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COST-733 WG4 Links between Weather Types and Flood events in Europe Christel Prudhomme - PowerPoint PPT Presentation


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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

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COST-733 WG4 Links between Weather Types and Flood events in EuropeChristel 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


Data Analysis (2)

  • 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


Results presentation

  • 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




Further work…

  • 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!



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