Item talk:Q269118
From geokb
{
"USGS Publications Warehouse": { "@context": "https://schema.org", "@type": "Article", "additionalType": "Journal Article", "name": "Prioritizing river basins for nutrient studies", "identifier": [ { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "70251746", "url": "https://pubs.usgs.gov/publication/70251746" }, { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70251746 }, { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1007/s10661-023-12266-7", "url": "https://doi.org/10.1007/s10661-023-12266-7" } ], "journal": { "@type": "Periodical", "name": "Environmental Monitoring and Assessment", "volumeNumber": "196", "issueNumber": null }, "inLanguage": "en", "isPartOf": [ { "@type": "CreativeWorkSeries", "name": "Environmental Monitoring and Assessment" } ], "datePublished": "2024", "dateModified": "2024-02-27", "abstract": "Increases in fluxes of nitrogen (N) and phosphorus (P) in the environment have led to negative impacts affecting drinking water, eutrophication, harmful algal blooms, climate change, and biodiversity\u00a0loss. Because of the importance, scale, and complexity of these issues, it may be useful to consider methods for prioritizing nutrient\u00a0research in representative drainage basins within a regional or national context. Two systematic, quantitative approaches were developed to (1) identify basins that geospatial data suggest are\u00a0most impacted by nutrients and (2) identify basins that have the most variability in factors affecting nutrient sources and transport in order to prioritize basins for studies that seek to understand the key drivers of nutrient impacts. The \u201cimpact\u201d approach relied on geospatial variables representing surface-water and groundwater nutrient concentrations, sources of N and P, and potential impacts on receptors (i.e., ecosystems and human health). The \u201cvariability\u201d approach relied on geospatial variables representing surface-water nutrient concentrations, factors affecting sources and transport of nutrients, model accuracy, and potential receptor impacts. One hundred and sixty-three drainage basins throughout the contiguous United States were ranked nationally and within 18 hydrologic regions. Nationally, the top-ranked basins from the impact approach were concentrated in the Midwest, while those from the variability approach were dispersed across the nation. Regionally, the top-ranked basin selected by the two approaches differed in 15 of the 18 regions, with top-ranked basins selected by the variability approach having lower minimum concentrations and larger ranges in concentrations than top-ranked basins selected by the impact approach. The highest ranked basins identified using the variability approach may\u00a0have advantages for exploring how landscape factors affect surface-water quality and how surface-water quality may affect ecosystems. In contrast, the impact approach prioritized basins in terms of human development and nutrient concentrations in both surface\u00a0water and groundwater, thereby targeting areas where actions to reduce nutrient concentrations could have the largest effect on improving water availability and reducing ecosystem impacts.", "description": "248, 21 p.", "publisher": { "@type": "Organization", "name": "Springer" }, "author": [ { "@type": "Person", "name": "Tesoriero, Anthony J. tesorier@usgs.gov", "givenName": "Anthony J.", "familyName": "Tesoriero", "email": "tesorier@usgs.gov", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0003-4674-7364", "url": "https://orcid.org/0000-0003-4674-7364" }, "affiliation": [ { "@type": "Organization", "name": "Oregon Water Science Center", "url": "https://www.usgs.gov/centers/oregon-water-science-center" } ] }, { "@type": "Person", "name": "Robertson, Dale M.", "givenName": "Dale M.", "familyName": "Robertson", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0001-6799-0596", "url": "https://orcid.org/0000-0001-6799-0596" }, "affiliation": [ { "@type": "Organization", "name": "Upper Midwest Water Science Center", "url": "https://www.usgs.gov/centers/upper-midwest-water-science-center" } ] }, { "@type": "Person", "name": "Green, Christopher", "givenName": "Christopher", "familyName": "Green", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-6480-8194", "url": "https://orcid.org/0000-0002-6480-8194" }, "affiliation": [ { "@type": "Organization", "name": "National Research Program - Western Branch", "url": "https://www.usgs.gov/centers/arizona-water-science-center" }, { "@type": "Organization", "name": "WMA - Integrated Modeling and Prediction Division", "url": "https://www.usgs.gov/mission-areas/water-resources" } ] }, { "@type": "Person", "name": "B\u00f6hlke, John K. jkbohlke@usgs.gov", "givenName": "John K.", "familyName": "B\u00f6hlke", "email": "jkbohlke@usgs.gov", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0001-5693-6455", "url": "https://orcid.org/0000-0001-5693-6455" }, "affiliation": [ { "@type": "Organization", "name": "WMA - Earth System Processes Division", "url": "https://www.usgs.gov/mission-areas/water-resources" }, { "@type": "Organization", "name": "Hydro-Ecological Interactions Branch", "url": "https://www.usgs.gov/mission-areas/water-resources" }, { "@type": "Organization", "name": "National Research Program - Eastern Branch", "url": "https://www.usgs.gov/centers/arizona-water-science-center" } ] }, { "@type": "Person", "name": "Harvey, Judson", "givenName": "Judson", "familyName": "Harvey", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-2654-9873", "url": "https://orcid.org/0000-0002-2654-9873" }, "affiliation": [ { "@type": "Organization", "name": "WMA - Earth System Processes Division", "url": "https://www.usgs.gov/mission-areas/water-resources" } ] }, { "@type": "Person", "name": "Qi, Sharon L. slqi@usgs.gov", "givenName": "Sharon L.", "familyName": "Qi", "email": "slqi@usgs.gov", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0001-7278-4498", "url": "https://orcid.org/0000-0001-7278-4498" }, "affiliation": [ { "@type": "Organization", "name": "Washington Water Science Center", "url": "https://www.usgs.gov/centers/washington-water-science-center" }, { "@type": "Organization", "name": "Oregon Water Science Center", "url": "https://www.usgs.gov/centers/oregon-water-science-center" }, { "@type": "Organization", "name": "Colorado Water Science Center", "url": "https://www.usgs.gov/centers/colorado-water-science-center" } ] } ], "funder": [ { "@type": "Organization", "name": "Oregon Water Science Center", "url": "https://www.usgs.gov/centers/oregon-water-science-center" }, { "@type": "Organization", "name": "WMA - Integrated Modeling and Prediction Division", "url": "https://www.usgs.gov/mission-areas/water-resources" }, { "@type": "Organization", "name": "Upper Midwest Water Science Center", "url": "https://www.usgs.gov/centers/upper-midwest-water-science-center" } ], "spatialCoverage": [ { "@type": "Place", "additionalType": "country", "name": "United States", "url": "https://geonames.org/4074035" } ] }, "OpenAlex": { "_id": "https://openalex.org/w4391690830", "abstract_inverted_index": { "Abstract": [ 0 ], "Increases": [ 1 ], "in": [ 2, 10, 51, 89, 96, 194, 218, 239, 283, 291 ], "fluxes": [ 3 ], "of": [ 4, 31, 37, 110, 127, 157, 220, 285 ], "nitrogen": [ 5 ], "(N)": [ 6 ], "and": [ 7, 27, 35, 80, 94, 122, 129, 131, 138, 155, 161, 167, 179, 236, 269, 288, 295, 315 ], "phosphorus": [ 8 ], "(P)": [ 9 ], "the": [ 11, 32, 86, 107, 172, 185, 189, 195, 200, 206, 209, 214, 221, 229, 246, 255, 278, 308 ], "environment": [ 12 ], "have": [ 13, 85, 259, 307 ], "led": [ 14 ], "to": [ 15, 44, 67, 98, 105, 302 ], "negative": [ 16 ], "impacts": [ 17, 133 ], "affecting": [ 18, 91, 153 ], "drinking": [ 19 ], "water,": [ 20 ], "eutrophication,": [ 21 ], "harmful": [ 22 ], "algal": [ 23 ], "blooms,": [ 24 ], "climate": [ 25 ], "change,": [ 26 ], "biodiversity": [ 28 ], "loss.": [ 29 ], "Because": [ 30 ], "importance,": [ 33 ], "scale,": [ 34 ], "complexity": [ 36 ], "these": [ 38 ], "issues,": [ 39 ], "it": [ 40 ], "may": [ 41, 258, 273 ], "be": [ 42 ], "useful": [ 43 ], "consider": [ 45 ], "methods": [ 46 ], "for": [ 47, 101, 261 ], "prioritizing": [ 48 ], "nutrient": [ 49, 92, 111, 124, 150, 289, 304 ], "research": [ 50 ], "representative": [ 52 ], "drainage": [ 53, 169 ], "basins": [ 54, 70, 83, 100, 170, 187, 226, 243, 252, 282 ], "within": [ 55, 180 ], "a": [ 56 ], "regional": [ 57 ], "or": [ 58 ], "national": [ 59 ], "context.": [ 60 ], "Two": [ 61 ], "systematic,": [ 62 ], "quantitative": [ 63 ], "approaches": [ 64, 216 ], "were": [ 65, 176, 192, 203 ], "developed": [ 66 ], "(1)": [ 68 ], "identify": [ 69, 82 ], "that": [ 71, 84, 103 ], "geospatial": [ 72, 118, 146 ], "data": [ 73 ], "suggest": [ 74 ], "are": [ 75 ], "most": [ 76, 87 ], "impacted": [ 77 ], "by": [ 78, 213, 228, 245 ], "nutrients": [ 79 ], "(2)": [ 81 ], "variability": [ 88, 201, 230, 256 ], "factors": [ 90, 152, 265 ], "sources": [ 93, 126, 154 ], "transport": [ 95, 156 ], "order": [ 97 ], "prioritize": [ 99 ], "studies": [ 102 ], "seek": [ 104 ], "understand": [ 106 ], "key": [ 108 ], "drivers": [ 109 ], "impacts.": [ 112, 164, 318 ], "The": [ 113, 141, 249 ], "\u201cimpact\u201d": [ 114 ], "approach": [ 115, 143, 191, 202, 231, 257, 280 ], "relied": [ 116, 144 ], "on": [ 117, 134, 145, 311 ], "variables": [ 119, 147 ], "representing": [ 120, 148 ], "surface-water": [ 121, 149, 267, 271 ], "groundwater": [ 123 ], "concentrations,": [ 125, 151 ], "N": [ 128 ], "P,": [ 130 ], "potential": [ 132, 162 ], "receptors": [ 135 ], "(i.e.,": [ 136 ], "ecosystems": [ 137 ], "human": [ 139, 286 ], "health).": [ 140 ], "\u201cvariability\u201d": [ 142 ], "nutrients,": [ 158 ], "model": [ 159 ], "accuracy,": [ 160 ], "receptor": [ 163 ], "One": [ 165 ], "hundred": [ 166 ], "sixty-three": [ 168 ], "throughout": [ 171 ], "contiguous": [ 173 ], "United": [ 174 ], "States": [ 175 ], "ranked": [ 177, 251 ], "nationally": [ 178 ], "18": [ 181, 222 ], "hydrologic": [ 182 ], "regions.": [ 183 ], "Nationally,": [ 184 ], "top-ranked": [ 186, 210, 225, 242 ], "from": [ 188, 199 ], "impact": [ 190, 247, 279 ], "concentrated": [ 193 ], "Midwest,": [ 196 ], "while": [ 197 ], "those": [ 198 ], "dispersed": [ 204 ], "across": [ 205 ], "nation.": [ 207 ], "Regionally,": [ 208 ], "basin": [ 211 ], "selected": [ 212, 227, 244 ], "two": [ 215 ], "differed": [ 217 ], "15": [ 219 ], "regions,": [ 223 ], "with": [ 224 ], "having": [ 232 ], "lower": [ 233 ], "minimum": [ 234 ], "concentrations": [ 235, 240, 290, 305 ], "larger": [ 237 ], "ranges": [ 238 ], "than": [ 241 ], "approach.": [ 248 ], "highest": [ 250 ], "identified": [ 253 ], "using": [ 254 ], "advantages": [ 260 ], "exploring": [ 262 ], "how": [ 263, 270 ], "landscape": [ 264 ], "affect": [ 266, 274 ], "quality": [ 268, 272 ], "ecosystems.": [ 275 ], "In": [ 276 ], "contrast,": [ 277 ], "prioritized": [ 281 ], "terms": [ 284 ], "development": [ 287 ], "both": [ 292 ], "surface": [ 293 ], "water": [ 294, 313 ], "groundwater,": [ 296 ], "thereby": [ 297 ], "targeting": [ 298 ], "areas": [ 299 ], "where": [ 300 ], "actions": [ 301 ], "reduce": [ 303 ], "could": [ 306 ], "largest": [ 309 ], "effect": [ 310 ], "improving": [ 312 ], "availability": [ 314 ], "reducing": [ 316 ], "ecosystem": [ 317 ] }, "apc_list": { "value": 2890, "currency": "EUR", "value_usd": 3690, "provenance": "doaj" }, "apc_paid": { "value": 2890, "currency": "EUR", "value_usd": 3690, "provenance": "doaj" }, "authorships": [ { "author_position": "first", "author": { "id": "https://openalex.org/A5013664782", "display_name": "Anthony J. 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