Modelling river flow in cold and ungauged regions: a review of the purposes, methods, and challenges

Chiara Belvederesi, Mohamed S. Zaghloul, Gopal Achari, Anil Gupta, Quazi K. Hassan

Research output: Contribution to journalReview articlepeer-review

11 Citations (Scopus)

Abstract

River flow forecasting models assist in the understanding, predicting, monitoring, and managing of issues related to surface-water resources, such as water quality deterioration and flooding, or developing adaptation strategies to cope with climate change and increasing water demand. This review presents an overview of the current research status and progress in river-flow forecasting, focusing on cold climates and ungauged locations. River-flow forecasting in cold regions represents a challenge because the natural processes that occur within catchments vary greatly both seasonally and annually. This variability, which highly depends on climatic and topo-geomorphological characteristics within a basin, translates into increased model uncertainty and a substantial limitation when attempting to forecast river flow in cold regions, which are often poorly gauged or ungauged. To address this limitation, the “Predictions in Ungauged Basins” initiative offers a variety of studies to improve forecasting performance by adopting regionalization, spatial calibration, interpolation, and regression approaches. Process-based models demonstrate significant improvement by including remote-sensing data to replicate and derive complex hydrological processes. Empirical models, which utilize observed data to formulate a graphical solution, unlike mathematical models that require formulating the relationships between the processes, are also implemented with the most recent developments in machine learning, showing exceptional forecasting accuracy. Although process-based models provide a wide understanding of a watershed hydrology, data are often unavailable, expensive, and time-consuming to collect. They also generate numerous calibration parameters, resulting in complex and computationally demanding methods to operate. River-flow forecasting using empirical models reduces the number of calibration parameters but could produce biased results when insufficient variables are available to explain the physical mechanisms of a watershed’s hydrology. Moreover, empirical models could be potentially sensitive to calibration and validation dataset selection. In this review, Canadian studies are primarily selected to highlight some of the efforts that may be necessary in other similar cold and ungauged regions, including: (i) coping with limited data availability through regionalization methods; (ii) providing user-friendly interfaces; (iii) advancing model structure; (iv) developing a universal method for transferring regionalization parameters; (v) standardizing calibration and validation dataset selection; (vi) integrating process-based and empirical models.

Original languageEnglish
Pages (from-to)159-173
Number of pages15
JournalEnvironmental Reviews
Volume30
Issue number1
DOIs
Publication statusPublished - 2022
Externally publishedYes

Keywords

  • Canada
  • Data-driven models
  • Empirical and process-based methods
  • Flood prevention
  • Machine learning
  • Streamflow prediction

ASJC Scopus subject areas

  • General Environmental Science

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