A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow.
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| Title: | A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow. |
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| Authors: | Nowak, Kenneth1,2, Prairie, James3, Rajagopalan, Balaji1,2,4, Lall, Upmanu5 |
| Source: | Water Resources Research. 2010, Vol. 46 Issue 8, pn/a-n/a. 13p. |
| Subjects: | Streamflow, Distribution (Probability theory), K-nearest neighbor classification, Hydrologic models, Hydraulics |
| Abstract: | Streamflow disaggregation techniques are used to distribute a single aggregate flow value to multiple sites in both space and time while preserving distributional statistics (i.e., mean, variance, skewness, and maximum and minimum values) from observed data. A number of techniques exist for accomplishing this task through a variety of parametric and nonparametric approaches. However, most of these methods do not perform well for disaggregation to daily time scales. This is generally due to a mismatch between the parametric distributions appropriate for daily flows versus monthly or annual flows, the high dimension of the disaggregation problem, compounded uncertainty in parameter estimation for multistage approaches, and the inability to maintain flow continuity across disaggregation time period boundaries. We present a method that directly simulates daily data at multiple locations from a single annual flow value via K-nearest neighbor (K-NN) resampling of daily flow proportion vectors. The procedure is simple and data driven and captures observed statistics quite well. Furthermore, the generated daily data are continuous and display lag correlation structure consistent with that of the observed data. The utility and effectiveness of this approach is demonstrated for selected sites in the San Juan River Basin, located in southwestern Colorado, and later compared with the disaggregation technique of Prairie et al. (2007) for several locations in the Colorado River Basin. [ABSTRACT FROM AUTHOR] |
| Copyright of Water Resources Research is the property of Wiley-Blackwell 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: 87147007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nowak%2C+Kenneth%22">Nowak, Kenneth</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Prairie%2C+James%22">Prairie, James</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Rajagopalan%2C+Balaji%22">Rajagopalan, Balaji</searchLink><relatesTo>1,2,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Lall%2C+Upmanu%22">Lall, Upmanu</searchLink><relatesTo>5</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Water+Resources+Research%22">Water Resources Research</searchLink>. 2010, Vol. 46 Issue 8, pn/a-n/a. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Streamflow%22">Streamflow</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Hydrologic+models%22">Hydrologic models</searchLink><br /><searchLink fieldCode="DE" term="%22Hydraulics%22">Hydraulics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Streamflow disaggregation techniques are used to distribute a single aggregate flow value to multiple sites in both space and time while preserving distributional statistics (i.e., mean, variance, skewness, and maximum and minimum values) from observed data. A number of techniques exist for accomplishing this task through a variety of parametric and nonparametric approaches. However, most of these methods do not perform well for disaggregation to daily time scales. This is generally due to a mismatch between the parametric distributions appropriate for daily flows versus monthly or annual flows, the high dimension of the disaggregation problem, compounded uncertainty in parameter estimation for multistage approaches, and the inability to maintain flow continuity across disaggregation time period boundaries. We present a method that directly simulates daily data at multiple locations from a single annual flow value via K-nearest neighbor (K-NN) resampling of daily flow proportion vectors. The procedure is simple and data driven and captures observed statistics quite well. Furthermore, the generated daily data are continuous and display lag correlation structure consistent with that of the observed data. The utility and effectiveness of this approach is demonstrated for selected sites in the San Juan River Basin, located in southwestern Colorado, and later compared with the disaggregation technique of Prairie et al. (2007) for several locations in the Colorado River Basin. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Water Resources Research is the property of Wiley-Blackwell 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1029/2009WR008530 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: n/a Subjects: – SubjectFull: Streamflow Type: general – SubjectFull: Distribution (Probability theory) Type: general – SubjectFull: K-nearest neighbor classification Type: general – SubjectFull: Hydrologic models Type: general – SubjectFull: Hydraulics Type: general Titles: – TitleFull: A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nowak, Kenneth – PersonEntity: Name: NameFull: Prairie, James – PersonEntity: Name: NameFull: Rajagopalan, Balaji – PersonEntity: Name: NameFull: Lall, Upmanu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: 2010 Type: published Y: 2010 Identifiers: – Type: issn-print Value: 00431397 Numbering: – Type: volume Value: 46 – Type: issue Value: 8 Titles: – TitleFull: Water Resources Research Type: main |
| ResultId | 1 |
