What forecast users might expect: an issue of forecast performance Tomas Vlasak, Radek Cekal & Jan Danhelka Czech Hydrometeorological Institute Czech republic

Hydrological service in the Czech republic Deterministic forecast (> 100 forecasting points 48hours / 1hour step) Flash flood guidance FFG-CZ Short-range probabilistic forecast (based on ensemble QPF) Medium-range probabilistic forecast (based on historical analogs)

Hydrological forecast evaluation Feedback for FORECASTERS: model parameters or model structure data input human impact strategy Feedback for forecast USERS: realize forecast uncertainty estimate forecast uncertainty avoid false expectation

Evaluation methods Comprehensible not only for expert. Simplification to 1 indicator: threshold exceeding - YES/NO total flow volume discharge maximum Evaluation of whole of forecasting process without prior differentiation of the source of uncertainty.

Evaluation methods – forecasts selection Target to flood forecasts Selection conditions: 1) forecasted or observed discharge exceeded flood threshold 2) last measured discharge was smaller then threshold

Categorical evaluation Observed YES NO Forecast HIT FALSE ALARM MISS CORRECT NEGATIVE portion of these category in different aspect: Threshold Basin area Season Year Forecasting office categorical statistics Hit Rate False Alarm Ratio Frequency Bias Critical Success Index ..... HIT HIT HIT MISS FALSE ALARM FALSE ALARM

low FLOOD EXTREMITY high Categorical evaluation – RESULTS – different thresholds Decrease of portion of HITS with increasing of flood extremity is small MISS forecasts prevail to FALSE ALARM low FLOOD EXTREMITY high

Categorical evaluation – RESULTS – basin area Forecasts for big rivers are more successful uncertainty of QPF produce FALSE ALARMS and uncertainty of hydrological modeling MISSES

Categorical evaluation – RESULTS – lead time Decrease of HIT rate is significant to first 24 hours of lead time. The most of forecasting points delimit basins with lag time shorter then 24h

Categorical evaluation – RESULTS – lead time Decrease of HIT rate is significant to first 24 hours of lead time. The most of forecasting points delimit basins with lag time shorter then 24h

Categorical evaluation – RESULTS – lead time Decrease of HIT rate is significant to first 24 hours of lead time. The most of forecasting points delimit basins with lag time shorter then 24h

Categorical evaluation – RESULTS - years Without significant trend

Categorical evaluation – RESULTS – years –regional office Plzeň change of hydrological model performance after model recalibration in 2010

Categorical evaluation – RESULTS

Categorical evaluation - RESULTS Forecasting point on big river has more successful forecasts

Predominance of FALSE ALARM category Categorical evaluation - RESULTS Influence of different forecaster‘s strategy Predominance of MISS category Predominance of FALSE ALARM category

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