Hydrological extremes and their meteorological causes

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

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

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 RheinMaxau1901 - 1999 RheinWorms1901 - 1999 RheinKaub1901 – 1999 RheinAndernach1901 – 1999 MoselCochem1901 – 1999 LahnKalkofen1901 – 1999 NeckarPlochingen1921 - 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

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 • Select random subsample (80 values) • Perform parameter estimation for subsample • Calculate design floods • Repeat 1-3 N times (N=1000) • 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