The disaggregation model proposed by Schaake et al. (1972) is revised to include linkages with the past at the different levels of aggregation. This modification produces a more realistic hydrologic model.

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This paper is a continuation of a previous one in which a stochastic model for analyzing excessive streamflows was presented. The model was based on the assumption that flood peak exceedances are independent, identically distributed random variables and that their occurrence is subject to the Poisson law. In the present paper a model is developed for nonidentically distributed exceedances on the assumption that only those exceedances during a particular season may be considered identically distributed. From this hypothesis the distribution function of the maximum flood peak exceedance in an arbitrary interval of time (0, t] is determined. The results are then applied to the 72‐year record of the Greenbrier River at Alderson, West Virginia. The theoretical and observed results agree reasonably well.

General Circulation Models (GCMs) are widely used tools to assess potential impacts of global climate warming. However, their outputs are diffi cult to use in regional impact studies with regard to water resources because of their coarse spatial resolution. Downscaling techniques have emerged as useful tools to reduce the problem of discordant scales by deriving regional climate information from global climate data. The objective of this study is to test the capability of one of these techniques, the Statistical DownScaling Model (SDSM), to derive local scale temperature and precipitation data series that can be used as inputs to a hydrologic model for streamfl ow modelling. Three river basins located in the province of Québec are analyzed. Results show that the SDSM provides reasonable downscaling data when using predictors representing the observed current climate. However, the performance is less reliable when using GCM predictors.Résumé : Les modèles de la circulation générale (MCG) sont des outils utilisés pour évaluer les impacts potentiels du réchauffement climatique global. Cependant, il est diffi cile d'utiliser directement leurs données dans le cadre d'études d'impacts régionales, tel que celles reliées aux ressources en eau, en raison de leur résolution spatiale grossière. Le développement des techniques de réduction d'échelle spatiale a permis de réduire le problème d'échelles discordantes en dérivant l'information du climat régional à partir de données sur le climat global. L'objectif de cette étude est de tester la capacité d'une de ces techniques, le Statistical DownScaling Model (SDSM), à fournir des données à échelle réduite adéquates de température et de précipitation à un modèle hydrologique pour la modélisation des débits en rivière. Trois bassins versants de la province de Québec sont à l'étude. Les résultats démontrent que SDSM réduit raisonnablement l'échelle spatiale des données en utilisant les variables atmosphériques à grande échelle représentant le climat actuel observé. Cependant, la performance diminue avec celles simulées par un MCG. 298Canadian Water Resources Journal/Revue canadienne des ressources hydriques

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