Nathalie Voisin1, Florian Pappenberger2, Dennis Lettenmaier1, Roberto Buizza2,

and John Schaake3

1 University of Washington2 ECMWF3 National Weather Service – NOAA

European Geophysical Union General Assembly , May 5 2010

2

Limpopo 2000Early Flood Alert System for Southern Africa (Artan et al. 2001)

South Asia 2000Mekong River Commission – basin wide approach for flood forecasting

Bangladesh 2004 (Hopson and Webster 2010)*

Horn of Africa 2004 (Thiemig et al. 2010, EU - AFAS)*

Zambezi 2001,2007,2008 (EU-AFAS, in process)*

Existing Flood Alert Systems in mostly-ungauged basins

* Ensemble flow forecasting

Develop a medium range probabilistic quantitative hydrologic forecast system applicable globally:

Using only (quasi-) globally available tools:▪ Global Circulation Model ensemble weather forecasts▪ High spatial resolution satellite-based remote sensing

Using a semi distributed hydrology model ▪ applicable for different basin sizes, not basin dependent▪ flow forecasts at several locations within large ungauged

basins

Daily time steps, up to 2 weeks lead time

Reliable and accurate for potential real time decision in areas with no flood warning system, sparse in situ observations (radars, gauge stations, etc) or no regional atmospheric model.

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4

Today

Voisin et al. (2010, in review)

Initial State

1.What is the forecast skill of the system?

2.What are the resulting hydrologic forecast errors related to errors in the calibrated and downscaled weather forecasts?

3.Is the forecast skill different for basins of different size?

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Analog method vs interpolation:- maintained resolution & discrimination- slightly lower predictability- BUT largely improved reliability- smaller mean error- more realistic precipitation patterns

15-member ensemble, 15-day daily forecast:Day 1-10: ECMWF EPS fcstDay 11-15: Zero precip.

ECMWF EPS fcstInterpolated to .25o

VIC2003-2007 period

Routing model2003-2007 period

ECMWF analysis fields:with TMPA precipitation

Daily, 2003-2007 period, 0.25 degree

Daily 2003-2007 simulated runoff,

soil moisture, SWESubstitute for observed

runoff

2003-2007 simulated daily flow

Substitute for observations

Reference(substitute for observations,

Climatology)

ECMWF EPS fcst Calibrated & downscaled( analog method)

15-member ensemble 15-day distributed

runoff forecast

15-member ensemble 15-day flow forecast

at 4 stations with different drainage areas

VIC15-day simulation

Routing model15-day simulation

Initial hydrologic

state

Initial flow

conditions

…

…

…

…

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2 3

Deterministic 15-day daily fcst

Day 1-15:- Zero precip.

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Forecast – Clim & null Precip

deterministic15-day distributed

runoff forecast

15-day deterministic flow forecast

at 4 stations with different drainage areas

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→ Use “simulated observed flow” as reference(ECMWF Analysis and TMPA precipitation)→Focus on weather forecasts errors

- No flow observation uncertainties

- No hydrology model and routing model ( structure, parameter estimation) uncertainties

Ohio River Basin2003-20071826 15-day forecasts (10 day fcst, +5 days 0-precip)848 0.25o grid cells

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Ensemble reliability at Metropolis and Elizabeth

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A preliminary probabilistic quantitative hydrologic forecast system for global application was developed and evaluated:

1. Skill for 10 days for spatially distributed runoff

2. Skill for 1-12+ day forecasts depending on concentration times at the flow forecast locations

For small basins : skills for 10 days, with good reliability for short lead times For larger basins: for 10 days + concentration time

3. Ensemble weather forecasts need to be calibrated: for better hydrologic probabilistic forecasts ( reliability ) For better forecast accuracy in sub basins locations

4. Will incorporate PUB and HEPEX results and ideas. ( PUB: Predictions in Ungauged Basins HEPEX: Hydrologic Ensemble Prediction Experiment)

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Which forecasts?- Spatially distributed ensemble runoff forecasts- Ensemble flow forecasts at 4 locations

Verification:Deterministic Forecast Skill Measures:- Bias ( accuracy, mean errors)- RMSE (accuracy)- Correlation (accuracy, predictability)

Probabilistic Forecasts Skill Measures:- Continuous Rank Probability Skill Score (accuracy, reliability, resolution,

predictability) - Rank Histograms ( ensemble spread i.e. probabilistic forecast reliability)

For forecast categories: What can I expect when a forecast falls in a certain forecast category? ( oriented for real-time decision )

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-Differences between TMPA and observed precipitation

-Daily flow fluctuations due to navigation, flood control, hydropower generation

-Uncertainties in VIC and routing models physical processes, structure and parameters

→ Use “simulated observed flow” as reference

→Focus on weather forecasts errors

Relative Operating characteristic (ROC)

Plot Hit Rate vs. False Alarm Rate for a set of increasing probability threshold to make the yes/no decision.

Diagonal = no skillSkill if above the 1:1 line

Measure resolutionA bias forecast may still have good

discrimination. 16

Ensemble reliability: Reliability plot: PROBABILISTIC fcsts

Choose an event = event specific Each time the event was forecasted with a specific

probability ( 20%, 40%, etc), how many times did it happen ( observation >= chosen event). It requires a sharpness diagram to give the confidence in each point. It should be on a 1:1 line.

Talagrand diagram (rank):PROBABILISTIC QUANTITAVE fcsts Give a rank to the observation with respect to the

ensemble forecast ( 0 if obs below all ensemble members, Nmember + 1 if obs larger )

Is uniform if ensemble spread is reliable, (inverse) U-shaped if ensemble is too small (large), asymetric is systematic bias.

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Probabilistic quantitative forecast verification

measures the difference between the predicted and observed cumulative distribution functions: resolution, reliability, predictability

For one forecast(gridcell, lead time, t): 18

d1

d2

d3

dNmember

magnitudeP

rob

Fcs

t∆P1

2

∆PN2

0

1

1 1 1