Informing Stochastic Streamflow Generation by Large Scale Climate Indices at Single and Multiple Sites

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F22%3A91622" target="_blank" >RIV/60460709:41330/22:91622 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0309170821001913?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0309170821001913?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.advwatres.2021.104037" target="_blank" >10.1016/j.advwatres.2021.104037</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Informing Stochastic Streamflow Generation by Large Scale Climate Indices at Single and Multiple Sites

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Informing Stochastic Streamflow Generation by Large Scale Climate Indices at Single and Multiple Sites

Popis výsledku anglicky

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

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ADVANCES IN WATER RESOURCES

ISSN

0309-1708

e-ISSN

1872-9657

Svazek periodika

2022

Číslo periodika v rámci svazku

156

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

18

Strana od-do

1-18

Kód UT WoS článku

000700574900005

EID výsledku v databázi Scopus

2-s2.0-85114922649