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Results for “Geoderma Regional”

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USGS2025

Smectite-rich horizons in Inceptisols trigger shallow landslides in tropical granitic terranes

Puerto Rico was affected by >70,000 landslides in the wake of 2017 Hurricane Maria, and landslide prevalence was especially high in the Utuado region in the Cordillera Central. Landslide density was highest where soil parent material is granodiorite; landslide slip surfaces tended to be shallow (<60 cm), and often were mobilized rapidly and with long runout distances. This study combines field observations with soil mineralogy (bulk and clay fractions), soil geochemistry (bulk fraction), and soil strength as determined by field cone penetrometer testing (CPT) to test the hypothesis that clay-rich subsoil horizons function as slip planes when water-logged. Soil pits were excavated to depths of ∼200 cm in Ultisols on an undulating plateau and to ∼100 cm in Inceptisols on steep slopes (36-43 o ) that flank the plateau and cone penetrometer tests (CPT) were done within 2 m of the soil pit. Six pits were located adjacent to scarps from previous landslides, enabling analysis of soil profiles downward through extrapolated slip surfaces. Results from X-ray diffraction (XRD), X-ray fluorescence (XRF) and thermogravimetric analysis (TGA) indicate that soils are heterogeneous, often with subsoil horizons enriched in clay minerals and immobile elements (Al, Fe, Ti). Inceptisols on steep slopes often contain smectite-rich horizons at 30–60 cm depth that appear to function as slip surfaces; in other Inceptisols, such horizons are not present and landslide susceptibility is potentially lower. In Ultisols, soil mineralogy is dominated by kaolinite with minor halloysite, and increased kaolinite content at soil depths ≥80 cm at some sites suggests potential slip surfaces enhancing probability of landslides. The origin of clay-rich horizons appears to be (1) fractures in granodiorite that facilitate water flow and leaching, accelerating mineral dissolution during early weathering stages, and (2) smectite-rich buried soils under permeable colluvium likely deposited by a prior mass wasting event. Where clay-rich layers occur beneath more-permeable horizons, rapid infiltration then absorption of water in clay-rich subsoil horizons causes decreased shear strength and increased landslide susceptibility.

Puerto Rico↗

USGS2017

Carbon cycling in the mantled karst of the Ozark Plateaus, central United States

The nature of carbon (C) cycling in the unsaturated zone where groundwater is in contact with abundant gas-filled voids is poorly understood. The objective of this study was to trace inorganic-C cycling in a karst landscape using stable-C isotopes, with emphasis on a shallow groundwater flow path through the soil, to an underlying cave, and to the spring outlet of a cave stream in the Ozark Plateaus of northwestern Arkansas. Carbon dioxide (CO 2 ) concentration and isotopic composition (δ 13 C-CO 2 ) in gas and dissolved inorganic carbon (DIC) concentration and isotopic composition (δ 13 C-DIC) in water were measured in samples collected from two suction-cup soil samplers above the cave, three sites in the cave, and at the spring outlet of the cave stream. Soil-gas CO 2 concentration (median 2,578 ppm) and δ 13 C-CO 2 (median − 21.5‰) were seasonally variable, reflecting the effects of surface temperature changes on soil-CO 2 production via respiration and organic-matter decomposition. Cave-air CO 2 (median 1,026 ppm) was sourced from the soil zone and the surface atmosphere, with seasonally changing proportions of each source controlled by surface temperature-driven air density gradients. Soil-DIC concentration (median 1.7 mg L − 1 ) was lower and soil-δ 13 C-DIC (median − 19.5‰) was lighter compared to the cave (median 23.3 mg L − 1 and − 14.3‰, respectively) because carbonate-bedrock dissolution provided an inorganic source of C to the cave. Carbon species in the soil had a unique, light stable-C isotopic signature compared to the cave. Discrimination of soil-C sources to karst groundwater was achieved, which is critical for developing hydrologic budgets using environmental tracers such as C.

Geoderma Regional↗

USGS2021

Regional ensemble modeling reduces uncertainty for digital soil mapping

Recent country and continental-scale digital soil mapping efforts have used a single model to predict soil properties across large regions. However, different ecophysiographic regions within large-extent areas are likely to have different soil-landscape relationships so models built specifically for these regions may more accurately capture these relationships relative to a ‘global’ model. We ask the question: Is a single ‘global’ model sufficient or are regionally-specific models useful for accurate digital soil mapping? We test this question by modeling soil depth classes across the 432,000 km 2 upper Colorado River Basin in the Western USA using a single global model, multiple ecophysiographic models, and ensembles of the ecophysiographic models. Effective soil depth class observations ( n = 12,194) were derived from multiple soil databases. Fifty-seven environmental covariates were derived from a 30 m digital elevation model, climate data, satellite imagery, and aeroradiometric data. Three independent land classifications were used to stratify the area. Two expert-derived land classifications, USDA Major Land Resource Areas (MLRA) and US-EPA Level III ecoregions, divided the study area into multiple ecophysiographic regions based on vegetation and broad-scale physiographic differences. The third land classification divided the study area into broad landforms. Soil depth observations were split into separate training ( n = 10,470) and validation ( n = 1,724) datasets. First, a ‘global’ random forest model was used to model soil depth classes using all training observations and covariates. ‘Global’ denotes a model built with all training data across the extent of the area, not a model at world extent. Second, the land classifications were used to subset the observations into ecophysiographic sub-datasets and random forest models were refit for each region. Models fit by ecophysiographic region are referred to as regional models. Thirdly, predictions from each regional model were fused into regional-ensemble models. Accuracy, Brier scores, and Shannon’s entropy were used to compare model accuracy and uncertainty. Regional ecophysiographic models were also compared to models built for geographic areas that were defined solely to be approximately equal in area. Training dataset density and the imbalance ratio were investigated to determine if data characteristics influenced regional accuracy/uncertainty metrics. Accuracy for the global model using the validation set was 62.8%. Regional model accuracies ranged between 56.1% and 75.0%. We found: 1) useful inter-regional differences in global model accuracy were revealed when the global model was validated by region, 2) no consistent relationship between training observation density and accuracy/uncertainty metrics, 3) no meaningful differences in accuracy and uncertainty metrics between physiographic and geographic regions, 4) ensembles of regionally-specific models were approximately as accurate as global models, and 5) both region-specific models and ensembles of regional models were less uncertain than the global model. Overall, we recommend the use of soil depth class predictions made from MLRA regional ensemble models because this prediction had higher accuracy than the ecoregion ensemble model prediction, but lower uncertainty than both the global model and the landform ensemble model predictions. We answer our question: Ensembles of regionally-specific models are approximately as accurate as global models, but result in less uncertainty.

Arizona, Colorado, Nevada, New Mexico, Utah, Wyomi↗

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↗

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↗

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↗

USGS1991

Weathering processes and the composition of inorganic material transported through the Orinoco River system, Venezuela and Colombia

The composition of river-borne material in the Orinoco River system is related primarily to erosion regime, which in turn is related to tectonic setting; especially notable is the contrast between material derived from tectonically active mountain belts and that from stable cratonic regions. For a particular morpho-tectonic region, the compositional suites of suspended sediment, bed material, overback deposits, and dissolved phases are fairly uniform are are typically distinct from whose of other regions. For each region, a consistent set of chemical weathering reactions can be formulated to explain the composition of dissolved and solid loads. In developing these formulations, erosion on slopes and storage of solids in soils and alluvial sediments are important considerations. Compositionally verymature sediment is derived from areas of thick soils where erosion is transport limited and from areas where sediments are stored for extended periods of time in alluvial deposits. Compositionally immature sediments are derived from tectonically active mountain belts where erosion is weathering limited. Weathering-limited erosion also is important in the elevated parts of the Guayana Shield within areas of sleep topography. Compared to the mountain belts, sediments derived from elevated parts of the Shield are more mature. A greater degree of chemical weathering seems to be needed to erode the rock types typical of the Shield. The major-element chemistry and mineral composition of sediment delivered by the Orinoco River to the ocean are controlled by rivers that have their headwaters in mountain belts and cross the Llanos, a region of alluvial plains within the foreland basin. The composition of sediments in rivers that drain the Shield seems to be established primarily at the site of soil formation, whereas for rivers that drain the mountain belts, additional weathering occurs during s episodes of storage on alluvial plains as sediments are transported across the Llanos to the main stem of the Orinoco. After mixing into the main stem, there seems to be little subsequent alteration of sediment.

Orinoco River↗

USGS1991

Soil chronosequence studies in temperate to subtropical, low-latitude, low-relief terrain with data from the eastern United States

The Coastal Plain of the eastern United States is a low-latitude, low-altitude, low-relief terrain composed primarily of gently dipping marine and marginal-marine sediments that range in age from Cretaceous to Quaternary. Population density of the area is moderate, and most of the population is concentrated along the coast. Inland of the coast, agriculture, including growing trees for pulp, is the dominant economy. In this region, soils have developed along two different pathways. One pathway is dominated by the dissolution and movement of oxyhydroxides and the accumulation of organic matter; the other by the accumulation of clays and oxyhydroxyides and the adsorption or oxidation of organic matter. The first pathway has resulted in the formation of Spodosols; the second, in the development of Ultisols. No clearly distinguishable age trends have been identified in the Spodosols, but the properties of Ultisols can be measured to quantify surface material alteration through time. Ultisols are, therefore, suited to order-of-magnitude chronostratigraphic interpretations. Potentially, data derived through the study of Ultisols can be used to develop models that predict how surface processes will change due to continued weathering and pedogenesis or as the result of climate change.

Alabama, Delaware, Florida, Georgia, Maryland, New↗

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↗

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↗

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↗

USGS2021

Biocrust and the soil surface: Influence of climate, disturbance, and biocrust recovery on soil surface roughness

Biocrust communities promote soil surface roughness, a key functional characteristic for soil ecology. However, the spatial scales at which biocrust communities contribute to surface roughness are not well understood. To refine our understanding of the spatial dynamics between biocrust and soil surface roughness, we used mm-resolution terrestrial LiDAR to measure micro-topographic roughness at seven sub-meter, 3-dimensional kernels (spatial scales) for undisturbed and disturbed biocrusts within the cool Great Basin and the hot Chihuahuan Deserts of western North America. This multi-scalar approach applied within the different desert regions allowed us to explore two objectives: 1) assess the relative importance of climate and disturbance on biocrust roughness, and 2) evaluate how soil surface roughness evolves with biocrust recovery. For objective 1, we found that undisturbed cool desert biocrust was up to three times rougher than hot desert biocrust. Much of the difference in roughness between the two desert biocrust communities appeared to be from climate or other regional factors. However, positive correlations between roughness and biocrust indicators, including soil chlorophyll-a and the field-based Level of Development (LOD) index, suggested that differences in roughness at spatial scales ≤ 10 cm are directly related to biocrust development. Mechanical disturbance aimed at removing biocrust resulted in significant reductions in roughness and removed much of the observed differences in roughness between cool and hot desert soils. We evaluated biocrust recovery within the cool desert study area two years after mechanical disturbance and found that the disturbed soil increased in roughness up-to 300%. The increased surface roughness at spatial scales ≤ 10 cm were positively correlated with increased aggregate stability and indicators of biocrust reestablishment. We found that topographic change area was also an important contributor to roughness at all spatial scales, particularly at spatial scales ≥ 20 cm where it was the most important factor evaluated. These results provide insight into how biocrust interacts with other biophysical processes to influence soil surface roughness and how soil surfaces evolve at time scales relevant to soil restoration activities.

New Mexico, Utah↗

USGS2006

Atmospheric dust in modern soil on aeolian sandstone, Colorado Plateau (USA): Variation with landscape position and contribution to potential plant nutrients

Rock-derived nutrients in soils originate from both local bedrock and atmospheric dust, including dust from far-distant sources. Distinction between fine particles derived from local bedrock and from dust provides better understanding of the landscape-scale distribution and abundance of soil nutrients. Sandy surficial deposits over dominantly sandstone substrates, covering vast upland areas of the central Colorado Plateau, typically contain 5-40% silt plus clay, depending on geomorphic setting and slope (excluding drainages and depressions). Aeolian dust in these deposits is indicated by the presence of titanium-bearing magnetite grains that are absent in the sedimentary rocks of the region. Thus, contents of far-traveled aeolian dust can be estimated from magnetic properties that primarily reflect magnetite content, such as isothermal remanent magnetization (IRM). Isothermal remanent magnetization was measured on bulk sediment samples taken along two transects in surficial sediment down gentle slopes away from sandstone headwalls. One transect was in undisturbed surficial sediment, the other in a setting that was grazed by domestic livestock until 1974. Calculation of far-traveled dust contents of the surficial deposits is based on measurements of the magnetic properties of rock, surficial deposits, and modern dust using a binary mixing model. At the undisturbed site, IRM-based calculations show a systematic down-slope increase in aeolian dust (ranging from 2% to 18% of the surface soil mass), similar to the down-slope increase in total fines (18-39% of surface soil mass). A combination of winnowing by wind during the past and down-slope movement of sediment likely accounts for the modern distribution of aeolian dust and associated nutrients. At the previously grazed site, dust also increases down slope (5-11%) in sediment with corresponding abundances of 13-25% fines. Estimates of the contributions of aeolian dust to the total soil nutrients range widely, depending on assumptions about grain-size partitioning of potential nutrients in weathered bedrock. Nevertheless, aeolian dust is important for this setting, contributing roughly 40-80% of the rock-derived nutrient stocks (P, K, Na, Mn, Zn, and Fe) in uppermost soil over most of the sampled slope at the undisturbed site, which shows no evidence of recent wind erosion.

Utah↗

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↗