Simulating Surface Energy Fluxes and Radiometric Surface Temperatures for Two Arid Vegetation Communities Using the SHAW Model

Abstract

While land?atmosphere transfer models have been pursued for over 30 years, Soil?Vegetation?Atmosphere?Transfer (SVAT) models are gaining attention only recently as the need to better represent the interaction between the soil and atmosphere in atmospheric circulation models becomes more apparent. The Simultaneous Heat and Water (SHAW) model, a detailed physical process model, simulates the effects of a multispecies plant canopy on heat and water transfer at the soil?atmosphere interface. The model was used in this study to simulate the surface energy balance and surface temperature of two vegetation communities using data collected during the Monsoon ?90 multidisciplinary field experiment. The two vegetation communities included a sparse, relatively homogeneous, grass-dominated community and a shrub-dominated site with large bare interspace areas between shrubs. The model mimicked the diurnal variation in the surface energy balance at both sites, while canopy leaf temperatures were simulated somewhat better at the relatively homogeneous grass-dominated site. The variation in surface fluxes accounted for by the model (i.e., model efficiency) ranged from 59% for latent heat flux at the shrub-dominated site to 98% for net radiation at both sites. Model efficiency for predicting latent heat flux at the grass-dominated site was 65%. Canopy leaf temperatures for the shrub-dominated site were consistently overpredicted by 1.8°C compared to measured values. Simulated soil surface temperatures at both sites had a model efficiency of 94% and a mean bias error of less of than 0.9°C. The ability of the model to simulate canopy and soil surface temperatures gives it the potential to be verified and periodically updated using remotely sensed radiometric surface temperature and soil moisture when extrapolating model-derived fluxes to other areas. A methodology is proposed whereby model predictions can be used with a combination of remotely sensed radiometric surface temperature and surface soil moisture to estimate soil water content within the rooting depth.