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Hydrological extremes and their meteorological causes András Bárdossy IWS University of Stuttgart.

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Presentation on theme: "Hydrological extremes and their meteorological causes András Bárdossy IWS University of Stuttgart."— Presentation transcript:

1 Hydrological extremes and their meteorological causes András Bárdossy IWS University of Stuttgart

2 1. Introduction The future is unknown Modelling cannot forecast We have to be prepared Extremes used for design –Wind – storm –Precipitation –Floods

3 2. Hydrological extremes Assumption: The future will be like past was „True“ for rain and wind Less for floods –Influences: River training Reservoirs Land use

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6 Choice of the variable: Water level –Important for flooding –Measurable –Strongly influenced Discharges (amounts) –Less influenced “natural” variable –Less important –Difficult to measure

7 Cross section

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9 2. Statistical assumptions Annual extremes Seasonal values (Summer Winter) Partial duration series Independent sample Homogeneous Future like past ?

10 Study Area Rhine catchment – Germany Rhein Maxau 1901 - 1999 Rhein Worms 1901 - 1999 Rhein Kaub 1901 – 1999 Rhein Andernach 1901 – 1999 Mosel Cochem 1901 – 1999 Lahn Kalkofen 1901 – 1999 Neckar Plochingen 1921 - 1999

11 Independence Independence   temporal changes Are there any unusual time intervals? Tests –Permutations and Moments –Autocorrelation (Bartlett) –Von Neumann ratio Test Negative Tests – only rejection possible

12 Permutations Randomness rejected for 6 out of 7

13 3. Understanding discharge series Goal: Equilibrium state Discharge: –Excess water –Meteorological origin –„Deterministic“ reaction

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15 Principle

16 Signal to be explained

17 Bodrog – CP07 (362% Increase)

18 Tisza CP10 (462% increase)

19 The 100 largest observed floods of the Tisza at Vásárosnamény 1900-1999 with the corresponding CPs.

20 Simulation Directly from CPs –

21 CP sequences Observed (1899-2003) GCM simulated Historical simulated Semi-Markov chain (persistence)

22 Llobregat – observed CPs

23 Llobregat – KIHZ CPs 1691- 1781

24 Summary and conclusions Hydrological extremes –Strongly influenced –Difficult to analyse –Not independent

25 Relationship between series Indicator series:

26 4. Probability distributions Choice of the distribution –Subjective –Objective statistical testing Kolmogorow-Smirnow Cramer – von Mises Khi-Square More than one not rejected (?!)

27 Significance of the results 1.Select random subsample (80 values) 2.Perform parameter estimation for subsample 3.Calculate design floods 4.Repeat 1-3 N times (N=1000) 5.Calculate mean and range for design flood

28 Bootstrap results

29 Principle

30 Downscaling Parameter estimation: –Maximum likelihood –Explicit separation of the data (CPs) Simulation: –For any given sequence of CPs Observed gridded SLP based NN based historical KIHZ based historical Extreme value statistics

31 Signal to be explained

32 Discharge changes Tisza

33 Frequency of CP10 (Tisza)

34 Relationship between extremes Correlation (daily) Correlation (Maxima) Rank correlation Correlation (dQ+) Tisza - Szamos 0.790.480.630.57 Tisza - Bodrog 0.700.400.490.48 Szamos - Bodrog 0.600.490.500.31


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