John W. Brakebill

Digital hydrologic networks supporting applications related to spatially referenced regression modeling

Abstract Digital hydrologic networks depicting surface-water pathways and their associated drainage catchments provide a key component to hydrologic analysis and modeling. Collectively, they form common spatial units that can be used to frame the descriptions of aquatic and watershed processes. In addition, they provide the ability to simulate and route the movement of water and associated constituents throughout the landscape. Digital hydrologic networks have evolved from derivatives of mapping products to detailed, interconnected, spatially referenced networks of water pathways, drainage areas, and stream and watershed characteristics. These properties are important because they enhance the ability to spatially evaluate factors that affect the sources and transport of water-quality constituents at various scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW), a process-based/statistical model, relies on a digital hydrologic network in order to establish relations between quantities of monitored contaminant flux, contaminant sources, and the associated physical characteristics affecting contaminant transport. Digital hydrologic networks modified from the River Reach File (RF1) and National Hydrography Dataset (NHD) geospatial datasets provided frameworks for SPARROW in six regions of the conterminous United States. In addition, characteristics of the modified RF1 were used to update estimates of mean-annual streamflow. This produced more current flow estimates for use in SPARROW modeling.

Digital data used to relate nutrient inputs 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, averageannual precipitation and temperature, slope, hydrogeomorphic regions, and soil permeability. Nutrient-input and land-surface characteristic data sets merged with the segmentedwatershed 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.

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 Agencys 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.