HR: 1340h
AN: GC23A-0981    [Abstracts]
TI: Coupling Satellite and Ground-Based Snow Data With Snow Cover Model for Estimating the Area-Averaged Snow Water Equivalent Over Large River Basins
AU: Kuchment, L
EM: kuchment@mail.ru
AF: Water Problem Institute of Russian Academy of Sciences, 3 Gubkin Str., WPI RAS, Moscow, 119333, Russian Federation
AU: Romanov, P
EM: Peter.Romanov@noaa.gov
AF: University of Maryland, 2207 Computer & Space Sciences Building, College Park, MD 20742, United States
AU: * Gelfan, A
EM: hydrowpi@aqua.laser.ru
AF: Water Problem Institute of Russian Academy of Sciences, 3 Gubkin Str., WPI RAS, Moscow, 119333, Russian Federation
AU: Demidov, V
EM: demidov@aqua.laser.ru
AF: Water Problem Institute of Russian Academy of Sciences, 3 Gubkin Str., WPI RAS, Moscow, 119333, Russian Federation
AU: Tarpley, D
EM: dtarpley@nesdis.noaa.gov
AF: NOAA Office of Research and Applications, Room 712, 5200 Auth Road,Camp Springs, Camp Springs, MD 20746, United States
AB: Improvement of long-range forecasts of snowmelt flood volume is one of key hydrological problems in Northern Russia. Accurate quantitative characterization of snow cover properties required in snowmelt runoff models is challenging in this region since the existing network of hydrometeorological stations is sparse. Application of satellite data for snow monitoring is hampered by large areas of coniferous forests masking the snow pack and by persistent cloudiness in the fall and winter season. In order to enhance quantitative characterization of snowpack properties we have developed a new technique where satellite data are coupled with a snow cover model. The physically-based snowpack model uses interpolated data from ground-based meteorological stations and incorporates a number of products derived from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites. The input satellite data include albedo, land surface temperature, leaf area index and the canopy coverage. The outputs of the model are the snow depth, snow density, ice and liquid water content of snow and the snow grain size. The model was tested over a region with a size of ~240 000 km2 (56°N to 60°N, and 48°E to 54°E) located within the NEESPI area. This region includes the Vyatka River basin with the catchment area of about 120 000 km2. Snow pack simulations were conducted for 1 x 1 km grid cells for the spring season of 2002 and 2003. Spatial correlation between the modeled snow extent and the MODIS-derived snow cover distribution over the study area ranged from 0.9-1.0 in the beginning and in the end of the melt season to 0.5-0.6 during the period of intensive snow melt. The analysis of MODIS snow retrievals over the study area demonstrated their good agreement with surface observations. Satellite information on snow cover was not used in the current version of the model, however high accuracy of satellite snow retrievals makes their incorporation in the next version of the model very attractive. In the presentation we will discuss ways to incorporate satellite snow retrievals in the snowpack model and advantages of the use of improved estimates of SWE in runoff hydrograph calculations.
DE: 1615 Biogeochemical cycles, processes, and modeling (0412, 0414, 0793, 4805, 4912)
DE: 1621 Cryospheric change (0776)
DE: 1631 Land/atmosphere interactions (1218, 1843, 3322)
DE: 1655 Water cycles (1836)
DE: 1834 Human impacts
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting