1. Potential for medium range global flood prediction Nathalie Voisin 1, Andrew W. Wood 1, Dennis P. Lettenmaier 1 1 Department of Civil and Environmental Engineering, University of Washington

2. Outline Background and Objectives Description of the prediction scheme: –The hydrology model –The scheme –The bias correction Use of satellite in the downscaling process of weather forecasts Preliminary results for –Rhine Flood 1995 ( mostly rain, then snowmelt) –Limpopo flood 2000 (tropical storm)

3. Background Need for flood prediction globally? www.dartmouth.edu/~floods, Dartmouth Flood Observatory

4. 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?

5. 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 downscale global weather forecasts into a higher spatial resolution without any local information ( gauges or radar)

6. 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.

7. 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)

8. The global prediction scheme Hydrologic model spin up (0.5 degree global simulation) Several years back Hydrologic forecast simulation Nowcasts INITIAL STATE Medium range forecasts ( up to 2 weeks) Daily ERA-40 downscaled to 0.5 degree using linear inverse distance square interpolation. NCEP Reforecasts (Hamill et al. 2006) 15 ensemble members – 15 day forecast – 2.5 degree (fixed GFS version of 1998) Bias correction at 2.5 degree, with respect to ERA- 40 (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 (0.5 degree global simulation: stream flow, soil moisture, SWE, runoff ) Atmospheric inputs Hydrology Model (here in retrospective mode)

9. Retrospective forecasting: Reforecasts Hamill et al. (2006) NOAA NCEP-MRF, 1998 version 1979-present 15-day forecasts issued daily 15 member ensemble forecast 2.5 degree resolution Near Real Time forecasting: ECMWF and/or NCEP analysis

10. The global prediction system The bias correction of GFS reforecasts (1) 1. Quantile-Quantile technique with respect to ERA-40 climatology -ERA-40 cdf based on a 9 day moving window, centered on the day of the forecast ( 9 * 23 values ) -GFS reforecast cdf for the 15 ensemble average, for each lead time, fixed 7 day window -Extreme values: low values fitted with Weibull distribution and high values fitted with Gumbel distribution Figure from Wood and Lettenmaier, 2004: A testbed for new seasonal hydrologic forecasting approaches in the western U.S.

11. The global prediction system The bias correction of GFS reforecasts (2) 2.Correction for daily intermittency ( with respect to ERA-40 climatology)

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

13. Use of satellite for downscaling forecasts Satellite Datasets → TRMM 3B42 –50 o S-50 o N –0.25 degree, 3 hourly, 2002-present –Use 2002-2006 ( to be updated yearly) → GPCP 1dd –Global, but used for 50 o N-90 o N and 50 o S-90 o S –1 degree, daily, Oct 1996 – 3 months before present –Use 1997-2005 (to be updated yearly), interpolated to 0.5 degree using an inverse distance square interpolation.

14. 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 i th Satellite, 2.5 deg, rank i th

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

16. 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 Satellite, 2.5 deg Satellite, 0.5 deg, corresponding record to the 2.5 degree cells Ratio of Satellite observations, 0.5 deg resolution / = Here for one ensemble member, one lead time :

17. 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 Bias corrected and downscaled (0.5 degree) GFS reforecast Bias corrected GFS refcst, 2.5 deg Ratio of satellite obs. 0.5 degree to 2.5 degree X = Here for one ensemble member, one lead time :

18. Preliminary Results Rhine Flood, 1995 (Forecast of January 20 th, 1995) 5 day precipitation accumulation fields ERA-40, simple interpol. GFS Det. Fcst., simple interpol. Bias corrected GFS Fcst. Ens. Avg, Downscaled 1 to 5 days 6 to 10 days

19. Preliminary Results Rhine Flood, 1995 (Forecast of January 20 th, 1995) 5 day runoff accumulation fields ERA-40, simple interpol. GFS Det. Fcst., simple interpol. Bias corrected GFS Fcst. Ens. Avg, Downscaled 1 to 5 days 6 to 10 days

20. Preliminary Results Rhine Flood, 1995 (Forecast of January 20 th, 1995) 5 day change in soil moisture ERA-40, simple interpol. GFS Det. Fcst., simple interpol. Bias corrected GFS Fcst. Ens. Avg, Downscaled 1 to 5 days 6 to 10 days

21. Preliminary Results Rhine Flood, 1995 (Forecast of January 20 th, 1995) 5 day change in SWE ERA-40, simple interpol. GFS Det. Fcst., simple interpol. Bias corrected GFS Fcst. Ens. Avg, Downscaled 1 to 5 days 6 to 10 days

22. Preliminary Results Rhine Flood, 1995 (Forecast of January 20 th, 1995) Discharge (cms)

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

24. Preliminary Results Limpopo Flood, 2000 (Forecast of February 3 rd, 2000) 5 day precipitation accumulation fields ERA-40 Rean., simple interpol. GFS Det. Fcst., simple interpol. Bias corrected GFS Fcst. Ens. Avg, Downscaled 1 to 5 days 6 to 10 days

25. Preliminary Results Limpopo Flood, 2000 (Forecast of February 3 rd, 2000) 5 day runoff accumulation fields ERA-40 Rean., simple interpol. GFS Det. Fcst., simple interpol. Bias corrected GFS Fcst. Ens. Avg, Downscaled 1 to 5 days 6 to 10 days

26. Preliminary Results Limpopo Flood, 2000 (Forecast of February 3 rd, 2000) 5 day change in soil moisture ERA-40 Rean., simple interpol. GFS Det. Fcst., simple interpol. Bias corrected GFS Fcst. Ens. Avg, Downscaled 1 to 5 days 6 to 10 days

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

28. 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

29. 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.

30. Thank You! April 2006 Flood in Romania, http://www.spiegel.de/fotostrecke/0,5538,13382,00.html

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

32. Link picture www.spiegel.de/img/0,1020,611798,00.jpg http://www.spiegel.de/fotostrecke/0,5538,13382, 00.html