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Accounting for the variability of hydrological processes and climate conditions between catchments and within catchments remains a challenge in rainfall–runoff modelling. Among the many approaches developed over the past decades, multi-model approaches provide a way to consider the uncertainty linked to the choice of model structure and its parameter estimates. Semi-distributed approaches make it possible to account explicitly for spatial variability while maintaining a limited level of complexity. However, these two approaches have rarely been used together. Such a combination would allow us to take advantage of both methods. The aim of this work is to answer the following question: what is the possible contribution of a multi-model approach within a variable spatial framework compared to lumped single models for streamflow simulation?To this end, a set of 121 catchments with limited anthropogenic influence in France was assembled, with precipitation, potential evapotranspiration, and streamflow data at the hourly time step over the period 1998–2018. The semi-distribution set-up was kept simple by considering a single downstream catchment defined by an outlet and one or more upstream sub-catchments. The multi-model approach was implemented with 13 rainfall–runoff model structures, three objective functions, and two spatial frameworks, for a total of 78 distinct modelling options. A simple averaging method was used to combine the various simulated streamflow at the outlet of the catchments and sub-catchments. The lumped model with the highest efficiency score over the whole catchment set was taken as the benchmark for model evaluation.Overall, the semi-distributed multi-model approach yields better performance than the different lumped models considered individually. The gain is mainly brought about by the multi-model set-up, with the spatial framework providing a benefit on a more occasional basis. These results, based on a large catchment set, evince the benefits of using a multi-model approach in a variable spatial framework to simulate streamflow.

The recent development of the FYRE climate (French Hydroclimate REanalysis), a high-resolution ensemble daily reanalysis of precipitation and temperature covering the period 1871–2012 and the whole of France, offers the opportunity to derive streamflow series over the country from 1871 onwards. The FYRE Climate dataset has been used as input for hydrological modelling over a large sample of 661 near-natural French catchments using the GR6J lumped conceptual model. This approach led to the creation of the 25-member hydrological reconstructions HyDRE spanning the 1871–2012 period. Two sources of uncertainties have been taken into account: (1) the climate uncertainty by using forcings from all 25 ensemble members provided by FYRE Climate, and (2) the streamflow measurement error by perturbing observations used during the calibration. The hydrological model error based on the relative discrepancies between observed and simulated streamflow has been further added to derive the HydREM streamflow reconstructions. These two reconstructions are compared to other hydrological reconstruction with different meteorological inputs, hydrological reconstructions from machine learning algorithm and independent/dependent observations. Overall the results show the added value of the HydRE and HydREM reconstructions in terms of quality, uncertainty estimation, and representation of extremes, therefore allowing to better understand the variability of past hydrology over France.

The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatio-temporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989-1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

SCOPE Climate (Spatially COherent Probabilistic Extended Climate dataset) is a 25-member ensemble of 142-year daily high-resolution reconstructions of precipitation, temperature, and Penman–Monteith reference evapotranspiration over France, from 1 January 1871 to 29 December 2012. SCOPE Climate provides an ensemble of 25 spatially coherent gridded multivariate time series. It is derived from the statistical downscaling of the Twentieth Century Reanalysis (20CR) by the SCOPE method, which is based on the analogue approach. SCOPE Climate performs well in comparison to both dependent and independent data for precipitation and temperature. The ensemble aspect corresponds to the uncertainty related to the SCOPE method. SCOPE Climate is the first century-long gridded high-resolution homogeneous dataset available over France and thus has paved the way for improving knowledge on specific past meteorological events or for improving knowledge on climate variability, since the end of the 19th century. This dataset has also been designed as a forcing dataset for long-term hydrological applications and studies of the hydrological consequences of climate variability over France. SCOPE Climate is freely available for any non-commercial use and can be downloaded as NetCDF files from https://doi.org/10.5281/zenodo.1299760 for precipitation, https://doi.org/10.5281/zenodo.1299712 for temperature, and https://doi.org/10.5281/zenodo.1251843 for reference evapotranspiration.

Statistical downscaling is widely used to overcome the scale gap between predictors from numerical weather prediction models or global circulation models and predictands like local precipitation, required for example for medium-term operational forecasts or climate change impact studies. The predictors are considered over a given spatial domain which is rarely optimised with respect to the target predictand location. In this study, an extended version of the growing rectangular domain algorithm is proposed to provide an ensemble of near-optimum predictor domains for a statistical downscaling method. This algorithm is applied to find five-member ensembles of near-optimum geopotential predictor domains for an analogue downscaling method for 608 individual target zones covering France. Results first show that very similar downscaling performances based on the continuous ranked probability score (CRPS) can be achieved by different predictor domains for any specific target zone, demonstrating the need for considering alternative domains in this context of high equifinality. A second result is the large diversity of optimised predictor domains over the country that questions the commonly made hypothesis of a common predictor domain for large areas. The domain centres are mainly distributed following the geographical location of the target location, but there are apparent differences between the windward and the lee side of mountain ridges. Moreover, domains for target zones located in southeastern France are centred more east and south than the ones for target locations on the same longitude. The size of the optimised domains tends to be larger in the southeastern part of the country, while domains with a very small meridional extent can be found in an east–west band around 47° N. Sensitivity experiments finally show that results are rather insensitive to the starting point of the optimisation algorithm except for zones located in the transition area north of this east–west band. Results also appear generally robust with respect to the archive length considered for the analogue method, except for zones with high interannual variability like in the Cévennes area. This study paves the way for defining regions with homogeneous geopotential predictor domains for precipitation downscaling over France, and therefore de facto ensuring the spatial coherence required for hydrological applications.

Small-scale models are useful tools to study the interactions between debris flows and structures and channels. Small-scale modelling of debris flows remains however complicated because of the complex rheology and scaling challenges of these geophysical processes. An on-going study of a debris basin and the downstream channel where two fords and a bridge are located is presented in this extended abstract. The studied torrent is the Manival catchment, located near Grenoble in France. We present the catchment features, the scientific questions studied, some preliminary calibration results describing the mixtures used to model debris flows as well as results from three debris-flood and two debris-flow runs. In essence, the model highlighted that the structure enable a large share of the bedload transport to pass downstream. Debris flows can be more or equally trapped depending on their rheology which controls the surges dynamics and the deposition slope in the debris basin.

Historical climate knowledge has become a big issue to better understand climate change and natural hazards. Climate reconstruction methods can be used however their performance are difficult to establish. This paper is part of an ongoing research project on floods and droughts in the Moselle basin (RECRET) for which reconstitution of long precipitation and temperature series were performed. A comparison with other climate reconstructions is carried out to allow a relative assessment of these series. The comparison is based on five methods: RECRET (Delus et al., 2018), a method based on the regional pressure gradient (Grelier et al., 2017), SCOPE Climate (Caillouet et al., 2019), FYRE Daily (Devers et al., 2019) and SAFRAN (Vidal et al., 2010). The paper focus on the consistency between precipitation series from the different methods. RECRET and FYRE Daily are quite similarand closed to SAFRAN.

An analysis of the long term morphological evolution of the Génissiat reservoir (France) is provided. First, a methodology for bathymetric data processing and reservoir sediment volume budget calculation is described. An estimation of global uncertainties in volume calculation is proposed. The reservoir bathymetric budget for several dam flushing events and interflush periods is presented, showing the global decrease of deposited sediment volume with time. The spatial dynamics of the reservoir subreaches is highlighted and typical patterns in flush and interflush periods are identified.

This study aims at comparing two quantitative precipitation forecasting techniques based on the meteorological analogy concept. Method A considers first a selection of analogous situations at synoptic scale. Second a subset of the most similar situations in terms of hygrometry is extracted. Method B extends method A by two innovative ways, which are restricting the search for analogues with temperature information instead of the common season criterion, and exploiting the information about vertical motion considering vertical velocity. Forecasts are evaluated in a perfect prognosis context and in operational conditions as well, by mean of verification measures (Continuous Ranked Probability Skill Score and scores computed from contingency tables). Results of the case study in France show that: (1) there is an increase in forecast skill when temperature and vertical velocity are included in the procedure, (2) it is possible to anticipate rainfall events up to one week ahead and (3) the introduction of new variables such as vertical velocity may be useless beyond few days ahead if the forecast of the weather model is not reliable.

An exploratory study is performed on the 850-hPa relative humidity data (RH850) extracted from the Re-Analysis Project (ERA-40) and National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) re-analyses covering a domain centred on western Europe (60°W-60°E, 15°N-75°N). The largest deviations between the two data archives are observed over the North Atlantic Ocean. In addition, unrealistic values of RH850 are detected in the ERA-40 re-analysis at resolutions of both 2.5° and 1.125°. There is no strong correlation between RH850 provided by ERA-40 and observations from radio sounding stations, thus ruling out a straightforward correction of the detected anomalous values. Copyright © 2009 Royal Meteorological Society