Using Indirect Observations to Infer Patterns of Precipitation in the Sierra Nevada

Mountain precipitation in the western United States is critical for the water resources of the region, but resolving 
spatial and temporal patterns of precipitation in complex terrain is challenging due to lack of observations, 
measurement uncertainty and high spatial variability. This research finds that widely-used gridded precipitation 
datasets exhibit substantial variation in water-year total precipitation over different areas of the range. In 
addition, trends in precipitation and snow computed from different datasets vary substantially. A methodology is 
developed for inferring water year total basin-mean precipitation from daily streamflow streamflow observations 
using lumped hydrologic models and Bayesian model calibration. To resolve patterns of precipitation over the Sierra 
Nevada, we infer precipitation from streamflow using 56 stream gauges that measure runoff from relatively unimpaired 
basins over 1950-2010. We compare inferred precipitation to gauge-based gridded precipitation data, finding that 
significant differences exist between the mean spatial patterns of precipitation over the range. In particular, 
inferred precipitation suggests that gridded products underestimate precipitation for higher-elevation basins whose 
aspect faces prevailing winds. These findings suggest that the development of spatially distributed precipitation 
datasets should consider other related hydrologic observations in order to better resolve patterns of precipitation 
in high-elevation areas.