Short-Term River Flow Forecasting Framework and Its Application in Cold Climatic Regions

Catchments located in cold weather regions are highly influenced by the natural seasonality that dictates all hydrological processes. This represents a challenge in the development of river flow forecasting models, which often require complex software that use multiple explanatory variables and a la...

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Bibliographic Details
Published in:Water
Main Authors: Chiara Belvederesi, John Albino Dominic, Quazi K. Hassan, Anil Gupta, Gopal Achari
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2020
Subjects:
Athabasca River
cold weather regions
predictive hydrology
simplistic environmental modelling
water resources
Hydraulic engineering
TC1-978
Water supply for domestic and industrial purposes
TD201-500
Canada
Online Access:https://doi.org/10.3390/w12113049
https://doaj.org/article/6b79f067edad4494a7bab8a2fd7f2225
Description
Summary:Catchments located in cold weather regions are highly influenced by the natural seasonality that dictates all hydrological processes. This represents a challenge in the development of river flow forecasting models, which often require complex software that use multiple explanatory variables and a large amount of data to forecast such seasonality. The Athabasca River Basin (ARB) in Alberta, Canada, receives no or very little rainfall and snowmelt during the winter and an abundant rainfall–runoff and snowmelt during the spring/summer. Using the ARB as a case study, this paper proposes a novel simplistic method for short-term (i.e., 6 days) river flow forecasting in cold regions and compares existing hydrological modelling techniques to demonstrate that it is possible to achieve a good level of accuracy using simple modelling. In particular, the performance of a regression model (RM), base difference model (BDM), and the newly developed flow difference model (FDM) were evaluated and compared. The results showed that the FDM could accurately forecast river flow (E NS = 0.95) using limited data inputs and calibration parameters. Moreover, the newly proposed FDM had similar performance to artificial intelligence (AI) techniques, demonstrating the capability of simplistic methods to forecast river flow while bypassing the fundamental processes that govern the natural annual river cycle.

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