Preprints
https://doi.org/10.5194/hess-2024-256
https://doi.org/10.5194/hess-2024-256
26 Aug 2024
 | 26 Aug 2024
Status: a revised version of this preprint is currently under review for the journal HESS.

Machine Learning in Stream/River Water Temperature Modelling: a review and metrics for evaluation

Claudia Rebecca Corona and Terri Sue Hogue

Abstract. As climate change continues to affect stream/river (henceforth stream) systems worldwide, stream water temperature (SWT) is an increasingly important indicator of distribution patterns and mortality rates among fish, amphibians, and macroinvertebrates. Technological advances tracing back to the mid-20th century have improved our ability to measure SWT at varying spatial and temporal resolutions for the fundamental goal of better understanding stream function and ensuring ecosystem health. Despite significant advances, there continue to be numerous stream reaches, stream segments and entire catchments that are difficult to access for a myriad of reasons, including but not limited to physical limitations. Moreover, there are noted access issues, financial constraints, and temporal and spatial inconsistencies or failures within situ instrumentation. Over the last few decades and in response to these limitations, statistical methods and physically based computer models have been steadily employed to examine SWT dynamics and controls. Most recently, the use of artificial intelligence, specifically machine learning (ML) algorithms, has garnered significant attention and utility in hydrologic sciences, specifically as a novel tool to learn undiscovered patterns from complex data and try to fill data streams and knowledge gaps. Our review found that in the recent five years (2020–2024), a similar number (27) of publications using ML, as were published in the previous 20 years, (2000–2019), totaling 54. The aim of this work is three-fold: first, to provide a concise review of the use of ML algorithms in SWT modeling and prediction, second, to review ML performance evaluation metrics as it pertains to SWT modeling and prediction and find the commonly used metrics and suggest guidelines for easier comparison of ML performance across SWT studies and third, to examine how ML use in SWT modeling has enhanced our understanding of spatial and temporal patterns of SWT and examine where progress is still needed.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Claudia Rebecca Corona and Terri Sue Hogue

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2024-256', Anonymous Referee #1, 23 Sep 2024
    • AC2: 'Reply on RC1', Claudia Corona, 28 Nov 2024
  • RC2: 'Comment on hess-2024-256', Anonymous Referee #2, 30 Sep 2024
    • AC1: 'Reply on RC2', Claudia Corona, 28 Nov 2024
  • RC3: 'Comment on hess-2024-256', Jeremy Diaz, 07 Oct 2024
    • AC3: 'Reply on RC3', Claudia Corona, 28 Nov 2024
Claudia Rebecca Corona and Terri Sue Hogue
Claudia Rebecca Corona and Terri Sue Hogue

Viewed

Total article views: 699 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
424 146 129 699 36 3 2
  • HTML: 424
  • PDF: 146
  • XML: 129
  • Total: 699
  • Supplement: 36
  • BibTeX: 3
  • EndNote: 2
Views and downloads (calculated since 26 Aug 2024)
Cumulative views and downloads (calculated since 26 Aug 2024)

Viewed (geographical distribution)

Total article views: 674 (including HTML, PDF, and XML) Thereof 674 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Dec 2024
Download
Short summary
Stream water temperature (SWT) is a key indicator of water quality that benefits public use and aquatic life health. Advances in computer modeling have helped improve our understanding of SWT dynamics, but challenges remain. Recently, scientists have begun to use machine learning (ML) in SWT modeling, but it is unclear how ML is increasing our understanding of SWT causes and effects. This work reviews the application of ML in SWT modeling and discusses where there is still room for improvement.