Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 18
Articles | Volume 30, issue 18
https://doi.org/10.5194/hess-30-5925-2026
Copyright waived. This work has been dedicated to the public domain (Creative Commons Public Domain Dedication).
https://doi.org/10.5194/hess-30-5925-2026
Copyright waived. This work has been dedicated to the public domain (Creative Commons Public Domain Dedication).
Articles | Volume 30, issue 18
Research article
 | 
23 Sep 2026
Research article |  | 23 Sep 2026

Predicting streamflow drought in the conterminous United States using machine learning and a donor-gage approach, 1982–2020

Aaron Heldmyer, Roy Sando, Caelan Simeone, Michael Wieczorek, Scott Hamshaw, Phillip Goodling, Ryan McShane, Jeremy Diaz, David Watkins, Bryce Pulver, Apoorva Shastry, Konrad Hafen, and John Hammond

Viewed

Total article views: 2,484 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,422 945 117 2,484 269 93 110
  • HTML: 1,422
  • PDF: 945
  • XML: 117
  • Total: 2,484
  • Supplement: 269
  • BibTeX: 93
  • EndNote: 110
Views and downloads (calculated since 21 Jan 2026)
Cumulative views and downloads (calculated since 21 Jan 2026)

Viewed (geographical distribution)

Total article views: 2,484 (including HTML, PDF, and XML) Thereof 2,444 with geography defined and 40 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Sep 2026
Download
Short summary
We used machine learning to explore what causes streamflow droughts across the U.S. We found that different regions are influenced by different factors like temperature, snow, and rainfall. Our new method can also predict droughts in areas without streamflow data, helping improve water resource planning.
Read more
Share