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Hydrological extremes and their meteorological causes. András Bárdossy IWS University of Stuttgart. 1. Introduction. The future is unknown Modelling cannot forecast We have to be prepared Extremes used for design Wind – storm Precipitation Floods. 2. Hydrological extremes.
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Hydrological extremes and their meteorological causes András Bárdossy IWS University of Stuttgart
1. Introduction • The future is unknown • Modelling cannot forecast • We have to be prepared • Extremes used for design • Wind – storm • Precipitation • Floods
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
Choice of the variable: • Water level • Important for flooding • Measurable • Strongly influenced • Discharges (amounts) • Less influenced “natural” variable • Less important • Difficult to measure
2. Statistical assumptions • Annual extremes • Seasonal values (Summer Winter) • Partial duration series Independent sample Homogeneous Future like past ?
Study Area • Rhine catchment – Germany RheinMaxau1901 - 1999 RheinWorms1901 - 1999 RheinKaub1901 – 1999 RheinAndernach1901 – 1999 MoselCochem1901 – 1999 LahnKalkofen1901 – 1999 NeckarPlochingen1921 - 1999
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
Permutations Randomness rejected for 6 out of 7
3. Understanding discharge series • Goal: Equilibrium state • Discharge: • Excess water • Meteorological origin • „Deterministic“ reaction
The 100 largest observed floods of the Tisza at Vásárosnamény 1900-1999 with the corresponding CPs.
Simulation Directly from CPs –
CP sequences • Observed (1899-2003) • GCM simulated • Historical simulated • Semi-Markov chain (persistence)
Summary and conclusions • Hydrological extremes • Strongly influenced • Difficult to analyse • Not independent
Relationship between series • Indicator series:
4. Probability distributions • Choice of the distribution • Subjective • Objective statistical testing • Kolmogorow-Smirnow • Cramer – von Mises • Khi-Square • More than one not rejected (?!)
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
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