E. Promponas, Statistical analysis and hydrological interpretation of the daily flow regime in a large record of European rivers, Diploma thesis, 175 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, November 2021.
The field of hydrology relies on the study-observation of natural-hydrological processes through the elaboration of existing measurements, in order to satisfactorily describe the chaotic nature of hydrological systems. Measurements therefore have an immediate and obvious utility since the study of natural processes-phenomena, their understanding and knowledge of their temporal evolution is based on the creation of time series with measurements and the processing of these time series. In recent years, one of the most common problems that anyone involved in the science of hydrology and in particular with the daily flow of rivers has to face is the lack (systematic or not) of time series measurements. It is therefore necessary to statistically analyze the existing data in order to obtain an overview of the statistical behavior of daily benefits and possibly to detect some grouping of behaviors according to the climatic characteristics of each region. For this statistical analysis, were used daily river flow data from the Global Historical Climatology Network (GHCN-Daily of N.O.A.A.). 209 stations were selected in the territory of Europe, based on quality criteria and completeness of their recordings. The data analysis was performed and controlled for the most part, through the source code applied in the R programming language and specifically in the free programming environment of R-Studio. The statistical characteristics of the time series were calculated and the distribution parameters were estimated using the L-moments method. Five probability distribution functions (PBF, Generalized Gamma, Lognormal, Weibull, Gamma) were adapted to the data, based on their suitability based on the available literature and on the other hand the ability to compare bi-parametric and tri-parametric distributions. The appropriateness of their adjustment was checked by the criterion of Mean Square Error (MSE) but also by the ratio diagrams on the scale L. Finally, supervisory spatial distribution maps were synthesized in the free QGIS software, which helped to draw conclusions and interpret the results data analysis.