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A multivariate conditional model for streamflow prediction and spatial precipitation refinement

The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high...

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Bibliographic Details
Main Authors: Liu, Zhiyong (Author) , Zhou, Ping (Author) , Chen, Xiuzhi (Author) , Guan, Yinghui (Author)
Format: Article (Journal)
Language:English
Published: 10 OCT 2015
In: Journal of geophysical research. Atmospheres
Year: 2015, Volume: 120, Issue: 19, Pages: 10,116-10,129
ISSN:2169-8996
DOI:10.1002/2015JD023787
Online Access:Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1002/2015JD023787
Verlag, lizenzpflichtig, Volltext: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2015JD023787
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Author Notes:Zhiyong Liu, Ping Zhou, Xiuzhi Chen, and Yinghui Guan
Description
Summary:The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high dimensional vine copulas, conditional bivariate copula simulations, and a quantile-copula function.
Item Description:Gesehen am 29.06.2020
Physical Description:Online Resource
ISSN:2169-8996
DOI:10.1002/2015JD023787

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