A basin wide stochastic salinity model
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| Title: | A basin wide stochastic salinity model |
|---|---|
| Authors: | Prairie, James R.1 prairie@colorado.edu, Rajagopalan, Balaji2,3 |
| Source: | Journal of Hydrology. Sep2007, Vol. 344 Issue 1/2, p43-54. 12p. |
| Subjects: | Rivers, Water quality, Water quality management |
| Geographic Terms: | United States |
| Abstract: | Summary: In the western United States many rivers experience high salinity resulting from natural and anthropogenic sources. This impacts the water quality and hence, is closely monitored. The salinity is closely linked with streamflow quantity in that, a higher flow brings with it more salt but also provides substantial dilution to reduce the salt concentration and vice-versa during low flow regimes. Decision makers typically plan strategies for salinity mitigation and evaluate impacts of water management policy options on salinity in the basin using decision support models. These models require statistically consistent basin wide scenarios of streamflow and salinity. Recognizing this need, we develop a basin wide stochastic generator of salinity that is consistent with the flows. The framework integrates a nonparametric space–time disaggregation model, applied to both flow and salt, and a nonparametric regression model, which simulates natural salt magnitude for a given natural flow. The nonparametric techniques do not require prior assumptions about the underlying functional relationship unlike traditional parametric methods, are data driven, and robust at capturing any functional form present in the data. They are also easily portable across sites. Within this integrated framework we propose two representative approaches, from several possible variations, for generating salt scenarios, each capturing different aspects of the flow and salt statistics. We demonstrate the performance of these approaches by applying them to flow and salt data from four gauges in the Upper Colorado River Basin. The spatial and temporal statistics of salt and their relationship with flows are well captured. This framework has utility in a variety of applications such as, short term salinity forecast conditioned on streamflow forecast, long term salinity scenarios conditioned on climate change projections, and also in reconstructing paleo salinity from paleo streamflows. [Copyright &y& Elsevier] |
| Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 26490648 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A basin wide stochastic salinity model – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Prairie%2C+James+R%2E%22">Prairie, James R.</searchLink><relatesTo>1</relatesTo><i> prairie@colorado.edu</i><br /><searchLink fieldCode="AR" term="%22Rajagopalan%2C+Balaji%22">Rajagopalan, Balaji</searchLink><relatesTo>2,3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Hydrology%22">Journal of Hydrology</searchLink>. Sep2007, Vol. 344 Issue 1/2, p43-54. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Rivers%22">Rivers</searchLink><br /><searchLink fieldCode="DE" term="%22Water+quality%22">Water quality</searchLink><br /><searchLink fieldCode="DE" term="%22Water+quality+management%22">Water quality management</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Summary: In the western United States many rivers experience high salinity resulting from natural and anthropogenic sources. This impacts the water quality and hence, is closely monitored. The salinity is closely linked with streamflow quantity in that, a higher flow brings with it more salt but also provides substantial dilution to reduce the salt concentration and vice-versa during low flow regimes. Decision makers typically plan strategies for salinity mitigation and evaluate impacts of water management policy options on salinity in the basin using decision support models. These models require statistically consistent basin wide scenarios of streamflow and salinity. Recognizing this need, we develop a basin wide stochastic generator of salinity that is consistent with the flows. The framework integrates a nonparametric space–time disaggregation model, applied to both flow and salt, and a nonparametric regression model, which simulates natural salt magnitude for a given natural flow. The nonparametric techniques do not require prior assumptions about the underlying functional relationship unlike traditional parametric methods, are data driven, and robust at capturing any functional form present in the data. They are also easily portable across sites. Within this integrated framework we propose two representative approaches, from several possible variations, for generating salt scenarios, each capturing different aspects of the flow and salt statistics. We demonstrate the performance of these approaches by applying them to flow and salt data from four gauges in the Upper Colorado River Basin. The spatial and temporal statistics of salt and their relationship with flows are well captured. This framework has utility in a variety of applications such as, short term salinity forecast conditioned on streamflow forecast, long term salinity scenarios conditioned on climate change projections, and also in reconstructing paleo salinity from paleo streamflows. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=26490648 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jhydrol.2007.06.029 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 43 Subjects: – SubjectFull: Rivers Type: general – SubjectFull: Water quality Type: general – SubjectFull: Water quality management Type: general – SubjectFull: United States Type: general Titles: – TitleFull: A basin wide stochastic salinity model Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Prairie, James R. – PersonEntity: Name: NameFull: Rajagopalan, Balaji IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 09 Text: Sep2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 00221694 Numbering: – Type: volume Value: 344 – Type: issue Value: 1/2 Titles: – TitleFull: Journal of Hydrology Type: main |
| ResultId | 1 |
