This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
Mountain snowmelt is an essential resource to 40% of the world?s population but is threatened by warming associated with climate change. Accurate model predictions of the climate sensitivity of individual watersheds are needed for watershed impacts analysis, local and regional planning processes, and the development of water policy. Currently the quality of meteorological driving data sets in mountain environments is a major obstacle to characterizing the sensitivity of particular watersheds and simulating climate related impacts using hydrologic models. In particular, accurately characterizing where precipitation falls as rain vs. snow on an event basis, and when and how fast snow melts is essential to the assessment of climate sensitivity at small to medium (10-1000 km2) spatial scales. Errors in precipitation, temperature and radiative data sets in mountain environments are related to the scarcity of high elevation measurements, poor measurement quality due to adverse conditions, relatively few measured variables, and interpolation errors in complex terrain. Such highly scale-dependent errors are propagated through hydrologic simulation models in a complex manner.
Because the density of in situ data will always be less than ideal for most watersheds, we propose to develop and evaluate new methods for combining physical process-based information from meso-scale climate models with low elevation meteorological stations and high resolution topographic information to remap available meteorological driving data more accurately across complex terrain. The performance of meso-scale climate models in this application is currently poorly understood, and we will use the intensively-monitored Tuolumne and North Fork American River watersheds in the Sierra Nevada, California to develop, evaluate, and refine these new techniques at basin scales ranging from 10 to 800 km2. While specific research experiments will focus on two mountain watersheds in the western United States, the research is designed to develop and evaluate tools that will be broadly applicable in mountain environments across the western U.S. and potentially at the global scale.
The following research questions will be addressed: 1) What forcing data errors contribute most to hydrologic errors in the simulation of snow accumulation and melt processes at various spatial scales? 2) How can simulations from high-resolution meteorological models best be combined with topography and limited low-elevation station data on multiple scales to produce distributed forcing data for hydrologic simulations in complex terrain? 3) What are the strengths and limitations of meso-scale climate models when used for long-term hydrologic predictions?
Throughout, the study will focus on impacts on water resources, and results will be shared with resource managers. We will develop a course on combined hydrologic data collection and modeling for UW graduate students and will employ both undergraduates and graduates in data collection and analysis. We will work with National Park Service interpretive rangers to communicate climate-hydrology connections to the general public.
