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SNOWPACK CONTROLS ON HYDROLOGIC RESPONSE TO EXTREME RAIN-ON-SNOW EVENTS IN THE NORTHERN SIERRA NEVADA
AuthorKatz, Lisa Jane
AdvisorHarpold, Adrian A.
Environmental and Natural Resource Sciences
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Continuous, near-real time predictions of winter flooding are critical to balancing the protection of life and property with providing water resources for consumptive use in California’s northern Sierra Nevada. Rain-on-snow (ROS) events are a major cause of floods in the region and are expected to increase as a result of climate change. During ROS, the amount of terrestrial water input (TWI) draining from the snowpack is the major driver of floods and depends on the snowpack's capacity to refreeze liquid water, its transmissivity, and the magnitude of snow melt during the event. The outcome is an interplay between (1) the amount and intensity of precipitation, (2) the antecedent conditions of the snowpack and (3) the potential for incoming energy to melt snow and drain additional water. An incomplete understanding and insufficient measurement of these interacting processes limits the skill of flood prediction in mountain regions. In this study, antecedent snowpack conditions, specifically cold content, density, liquid water content and SWE, are examined to understand how these factors modulate TWI during ROS. Data from three SNOTEL stations, common in the Western U.S., across a 500 m elevation gradient on the eastern side of California’s Sierra Nevada mountains are used as input to a physically-based model that simulates liquid water drainage explicitly (SNOWPACK). Hourly forcing parameters were developed to calibrate and validate the SNOWPACK model to the SNOTEL stations spanning water years 1981-2019 and 149 ROS events. During the 149 historical events, the snowpack mitigated TWI for 80% of events, 13% had no mitigation, and 7% had conditions for active melt. Mean TWI increased 32% from the lowest elevation (58 mm of water) to the highest elevation (85 mm of water). As expected, the amount of TWI depends on total rainfall, however, that events with TWI/rain ratios>1.0 produce the largest event streamflows. When antecedent conditions were varied in reasonable ways, total TWI response varies 46% on average across eight extreme events. A key result is that snowpack cold content explains the majority of TWI variability. Riper snowpacks generated the highest TWI values for most events, with a 1 MJ decrease in cold content corresponding to 0.74 more TWI. Our results highlight the importance of cold content in TWI response across realistic antecedent conditions. Cold content is rarely measured and effectively not included in operational flood forecast models. As ROS becomes increasingly frequent in a warming climate, enhanced observations of cold content and modeling could have important implications for improved flood forecasting.