Date of Award

Summer 2025

Language

English

Embargo Period

8-8-2025

Document Type

Master's Thesis

Degree Name

Master of Science (MS)

College/School/Department

Department of Atmospheric and Environmental Sciences

Program

Atmospheric Science

First Advisor

Justin Minder

Committee Members

Brian Tang

Keywords

Hydrology, Climatology, High-Resolution Rapid Refresh, Forecast Verification

Subject Categories

Atmospheric Sciences

Abstract

Extreme rainfall and streamflow often lead to increased turbidity in reservoirs in the Catskill/Delaware catchment, which is part of the New York City water supply system. To better understand extreme events that lead to increased turbidity, this study takes a dual approach: first, we identify hydrologic conditions during extremes to understand when and why extremes occur. Next, we evaluate an identified extreme case to evaluate mesoscale model skill in this region. Measurements from the New York State Mesonet and United States Geological Survey are used to identify hydrologic conditions from 2017-2024 associated with three extreme event types: rainfall, streamflow, and concurrent rainfall that leads to streamflow. We find that each event type has a distinct seasonal distribution and soil moisture conditions. Streamflow and concurrent events during the cold season often occur with snow melt, with the latter suggesting the prevalence of rain-on-snow events. Meanwhile, rainfall events during the warm season often do not result in streamflow events due to dry antecedent soils. Following this analysis, we evaluate the High-Resolution Rapid Refresh (HRRR) for a high-impact rain-on-snow event from 24-25 December 2020. We find that the HRRR has high skill in forecasting the accumulated precipitation and change in snow water equivalent and low skill in snow depth forecasts. Together, these analyses further the current understanding of extreme rainfall and streamflow and provide insight into mesoscale model performance in the Catskill/Delaware catchment.

License

This work is licensed under the University at Albany Standard Author Agreement.

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