Climate changes in the Mediterranean region especially those related to changes in rainfall distribution and occurrence of extreme events affect local economies. Agriculture is a sector strongly affected by climate conditions and concerns the majority of the Greek territory. The Gallikos river basin is an area of great interest regarding climate change impacts since it is an agricultural area depended on surface water resources and an area in which extreme events relatively often take place (e.g. floods). Long time series precipitation (27 years) and temperature data derived from measurement stations along with reanalysis data (ERA INTERIM) were used for the estimation of water availability and climate type over time in the area. The Standardized Precipitation Index and De Martonne aridity index was employed. The water flow measurements were correlated in order to investigate the interrelation between the different river branches and the extent of the meteorological changes effect in the basin. Descriptive statistics and cumulative curves were applied to check homogeneity of data. The results revealed that the climate type varies from semi arid to very wet and water availability ranges from moderately dry to extremely wet years. Reanalysis data overestimate precipitation. The meteorological changes affect at the same time the entire basin since the flow rate peaks occur simultaneously in the hydrographic network at different areas.
The projection of extreme precipitation events with higher accuracy and reliability, which engender severe socioeconomic impacts more frequently, is considered a priority research topic in the scientific community. Although large scale initiatives for monitoring meteorological and hydrological variables exist, the lack of data is still evident particularly in regions with complex topographic characteristics. The latter results in the use of reanalysis data or data derived from Regional Climate Models, however both datasets are biased to the observations resulting in non-accurate results in hydrological studies. The current research presents a newly developed statistical method for the bias correction of the maximum rainfall amount at watershed scale. In particular, the proposed approach necessitates the coupling of a spatial distribution method, namely Thiessen polygons, with a multivariate probabilistic distribution method, namely copulas, for the bias correction of the maximum precipitation. The case study area is the Nestos river basin where the several extreme episodes that have been recorded have direct impacts to the regional agricultural economy. Thus, using daily data by three monitoring stations and daily reanalysis precipitation values from the grids closest to these stations, the results demonstrated that the bias corrected maximum precipitation totals (greater than 90%) is much closer to the real max precipitation totals, while the respective reanalysis value underestimates the real precipitation totals. The overall improvement of the outputs, shows that the proposed Thiessen-copula method could constitute a significant asset to hydrologic simulations.
Extreme rainfall is one of the most devastating natural events. The frequency and intensity of these events has increased. This trend will likely continue as the effects of climate change become more pronounced. As a consequence, it is necessary to evaluate the different statistical methods that assess the occurrence of the extreme rainfalls. This research evaluates some of the most important statistical methods that are used for the analysis of the extreme precipitation events. Extreme Value Theory is applied on ten station data located in the Mediterranean region. Furthermore, its two main fundamental approaches (Block-Maxima and POT) and three commonly used methods for the calculation of the extreme distributions parameters (Maximum Likelihood, L-Moments, and Bayesian) are analyzed and compared. The results showed that the Generalized Pareto Distribution provides better theoretical justification to predict extreme precipitation compared to Generalized Extreme Value (GEV) distribution while in the majority of stations the most accurate parameters for the highest precipitation levels are estimated with the Bayesian method. Extreme precipitation for return period of 50, 150 and 300 years were finally obtained which indicated that Generalized Extreme Value Distribution with Bayesian estimator presents the highest return levels for western stations, while for the eastern Mediterranean stations the Generalized Pareto Distribution with Bayesian estimator presents the highest ones.