A44B-04
Precipitation trends in the High Sierra of California inferred from streamflow and snowpack observations

Thursday, 17 December 2015: 16:45
3008 (Moscone West)
Brian M Henn, University of Washington Seattle Campus, Civil and Environmental Engineering, Seattle, WA, United States, Martyn P Clark, National Center for Atmospheric Research, Boulder, CO, United States, Dmitri Kavetski, School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, South Australia, Australia, Bruce J McGurk, Self Employed, McGurk Hydrologic, Washington, DC, United States, Thomas H Painter, NASA Jet Propulsion Laboratory, Pasadena, CA, United States and Jessica D Lundquist, University of Washington, Seattle, WA, United States
Abstract:
Directly observing basin-mean precipitation in the high-elevation basins of the Sierra Nevada of California is challenging, due to the sparse network of precipitation gauges at these altitudes and due to snow undercatch errors. However, numerous observations of streamflow and snowpack are also made in the Sierra, which offer additional information about the water balance of the basin with which to estimate precipitation.

We develop and apply a Bayesian methodology for inferring basin-mean precipitation from streamflow and snowpack observations, in multiple snow-dominated Sierra basins. To do this, we calibrate semi-lumped hydrologic models representing each basin, using streamflow and snowpack to infer multiplicative corrections to precipitation forcing data, and thus improve basin-mean precipitation estimates. We use unimpaired streamflow observations from multiple basins across the Sierra from the mid-20th century to the present, as well as snowpack observations combined from snow pillows, courses and airborne LIDAR observations (ASO).

For each basin, mean precipitation is inferred both as a long-term average and for individual water years. We investigate the inferred spatial patterns of precipitation in comparison to standard climatological and daily gridded precipitation datasets, finding significant differences in some basins and in some water years. For example, see figure of different estimates of 1982-2006 mean annual precipitation for several Yosemite-area basins (figure from manuscript currently in review at Water Resources Research). We also investigate the precision and robustness of inferring basin-mean precipitation rates from streamflow and snowpack observations. Uncertainties in this approach are associated with uncertainties in lumped hydrologic model structure and in the methodology of translating point snow observations to the basin scale. Using both snowpack and streamflow observations in the inference likely reduces these uncertainties, relative to using streamflow or snow alone.

Based on this work, we hypothesize that the addition of streamflow and snowpack observations to standard approaches of distributing precipitation would improve their accuracy in areas of high-elevation, complex terrain.

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