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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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
10 OCT 2015
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| In: |
Journal of geophysical research. Atmospheres
Year: 2015, Jahrgang: 120, Heft: 19, Pages: 10,116-10,129 |
| ISSN: | 2169-8996 |
| DOI: | 10.1002/2015JD023787 |
| Online-Zugang: | 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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| Verfasserangaben: | Zhiyong Liu, Ping Zhou, Xiuzhi Chen, and Yinghui Guan |
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| 520 | |a 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. | ||
| 650 | 4 | |a conditional distribution | |
| 650 | 4 | |a spatial precipitation refinement | |
| 650 | 4 | |a streamflow prediction | |
| 650 | 4 | |a uncertainty estimation | |
| 650 | 4 | |a vine copula | |
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