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USGS2010

Phosphorus dynamics in soils irrigated with reclaimed waste water or fresh water - A study using oxygen isotopic composition of phosphate

Transformations of phosphate (Pi) in different soil fractions were tracked using the stable isotopic composition of oxygen in phosphate ( δ 18 O p ) and Pi concentrations. Clay soil from Israel was treated with either reclaimed waste water (secondary, low grade) or with fresh water amended with a chemical fertilizer of a known isotopic signature. Changes of δ 18 O p and Pi within different soil fractions, during a month of incubation, elucidate biogeochemical processes in the soil, revealing the biological and the chemical transformation impacting the various P pools. P in the soil solution is affected primarily by enzymatic activity that yields isotopic equilibrium with the water molecules in the soil solution. The dissolved P interacts rapidly with the loosely bound P (extracted by bicarbonate). The oxides and mineral P fractions (extracted by NaOH and HCl, respectively), which are considered as relatively stable pools of P, also exhibited isotopic alterations in the first two weeks after P application, likely related to the activity of microbial populations associated with soil surfaces. Specifically, isotopic depletion which could result from organic P mineralization was followed by isotopic enrichment which could result from preferential biological uptake of depleted P from the mineralized pool. Similar transformations were observed in both soils although transformations related to biological activity were more pronounced in the soil treated with reclaimed waste water compared to the fertilizer treated soil.

Geoderma↗

USGS2011

Estimating phosphorus availability for microbial growth in an emerging landscape

Estimating phosphorus (P) availability is difficult—particularly in infertile soils such as those exposed after glacial recession—because standard P extraction methods may not mimic biological acquisition pathways. We developed an approach, based on microbial CO 2 production kinetics and conserved carbon:phosphorus (C:P) ratios, to estimate the amount of P available for microbial growth in soils and compared this method to traditional, operationally-defined indicators of P availability. Along a primary succession gradient in the High Andes of Perú, P additions stimulated the growth-related (logistic) kinetics of glutamate mineralization in soils that had been deglaciated from 0 to 5 years suggesting that microbial growth was limited by soil P availability. We then used a logistic model to estimate the amount of C incorporated into biomass in P-limited soils, allowing us to estimate total microbial P uptake based on a conservative C:P ratio of 28:1 (mass:mass). Using this approach, we estimated that there was < 1 μg/g of microbial-available P in recently de-glaciated soils in both years of this study. These estimates fell well below estimates of available soil P obtained using traditional extraction procedures. Our results give both theoretical and practical insights into the kinetics of C and P utilization in young soils, as well as show changes in microbial P availability during early stages of soil development.

Geoderma↗

USGS2011

Soil carbon distribution in Alaska in relation to soil-forming factors

The direction and magnitude of soil organic carbon (SOC) changes in response to climate change remain unclear and depend on the spatial distribution of SOC across landscapes. Uncertainties regarding the fate of SOC are greater in high-latitude systems where data are sparse and the soils are affected by sub-zero temperatures. To address these issues in Alaska, a first-order assessment of data gaps and spatial distributions of SOC was conducted from a recently compiled soil carbon database. Temperature and landform type were the dominant controls on SOC distribution for selected ecoregions. Mean SOC pools (to a depth of 1-m) varied by three, seven and ten-fold across ecoregion, landform, and ecosystem types, respectively. Climate interactions with landform type and SOC were greatest in the uplands. For upland SOC there was a six-fold non-linear increase in SOC with latitude (i.e., temperature) where SOC was lowest in the Intermontane Boreal compared to the Arctic Tundra and Coastal Rainforest. Additionally, in upland systems mineral SOC pools decreased as climate became more continental, suggesting that the lower productivity, higher decomposition rates and fire activity, common in continental climates, interacted to reduce mineral SOC. For lowland systems, in contrast, these interactions and their impacts on SOC were muted or absent making SOC in these environments more comparable across latitudes. Thus, the magnitudes of SOC change across temperature gradients were non-uniform and depended on landform type. Additional factors that appeared to be related to SOC distribution within ecoregions included stand age, aspect, and permafrost presence or absence in black spruce stands. Overall, these results indicate the influence of major interactions between temperature-controlled decomposition and topography on SOC in high-latitude systems. However, there remains a need for more SOC data from wetlands and boreal-region permafrost soils, especially at depths > 1 m in order to fully understand the effects of climate on soil carbon in Alaska.

Geoderma↗

USGS2012

Intelligent estimation of spatially distributed soil physical properties

Spatial analysis of soil samples is often times not possible when measurements are limited in number or clustered. To obviate potential problems, we propose a new approach based on the self-organizing map (SOM) technique. This approach exploits underlying nonlinear relation of the steady-state geomorphic concave–convex nature of hillslopes (from hilltop to bottom of the valley) to spatially limited soil textural data. The topographic features are extracted from Shuttle Radar Topographic Mission elevation data; whereas soil textural (clay, silt, and sand) and hydraulic data were collected in 29 spatially random locations (50 to 75 cm depth). In contrast to traditional principal component analysis, the SOM identifies relations among relief features, such as, slope, horizontal curvature and vertical curvature. Stochastic cross-validation indicates that the SOM is unbiased and provides a way to measure the magnitude of prediction uncertainty for all variables. The SOM cross-component plots of the soil texture reveals higher clay proportions at concave areas with convergent hydrological flux and lower proportions for convex areas with divergent flux. The sand ratio has an opposite pattern with higher values near the ridge and lower values near the valley. Silt has a trend similar to sand, although less pronounced. The relation between soil texture and concave–convex hillslope features reveals that subsurface weathering and transport is an important process that changed from loss-to-gain at the rectilinear hillslope point. These results illustrate that the SOM can be used to capture and predict nonlinear hillslope relations among relief, soil texture, and hydraulic conductivity data.

Geoderma↗

USGS2012

Photodissolution of soil organic matter

Sunlight has been shown to enhance loss of organic matter from aquatic sediments and terrestrial plant litter, so we tested for similar reactions in mineral soil horizons. Losses of up to a third of particulate organic carbon occurred after continuous exposure to full-strength sunlight for dozens of hours, with similar amounts appearing as photodissolved organic carbon. Nitrogen dissolved similarly, appearing partly as ammonium. Modified experiments with interruption of irradiation to include extended dark incubation periods increased loss of total organic carbon, implying remineralization by some combination of light and microbes. These photodissolution reactions respond strongly to water content, with reaction extent under air-dry to fully wet conditions increasing by a factor of 3–4 fold. Light limitation was explored using lamp intensity and soil depth experiments. Reaction extent varied linearly with lamp intensity. Depth experiments indicate that attenuation of reaction occurs within the top tens to hundreds of micrometers of soil depth. Our data allow only order-of-magnitude extrapolations to field conditions, but suggest that this type of reaction could induce loss of 10–20% of soil organic carbon in the top 10 cm horizon over a century. It may therefore have contributed to historical losses of soil carbon via agriculture, and should be considered in soil management on similar time scales.

Geoderma↗

USGS2012

Strontium isotopes and nutrient sourcing in a semi-arid woodland

Sr isotopes are widely used as a tracer of Sr and Ca in surficial systems. Basalt flows ranging in age from 3 ka (kiloyears ago) to > 200 ka from El Malpais National Monument (EMNM), New Mexico provide an ideal setting to examine strontium, and hence calcium cycling by plants in a semi-arid woodland. To gauge plant dependence on atmospheric dust versus local weathering products for strontium and calcium, we measured 87 Sr/ 86 Sr ratios in local bedrock and soils, and compared them to leaf/wood cellulose of four different conifers, a deciduous tree, three shrubs, an annual C 4 grass, and a lichen. Sampling sites varied by parent material (limestone, sandstone, granite, and basalt) and age (Quaternary to Precambrian), providing a wide range in end-member 87 Sr/ 86 Sr ratios, whereas the target plant species varied in physiognomy, life history, and rooting depth. On non-basalt parent material, the contribution from dust changed with the supply of weatherable Sr-bearing minerals in local bedrock. Soils developed on Paleozoic limestone showed significant bedrock contributions. On basalts, the Sr budget of soils at EMNM is dominated by atmospheric dust on young, 3 ka flows, incorporates a mixture of basalt-dust in 9 ka flows, and is basalt-dominated in 120 ka flows. This is unlike the pattern observed in tropical soils developed on basalt in Hawaii, where basalt weathering dominates the Sr inventory of the youngest soils and aerosols dominate in older, deeply weathered soils. This contrast is mainly due to different water/rock (W/R) ratios: bedrock subjected to high W/R over short periods is quickly ( < 10 ka) depleted in Sr (and Ca), except for the ongoing replenishment from aerosols. In arid settings where W/R are lower, soil Sr is still abundantly available first from dust, and increasingly from bedrock even after 120 ka. For plants, 87 Sr/ 86 Sr variations within and across sites at EMNP showed that evergreen trees varied most in 87 Sr/ 86 Sr ratios, shrubs were least dependent on eolian input of Sr, and both foliage density and rooting depths influence soil Sr pools.

New Mexico↗

USGS2013

Chemical transfers along slowly eroding catenas developed on granitic cratons in southern Africa

A catena is a series of distinct but co-evolving soils arrayed along a slope. On low-slope, slowly eroding catenas the redistribution of mass occurs predominantly as plasma, the dissolved and suspended constituents in soil water. We applied mass balance methods to track how redistribution via plasma contributed to physical and geochemical differentiation of nine slowly eroding (~ 5 mm ky − 1 ) granitic catenas. The catenas were arrayed in a 3 × 3 climate by relief matrix and located in Kruger National Park, South Africa. Most of the catenas contained at least one illuviated soil profile that had undergone more volumetric expansion and less mass loss, and these soils were located in the lower halves of the slopes. By comparison, the majority of slope positions were eluviated. Soils from the wetter climates (550 and 730 mm precipitation yr − 1 ) generally had undergone greater collapse and lost more mass, while soils in the drier climate (470 mm yr − 1 ) had undergone expansion and lost less mass. Effects of differences in catena relief were less clear. Within each climate zone, soil horizon mass loss and strain were correlated, as were losses of most major elements, illustrating the predominant influence of primary mineral weathering. Nevertheless, mass loss and volumetric collapse did not become extreme because of the skeleton of resistant primary mineral grains inherited from the granite. Colloidal clay redistribution, as traced by the ratio of Ti to Zr in soil, suggested clay losses via suspension from catena eluvial zones. Thus illuviation of colloidal clays into downslope soils may be crucial to catena development by restricting subsurface flow there. Our analysis provides quantitative support for the conceptual understanding of catenas in cratonic landscapes and provides an endmember reference point in understanding the development of slowly eroding soil landscapes.

Geoderma↗

USGS2013

Quantifying mineral abundances of complex mixtures by coupling spectral deconvolution of SWIR spectra (2.1-2.4 μm) and regression tree analysis

This paper presents a methodology for assessing mineral abundances of mixtures having more than two constituents using absorption features in the 2.1-2.4 μm wavelength region. In the first step, the absorption behaviour of mineral mixtures is parameterised by exponential Gaussian optimisation. Next, mineral abundances are predicted by regression tree analysis using these parameters as inputs. The approach is demonstrated on a range of prepared samples with known abundances of kaolinite, dioctahedral mica, smectite, calcite and quartz and on a set of field samples from Morocco. The latter contained varying quantities of other minerals, some of which did not have diagnostic absorption features in the 2.1-2.4 μm region. Cross validation showed that the prepared samples of kaolinite, dioctahedral mica, smectite and calcite were predicted with a root mean square error (RMSE) less than 9 wt.%. For the field samples, the RMSE was less than 8 wt.% for calcite, dioctahedral mica and kaolinite abundances. Smectite could not be well predicted, which was attributed to spectral variation of the cations within the dioctahedral layered smectites. Substitution of part of the quartz by chlorite at the prediction phase hardly affected the accuracy of the predicted mineral content; this suggests that the method is robust in handling the omission of minerals during the training phase. The degree of expression of absorption components was different between the field sample and the laboratory mixtures. This demonstrates that the method should be calibrated and trained on local samples. Our method allows the simultaneous quantification of more than two minerals within a complex mixture and thereby enhances the perspectives of spectral analysis for mineral abundances.

Geoderma↗

USGS2014

Distribution and landscape controls of organic layer thickness and carbon within the Alaskan Yukon River Basin

Understanding of the organic layer thickness (OLT) and organic layer carbon (OLC) stocks in subarctic ecosystems is critical due to their importance in the global carbon cycle. Moreover, post-fire OLT provides an indicator of long-term successional trajectories and permafrost susceptibility to thaw. To these ends, we 1) mapped OLT and associated uncertainty at 30 m resolution in the Yukon River Basin (YRB), Alaska, employing decision tree models linking remotely sensed imagery with field and ancillary data, 2) converted OLT to OLC using a non-linear regression, 3) evaluate landscape controls on OLT and OLC, and 4) quantified the post-fire recovery of OLT and OLC. Areas of shallow (< 10 cm), moderate (≥ 10 cm and < 20 cm), moderately thick (≥ 20 cm and < 30 cm), and thick (≥ 30 cm) OLT, composed 34, 20, 14, and 18% of the YRB, respectively; the average OLT was 19.4 cm. Total OLC was estimated to be 3.38 Pg. A regional chronosequence analysis over 30 years revealed that OLT and OLC increased with stand age (OLT: R 2 = 0.68; OLC: R 2 = 0.66), where an average of 16 cm OLT and 5.3 kg/m 2 OLC were consumed by fires. Strong predictors of OLT included climate, topography, near-surface permafrost distributions, soil wetness, and spectral information. Our modeling approach enabled us to produce regional maps of OLT and OLC, which will be useful in understanding risks and feedbacks associated with fires and climate feedbacks.

Alaska↗

USGS2014

Evaluation of a model framework to estimate soil and soil organic carbon redistribution by water and tillage using 137 Cs in two U.S. Midwest agricultural fields

Cultivated lands in the U.S. Midwest have been affected by soil erosion, causing soil organic carbon (SOC) redistribution in the landscape and other environmental and agricultural problems. The importance of SOC redistribution on soil productivity and crop yield, however, is still uncertain. In this study, we used a model framework, which includes the Unit Stream Power-based Erosion Deposition (USPED) and the Tillage Erosion Prediction (TEP) models, to understand the soil and SOC redistribution caused by water and tillage erosion in two agricultural fields in the U.S. Midwest. This model framework was evaluated for different digital elevation model (DEM) spatial resolutions (10-m, 24-m, 30-m, and 56-m) and topographic exponents ( m = 1.0–1.6 and n = 1.0–1.3) using soil redistribution rates from 137 Cs measurements. The results showed that the aggregated 24-m DEM, m = 1.4 and n = 1.0 for rill erosion, and m = 1.0 and n = 1.0 for sheet erosion, provided the best fit with the observation data at both sites. Moreover, estimated average SOC redistributions were 1.3 ± 9.8 g C m − 2 yr − 1 in field site 1 and 3.6 ± 14.3 g C m − 2 yr − 1 in field site 2. Spatial distribution patterns showed SOC loss (negative values) in the eroded areas and SOC gain (positive value) in the deposition areas. This study demonstrated the importance of the spatial resolution and the topographic exponents to estimate and map soil redistribution and the SOC dynamics throughout the landscape, helping to identify places where erosion and deposition from water and tillage are occurring at high rates. Additional research is needed to improve the application of the model framework for use in local and regional studies where rainfall erosivity and cover management factors vary. Therefore, using this model framework can help to improve the information about the spatial distribution of soil erosion across agricultural landscapes and to gain a better understanding of SOC dynamics within eroding and previously eroded fields.

Iowa↗

USGS2015

Machine learning for predicting soil classes in three semi-arid landscapes

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination. Overall, complex models were consistently more accurate than simple or moderately complex models. Random forests (RF) using covariates selected via recursive feature elimination was consistently the most accurate, or was among the most accurate, classifiers between study areas and between covariate sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used. Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. Individual subgroup class accuracy was generally dependent upon the number of soil pedon observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil–landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area.

New Mexico, Utah, Wyoming↗

USGS2015

Long-term controls of soil organic carbon with depth and time: a case study from the Cowlitz River Chronosequence, WA USA

Over timescales of soil development (millennia), the capacity of soils to stabilize soil organic carbon (SOC) is linked to soil development through changes in soil mineralogy and other soil properties. In this study, an extensive dataset of soil profile chemistry and mineralogy is compiled from the Cowlitz River Chronosequence (CRC), WA USA. The CRC soils range in age from 0.25 to 1200 kyr, spanning a developmental gradient encompassing clear changes in soil mineralogy, chemistry, and surface area. Comparison of these and other metrics of soil development with SOC properties reveal several relationships that may be diagnostic of the long-term coupling of soil development and C cycling. Specifically, SOC content was significantly correlated with sodium pyrophosphate extractable metals emphasizing the relevance of organo-metal complexes in volcanic soils. The depth distributions of organo-metals and other secondary weathering products, including the kaolin and short-range order (SRO) minerals, support the so-called “binary composition” of volcanic soils. The formation of organo-metal complexes limits the accumulation of secondary minerals in shallow soils, whereas in deep soils with lower SOC content, secondary minerals accumulate. In the CRC soils, secondary minerals formed in deep soils (below 50 cm) including smectite, allophane, Fe-oxides and dominated by the kaolin mineral halloysite. The abundance of halloysite was significantly correlated with bulk soil surface area and 14 C content (a proxy for the mean age of SOC), implying enhanced stability of C in deep soils. Allophane, an SRO mineral commonly associated with SOC storage, was not correlated with SOC content or 14 C values in CRC soils. We propose conceptual framework to describe these observations based on a general understanding of pedogenesis in volcanic soils, where SOC cycling is coupled with soil development through the formation of and fate of organo-metal or other mobile weathering products. This framework highlights interactions between SOC and soil development, which may be applicable to other soils where organic inputs interact with the products of chemical weathering.

Washington↗

USGS2016

Distribution and geochemical speciation of soil mercury in Wanshan Hg mine: Effects of cultivation

The distribution and speciation of mercury (Hg) were investigated in contaminated soils collected from two adjacent land use systems (arid land and rice paddy) near the Wanshan Mercury mine, SW China. In both sites, fine soil aggregate size fractions (<231μm) showed higher total Hg concentrations and higher soil organic matter contents than in larger aggregate size fractions (231 to 2000μm). Compared to arid land, paddy soils are characterized by higher proportions of fine soil aggregates, higher soil organic matter and higher total Hg content. Soil Hg speciation, based on X-ray absorption spectroscopy (XANES) analysis, indicated that the majority (64–81%) of Hg in soils under both land use systems was associated with metacinnabar (β-HgS), indicating the precipitation of β-HgS in soils. We also observed the presence of bioavailable HgCl 2 and Hg(0) in soils at both sites, which may represent a considerable environmental concern. Our study clearly showed that different cultivation practices can largely change the distribution and speciation of Hg in agriculture soils.

Guizhou Province↗

USGS2016

POLARIS: A 30-meter probabilistic soil series map of the contiguous United States

A new complete map of soil series probabilities has been produced for the contiguous United States at a 30 m spatial resolution. This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database is possible using available high performance computing resources. POLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons. The geospatial environmental covariates that have the largest role in assembling POLARIS over the contiguous United States (CONUS) are fine-scale (30 m) elevation data and coarse-scale (~ 2 km) estimates of the geographic distribution of uranium, thorium, and potassium. A preliminary validation of POLARIS using the NRCS National Soil Information System (NASIS) database shows variable performance over CONUS. In general, the best performance is obtained at grid cells where DSMART-HPC is most able to reduce the chance of misclassification. The important role of environmental covariates in limiting prediction uncertainty suggests including additional covariates is pivotal to improving POLARIS' accuracy. This database has the potential to improve the modeling of biogeochemical, water, and energy cycles in environmental models; enhance availability of data for precision agriculture; and assist hydrologic monitoring and forecasting to ensure food and water security.

Geoderma↗

USGS2017

Pyrogenic carbon distribution in mineral topsoils of the northeastern United States

Due to its slow turnover rates in soil, pyrogenic carbon (PyC) is considered an important C pool and relevant to climate change processes. Therefore, the amounts of soil PyC were compared to environmental covariates over an area of 327,757 km 2 in the northeastern United States in order to understand the controls on PyC distribution over large areas. Topsoil (defined as the soil A horizon, after removal of any organic horizons) samples were collected at 165 field sites in a generalised random tessellation stratified design that corresponded to approximately 1 site per 1600 km 2 and PyC was estimated from diffuse reflectance mid-infrared spectroscopy measurements using a partial least-squares regression analysis in conjunction with a large database of PyC measurements based on a solid-state 13 C nuclear magnetic resonance spectroscopy technique. Three spatial models were applied to the data in order to relate critical environmental covariates to the changes in spatial density of PyC over the landscape. Regional mean density estimates of PyC were 11.0 g kg − 1 (0.84 Gg km − 2 ) for Ordinary Kriging, 25.8 g kg − 1 (12.2 Gg km − 2 ) for Multivariate Linear Regression, and 26.1 g kg − 1 (12.4 Gg km − 2 ) for Bayesian Regression Kriging. Akaike Information Criterion (AIC) indicated that the Multivariate Linear Regression model performed best (AIC = 842.6; n = 165) compared to Ordinary Kriging (AIC = 982.4) and Bayesian Regression Kriging (AIC = 979.2). Soil PyC concentrations correlated well with total soil sulphur (P < 0.001; n = 165), plant tissue lignin (P = 0.003), and drainage class (P = 0.008). This suggests the opportunity of including related environmental parameters in the spatial assessment of PyC in soils. Better estimates of the contribution of PyC to the global carbon cycle will thus also require more accurate assessments of these covariates.

Geoderma↗

USGS2018

A sequential selective dissolution method to quantify storage and stability of organic carbon associated with Al and Fe hydroxide phases

Stabilization of SOM (soil organic matter) is regulated in part by sorption and desorption reactions happening at mineral surfaces, as well as precipitation and dissolution of organo-metal complexes. Fe and Al hydroxides play a particularly significant role in SOM stabilization in soils due to their ubiquitous distribution and their highly reactive surface properties. Iron and Al hydroxides exist in soils across a wide spectrum of crystallinity, ranging from dissolved Fe and Al cations which combine with organics to form organo-metal precipitates to the more crystalline end members, goethite and gibbsite, which sorb SOM through a variety of molecular interactions. Though the importance of these sorption and precipitation reactions has long been recognized, the distribution of SOM among Fe and Al hydroxides of differing crystallinity has not been well quantified, nor has the timescale over which these stabilization mechanisms operate. In an attempt to measure the distribution of organic C among (i) Al- and Fe-humus complexes (ii) short-range-order (SRO) Al and Fe hydroxide surfaces and (iii) crystalline Fe oxyhydroxide surfaces, a single method combining several selective mineral dissolutions was applied to soils of four different geneses (a tropical forest Andisol, a temperate forest basaltic Mollisol, a Mediterranean coastal prairie Mollisol, and a northern mixed hardwood forest Spodosol). The traditional reactants used in selective dissolutions were replaced with carbon-free analogues so that the carbon released along with the Fe and Al at each stage of the selective dissolution process could be measured. Selective dissolutions were performed sequentially: Na-pyrophosphate (organo-Al and Fe complexes) followed by hydroxylamine (SRO Al and Fe hydroxides) followed by dithionite-HCl (crystalline Fe hydroxides). Carbon, Al, and Fe concentrations, as well as radiocarbon abundance were measured in the solutions yielded by each stage of the selective dissolution process. Results suggest that precipitation of organo-metal complexes (Na-pyrophosphate extractable C) often accounts for the largest pool of stabilized C among the three selectively dissolved pools, but these complexes were 14 C enriched in comparison to C from the other selectively dissolved pools and the residual C left on crystalline mineral surfaces after all three stages of selective dissolution. Hydroxylamine and dithionite-HCl extractable C pools were, on average, small and often below detection level in temperate soils. However, radiocarbon values for these C pools were generally depleted in comparison to other pools. These results suggest variation in organo-mineral complex stability is associated with degree of crystallinity of the mineral phase. Overall, this work suggests that sequential selective dissolution methods are a promising tool for characterizing the content and isotopic composition of soil C associated with distinct organo-mineral and organo-metal associations.

Geoderma↗

USGS2019

Controls of the spatial variability of denitrification potential in nontidal floodplains of the Chesapeake Bay watershed, USA

Identifying floodplains with high rates of denitrification will help prioritize restoration projects for the removal of nitrogen. Currently, relationships of denitrification with hydrogeomorphic, physiographic, and climate ( i.e. , largescale) characteristics of floodplains are relatively unknown, even though these characteristics have datasets ( e.g. , geographic mapping tools) that are publicly available (or soon-to-become) that could be used to understand denitrification variability. Thus, we investigated control of denitrification by these largescale characteristics in eighteen nontidal floodplains of the Chesapeake Bay watershed ( i.e. , at regional scale, >100 km, scale), using measurements or compiled data at the scales of the stream reach and respective catchment; floodplain soil and herbaceous vegetation ( i.e. , local) characteristics were additionally investigated. Soil denitrification potentials were measured in May, July, and August using complementary acetylene-based techniques under an anoxic environment. Linear largescale predictors of denitrification potential measurements included stream nitrogen and phosphorus concentrations (+), channel width-to-depth ratio (+), floodplain sedimentation (+), forested (−) and urban (+) catchment land cover, and seasonal air temperature (−). Three predictors, catchment forested land cover (strongly related to agricultural land cover), catchment urban land cover, and floodplain sedimentation were related to the most number of denitrification potential measurements. Soil structure, soil nutrient concentrations, and herbaceous vegetation characteristics that were seasonally measured (with a few exceptions) were linear predictors of denitrification potentials in May and August, with nitrogen and carbon characteristics the most consistent (positive) predictors across measurements. Nutrient amendment assays further supported the importance of nitrogen and carbon controls. Using the local characteristics as statistical mediators in path analysis, greater non-forested catchment land cover indirectly increased denitrification through greater floodplain soil nitrate, total phosphorus, and herbaceous aboveground biomass. Additionally, greater floodplain sedimentation indirectly increased denitrification through greater soil pH, total phosphorus, and potential carbon mineralization. Due to the consistency of relationships across denitrification potential measurements along with path modeling results, hotspots of floodplain denitrification should be found in urban and agricultural catchments where river-floodplain hydrologic connectivity promotes sedimentation. Largescale predictors explained 43–57% of the variation in denitrification potentials and should be useful for prediction in floodplains. Siting restoration projects in watersheds for maximum nitrate removal using publicly available largescale datasets is both feasible and effective.

Chesapeake Bay watershed↗

USGS2019

Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data

Fine scale maps of soil properties enable efficient land management and inform earth system models. Recent efforts to create soil property maps from field observations tend to use similar tree-based machine learning interpolation approaches, but often deal with depth of predictions, validation, and uncertainty differently. One of the main differences in approaches is whether to model individual depths of interest separately as ‘2D’ models, or to create models that incorporate depth as a predictor variable creating a ‘3D’ model that can make pre-dictions for all depths. It is unclear how choice of 2D or 3D approach influences model accuracy and uncertainty due to lack of direct comparison and inconsistent presentation of results in past studies. This study compares 2D and 3D methods for mapping soil electrical conductivity (salinity), pH, sum of fine and very fine sands, and organic carbon at 30 m resolution for the upper 432,000 km 2 of the Colorado River Watershed of the United States of America. A new, simple, model-agnostic relative prediction interval (RPI) approach to report un-certainty is presented that scales prediction interval width to the 95% interquantile width of the original training sample distribution. The RPI approach enables direct comparison of uncertainty between properties and depths and is easily interpretable by end users. Results indicate that 3D mapping of soil properties with strong variation with depth can result in substantial areas with much higher uncertainty that coincide with unrealistic predictions relative to 2D models, even though 3D models had slightly better global cross-validation scores. Maps and global model summaries of RPI proved helpful in identifying these issues with 3D models. These results suggest that the use of RPI or similar approaches to evaluate models can identify accuracy problems not evident in global va-lidation diagnostics.

Geoderma↗