Potential for medium range global flood prediction Nathalie Voisin 1, Andrew W. Wood 1, Dennis P....

Potential for medium range global flood prediction

Nathalie Voisin1 , Andrew W. Wood1 , Dennis P. Lettenmaier1

1 Department of Civil and Environmental Engineering, University of Washington

Outline

• Background and Objectives• Description of the prediction scheme:

– The hydrology model– The scheme– The bias correction

• Use of satellite in the downscaling process of weather forecasts

• Preliminary results for– Rhine Flood 1995 ( mostly rain, then snowmelt)– Limpopo flood 2000 (tropical storm)

Background

Need for flood prediction globally?

www.dartmouth.edu/~floods, Dartmouth Flood Observatory

Background

Flood prediction systems exist • in developed Countries• What about developing countries?

The potential for global flood prediction system exists

• Global weather models : analysis and forecasts are available

• Issues: scale?

Objectives

Ultimate objective: to predict streamflow and associated hydrologic variables, soil moisture, runoff, evaporation and snow water equivalent :– At a global scale

• Spatial consistency

• Especially in ungauged or poorly gauged basins

– medium-range time scale ( up to 2 weeks)

This talk: to suggest a method to downscale global weather forecasts into a higher spatial resolution without any local information ( gauges or radar)

The global prediction scheme

The hydrology model VIC

- Semi-distributed model driven by a set of surface meteorological data ( precipitation, wind, solar radiation derived from Tmin and Tmax, etc)

- Represents vegetation, has three soil layers with variable infiltration, non linear base flow.


The river routing model

- Runoff and baseflow for each cell is then routed toward selected locations, following directions equivalent to channels.

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

- Already calibrated and validated at 2 degree resolution over 26 basins worldwide(Nijssen et al. 2001)


Hydrologic model spin up (0.5 degree global simulation)

Several years back

Hydrologic forecast simulation

Nowcasts

INITIAL STATE

Medium range forecasts

( up to 2 weeks)

Daily ERA-40 downscaled to 0.5 degree using linear

inverse distance square interpolation.

NCEP Reforecasts (Hamill et al. 2006)15 ensemble members – 15 day forecast – 2.5 degree (fixed GFS version of 1998)

Bias correction at 2.5 degree, with respect to ERA-40 (Ensures consistency between spinup and the reforecasts)

Downscaling from 2.5 to 0.5 degree using the Schaake Shuffle ( Clark et al. 2004) with higher spatial resolution satellite GPCP 1dd (Huffman et al. 2001) and TRMM 3B42 precipitations

(0.5 degree global simulation: stream flow, soil moisture, SWE, runoff )

Atmospheric inputs

Hydrology Model

(here in retrospective mode)

Retrospective forecasting: Reforecasts• Hamill et al. (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 analysis

The global prediction system

The bias correction of GFS reforecasts (1)1. Quantile-Quantile technique with respect to ERA-40 climatology

- ERA-40 cdf based on a 9 day moving window, centered on the day of the forecast ( 9 * 23 values )

- GFS reforecast cdf for the 15 ensemble average, for each lead time, fixed 7 day window

- Extreme values: low values fitted with Weibull distribution and high values fitted with Gumbel distribution

Figure from Wood and Lettenmaier, 2004: A testbed for new seasonal hydrologic forecasting approaches in the western U.S.

The global prediction system

The bias correction of GFS reforecasts (2)

2.Correction for daily intermittency ( with respect to ERA-40 climatology)

Use of satellite for downscaling forecasts

South Africa, 2.5 degree grid

Limpopo Basin, 2.5 degree grid

Limpopo Basin, 0.5 degree grid


Satellite Datasets→ TRMM 3B42

– 50oS-50oN– 0.25 degree, 3 hourly, 2002-present– Use 2002-2006 ( to be updated yearly)

→ GPCP 1dd – Global, but used for 50oN-90oN and 50oS-90oS– 1 degree, daily, Oct 1996 – 3 months before present– Use 1997-2005 (to be updated yearly), interpolated to 0.5

degree using an inverse distance square interpolation.


Simplified Schaake Schuffle (Clark et al. 2004)– to construct spatial patterns of precipitation

within each 2.5 degree cell based on observations ( here, satellite)

– For each 2.5 degree cell, for each lead time:• 15 satellite observations are randomly selected (

based on rain / no rain, specific to calendar month )

• for each ranked forecast ensemble member, it associates the corresponding ranked observation ( 15 ensemble members).

→ ensures that the selected highest observed precipitation event is assigned to the highest forecast

GFS refcst, 2.5 deg, rank ith

Satellite, 2.5 deg, rank ith


TRMM 3B42,

2.5 degree

TRMM 3B42,

0.5 degree

(mm/day)

TRMM 3B42 aggregated to daily and 2.5 degree resolution→ resolution of the weather forecasts

TRMM 3B42 aggregated to daily and 0.5 degree

resolution→ resolution of the hydrologic

model

→Need a downscaling method that inserts localized precipitation patterns

Jan 31st, 2001

Jan 31st, 2001


Simplified Schaake Shuffle (2):– The corresponding observed value field at 0.5 degree resolution

gives the spatial distribution of precipitation, but NOT the magnitude

Satellite, 2.5 deg

Satellite, 0.5 deg,

corresponding record to the 2.5 degree

cells

Ratio of Satellite

observations, 0.5 deg

resolution

/ =

Here for one ensemble member, one lead time :


Dowscaling of precipitation characteristics:- Spatial distribution from satellite observations

- Magnitude of the bias corrected GFS reforecasts

- Consistency between spin up dataset and bias corrected downscaled forecasts

Bias corrected and downscaled (0.5

degree) GFS reforecast

Bias corrected GFS refcst,

2.5 deg

Ratio of satellite obs. 0.5 degree to

2.5 degree

X =

reeree

reeree GFSrefcst

Satellite

SatelliteGFSrefcst deg5.0

deg5.2

deg5.0deg5.2

Here for one ensemble member, one lead time :

Preliminary Results

Rhine Flood, 1995 (Forecast of January 20th, 1995)

• 5 day precipitation accumulation fields

ERA-40, simple interpol.

GFS Det. Fcst.,simple interpol.

Bias corrected GFS Fcst. Ens. Avg,

Downscaled

1 to

5 d

ays

6 to

10

days

Preliminary Results


• 5 day runoff accumulation fields




Downscaled

1 to

5 d

ays

6 to

10

days

Preliminary Results


• 5 day change in soil moisture




Downscaled

1 to

5 d

ays

6 to

10

days

Preliminary Results


• 5 day change in SWE




Downscaled

1 to

5 d

ays

6 to

10

days

Preliminary Results


• Discharge (cms)

Preliminary Results



NCEP Rean., simple interpol.



Downscaled

1 to

5 d

ays

6 to

10

days


simple interpol.

Preliminary Results

Limpopo Flood, 2000 (Forecast of February 3rd, 2000)


ERA-40 Rean., simple interpol.



Downscaled

1 to

5 d

ays

6 to

10

days

Preliminary Results


• 5 day runoff accumulation fields




Downscaled

1 to

5 d

ays

6 to

10

days

Preliminary Results


• 5 day change in soil moisture




Downscaled

1 to

5 d

ays

6 to

10

days

Preliminary Results



NCEP Rean., simple interpol.



Downscaled


simple interpol.

1 to

5 d

ays

6 to

10

days

Conclusions

1) Improvement of using this downscaling method rather than a simple inverse distance square interpolation method

– E.g. representation of topography ( snowmelt)– Less obvious for tropical storm in flat and arid areas like South

Eastern Africa

2) Need to compare it with more sophisticated, but local downscaling methods

– Using a nested regional scale model – Equivalent downscaling techniques using high resolution

datasets based on gauges, in regions where in situ network exists

Conclusions

About the entire prediction scheme …

3) The scheme performance is very dependent on the quality of the forecasts.

4) The full scheme, including hydrologic simulations, will be evaluated with respects to other existing flood prediction systems. Calibration will be an essential step.

Thank You!

April 2006 Flood in Romania, http://www.spiegel.de/fotostrecke/0,5538,13382,00.html


Scaling of precipitation

Schaake Shuffle

Corresponding record for each cell, 0.5 degree

Downscaled GFS reforecast

12

34

GFS refcst, 2.5 deg

SATELLITE, 2.5 deg

SATELLITE, 0.5 deg

Ratio of SAT 0.5 degree to

2.5 degree

RatioScale 2.5 degree reforecast with SAT ratio

• Link picture• www.spiegel.de/img/0,1020,611798,00.jpg • http://www.spiegel.de/fotostrecke/

0,5538,13382,00.html

Potential for medium range global flood prediction Nathalie Voisin 1, Andrew W. Wood 1, Dennis P....

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