1 / 18

Towards Remote Sensing for Hydrologic Prediction in Ungauged Basins

Explore the use of remote sensing for hydrologic prediction in ungauged basins. Address challenges in data collection and modeling while focusing on key elements like evapotranspiration, runoff, and soil properties.

botelho
Download Presentation

Towards Remote Sensing for Hydrologic Prediction in Ungauged Basins

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Towards Remote Sensing for Hydrologic Prediction in Ungauged Basins Jeffrey Walker Dept of Civil and Env Engg, University of Melbourne, Australia http://www.civenv.unimelb.edu.au/~jwalker

  2. The Challenge …. • In-situ observations: cover almost nothing but most of the time • Aircraft observations: cover almost everything but hardly ever • Satellite observations: cover everythingall of the time but not what we want • Modelling: pretends to cover everythingall of the time

  3. Hydrologic Prediction • Fluxes • Evapotranspiration • Sensible Heat Flux • Runoff • Drainage • Forcing • Precipitation • Radiation • Wind • Humidity • Air Temperature RO= P - ET –SM –GW -SWE • Parameters • Vegetation Properties • Topography • Soil Properties • States • Soil Moisture • Groundwater • Snow    met. inputs model parameters and physics prediction

  4. Hydrologic Remote Sending Radiation Soil Moisture Vegetation Precipitation Snow ET

  5. Precipitation: GOES, TRMM, SSM/I NRL 0.25o 6hr IR NCEP 40km 3hr NWP U of Az 0.25o 1hr IR-neural net NRL 0.25o 6hr Microwave NCEP 4km 1hr Gage/Radar CPC 0.25o 24hr Gauge Paul Houser

  6. Shortwave down 1 March 2003, W/m2 Radiation: GOES

  7. Model Parameters: LandSAT, MODIS Leaf Area Index Greenness Elevation

  8. Effect of Model Parameters Grayson, Western and Walker (2005)

  9. Evapotranspiration: Landsat, MODIS Savige, Western and Walker (2005)

  10. Evapotranspiration: Landsat, MODIS Sensible Heat Latent Heat Soil Temperature Soil Moisture observations model Pipunic, Walker and Western (2005)

  11. Soil Moisture: AMSR, SMOS, Hydros Walker et al. (2003) and Hemakumura, Kalma, Walker and Willgoose (2005)

  12. NDVI Data Walker et al (2003)

  13. Terrestrial Water Storage: GRACE Gravity Anomalie (mGal)

  14. Terrestrial Water Storage: GRACE Truth and Observations Ellett, Walker and Western (2005)

  15. Snow Cover: MODIS Rodell and Houser (2004)

  16. Snow Water Equivalent: AMSR Dong, Walker and Houser (2005)

  17. Streamflow: TOPEX/POSEIDON Ruediger, Walker, Kalma, Willgoose and Houser (2004)

  18. www.civenv.unimelb.edu.au/~jwalker/data/nafe Nov 2005 Nov 2006 Field Experiments

More Related