The generalized extreme value (GEV) distribution is often fitted to environmental time series of extreme values such as annual maxima of daily precipitation. We study two methodological issues here. First, we compare criteria for selecting the best model among 16 GEV models that allow nonstationary scale and location parameters. Simulation results showed that both the corrected Akaike information criterion and Bayesian information criterion (BIC) always detected nonstationarity, but the BIC selected the correct model more often except in very small samples. Second, we examined confidence intervals (CIs) for model parameters and other quantities such as the return levels that are usually required for hydrological and climatological time series. Four bootstrap CIs-normal, percentile, basic and bias-corrected and accelerated-constructed by random-t resampling, fixed-t resampling and the parametric bootstrap methods were compared. CIs for parameters of the stationary model do not present major differences. CIs for the more extreme quantiles tend to become very wide for all bootstrap methods. For nonstationary GEV models with linear time dependence of location or log-linear time dependence of scale, CI coverage probabilities are reasonably accurate for the parameters. For the extreme percentiles, the bias-corrected and accelerated method is best overall, and the fixed-t method also has good average coverage probabilities. A case study is presented of annual maximum daily precipitation over the mountainous Mesochora catchment in Greece. Analysis of historical data and data generated under two climate scenarios (control run and climate change) supported a stationary GEV model reducing to the Gumbel distribution.

No abstract

The long term hydrological response of a medium-sized mountainous catchment to climate changes has been examined, The climate changes were represented by a set of hypothetical scenarios of temperature increases coupled with precipitation and potential évapotranspiration changes. Snow accumulation and ablation, plus runoff from the study catchment (the Mesochora catchment in central Greece) were simulated under present (historical) and altered climate conditions using the US National Weather Service snowmelt and soil moisture accounting models. The results of this research obtained through alternative scenarios suggest strongly that all the hypothetical climate change scenarios would cause major decreases in winter snow accumulation and hence increases in winter runoff, as well as decreases in spring and summer runoff. The simulated changes in annual runoff were minor compared with the changes in the monthly distribution of runoff. Attendant changes in the monthly distribution of soil moisture and actual évapotranspiration would also occur. Such hydrological results would have significant implications on future water resources design and management.

An algorithm coupling linear least squares and simplex optimization (LLSSIM) is used to examine the ability of a three-layer feedforward artificial neural network (ANN) to simulate the high and low flows in various climate regimes over a mountainous catchment (the Mesochora catchment in central Greece). The plot of the long-term annual catchment pseudo-precipitation (rain plus snowmelt) simulated by the snow accumulation and ablation model (SAA) of the US National Weather Service (US NWS) showed trends of three climatically distinct periods, described by clearly descending, rising and moderately descending segments in pseudo-precipitation. The ANN model was calibrated for each of the three climate types and each was validated against the others. A set of statistical measures and graphs adapted for high and low flows showed the robustness of the ANN model under various climates and transient conditions. The ANN model proved capable of simulating well the daily high and low flows when it is calibrated for increasing pseudo-precipitation and validated for moderately decreasing pseudo-precipitation. For the entire period, the ANN model provided a better simulation of high and low flows than the conceptual soil moisture accounting (SMA) model of the US NWS, which was also employed in this study. Because the ANN is not a physically-based model, it is by no means a substitute for the SMA model. However, it is concluded that the ANN approach is an effective alternative for daily high-and low-flow simulation and forecasting in climatically varied regimes, particularly in cases where the internal dynamics of the catchment do not require an explicit representation.Key words artificial neural network; conceptual modelling; high flows; linear least squares; low flows; simplex optimization Réseaux de neurones artificiels et crues et étiages en régimes climatiques variésRésumé Un algorithme couplé de moindres carrés linéaires et d'optimisation simplex (LLSSIM) a été utilisé afin d'examiner l'aptitude d'un réseau de neurones artificiel (RNA) sans rétroaction à trois niveaux à simuler les crues et les étiages selon les régimes climatiques variés d'un bassin versant montagneux (le bassin versant de Mesochora en Grèce centrale). Les graphiques de la pseudoprécipitation (pluie plus fonte nivale) à long terme, simulée par le modèle d'accumulation et d'ablation de la neige (SAA) du service national météorologique des Etats Unis (US NWS), ont révélé les tendances de trois périodes climatiques distinctes, correspondant à des segments de décroissance forte, croissance et décroissance modérée. Le modèle RNA a été calé pour chacun des trois types climatiques et validé par rapport aux deux autres. Un ensemble d'indices statistiques et de graphiques adaptés aux crues et aux étiages a montré la robustesse du modèle RNA selon différents climats ainsi qu'en conditions transitoires. Il est apparu que le modèle RNA est apte à bien simuler les crues et les étiages journaliers lorsqu'il est calé en période de pseudo-précipitation croissan...

Nestos River flows through Bulgaria and Greece and discharges into the North Aegean Sea. Its total catchment area is around 6,200 km 2 , while the mean annual precipitation and runoff are 680 mm and 40 m 3 /s, respectively. The Hellenic part of the catchment has undergone a substantial hydroelectric development, since two dams associated with major hydropower pumped-storage facilities are in operation. The main objective of the paper is to assess the expected sediment delivery of Nestos R. at the uppermost Thisavros reservoir site. This has been carried out by implementing the Universal Soil Loss Equation in a GIS environment for determining the mean annual soil erosion in conjunction with a suspended sediment measurement program (114 measurements in total) accomplished between 1965 and 1983 adjacent to the dam site. The sediment discharge rating curve between sediment and river discharges in a power form has been constructed using five alternative techniques, namely (a) the linear regression of the log-transformed variables, (b) the same as (a) but with the Ferguson correction, (c) different ratings for the dry and wet seasons of the year, (d) the nonlinear regression, and (e) the broken line interpolation that utilizes different rating parameters for two discharge classes. It is shown that the mean annual sediment yield is almost equal for all rating curve formulations and varies between 178.5 tkm −2 and 203.4 tkm −2 and the highest value results from the broken line interpolation method. Accordingly, the sediment delivery ratios vary slightly between 17% and 19% of the upstream soil erosion.