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In many countries, rivers are the primary supply of water. A number of uses are concerned (drinking water, irrigation, hydropower…) and can be strongly affected by water shortages. Therefore, there is a need of early anticipation of low-flow periods to improve water management. This is strengthened by the perspective of having more severe summer low-flows in the context of climate change. Several French institutes (BRGM, EDF, INRAE, Lorraine University and Météo-France) have been collaborating to develop an operational tool for low-flow forecasting, called PREMHYCE (Tilmant et al., 2020). This platform produces forecasts in real time on more than 900 catchments in metropolitan France since 2017, in cooperation with French operational services of water management. PREMHYCE includes five hydrological models which can be calibrated on gauged catchments and which assimilate flow observations. Low-flow forecasts based on ensemble streamflow prediction (ESP) can be issued up to either 15 days ahead, based on scenarios from the European Centre for Medium-Range Weather Forecasts (ECMWF) or 90 days ahead, using historical climatic data as ensembles of future input scenarios. These climatic data (precipitation and temperature) are provided by Météo-France with the daily gridded SAFRAN reanalysis on the 1958-2020 period, which includes a wide range of conditions. Outputs from the different hydrological models are combined into a simple multi-model approach to improve robustness of the forecasts. The tool provides text files and graphical representation of forecasted low-flows, and probability to be under low-flow thresholds provided by users. In 2020, a website has been developed allowing easier access to the forecasts for users. The presentation will show the main characteristics of this operational tool and results on the recent low-flow periods. Keywords: low-flow forecasting, hydrological model, ensemble streamflow prediction, water resource managementReferenceTilmant, F., Nicolle, P., Bourgin, F., Besson, F., Delaigue, O., Etchevers, P., François, D., Le Lay, M., Perrin, C., Rousset, F., Thiéry, D., Magand, C., Leurent, T., Jacob, E., 2020: PREMHYCE: an operational tool for low-flow forecasting, La Houille Blanche (5) 37-44, DOI: 10.1051/lhb/2020043

International audience; In many countries, rivers are the primary supply of water. A number of uses (drinking water, irrigation, hydropower…) can be strongly affected by water shortages. Therefore, there is a need for the early anticipation of low-flow periods to improve water management. This is strengthened by the perspective of having more severe low flows in the context of climate change. Several French institutions have been collaborating over the last years to develop an operational tool for low-flow forecasting, called PREMHYCE. It was tested in real time on more than one hundred catchments in continental France since 2017. PREMHYCE includes five hydrological models, which are calibrated on gauged catchments and which assimilate flow observations. Low-flow forecasts can be issued up to 90 days ahead, based on Ensemble Streamflow Prediction (ESP) using historical climatic data as ensembles of future input scenarios. The performance of the forecasting tool is analysed on the 2017–2018 low flows on 38 catchments for which forecasts are available for all the models. The capacity to anticipate low-flow events shows an average anticipation of 40 days. The short-term forecast (7 days ahead) is good for most of the models.; De nombreux usages de l'eau peuvent être fortement impactés par les pénuries d'eau (eau potable, irrigation, hydroéléctricité...). Il est donc nécessaire d'anticiper les périodes d'étiage afin d'améliorer la gestion de l'eau. Ceci est renforcé par la perspective d'étiages futurs plus sévères dans le contexte du changement climatique. Cinq institutions françaises ont développé un outil opérationnel de prévision des bas débits, PREMHYCE. Il est testé en temps réel sur une centaine de bassins versants de France métropolitaine depuis 2017. PREMHYCE comprend cinq modèles hydrologiques qui peuvent être calés sur des bassins versants jaugés et assimilent les dernières observations de débit. Les prévisions de débits sont émises jusqu'à un horizon de 90 jours selon l'approche Ensemble Streamflow Prediction (ESP) (données météorologiques historiques utilisées comme ensemble de scénarios d'entrée). Ces données météorologiques (précipitations, évapotranspiration et température) sont issues de la réanalyse SAFRAN journalière de Météo-France, sur la période 1958–2018. Les performances de l'outil sont analysées sur les étiages 2017–2018 pour 38 bassins versants sur lesquels les prévisions sont disponibles pour tous les modèles. Ces derniers ont montré des capacités d'anticipation de l'ordre de 40 jours en moyenne. La plupart des modèles présentent une précision satisfaisante pour prévoir les bas débits à courte échéance (j + 7).

Real-time flood forecasting and other hydrological applications in mountainous areas require a good understanding and accounting of snow accumulation and melt. The CemaNeige snow model is currently used with the GRP hydrological model by most regional operational services in France to produce floods forecasts with lead times varying from a few hours to a few days. The snow model is based on a degree-day approach and needs limited inputs (precipitation and air temperature) spatialized on altitude bands over the catchment. It was initially developed on snow-dominated catchments by Valéry et al. (2014) using only streamflow series as calibration information. The model was then adapted by Riboust et al. (2019) to better simulate snow-covered areas as estimated by MODIS satellite images. These data were used as a secondary source of information for parameter calibration. All these developments were made at the daily time step. However, for real-time purposes, the outputs from the snow model are often needed at a finer time step, typically hourly or sub-hourly.Here the transposability and the consistency of the CemaNeige model were studied across a range of time steps, from hourly to daily, on a set of snow-influenced catchments. This follows previous works on the hydrological model to improve its consistency across time scales (see e.g. Viatgé et al., 2019). Several questions were addressed:To which extent are the outputs of the snow model and its parameters consistent across various time steps? Is the current snow model complexity (structure and parameters) sufficient to simulate the snow influence at the catchment scale at sub-daily time steps? Can we expect better results by running the snow model at sub-daily time steps than by disaggregating the outputs of the snow model run at the daily time? The answers to these questions will be presented based on a comprehensive testing scheme and a set of numerical criteria. Perspectives in terms of operational use will be discussed.

National audience; Nowadays, flood forecasting is often done at the hourly time step, which is a good compromise between data availability and the necessary resolution to simulate flood events. However, in a number of cases, this fixed time step shows limitations, and shorter (or even larger) time steps are required. Therefore, it is necessary to have hydrological models able to adapt to various time steps. Generally, this adaptation does not limit to a simple change of the length of the time step in the model equations. The time-step dependency must be made explicit in some model parameters, and the model structure must also be adapted to consistently represent processes across time steps. The article presents the adaptation of the GRP hydrological model, which is widely used by flood forecasting services in operational conditions in France, so that it can behave and run consistently at different time steps. The tests were made on a set of 240 French catchments.; La prévision des crues est souvent réalisée au pas de temps horaire, qui est un bon compromis entre disponibilité des données et résolution nécessaire pour bien rendre compte des phénomènes de crue. Cependant, dans un certain nombre de cas, ce pas de temps fixe est limitant, et des pas de temps plus fins (ou éventuellement plus larges) sont mieux adaptés. Il est donc nécessaire d'avoir des modèles hydrologiques capables de s'adapter à un fonctionnement à différents pas de temps. Or, cette adaptation ne se résume généralement pas à un simple changement de la longueur du pas de temps dans les équations du modèle. Elle implique une explicitation de la dépendance possible de certains paramètres du modèle au pas de temps, et également une adaptation de la structure du modèle pour que les processus soient représentés de manière cohérente entre les pas de temps. L'article présente une adaptation du modèle hydrologique GRP, qui est largement utilisé en conditions opérationnelles par les services de prévision des crues français, pour qu'il fonctionne correctement à différents pas de temps. Les tests sont réalisés sur une base de 240 bassins versants français.

Low-flow forecasting can help to improve water management at places where a number of uses can be affected by diminishing water supply from rivers. Several French institutes (INRAE, BRGM, EDF, Lorraine University and Météo-France) have been collaborating to set up an operational platform, called PREMHYCE, for low-flow forecasting at the national scale, in cooperation with operational services. PREMHYCE includes five hydrological models and low-flow forecasts can be issued up to 90 days ahead for more than 800 basins. Several input scenarios are considered: ECMWF 14-days ensemble forecasts, ensemble streamflow prediction (ESP) using historical climatic data, and a no precipitation scenario. Outputs from the different hydrological models are combined into a multi-model approach to improve robustness of the forecasts. The tool provides text files and graphical representation of forecasted low-flows, as well as key low-flow indicators, such as the probabilities of being under low-flow thresholds provided by operational services. The presentation will show the main characteristics of this operational forecast platform, its latest developments and the results on the recent low-flow periods.

In many countries, rivers are the primary supply of water. A number of uses are concerned (drinking water, irrigation, hydropower…) and they can be strongly affected by water shortages. Therefore, there is a need of early anticipation of low-flow periods to improve water management. This is strengthened by the perspective of having more severe summer low-flows in the context of climate change. Several French institutes (Irstea, BRGM, Météo-France, EDF and Lorraine University) have been collaborating to develop an operational tool for low-flow forecasting, called PREMHYCE. It is tested in real time since 2017, and implemented on 259 catchments in metropolitan France, in cooperation with operational services which provide streamflow observations and use low-flow forecasts from the tool. PREMHYCE includes five hydrological models which can be calibrated on gauged catchments and which assimilate flow observations. Low-flow forecasts can be issued up to 90 days ahead, based on several inputs scenarios: ECMWF 10-days ensemble forecasts, ensemble streamflow prediction (ESP) using historical climatic data as ensembles of future input scenarios, and a no precipitation scenario. Climatic data (precipitation, evapotranspiration and temperature) are provided by Météo-France with the daily gridded SAFRAN reanalysis on the 1959-2019 period, which includes a wide range of conditions. The tool provides text files and graphical representation of forecasted low-flows, and probability to be under low-flow thresholds provided by users. Outputs from the different hydrological models can be combined within a multi-model approach to improve robustness of the forecastsThe presentation will show the main characteristics of this operational tool, the probabilistic evaluation framework, results on the recent low-flow periods, and how feedbacks from end-users can help improving the tool.

International audience; In many countries, rivers are the primary supply of water. A number of uses are concerned (drinking water, irrigation, hydropower...) and they can be strongly affected by water shortages. Therefore, there is a need of early anticipation of low-flow periods to improve water management. This is strengthened by the perspective of having more severe summer low-flows in the context of climate change. Several French institutes (Irstea, BRGM, Météo-France, EDF and Lorraine University) have been collaborating to develop an operational tool for low-flow forecasting, called PREMHYCE. It is tested in real time on 118 catchments in metropolitan France since 2017, in cooperation with operational services which provide streamflow observations and use low-flow forecasts from the tool. PREMHYCE includes five hydrological models which can be calibrated on gauged catchments and which assimilate flow observations. Low-flow forecasts can be issued up to 90 days ahead, based on ensemble streamflow prediction (ESP) using historical climatic data as ensembles of future input scenarios. These climatic data (precipitation, evapotranspiration and temperature) are provided by Météo-France with the daily gridded SAFRAN reanalysis on the 1958-2018 period, which includes a wide range of conditions. The tool provides text files and graphical representation of forecasted low-flows, and probability to be under low-flow thresholds provided by users. Outputs from the different hydrological models can be combined into a simple multi-model approach to improve robustness of the forecasts. The presentation will show the main characteristics of this operational tool and results on the recent low-flow periods.

International audience; ABSTRACT.-In mountainous regions, near-surface meteorological reanalyses can carry significant uncertainties due to the complex topography and the scarcity of observations. Combining different reanalyses has proved to be efficient in reducing these uncertainties. This technique is applied over a French mountainous basin in the southern Alps, the Durance basin, where two reanalyses exist : SAFRAN and SPAZM. The comparison of the features of these two rea-nalyses shows that they are complementary. The precipitations provided by SPAZM are at a finer spatial resolution than SAFRAN and are based on more observations at high altitudes. However, on the contrary to SAFRAN, SPAZM does not provide the full set of atmospheric variables required by hydrological models based on water and energy balance. SPAZM and SAFRAN were thus combined. Precipitation and temperature from SAFRAN are corrected by monthly temperature and precipitation from SPAZM, and the other variables from SAFRAN are then corrected accordingly. As a result, the meteorological product called DuO was obtained, providing eight atmospheric variables (temperature at 2 m, rainfall, snowfall, incoming solar and infrared radiation, air humidity, wind speed and potential evapotranspiration), at the hourly timescale and at the 1-km 2 resolution. The strong topographical heterogeneity is thus better represented in the upstream part of the Durance catchment. DuO is tested for hydrological modelling, using the Catchment Land Surface Model (CLSM), a land surface model distributed over sub-catchments. The analysis of various output variables (stream-flow, snowpack and evapotranspiration) shows that the hybridization of the two reanalyses benefits from their respective qualities and yields a more realistic representation of the hydrological processes in the CLSM model.; La rareté des mesures et la complexité du terrain dans les régions montagneuses engendrent d'importantes incertitudes dans les réanalyses météorologiques de surface. L'hybridation de plusieurs réanalyses est une technique efficace pour limiter ces incertitudes. Dans le bassin versant de la Durance, deux réanalyses se basant sur des réseaux de mesures et des techniques d'extrapolation différentes coexistent, SAFRAN et SPAZM. Leur comparaison montre qu'elles sont complémentaires car les précipitations de SPAZM à une résolution spatiale plus fine que celle de SAFRAN, se basent sur un plus grand nombre d'observations en haute altitude. Cependant, SPAZM ne fournit pas l'ensemble des variables atmosphériques dont un modèle hydrologique à bilans d'eau et d'énergie a besoin, contrairement à SAFRAN. L'hybridation de ces deux réanalyses a donc été effectuée. Le produit météorologique obtenu, DuO, fournit huit variables atmosphériques (température de l'air à 2 m, pluie, neige, rayonnements incidents, humidité de l'air, vent et évapo-transpiration potentielle) au pas de temps horaire et à la résolution de 1 km 2. DuO a été testé dans un objectif de modé-lisation hydrologique, avec le modèle semi-distribué CLSM (Catchment Land Surface Model). L'analyse des différentes variables de sortie (débits, stock de neige et évapotranspiration) montre que l'hybridation des deux réanalyses, valorisant les qualités respectives de SAFRAN et de SPAZM, permet une représentation plus réaliste des processus hydrologiques au sein du modèle CLSM. Building hybrid near-surface reanalysis adapted to mountainous regions: example of the DuO product over the Durance basin, France

In this study, we assess the interest of using a Soil – Vegetation – Atmosphere – Transfer model, the SiSPAT (Simple Soil Vegetation Atmosphere Transfer) model, which solves the surface energy balance, for the evaluation of theoretical crop water requirements in south-east France. First the relevance of the model results, when parameterized using information extracted from a soil data base and pedotransfer functions for the estimation of soil hydraulic properties, and when vegetation characteristics are prescribed using available data bases is assessed. We use long term time series of soil water content profiles for this purpose. The results show that evapotranspiration, as simulated by SiSPAT is sensitive to the soil parameter specification leading to large uncertainties in the model results. Then, we present two methods implemented in SiSPAT to compute irrigation requirements. The first option mimics the soil water balance model principles by estimating the irrigation from the available soil water capacity filling. The second option relies on the model physics and estimates the difference between actual transpiration and the value corresponding to a minimal stomatal resistance, i.e. without water stress. Aspersion and drip irrigation can be simulated. Nine crop are chosen for the model evaluation. A comparison with two other water balance models is performed. The three models are consistent with determination coefficient between the simulated annual irrigation generally larger than 0.4. However, differences of the interannual irrigation needs, larger than several 100 mm, are sometimes found, especially for drip irrigation. This work provides a quantification of expected uncertainties when using water balance models or physically-based models for irrigation needs estimation.

There has been a growing interest in extending forecast lead times to facilitate water resources planning and management during droughts and low-flow events. This can be partly achieved by improving integrated hydrometeorological forecasting systems, which offer forecasts of future meteorological and hydrological conditions over continuous space and time scales. Several initiatives have been recently carried out at continental or global scales, in Europe and worldwide, to set up forecasting chains that run with numerical weather and climate model predictions as input to distributed hydrological models. Overall, these systems have been developed in response to a call for seamless forecasts in time (from short to medium and long-ranges) and space (at well-monitored and partially-monitored or ungauged catchments). These systems usually have to deal with limited calibration and evaluation against local data.In France, the CIPRHES project (2021-2025) aims at building an efficient and integrated methodology and an online operational service for a country-wide hydrological drought and low-flow forecasting system, based on the proof-of-concept PREMHYCE platform. The first step focuses on producing seamless atmospheric forecasts, combining information from climatology, weather predictions and seasonal forecasts. Then forecasts are post-processed and tailored for hydrological purposes of drought and low-flow forecasting.The first outcomes of the project will be presented, towards providing short- to long-term seamless hydrological forecasts. The outputs of a workshop organized with end-users to improve a forecasting prototype will be discussed.The CIPRHES project is funded by the French National Research Agency (grant ANR-20-CE04-0009-03).