Fusing Remote Sensing and Modeling to Advance Hydrologic Prediction in Snow Dominated Headwater Catchments
Snowpacks in mountain environments have been called "the water towers of the world". As snowpacks melt they provide water for rivers, soil and groundwater into the summer when its most needed. Knowing how much water is in snow, and when and how quickly it melts is essential information for managing water supply and estimating fire risk and other ecosystem services. Come find out more about the latest science that tells us about snow!
—Naomi Tague, Professor, Bren School
Watch a recording of this talk here
ABSTRACT
Seasonal mountain snowmelt is an important contributor to surface water resources and groundwater recharge in the Western US, making forecasting of snowmelt timing and duration critical for accurate hydrologic prediction. Net solar radiation, controlled primarily by variation in snow albedo, is the main driver of snowmelt in most snow-covered environments. However, solar radiation is rarely measured in the mountains, and available measurements are not representative of the heterogenous environment. Additionally, lowering of snow albedo from episodic dust deposition has been shown to be an important, but highly variable, contributor to snowmelt timing and magnitude. To account for spatial variability in net solar radiation and interannual variability in snow darkening this talk will present a new modeling framework designed for operational forecasting environments; a spatially distributed process-based snowmelt model that incorporates near-real time snow albedo from remote sensing and incoming solar radiation from numerical weather prediction. The model improves prediction of snowmelt initiation and duration in mountain headwater catchments, even in years with lower dust impacts, demonstrating the importance of accounting for net solar radiation and snow energy balance in snowmelt modeling.
BIO
Dr. McKenzie Skiles is an associate professor, and director of the Snow Hydrology Resarch-to-Operations Laboratory (Snow HydRO Lab), in the School of Environment, Society & Sustainability at the University of Utah. Her research methods combine distributed modeling with multi-scale observations to understand how much water is held as snow, and how snow is changing over time, in mountainous headwaters. Her expertise spans in situ snow observation, process-based numerical modeling, and optical remote sensing of snow and ice. One of her main research trajectories is investigating the impacts of mineral dust and other light absorbing particles on snow in the Western US, where changing snowmelt patterns have important implications for water security. Originally from Anchorage, Alaska she received her PhD from UCLA and carried out postdoctoral research at Caltech/NASA-JPL.