Global Flood and Drought Prediction Nathalie Voisin and Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington Seattle, USA images/parisflood.jpg

Outline 1.Background and Objective 2.Data and models …Or why I think GCM met data are more appropriate for global streamflow forecasting for now 3.Toward developing global hydrology forecast capability Approach Data Processing : bias correction and downscaling of the forecasts Preliminary results: Evaluation of the bias correction and the downscaling on two specific events 4.Future work

-1- Background

Need for flood prediction globally? Dartmouth Flood Observatory

Global Floods and Droughts Floods –$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 Droughts –1988 US Drought: $40 billion (1988 drought: NCDC ) –Famine in many countries: 200,000 people killed in Ethiopia in Source: United Nations University, drought: NCDC :

Objective Predict streamflow and associated hydrologic variables, 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) (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

Meteorological Data - Comparison …Or why I think GCM met data are more appropriate for global streamflow forecasting for now Precip Runoff Evap Soil Moist SWE Comparison of monthly basin averages

Meteorological Data - Comparison …Or why I think GCM met data are more appropriate for global streamflow forecasting for now Precip Runoff Evap Soil Moist SWE

Meteorological Data - Comparison …Or why I think GCM met data are more appropriate for global streamflow forecasting for now Precip Runoff Evap Soil Moist SWE

Meteorological Data In this paper, I conclude that : (1) Satellite precip need more “calibration” based on topography and in the ITCZ (2) Great hope that satellite will bring the “observed” small scale spatial variability available. Need longer climatology to be able to perform bias correction in order to correct for (1) (3) Missing data, issue in real time forecasting

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 -Routing at 0.5 degree derived from SRTM30 and from the manually corrected global direction file from Doell and Lehner (2002)

Routed Basins

-3- Toward developing global hydrology forecast capability

Forecast System Schematic * ECMWF ERA40 (or Analysis) 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 Several years back Hydrologic forecast simulation NOWCASTS INITIAL STATE Ensemble Reforecasts NCEP Reforecasts (Hamill 2006), bias corrected w/r to ERA40 and downscaled w/r to Adam et al. (2006) observations ( NCEP GFS, ECMWF ESP) * Similar experimental procedure as used by Wood et al (2005) West-wide seasonal hydrologic forecast system SEASONAL FORECASTS (drought) SHORT TERM FORECASTS (flood) Later on: CMORPH, MODIS, AMSR-E, others

Spin Up ECMWF ERA40 reanalysis for retrospective forecasting Assume ERA40 is the truth Later use ECMWF 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 (Near) 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)

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

Data processing: Bias Correction Systematic bias Using quantile-quantile mapping technique Occurrence of Precipitation

Data processing: Downscaling 1.Inverse square distance interpolation from 2.5 down to 0.5 degree resolution 2.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

Downscaling: observation based spatial variability at 0.5 degree: 1.Choose the year that will give the variability: Choose randomly one single year ( ) for all cells and all lead times For retrospective forecasting, choose the year of the forecast. 2.Rainy and non rainy days records from Adam et al (averaged to 2.5 degrees) dataset, are classified and saved for the month of the lead time: RecordRain(cell, month of fcst) and RecordNonRain(cell, month of fcst) 3.Shitfing and/or scaling: If Raining: select randomly a record in the RecordRain database, the record is conserved for the 4 variables. Precip 0.5 =GFS biascorrected,2.5 / Adam 2.5 (recordrain) * Adam 0.5 (recordrain) Tavg 0.5 =GFS biascorrected,2.5 - Adam 2.5 (recordrain) + Adam 0.5 (recordrain) Wind 0.5 =GFS biascorrected,2.5 / Adam 2.5 (recordrain) * Adam 0.5 (recordrain) 4.Adjust Tmin and Tmax DeltaTemp=Tmax Adam0.5 (recordrain)-Tmin Adam0.5 (recordrain) Tmax 0.5 =Tavg /2*DeltaTemp and Tmin 0.5 =Tavg /2*DeltaTemp

Downscaling Bias corrected GFS reforecast 2.5 degree Adam et al. Random rainy day ( in the mth of fcst) 0.5 degree2.5 degree Scaling: Normalize the 0.5 degree spatial variability Shifting: Save the (Value 0.5 -Value 2.5 ) Rain No RainShifting of temperature and scaling of wind based on any Non Rainy day

Downscaling Notes: Sometimes, bias corrected GFS reforecast calls for rain but there is No rain in the Adam et al. dataset for the (randomly) selected year. In this case, the 2.5 degree cell is downscaled to 0.5 degree by inverse square distance interpolation. Tmin and Tmax are derived from a randomly selected record. If I was to select another year: –No guarantee that there is rain in a different year –The additional computing time and resources needed make it not worth doing it Downside of downscaling this way: any spatially large rain event (> 2.5 degree like fronts, etc) is not conserved because each 2.5 degree cell has an independently randomly selected RainRecord.

Retrospective Forecasting Interests: –Skills over different climate and basins –Skills for different type of floods: short term strong events ( fronts, extratropical or tropical storms) or sustained rain events ( monsoons). Events of interest: –Event 1: End of January 1995 : flood in the Meuse and Rhine basins (Europe) –Event 2: July 1997: flood in the Oder Basin (Europe) –Event 3: February 2000, flood in the Limpopo –Event 4: July-September 2000, flood in the Mekong

Retrospective Forecasting of Short Events Event 1: End of January 1995 : flood in the Meuse and Rhine basins (Europe) Event 3: February 2000, flood in the Limpopo

1995/01/20 Rhine – Effect of SMSC The Rhine Basin

1995/01/20 Rhine LEAD 1 LEAD 2 LEAD 3 Precipitation w/o SMSC SMSC BC SMSC ERA40

1995/01/20 Rhine Runoff w/o SMSC SMSC BC SMSC ERA40 LEAD 1 LEAD 2 LEAD 3

1995/01/20 Rhine PRECIPITATION W/o SMSC With SMSC ERA40

1995/01/20 Rhine RUNOFF W/o SMSC With SMSC ERA40

1995/01/20 Rhine Change in SWE W/o SMSC With SMSC ERA40

1995/01/20 Rhine Basin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black, NMC reanalysis in blue ) NO SMSC SMSC BC SMSCBC BC improves SWE (?) but does not do any miracle on missed precip peaks ( timing issue). Not significant improvement here though … SMSC gives a few “crazy” ensembles

2000/02/03 - Limpopo Limpopo

2000/02/03 Limpopo – Effect of SMSC PRECIPITATION W/o SMSC With SMSC ERA40

2000/02/03 Limpopo – Effect of SMSC Runoff W/o SMSC With SMSC ERA40

2000/02/03 Limpopo – CTRL PRECIPITATION without SMSC and BC GFS ens avg NMC rean ERA40

Conclusion Bias correction implemented Correction for occurrence of precip implemented Downscaling implemented Output variables: –By lead time: Precip, Runoff, Evap, Change in SWE, Change in Soil Moisture, Degree Days –5 day accumulation: same –Time series: of basin average hydrologic variables and streamflow at selected stations

Conclusion on the Bias Correction 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 ( not shown ) brings consistency between the control run ( model or observations, or both) and the forecasts

Conclusion on the Downscaling A few ensembles get crazy values ( should check the scaling factor) The location of the front is not really respected, some information get lost ( maybe should interpolate linearly first, then shift or scale) Adam et al precipitation was disaggregated statistically in time. Should I expect real precipitation patterns (frontal shape, etc) within a 2.5 degree cell? Not appropriate for downscaling in a tropical storm event ; linear interpolation might be better in this case.

-4- Future Work

Future Work Retrospective forecasting: –Investigate more events and fix the remaining “bugs?”/make last improvements –Extend the retrospective forecasts: using 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/Twice a week for short term forecasts –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!

2000/02/03 Limpopo – Effect of SMSC Basin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black, NMC reanalysis in blue ) NO SMSC SMSC