Changing Seasonality of Annual Maximum Floods over the Conterminous US: Potential Drivers and Regional Synthesis
Publication: Journal of Hydrologic Engineering
Volume 28, Issue 4
Abstract
Understanding the flood-generating mechanisms that influence flood seasonality in a region provides information on setting up relevant contingency measures. Although former studies estimated flood seasonality at regional/continental scale, limited/no studies have investigated the climate/basin drivers that influence the changes in flood seasonality. Considering this, the current study performed two analyses: (1) estimated the changes in the seasonality of annual maximum floods (AMF) between pre- and post-1970 across Hydroclimate Data Network basins over the conterminous US, and (2) identified the predictors that influence the change in the seasonality from a set of climate and geomorphic variables. Significant changes in the AMF seasonality were noted for approximately half of the basins in the eastern US, but low to no change was found in most basins in the central/western US. We found, except in the Northeast and mid-Atlantic basins, a decrease in the seasonality index, indicating floods arriving more uniformly is typically associated with an increase in the precipitation days in basins. On the other hand, increase in the seasonality index, indicating floods occurring more concentrated in time, is typically associated with an increase in the extreme precipitation in basins. Among the basin characteristics, elevation has a more dominant role than the drainage area in changing the flood seasonality. Elevation affects the form of precipitation, particularly in the western US, because floods arrive more distributed over the year (i.e., decrease in flood seasonality index), which potentially indicates increased warming resulting in early snowmelt.
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Data Availability Statement
Some or all data, models, or codes generated or used during the study are available in a repository online in accordance with funder data retention policies. The data set and computer code used to complete the analysis in this paper can be downloaded from Figshare (Basu et al. 2020).
Acknowledgments
This research work is partially funded by the project NSF Grants CBET-1442909 and CBET-0954405.
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Journal of Hydrologic Engineering
Volume 28 • Issue 4 • April 2023
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© 2023 American Society of Civil Engineers.
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Received: Mar 3, 2022
Accepted: Oct 19, 2022
Published online: Feb 11, 2023
Published in print: Apr 1, 2023
Discussion open until: Jul 11, 2023
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