Nathalie Voisin 1, Florian Pappenberger 2, Dennis Lettenmaier 1, Roberto Buizza 2, and John Schaake 3 1 University of Washington 2 ECMWF 3 National Weather Service – NOAA CEE Department Seminar, University of Washington, June

2 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)* Existing Flood Alert Systems in mostly-ungauged basins * Ensemble flow forecasting

 Basics  What is meteorological ensemble forecasting?  What is hydrological ensemble forecasting?  Structure of a medium-range flood forecast system  A medium range flood forecast system for global application  Ensemble hydrological forecast verification 3

 Generation of the ensemble members 4 Weather forecast Model (global or regional) Set of perturbed initial conditions … X members X ensemble members Surface air temperature, 24h precipitation, surface wind … Quasi 3D models Spatially distributed Processing of the probabilities … Calibration of forecast

 Probabilistic forecast / ensemble forecast 5

 Maps of probabilistic forecasts 6

 EFAS – CHPS – other probabilistic flood forecast system 7 (Semi-)distributed/lumped hydrological model Ensemble weather forecast … 51 members Ensemble flow forecast Post-processor : calibration of the flow forecast Calibration of weather forecasts (OPTIONAL) … … In-situ observation: Precipitation, temperature, wind OBSERVED river flow

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. 8

9 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? 10

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

(Semi-)distributed/lumped hydrological model Ensemble weather forecast … 51 members Ensemble flow forecast Calibration of weather forecasts (OPTIONAL) … … Simulated Temp, Wind Satellite Precip Experiment 1 : WITHOUT calibration of weather forecasts Experiment 2 : WITH calibration of weather forecasts Experiment 3 : 0 precip forecasts

13 → 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

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 ) 14

Ohio River Basin day forecasts (10 day fcst, +5 days 0-precip) o grid cells 15

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Ensemble reliability at Metropolis and Elizabeth 17

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 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) - 18

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20 -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 skill Skill if above the 1:1 line Measure resolution A bias forecast may still have good discrimination. 21

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. 22

 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): 23 d1d1 d2d2 d3d3 d Nmember magnitude Prob Fcst ∆P 1 2 ∆P N

 Seasonal flow forecasting using ESP 24 Initial conditions: -Observed : SWE -Simulated : soil moisture Semi distributed Hydrology Model VIC One basin at the time ESP ( Extended Streamflow Prediction) Previous year meteorological info # of years members … Processing of the probabilities … … Ensemble of flow forecasts

University of Washington – West Wide Seasonal Flow Forecast System CEE, Land Surface Hydrology Group