HR: 0800h
AN: H31H-0766    [Abstracts]
TI: Improvements to modeled latent heat fluxes through the assimilation of soil moisture variability
AU: Alavi, N
EM: nalavi@uoguelph.ca
AF: Dept. of Land Resource Science, University of Guelph, Guelph, ONT N1G2W1, Canada
AU: * Berg, A
EM: aberg@uoguelph.ca
AF: Dept. of Geography, University of Guelph, Guelph, ONT N1G2W1, Canada
AU: Warland, J
EM: jwarland@uoguelph.ca
AF: Dept. of Land Resource Science, University of Guelph, Guelph, ONT N1G2W1, Canada
AB: Soil moisture is highly variable in space and time due to variations in climatic, topographic, vegetative, and soil properties. While it has been demonstrated in numerous studies that assimilation of soil moisture improves modeled estimates of evapotranspiration (ET), few studies have examined the incorporation of sub-grid-scale soil moisture variability into data assimilation schemes. In this study we assimilate soil moisture data collected at the field scale over a 70x70m grid into the CLASS model (Canadian Land Surface Scheme) using three versions of an ensemble Kalman filter. The results demonstrate that assimilating field soil moisture variability into CLASS improved model latent heat flux estimates by up to 14 percent when compared to estimates from a flux tower. However, the amount of improvement depends on the method and timing of assimilation. The effect was maximum at the beginning of the growing season, while it was minimum at peak growth. This study also demonstrates that assimilation of soil moisture variability into CLASS can result in greater improvement in modeled ET than assimilation of the mean of the sampling area.
DE: 1847 Modeling
DE: 1866 Soil moisture
SC: Hydrology [H]
MN: 2007 Fall Meeting