1. Use of satellite remote sensing in a medium range global flood prediction scheme
Nathalie Voisin1 , Andrew W. Wood1 , Dennis P. Lettenmaier1 and Eric F. Wood2
1 Department of Civil and Environmental Engineering, University of Washington
2 Department of Civil and Environmental Engineering, Princeton University

2. Outline Background and Objectives
Brief description of the prediction scheme
Use of satellite in the downscaling process of weather forecasts
Preliminary results for
Rhine Flood 1995 ( mostly rain, then snowmelt)
Danube Flood 2006 (snowmelt and rain)
Limpopo flood 2000 (tropical storm)

3. Background Flood prediction systems exist in developed Countries
What about developing countries?
The potential for global flood prediction system exists
Global weather models : analysis and forecasts are available
Issues: scale?

4. Objectives
Ultimate objective: to predict streamflow and associated hydrologic variables, soil moisture, runoff, evaporation and snow water equivalent :
At a global scale
Spatial consistency
Especially in ungauged or poorly gauged basins
medium-range time scale ( up to 2 weeks)
This talk: to suggest a method to spatially downscale global weather forecasts without any local information ( gauges or radar)

5. The global prediction scheme
(here in retrospective mode)
Atmospheric inputs
NCEP Reforecasts (Hamill et al. 2006) 15 ensemble members – 15 day forecast – 2.5 degree (fixed GFS version of 1998)
Daily NCEP Reanalysis 1
downscaled to 0.5 degree using linear inverse distance square interpolation.
Bias correction at 2.5 degree, with respect to NCEP Reanalysis (Ensures consistency between spinup and the reforecasts)
Downscaling from 2.5 to 0.5 degree
using the Schaake Shuffle ( Clark et al. 2004) with higher spatial resolution satellite GPCP 1dd (Huffman et al. 2001) and TRMM 3B42 precipitations
Hydrologic
model spin up
(0.5 degree global simulation)
Hydrologic forecast simulation
INITIAL STATE
(0.5 degree global simulation: stream flow, soil moisture, SWE, runoff )
Several years back
Nowcasts
Medium range forecasts
( up to 2 weeks)
Hydrology Model

6. The global prediction system
The bias correction of GFS reforecasts
1.Bias Correction ( Quantile-Quantile technique with respect to NCEP Reanalysis climatology)
2.Correction for Daily Intermittency ( with respect to NCEP Reanalysis climatology)
Figure from Wood and Lettenmaier, 2004: A testbed for new seasonal hydrologic forecasting approaches in the western U.S.

7. Use of satellite for downscaling forecasts
Limpopo Basin, 2.5 degree grid
South Africa, 2.5 degree grid
Limpopo Basin, 0.5 degree grid

8. Use of satellite for downscaling forecasts
Satellite Datasets
TRMM 3B42
50oS-50oN
0.25 degree, 3 hourly, 2002-present
Use ( to be updated yearly)
GPCP 1dd
Global, but used for 50oN-90oN and 50oS-90oS
1 degree, daily, Oct 1996 – 3 months before present
Use (to be updated yearly), interpolated to 0.5 degree using an inverse distance square interpolation.

9. Use of satellite for downscaling forecasts
Simplified Schaake Schuffle (Clark et al. 2004)
to construct spatial patterns of precipitation within each 2.5 degree cell based on observations ( here, satellite)
For each 2.5 degree cell, for each lead time:
15 satellite observations are randomly selected ( based on rain / no rain, specific to calendar month )
for each ranked forecast ensemble member, it associates the corresponding ranked observation ( 15 ensemble members).
→ ensures that the selected highest observed precipitation event is assigned to the highest forecast
GFS refcst, 2.5 deg, rank ith
Satellite, 2.5 deg, rank ith

10. Use of satellite for downscaling forecasts
TRMM 3B42 aggregated to daily and 2.5 degree resolution
→ resolution of the weather forecasts
TRMM 3B42,
2.5 degree
Jan 31st, 2001
TRMM 3B42 aggregated to daily and 0.5 degree resolution
→ resolution of the hydrologic model
TRMM 3B42,
0.5 degree
Jan 31st, 2001
→Need a downscaling method that inserts localized precipitation patterns
(mm/day)

11. Use of satellite for downscaling forecasts
Simplified Schaake Shuffle (2):
The corresponding observed value field at 0.5 degree resolution gives the spatial distribution of precipitation, but NOT the magnitude
Here for one ensemble member, one lead time : / =
Ratio of Satellite observations, 0.5 deg resolution
Satellite, 0.5 deg, corresponding record to the 2.5 degree cells
Satellite, 2.5 deg

12. Use of satellite for downscaling forecasts
Dowscaling of precipitation characteristics:
Spatial distribution from satellite observations
Magnitude of the bias corrected GFS reforecasts
Consistency between spin up dataset and bias corrected downscaled forecasts
Here for one ensemble member, one lead time : X =
Bias corrected GFS refcst, 2.5 deg
Ratio of satellite obs. 0.5 degree to 2.5 degree
Bias corrected and downscaled (0.5 degree) GFS reforecast

13. Preliminary Results Rhine Flood, 1995 (Forecast of January 20th, 1995)
5 day precipitation accumulation fields
Bias corrected GFS Fcst.
Ens. Avg,
simple interpol.
Bias corrected GFS Fcst.
Ens. Avg,
Downscaled
NCEP Rean.,
simple interpol.
GFS Det. Fcst.,
simple interpol.
1 to 5 days
6 to 10 days

14. Preliminary Results Danube Flood, 2006 (Forecast of April 1st, 2006)
5 day precipitation accumulation fields
Bias corrected GFS Fcst.
Ens. Avg,
simple interpol.
Bias corrected GFS Fcst.
Ens. Avg,
Downscaled
NCEP Rean.,
simple interpol.
1 to 5 days
6 to 10 days

15. Preliminary Results
Limpopo Flood, 2000 (Forecast of February 3rd, 2000)
5 day precipitation accumulation fields
Bias corrected GFS Fcst.
Ens. Avg,
simple interpol.
Bias corrected GFS Fcst.
Ens. Avg,
Downscaled
NCEP Rean.,
simple interpol.
GFS Det. Fcst.,
simple interpol.
1 to 5 days
6 to 10 days

16. Conclusions
1) Improvement of using this downscaling method rather than a simple inverse distance square interpolation method
E.g. representation of topography ( snowmelt)
Less obvious for tropical storm in flat and arid areas like South Eastern Africa
2) Need to compare it with more sophisticated, but local downscaling methods
Using a nested regional scale model
Equivalent downscaling techniques using high resolution datasets based on gauges, in regions where in situ network exists

17. Conclusions About the entire prediction scheme …
3) The scheme performance is very dependent on the quality of the forecasts.
4) The full scheme, including hydrologic simulations, will be evaluated with respects to other existing flood prediction systems. Calibration will be an essential step.

18. Thank You!
April 2006 Flood in Romania,

19. The global prediction scheme
The hydrology model VIC
- Semi-distributed model driven by a set of surface meteorological data ( precipitation, wind, solar radiation derived from Tmin and Tmax, etc)
- Represents vegetation, has three soil layers with variable infiltration, non linear base flow .

20. The global prediction scheme
The river routing model
- Runoff and baseflow for each cell is then routed toward selected locations, following directions equivalent to channels.
Routing at 0.5 degree derived from the manually corrected global direction file from Döll and Lehner (2002)
- Already calibrated and validated at 2 degree resolution over 26 basins worldwide (Nijssen et al. 2001)

21. Use of satellite for downscaling forecasts
Scaling of precipitation 2 1
Corresponding record for each cell, 0.5 degree
Schaake
Shuffle
SATELLITE, 0.5 deg
GFS refcst, 2.5 deg
SATELLITE, 2.5 deg 3 4
Ratio
Scale 2.5 degree reforecast with SAT ratio
Ratio of SAT 0.5 degree to 2.5 degree
Downscaled GFS reforecast

22. Link picture