1. COST-733 WG4 Links between Weather Types and Flood events in Europe Christel Prudhomme

2. Understanding large scale antecedent conditions Weather Types/ Classifications from COST –A priori, all classifications At present only for Europe: D00 For each large flood events –Frequency of weather type : preceding day(s) – “ : preceding weeks –Frequency anomaly (i.e. is situation exceptional?) –Systematic occurrence of some WT ?

3. Flood events Daily flow data series from different data bases –Global Runoff Data Centre (GRDC). Selected 176 –Flow Regimes from International Network Data (FRIEND): 95 –French Banque Hydro (with restriction): 132 [not yet analysed] –UK National River Archive (NRFA): 87 [not yet analysed] –European Water Archive: [not yet retrieved] –Total: 358 [later date : 490 + EWA] Selected all over Europe For each catchment select the largest flood peak events –Number of flood peak: 3 * number of years –Criterium of independence between each selected flood peak –POT3 data, with Flood in m3/s, and date

4. Data analysis For each river basin –POT3 series: flood magnitude, date –For each day find corresponding Classification/Weather Type ClassA[WTi] Index 1: Frequency anomaly of weather types PI1 –For each river basin –PI1 = 100*(freq. ClassA[WTi] during flood day - freq. ClassA[WTi] any day )/ freq. ClassA[WTi] any day –If PI1 = -100: ClassA[WTi] never occurred during flood day –If PI1 <0 : ClassA[WTi] occurred less often during flood day than usual –If PI1 >> 100 : ClassA[WTi] occ. more often during flood day than usual –We want to see if, across Europe, some ClassA[Wti] systematically occur more/less often during flood days –Can look at days preceding flood as well

5. Index 2: Persistence of weather type PI2 –‘Is the persistence of k days with ClassA[Wti] linked to a flood’ –Measure the number of times kday with ClassA[WTi] within a window of xday prior to flood events –Calculate conditional probability of kday given there is a flood event: PI2 –Compare PI2 with value expected purely by chance, knowing the probability of occurence of ClassA[WTi] [Binomial/Bernouilli] –If PI2 greater than expected by chance, the persistence of ClassA[WTi] at least kday within xday followed by flood event is statistically significant Calculate PI2 and Bernouilli for windows up to 5 days, k day varying from 0 to 5 Data Analysis (2)

6. Maps: for each ClassA[WTi], one dot per catchment –PI1 – Positive : back / negative : grey – Size dot: PI1 magnitude –PI2 – Significant: black / non significant : grey – Size dot: PI2 magnitude –Done for the day of the flood, and up to 5 days before –One index per season –Huge number of maps (for CEC: 200 * 5 PI1; 200*5*5 PI2) Histographs: for each ClassA[WTi] –Proportion of catchments in PI1/PI2 categories –Aim: to identify ClassA[WTi] with largest number of catchment with high PI1 / PI2 –Future: plot diagrams, for each category, with evolution with Lag time; all WT together Results presentation

7. Some Results for PI1 – NO CONCLUSION YET – Lag = 1 day

8. Some examples of Results PI2 – NO CONCLUSION YET

9. Analysis for all catchments –First need to get data from other databases –Work on ‘summary’ graphics Focus on –Lag: any evolution on windows of analysis –Meaning of PI2 compared to PI1. Which one is best –Threshold to assess significance of link between ClassA[WTi] and flood events –Regional analysis: catchments in different regions might be linked to different weather type –Importance of seasonal analysis Identification of ‘regional flood’ days, depending on the proportion of catchment in study area has a POT3 event that day Continue lit review to have more ideas for analysis! Further work…

10. Thank you