Low-flow simulation and forecasting remains a difficult issue for hydrological modellers, and intercomparisons can be extremely instructive for assessing existing low-flow prediction models and for developing more efficient operational tools. This research presents the results of a collaborative experiment conducted to compare low-flow simulation and forecasting models on 21 unregulated catchments in France. Five hydrological models (four lumped storage-type models – Gardenia, GR6J, Mordor and Presages – and one distributed physically oriented model – SIM) were applied within a common evaluation framework and assessed using a common set of criteria. Two simple benchmarks describing the average streamflow variability were used to set minimum levels of acceptability for model performance in simulation and forecasting modes. Results showed that, in simulation as well as in forecasting modes, all hydrological models performed almost systematically better than the benchmarks. Although no single model outperformed all the others for all catchments and criteria, a few models appeared to be more satisfactory than the others on average. In simulation mode, all attempts to relate model efficiency to catchment or streamflow characteristics remained inconclusive. In forecasting mode, we defined maximum useful forecasting lead times beyond which the model does not bring useful information compared to the benchmark. This maximum useful lead time logically varies between catchments, but also depends on the model used. Simple multi-model approaches that combine the outputs of the five hydrological models were tested to improve simulation and forecasting efficiency. We found that the multi-model approach was more robust and could provide better performance than individual models on average.

The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

This work discusses the ability of a bias-adjustment method using empirical quantile mapping to improve the skills of seasonal forecasts over Europe for three key climate variables, i.e., temperature, precipitation and wind speed. In particular, the suitability of the approach to be integrated in climate services and to provide tailored predictions for local applications was evaluated. The workflow was defined in order to allow a flexible implementation and applicability while providing accurate results. The scheme adjusted monthly quantities from the seasonal forecasting system SEAS5 of the European Centre for Medium-Range Forecasts (ECMWF) by using ERA5 reanalysis as reference. Raw and adjusted forecasts were verified through several metrics analyzing different aspects of forecast skills. The applied method reduced model biases for all variables and seasons even though more limited improvements were obtained for precipitation. In order to further assess the benefits and limitations of the procedure, the results were compared with those obtained by the ADAMONT method, which calibrates daily quantities by empirical quantile mapping conditioned by weather regimes. The comparable performances demonstrated the overall suitability of the proposed method to provide end users with calibrated predictions of monthly and seasonal quantities.

In order to develop seasonal forecast applications, raw forecast data generally need to be corrected to remove their systematic errors and drifts in time. In the climate community, methods based on quantile mapping techniques are quite common for their easy implementation. In the framework of the SECLI-FIRM project, we have tested a refinement of quantile mapping by conditioning the correction to weather regimes, in order to take large-scale circulation into account. For that purpose, we have used ADAMONT, a tool originally developed by Météo-France to correct climate projection scenarios. It was applied on four C3S seasonal forecast models over Europe, using ERA5 as a reference. Three parameters were treated at daily time-step: 2-metre temperature, precipitation and 10-metre wind-speed.One of the main objectives of this study was to better understand the role weather regimes can play, if/when/where/for which parameter we gain in quality and predictability. For instance, a series of experiments were conducted on an idealized case of “perfect forecasts” of weather regimes, to point out the maximum benefits we could expect from the method.Another focus of research was to test some strategies to optimize the positive impact of the introduction of weather regimes, by selecting members in one model ensemble or by using a multi-model approach. The selection was based on a sub-sampling of the best members in terms of weather regime frequency forecast, in order to determine the needed precision of weather regime forecast, for it to be useful in the correction.We will present the main results of this work and some operational perspectives.

In the framework of the MEDSCOPE project, Météo-France has initiated the development of new prototypes for seasonal water resource management in the Mediterranean region, addressing different scientific and technical challenges essential for a future operationalization of the services . In order to have a replicable result on the Mediterranean area, we decided first to consider the three large watersheds onof the Rhone river in France, the Ebro river in Spain and the Po river in Italy.Our first challenge was to use a new hydrologic model SURFEX-CTRIP, covering the whole Mediterranean area. Another point was to perfect and evaluate a new downscaling tool named ADAMONT permitting to debiase all seasonal forecast input variables needed for hydrology applications and not only (temperature and, precipitation and 5 other surface meteorological parameters). We decided also to assess the new UERRA hydrological analyse available on these three countries. Lthe last challenge was to identify local end users facing with decision making process at seasonal scale for water resources management and develop decision help products adapted to their needs.The evaluation of these prototypes, carried out over the period 2019-2020 using the MF Syst 6 and then Syst 7 seasonal forecasting model, has highlighted a significant potential in a future operational application but also difficulties to be overcome.The communication will present the main results of this work and discuss the lessons to be learnet from this experience 

Hydrological drought conditions have negative implications for environmental and socio-economic activities in France. Operational drought forecasts with adequate timing and quality can guide decision-makers to prepare and make informed decisions on water management. Available ensemble meteorological forecasting systems, used to force hydrological models, differ in configuration, resolution, timescale, release frequency, and accuracy. These differences in atmospheric forecasting systems limit their usefulness for operational drought forecasts. To address this challenge, we proposed an approach to produce seamless atmospheric forecasts (SeamF). SeamF were constructed from the extended- and long-range ensemble prediction systems (ECMWF and Météo-France, respectively) using a random concatenation member by member method in a first step.The new SeamF system provides a 144-day outlook of hourly and daily precipitation and temperature variables, updated once a week, suitable for hydrological modelling over the country. In addition to the practicability of these integrated forecasts, as compared to stand-alone extended- or long-range forecasts, the level of deterministic and probabilistic verifications metrics suggests a promising improvement in hydrological applications. This approach builds a basis for more sophisticated seamless forecast production and application for hydrological decision-making in France.