Doing Hydrology Backward: Estimating Mountain Precipitation Patterns from Streamflow

Estimation of basin-mean precipitation in mountainous areas may be difficult due to high spatial variability and time-varying precipitation patterns. Streamflow offers additional information about the water balance of the basin with which to estimate precipitation. We apply a methodology for inferring basin-mean precipitation from streamflow using Bayes’ Theorem, and adapt this approach to snow-dominated basins in the Sierra Nevada mountain range of California. As part of this approach, we develop and couple a temperature-index snow model to an existing lumped hydrologic model. We infer 1981-2006 annual average precipitation across four basins and compare the results to those obtained from similar approaches based on climatological precipitation patterns. We also use the approach to identify an example of year-to-year variability in precipitation patterns, finding that the inferred precipitation patterns generally match other observations from two anomalous water years. The method offers the potential for inferring spatial precipitation patterns at a level of precision that could improve upon current methods. Future work on this approach will focus on identifying spatial patterns of precipitation across a more extensive collection of basins and water years.