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Stephen D. Preston

Publications and source records attributed to Stephen D. Preston.

13 recordsLinked to original sources
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

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↗
USGS2016

An evaluation of methods for estimating decadal stream loads

Effective management of water resources requires accurate information on the mass, or load of water-quality constituents transported from upstream watersheds to downstream receiving waters. Despite this need, no single method has been shown to consistently provide accurate load estimates among different water-quality constituents, sampling sites, and sampling regimes. We evaluate the accuracy of several load estimation methods across a broad range of sampling and environmental conditions. This analysis uses random sub-samples drawn from temporally-dense data sets of total nitrogen, total phosphorus, nitrate, and suspended-sediment concentration, and includes measurements of specific conductance which was used as a surrogate for dissolved solids concentration. Methods considered include linear interpolation and ratio estimators, regression-based methods historically employed by the U.S. Geological Survey, and newer flexible techniques including Weighted Regressions on Time, Season, and Discharge (WRTDS) and a generalized non-linear additive model. No single method is identified to have the greatest accuracy across all constituents, sites, and sampling scenarios. Most methods provide accurate estimates of specific conductance (used as a surrogate for total dissolved solids or specific major ions) and total nitrogen – lower accuracy is observed for the estimation of nitrate, total phosphorus and suspended sediment loads. Methods that allow for flexibility in the relation between concentration and flow conditions, specifically Beale’s ratio estimator and WRTDS, exhibit greater estimation accuracy and lower bias. Evaluation of methods across simulated sampling scenarios indicate that (1) high-flow sampling is necessary to produce accurate load estimates, (2) extrapolation of sample data through time or across more extreme flow conditions reduces load estimate accuracy, and (3) WRTDS and methods that use a Kalman filter or smoothing to correct for departures between individual modeled and observed values benefit most from more frequent water-quality sampling.

Journal of Hydrology↗
USGS2014

Spatially explicit modeling to evaluate regional stream water quality

Spatially referenced regressions on watershed attributes (SPARROW) models have been developed and applied over the past two decades to address the need for large-scale, spatially explicit information on stream water quality conditions. The strength of SPARROW models is that they describe the primary environmental processes that affect the supply and transport of contaminant mass in watersheds, based on the use of stream monitoring and geospatial data to statistically estimate model parameters. SPARROW models were first applied at the scale of the conterminous US, but their use has grown through applications in many smaller regions of the US and in other countries. Recent developments include a web-based decision support system that provides open access to model results without the assistance of technical experts or special software. As it is highlighted in this chapter, SPARROW modeling provides a flexible framework for studying many aspects of water quality to support both research and resource management objectives.

Book chapter↗
USGS2011

The Regionalization of National-Scale SPARROW Models for Stream Nutrients

This analysis modifies the parsimonious specification of recently published total nitrogen (TN) and total phosphorus (TP) national‐scale SPAtially Referenced Regressions On Watershed attributes models to allow each model coefficient to vary geographically among three major river basins of the conterminous United States. Regionalization of the national models reduces the standard errors in the prediction of TN and TP loads, expressed as a percentage of the predicted load, by about 6 and 7%. We develop and apply a method for combining national‐scale and regional‐scale information to estimate a hybrid model that imposes cross‐region constraints that limit regional variation in model coefficients, effectively reducing the number of free model parameters as compared to a collection of independent regional models. The hybrid TN and TP regional models have improved model fit relative to the respective national models, reducing the standard error in the prediction of loads, expressed as a percentage of load, by about 5 and 4%. Only 19% of the TN hybrid model coefficients and just 2% of the TP hybrid model coefficients show evidence of substantial regional specificity (more than ±100% deviation from the national model estimate). The hybrid models have much greater precision in the estimated coefficients than do the unconstrained regional models, demonstrating the efficacy of pooling information across regions to improve regional models.

Journal of the American Water Resources Associatio↗
USGS2009

SPARROW MODELING - Enhancing Understanding of the Nation's Water Quality

The information provided here is intended to assist water-resources managers with interpretation of the U.S. Geological Survey (USGS) SPARROW model and its products. SPARROW models can be used to explain spatial patterns in monitored stream-water quality in relation to human activities and natural processes as defined by detailed geospatial information. Previous SPARROW applications have identified the sources and transport of nutrients in the Mississippi River basin, Chesapeake Bay watershed, and other major drainages of the United States. New SPARROW models with improved accuracy and interpretability are now being developed by the USGS National Water Quality Assessment (NAWQA) Program for six major regions of the conterminous United States. These new SPARROW models are based on updated geospatial data and stream-monitoring records from local, State, and other federal agencies.

Fact Sheet↗
USGS2007

Synthesis of U.S. Geological Survey science for the Chesapeake Bay ecosystem and implications for environmental management

The purpose of this report is to present a synthesis of the USGS Chesapeake Bay science related to the 2001-06 goals and provide implications for environmental management. The report provides USGS findings that address the science needs of the Chesapeake Bay Program (CBP) restoration goals and includes summaries of 1. land-use change; 2. water quality in the watershed, including nutrients, sediment, and contaminants; 3. long-term changes in climate and estuarine water quality; 4. estuary habitats, focusing on submerged aquatic vegetation (SAV) and tidal wetlands; and 5. factors affecting fish and waterbird populations.

Delaware, Maryland, New York, Pennsylvania, Virgin↗
USGS2004

Digital data used to relate nutrient inputs to water quality in the Chesapeake Bay watershed, version 3.0

Chesapeake Bay restoration efforts are focused on improving water quality, living resources, and ecological habitats by 2010. One aspect of the water-quality restoration is the refinement of strategies designed to implement nutrient-reduction practices within the Bay watershed. These strategies are being refined and implemented by resource managers of the Chesapeake Bay Program (CBP), a partnership comprised of various Federal, State, and local agencies that includes jurisdictions within Delaware, Maryland, New York, Pennsylvania, Virginia, West Virginia, and the District of Columbia. The U.S. Geological Survey (USGS), an active member of the CBP, provides necessary water-quality information for these Chesapeake Bay nutrient-reduction strategy revisions and evaluations. The formulation and revision of effective nutrient-reduction strategies requires detailed scientific information and an analytical understanding of the sources, transport, and delivery of nutrients to the Chesapeake Bay. The USGS is supporting these strategies by providing scientific information to resource managers that can help them evaluate and understand these processes. One statistical model available to resource managers is a collection of SPAtially Referenced Regressions On Watershed (SPARROW) attributes, which uses a nonlinear regression approach to spatially relate nutrient sources and watershed characteristics to nutrient loads of streams throughout the Chesapeake Bay watershed. Developed by the USGS, information generated by SPARROW can help resource managers determine the geographical distribution and relative contribution of nutrient sources and the factors that affect their transport to the Bay. Nutrient source information representing the late 1990s time period was obtained from several agencies and used to create and compile digital spatial datasets of total nitrogen and total phosphorus contributions that served as input sources to the SPARROW models. These data represent atmospheric deposition, point-source locations, land-use, land-cover, and agricultural sources such as commercial fertilizer and manure applications. Watershed-characteristics datasets representing factors that affect the transport of nutrients also were compiled from previous applications of the SPARROW models in the Chesapeake Bay watershed. Datasets include average-annual precipitation and temperature, slope, soil permeability, and hydrogeomorphic regions. Nutrient-input and watershed-characteristics datasets representing conditions during the late 1990s were merged with a connected network of stream reaches and watersheds to provide the spatial detail required by SPARROW. Stream-nutrient load estimates for 125 sampling sites (87 for total nitrogen and 103 for total phosphorus) served as the dependent variables for the regressions, and were used to calibrate models of total nitrogen and total phosphorus depicting late 1990s conditions in the Chesapeake Bay watershed. Spatial data generated for the models can be used to identify the location of nutrient sources, while the models' nutrient estimates can be used to evaluate stream-nutrient load contributed locally by each source evaluated, the amount of local load generated that is transported to the Bay, and the factors that affect the nutrient transport. Applying the SPARROW methodology to late 1990s information completes three time periods (late 1980s, early 1990s, and late 1990s) of viable data that resource managers can use to evaluate the water-quality conditions within the Bay watershed in order to refine restoration goals and nutrient-reduction strategies.

Chesapeake Bay watershed↗
USGS2001

Digital data used to relate nutrient input to water quality in the Chesapeake Bay watershed

Digital data sets compiled by the U.S. Geological Survey were used as input for a collection of Spatially Referenced Regressions On Watershed (SPARROW) attributes for the Chesapeake Bay region including parts of Delaware, Maryland, New York, Pennsylvania, Virginia, West Virginia, and the District of Columbia. These regressions use a nonlinear statistical approach to relate nutrient sources and land-surface characteristics to nutrient loads of streams throughout the Chesapeake Bay watershed. A digital segmented-watershed network serves as the primary framework for spatially referencing nutrient-source and land-surface characteristic data within a geographic information system. Flow direction and flow accumulation generated from a 30-meter cell-size Digital Elevation Model and attributes from 1:500,000-scale stream data were used to generate stream and watershed networks. Spatial data sets representing nutrient inputs of total nitrogen and total phosphorus from the early 1990's were created and compiled from numerous sources. Data include atmospheric deposition, septic systems, point-source locations, land use, land cover, and agricultural sources such as commercial fertilizer and manure. Some land-surface characteristic data sets representing factors that affect the transport of nutrients also were compiled. Data sets include land use, land cover, average-annual precipitation and temperature, slope, hydrogeomorphic regions, and soil permeability. Nutrient-input and land-surface characteristic data sets merged with the segmented-watershed network provide the spatial detail by watershed segment required by SPARROW. Stream-nutrient load estimates for 132 sampling sites representing the early 1990's (103 for total nitrogen and 121 for total phosphorus) serve as the dependent variables for the regressions. These estimates were used to calibrate models of total nitrogen and total phosphorus depicting 1992 land-surface conditions. Examples of model predictions consist of stream-nutrient load and source percentages contributed locally to each stream reach, as well as percentages of the load that reach Chesapeake Bay.

Open-File Report↗
USGS1999

Digital data used to relate nutrient inputs to water quality in the Chesapeake Bay watershed

Digital data sets were compiled by the U. S. Geological Survey (USGS) and used as input for a collection of Spatially Referenced Regressions On Watershed attributes for the Chesapeake Bay region. These regressions relate streamwater loads to nutrient sources and the factors that affect the transport of these nutrients throughout the watershed. A digital segmented network based on watershed boundaries serves as the primary foundation for spatially referencing total nitrogen and total phosphorus source and land-surface characteristic data sets within a Geographic Information System. Digital data sets of atmospheric wet deposition of nitrate, point-source discharge locations, land cover, and agricultural sources such as fertilizer and manure were created and compiled from numerous sources and represent nitrogen and phosphorus inputs. Some land-surface characteristics representing factors that affect the transport of nutrients include land use, land cover, average annual precipitation and temperature, slope, and soil permeability. Nutrient input and land-surface characteristic data sets merged with the segmented watershed network provide the spatial detail by watershed segment required by the models. Nutrient stream loads were estimated for total nitrogen, total phosphorus, nitrate/nitrite, amonium, phosphate, and total suspended soilds at as many as 109 sites within the Chesapeake Bay watershed. The total nitrogen and total phosphorus load estimates are the dependent variables for the regressions and were used for model calibration. Other nutrient-load estimates may be used for calibration in future applications of the models.

Open-File Report↗
USGS1999

Application of spatially referenced regression modeling for the evaluation of total nitrogen loading in the Chesapeake Bay watershed

The reduction of stream nutrient loads is an important part of current efforts to improve water quality in the Chesapeake Bay. To design programs that will effectively reduce stream nutrient loading, resource managers need spatially detailed information that describes the location of nutrient sources and the watershed factors that affect delivery of nutrients to the Bay. To address this need, the U.S. Geological Survey has developed a set of spatially referenced regression models for the evaluation of nutrient loading in the watershed. The technique applied for this purpose is referred to as ?SPARROW? (SPAtially Referenced Regressions On Watershed attributes), which is a statistical modeling approach that retains spatial referencing for illustrating predictions, and for relating upstream nutrient sources to downstream nutrient loads. SPARROW is based on a digital stream-network data set that is composed of stream segments (reaches) that are attributed with traveltime and connectivity information. Drainage-basin boundaries are defined for each stream reach in the network data set through the use of a digital elevation model. For the Chesapeake Bay watershed, the spatial network was developed using the U.S. Environmental Protection Agency?s River Reach File 1 digital stream network, and is composed of 1,408 stream reaches and watershed segments. To develop a SPARROW model for total nitrogen in the Chesapeake Bay watershed, data sets for sources and basin characteristics were incorporated into the spatial network and related to stream-loading information by using a nonlinear regression model approach. Total nitrogen source variables that were statistically significant in the model include point sources, urban area, fertilizer application, manure generation and atmospheric deposition. Total nitrogen loss variables that were significant in the model include soil permeability and instream-loss rates for four stream-reach classes. Applications of SPARROW for evaluating total nitrogen loading in the Chesapeake Bay watershed include the illustration of the spatial distributions of total nitrogen yields and of the potential for delivery of those yields to the Bay. This information is being used by the Chesapeake Bay Program to target nutrient-reduction areas (Priority Nutrient Reduction Areas) and to design nutrient-load reduction plans that are specific to each tributary (Tributary Strategies).

Chesapeake Bay watershed↗