Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States.
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| Title: | Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States. |
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| Authors: | Luke, Adam1, Vrugt, Jasper A.1,2, AghaKouchak, Amir1, Matthew, Richard3, Sanders, Brett F.1,3 |
| Source: | Water Resources Research. Jul2017, Vol. 53 Issue 7, p5469-5494. 26p. |
| Subjects: | Floods, Flood risk |
| Abstract: | Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference. [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: 124718681 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Luke%2C+Adam%22">Luke, Adam</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Vrugt%2C+Jasper+A%2E%22">Vrugt, Jasper A.</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22AghaKouchak%2C+Amir%22">AghaKouchak, Amir</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Matthew%2C+Richard%22">Matthew, Richard</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Sanders%2C+Brett+F%2E%22">Sanders, Brett F.</searchLink><relatesTo>1,3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Water+Resources+Research%22">Water Resources Research</searchLink>. Jul2017, Vol. 53 Issue 7, p5469-5494. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Floods%22">Floods</searchLink><br /><searchLink fieldCode="DE" term="%22Flood+risk%22">Flood risk</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference. [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: 10.1002/2016WR019676 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 5469 Subjects: – SubjectFull: Floods Type: general – SubjectFull: Flood risk Type: general Titles: – TitleFull: Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Luke, Adam – PersonEntity: Name: NameFull: Vrugt, Jasper A. – PersonEntity: Name: NameFull: AghaKouchak, Amir – PersonEntity: Name: NameFull: Matthew, Richard – PersonEntity: Name: NameFull: Sanders, Brett F. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 00431397 Numbering: – Type: volume Value: 53 – Type: issue Value: 7 Titles: – TitleFull: Water Resources Research Type: main |
| ResultId | 1 |
