With the increasing changes in our environment due to climate change, extreme weather events are also becoming more frequent. We witnessed this in 2023 when heavy rainfall caused floods unprecedented in the history of Slovenia. These floods caused extensive damage to infrastructure, the economy, property, and the mental health of residents.
In this thesis, we explored one of several methods that allow us to predict or computationally determine such extreme events and design discharge values. We examined the catchment area of the Nazarje gauging station, located in the Upper Savinja Valley on the Savinja River. Based on the data obtained from the Slovenian Environmental Agency (ARSO), we established a stochastic precipitation model in the R software for generating synthetic precipitation and air temperature data. Within the same software, we also established a rainfall-runoff hydrological model, which enabled us to determine design discharge values in the studied area based on the calculated synthetic precipitation and air temperature data, and flood frequency analysis.
The analysis of generated data for different time periods using the stochastic model revealed that scenarios and calculated values can vary with each simulation of the model, even if the number of scenarios remains unchanged. This indicates the need for repeated calculations with more scenarios for more robust results. Further analyses showed that design discharge values for different return periods do not change if the number of scenarios increases while maintaining the same length of simulations, but rather are supplemented with results from new or additional scenarios. Comparison between measured and generated data showed similarities in the shapes of design discahrge curves, although differences were more significant for return periods over 100 years. For better model performance, it would be necessary to use hourly time step and more precise air temperature data, which we could not provide due to the sparse network of measuring stations.
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