Global Flood and Drought Prediction

European Geosciences UnionGeneral Assembly 2006

Vienna, Austria, 2006 April 4th

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

University of WashingtonSeattle, USA

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

Outline1. Background and Objective

2. Data and models

3. Toward developing global hydrology forecast capability

• Approach• Data Processing : bias correction and downscaling of

the forecasts• Preliminary results: forecast issued on Feb 4th 2000

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

ObjectivePredict 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 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

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 Forecastoperational over the entire western USseasonal 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 *

ECMWF ERA40

(orAnalysis)

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

Downscaling using

observations (Adam et al

2006)

Hydrologic forecast simulation

NOWCASTS

INITIAL STATE

Ensemble ReforecastsNCEP Reforecasts (Hamill 2006), bias corrected and downscaled

( 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 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 CorrectionNon-exceedance probability plots (MRF in green, ERA40 in black ) Systematic Bias Occurrence of Precipitation

Data processing: Downscaling1. 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

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 2006http://gisdata.usgs.net/sa_floods/

Tropical depression Boloetse track (pink) and forecasted direction (red)

Preliminary results – 2000 Feb 4th 5 day acc. PRECIPITATION

ERA 40 GFS reforecast 15 ensembles avg

LEAD 1

LEAD 2 LEAD 2

LEAD 1

Preliminary results – 2000 Feb 4th 5 day acc. RUNOFF

ERA 40 GFS reforecast 15 ensembles avg

LEAD 1

LEAD 2 LEAD 2

LEAD 1

Preliminary results – 2000 Feb 4thBasin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black )

Zambeze Basin, Africa NO BIAS CORRECTION BIAS CORRECTION

Preliminary results – 2000 Feb 4thBasin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black )

Limpopo Basin, Africa NO BIAS CORRECTION BIAS CORRECTION

Preliminary results – 2000 Feb 4thBasin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black )

Colorado Basin, North America NO BIAS CORRECTION BIAS CORRECTION

Preliminary results – 2000 Feb 4thBasin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black )

Ganges Basin, Asia NO BIAS CORRECTION BIAS CORRECTION

Preliminary results – 2000 Feb 4thBasin 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 4thSnapshots 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