Global Flood and Drought Prediction

Nathalie Voisin and Dennis P. LettenmaierDepartment of Civil and Environmental Engineering

University of WashingtonSeattle, USA

www.ektopia.co.uk/ektopia/ images/parisflood.jpg

Outline1. 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?

www.dartmouth.edu/~floods, 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 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.htmhttp://www.unu.edu/env/govern/ElNIno/CountryReports/inside/ethopia/Executive%20Summary/Executive%20Summary-txt.html1988 drought: NCDC : http://lwf.ncdc.noaa.gov/oa/reports/billionz.html

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 coverageVarious attempts to grid globallyWe 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-redIssue with climatology and consistency ( especially important for

seasonal prediction)

- Climate Models: ECMWF and NCEPRe-analysis products, for at least 25 yearsEnsemble forecast productsQuasi 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

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Comparison of 1997-1999 monthly basin averages

Meteorological Data - Comparison

…Or why I think GCM met data are more appropriate for global streamflow forecasting for now

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Meteorological Data - Comparison

…Or why I think GCM met data are more appropriate for global streamflow forecasting for now

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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 moisturesnowpack

Hydrologic model spin up

SNOTEL

Update

streamflow, soil moisture, snow water equivalent, runoff

Month 0

Several years back

Hydrologic forecast simulation

NOWCASTS

INITIAL STATE

Ensemble ReforecastsNCEP 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 CorrectionNon-exceedance probability plots (MRF in green, ERA40 in black )

Systematic Bias Occurrence of Precipitation

Data processing: Bias Correction

Systematic biasUsing 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 (1979-1999) 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. 2006 (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.Precip0.5=GFSbiascorrected,2.5 / Adam2.5(recordrain) * Adam0.5(recordrain)Tavg0.5=GFSbiascorrected,2.5- Adam2.5(recordrain) + Adam0.5(recordrain)Wind0.5=GFSbiascorrected,2.5 / Adam2.5(recordrain) * Adam0.5(recordrain)

4. Adjust Tmin and TmaxDeltaTemp=TmaxAdam0.5(recordrain)-TminAdam0.5(recordrain)Tmax0.5=Tavg0.5+1/2*DeltaTemp and Tmin0.5=Tavg0.5-1/2*DeltaTemp

DownscalingBias correctedGFS reforecast

2.5 degree

Adam et al.Random rainy day ( in the mth of fcst)

0.5 degree 2.5 degree

Scaling: Normalize the 0.5 degree spatial variabilityShifting: Save the (Value0.5-Value2.5)

Rain

No Rain Shifting 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

Precipitationw/o SMSC SMSC BC SMSC ERA40

1995/01/20 Rhine

Runoffw/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 RhineBasin 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. 2006 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 SMSCBasin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black, NMC reanalysis in blue )

NO SMSC SMSC