Global Flood and Drought Prediction European Geosciences Union General Assembly 2006 Vienna, Austria, 2006 April 4th Nathalie Voisin, Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington Seattle, USA Credit: Philip Wijmans/ACT-LWF Trevo, Mozambique, February 2000 , http://gbgm-umc.org/umcor/00/mozphotos.stm

Outline Background and Objective Data and models Toward developing global hydrology forecast capability Approach Data Processing : bias correction and downscaling of the forecasts Preliminary results: forecast issued on Feb 4th 2000 Future work

-1- Background

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

Global Floods and Droughts $50-60 billion USD /year, worldwide ( United Nations University) 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 (1988 drought: NCDC ) 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

Objective Predict streamflow and associated hydrologic , soil moisture, runoff, evaporation and snow water equivalent : 1. At a global scale Spatial consistency To cover ungauged or poorly gauged basins 2. Time scales: Short term for floods Seasonal (or longer) for drought 3. Freely disseminate information for agriculture, energy, food security ,and protection of life and property

-2- Data and models

Meteorological Data - Surface observations: Uneven global coverage Various attempts to grid globally We use Adam et al. (2006) 1979-1999 (0.5 degrees) and ERA-40 - Precipitation derived from satellite: Various products available, mostly either passive microwave and/or infra-red Issue with climatology and consistency ( especially important for seasonal prediction) - Climate Models: ECMWF and NCEP Re-analysis products, for at least 25 years Ensemble forecast products Quasi all or all required input data for our hydrologic model available

The Hydrologic Model VIC - Already calibrated and validated at 2 degree resolution on over 26 basins worldwide (Nijjsen et al. 2001) Calibrated and validated at 0.5 degree over the Arctic domain Ongoing with UW and Princeton globally at 0.5 degree resolution

Real time forecasting using VIC The Seasonal Westwide Forecast operational over the entire western US seasonal forecast of streamflow The Surface Water Monitor operational over the entire western US daily analysis of soil moisture

-3- Toward developing global hydrology forecast capability

Forecast System Schematic * soil moisture snowpack streamflow, soil moisture, snow water equivalent, runoff local scale (1/2 degree) weather inputs INITIAL STATE Hydrologic model spin up Hydrologic forecast simulation ECMWF ERA40 (or Analysis) Downscaling using observations (Adam et al 2006) Ensemble Reforecasts NCEP Reforecasts (Hamill 2006), bias corrected and downscaled ( NCEP GFS, ECMWF ESP) Later on: CMORPH, MODIS, AMSR-E, others SNOTEL Update Several years back NOWCASTS SEASONAL FORECASTS (drought) Month 0 SHORT TERM FORECASTS (flood) * Similar experimental procedure as used by Wood et al (2005) West-wide seasonal hydrologic forecast system

Spin Up ECMWF ERA40 reanalysis for retrospective forecasting Assume ERA40 is the truth Later use ERA40 analysis field, bias corrected to match ERA40 characteristics

The Meteorological Forecasts Retrospective forecasting: Reforecasts Tom Hamill (2006) NOAA NCEP-MRF, 1998 version 1979-present 15-day forecasts issued daily 15 member ensemble forecast 2.5 degree resolution Real Time forecasting: ECMWF and/or NCEP (future)

Data processing The climatology statistics to be conserved in the forecasts are : - the frequency of occurrence of rain - the peaks - accumulated amounts (mean) Using quantile-quantile mapping technique

Data processing: Bias Correction Non-exceedance probability plots (MRF in green, ERA40 in black ) Systematic Bias Occurrence of Precipitation

Data processing: Downscaling Inverse square distance interpolation from 2.5 down to 0.5 degree resolution Integration of observation based spatial variability at 0.5 degree: Use observations based Adam et al. (2006) global dataset (0.5 degree resolution) Shifting : makes the Adam et al. average temperature field at 2.5 degree match ERA40, Derive the temperature range for each 0.5 degree cell within the 2.5 degree cell Scaling of the precipitation and the wind field so that the ratio Value(0.5)/Value(2.5) is conserved

Preliminary results February 2000 floods in the Northern Part of South Africa: Tropical depression moving southward from Beira, then continuing west into Zimbabwe, Botswana and South Africa Sustained rain during the period 4 to about 14 February Satellite imagery of cloud activity on 1 Feb 2006 Satellite imagery of cloud activity on 2 Feb 2006 Tropical depression Boloetse track (pink) and forecasted direction (red) http://gisdata.usgs.net/sa_floods/

Preliminary results – 2000 Feb 4th 5 day acc. PRECIPITATION ERA 40 GFS reforecast 15 ensembles avg LEAD 1 LEAD 1 LEAD 2 LEAD 2

Preliminary results – 2000 Feb 4th 5 day acc. RUNOFF ERA 40 GFS reforecast 15 ensembles avg LEAD 1 LEAD 1 LEAD 2 LEAD 2

Preliminary results – 2000 Feb 4th Basin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black ) Zambeze Basin, Africa NO BIAS CORRECTION BIAS CORRECTION

Preliminary results – 2000 Feb 4th Basin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black ) Limpopo Basin, Africa NO BIAS CORRECTION BIAS CORRECTION

Preliminary results – 2000 Feb 4th Basin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black ) Colorado Basin, North America NO BIAS CORRECTION BIAS CORRECTION

Preliminary results – 2000 Feb 4th Basin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black ) Ganges Basin, Asia NO BIAS CORRECTION BIAS CORRECTION

Preliminary results – 2000 Feb 4th Basin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black ) Elbe Basin, Europe NO BIAS CORRECTION BIAS CORRECTION

Preliminary results – 2000 Feb 4th The bias correction : beneficial for ALL input variables (P, Tavg,Wind) does not substitute for missed precipitation/temperature peaks/lows BUT the correction for the occurrence of rain correction should help brings consistency between the control run ( model or observations, or both) and the forecasts

-4- Future Work

Future Work Retrospective forecasting: Finish up the small scale variability implementation in the code Refined precipitation occurrence correction Further evaluation of the retrospective forecasts: using GFS reforecasts and eventually archived ECMWF 10 day, monthly and seasonal forecasts Predictions in forms of percentile and anomalies with respect to the climatology

Future Work Operational real time forecasting: Once a week Use several climate model forecasts: ECMWF 10 day forecast ECMWF monthly forecast ECMWF seasonal forecast GFS 6-10 day forecast Improvement of the initial conditions: e.g. assimilation of satellite soil moisture; snow

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

Preliminary results – 2000 Feb 4th Snapshots Hydrologic Variables Prediction (ERA 40) 5 day acc. PRECIPITATION 5 day acc. RUNOFF

Preliminary results – 2000 Feb 4th Snapshots Hydrologic Variables Prediction (ERA 40) 5 day avg. SOIL MOISTURE 5 day avg. SWE

Preliminary results – 2000 Feb 4th 5 day acc. PRECIPITATION ERA 40 GFS reforecast ensemble #14

Preliminary results – 2000 Feb 4th 5 day acc. RUNOFF ERA 40 GFS reforecast ensemble #14