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Towards an integrated chain for hydrometeorological forecasting of low flows and droughts – Enhancement of the PREMHYCE platform through the CIPRHES projectThe “Integrated hydrometeorological forecasting chain for low flows and droughts” (CIPRHES (2021-2026) - Chaîne intégrée de prévision hydrométéorologique des étiages et des sécheresses) project aimed to improve the modelling chains used in the PREMHYCE operational platform. It brought together eight research teams from INRAE, BRGM, EDF, Météo-France and the University of Lorraine, and helped to strengthen links with institutional and operational stakeholders involved in water management. Main issues raised & general objectives - A growing need for anticipation to address the challenges of water sharing and the protection of water resources and ecosystems during droughtsIn many countries, rivers are the main source of water supply for various purposes (drinking water, irrigation, energy, navigation, etc.), which can be severely affected by water shortages. Furthermore, maintaining a minimum environmental flow is crucial for preserving the quality of the aquatic environment and biodiversity. In 2022, France experienced a drought of exceptional intensity, with severe consequences for various sectors and damages estimated at over €5 billion. This type of event foreshadows what hydroclimatic projections suggest will become common occurrences in the remainder of the 21st century, due to climate change. This prospect of more severe, prolonged and late low-flow periods highlights the need for tools to better prepare for and anticipate their impacts, improve crisis management and facilitate decision-making for better water sharing. Assessments and developments of drought forecasting chains have been carried out in various countries to meet these objectives, with forecast horizons ranging from the medium term (around ten days) to the seasonal term (three months). In France, an initiative to evaluate hydrological models for the purpose of low-flows forecasting, known as PREMHYCE (Low-flow Forecasting using Hydrological Models, Comparison and Evaluation), has led to the development of an operational prototype hydrological service, based on a multi-model approach and tested from 2018 onwards to produce real-time forecasts for the French river network. The CIPRHES project was designed as a laboratory for the development and refinement of methods associated with the PREMHYCE platform. The aim was to establish an integrated forecasting chain able to produce consistent long-term hydrometeorological forecasts (ranging from a few days to several months) that are coherent across different spatial scales (river basins and sub-basins). The proposed developments were tested on a large dataset to assess the strengths and weaknesses of the forecasting chain. More specifically, the CIPRHES project was guided by five main objectives: (1) To produce effective and homogeneous atmospheric forecasts ranging from medium to seasonal lead time; (2) To develop an integrated hydrometeorological modelling approach for low-flow forecasting; (3) To develop methods for quantifying the various sources of uncertainty affecting low-flow forecasts; (4) To establish advanced ‘crash-test’ frameworks to assess the performance, robustness and usefulness of low-flow forecasts; (5) To design a user-centred online hydrometeorological service to provide informative real-time forecasts.Methods used - A large sample of catchments and hydroclimatic data to enhance the robustness and generalisability of the statistical models and methods testedThe project drew on a wide range of mathematical, numerical, statistical and modelling methods, applied to the fields covered by the project (meteorology, hydrology, hydrogeology). It used five hydrological models of different types and levels of complexity, with the aim of ensuring a degree of generality and robustness in the modelling chain. These models, developed by the project partners, represent various ways of modelling the processes underlying low flows. Various data assimilation techniques, using different types of observations (discharge, groundwater level) to calibrate the models, were applied. In addition, a wide range of statistical approaches was used for the post-processing of meteorological and hydrological forecasts, and for quantifying the uncertainties associated with observations and forecasts.The project also used data from a wide range of catchments in mainland France in order to test the proposed methodologies under various conditions and to draw general conclusions. The data were mainly extracted from public databases (Météo-France for climate data, HydroPortail for hydrological data, and the ADES database for groundwater data). More specific data were used for certain studies, such as historical weather forecast records or rating curves associated with specific hydrometric stations. These data underwent detailed analysis to ensure their quality and spatio-temporal consistency. The catchments used for the tests were selected on the basis of various criteria relating to data availability, data quality and spatial coverage. The national sample was compiled by cross-referencing the catchment database from the PREMHYCE operational platform (approximately 1,300 catchments) with the CAMELS-FR national reference sample of catchments (654 catchments). Cross-referencing these two catchment samples enabled the definition of a set of 478 catchments well distributed across mainland France. Variations of this national sample have been used in various studies, depending on additional constraints imposed during certain tests (data availability, choice of time periods, etc.). Some studies linked to the project have also used specific databases independent of this national database.A more detailed database has also been established for the Meuse catchment at Chooz (approximately 10,000 km²), used as a demonstration case study within the project, particularly for the evaluation of semi-distributed hydrological models. Main resultsThe project has led to progress in several areas. With regard to weather forecasting, a statistical method for combining medium-range and seasonal forecasts has been proposed. This method enables the production of continuous forecasts across a wide range of lead times, whilst capitalising on the strengths of both types of forecasts. Implemented in the operational production chain, it simplifies data fluxes in real time and the analysis of results.A significant part of the project’s work focused on improving the hydrological models used to forecast low flows. Improvements were proposed for the functions responsible for simulating low flows and for the conceptualisation of exchanges between surface and groundwater. A more explicit representation of aquifers was proposed, along with the inclusion of storage structures. Modelling schemes that more explicitly represent spatial variability within catchments (semi-distributed approach) were also evaluated, with encouraging results for the Meuse catchment. The implementation of hydrological models in forecasting mode has been the subject of several studies aimed at correcting the models in real time by incorporating available observations (data assimilation). Whilst observed river flow is a classical source of information, the research has also sought to incorporate groundwater levels. This additional information has not yielded significant improvements compared with flow data alone, but it enhances the models’ ability to simulate both variables jointly. As forecasting is inherently uncertain, the project focused specifically on three sources of uncertainty: weather forecasts, hydrometric observations and hydrological modelling. Methods were proposed to quantify hydrometric uncertainties, related to rating curves or the quality of gauging stations. Statistical approaches were also developed to ensure better temporal consistency in uncertainty estimates related to hydrological modelling and weather forecasts.Furthermore, analyses of a large sample of catchments made it possible to quantify the time horizons for which informative forecasts can be provided (predictability) and to identify the factors determining this (hydroclimatic or physical context). Finally, the operational platform has been improved, with a significant increase in spatial coverage of the territory (now around 1,300 forecast points), a strengthened user network, and a more functional and user-friendly interface incorporating user requests and needs.Outstanding feature and future prospectThe CIPRHES project has advanced our understanding of the factors underlying the efficiency of operational drought forecasting chains. It opens up various avenues, including the gradual implementation of the CIPRHES project’s results within the PREMHYCE operational platform, further developments in seasonal forecasting (atmosphere – surface – subsurface) as part of other projects, the exploration of complementary methods and data (machine learning methods, use of satellite information), a more detailed consideration of anthropogenic influences, and the production of indicators tailored to different water user sectors.The project was carried out across a number of research directions with focus on the main sources of uncertainty in the forecasting chain. A feedback seminar organised in late 2025 brought together around 220 people from various fields. It served as a forum for multidisciplinary and multi-sectoral exchange, with the participation of stakeholders from the fields of research, operational management, engineering and public decision-making. The discussions brought together different perspectives and highlighted the outlook, needs and expectations regarding research developments, institutional challenges, the operational aspects of low-flow management and, finally, potential applications across various water-related sectors, particularl