Articles | Volume 24, issue 10
https://doi.org/10.5194/nhess-24-3315-2024
https://doi.org/10.5194/nhess-24-3315-2024
Research article
 | 
30 Sep 2024
Research article |  | 30 Sep 2024

Algorithmically detected rain-on-snow flood events in different climate datasets: a case study of the Susquehanna River basin

Colin M. Zarzycki, Benjamin D. Ascher, Alan M. Rhoades, and Rachel R. McCrary

Viewed

Total article views: 608 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
384 120 104 608 33 31
  • HTML: 384
  • PDF: 120
  • XML: 104
  • Total: 608
  • BibTeX: 33
  • EndNote: 31
Views and downloads (calculated since 14 Mar 2024)
Cumulative views and downloads (calculated since 14 Mar 2024)

Viewed (geographical distribution)

Total article views: 608 (including HTML, PDF, and XML) Thereof 611 with geography defined and -3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
We developed an automated workflow to detect rain-on-snow events, which cause flooding in the northeastern United States, in climate data. Analyzing the Susquehanna River basin, this technique identified known events affecting river flow. Comparing four gridded datasets revealed variations in event frequency and severity, driven by different snowmelt and runoff estimates. This highlights the need for accurate climate data in flood management and risk prediction for these compound extremes.
Altmetrics
Final-revised paper
Preprint