ESRL/PSD Seminar Series
Improving Spatial Precipitation Estimates in Complex Terrain Using Streamflow and Snow Observations.
Associate Professor, Civil and Environmental Engineering Department, University of Washington
Precipitation in mountain watersheds is difficult to measure accurately over large areas due to its spatial variability, which is not captured well by the sparse network of gauges located at high altitudes. Therefore, predictions which rely on mountain basin-average precipitation, such as flood and water supply volume forecasts, often have large uncertainty. However, other point observations are made more reliably in these areas, including streamflow measurements. Therefore, we propose a method of retrospectively using observations of streamflow to infer basin-averaged mountain precipitation. We use streamflow observations to estimate basin-averaged annual precipitation for four Yosemite National Park (CA) watersheds. We use a simple hydrologic model of snowpack, soil water storage and runoff to assess the likelihood of the precipitation observations being representative of the basin, given our observations of streamflow. The results of this method provide an improved estimate of basin-average precipitation and its confidence bounds compared to precipitation gauge records alone. We show that it is possible to infer both climatological and year-to-year precipitation patterns over a watershed from seasonal streamflow quantities and timing. We compare our findings to other basin-average precipitation estimates (PRISM and weather model output). Annual-scale spatial deviations from precipitation climatology are currently difficult to identify; our approach could identify storm patterns that lead to such deviations and thus improve future forecasts.
Friday Feb 21st
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