Geology topics

Gregory E. Schwarz

Publications and source records attributed to Gregory E. Schwarz.

At least 19 recordsLinked to original sources

USGS2026

Seasonal estimates of nutrient loading, sources, and impacts on water availability in streams across the conterminous United States, 2000–2020

Excess riverine nutrients can limit water availability for humans and ecosystems. Nutrient monitoring on individual stream reaches provides important information about current and past conditions, yet most river reaches are unmonitored. Additionally, monitored data integrate effects of upstream sources, sinks, and pathways by which nutrients get into streams, making explicit planning for effective nutrient reduction strategies challenging. We developed and applied dynamic SPAtially Referenced Regression On Watershed attributes (SPARROW) models to quantify total nitrogen (TN) and total phosphorus (TP) delivery and source-specific loading in all stream reaches of the conterminous United States (CONUS) at a seasonal timestep from 2000 through 2020. Between one third and one half of non-point source nutrients lagged more than one season in their delivery to CONUS streams over the study period. The largest sources of nutrients that were applied and delivered to streams within a season were agricultural fertilizers, treated wastewater, atmospheric deposition, and manure. Increases in loads were widespread across the CONUS and predominantly associated with increased loading from lagged delivery of non-point source inputs and current season fertilizer application. Model results also identified extensive areas where predicted seasonal concentrations frequently exceeded the National Rivers and Streams Assessment “poor” criteria. Increased loading occurred in areas with high yields and delivery to downstream receiving waterbodies, particularly across the Midwest, raising the risk of negative impacts to downstream water availability. The modeling approach presented here provides a continental-scale framework for identifying when, where, and why nutrients are generated and how that may ultimately impact receiving waterbodies.

conterminous United States↗

USGS2024

Temporal variability in irrigated land and climate influences on salinity loading across the Upper Colorado River Basin, 1986-2017

Freshwater salinization is a growing global concern impacting human and ecosystem needs with impacts to water availability for human and ecological uses. In the Upper Colorado River Basin (UCRB), dissolved solids in streams compound ongoing water supply challenges to further limit water availability and cause economic damages. Much effort has been dedicated to understanding dissolved solid sources, transport, and management in the region, yet temporal variability in loading from key sources such as irrigated lands and the influence of climate on dissolved solids loading remains unknown. Quantifying the contributions and temporal variability of dissolved solids loads from irrigated lands may benefit salinity management efforts. This study applies a time-varying (dynamic) modeling approach to predict annual dissolved solids loads across the UCRB from 1986 through 2017. Between 66% and 82% of the total accumulated dissolved solids load in the basin is from groundwater (storage and baseflow). Our findings link climate, irrigation, and groundwater, and confirm large storage contributions that have declined slightly with time. Dissolved solids loads increase during wet periods and decrease during dry periods, although the relative contributions of different sources vary little with time. Irrigation enhances loading efficiency relative to unirrigated areas through runoff and groundwater, and can locally be a major source of dissolved solids where irrigation occurs. Results indicate that loads from irrigated areas increase when irrigated area and/or water available for runoff increase. Increased regional aridification over the study period may have contributed to decreasing stream salinity through both quicker surface runoff and lagged groundwater storage processes. Study results may be relevant to salinity management in arid environments where water availability is limited and where irrigation influences salinity loading to streams.

Upper Colorado River Basin↗

USGS2024

Seasonally varying contributions of contemporaneous and lagged sources of instream total nitrogen and phosphorus load across the Illinois River basin

Quantifying nutrient sources in streams, their temporal and spatial variability, and drivers of that variability can support effective water resources management. Yet a lack of data and modeling capabilities has previously prevented comprehensive quantification across both space and time. Here a dynamic SPARROW (Spatially Referenced Regressions on Watershed attributes) model that accounts for a lagged delivery of nutrients to streams was developed and applied to simulate seasonal and source-specific total nitrogen (TN) and total phosphorus (TP) loads in streams across the Illinois River basin (IRB). Dynamic load predictions from 2000 through 2020 revealed that a third of the TN and a quarter of the TP instream load originated from non-point sources that were lagged in their delivery from land-application to streams by more than a season. This lagged mass was the largest overall TN source—which was estimated as a lagged expression of previous seasonal non-point sources including fertilizer, manure, atmospheric deposition and fixation, and urban land use. Treated wastewater effluent was the largest TP source exported from the basin, contributing 39 % of the TP load and 15 % of the TN load, and dominated the load in the upper Illinois River near Chicago. Loads in the lower river during this period, conversely, were attributed primarily to a mix of agricultural sources and their lagged fractions from headwater tributaries. Instream processes removed 10 % of the TN load while only 4 % of the TP load was removed during instream transport. With appropriate datasets, the models could be extended to other basins or time periods and used to forecast future seasonal nutrient loads.

Illinois↗

USGS2023

Sand- and gravel-trapping efficiencies derived for four types of pressure-difference bedload samplers

Bedload-trapping efficiencies (coefficients) were derived for four types of pressure-difference bedload samplers at the St. Anthony Falls Laboratory, University of Minnesota during the first two phases of flume experiments in January-March, 2006, referred to as “StreamLab06.” The bedload-sampler research component was part of a series of community-led, large-scale laboratory experiments performed under the auspices of the National Center for Earth-surface Dynamics (Marr and others, 2010; Singh and others, 2013; Gray and others, 2010, 2019, 2021). A bedload-trapping coefficient is the ratio of the mass of bedload – sediment transported by rolling, sliding, or skipping in close contact with the riverbed – collected by the deployed sampler, to the mass of bedload that would have passed through the width of the sample section at the same time but in the absence of the sampler (Hubbell, 1964). A trapping coefficient of 1.0 would mean the mass of every particle-size fraction of sediment in the collected sample is in the same proportion as those in transport. For the 2006 experiments, a Helley-Smith (intake-nozzle width of 76.2 millimeter [mm] and height of 76.2 mm), BLH-84 (76.2 mm × 76.2 mm), Elwha (203 mm × 102 mm) and Toutle River-2 (TR-2; 305 mm × 152 mm) were repeatedly deployed by a hand-held rod with a stabilizing tether line in the main flume. Six combinations of bedload sampler types and bed compositions were tested: The BLH-84, Elwha, and Helley-Smith samplers were deployed on a sand bed (d50 = 1.0 mm) during five steady flows ranging from 2.0-3.6 cubic meters per second (m3/s). The BLH-84, Elwha, and TR-2 samplers were deployed on a gravel bed (d50 = 11.2 mm) at four steady flows ranging from 4.0-5.5 m3/s. Bedload samples collected manually as part of 37 trials – each associated with a unique combination of a bedload sampler type, steady-flow rate, and bed composition – and associated ancillary data were used to calculate 2,030 instantaneous, at-a-point bedload-transport rates (1,000 as part of 19 sand-bed trials, and 1,030 as part of 27 gravel-bed trials.). Five contiguous weigh drums embedded in a slot spanning the width of the flume independently and continuously weighed captured bedload on approximately 1.1-second intervals. Approximately 3.8-million individual weigh-drum time-series measurements were recorded during the bedload sampler experiments (Groten and Gray, 2021; Gray and others, 2021).

Conference Paper↗

USGS2022

Predicting near-term effects of climate change on nitrogen transport to Chesapeake Bay

Understanding effects of climate change on nitrogen fate and transport in the environment is critical to nutrient management. We used climate projections within a previously calibrated spatially referenced regression (SPARROW) model to predict effects of expected climate change over 1995 through 2025 on total nitrogen fluxes to Chesapeake Bay and in watershed streams. Assuming nitrogen inputs and other watershed conditions remain at 2012 levels, effects of increasing temperature, runoff, streamflow, and stream velocity expected between 1995 and 2025 will include an estimated net 6.5% decline in annual nitrogen delivery to the bay from its watershed. This predicted decline is attributable to declines in the delivery of nitrogen from upland nonpoint sources to streams due to predicted warmer temperatures. Such temperature-driven declines in the delivery of nitrogen to streams more than offset predicted increased delivery to and within streams due to increased runoff and streamflow and may be attributable to increasing rates of denitrification or ammonia volatilization or to changes in plant phenology. Predicted climate-driven declines in nitrogen flux are generally similar across the watershed but vary slightly among major nonpoint source sectors and tributary watersheds. Nitrogen contributions to the bay from point sources are not affected by temperature-driven changes in delivery from uplands and are therefore predicted to increase slightly between 1995 and 2025.

Chesapeake Bay watershed↗

USGS2021

Quantifying regional effects of best management practices on nutrient losses from agricultural lands

Nitrogen (N) and phosphorus (P) losses from agricultural areas have degraded the water quality of downstream rivers, lakes, and oceans. As a result, investment in the adoption of agricultural best management practices (BMPs) has grown, but assessments of their effectiveness at large spatial scales have lagged. This study applies regional Spatially Referenced Regression On Watershed-attributes (SPARROW) models developed for the Midwest, Northeast, and Southeast United States to quantify potential regional effects of BMPs on nutrient losses from agricultural lands. These models were used because they account for specific BMPs in the prediction of instream nutrient loads. The BMPs included in the models were cover crops, no-till, and conservation tillage. Sensitivity testing for the BMPs on agricultural nutrient loads was done using simulations that varied the intensity of BMPs specified in each region. When the BMP intensity was increased 50% relative to the 2012 intensity, the predicted agricultural load of total P decreased across all regions (4% to 14%). The predicted reduction in average P yields in the Midwest, Northeast, and Southeast was 706, 544, and 26 kg km–2, respectively. Increasing BMPs by 50% decreased predicted agricultural total N loads by 3.5% in the Southeast but increased predicted N loads in the Midwest and Northeast by 4.7% and 1.8%, respectively. Model-predicted average N yields increased by 402 kg km–2 and 302 kg km–2 in the Midwest and Northeast, respectively, and decreased in the Southeast by 329 kg km–2. In model simulations, cover crops were more effective at reducing N and P loads than the tillage BMPs despite lower intensity of implementation in 2012. However, at the regional scale of this investigation, implementation of BMPs result in only moderate predicted effects on agricultural nutrient loads

Journal of Soil and Water Conservation↗

USGS2021

Seasonally dynamic nutrient modeling quantifies storage lags and time-varying reactivity across large river basins

Nutrients that have gradually accumulated in soils, groundwaters, and river sediments in the United States over the past century can remobilize and increase current downstream loading, obscuring effects of conservation practices aimed at protecting water resources. Drivers of storage accumulation and release of nutrients are poorly understood at the spatial scale of basins to watersheds. Predicting water quality outcomes in large river basins demands modeling storage lags and time varying reactivity that models of mean conditions typically cannot elucidate. We developed a seasonally dynamic approach to large-scale nutrient modeling based on a multiscale framework and nutrient storage lags were quantified for the nearly 190 000 small catchments that feed the rivers across the northeastern United States where catchment mean transit times were found to be around 4.7 (2–10) years for nitrogen and 1.3 (0.7–2) years for phosphorus. Nutrient loads carried in river flow in the current season contained a significant—and sometimes dominant—portion of mass lagged in its release from catchment storage repositories. Our approach of integrating storage releases with seasonally dynamic hydroclimatic drivers sets the stage to assess the accumulated effects of nutrient storage and lagged releases to the river interacting with seasonally varying nutrient reactivity and societal management actions throughout large river basins.

Environmental Research Letters↗

USGS2021

Instruments, methods, rationale, and derived data used to quantify and compare the trapping efficiencies of four types of pressure-difference bedload samplers

Bedload and ancillary data were collected to calculate and compare the bedload trapping efficiencies of four types of pressure-difference bedload samplers as part of episodic, sediment-recirculating flume experiments at the St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, in January–March 2006. The bedload-sampler experiments, which were conceived, organized, and led by the U.S. Geological Survey’s Office of Surface Water, were part of a broader suite of experiments performed in the rectangular, concrete-lined, sediment-recirculating Main Channel Facility (“main channel flume”). Collectively referred to as “StreamLab06,” the experiments were conducted under the auspices of the National Center for Earth-Surface Dynamics, University of Minnesota. Four pressure-difference-type bedload samplers—a standard Helley-Smith, US BLH-84, Elwha, and Toutle River-2—were deployed by using hand-held rods in the main flume in a series of trials during steady flows as part of the first two of seven phases of the StreamLab06 experiments. The Phase I flows were released over a sand bed. Gravel composed the bed during the Phase II flows. Bedload samples were collected during flows ranging from 2.0 cubic meters per second (near the incipient motion of bed material) to 5.5 cubic meters per second. A total of 2,030 bedload samples were collected—1,000 as part of 19 sand-bed trials, and 1,030 as part of 27 gravel-bed trials. Bedload was captured in five contiguous weigh drums inside a slot spanning the full width of the main flume channel 8.5 meters downstream from the cross-section in which the bedload samplers were deployed. The contents of each drum were automatically weighed and recorded as a time series about every 1.1 seconds. Each drum automatically, independently, and episodically dumped its contents into the bottom of the slot upon the accumulation of a pre-determined mass of entrapped sediment, after which the drum continued to capture and weigh bedload. An auger at the bottom of the slot evacuated the accumulating sediment to a side-channel pump that piped the captured sediments upstream and discharged them back to the flume. Bedload-transport rates were calculated from measurements of the masses of material trapped by the bedload samplers and from the data produced by the automated bedload capture-and-weigh system of the main channel flume. These data were used to compute at-a-point and mean bedload-transport rates for subsequent use in developing bedload-trapping efficiency (calibration) coefficients for each bedload sampler and for comparing the relative trapping efficiencies of the manually deployed bedload samplers. The data were collected to enable the use of several computational methods for deriving bedload-trapping coefficients. Continuous ancillary data including stage, water discharge, and water temperature were automatically collected and stored. Flow depths were manually measured and recorded concurrent with each at-a-point bedload-sampler deployment. Other information obtained during parts of the experiments included longitudinal water-surface slope, bedload particle-size distributions, and suspended-sediment concentrations and percent sand analyzed from samples collected by depth integration with a US DH-48 isokinetic suspended-sediment sampler. This report describes the types and availability of the bedload and ancillary data derived through the StreamLab06 experiments. The data are available from the St. Anthony Falls Laboratory and the U.S. Geological Survey through a data release. Also included are selected descriptive and historical information as well as the background, experimental design, experimental caveats, and other factors relevant to the production of the bedload-transport and ancillary data produced through Phases I and II of the StreamLab06 experiments.

Open-File Report↗

USGS2020

Accounting for temporal variability of streamflow in estimates of travel time

Retention, processing, and transport of solutes and particulates in stream corridors are influenced by the travel time of streamflow through stream channels, which varies dynamically with discharge. The effects of streamflow variability across sites and over time cannot be addressed by time-averaged models if parameters are based solely on the characteristics of mean streamflow. We develop methods to account for the effects of streamflow variability on travel time and compare our estimates to flow-weighted (“effective”) travel time at 100 streams in the southeastern United States. Velocity time series were generated for each stream from multiple-year (median 15.5 years), high-frequency (15 min interval) records of instantaneous streamflow and field measurements of velocity and inverted to produce time series of specific travel time [T/L]. The effective travel times for streams are 60–90% of the specific travel time of mean streamflow because a large fraction of the total streamflow volume is discharged during higher flows with higher velocities. We find that adjusting the specific travel time of mean streamflow at a site by a factor of 0.81 generally accounts for the effect of a skewed streamflow distribution, but at-site estimates of the coefficient of variation of streamflow are necessary to resolve differences in streamflow variability between streams or changes in variability over time. For example, the effective travel time of urban streams is less than the effective travel of forested streams in the southeastern United States as a result of increased streamflow variability in urban streams. Effective travel time accounts for both the variation in velocity with streamflow and the large fraction of streamflow discharged during high flows in most streams and provides time-averaged models with limited capability to account for effects of streamflow variability that otherwise they lack. This capability is needed for continental-scale modeling where streamflow variability is not uniform because of heterogeneous surficial geology, hydro-climatology, and vegetation and for applications where streamflow variability is not stationary as a response to climate change or hydrologic alteration.

Alabama, Georgia, North Carolina, Virginia, South ↗

USGS2020

Low threshold for nitrogen concentration saturation in headwaters increases regional and coastal delivery

River corridors store, convey, and process nutrients from terrestrial and upstream sources, regulating delivery from headwaters to estuaries. A consequence of chronic excess nitrogen loading, as supported by theory and field studies in specific areas, is saturation of the biogeochemically-mediated nitrogen removal processes that weakens the capacity of the river corridor to remove nitrogen. Regional nitrogen models typically assume that removal capacity exhibits first-order behavior, scaling positively and linearly with increasing concentration, which may bias the estimation of where and at what rate nitrogen is removed by river corridors. Here we estimate the nitrogen concentration saturation threshold and its effects on nitrogen export from the Northeastern United States, revealing an average 42% concentration-induced reduction in headwater removal capacity. The weakened capacity caused an average 10% increase in the predicted delivery of riverine nitrogen from urban and agricultural watersheds compared to estimates using first-order process assumptions. Our results suggest that nitrogen removal may fall below a first-order process at a low riverine threshold concentration of 0.5 mg N L-1. Threshold behavior indicates that even modest mitigation of nitrogen concentration in river corridors above the threshold can cause a self-reinforcing boost to nitrogen removal.

Connecticut, District of Columbia, Delaware, Massa↗

USGS2020

An enhanced hydrologic stream network based on the NHDPlus medium resolution dataset

The National Hydrography Dataset Plus, Version 2.1 (NHDPlusV2.1) is an attribute-rich digital stream network for the conterminous United States, serving as a foundational infrastructure for reporting hydrologic information at both regional and national scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW) is a process-based statistical model that relies on a digital hydrologic network like NHDPlusV2.1 to establish spatial relations between quantities of monitored contaminant loads and contaminant sources, accounting for the physical characteristics along flow paths affecting contaminant transport. The U.S. Geological Survey National Water Quality Assessment project adopted and modified the medium-resolution NHDPlusV2.1 network for use as the primary framework supporting SPARROW modeling. This report describes the enhancements made to improve the routing capabilities and the value-added attributes of NHDPlusV2.1 to support modeling and other hydrologic analyses. These enhancements include corrections to inconsistencies in network/routing information, filling in missing attribute values of associated characteristics, accounting of water use affecting flow, new variables useful for interpreting network data, revised flowline attributes such as slope and flow, and incorporation of ancillary spatial data into the network. The resulting dataset containing the enhancements to the network is named E2NHDPlusV2_US. Although the enhancements described in the report were developed for use in SPARROW modeling, the enhancements are expected to be useful for a wide variety of hydrologic studies within the United States.

Conterminous United States↗

USGS2019

Advances in quantifying streamflow variability across continental scales: 2. Improved model regionalization and prediction uncertainties using hierarchical Bayesian methods

The precise estimation of process effects in hydrological models requires applying models to large scales with extensive spatial variability in controlling factors. Despite progress in large‐scale applications of hydrological models in conterminous United States (CONUS) river basins, spatial constraints in model parameters have prevented the interbasin sharing of data, complicating quantification of process effects and limiting the accuracy of model predictions and uncertainties. Hierarchical Bayesian methods enable data sharing between basins and the identification of the causes of model uncertainties, which can improve model accuracy and interpretability; however, computational inefficiencies have been an obstacle to their large‐scale application. We used a new generation of Bayesian methods to develop a hierarchical version of a previous hybrid (statistical‐mechanistic) SPAtially Referenced Regression On Watershed attributes model of long‐term mean annual streamflow in the CONUS. We identified hierarchical (regional) variations in model coefficients and uncertainties and evaluated their effects on model accuracy and interpretability across diverse environments in 16 major CONUS regions. Hierarchical coefficients significantly improved spatial accuracy of model predictions, with the largest improvements in humid eastern regions, where uncertainties were approximately one third of those in arid western regions. Half of the coefficients varied regionally, with the largest variations in coefficients associated with water losses in streams and reservoirs. Our unraveling of the causes of model uncertainties identified a small latent process component of runoff that varies inversely with river size in most CONUS regions. Our study advances the use of hierarchical Bayesian methods to improve the predictive capabilities of hydrological models.

Water Resources Research↗

USGS2019

Phosphorus and nitrogen transport in the binational Great Lakes Basin estimated using SPARROW watershed models

Eutrophication problems in the Great Lakes are caused by excessive nutrient inputs (primarily phosphorus, P, and nitrogen, N) from various sources throughout its basin. In developing protection and restoration plans, it is important to know where and from what sources the nutrients originate. As part of a binational effort, Midcontinent SPARROW (SPAtially Referenced Regression On Watershed attributes) models were developed and used to estimate P and N loading from throughout the entire basin based on nutrient inputs similar to 2002; previous SPARROW models only estimated U.S. contributions. The new models have a higher resolution (~2‐km 2 catchments) enabling improved descriptions of where nutrients originate and the sources at various spatial scales. The models were developed using harmonized geospatial datasets describing the stream network, nutrient sources, and environmental characteristics affecting P and N delivery. The models were calibrated using loads from sites estimated with ratio estimator and regression techniques and additional statistical approaches to reduce spatial correlation in the residuals and have all monitoring sites equally influence model development. SPARROW results, along with interlake transfers and direct atmospheric inputs, were used to quantify the entire P and N input to each lake and describe the importance of each nutrient source. Model results can be used to compare loading and yields from various tributaries and jurisdictions.

Great Lakes↗

USGS2019

Small ponds in headwater catchments are a dominant influence on regional nutrient and sediment budgets

Small ponds—farm ponds, detention ponds, or impoundments below 0.01 km 2 —serve important human needs throughout most large river basins. Yet the role of small ponds in regional nutrient and sediment budgets is essentially unknown, currently making it impossible to evaluate their management potential to achieve water quality objectives. Here we used new hydrography data sets and found that small ponds, depending on their spatial position within both their local catchments and the larger river network, can dominate the retention of nitrogen, phosphorus, and sediment compared to rivers, lakes, and reservoirs. Over 300,000 small ponds are collectively responsible for 34%, 69%, and 12% of the mean annual retention of nitrogen, phosphorus, and sediment in the Northeastern United States, respectively, with a dominant influence in headwater catchments (54%, 85%, and 50%, respectively). Small ponds play a critical role among the many aquatic features in long‐term nutrient and sediment loading to downstream waters.

Geophysical Research Letters↗

USGS2019

Estimates of long-term mean daily streamflow and annual nutrient and suspended-sediment loads considered for use in regional SPARROW models of the Conterminous United States, 2012 base year

Streamflow, nutrient, and sediment concentration data needed to estimate long-term mean daily streamflow and annual constituent loads were compiled from Federal, State, Tribal, and regional agencies, universities, and nongovernmental organizations. The streamflow and loads are used to develop Spatially Referenced Regressions on Watershed Attributes (SPARROW) models. SPARROW models help describe the distribution, sources, and transport of streamflow, nutrients, and sediment in streams throughout five regions of the conterminous United States. After the data were screened, approximately 5,200 streamflow, 3,000 sediment, and 3,300 nutrient sites, sampled by 137 agencies and organizations were identified as having suitable data for calculating the long-term mean daily streamflow and annual nutrient and sediment loads required for SPARROW model estimation. These sites are representative of a wide range in terms of watershed size, contaminant source types, and land-use and other important watershed characteristics. The methods used to estimate long-term mean annual loads include the Beale ratio estimator and Fluxmaster regression method with Kalman smoothing.

Conterminous United States↗

USGS2019

Toward explaining nitrogen and phosphorus trends in Chesapeake Bay tributaries, 1992-2012

Understanding trends in stream chemistry is critical to watershed management, and often complicated by multiple contaminant sources and landscape conditions changing over varying time scales. We adapted spatially-referenced regression (SPARROW) to infer causes of recent nutrient trends in Chesapeake Bay tributaries by relating observed fluxes during 1992, 2002, and 2012 to contemporary inputs and watershed conditions. The annual flow-normalized nitrogen flux to the bay from its watershed declined by 14 percent to 127,000 Mg (metric tons) between 1992 and 2012, due primarily (more than 80 percent of the decline) to reduced point sources. The remainder of the decline was due to reduced atmospheric deposition (13 percent) and urban non-point sources. Agricultural inputs, which contribute most nitrogen to the bay, changed little, although trends in the average nitrogen yield (flux per unit area) from cropland and pasture to streams in some settings suggest possible effects of evolving nutrient applications or other land management practices. Point sources of phosphorus to local streams declined by half between 1992 and 2012, while non-point inputs were relatively unchanged. Annual phosphorus delivery to the bay increased by 9 percent to 9,570 Mg between 1992 and 2012, however, due mainly to reduced retention in the Susquehanna River at Conowingo Reservoir.

Delaware, Maryland, New Jersey, Pennsylvania, Virg↗

USGS2019

Catchment-level estimates of nitrogen and phosphorus agricultural use from commercial fertilizer sales for the conterminous United States, 2012

Nutrient inputs from commercial agricultural fertilizer, particularly nitrogen and phosphorus, are important factors contributing to the degradation of surface-water quality and the alteration of aquatic ecosystems. Despite this importance, information about the application of fertilizer to agricultural land is not available in a consistent manner across the United States at a scale useful for regional water-quality assessment. To address this need, an approach is developed to relate commercial fertilizer sales to a set of explanatory variables using spatially referenced modeling methods. Spatially referenced modeling in this study refers to statistically relating fertilizer use, estimated from commercial fertilizer sales data, to spatially referenced data on watershed attributes. Separate models for nitrogen and phosphorus are developed to estimate elemental fertilizer use on agricultural lands for the conterminous United States at the National Hydrography Dataset Plus (NHDPlus) catchment scale for the year 2012. The approach builds on earlier efforts that use Association of American Plant Food Control Officials data on fertilizer sales to provide county-level estimates of nitrogen and phosphorus fertilizer use. The spatially referenced method improves on these efforts by allowing for varying nitrogen to phosphorus ratios at the catchment scale and expanding the set of variables used to allocate county-level sales data to the catchment scale. The models include catchment-level factors that are either primary determinants of fertilizer use, such as the acreage of different crop types, or measures reflecting the intensity of use, such as climate. Explanatory variables available only at the county scale, such as U.S. Department of Agriculture Census of Agriculture estimates of fertilizer expenditures, are included to improve the model predictions of elemental use. The nitrogen and phosphorus models explain more than 90 percent of the variation in elemental use at the state level, and the statistical approach allows for the estimation of uncertainty of predicted use in each catchment. The spatial patterns of model predictions reflect known agricultural cropping practices across the United States that transcend political boundaries, despite the county/state orientation of the fertilizer sales information. The results are expected to be useful for a variety of water-quality assessments that are intended to estimate nitrogen and phosphorus loads to streams.

Scientific Investigations Report↗

USGS2019

Adapting a regional water-quality model for local application: A case study for Tennessee, USA

We evaluated whether SPAtially Referenced Regression On Watershed attributes (SPARROW) models calibrated for two adjacent USA regions could be applied at the local scale to support management decisions for streams in Tennessee. Nutrient-source apportionment of load is important for this local-scale application and demands careful consideration of uncertainty in the calibrated coefficients. We used Gauss-Newton regression to test the published SPARROW models for constancy of coefficient estimates between calibration sites on streams within (n = 59) versus outside (n = 327) Tennessee and concluded source apportionment was unbiased for Tennessee streams. The SPARROW models were then applied without re-calibration to predict stream loads and source apportionment for Tennessee streams and to build tools for displaying model results and evaluating source-change scenarios. This approach may inform the adaptation of other regional-scale regression models for use to address water-resource management issues in smaller-scale watersheds.

Tennessee↗