Snow water equivalent time‐series forecasting in Ontario, Canada, in link to large atmospheric circulations
Abstract The present study applies different time‐series models for forecasting daily and monthly snow water equivalent (SWE) data in Ontario, Canada, during 1987–2011. For daily time series, which showed a significant negative trend, four categories of the autoregressive moving‐average (ARMA) and A...
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crwiley:10.1002/hyp.10184 2024-06-02T08:11:29+00:00 Snow water equivalent time‐series forecasting in Ontario, Canada, in link to large atmospheric circulations Sarhadi, Ali Kelly, Richard Modarres, Reza 2014 http://dx.doi.org/10.1002/hyp.10184 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fhyp.10184 https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.10184 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Hydrological Processes volume 28, issue 16, page 4640-4653 ISSN 0885-6087 1099-1085 journal-article 2014 crwiley https://doi.org/10.1002/hyp.10184 2024-05-03T11:13:14Z Abstract The present study applies different time‐series models for forecasting daily and monthly snow water equivalent (SWE) data in Ontario, Canada, during 1987–2011. For daily time series, which showed a significant negative trend, four categories of the autoregressive moving‐average (ARMA) and ARMA model with exogenous variables (ARMAX) were applied. The North Atlantic Oscillation, Southern Oscillation Index and Pacific/North American Pattern, as large‐scale atmospheric anomalies, as well as temperature time series are considered as exogenous variables for ARMAX models. According to the multicriteria performance evaluation, a time‐trend ARMAX model demonstrated the best performance for modelling and forecasting daily SWE. Two models, seasonal autoregressive integrated moving average (SARIMA) and SARIMA with exogenous variables (SARIMAX), were also fitted to the monthly SWE time series. The results revealed that the SARIMAX model showed a better performance than the SARIMA model according to multicriteria evaluation. The three nonparametric tests, Wilcoxon, Levene and Kolmogorov–Smirnov for forecasting evaluation demonstrated that the selected time‐series models had enough reliability for short‐term SWE forecasting in Ontario. The results of this study also demonstrate the importance of incorporating both trend and appropriate exogenous variables for SWE time‐series modelling and forecasting. Copyright © 2014 John Wiley & Sons, Ltd. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Wiley Online Library Canada Pacific Sarima ENVELOPE(29.040,29.040,69.037,69.037) Hydrological Processes 28 16 4640 4653 |
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Wiley Online Library |
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English |
description |
Abstract The present study applies different time‐series models for forecasting daily and monthly snow water equivalent (SWE) data in Ontario, Canada, during 1987–2011. For daily time series, which showed a significant negative trend, four categories of the autoregressive moving‐average (ARMA) and ARMA model with exogenous variables (ARMAX) were applied. The North Atlantic Oscillation, Southern Oscillation Index and Pacific/North American Pattern, as large‐scale atmospheric anomalies, as well as temperature time series are considered as exogenous variables for ARMAX models. According to the multicriteria performance evaluation, a time‐trend ARMAX model demonstrated the best performance for modelling and forecasting daily SWE. Two models, seasonal autoregressive integrated moving average (SARIMA) and SARIMA with exogenous variables (SARIMAX), were also fitted to the monthly SWE time series. The results revealed that the SARIMAX model showed a better performance than the SARIMA model according to multicriteria evaluation. The three nonparametric tests, Wilcoxon, Levene and Kolmogorov–Smirnov for forecasting evaluation demonstrated that the selected time‐series models had enough reliability for short‐term SWE forecasting in Ontario. The results of this study also demonstrate the importance of incorporating both trend and appropriate exogenous variables for SWE time‐series modelling and forecasting. Copyright © 2014 John Wiley & Sons, Ltd. |
format |
Article in Journal/Newspaper |
author |
Sarhadi, Ali Kelly, Richard Modarres, Reza |
spellingShingle |
Sarhadi, Ali Kelly, Richard Modarres, Reza Snow water equivalent time‐series forecasting in Ontario, Canada, in link to large atmospheric circulations |
author_facet |
Sarhadi, Ali Kelly, Richard Modarres, Reza |
author_sort |
Sarhadi, Ali |
title |
Snow water equivalent time‐series forecasting in Ontario, Canada, in link to large atmospheric circulations |
title_short |
Snow water equivalent time‐series forecasting in Ontario, Canada, in link to large atmospheric circulations |
title_full |
Snow water equivalent time‐series forecasting in Ontario, Canada, in link to large atmospheric circulations |
title_fullStr |
Snow water equivalent time‐series forecasting in Ontario, Canada, in link to large atmospheric circulations |
title_full_unstemmed |
Snow water equivalent time‐series forecasting in Ontario, Canada, in link to large atmospheric circulations |
title_sort |
snow water equivalent time‐series forecasting in ontario, canada, in link to large atmospheric circulations |
publisher |
Wiley |
publishDate |
2014 |
url |
http://dx.doi.org/10.1002/hyp.10184 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fhyp.10184 https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.10184 |
long_lat |
ENVELOPE(29.040,29.040,69.037,69.037) |
geographic |
Canada Pacific Sarima |
geographic_facet |
Canada Pacific Sarima |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Hydrological Processes volume 28, issue 16, page 4640-4653 ISSN 0885-6087 1099-1085 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1002/hyp.10184 |
container_title |
Hydrological Processes |
container_volume |
28 |
container_issue |
16 |
container_start_page |
4640 |
op_container_end_page |
4653 |
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1800757637592121344 |