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Article
Corruption of parameter behavior and regionalization by model and forcing data errors: A Bayesian example using the SNOW17 model
Water Resources Research
  • Minxue He, University of California, Los Angeles
  • Terri S. Hogue, University of California, Los Angeles
  • Kristie J. Franz, Iowa State University
  • Steven A. Margulis, University of California, Los Angeles
  • Jasper A. Vrugt, University of California, Irvine
Document Type
Article
Publication Version
Published Version
Publication Date
7-1-2011
DOI
10.1029/2010WR009753
Abstract

The current study evaluates the impacts of various sources of uncertainty involved in hydrologic modeling on parameter behavior and regionalization utilizing different Bayesian likelihood functions and the Differential Evolution Adaptive Metropolis (DREAM) algorithm. The developed likelihood functions differ in their underlying assumptions and treatment of error sources. We apply the developed method to a snow accumulation and ablation model (National Weather Service SNOW17) and generate parameter ensembles to predict snow water equivalent (SWE). Observational data include precipitation and air temperature forcing along with SWE measurements from 24 sites with diverse hydroclimatic characteristics. A multiple linear regression model is used to construct regionalization relationships between model parameters and site characteristics. Results indicate that model structural uncertainty has the largest influence on SNOW17 parameter behavior. Precipitation uncertainty is the second largest source of uncertainty, showing greater impact at wetter sites. Measurement uncertainty in SWE tends to have little impact on the final model parameters and resulting SWE predictions. Considering all sources of uncertainty, parameters related to air temperature and snowfall fraction exhibit the strongest correlations to site characteristics. Parameters related to the length of the melting period also show high correlation to site characteristics. Finally, model structural uncertainty and precipitation uncertainty dramatically alter parameter regionalization relationships in comparison to cases where only uncertainty in model parameters or output measurements is considered. Our results demonstrate that accurate treatment of forcing, parameter, model structural, and calibration data errors is critical for deriving robust regionalization relationships.

Comments

This article is from Water Resources Research 47 (2011): art. no. W07546, doi: 10.1029/2010WR009753. Posted with permission.

Copyright Owner
American Geophysical Union
Language
en
File Format
application/pdf
Citation Information
Minxue He, Terri S. Hogue, Kristie J. Franz, Steven A. Margulis, et al.. "Corruption of parameter behavior and regionalization by model and forcing data errors: A Bayesian example using the SNOW17 model" Water Resources Research Vol. 47 Iss. 7 (2011) p. art. no. W07546
Available at: http://works.bepress.com/kristie-franz/7/