California Institute of Technology, Pasadena, CA, USA
Viewed
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 853 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
782
40
31
853
18
25
HTML: 782
PDF: 40
XML: 31
Total: 853
BibTeX: 18
EndNote: 25
Views and downloads (calculated since 03 Jun 2024)
Cumulative views and downloads
(calculated since 03 Jun 2024)
Total article views: 477 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
410
40
27
477
18
25
HTML: 410
PDF: 40
XML: 27
Total: 477
BibTeX: 18
EndNote: 25
Views and downloads (calculated since 24 Jul 2025)
Cumulative views and downloads
(calculated since 24 Jul 2025)
Total article views: 376 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
372
0
4
376
0
0
HTML: 372
PDF: 0
XML: 4
Total: 376
BibTeX: 0
EndNote: 0
Views and downloads (calculated since 03 Jun 2024)
Cumulative views and downloads
(calculated since 03 Jun 2024)
Viewed (geographical distribution)
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 853 (including HTML, PDF, and XML)
Thereof 835 with geography defined
and 18 with unknown origin.
Total article views: 477 (including HTML, PDF, and XML)
Thereof 475 with geography defined
and 2 with unknown origin.
Total article views: 376 (including HTML, PDF, and XML)
Thereof 360 with geography defined
and 16 with unknown origin.
Machine learning is playing an increasingly important role in hydrological modeling. In this paper, we introduce an adaptation of existing machine learning models for simulating streamflow in river basins, redesigning them with the goal of integrating them in climate models. We demonstrate the effectiveness of our adapted model by showing that it outperforms a physics-based river model. These results motivate further studies of the use of machine-learning-based river models inside climate models.
Machine learning is playing an increasingly important role in hydrological modeling. In this...