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This study compares the multivariate predictions of daily temperature, temperature range, precipitation, surface wind and solar radiation of a single-model analogue approach with an original multi-model analogy over 12 regions in Europe and Maghreb. Both approaches are based on two-level analogue models where atmospheric predictors are either dynamic or thermodynamic. In the multi-model approach, independent analogue models with predictand-specific predictors are used. In the single-model one, a unique analogue model and its associated set of predictors is applied to all predictands. Testing numerous large-scale predictors, we first identify the best predictor sets for each modelling strategy. Those obtained for the single-model approach are significantly different from those of the predictand-specific models. This is especially the case for local temperature and wind speed. Both methods perform similarly for precipitation, temperature range and radiation. We next assess the ability of both approaches to simulate physically coherent multivariate weather scenarios. With the single-model method, weather scenarios are obtained for each prediction day from observations sampled simultaneously on one analogue day. The physical consistency between variables is thus automatically fulfilled each day. This allows the single-model method to reproduce well the observed inter-predictand correlations, especially the significant correlations between radiation and precipitation and between radiation and temperature range. These results suggest a hybrid analogue model using a single-model for radiation, temperature range and precipitation, combined with a univariate approach for wind. Two options are proposed for temperature for which either the predictand-specific method or a single-model approach with an additional correction are conceivable. This hybrid approach leads to a possible compromise between reasonable univariate prediction skills and realistic inter-predictands correlations, both classically required for many impact studies.

Recent flash flood impact studies highlight that road networks are often disrupted due to adverse weather and flash flood events. Road users are thus particularly exposed to road flooding during their daily mobility. Previous exposure studies, however, do not take into consideration population mobility. Recent advances in transportation research provide an appropriate framework for simulating individual travel-activity patterns using an activity-based approach. These activity-based mobility models enable the prediction of the sequence of activities performed by individuals and locating them with a high spatial–temporal resolution. This paper describes the development of the MobRISK microsimulation system: a model for assessing the exposure of road users to extreme hydrometeorological events. MobRISK aims at providing an accurate spatiotemporal exposure assessment by integrating travel-activity behaviors and mobility adaptation with respect to weather disruptions. The model is applied in a flash-flood-prone area in southern France to assess motorists' exposure to the September 2002 flash flood event. The results show that risk of flooding mainly occurs in principal road links with considerable traffic load. However, a lag time between the timing of the road submersion and persons crossing these roads contributes to reducing the potential vehicle-related fatal accidents. It is also found that sociodemographic variables have a significant effect on individual exposure. Thus, the proposed model demonstrates the benefits of considering spatiotemporal dynamics of population exposure to flash floods and presents an important improvement in exposure assessment methods. Such improved characterization of road user exposures can present valuable information for flood risk management services.

Summary Flash floods are responsible for a majority of natural disaster fatalities in the USA and Europe and most of them are vehicle-related. If human exposure to flood is generally assessed through the number of inhabitants per buildings located in flood prone zone, it is clear that this number varies dramatically throughout the day as people move from place to place to follow their daily program of activities. Knowing the number of motorists exposed on flood prone road sections or the factors determining their exposure would allow providing a more realistic evaluation of the degree of exposure. In order to bridge this gap and provide emergency managers with methods to assess the risk level for motorists, this paper describes two methods, a simple rough-and-ready estimate and a traffic attribution method, and applies both of them on datasets of the Gard departement, an administrative region of Southern France with about 700 000 inhabitants over 5875 km2. The first method to obtain an overall estimation of motorists flood exposure is to combine (i) the regional density of roads and rivers to derive a count of potential road cuts and (ii) the average daily kilometers driven by commuters of the study area to derive the number of people passing these potential cuts. If useful as a first approximation, this method fails to capture the spatial heterogeneities introduced by the geometry of river and road networks and the distribution of commuters’ itineraries. To address this point, this paper (i) uses a pre-established detailed identification of road cuts (Naulin et al., 2013) and (ii) applies a well-known traffic attribution method to existing and freely available census datasets. Both methods indicate that commuters’ exposure is much larger than the number of commuters itself, illustrating the risk amplification effect of mobility. Comparing the results from both methods shows that (i) the road network geometry plays a significant role in reducing the risk of river-road dangerous intersections and (ii) not all commuters are equally exposed. Evidently commuters who have longer routes are more exposed, but residents of rural municipalities as well as professionals with highly qualified jobs are also more exposed. Finally, these exposure assessment methods applied to the Gard area allows locating road sections where commuters’ exposure to flood is high. It also sets the first step toward the implementation of a modeling platform able to combine the estimation of daily travel patterns exposure and behavioral response of motorists to road flooding, a critical input for emergency services and services in charge of the management of road networks in flash flood prone areas.

Summary Distributed hydrological models are useful tools for process understanding and water management, especially in peri-urban catchments where the landscape heterogeneity is large, caused by a patchwork of natural and urbanized areas. This paper presents the Peri-Urban Model for landscape MAnagement (PUMMA) built within the LIQUID® modeling framework, specifically designed to study the hydrology of peri-urban catchments. It combines rural and urban hydrological models, and is used for process understanding. The originality of PUMMA is to follow a fully object-oriented approach, for both model mesh building and process representation. Urban areas, represented by cadastral units and rural areas divided in Hydrological Response Units are thus modeled with different interacting process modules. This provides a detailed representation of the runoff generation on natural and impervious areas. Furthermore, the exchange between process modules facilitates the simulation of subsurface and overland flow, as well as groundwater drainage by sewer pipes. Several drainage networks can coexist and interact (e.g. via storm water overflow devices) and water can be stored in retention basins, which allows the modeling of complex suburban drainage systems with multiple outlets. The model is then applied to the Chaudanne catchment (2.7 km 2 ), located in the suburbs of Lyon, France. The uncalibrated model results show the importance of surface runoff from impervious areas for summer events and flow contributions from rural zones for winter events. Furthermore, the model reveals that the retention capacity of the Chaudanne catchment is larger than for classical urban catchments due to the peri-urban character of the catchment.

In distributed hydrological models, elementary modelling units are often derived from raster and vector geographical maps such as digital elevation models (DEM), land use and soil properties maps. Model pre-processing can be defined as the operations transforming raw GIS data into the adequate modelling units. This paper describes our experience with open-source GIS for these pre-processing operations through a set of examples. The softwares tested were PostGIS, OpenJump, OrbisGIS, GRASS, SAGA, MapWindow and QuantumGIS. The pre-processing operations dealt with DEM processing, topology corrections, and advanced editing of vector layers. None of the tested software provided a ready to use solution. However GRASS GIS appeared as the most promising candidate for performing all the preprocessing operations and automating them. Although sometimes tricky to handle by GIS non-expert users, open-source GIS allow easy tests and comparisons and therefore are well suited to research purposes.

18 Pags., To better handle landscape heterogeneities in distributed hydrological modeling, an earlier work proposed a discretization based on nested levels, which leads to fully unstructured modeling meshes. Upon such a discretization, traditional numerical solutions must be adapted, especially to describe lateral flow between the unstructured mesh elements. In this paper, we illustrated the feasibility of the numeric solution of the diffusion equation, representing groundwater flow, using unstructured meshes. Thus, a two-dimensional (2D) groundwater model (BOUSS2D), adapted to convex unstructured and irregular meshes was developed. It is based on the approximation of the 2D Boussinesq equation using numeric techniques suitable for nonorthogonal grids. The handling of vertical and horizontal aquifer heterogeneities is also addressed. The fluxes through the interfaces among joined mesh elements are estimated by the finite volume method and the gradient approximation method. Comparisons between the BOUSS2D predictions and analytical solutions or predictions from existing codes suggest the acceptable performance of the BOUSS2D model. These results therefore encourage the further development of hydrological models using unstructured meshes that are capable of better representing the landscape heterogeneities.

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU; Distributed hydrological models are valuable tools that can be used to support water management in catchments. However, the complexity of management issues, the variety of modelling objectives, and the variable availability of data require a flexible way to customize models and adapt them to each individual problem. Environmental modelling frameworks offer such flexibility; they are designed to build and run integrated models on the basis of reusable and exchangeable components. This paper presents the LIQUID® framework, developed by Hydrowide since 2005. The purpose of developing LIQUID® was to provide both easier integration of hydrological processes and preservation of their characteristic temporal and spatial scales. It suits a wide range of applications, both in terms of spatial scales and of process conceptualisations. LIQUID® is able to synchronize different time steps, to handle irregular geometries, and to simulate complex connections between components, in particular involving feedback. The paper presents the concepts of LIQUID® and the technical choices made to meet the above requirements, with focuses on the simulation run system and on the spatial discretization of process components. The use of the framework is illustrated by five application cases associated with contrasted spatial and temporal scales.