Vízügyi Közlemények, 1997 (79. évfolyam)
1. füzet - Zsuffa István: Folyóink árvízviszonyainak statisztikai értékelése
Folyóink árvizviszonyainak statisztikai értékelése 75 Zelenhasic, E. : A stochastic model for flood analysis. Dissertation Colorado State University, Fort Collins Zsuffa /.: A hidrológiai folyamatok elmélete és a műszaki hidrológia A Műszaki Tudomány doktori értekezése. 1991. * * * Statistical analysis of the conditions of Hungarian river floods by Prof. Dr. István ZSUFFA, C. E. Floods which endanger man, the society and the living environment are stochastic elements of the hydrological regime of rivers. They are stochastic variables the probability distributions of whose can be used for the unambiguous numerical determination of the level of risk which is involved in designing flood control facilities. In Hungary appropriately reliable records (Figure 1.) are available for statistical sampling for almost all sites of importance and the theoretical distribution functions can be easily generated by making use of modem computer techniques (Figure 2.). The reliability of results depends on the magnitude of the river and of the length of the records and the theoretical method of analysis depends also in these two factors (Table I.) Exact means of mathematical statistics are available for taking all probabilistic processes, such as the water level raised by wind forces, and can also be used for the estimation of the „uncertainty of sampling" that also depends on the length of the record ( Tables II— III.). Each of the water courses can and should be analyzed independently and up to date computer techniques are available for obtaining the exact solutions (Figure 6.). Artifically generalized social regulations are thus opposed by the facts of natural sciences. Hungary is in the lead in international fore not only in terms of the completeness of records of the hydrological regime of rivers but also in terms of counteracting, eliminating flooding catastrophes. There have been no flooding catastrophes, resulting in mass-death, since 1879 and even those dike ruptures that would have resulted in economic losses of national importance were eliminated ever since 1956. Flood catastrophes were not caused by the overflowing of the levees but by the „dike-load" that resulted in a break-through of the levee or of the base-soil. This probabilistic variable, which had been defined by Zoltán Károlyi in 1962, can be exactly analyzed by the Cramér-Leadbetter method. The hazard of catastrophes due the devastating „dike load" that soaks the body of the levee and the subsoil had been recognized by Pál Vásárhelyi as early as the beginning of the last century, when the travel time of flood hydrographs had been reduced to one-third on the River Tisza by the regulation works that were directed by him. There have never been any dike failures over the Tisza river reaches where the regulation was made in accordance with the genial plans of Vásárhelyi. After the contraction of the Austrian hydropower stations the release of flood waves has also been accelerated and the accurate mathematical-statistical analysis indicate that the "dike-load" was reduced to one-third — one-fourth (Figures 9—10.). A special mathematical statistical task is analyze the probability of occurrence of past flooding events. On the basis of such analysis it was found that the flood of 1965, which was successfully defended, has a return period of 300 years. A basic task, in addition to analyzing the one-dimensional probability variables of peaking water stages and dike-loads, is to statistically evaluate the time-variaton of the hydrographs of flooding events that are of decisive importance from the point of view of flood defence (Table IV.). The method of professor Bergman, related to independent peaking events and flood volumes and to the normal distribution of two probability variables, has been completed to include a relationship between the peaking level and the flood volume and also with the to dimensional analysis of the Gumbel distribution of two probability variables (Figures 11—14.). * * *
