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| H37: | Remote Sensing and Modeling of Land Surface Hydrological Processes |
| Sponsor: |
Hydrology
|
| CoSponsor: |
Atmospheric Sciences |
| Convener: |
Michael H. Cosh USDA-ARS-HRSL USA Michael.Cosh@ars.usda.gov Jasmeet Judge University of Florida USA jasmeet@ufl.edu Yang Hong University of Oklahoma School of Civil Engineering and Environmental Sciences Norman, OK, USA yanghong@ou.edu Craig Ferguson Princeton University Deparment of Civil and Environmental Engineering Princeton, NJ, USA cferguso@princeton.edu Qiuhong Tang University of Washington Department of Civil and Environmental Engineering Box 352700 Seattle, WA, USA 206-685-3202 qiuhong@hydro.washington.edu |
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1855 1839 1847 1814 1843 .
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| Description: | This session seeks contributions from the research, operational, and user communities that address various applications of remote sensing (RS) observations for development, improvement, validation, and use of hydrological models. Recent studies have demonstrated that combination of RS observations with land surface modeling, through initialization, parameterization, and data assimilation, can provide significant improvement in representation of hydrological processes. The goal of this session is to host a dialogue among the three communities, particularly between those involved in development of operational RS algorithms for retrieving hydrological variables and those involved in the use of RS observations in land surface models. Of particular interest are ongoing investigations that have developed models or have implemented applications in which RS observations are utilized, either as inputs (e.g. radiative energy fluxes, soil moisture, precipitation, discharge, evapotranspiration, snow depth, and snow water equivalent), or as parameters (e.g. land use/cover, vegetation fraction/water content, emissivity, and albedo). Contributions are also encouraged that address quantification of uncertainties in hydrological products due to errors in temporal sampling, sensor, and algorithm, magnitudes of which depend on the hydrological variable(s), sensor, and spatio-temporal scale of interest. |
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