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At high latitudes, three quarters of the land surface is boreal conifer forests, and snow remains for six to eight months of the year. Correctly modeling snowmelt at high-latitudes has a significant impact on meteorological and hydrological processes. Snowmelt affects surface energy and water balance. Spring water supply in those regions is highly dependent on snowmelt. However the hierarchically heterogeneous and clumped forest structure modifies both the shortwave and longwave radiation reaching the underlying surface, which has important implications on snowmelt. In this talk, I will present our recent research results on examining the influence of heterogeneous and clumped vegetation structure on land surface radiation and energy balance and the snowmelt rate in boreal ecosystems through coupling a simple but physically based canopy radiative transfer scheme developed for a Dynamic Global Terrestrial Ecosystem Model (DGTEM) with the Variable Infiltration Capacity (VIC) macroscale hydrological model. Validation in boreal forests demonstrates that consideration of the clumped vegetation structure on land surface radiation scheme improves snowmelt rate dramatically.
Dr. Wenge Ni-Meister is Associate Professor of Geography at Hunter College of The City University of New York. She received her B.S. and M.S. in meteorology and climatology in China, M.S. in land-atmosphere interactions from the University of Connecticut, and PhD in remote sensing science of terrestrial ecosystem from Boston University. She worked as a research scientist at the University of Maryland and NASA Goddard Space Flight Center before joining Hunter College. Her main areas of interest are modeling of terrestrial ecosystem dynamics and land-atmosphere interactions and terrestrial ecosystem structure characterization from lidar remote sensing, fusion of remote sensing data and physical models through data assimilation, canopy radiative transfer and snow vegetation interactions. The main goal of her research is to develop schemes to merge remote sensing satellite data with ecological and hydrological physical models for improved estimate of terrestrial carbon, water and energy.