Multiobjective calibration of land surface model evapotranspiration predictions using streamflow observations and spaceborne surface radiometric temperature retrievals

W. T. Crow, Eric F. Wood, M. Pan

Research output: Contribution to journalArticle

49 Scopus citations

Abstract

Physically based models of surface water and energy balance processes typically require a large number of soil and vegetation parameters as inputs. Accurate specification of these parameters is often difficult without resorting to calibration of model predictions against independent observations. Along with streamflow observations from gauging stations, spaceborne surface radiometric temperature retrievals offer the only independent observation of land surface model output commonly available at regional spatial scales (i.e., >502 km2). This analysis examines the potential benefits of incorporating spaceborne radiometric surface temperature retrievals and streamflow observations in a multiobjective calibration framework to accurately constrain regional-scale model evapotranspiration predictions. Results for the VIC (Variable Infiltration Capacity) model over the Southern Great Plains of the United States suggest that multiobjective model calibration against radiometric skin temperatures and steamflow observations can reduce error in model monthly evapotranspiration predictions by up to 20% relative to single-objective model calibration against streamflow alone.

Original languageEnglish (US)
Pages (from-to)ACL 5-1 - ACL 5-12
JournalJournal of Geophysical Research: Atmospheres
Volume108
Issue number23
DOIs
StatePublished - Dec 16 2003

All Science Journal Classification (ASJC) codes

  • Geophysics
  • Forestry
  • Oceanography
  • Aquatic Science
  • Ecology
  • Water Science and Technology
  • Soil Science
  • Geochemistry and Petrology
  • Earth-Surface Processes
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science
  • Palaeontology

Keywords

  • Land surface modeling
  • Multiobjective calibration
  • Remote sensing

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