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

41 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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