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Digital Hydrologic Networks Supporting Applications Related to Spatially Referenced Regression Modeling 1
Author(s) -
Brakebill J.W.,
Wolock D.M.,
Terziotti S.E.
Publication year - 2011
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.2011.00578.x
Subject(s) - watershed , environmental science , hydrology (agriculture) , hydrological modelling , digital elevation model , streamflow , geospatial analysis , computer science , geography , remote sensing , cartography , geology , drainage basin , geotechnical engineering , climatology , machine learning
Brakebill, J.W., D.M. Wolock, and S.E. Terziotti, 2011. Digital Hydrologic Networks Supporting Applications Related to Spatially Referenced Regression Modeling. Journal of the American Water Resources Association (JAWRA) 47(5):916‐932. DOI: 10.1111/j.1752‐1688.2011.00578.x 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.