Application of a global probabilistic hydrologic forecast system to the Ohio River Basin Nathalie Voisin1, Florian Pappenberger2, Dennis Lettenmaier1, Roberto Buizza2, and John Schaake3 1 University of Washington 2 ECMWF 3 National Weather Service – NOAA European Geophysical Union General Assembly , May 5 2010

Background Existing Flood Alert Systems in mostly-ungauged basins Limpopo 2000 Early Flood Alert System for Southern Africa (Artan et al. 2001) South Asia 2000 Mekong 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)* Message: 1. existing flood alert system in developed countries : many use ensemble weather forecasts, most important they use in-situ data: rain gauge station observations, radars, river flow gauge etc. And they are for one basin in particular 2. in ungauged basins: - usually financed by international organization following desastrous events - case by case basis Some are ensemble some are not - basin dependent - maintenance issue : educated people leave AND expensive 3. Goal here: - develop a system hydrological forecast that would use ensemble weather forecast - for large domains : not one basin at a time but a continent ( except for flow ) -global approach for calibrating the weather forecasts - using only globally available tools, and can be run remotely to avoid cost and maintenance to population TOO LONG Global application i.e. have one consistent system over entire Africa and South East Asia run at once remotely: -> main interest: reduce the financial burden of maintenance and operation  cover entire continent instead of being on a basin per basin basis ( AFAS like) * Ensemble flow forecasting

Objective 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. Note that similar to EFAS

Forecast scheme Today Initial State Voisin et al. (2010, in review) Explain the scheme and state how it differs from the existing systems in ungauged basins ( global application) Today Initial State

Science Questions What is the forecast skill of the system? What are the resulting hydrologic forecast errors related to errors in the calibrated and downscaled weather forecasts? Is the forecast skill different for basins of different size?

Ensemble precipitation forecast calibration and downscaling Shows snapshots of two observed events: First one is the TMPA largest basin average precip day in the 2002-2006 period -> interpolation-like for RMSD and RMSDmean because….. -> unrealistic spottyness for BCSD - Second is a day with significant precipitation (upper tercile ) but without interpolation substitution in RMSD and RMSDmean All look more realistic than interpolation method : higher precipitation values in specific grid cells, precipitation patterns. Analog method vs interpolation: - maintained resolution & discrimination slightly lower predictability BUT largely improved reliability smaller mean error more realistic precipitation patterns

(substitute for observations, Climatology) Forecast – Reference (substitute for observations, Climatology) Forecast – Clim & null Precip 4 15-member ensemble, 15-day daily forecast: Day 1-10: ECMWF EPS fcst Day 11-15: Zero precip. 1 Deterministic 15-day daily fcst Day 1-15: - Zero precip. ECMWF analysis fields: with TMPA precipitation Daily, 2003-2007 period, 0.25 degree 2 3 ECMWF EPS fcst Interpolated to .25o ECMWF EPS fcst Calibrated & downscaled ( analog method) … Initial hydrologic state VIC 2003-2007 period VIC 15-day simulation … Daily 2003-2007 simulated runoff, soil moisture, SWE Substitute for observed runoff 15-member ensemble 15-day distributed runoff forecast deterministic 15-day distributed runoff forecast … Initial flow conditions Routing model 15-day simulation Routing model 2003-2007 period … 15-member ensemble 15-day flow forecast at 4 stations with different drainage areas 15-day deterministic flow forecast at 4 stations with different drainage areas 2003-2007 simulated daily flow Substitute for observations

Calibration of the hydrology and routing models → 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 Re-explain briefly why the application is over the Ohio River Basin ( previous work)

Verification of ensemble runoff forecasts Ohio River Basin 2003-2007 1826 15-day forecasts (10 day fcst, +5 days 0-precip) 848 0.25o grid cells CRPS is a great measure for probabilistic fcst: Handy because get a measure for one forecast BUT very sensitive to the size of the ensemble spread ( range)

Ensemble flow forecasts verification

Ensemble flow forecasts verification Ensemble reliability at Metropolis and Elizabeth

Conclusions A preliminary probabilistic quantitative hydrologic forecast system for global application was developed and evaluated: Skill for 10 days for spatially distributed runoff 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 Ensemble weather forecasts need to be calibrated: for better hydrologic probabilistic forecasts ( reliability ) For better forecast accuracy in sub basins locations Will incorporate PUB and HEPEX results and ideas. ( PUB: Predictions in Ungauged Basins HEPEX: Hydrologic Ensemble Prediction Experiment)

Thank you – Questions?

Forecast Verification 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 )

Calibration of the hydrology and routing models -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 Re-explain briefly why the application is over the Ohio River Basin ( previous work) → Use “simulated observed flow” as reference →Focus on weather forecasts errors

Ensemble forecast verification 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 skill Skill if above the 1:1 line Measure resolution A bias forecast may still have good discrimination.

Ensemble Forecast Verification 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.

Continuous Rank Probability Score 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): d1 d2 d3 dNmember magnitude Prob Fcst ∆P12 ∆PN2 1 forecast is best. 1 1 1