Doctor of Philosophy, The Ohio State University, 2024, Earth Sciences
Seasonal snow plays a large role in the water cycle and local ecosystem dynamics in snow dominated regions. However, two characteristics of the snowpack, the snow water equivalent (SWE) and density, are challenging to measure at scale. Modeling and remote sensing allow for the estimation of these characteristics at wide spatial scales, but practical limitations remain on our ability to estimate at a fine spatial fidelity, wide spatial extent, and daily temporal resolution. Regional Climate Models (RCMs) have been shown to successfully estimate SWE at basin-wide scales but remain too computationally expensive to run at sub-kilometer resolutions over large domains.
In this thesis, I present two alternative methods to estimate daily SWE at a high spatial and temporal resolution on a basin-wide scale. The first, Blender, presented in Chapter 2, merges 9 km RCM estimates of SWE, precipitation, and top of the snowpack energy balance from the Weather and Research Forecasting (WRF) model with remotely sensed snow cover fraction (SCF) measurements to produce 500 m estimates of SWE timeseries. Blender re-solves the mass and energy balance of the snowpack with a constrained non-linear optimization, forced by the timing of the snow on and off dates from the SCF data. Compared against 50 m LiDAR estimates of SWE from 18 Airborne Space Observatory (ASO) flights, Blender has an average spatial RMSE of 11.5% of maximum SWE, while the prior from WRF has an average spatial RMSE of 17% of maximum SWE. The mean absolute bias of the total basin snow water storage (SWS) for the Blender estimates is 7.3% in the winter, and 31.6% for the WRF prior. This method, Blender, requires ~ 20% extra computing time on top of the original WRF runs, and improves both the spatial RMSE and basin SWS absolute bias, all while better matching the melt timing to the remotely sensed SCF.
In Chapter 3 we present the second method, Linear Blender, is a linearized version of Blender, Chapter 2. This meth (open full item for complete abstract)
Committee: Michael Durand (Advisor); Ian Howat (Committee Member); Jim Stagge (Committee Member); Demián Gómez (Committee Member)
Subjects: Earth; Environmental Science; Geography