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  4. Informing stochastic streamflow generation by large-scale climate indices at single and multiple sites
 
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Informing stochastic streamflow generation by large-scale climate indices at single and multiple sites

Publikationstyp
Journal Article
Date Issued
2021-09-10
Sprache
English
Author(s)
Zaerpour, Masoud  
Papalexiou, Simon Michael  
Nazemi, Ali  
TORE-URI
https://hdl.handle.net/11420/57759
Journal
Advances in water resources  
Volume
156
Article Number
104037
Citation
Advances in Water Resources 156: 104037 (2021)
Publisher DOI
10.1016/j.advwatres.2021.104037
Scopus ID
2-s2.0-85114922649
Publisher
Elsevier
Despite the existence of several stochastic streamflow generators, not much attention has been given to representing the impacts of large-scale climate indices on seasonal to interannual streamflow variability. By merging a formal predictor selection scheme with vine copulas, we propose a generic approach to explicitly incorporate large-scale climate indices in ensemble streamflow generation at single and multiple sites and in both short-term prediction and long-term projection modes. The proposed framework is applied at three headwater streams in the Oldman River Basin in southern Alberta, Canada. The results demonstrate higher skills than existing models both in terms of representing intra- and inter-annual variability, as well as accuracy and predictability of streamflow, particularly during high flow seasons. The proposed algorithm presents a globally relevant scheme for the stochastic streamflow generation, where the impacts of large-scale climate indices on streamflow variability across time and space are significant.
Subjects
Algorithm development | Large-scale climate indices | Predictor selection | Stochastic streamflow generation | Streamflow variability | Vine copulas
DDC Class
600: Technology
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