COST-733 WG4 Links between Weather Types and Flood

events in Europe

Christel Prudhomme

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 ?

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

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

• 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)

• 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

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

Some examples of Results PI2 – NO CONCLUSION YET

• 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…

Thank you