PhD Research - Timbo Stillinger

BS, Molecular Environmental Biology, University of California, Berkeley; MESM, Bren School of Environmental Science & Management, UCSB

Runoff from mountain ranges is the water supply for 40% of the global population, and seasonal mountain snow and glaciers supply water for more than one billion people. Timely and accurate information about the snow is crucial for water supply management, yet runoff forecasts are not operational everywhere and existing forecasts of seasonal melt significantly under- and over-predict flows, diminishing the ability to maximize societal benefits from storing and using water. Mountain basins worldwide have harsh climate, rugged terrain, and are sparsely gauged at best, making space borne remote sensing missions the most dependable tools available for providing water supply information on mountain snowpacks globally. Additionally, forecasting solutions for water supply management from operational space born remote sensors are altruistic, applicable irrespective of local geo-political stability or socio-economic conditions of the nations and regions in need of information on their water supplies. The broad goal of Timbo’s dissertation is to both advance the ability to map snow from space and improve the understanding of the current use and potential opportunities for snowpack data integration into water resources management. His work addresses two problems in the research and applied uses of snowpack data for managing water.

First, the misidentification between clouds and snow in multispectral satellite imagery is a serious source of error in existing remote sensing snow products. An improved cloud masking methodology will have high utility to a variety of user groups interested in snow and clouds in mountainous imagery. Timbo is investigating theoretical and empirical evidence for explaining why this is a difficult problem, and working on a proposed solution for improving upon existing cloud masking algorithms.

The second objective of Timbo’s research is to determine the economic value of snowpack forecasts. The goal is to understand how changes in mountain snowpack storage impact the value of forecasts and functioning of the water supply system. Timbo is integrating the economic value of observations with a quantitative assessment of the tradeoff between earlier but uncertain predictions versus later, more robust ones. Analysis of existing forecasts and their errors is combined with simple models that explore and explain the relationship between snowpack forecasts and water management.

Year Admitted: 2014
Research Areas: Remote Sensing, Snow, Water Resources Management
Faculty Advisor: Jeff Dozier

Office: Bren Hall 1001
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Curriculum Vitae
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