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{"@context": "https://schema.org", "@type": "CreativeWork", "additionalType": "USGS Numbered Series", "name": "Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment transport in streams of the southwestern United States", "identifier": [{"@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "sir20195106", "url": "https://pubs.usgs.gov/publication/sir20195106"}, {"@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70205576}, {"@type": "PropertyValue", "propertyID": "DOI", "value": "10.3133/sir20195106", "url": "https://doi.org/10.3133/sir20195106"}], "inLanguage": "en", "isPartOf": [{"@type": "CreativeWorkSeries", "name": "Scientific Investigations Report"}], "datePublished": "2019", "dateModified": "2020-07-03", "abstract": "Given the predicted imbalance between water supply and demand in the Southwest region of the United States, and the widespread problems with excessive nutrients and suspended sediment, there is a growing need to quantify current streamflow and water quality conditions throughout the region. Furthermore, current monitoring stations exist at a limited number of locations, and many streams lack streamflow and water quality information. SPAtially Referenced Regression On Watershed attributes (SPARROW) models were developed for hydrologic conditions representative of 2012 in order to understand how climate, land use, and other landscape characteristics control the yields of water, total nitrogen, total phosphorus, and suspended sediment across the Southwest region. The calibration data (mean annual streamflow and loads) for each of the four SPARROW models were based on continuous streamflow and discrete water-quality observations from throughout the region. Explanatory variables for the models consisted of regional datasets representing a range of potential sources of streamflow, nitrogen, phosphorous, and sediment, and processes that control the transport from land to water and attenuate loads within streams and waterbodies. Calibration and explanatory data were referenced to a surface water drainage network that allowed for routing and transport of water and loads through the region. The model results showed that wastewater discharge is the largest contributor to total nitrogen and total phosphorus yield from the Southwest region and forest land is the largest contributor to suspended-sediment yield, but that other sources such as atmospheric nitrogen deposition, agricultural runoff, and runoff from developed land are locally important across the region. The results from this study could complement research and inform water-quality management activities in the Southwest region. Examples might include identifying potentially impaired waterbodies and guiding remediation efforts where impairment has been documented, explaining the spatial patterns in harmful algal blooms, and providing estimates of sediment and nutrient loadings where such data are scarce or non-existent.", "description": "Report: viii, 66 p.; Data Release", "publisher": {"@type": "Organization", "name": "U.S. Geological Survey"}, "author": [{"@type": "Person", "name": "Wise, Daniel R.", "givenName": "Daniel R.", "familyName": "Wise", "identifier": {"@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-1215-9612", "url": "https://orcid.org/0000-0002-1215-9612"}, "affiliation": [{"@type": "Organization", "name": "Oregon Water Science Center", "url": "https://www.usgs.gov/centers/oregon-water-science-center"}]}, {"@type": "Person", "name": "Anning, David W.", "givenName": "David W.", "familyName": "Anning", "identifier": {"@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-4470-3387", "url": "https://orcid.org/0000-0002-4470-3387"}, "affiliation": [{"@type": "Organization", "name": "USGS volunteer"}]}, {"@type": "Person", "name": "Miller, Olivia L.", "givenName": "Olivia L.", "familyName": "Miller", "identifier": {"@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-8846-7048", "url": "https://orcid.org/0000-0002-8846-7048"}, "affiliation": [{"@type": "Organization", "name": "Utah Water Science Center", "url": "https://www.usgs.gov/centers/utah-water-science-center"}]}], "funder": [{"@type": "Organization", "name": "Oregon Water Science Center", "url": "https://www.usgs.gov/centers/oregon-water-science-center"}, {"@type": "Organization", "name": "Utah Water Science Center", "url": "https://www.usgs.gov/centers/utah-water-science-center"}], "spatialCoverage": [{"@type": "Place", "additionalType": "country", "name": "United States", "url": "https://geonames.org/6252001"}, {"@type": "Place", "additionalType": "unknown", "name": "Southwestern United States", "url": "https://geonames.org/12212300"}, {"@type": "Place", "geo": [{"@type": "GeoShape", "additionalProperty": {"@type": "PropertyValue", "name": "GeoJSON", "value": {"type": "FeatureCollection", "features": [{"type": "Feature", "properties": {}, "geometry": {"type": "Polygon", "coordinates": [[[-115.927734375, 32.39851580247402], [-114.873046875, 32.76880048488168], [-111.09374999999999, 31.203404950917395], [-108.28125, 31.052933985705163], [-108.19335937499999, 32.02670629333614], [-105.8203125, 30.977609093348686], [-103.447265625, 28.76765910569123], [-102.83203125, 29.6880527498568], [-101.689453125, 29.76437737516313], [-100.1953125, 27.916766641249065], [-98.349609375, 26.115985925333536], [-97.20703125, 26.509904531413927], [-96.767578125, 28.22697003891834], [-93.515625, 29.53522956294847], [-99.49218749999999, 33.87041555094183], [-103.623046875, 38.8225909761771], [-104.94140625, 40.245991504199026], [-111.97265625, 42.94033923363181], [-113.90625, 41.376808565702355], [-116.806640625, 41.77131167976407], [-118.740234375, 44.213709909702054], [-120.498046875, 44.59046718130883], [-120.84960937499999, 41.44272637767212], [-118.65234374999999, 36.10237644873644], [-115.927734375, 32.39851580247402]]]}}]}}}, {"@type": "GeoCoordinates", "latitude": 35.649123811971215, "longitude": -108.60514476107112}]}]} | |||
Given the predicted imbalance between water supply and demand in the Southwest region of the United States, and the widespread problems with excessive nutrients and suspended sediment, there is a growing need to quantify current streamflow and water quality conditions throughout the region. Furthermore, current monitoring stations exist at a limited number of locations, and many streams lack streamflow and water quality information. SPAtially Referenced Regression On Watershed attributes (SPARROW) models were developed for hydrologic conditions representative of 2012 in order to understand how climate, land use, and other landscape characteristics control the yields of water, total nitrogen, total phosphorus, and suspended sediment across the Southwest region. The calibration data (mean annual streamflow and loads) for each of the four SPARROW models were based on continuous streamflow and discrete water-quality observations from throughout the region. Explanatory variables for the models consisted of regional datasets representing a range of potential sources of streamflow, nitrogen, phosphorous, and sediment, and processes that control the transport from land to water and attenuate loads within streams and waterbodies. Calibration and explanatory data were referenced to a surface water drainage network that allowed for routing and transport of water and loads through the region. The model results showed that wastewater discharge is the largest contributor to total nitrogen and total phosphorus yield from the Southwest region and forest land is the largest contributor to suspended-sediment yield, but that other sources such as atmospheric nitrogen deposition, agricultural runoff, and runoff from developed land are locally important across the region. The results from this study could complement research and inform water-quality management activities in the Southwest region. Examples might include identifying potentially impaired waterbodies and guiding remediation efforts where impairment has been documented, explaining the spatial patterns in harmful algal blooms, and providing estimates of sediment and nutrient loadings where such data are scarce or non-existent. | |||
Revision as of 19:13, 15 July 2024
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