• 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