Articles | Volume 27, issue 9
https://doi.org/10.5194/hess-27-1755-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-1755-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 May 2023
Research article |
| 04 May 2023
| 04 May 2023
Hydrologic implications of projected changes in rain-on-snow melt for Great Lakes Basin watersheds
Department of Geography, Indiana University, Bloomington, Indiana
47405, USA
Darren L. Ficklin
Department of Geography, Indiana University, Bloomington, Indiana
47405, USA
Scott M. Robeson
Department of Geography, Indiana University, Bloomington, Indiana
47405, USA
Related authors
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024, https://doi.org/10.5194/hess-28-5295-2024, 2024
Short summary
Short summary
We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024, https://doi.org/10.5194/hess-28-5295-2024, 2024
Short summary
Short summary
We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
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Short summary
We projected climate change impacts to rain-on-snow (ROS) melt events in the Great Lakes Basin. Decreases in snowpack limit future ROS melt. Areas with mean winter/spring air temperatures near freezing are most sensitive to ROS changes. The projected proportion of total monthly snowmelt from ROS decreases. The timing for ROS melt is projected to be 2 weeks earlier by the mid-21st century and affects spring streamflow. This could affect freshwater resources management.
We projected climate change impacts to rain-on-snow (ROS) melt events in the Great Lakes Basin....
