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

Yenan Wu; Thian Yew Gan; Yuntong She; Chongyu Xu; Haibin Yan

doi:10.1016/j.scitotenv.2020.141330

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

Sci Total Environ. 2020 Dec 1:746:141330. doi: 10.1016/j.scitotenv.2020.141330. Epub 2020 Jul 30.

Authors

Yenan Wu¹, Thian Yew Gan², Yuntong She³, Chongyu Xu⁴, Haibin Yan⁵

Affiliations

¹ Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada; College of Hydrology and Water Resources, Hohai University, Nanjing, China.
² Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada. Electronic address: tgan@ualberta.ca.
³ Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada.
⁴ Department of Geosciences, University of Oslo, Oslo, Norway.
⁵ College of Hydrology and Water Resources, Hohai University, Nanjing, China.

PMID: 32771763
DOI: 10.1016/j.scitotenv.2020.141330

Abstract

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.

Keywords: Composite analysis; Hierarchical Bayesian regression model; Large-scale climate patterns; Non-stationarity; Streamflow reconstruction; Tree ring chronologies.