Five centuries of reconstructed streamflow in Athabasca River Basin, Canada: Non-stationarity and teleconnection to climate patterns

Given the challenge to estimate representative long-term natural variability of streamflow from limited observed data, a hierarchical, multilevel Bayesian regression (HBR) was developed to reconstruct the 1489–2006 annual streamflow data at six Athabasca River Basin (ARB) gauging stations based on 1...

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Published in:Science of The Total Environment
Main Authors: Wu, Yenan, Gan, Thian Yew, She, Yuntong, Xu, Chong-Yu, Yan, Haibin
Format: Article in Journal/Newspaper
Language:English
Published: 2020
Subjects:
Online Access:http://hdl.handle.net/10852/82226
http://urn.nb.no/URN:NBN:no-85124
https://doi.org/10.1016/j.scitotenv.2020.141330
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spelling ftoslouniv:oai:www.duo.uio.no:10852/82226 2023-05-15T15:26:03+02:00 Five centuries of reconstructed streamflow in Athabasca River Basin, Canada: Non-stationarity and teleconnection to climate patterns Wu, Yenan Gan, Thian Yew She, Yuntong Xu, Chong-Yu Yan, Haibin 2020-12-28T15:41:03Z http://hdl.handle.net/10852/82226 http://urn.nb.no/URN:NBN:no-85124 https://doi.org/10.1016/j.scitotenv.2020.141330 EN eng http://urn.nb.no/URN:NBN:no-85124 Wu, Yenan Gan, Thian Yew She, Yuntong Xu, Chong-Yu Yan, Haibin . Five centuries of reconstructed streamflow in Athabasca River Basin, Canada: Non-stationarity and teleconnection to climate patterns. Science of the Total Environment. 2020, 746 http://hdl.handle.net/10852/82226 1863580 info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Science of the Total Environment&rft.volume=746&rft.spage=&rft.date=2020 Science of the Total Environment 746 https://doi.org/10.1016/j.scitotenv.2020.141330 URN:NBN:no-85124 Fulltext https://www.duo.uio.no/bitstream/handle/10852/82226/2/WU%2BYenan_preprint.pdf 0048-9697 Journal article Tidsskriftartikkel SubmittedVersion 2020 ftoslouniv https://doi.org/10.1016/j.scitotenv.2020.141330 2021-01-20T23:30:59Z Given the challenge to estimate representative long-term natural variability of streamflow from limited observed data, a hierarchical, multilevel Bayesian regression (HBR) was developed to reconstruct the 1489–2006 annual streamflow data at six Athabasca River Basin (ARB) gauging stations based on 14 tree ring chronologies. Seven nested models were developed to maximize the applications of available tree ring predictors. Based on results of goodness-of-fit tests, the HBR developed was skillful and reliable in reconstructing the streamflow of ARB. From five centuries of reconstructed streamflow for ARB, five or six abrupt change points are detected. The streamflow time series obtained from a backward moving, 46-year window for six gauging sites in ARB vary significantly over five centuries (1489–2006) and at times could exceed the 90% and/or 95% confidence intervals, denoting significant non-stationarities. Apparently changes in the mean state and the lag-1 autocorrelation of reconstructed streamflow across the gauging sites can be similar or radically different from each other. These nonstationary features imply that the default stationary assumption is not applicable in ARB. Further, the reconstructed streamflow shows statistically significant oscillations at interannual, interdecadal and multidecadal time scales and are teleconnected to climate patterns such as El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO). A composite analysis shows that La Niña (El Niño), cold (warm) PDO, and cold (warm) AMO events are typically associated with increased (decreased) streamflow anomalies of ARB. The reconstructed streamflow data provides us the full range of streamflow variability and recurrence characteristics of extremes spanned over five centuries from which it is useful for us to evaluate and manage the current water systems of ARB more effectively and a better risk analysis of future droughts of ARB. Article in Journal/Newspaper Athabasca River Universitet i Oslo: Digitale utgivelser ved UiO (DUO) Athabasca River Canada Pacific Science of The Total Environment 746 141330
institution Open Polar
collection Universitet i Oslo: Digitale utgivelser ved UiO (DUO)
op_collection_id ftoslouniv
language English
description Given the challenge to estimate representative long-term natural variability of streamflow from limited observed data, a hierarchical, multilevel Bayesian regression (HBR) was developed to reconstruct the 1489–2006 annual streamflow data at six Athabasca River Basin (ARB) gauging stations based on 14 tree ring chronologies. Seven nested models were developed to maximize the applications of available tree ring predictors. Based on results of goodness-of-fit tests, the HBR developed was skillful and reliable in reconstructing the streamflow of ARB. From five centuries of reconstructed streamflow for ARB, five or six abrupt change points are detected. The streamflow time series obtained from a backward moving, 46-year window for six gauging sites in ARB vary significantly over five centuries (1489–2006) and at times could exceed the 90% and/or 95% confidence intervals, denoting significant non-stationarities. Apparently changes in the mean state and the lag-1 autocorrelation of reconstructed streamflow across the gauging sites can be similar or radically different from each other. These nonstationary features imply that the default stationary assumption is not applicable in ARB. Further, the reconstructed streamflow shows statistically significant oscillations at interannual, interdecadal and multidecadal time scales and are teleconnected to climate patterns such as El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO). A composite analysis shows that La Niña (El Niño), cold (warm) PDO, and cold (warm) AMO events are typically associated with increased (decreased) streamflow anomalies of ARB. The reconstructed streamflow data provides us the full range of streamflow variability and recurrence characteristics of extremes spanned over five centuries from which it is useful for us to evaluate and manage the current water systems of ARB more effectively and a better risk analysis of future droughts of ARB.
format Article in Journal/Newspaper
author Wu, Yenan
Gan, Thian Yew
She, Yuntong
Xu, Chong-Yu
Yan, Haibin
spellingShingle Wu, Yenan
Gan, Thian Yew
She, Yuntong
Xu, Chong-Yu
Yan, Haibin
Five centuries of reconstructed streamflow in Athabasca River Basin, Canada: Non-stationarity and teleconnection to climate patterns
author_facet Wu, Yenan
Gan, Thian Yew
She, Yuntong
Xu, Chong-Yu
Yan, Haibin
author_sort Wu, Yenan
title Five centuries of reconstructed streamflow in Athabasca River Basin, Canada: Non-stationarity and teleconnection to climate patterns
title_short Five centuries of reconstructed streamflow in Athabasca River Basin, Canada: Non-stationarity and teleconnection to climate patterns
title_full Five centuries of reconstructed streamflow in Athabasca River Basin, Canada: Non-stationarity and teleconnection to climate patterns
title_fullStr Five centuries of reconstructed streamflow in Athabasca River Basin, Canada: Non-stationarity and teleconnection to climate patterns
title_full_unstemmed Five centuries of reconstructed streamflow in Athabasca River Basin, Canada: Non-stationarity and teleconnection to climate patterns
title_sort five centuries of reconstructed streamflow in athabasca river basin, canada: non-stationarity and teleconnection to climate patterns
publishDate 2020
url http://hdl.handle.net/10852/82226
http://urn.nb.no/URN:NBN:no-85124
https://doi.org/10.1016/j.scitotenv.2020.141330
geographic Athabasca River
Canada
Pacific
geographic_facet Athabasca River
Canada
Pacific
genre Athabasca River
genre_facet Athabasca River
op_source 0048-9697
op_relation http://urn.nb.no/URN:NBN:no-85124
Wu, Yenan Gan, Thian Yew She, Yuntong Xu, Chong-Yu Yan, Haibin . Five centuries of reconstructed streamflow in Athabasca River Basin, Canada: Non-stationarity and teleconnection to climate patterns. Science of the Total Environment. 2020, 746
http://hdl.handle.net/10852/82226
1863580
info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Science of the Total Environment&rft.volume=746&rft.spage=&rft.date=2020
Science of the Total Environment
746
https://doi.org/10.1016/j.scitotenv.2020.141330
URN:NBN:no-85124
Fulltext https://www.duo.uio.no/bitstream/handle/10852/82226/2/WU%2BYenan_preprint.pdf
op_doi https://doi.org/10.1016/j.scitotenv.2020.141330
container_title Science of The Total Environment
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