Višedimenzionalni pristup proračunu merodavnih velikih voda na sektorima reka sa pritokama

Abstract

Planning and design of flood mitigation and protection measures are based on a probabilistic assessment of flood wave parameters. Defining the design floods on rivers is necessary for various engineering needs, where the need to determine the design water levels in order to design flood protection system, which includes facilities such as embankments, walls and the like, is the most prominent. For this purpose, the procedure of adopting the maximum river flow of a certain return period (flood wave peak) estimated on the basis of historical data on maximum annual flows has been established. If river reach include tributaries, such an approach cannot be justified, because at least two flows (mainstem and tributary) have to be considered. Accordingly, the complexity of flood occurrence and its assessment require the linking of marginal distributions of multiple variables in order to define a unique distribution law that describes the flood. The main aim of doctoral dissertation is twofold: to elaborate a procedure that will define the flood exceedance probability in the analyzed area within a multidimensional probability space and to develop a methodology for defining flood coincidence in the mainstem reach with its main tributaries and thus determine design floods in order to design the flood control system in the confluence area. Mathematical models are based on (1) two-dimensional probability distribution (PROIL model) and (2) the copula function (Archimedean class of copulas), and fitted to a practical application in a two-dimensional probability distribution space. In case of random variables, simultaneous quantitative characteristics of flood wave (flood flow) hydrographs for the mainstem and a tributary are considered. The paper discusses the specific reach of the Danube, from the Hungarian-Serbian border to the city of Smederevo, which encompasses three significant tributaries – the Drava, the Tisa, and the Sava.