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

3. 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)

4. 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

5. 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.

6. 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

7. 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

8. 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

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

10. 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

11. 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

12. 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

13. Categorical evaluation – RESULTS - years
Without significant trend

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

15. Categorical evaluation – RESULTS

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

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

23.
THANK YOU