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Operational hydrometeorological forecasting on the Rhône River in France: moving toward a seamless probabilistic approach
Compagnie Nationale du Rhône (CNR) has operated the Rhône River since 1934 according to three core missions - hydropower generation, inland navigation and irrigation - using 19 run of-river multi-purpose hydropower schemes. To ensure hydraulic safety and optimise hydro power production and commercia...
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Published in: | LHB 2022-12, Vol.108 (1) |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | Compagnie Nationale du Rhône (CNR) has operated the Rhône River since 1934 according to three core missions - hydropower generation, inland navigation and irrigation - using 19 run of-river multi-purpose hydropower schemes. To ensure hydraulic safety and optimise hydro power production and commercialisation, CNR developed a range of hydrometeorological forecasting tools, currently run every day in real time by CNR forecasters. The first is an hourly deterministic tool, giving forecasts up to the next 4 days. This tool does not allow quantification of uncertainties, which is a key issue to improve forecasts and take better operational decisions. At the same time, national meteorological centres provide ensemble meteorological forecasts, and thus information about uncertainty. Therefore, CNR is developing a chain of ensemble forecasting tools based on ensemble meteorological forecasts and various post-processing methods. These tools provide hourly/daily hydrological forecasts up to the next 4/12 days. The operational use of such a probabilistic forecasting chain is intended to facilitate decision-making processes. All these tools have been designed to leave large room for human expertise. This paper presents the tools described above, but also their interaction with human forecasters as well as the first attempts to link them together to add consistency between the different forecast types. |
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ISSN: | 2767-8490 2767-8490 |
DOI: | 10.1080/27678490.2022.2061312 |