University of Oulu

Kabir Rasouli, Bouchra R. Nasri, Armina Soleymani, Taufique H. Mahmood, Masahiro Hori, Ali Torabi Haghighi; Forecast of streamflows to the Arctic Ocean by a Bayesian neural network model with snowcover and climate inputs. Hydrology Research 1 June 2020; 51 (3): 541–561. doi:

Forecast of streamflows to the Arctic Ocean by a Bayesian neural network model with snowcover and climate inputs

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Author: Rasouli, Kabir1,2; Nasri, Bouchra R.3; Soleymani, Armina4;
Organizations: 1Meteorological Service of Canada, Environment and Climate Change Canada, 2121 Trans Canada Route, Dorval, QC, H9P 1J3, Canada
2Department of Geoscience, University of Calgary, Calgary, AB, Canada
3GERAD and Department of Mathematics and Statistics, McGill University, Montreal, QC, Canada
4Department of Systems Design Engineering, University of Waterloo, ON, Canada
5Harold Hamm, School of Geology and Geological Engineering, University of North Dakota, Grand Forks, ND, USA
6Earth Observation Research Center, Japan Aerospace Exploration Agency, Japan
7Water, Energy and Environmental Engineering Research Unit, University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.6 MB)
Persistent link:
Language: English
Published: IWA Publishing, 2020
Publish Date: 2020-07-01


Increasing water flowing into the Arctic Ocean affects oceanic freshwater balance, which may lead to the thermohaline circulation collapse and unpredictable climatic conditions if freshwater inputs continue to increase. Despite the crucial role of ocean inflow in the climate system, less is known about its predictability, variability, and connectivity to cryospheric and climatic patterns on different time scales. In this study, multi-scale variation modes were decomposed from observed daily and monthly snowcover and river flows to improve the predictability of Arctic Ocean inflows from the Mackenzie River Basin in Canada. Two multi-linear regression and Bayesian neural network models were used with different combinations of remotely sensed snowcover, in-situ inflow observations, and climatic teleconnection patterns as predictors. The results showed that daily and monthly ocean inflows are associated positively with decadal snowcover fluctuations and negatively with interannual snowcover fluctuations. Interannual snowcover and antecedent flow oscillations have a more important role in describing the variability of ocean inflows than seasonal snowmelt and large-scale climatic teleconnection. Both models forecasted inflows seven months in advance with a Nash–Sutcliffe efficiency score of ≈0.8. The proposed methodology can be used to assess the variability of the freshwater input to northern oceans, affecting thermohaline and atmospheric circulations.

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Series: Hydrology research
ISSN: 1998-9563
ISSN-E: 2224-7955
ISSN-L: 1998-9563
Volume: 51
Issue: 3
Pages: 541 - 561
DOI: 10.2166/nh.2020.164
Type of Publication: A1 Journal article – refereed
Field of Science: 1171 Geosciences
Funding: The authors gratefully acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) through Post-doctoral Fellowship to KR, NSF funded ND EPSCoR (NSF grant #IIA‐135466) to THM and from the Fonds de recherche du Québec-Nature et technologies and the Canadian Statistical Sciences Institute through Post-doctoral Fellowship to BRN. The authors also thank the Earth Observation Research Center of JAXA for providing them with the JASMES snowcover extent data. The authors of this paper, hereby, confirm that they do not have any conflict of interest to declare. Datasets related to this article can be found from the website of the Water Survey of Canada ( for streamflow, National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA) website ( for climate teleconnections, and Japanese Satellite Monitoring for Environmental Studies (JASMES) website ( for snowcover extent data.
Copyright information: © 2020 The Authors. This is an Open Access article distributed under the terms of the CreativeCommons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (