Flood risk assessment on small catchment with ECMWF ensemble forecasts in operational conditions Debarre C. Belleudy A. Zuber F. - MTE/SCHAPI Goals - Better anticipate flood risk - Internal organisation - Medium term flood risk map - Goals evoluted during study from discharge forecasting to risk assessment Empirical uncertainty processor Rank histogram 108-120h 2500 2000 1500 1000 500 0 0 1000 2000 3000 4000 5000 6000 GRP GRP+OTAMIN - OTAMIN : [Bourgin, F. (2014)] - Improves reliability - Losing of temporal coherency - No improvement of event detection Bourgin, F. (2014). Comment quantifier l’incertitude prédictive en modélisation hydrologique?: Travail exploratoire sur un grand échantillon de bassins versants (Doctoral dissertation, AgroParisTech) Method Ensemble rainfall forecast ECMWF 50 members Catchement areal rainfall calculation GRP Rainfall runoff model 50 discharge members No post processing Empirical uncertainty processor Discrimination tree Logistic regression Post-processing Reliability Rank histogram POD/FAR Evaluation Raw ensemble and statistical post-processing - Predict risk of flood event - Discharge>Threshold - Predictors = 50 daily maximum discharge from GRP ensemble sorted by values - Raw ensemble = P(Q>Threshold) - Logistic regression & CART : predictor selection & cross-validation Study period 2017-2020 GRP : continuous - lumped - storage type rainfall runoff model from INRAE One catchment : le Grand Morin à Pommeuse (770 km²) response time ~16h Conclusion - Challenge of rare event prediction - Can detect ~75% of the events with ~75% of false alarm - No improvement with statistical post processing on test sample Results To be continued - More catchments : mediteranean area... - Spatial aggregation - Longer study period
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Flood risk assessment on small catchment with ECMWF ensemble forecasts in operational conditions Debarre C. Belleudy A. Zuber F. - MTE/SCHAPI
Goals
- Better anticipate flood risk- Internal organisation- Medium term flood risk map- Goals evoluted during study from discharge forecasting to risk assessment
Empirical uncertainty processor Rank histogram108-120h
2500
2000
1500
1000
500
00
1000
2000
3000
4000
5000
6000
GRP GRP+OTAMIN
- OTAMIN : [Bourgin, F. (2014)]
- Improves reliability
- Losing of temporal coherency
- No improvement of event
detection
Bourgin, F. (2014). Comment quantifier l’incertitude prédictive en modélisation hydrologique?: Travail exploratoire sur un
grand échantillon de bassins versants (Doctoral dissertation, AgroParisTech)
Method
Ensemble rainfall forecast ECMWF
50 members
Catchement arealrainfall calculation
GRPRainfall runoff model
50 discharge members
No post processing
Empirical uncertainty processor
Discrimination tree
Logistic regression
Post-processing
ReliabilityRank histogram
POD/FAR
Evaluation
Raw ensemble and statistical post-processing
- Predict risk of flood event - Discharge>Threshold- Predictors = 50 daily maximum discharge from GRP ensemble sorted by values- Raw ensemble = P(Q>Threshold)- Logistic regression & CART : predictor selection & cross-validation
Study period 2017-2020GRP : continuous - lumped - storage type rainfall runoff model from INRAEOne catchment : le Grand Morin à Pommeuse (770 km²) response time ~16h
Conclusion
- Challenge of rare event prediction - Can detect ~75% of the events with ~75% of false alarm- No improvement with statistical post processing on test sample
Results To be continued- More catchments : mediteranean area...- Spatial aggregation- Longer study period
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