DocumentCode :
2783089
Title :
A macro hydrologic model simulation based on remote sensing data
Author :
Cai Yulin ; Guo Zhifeng ; Yang Li
Author_Institution :
Inst. of Remote Sensing Applic., CAS, Beijing
fYear :
2008
fDate :
June 30 2008-July 2 2008
Firstpage :
1
Lastpage :
4
Abstract :
The hydrologically based variable infiltration capacity (VIC) macroscale hydrologic model was applied to simulate streamflow for Poyang Lake Basin in China. DEM needed to get basin characteristics is from SRTM. The required soil parameters are derived from the soil classification information of global 5 min data provided by the National Atmospheric and Oceanic Administration (NOAA) Hydrology Office, the vegetation parameters are derived based on MODIS products and land data assimilation system (LDAS) and the forcing data are obtained through interpolation method based on 151 stations. All of the data (i.e. soil, vegetation, and forcings) needed by VIC-3L are compiled with at 8times8 km2 resolution. The VIC-3L model is applied to the Yellow River basin and the simulated daily runoff is routed to the outlet of two stations using ARNO model and compared to daily observed streamflow at these stations. Results show that remote sensing data can play the important role in model simulation process, though application of remote sensing data can not improve the performance of the model very much.
Keywords :
data assimilation; hydrological techniques; remote sensing; rivers; simulation; soil; vegetation; ARNO model; China; DEM; LDAS; MODIS products; NOAA Hydrology Office; National Atmospheric and Oceanic Administration; Poyang lake basin; SRTM; VIC macroscale hydrologic model; VIC-3L model; Yellow river basin; forcing data; interpolation method; land data assimilation system; macrohydrological model simulation; remote sensing data; simulated daily runoff; soil classification information; soil parameters; streamflow simulation; variable infiltration capacity; vegetation parameters; Atmospheric modeling; Data assimilation; Hydrology; Interpolation; Lakes; Linear discriminant analysis; MODIS; Remote sensing; Soil; Vegetation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Earth Observation and Remote Sensing Applications, 2008. EORSA 2008. International Workshop on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2393-4
Electronic_ISBN :
978-1-4244-2394-1
Type :
conf
DOI :
10.1109/EORSA.2008.4620290
Filename :
4620290
Link To Document :
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