Details
| Original language | English |
|---|---|
| Pages (from-to) | 447-463 |
| Number of pages | 17 |
| Journal | Hydrology and Earth System Sciences |
| Volume | 23 |
| Issue number | 1 |
| Early online date | 25 Jan 2019 |
| Publication status | E-pub ahead of print - 25 Jan 2019 |
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.
ASJC Scopus subject areas
- Environmental Science(all)
- Water Science and Technology
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
Sustainable Development Goals
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In: Hydrology and Earth System Sciences, Vol. 23, No. 1, 25.01.2019, p. 447-463.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Statistical approaches for identification of low-flow drivers: temporal aspects
AU - Fangmann, Anne
AU - Haberlandt, Uwe
N1 - Funding information:. The results presented in this study are part of the KliBiW project (Wasserwirtschaftliche Folgenabschätzung des globalen Klimawandels für die Binnengewässer in Niedersachsen), funded by the Lower Saxon Ministry for Environment, Energy and Climate Protection (Niedersächsisches Ministerium für Umwelt, Energie und Klimaschutz), who are gratefully acknowledged. We thank the NLWKN (Niedersächsischer Landesbetrieb für Wasserwirtschaft, Küsten-und Naturschutz) for provision and pre-processing of the data used in this study. We would also like to thank Gregor Laaha and Martin Hanel, whose suggestions and comments helped to improve the manuscript. The publication of this article was funded by the open-access fund of Leibniz Universität Hannover.
PY - 2019/1/25
Y1 - 2019/1/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85060615158&partnerID=8YFLogxK
U2 - 10.5194/hess-23-447-2019
DO - 10.5194/hess-23-447-2019
M3 - Article
AN - SCOPUS:85060615158
VL - 23
SP - 447
EP - 463
JO - Hydrology and Earth System Sciences
JF - Hydrology and Earth System Sciences
SN - 1027-5606
IS - 1
ER -