Sparrow modeling to understand water quality conditions in major regions of the United States: A featured collection introduction
No abstract available.
Journal of the American Water Resources Associatio↗
Geology topics
S. D. Preston
Publications and source records attributed to S. D. Preston.
A hydrologic network supporting spatially referenced regression modeling in the Chesapeake Bay watershed
The U.S. Geological Survey has developed a methodology for statistically relating nutrient sources and land-surface characteristics to nutrient loads of streams. The methodology is referred to as SPAtially Referenced Regressions On Watershed attributes (SPARROW), and relates measured stream nutrient loads to nutrient sources using nonlinear statistical regression models. A spatially detailed digital hydrologic network of stream reaches, stream-reach characteristics such as mean streamflow, water velocity, reach length, and travel time, and their associated watersheds supports the regression models. This network serves as the primary framework for spatially referencing potential nutrient source information such as atmospheric deposition, septic systems, point-sources, land use, land cover, and agricultural sources and land-surface characteristics such as land use, land cover, average-annual precipitation and temperature, slope, and soil permeability. In the Chesapeake Bay watershed that covers parts of Delaware, Maryland, Pennsylvania, New York, Virginia, West Virginia, and Washington D.C., SPARROW was used to generate models estimating loads of total nitrogen and total phosphorus representing 1987 and 1992 land-surface conditions. The 1987 models used a hydrologic network derived from an enhanced version of the U.S. Environmental Protection Agency's digital River Reach File, and course resolution Digital Elevation Models (DEMs). A new hydrologic network was created to support the 1992 models by generating stream reaches representing surface-water pathways defined by flow direction and flow accumulation algorithms from higher resolution DEMs. On a reach-by-reach basis, stream reach characteristics essential to the modeling were transferred to the newly generated pathways or reaches from the enhanced River Reach File used to support the 1987 models. To complete the new network, watersheds for each reach were generated using the direction of surface-water flow derived from the DEMs. This network improves upon existing digital stream data by increasing the level of spatial detail and providing consistency between the reach locations and topography. The hydrologic network also aids in illustrating the spatial patterns of predicted nutrient loads and sources contributed locally to each stream, and the percentages of nutrient load that reach Chesapeake Bay.
Environmental Monitoring and Assessment↗
Factors affecting nutrient trends in major rivers of the Chesapeake Bay Watershed
Trends in nutrient loads and flow-adjusted concentrations in the major rivers entering Chesapeake Bay were computed on the basis of water-quality data collected between 1985 and 1998 at 29 monitoring stations in the Susquehanna, Potomac, James, Rappahannock, York, Patuxent, and Choptank River Basins. Two computer models-the Chesapeake Bay Watershed Model (WSM) and the U.S. Geological Survey's 'Spatially Referenced Regressions on Watershed attributes' (SPARROW) Model-were used to help explain the major factors affecting the trends. Results from WSM simulations provided information on temporal changes in contributions from major nutrient sources, and results from SPARROW model simulations provided spatial detail on the distribution of nutrient yields in these basins. Additional data on nutrient sources, basin characteristics, implementation of management practices, and ground-water inputs to surface water were analyzed to help explain the trends. The major factors affecting the trends were changes in nutrient sources and natural variations in streamflow. The dominant source of nitrogen and phosphorus from 1985 to 1998 in six of the seven tributary basins to Chesapeake Bay was determined to be agriculture. Because of the predominance of agricultural inputs, changes in agricultural nutrient sources such as manure and fertilizer, combined with decreases in agricultural acreage and implementation of best management practices (BMPs), had the greatest impact on the trends in flow-adjusted nutrient concentrations. Urban acreage and population, however, were noted to be increasing throughout the Chesapeake Bay Watershed, and as a result, delivered loads of nutrients from urban areas increased during the study period. Overall, agricultural nutrient management, in combination with load decreases from point sources due to facility upgrades and the phosphate detergent ban, led to downward trends in flow-adjusted nutrient concentrations atmany of the monitoring stations in the watershed. The loads of nutrients, however, were not reduced significantly at most of the monitoring stations. This is due primarily to higher streamflow in the latter years of the monitoring period, which led to higher loading in those years. Results of this study indicate a need for more detailed information on BMP effectiveness under a full range of hydrologic conditions and in different areas of the watershed; an internally consistent fertilizer data set; greater consideration of the effects of watershed processes on nutrient transport; a refinement of current modeling efforts; and an expansion of the non-tidal monitoring network in the Chesapeake Bay Watershed.
Delaware, Maryland, New York, Pennsylvania, Virgin↗
Statistical identification of hydrochemical response units for hydrologic monitoring and modeling in Maryland
In support of Maryland's efforts to develop statewide water-quality management plans, a statistical analysis was performed to identify a set of representative and relatively homogeneous areas referred to as Hydrochemical Response Units (HRUs). The State intends to select representative areas within each hydrochemical response unit for monitoring and model development, and then apply the model to the entire unit. To identify hydrochemical response units, cluster analysis was applied to 1,136 digitally defined watershed units. Basin characteristics including land cover, soil type, slope, and geology were determined for each watershed unit and a clustering algorithm was applied to the data sets. A total of 11 hydrochemical response units were identified by the analysis. Major features that were important in distinguishing different areas of the state include: 1) upland and lowland Coastal Plain settings, 2) igneous, shale, and carbonate geology; and 3) urban land cover. The hydrochemical response units described in this report are considered to be an initial classification of watersheds in Maryland that can be refined as geographic data sets are improved and additional hydrologic data are collected.
Water-Resources Investigations Report↗
Estimation of nutrient and suspended-sediment loads in the Patuxent River basin, Maryland, water years 1986-90
Water-quality data collected at stream sites in the Patuxent River Basin from 1986 through 1990 were used to estimate loads of nutrients and otherconstituents. Studies were performed to determine the adequacy of the water-quality data for load estimation and to evaluate load estimation methods.A regression-based estimator and a ratio estimator were used to estimate loads. Comparisons indicated that the estimators provided similar levels of accuracy when constituent concentration data were available from the entire discharge range.When high-discharge concentration data were not available, it appeared that the regression-based estimator could overestimate loads of some constituents, whereas the ratio estimator appeared to underestimate some loads. The ratio estimator was selected for application in this study because the temporal inconsistencies in the sampling frequencies and patterns represented violations of the assumptions of the regression-based method.Ratio estimator load-estimate quality varied because high-flow concentration data were not available during some years. Preliminary estimation of the base-flow percentages of total loads was performed by calculating conservatively high and conservatively low base-flow load estimates, to provide limits for the actual base-flow percentage. The highest base-flow percentages--at the Unity, Savage, and Killpeck Creek sites--were for total nitrogen, because nitate from ground-water input isthe largest percentage of total nitrogen at those sites.
Water-Resources Investigations Report↗
Study of nonpoint source nutrient loading in the Patuxent River basin, Maryland
Study of nonpoint-source (NPS) nutrient loading in Maryland has focused on the Patuxent watershed because of its importance and representativeness of conditions in the State. Evaluation of NPS nutrient loading has been comprehensive and has included long-term monitoring, detailed watershed modeling, and synoptic sampling studies. A large amount of information has been compiled for the watershed and that information is being used to identify primary controls and efficient management strategies for NPS nutrient loading. Results of the Patuxent NPS study have identified spatial trends in water quality that appear to be related to basin charcteristics such as land use, physiography, andgeology. Evaluation of the data compiled by the study components is continuing and is expected to provide more detailed assessments of the reasons for spatial trends. In particular, ongoing evaluation of the watershed model output is expected to provide detailed information on the relative importance of nutrient sources and transport pathways across the entire watershed. Planned future directions of NPS evaluation in the State of Maryland include continued study of water quality in the Patuxent watershed and a shift in emphasis to a statewide approach. Eventually, the statewide approach will become the primary approach usedby the State to evaluate NPS loading. The information gained in the Patuxent study and the tools developed will represent valuable assets indeveloping the statewide NPS assessment program.
Maryland↗