Download presentation
Presentation is loading. Please wait.
Published byWesley Johnson Modified over 9 years ago
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 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
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
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.