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

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.