UC Santa Barbara

The dynamic nature in which the atmosphere and land interact can render the spatial distribution of precipitation highly variable. This is particularly the case in mountain environments where the topography is steep and the elevational variability is large. To capture this variability, we presently rely on a network of gauges, and to make these measurements meaningful we utilize statistics or models to “fill the gaps”. However, the “gaps” often include vast areas of topographic variability, making model validation challenging at best. Nonetheless, these data are used in streamflow forecasts and even in some of the most technologically advanced regions in the world these forecasts can have large errors, in some cases exceeding 100% absolute error. A large part of this error is due to an inadequate understanding of the spatial distribution of mountain precipitation. Snow, unlike rain, remains roughly in place post snowfall, and can be measured both from the ground and now from the air. In my first chapter, I take advantage of this by using snow remote sensing from the Airborne Snow Observatory (ASO) to establish a methodology for measuring the spatial distribution of snowfall during a single storm. The second chapter utilizes spatial scaling of the ASO data to parse changes in snow accumulation by the principal driving force—either precipitation or wind redistribution. The third and final chapter evaluates the Weather Research and Forecasting (WRF) model’s ability to replicate the spatial patterns in yearly snow accumulation as observed by ASO. Importantly, all three chapters lay the groundwork for the increased use of snow remote sensing to both help augment and evaluate existing models for precipitation monitoring and forecasting in mountain environments.