extends hydrologie ensemble services from 6-hour to year-ahead forecasts and includes additional weather and climate infoi'mation as well as improved quantification of major uncertainties.

Abstract. A procedure is presented to construct ensemble forecasts from single-value forecasts of precipitation and temperature. This involves dividing the spatial forecast domain and total forecast period into a number of parts that are treated as separate forecast events. The spatial domain is divided into hydrologic sub-basins. The total forecast period is divided into time periods, one for each model time step. For each event archived values of forecasts and corresponding observations are used to model the joint distribution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distribution of events that may occur for that forecast. This conditional forecast distribution subsequently is used to create ensemble members that vary in space and time using the "Schaake Shuffle" (Clark et al, 2004). The resulting ensemble members have the same space-time patterns as historical observations so that space-time joint relationships between events that have a significant effect on hydrological response tend to be preserved. Forecast uncertainty is space and time-scale dependent. For a given lead time to the beginning of the valid period of an event, forecast uncertainty depends on the length of the forecast valid time period and the spatial area to which the forecast applies. Although the "Schaake Shuffle" procedure, when applied to construct ensemble members from a time-series of single value forecasts, may preserve some of this scale dependency, it may not be sufficient without additional constraint. To account more fully for the time-dependent structure of forecast uncertainty, events for additional "aggregate" forecast periods are defined as accumulations of different "base" forecast periods. The generated ensemble members can be ingested by an Ensemble Streamflow Prediction system to produce ensemble forecasts of streamflow and other hydrological variables that reflect the meteorological uncertainty. The methodology is illustrated by an application to generate temperature and precipitation ensemble forecasts for the American River in California. Parameter estimation and dependent validation results are presented based on operational single-value forecasts archives of short-range River Forecast Center (RFC) forecasts and medium-range ensemble mean forecasts from the National Weather Service (NWS) Global Forecast System (GFS).

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This article presents a comparison between real-time discharges calculated by a flash-flood warning system and post-event flood peak estimates. The studied event occurred on 15 and 16 June 2010 at the Argens catchment located in the south of France. Real-time flood warnings were provided by the AIGA (Adaptation d'Information Géographique pour l'Alerte en Crue) warning system, which is based on a simple distributed hydrological model run at a 1-km 2 resolution using radar rainfall information. The timing of the warnings (updated every 15 min) was compared to the observed flood impacts. Furthermore, "consolidated" flood peaks estimated by an intensive post-event survey were used to evaluate the AIGA-estimated peak discharges. The results indicated that the AIGA warnings clearly identified the most affected areas. However, the effective lead-time of the event detection was short, especially for fastresponse catchments, because the current method does not take into account any rainfall forecast. The flood peak analysis showed a relatively good correspondence between AIGA-and field-estimated peak values, although some differences were due to the rainfall underestimation by the radar and rainfall-runoff model limitations.Key words evaluation; flash flood; ungauged; warning system; post-event survey Evaluation des avertissements de crue éclair sur des bassins non-jaugés en utilisant des relevés post-événement: application avec le système d'avertissement AIGA Résumé Cet article présente une comparaison des débits calculés en temps réel par un système d'avertissement de crues éclair avec des estimations post-événement des pics de crue. L'événement étudié s'est déroulé les 15 et 16 juin 2010 sur le bassin de l'Argens, situé dans le Sud de la France. Les débits en temps réel ont été calculés par le système d'avertissement AIGA (Adaptation d'Information Géographique pour l'Alerte en Crue), qui associe un modèle hydrologique distribué simple et une lame d'eau radar à la résolution de 1 km 2 . La chronologie des avertissements émis a été comparée aux dégâts observés. De plus, des estimations de pic de crue réalisées au cours de relevés de terrain après l'événement ont permis d'évaluer la pertinence des débits estimés par AIGA. Les résultats ont montré qu'AIGA a clairement indiqué les zones les plus affectées par la crue. Cependant, l'anticipation des avertissements a été courte, en particulier sur les bassins rapides, du fait que le système n'intègre pas de prévision de pluie. L'analyse des pics de crue a montré une relative bonne correspondance entre les débits calculés par AIGA et ceux estimés sur le terrain, avec cependant des différences. Celles-ci sont liées à une sous-estimation de la lame d'eau radar sur certains bassins et aux limites du modèle hydrologique.Mots clefs non jaugé ; crue éclair ; système d'avertissement ; relevés post-événement

This paper presents a strategy for diagnostic verification of hydrologic ensembles, based on the selection of summary verification metrics (which could be extended to more detailed metrics) and the analysis of the relative contribution of the different sources of error. Such diagnostic verification could be conducted with the Ensemble Verification System (EVS) and is illustrated with a verification case study of experimental precipitation and streamflow ensemble reforecasts over a 24-year period. The EVS is proposed as a flexible and modular tool for the HEPEX verification test-bed to evaluate existing and emerging verification methods that are appropriate for hydrologic applications. Published in

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