Hydrological Modelling of River Flow Forecasting in Cold Regions and Its Application over the Athabasca River
Floods are disasters that represent a growing threat to the communities living close to rivers. To maximize community resilience, the main objective was to formulate a transferable framework for river flow forecasting in cold and poorly gauged/ungauged regions. First, the literature was reviewed, su...
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2023
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Online Access: | http://hdl.handle.net/1880/115958 https://doi.org/10.11575/PRISM/40807 |
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ftunivcalgary:oai:prism.ucalgary.ca:1880/115958 2023-10-29T02:34:52+01:00 Hydrological Modelling of River Flow Forecasting in Cold Regions and Its Application over the Athabasca River Belvederesi, Chiara Hassan, Quazi Achari, Gopal Rangelova, Elena Gupta, Anil 2023-03-23 application/pdf http://hdl.handle.net/1880/115958 https://doi.org/10.11575/PRISM/40807 eng eng Schulich School of Engineering University of Calgary Belvederesi, C. (2023). Hydrological modelling of river flow forecasting in cold regions and its application over the Athabasca River (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. http://hdl.handle.net/1880/115958 https://dx.doi.org/10.11575/PRISM/40807 University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. ungauged cold hydrology review empirical Athabasca River Basin Engineering Engineering--Environmental doctoral thesis 2023 ftunivcalgary https://doi.org/10.11575/PRISM/40807 2023-10-01T17:42:57Z Floods are disasters that represent a growing threat to the communities living close to rivers. To maximize community resilience, the main objective was to formulate a transferable framework for river flow forecasting in cold and poorly gauged/ungauged regions. First, the literature was reviewed, summarizing the recent findings in river flow forecasting in these regions. Here, hydrological processes greatly vary seasonally and annually, translating into increased model uncertainty. Regionalization, spatial calibration, and other methods were implemented into process-based and empirical models. Although process-based models provided a wide understanding of a watershed’s hydrology, they were often complex and computationally demanding. Empirical models produced fewer calibration parameters although generated biased results when insufficient descriptors were available. The results from this review highlighted some efforts necessary to improve river flow forecasting, including: coping with limited data; providing user-friendly interfaces; advancing model structure; developing a universal method for transferring parameters; standardizing calibration and validation; integrating process-based and empirical models. In addition, a machine learning-based model was developed using a single-input sequential adaptive neuro-fuzzy inference system (ANFIS) in the Athabasca River Basin (ARB) in Alberta, Canada. After estimating the optimal lead time between four hydrometric stations, data measured near the source were used to compute flows near the mouth, over approximately 1,000 km. This technique was compared to nonsequential and multi-input ANFIS, which used data from all the four hydrometric stations. The results showed that sequential ANFIS could accurately predict flows (r2 = 0.99, Nash–Sutcliffe = 0.98) with a longer lead time (6 days) using a single input. Finally, a novel simplistic method for short-term (6 days) forecasting called Flow Difference Model (FDM) was developed and compared against existing hydrological ... Doctoral or Postdoctoral Thesis Athabasca River PRISM - University of Calgary Digital Repository |
institution |
Open Polar |
collection |
PRISM - University of Calgary Digital Repository |
op_collection_id |
ftunivcalgary |
language |
English |
topic |
ungauged cold hydrology review empirical Athabasca River Basin Engineering Engineering--Environmental |
spellingShingle |
ungauged cold hydrology review empirical Athabasca River Basin Engineering Engineering--Environmental Belvederesi, Chiara Hydrological Modelling of River Flow Forecasting in Cold Regions and Its Application over the Athabasca River |
topic_facet |
ungauged cold hydrology review empirical Athabasca River Basin Engineering Engineering--Environmental |
description |
Floods are disasters that represent a growing threat to the communities living close to rivers. To maximize community resilience, the main objective was to formulate a transferable framework for river flow forecasting in cold and poorly gauged/ungauged regions. First, the literature was reviewed, summarizing the recent findings in river flow forecasting in these regions. Here, hydrological processes greatly vary seasonally and annually, translating into increased model uncertainty. Regionalization, spatial calibration, and other methods were implemented into process-based and empirical models. Although process-based models provided a wide understanding of a watershed’s hydrology, they were often complex and computationally demanding. Empirical models produced fewer calibration parameters although generated biased results when insufficient descriptors were available. The results from this review highlighted some efforts necessary to improve river flow forecasting, including: coping with limited data; providing user-friendly interfaces; advancing model structure; developing a universal method for transferring parameters; standardizing calibration and validation; integrating process-based and empirical models. In addition, a machine learning-based model was developed using a single-input sequential adaptive neuro-fuzzy inference system (ANFIS) in the Athabasca River Basin (ARB) in Alberta, Canada. After estimating the optimal lead time between four hydrometric stations, data measured near the source were used to compute flows near the mouth, over approximately 1,000 km. This technique was compared to nonsequential and multi-input ANFIS, which used data from all the four hydrometric stations. The results showed that sequential ANFIS could accurately predict flows (r2 = 0.99, Nash–Sutcliffe = 0.98) with a longer lead time (6 days) using a single input. Finally, a novel simplistic method for short-term (6 days) forecasting called Flow Difference Model (FDM) was developed and compared against existing hydrological ... |
author2 |
Hassan, Quazi Achari, Gopal Rangelova, Elena Gupta, Anil |
format |
Doctoral or Postdoctoral Thesis |
author |
Belvederesi, Chiara |
author_facet |
Belvederesi, Chiara |
author_sort |
Belvederesi, Chiara |
title |
Hydrological Modelling of River Flow Forecasting in Cold Regions and Its Application over the Athabasca River |
title_short |
Hydrological Modelling of River Flow Forecasting in Cold Regions and Its Application over the Athabasca River |
title_full |
Hydrological Modelling of River Flow Forecasting in Cold Regions and Its Application over the Athabasca River |
title_fullStr |
Hydrological Modelling of River Flow Forecasting in Cold Regions and Its Application over the Athabasca River |
title_full_unstemmed |
Hydrological Modelling of River Flow Forecasting in Cold Regions and Its Application over the Athabasca River |
title_sort |
hydrological modelling of river flow forecasting in cold regions and its application over the athabasca river |
publisher |
Schulich School of Engineering |
publishDate |
2023 |
url |
http://hdl.handle.net/1880/115958 https://doi.org/10.11575/PRISM/40807 |
genre |
Athabasca River |
genre_facet |
Athabasca River |
op_relation |
Belvederesi, C. (2023). Hydrological modelling of river flow forecasting in cold regions and its application over the Athabasca River (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. http://hdl.handle.net/1880/115958 https://dx.doi.org/10.11575/PRISM/40807 |
op_rights |
University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. |
op_doi |
https://doi.org/10.11575/PRISM/40807 |
_version_ |
1781057756311060480 |