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.

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.

Hydrological dam safety assessment methods traditionally assume that the reservoir is full while it receives the design flood. In practice, reservoir management strategy determines the probability distribution of reservoir levels at the beginning of flood episodes. In this study we present a method to economically assess the influence of reservoir management strategy on hydrological dam safety and downstream flood risk. The method was applied to a gated-spillway dam located in the Tagus river basin. A set of 100000 inflow hydrographs was generated through a Monte Carlo procedure, reproducing the observed statistics of main hydrograph characteristics: peak flow, volume and duration. The set of 100000 hydrographs was routed through the reservoir applying the Volumetric Operation Method as flood control strategy. Three different scenarios were studied: initial reservoir level equal to maximum normal level, equal to a maximum conservation level and following the probability distribution of initial reservoir levels. In order to evaluate economically the influence of initial variable reservoir level and compare the three scenarios, a global risk index was applied. The index combines the hydrological risk for the dam, linked to the maximum water level experienced in the reservoir while the flood is routed, and the flood risk in the downstream river reach, linked to the discharge releases from the dam. The results highlighted the importance of considering the fluctuation of initial reservoir level for assessing the risk related to hydrological dam safety. Within the case study, the global risk index reduced its value up to 93 % if variable initial reservoir level is accounted, from 1445.6 x10³ to 83 x10³ euros.

This paper presents certain characteristics of trends in rainfall erosivity density (ED), that have not been so far investigated in depth in the current literature. Global warming is expected to increase the intensity of rainfall in Europe and consequently to increase the soil erosion rates. This potential may have a significant impact, especially on Greece, which is inflicted by the phenomenon of desertification. Raw pluviograph data were acquired from the Greek National Bank of Hydrological and Meteorological Information for 108 stations. Precipitation time series values were cleared from noise and errors and the ratio of missing values was computed. Erosive rainfalls were identified, their return period was determined using Intensity–Duration–Frequency (IDF) curves and erosivity values were computed. A Monte Carlo method was utilized to assess the impact of missing values ratio to the computation of annual erosivity (R) and ED values. It was found that the R values are underestimated in a linear way, while ED is more robust against the presence of missing precipitation values. Indicatively, the R values are underestimated by 49%, when only 50% of the erosive rainfall events are used while at the same time the estimation error of ED is 20%. Using predefined quality criteria for coverage and time length a subset of 18 stations was selected. Their annual ED values, as well as the samples' autocorrelation and partial autocorrelation functions were computed, in order to investigate the presence of stochastic trends. Subsequently, Kendall's Tau was used in order to yield a measure of the monotonic relationship between annual ED values and time. Finally, the hypothesis that ED values are affected by elevation was tested. In conclusion: a) it is suggested to compute ED for the assessment of erosivity in Greece instead of the direct computation of R, b) stationarity of ED was found for the majority of the selected stations, in contrast to reported precipitation trends for the same time period and c) the hypothesis that ED values are not correlated to elevation could not be rejected.

Climate model outputs can be used to assess the expected behaviour of extreme precipitations in the future due to climate change. In Europe, the EURO-CORDEX project provides precipitation projections in the future under various representative concentration pathways (RCP), proposed by the Fifth Assessment Report (AR5) of the IPCC. The EURO-CORDEX project regionalised Global Climate Model (GCM) outputs in Europe through a set of Regional Climate Models (RCM).

In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX programme are analysed in the Iberian Peninsula. Annual maximum daily precipitation series are considered. Precipitation quantiles for a set of probabilities of exceedance are estimated by using the three-parameter Generalized Extreme Value distribution and L-moments. Precipitation quantiles expected in the future are compared with the precipitation quantiles in the control period, for each climate model. Delta changes in precipitation quantiles are obtained as thresholds exceeded by a percentage of climate models: 50%, 68% and 90%. Three periods in the future are considered: 2011-2040, 2041-2070 and 2071-2100.

An approach based on Monte Carlo simulations is developed, in order to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period. Thus, statistical significant changes are identified. The higher the significance threshold, the fewer cells with significant changes are identified. Consequently, a set of maps are obtained for various thresholds, in order to assist the decision making process in subsequent climate change studies.

The results of this study are currently being applied to implement the Floods Directive in Spain, regarding the preliminary assessment of flood risks.