Spatiotemporal patterns of precipitation inferred from streamflow observations across the Sierra Nevada mountain range

Brian Henn, Martyn P. Clark, Dmitri Kavetski, Andrew J. Newman, Mimi Hughes, Bruce McGurk, Jessica D. Lundquist

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Given uncertainty in precipitation gauge-based gridded datasets over complex terrain, we use multiple streamflow observations as an additional source of information about precipitation, in order to identify spatial and temporal differences between a gridded precipitation dataset and precipitation inferred from streamflow. We test whether gridded datasets capture across-crest and regional spatial patterns of variability, as well as year-to-year variability and trends in precipitation, in comparison to precipitation inferred from streamflow. We use a Bayesian model calibration routine with multiple lumped hydrologic model structures to infer the most likely basin-mean, water-year total precipitation for 56 basins with long-term (>30 year) streamflow records in the Sierra Nevada mountain range of California. We compare basin-mean precipitation derived from this approach with basin-mean precipitation from a precipitation gauge-based, 1/16° gridded dataset that has been used to simulate and evaluate trends in Western United States streamflow and snowpack over the 20th century. We find that the long-term average spatial patterns differ: in particular, there is less precipitation in the gridded dataset in higher-elevation basins whose aspect faces prevailing cool-season winds, as compared to precipitation inferred from streamflow. In a few years and basins, there is less gridded precipitation than there is observed streamflow. Lower-elevation, southern, and east-of-crest basins show better agreement between gridded and inferred precipitation. Implied actual evapotranspiration (calculated as precipitation minus streamflow) then also varies between the streamflow-based estimates and the gridded dataset. Absolute uncertainty in precipitation inferred from streamflow is substantial, but the signal of basin-to-basin and year-to-year differences are likely more robust. The findings suggest that considering streamflow when spatially distributing precipitation in complex terrain may improve its representation, particularly for basins whose orientations (e.g., windward-facing) are favored for orographic precipitation enhancement.

Original languageEnglish
Pages (from-to)993-1012
Number of pages20
JournalJournal of Hydrology
Volume556
DOIs
StatePublished - Jan 2018
Externally publishedYes

Keywords

  • Bayesian inference
  • Mountain hydrology
  • Orographic enhancement
  • Precipitation
  • Sierra Nevada
  • Streamflow

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