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

Chiara Belvederesi,Gopal Achari,Anil Gupta,John Dominic,Quazi Hassan

doi:10.3390/w12113049

Chiara Belvederesi, Gopal Achari + Show 3 more

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https://doi.org/10.3390/w12113049

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Abstract

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 (ENS = 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.

Highlights

Hydrological processes are the results of the continuous natural changes of the state of water between the atmosphere and the earth, and several models exist in the literature to simulate and forecast such processes
The findings from this study showed that highly accurate river flow estimates in cold regions could be obtained using simple models
The performance of three simple methods, i.e., the base difference model (BDM), flow difference model (FDM), and regression model (RM) was investigated over the Athabasca River, Alberta, Canada, as a case study

Summary

Introduction

Hydrological processes are the results of the continuous natural changes of the state of water between the atmosphere and the earth, and several models exist in the literature to simulate and forecast such processes. Hydrological modelling for large watersheds, which could include multiple basins, is often challenging due to the complexity of hydroclimatic regimes related to intra- and inter-basin variations in topography, climatic patterns, land cover, basin drainage density, soil drainage capacity, and other similar factors [2,3] These factors play an important role in hydrological modelling in cold weather regions such as the Athabasca River Basin (ARB) considered in this study. The basin drainage density is reduced during the colder months as creeks and minor watercourses tend to freeze as well as topsoil, which does not provide any significant drainage capacity Due to these reasons, accurate forecasting of stream flows in cold climatic regions is highly challenging and often requires a large amount of input data in the form of explanatory variables to capture the large variations in climatic regimes, resulting in long computational times for calibration and, numerous calibration parameters [5]

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