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Comparing and combining SWE estimates from the SNOW‐17 model using PRISM and SWE reconstruction
Author(s) -
Raleigh Mark S.,
Lundquist Jessica D.
Publication year - 2012
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2011wr010542
Subject(s) - snow , extrapolation , forcing (mathematics) , snowmelt , precipitation , elevation (ballistics) , environmental science , terrain , meteorology , sensitivity (control systems) , climatology , geology , mathematics , statistics , geometry , geography , engineering , cartography , electronic engineering
Snow models such as SNOW‐17 may estimate past snow water equivalent (SWE) using either a forward configuration based on spatial extrapolation of measured precipitation, such as with the parameter‐elevation regressions on independent slopes model (PRISM), or a reconstruction configuration based on snow disappearance timing and back‐calculated snowmelt. However, little guidance exists as to which configuration is preferable. Because the two approaches theoretically have opposite sensitivities to model forcing, combining (averaging) their SWE estimates may be advantageous. Using 154 snow pillow sites located in maritime mountains of the western United States, we compared forward, reconstruction, and combined configurations of a simplified SNOW‐17. We evaluated model errors in annual precipitation, peak SWE, and SWE errors during the accumulation and ablation seasons. We also conducted a separate analysis to assess the sensitivity of peak SWE to biased forcing data and model parameters. The forward model had the greatest precipitation accuracy, while the combined model had the greatest accuracy in peak SWE and SWE during the accumulation and ablation seasons. In determining peak SWE, the forward and reconstruction models demonstrated opposite sensitivities to errors in air temperature and model parameters, and the combined model minimized errors due to temperature bias and parameter uncertainty. In basins with precipitation gages, we recommend PRISM for precipitation estimation and the combined model for SWE estimation. In areas with high precipitation uncertainty, reconstruction is more viable. Accurate model parameters dramatically improved reconstruction, so more work is needed to advance parameter estimation techniques in complex terrain.