1. Global Flood and Drought Prediction GEWEX 2005 Meeting, June 20-24 Role of Modeling in Predictability and Prediction Studies Nathalie Voisin, Dennis P. Lettenmaier, Eric F. Wood

2. Global Floods and Droughts Floods –$50-60 billion USD /year, worldwide –520+ million people impacted per year worldwide –Estimates of up to 25,000 annual deaths Mostly in developing countries; Mozambique in 2000 and 2001, Vietnam and others (Mekong) in 2000. Droughts –1988 US Drought: $40 billion –Famine in many countries: 200,000 people killed in Ethiopia in 1973-74 Source: United Nations University, http://update.unu.edu/archive/issue32_2.htm http://www.unu.edu/env/govern/ElNIno/CountryReports/inside/ethopia/Executive%20Summary/Executive%20Summary-txt.html 1988 drought: NCDC : http://lwf.ncdc.noaa.gov/oa/reports/billionz.html

3. New technologies In the last 25 years: Climate models Hydrological models Land surface schemes Remote sensing devices Archives, storage Despite all these advances, no capability for performing global hydrological prediction

4. But discontinuity on a global scale… –Uneven observations –local hydrological models Hydrologic warnings tend to be localized

5. Objectives Develop a global flood and drought nowcast and prediction system Using climate ensemble forecasts Distributed hydrologic model VIC ( U. of Washington, Princeton University) Satellite remote sensing information NCEP / ECMWF data sets

6. Forecast System Schematic * Satellite precipitatio n estimates local scale (1/2 degree) weather inputs soil moisture snowpack Hydrologic model spin up SNOTE L Update streamflow, soil moisture, snow water equivalent, runoff Month 0 1-2 years back G-LDAS /other real- time met. forcings for spin-up Hydrologic forecast simulation NOWCASTS INITIAL STATE AMSR-E MODIS Update ensemble forecasts NCEP GSM ensemble * Similar experimental procedure as used by Wood et al (2005) West-wide seasonal hydrologic forecast system SEASONAL FORECASTS (drought) SHORT TERM FORECASTS (flood)

7. Hydrologic Model Spin Up Preliminary studies Compare Hydrological variables as simulated by the distributed model VIC using –Climatology: Adam and Lettenmaier (2003) and Adam et al (2005) precipitation data sets gauge undercatch and orography correction 1979-1999 Refer to A & al. later on –Satellite precipitation estimates

8. Satellite datasets Choosing satellite data sets –Availability ( near real time later on) –Time resolution (daily and less) –Spatial resolution ( ½ lat/lon degree maximum) –Spatial coverage (global)

9. Satellite based precipitation estimates Combined IR and PMW data sets Spatial Domain Spatial res. Time res.Period available Avail. CMORPH (Joyce et al 2004) Global0.25hourlyDec 2002-daily PERSIANN (Sorooshian et al 2000) 50oS- 50oN 0.256 hourlyMar 2000-2 days CMAP (Xie and Arkin 1996, 1997) Global2.5monthlyJan 1979-1 week GPCP 1DD (Huffman et al 2001) Global1dailyOct 1996-3 mths 3B42RT (Huffman et al 2002) 50oS- 50oN 0.253 hourlyFeb 2002 -6 hrs

10. Satellite precipitation estimates Surrogate for future near real time satellite estimates: GPCP 1DD daily precipitation –Huffman et al 2001 –Infra-Red (TMPI) over 40 o S-40 o N –Recalibrated TIROS Operational Vertical Sounder (TOVS) beyond 40 o S and 40 o N –Scaled to match monthly GPCP Version 2 Satellite- Gauge precipitation estimates –1997-present

11. Major Basins to be simulated first World Basins Study basins 6 simulated basins Mackenzie Mississippi Mekong Danube Congo Amazon

12. 1997-99 Water Balance (mm) We compare hydrologic variables as simulated by VIC driven by 1997-99: A & al.precipitation estimates GPCP 1DD (Huffman et al 2001) precipitation estimates

13. Monthly Water Balances 1997-99

17. Future work Model Spin up –Further analysis : assess bias in simulated hydrologic variables when using satellite precipitation estimates Extend A & al data sets to 2004 Use CMORPH (Joyce et al 2004) –Use other precipitation estimates: NCEP ECMWF ERA 40 –Bias adjustment of forcing data set : need 10 years of observations at least

18. Future Work Data Assimilation: –use satellite soil moisture still experimental, need further validation and assess the additional skill in forecast –Use MODIS: experimental as well Forecasts: –Validation with retrospective forecasts, near real time forecasts / nowcasts –Assess predictability skills ; initial conditions precipitation forecast

19. Thank You! Credit: Philip Wijmans/ACT-LWF Trevo, Mozambique, February 2000, http://gbgm-umc.org/umcor/00/mozphotos.stm

20. Amazon

21. Mekong

22. Congo

23. Mackenzie

24. Danube

25. Mississippi