Statistical approaches for identification of low-flow drivers: Temporal aspects

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dc.identifier.uri Fangmann, Anne Haberlandt, Uwe 2019-03-08T10:25:28Z 2019-03-08T10:25:28Z 2019
dc.identifier.citation Fangmann, A.; Haberlandt, U.: Statistical approaches for identification of low-flow drivers: Temporal aspects. In: Hydrology and Earth System Sciences 23 (2019), Nr. 1, S. 447-463. DOI:
dc.description.abstract The characteristics of low-flow periods, especially regarding their low temporal dynamics, suggest that the dimensions of the metrics related to these periods may be easily related to their meteorological drivers using simplified statistical model approaches. In this study, linear statistical models based on multiple linear regressions (MLRs) are proposed. The study area chosen is the German federal state of Lower Saxony with 28 available gauges used for analysis. A number of regression approaches are evaluated. An approach using principal components of local meteorological indices as input appeared to show the best performance. In a second analysis it was assessed whether the formulated models may be eligible for application in climate change impact analysis. The models were therefore applied to a climate model ensemble based on the RCP8.5 scenario. Analyses in the baseline period revealed that some of the meteorological indices needed for model input could not be fully reproduced by the climate models. The predictions for the future show an overall increase in the lowest average 7-day flow (NM7Q), projected by the majority of ensemble members and for the majority of stations. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartofseries Hydrology and Earth System Sciences 23 (2019), Nr. 1
dc.rights CC BY 4.0 Unported
dc.subject Atmospheric movements eng
dc.subject Climate change eng
dc.subject Climate change impact eng
dc.subject Meteorological index eng
dc.subject Multiple linear regressions eng
dc.subject Principal Components eng
dc.subject Statistical approach eng
dc.subject Statistical modeling eng
dc.subject Temporal aspects eng
dc.subject Temporal dynamics eng
dc.subject Climate models eng
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Statistical approaches for identification of low-flow drivers: Temporal aspects eng
dc.type Article
dc.type Text
dc.relation.issn 1027-5606
dc.bibliographicCitation.issue 1
dc.bibliographicCitation.volume 23
dc.bibliographicCitation.firstPage 447
dc.bibliographicCitation.lastPage 463
dc.description.version publishedVersion
tib.accessRights frei zug�nglich

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