2010) Flood frequency analysis using historical data: accounting for random and systematic errors. Hydrol. Sci. J. 55(2), 192-208. Abstract Flood frequency analysis based on a set of systematic data and a set of historical floods is applied to several Mediterranean catchments. After identification and collection of data on historical floods, several hydraulic models were constructed to account for geomorphological changes. Recent and historical rating curves were constructed and applied to reconstruct flood discharge series, together with their uncertainty. This uncertainty stems from two types of error: (a) random errors related to the water-level readings; and (b) systematic errors related to over-or under-estimation of the rating curve. A Bayesian frequency analysis is performed to take both sources of uncertainty into account. It is shown that the uncertainty affecting discharges should be carefully evaluated and taken into account in the flood frequency analysis, as it can increase the quantiles confidence interval. The quantiles are found to be consistent with those obtained with empirical methods, for two out of four of the catchments. Analyse fréquentielle des débits de crues avec des données historiques en prenant en compte les erreurs aléatoires et systématiquesRésumé Ce papier présente une analyse fréquentielle des crues basée sur un échantillon de crues collecté sur une période systématique et sur une période historique. Elle est appliquée sur plusieurs petits bassins versants méditerranéens. Après le recensement et la collecte des données sur les crues historiques, plusieurs modèles hydrauliques ont été construits pour prendre en compte l'évolution géomorphologique des cours d'eau. Des courbes de tarage pour les périodes récentes et historiques ont été construites et utilisées pour estimer les débits de crues avec leurs incertitudes. Ces incertitudes prennent en compte deux types d'erreurs: (a) une erreur aléatoire liée à la lecture de la hauteur d'eau, et (b) une erreur systématique liée à une sur ou sous estimation de la courbe de tarage. Un modèle bayésien d'analyse fréquentielle est développé pour prendre en compte ces deux sources d'incertitudes. Il est montré que les incertitudes affectant les débits doivent être prise en compte dans l'analyse fréquentielle des crues car elles peuvent significativement modifier les intervalles de confiance des quantiles. Les quantiles de crues obtenus semblent concordant avec les estimations de formules empiriques pour deux des quatre bassins étudiés.

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After a presentation of the nonlinear properties of neural networks, their applications to hydrology are described. A neural predictor is satisfactorily used to estimate a flood peak. The main contribution of the paper concerns an original method for visualising a hidden underground flow Satisfactory experimental results were obtained that fitted well with the knowledge of local hydrogeology, opening up an interesting avenue for modelling using neural networks.

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Abstract. The increasing severity of hydrological droughts in the Mediterranean basin related to climate change raises the need to understand the processes sustaining low flow. The purpose of this paper is to evaluate simple mixing model approaches, first to identify and then to quantify streamflow contribution during low-water periods. An approach based on the coupling of geochemical data with hydrological data allows the quantification of flow contributions. In addition, monitoring during the low-water period was used to investigate the drying-up trajectory of each geological reservoir individually. Data were collected during the summers of 2018 and 2019 on a Mediterranean river (Gardon de Sainte-Croix). The identification of the end-members was performed after the identification of a groundwater geochemical signature clustered according to the geological nature of the reservoir. Two complementary methods validate further the characterisation: rock-leaching experiments and unsupervised classification (k-means). The use of the end-member mixture analysis (EMMA) coupled with a generalised likelihood uncertainty estimate (GLUE) (G-EMMA) mixing model coupled with hydrological monitoring of the main river discharge rate highlights major disparities in the contribution of the geological units, showing a reservoir with a minor contribution in high flow becoming preponderant during the low-flow period. This finding was revealed to be of the utmost importance for the management of water resources during the dry period.