Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications
Sub-seasonal to seasonal (S2S) streamflow forecasts play a critical role in the planning and management of water resources for various purposes, such as optimization of hydropower production, ensuring sufficient water supplies for various usages, mitigating flood and drought risks, and management of...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025024168 |
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| author | Duc Hai Nguyen Amin Elshorbagy Muhammad Naveed Khaliq Chaopeng Shen Mohammad Khaled Akhtar Mohamed Moghairib Fisaha Unduche Saman Razavi Philippe Lamontagne |
| author_facet | Duc Hai Nguyen Amin Elshorbagy Muhammad Naveed Khaliq Chaopeng Shen Mohammad Khaled Akhtar Mohamed Moghairib Fisaha Unduche Saman Razavi Philippe Lamontagne |
| author_sort | Duc Hai Nguyen |
| collection | DOAJ |
| description | Sub-seasonal to seasonal (S2S) streamflow forecasts play a critical role in the planning and management of water resources for various purposes, such as optimization of hydropower production, ensuring sufficient water supplies for various usages, mitigating flood and drought risks, and management of nutrients from industrial and agricultural sources. Contrary to day-to-day operational activities, such forecasts can provide an extended operational window to various levels of the government for taking appropriate actions and issuing timely directives. Compared to the vast amount of hydrologic literature on short-term streamflow forecasting, S2S forecasting area is still not well-developed. This paper reviews state-of-the-art in S2S streamflow forecasting, considering conventional process-based and statistical modeling approaches, emerging machine learning (ML) techniques, and hybrid options. The generated knowledge and insights are intended to guide the development of operational tools for S2S forecasting for Alberta, Saskatchewan, and Manitoba provinces of Canada, and can also be used for developing similar tools for other regions of the world. Apart from discussing various modeling challenges, data availability constraints, and quantification of uncertainties, the paper also presents a systematic framework for developing ML-based S2S streamflow forecasting tools. Various limitations of the reviewed approaches and potential avenues of future research are also discussed to advance research and applications in S2S forecasting area. It is found that the potential of ML in addressing scaling issues in hydrology, through S2S forecasting, and investigating relevant hydrologic mechanisms at coarse spatial and temporal resolutions are not adequately explored. This is a significant path forward for ML in hydrology. |
| format | Article |
| id | doaj-art-0e7a8bb9e3aa4cd98ff533077fc85119 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-0e7a8bb9e3aa4cd98ff533077fc851192025-08-20T03:08:17ZengElsevierResults in Engineering2590-12302025-09-012710634510.1016/j.rineng.2025.106345Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational ApplicationsDuc Hai Nguyen0Amin Elshorbagy1Muhammad Naveed Khaliq2Chaopeng Shen3Mohammad Khaled Akhtar4Mohamed Moghairib5Fisaha Unduche6Saman Razavi7Philippe Lamontagne8Department of Civil, Geological, and Environmental Engineering, University of Saskatchewan, Saskatoon, SK, Canada; Smart Computing in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, VietnamDepartment of Civil, Geological, and Environmental Engineering, University of Saskatchewan, Saskatoon, SK, Canada; Corresponding author.Ocean, Coastal, and River Engineering Research Centre, National Research Council Canada, Ottawa, ON, CanadaDepartment of Civil and Environmental Engineering, Pennsylvania State University, PA, USAAlberta Environment and Protected Areas, Government of Alberta, Edmonton, AB, CanadaSchulich School of Engineering, University of Calgary, Calgary, AB, CanadaManitoba Transportation and Infrastructure, Government of Manitoba, Winnipeg, MB, CanadaSchool of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK, CanadaOcean, Coastal, and River Engineering Research Centre, National Research Council Canada, Ottawa, ON, CanadaSub-seasonal to seasonal (S2S) streamflow forecasts play a critical role in the planning and management of water resources for various purposes, such as optimization of hydropower production, ensuring sufficient water supplies for various usages, mitigating flood and drought risks, and management of nutrients from industrial and agricultural sources. Contrary to day-to-day operational activities, such forecasts can provide an extended operational window to various levels of the government for taking appropriate actions and issuing timely directives. Compared to the vast amount of hydrologic literature on short-term streamflow forecasting, S2S forecasting area is still not well-developed. This paper reviews state-of-the-art in S2S streamflow forecasting, considering conventional process-based and statistical modeling approaches, emerging machine learning (ML) techniques, and hybrid options. The generated knowledge and insights are intended to guide the development of operational tools for S2S forecasting for Alberta, Saskatchewan, and Manitoba provinces of Canada, and can also be used for developing similar tools for other regions of the world. Apart from discussing various modeling challenges, data availability constraints, and quantification of uncertainties, the paper also presents a systematic framework for developing ML-based S2S streamflow forecasting tools. Various limitations of the reviewed approaches and potential avenues of future research are also discussed to advance research and applications in S2S forecasting area. It is found that the potential of ML in addressing scaling issues in hydrology, through S2S forecasting, and investigating relevant hydrologic mechanisms at coarse spatial and temporal resolutions are not adequately explored. This is a significant path forward for ML in hydrology.http://www.sciencedirect.com/science/article/pii/S2590123025024168Hydrological modelingMachine learningPrairie ProvincesSeasonal forecastingStatistical modelingWater resources management |
| spellingShingle | Duc Hai Nguyen Amin Elshorbagy Muhammad Naveed Khaliq Chaopeng Shen Mohammad Khaled Akhtar Mohamed Moghairib Fisaha Unduche Saman Razavi Philippe Lamontagne Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications Results in Engineering Hydrological modeling Machine learning Prairie Provinces Seasonal forecasting Statistical modeling Water resources management |
| title | Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications |
| title_full | Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications |
| title_fullStr | Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications |
| title_full_unstemmed | Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications |
| title_short | Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications |
| title_sort | advancing sub seasonal to seasonal streamflow forecasting in canada a review of conventional and emerging approaches for operational applications |
| topic | Hydrological modeling Machine learning Prairie Provinces Seasonal forecasting Statistical modeling Water resources management |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025024168 |
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