globalchange  > 气候变化与战略
DOI: 10.5194/hess-23-73-2019
论文题名:
A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers
作者: Iliopoulou T.; Aguilar C.; Arheimer B.; Bermúdez M.; Bezak N.; Ficchì A.; Koutsoyiannis D.; Parajka J.; Polo M.J.; Thirel G.; Montanari A.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2019
卷: 23, 期:1
起始页码: 73
结束页码: 91
语种: 英语
Scopus关键词: Catchments ; Frequency estimation ; Probability distributions ; Stream flow ; Average flows ; Catchment characteristics ; European Countries ; Flow signature ; Frequency distributions ; Gaussian probability distributions ; Hydrological process ; Large sample analysis ; Rivers ; antecedent conditions ; data set ; probability ; river flow ; seasonal variation ; streamflow ; timescale
英文摘要: The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes. © Author(s) 2019.All Rights Reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163086
Appears in Collections:气候变化与战略

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作者单位: Iliopoulou, T., Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Zographou, 15780, Greece; Aguilar, C., Fluvial Dynamics and Hydrology Research Group, Andalusian Institute of Earth System Research, University of Córdoba, Córdoba, 14071, Spain; Arheimer, B., Swedish Meteorological and Hydrological Institute, Norrköping, 601 76, Sweden; Bermúdez, M., Water and Environmental Engineering Group, Department of Civil Engineering, University of A Coruña, Coruña, 15071 A, Spain; Bezak, N., Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova 2, Ljubljana, 1000, Slovenia; Ficchì, A., Department of Geography and Environmental Science, University of Reading, Reading, RG6 6AB, United Kingdom, IRSTEA, Hydrology Research Group (HYCAR), Antony, 92761, France; Koutsoyiannis, D., Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Zographou, 15780, Greece; Parajka, J., Vienna University of Technology, Institute of Hydraulic Engineering and Water Resources Management, Karlsplatz 13/222, Vienna, 1040, Austria; Polo, M.J., Fluvial Dynamics and Hydrology Research Group, Andalusian Institute of Earth System Research, University of Córdoba, Córdoba, 14071, Spain; Thirel, G., IRSTEA, Hydrology Research Group (HYCAR), Antony, 92761, France; Montanari, A., Department DICAM, University of Bologna, Bologna, 40136, Italy

Recommended Citation:
Iliopoulou T.,Aguilar C.,Arheimer B.,et al. A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers[J]. Hydrology and Earth System Sciences,2019-01-01,23(1)
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