Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction

Anna Ryken, Lindsay A. Bearup, Jennifer L. Jefferson, Paul Constantine, Reed M. Maxwell

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The hydrology of high-elevation, mountainous regions is poorly represented in Earth Systems Models (ESMs), yet these ecosystems play an important role in the storage and land-atmosphere exchange of water. As much of the western United States’ water comes from water stored in the snowpack (snow water equivalent, SWE), model representation of these regions is important. This study assesses how uncertainty in both model parameters and forcing affect simulated snow processes through sensitivity analysis (active subspaces) on model inputs (meteorological forcing and model input parameters) for a widely used snow model. Observations from an AmeriFlux tower at the Niwot Ridge research site are used to force an integrated, single-column hydrologic model, ParFlow-CLM. This study finds that trees can mute the effects of snow albedo causing the evergreen needleleaf scenarios to be sensitive primarily to hydrologic forcing while bare ground simulations are more sensitive to the snow parameters. The bare ground scenarios are most sensitive overall. Both forcing and model input parameters are important for obtaining accurate hydrologic model results.

Original languageEnglish (US)
Article number103473
JournalAdvances in Water Resources
Volume135
DOIs
StatePublished - Jan 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

Keywords

  • Active subspaces
  • Hydrologic modeling
  • Sensitivity analysis
  • Snow water equivalent

Fingerprint

Dive into the research topics of 'Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction'. Together they form a unique fingerprint.

Cite this