Articles | Volume 24, issue 10
https://doi.org/10.5194/nhess-24-3315-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-24-3315-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Sep 2024
Research article |
| 30 Sep 2024
| 30 Sep 2024
Algorithmically detected rain-on-snow flood events in different climate datasets: a case study of the Susquehanna River basin
Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA, USA
Benjamin D. Ascher
Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA, USA
now at: Meteorologisches Institut, Ludwig-Maximilians-Universität, Munich, Germany
Alan M. Rhoades
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Rachel R. McCrary
Research Applications Laboratory, NSF National Center for Atmospheric Research, Boulder, CO, USA
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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.
We developed an automated workflow to detect rain-on-snow events, which cause flooding in the...
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