the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Algorithmically Detected Rain-on-Snow Flood Events in Different Climate Datasets: A Case Study of the Susquehanna River Basin
Abstract. Rain-on-snow (RoS) events in regions of ephemeral snowpack – such as the northeastern United States – can be key drivers of cool-season flooding. We describe an automated algorithm for detecting basin-scale RoS events in gridded climate data by generating an area-averaged time-series and then searching for periods of concurrent precipitation, surface runoff, and snowmelt exceeding pre-defined thresholds. When evaluated using historical data over the Susquehanna River Basin (SRB), the technique credibly finds RoS events in published literature and flags events that are followed by anomalously high streamflow as measured by gage data along the river. When comparing four different datasets representing the same 21-year period, we find large differences in RoS event magnitude and frequency, primarily driven by differences in estimated surface runoff and snowmelt. Using dataset-specific thresholds improves agreement between datasets but does not account for all discrepancies. We show that factors such as meteorological forcing and coupling frequency as well as choice of land surface model play roles in how data products capture these compound extremes and suggest care is to be taken when climate datasets are used by stakeholders for operational decision-making.
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Status: open (until 26 Apr 2024)
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RC1: 'Comment on egusphere-2023-3094', Anonymous Referee #1, 31 Mar 2024
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This study makes a useful contribution to studying rain-on-events (at ephemeral snowpacks) by developing and evaluating an algorithm that can detect basin-scale rain-on-snow events using gridded climate data. The method searches for periods of concurrent (area-averaged) precipitation, surface runoff, and snowmelt exceeding pre-defined values. Application of this to Susquehanna River Basin (SRB) indicates that (using dataset-specific thresholds) the method seems to work appropriately. The method also highlights there can be large differences in RoS event magnitude and frequency caused by differences in the various driving factors.
In principle, this paper could be published, as the developments seem useful, and are logically shown. However, at the same time, the sort of ad-hoc presentation of a single basin results makes it unclear how useful the methods are, and how it’s usefulness varies between scales and settings. This would make the paper more useful and thereby a better fit for this journal.
- It would be useful if a clearer workflow is presented. This helps other people to better understand and potentially adopt the approach
- The method is applied to one basin. This is OK, but also a bit a thin basis for introducing a method. Across what range of areas is your method likely applicable?
- The comparison of climate data for a single basin (section 3.1) is useful, but it would be a lot more useful to systematically test how these things vary? (i.e. beyond this specific basin).
- The timescale over which RoS are relevant will be very dependent on the spatial scale of the watershed. For example, headwater catchments may need timescales of hours whereas for large basins it may be multiple days. Can this be discussed?
- Provide units at all axes go all figures.
Citation: https://doi.org/10.5194/egusphere-2023-3094-RC1
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