
The influence of shield wires on Geomagnetically Induced Currents (GICs) in power systems is considered. For the most simple power network, with one single transmission line and one shield wire connecting two substations, we derive the expressions for the voltage source and the resistance of the Thévenin equivalent circuits that, in parallel with the substations grounding resistances, produce the same effects on GICs as the full circuit. Our model extends results from previous studies that considered the effect of shield wires resistances by also including the induced geoelectric field.
Vertisols shrink and swell with changes in soil moisture, influencing hydraulic properties. Vertisols are often in floodplains, yet the importance of flooding as a source of soil moisture remains poorly understood. We used blue dye and deuterated water as tracers to determine the role of the macropore network in matrix recharge under artificial flood durations of 3 and 31 d in large soil monoliths extracted from a forested soil. Gravimetric soil moisture content increased by 47% in the first three days, then increased only 3.5% from day 3 to 31. Post-flood moisture content was greatest in the organic-rich, top 10 cm and was lower at 10 to 75 cm where organic matter was less. Deuterium concentration revealed that soil moisture in the top 10 cm was quickly dominated by artificial flood water, but at depth remained <80% floodwater even after 31 d. Pervasive dye staining of ped surfaces in the top 4 cm indicated connectivity to flood waters but staining at depth was less and highly variable. The isotopic composition of soil water at depth continued to shift toward flood water despite no differences in dye staining between days 3 and 31. Results indicate flooding initially but incompletely recharges matrix water via macropores and suggest the importance of flooding as a source of matrix recharge in vertic floodplain soils may depend more on flood frequency than duration. Isotopic composition of matrix water in vertic soils depends on both advective and diffusional processes, with diffusion becoming more dominant as porosity decreases.