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EDF group – a brief presentation

Snow Water Equivalent measurement with Cosmic Ray Snow Gauges Marie-Thérèse LAVAL – EDF/DTG marie-therese.laval@edf.fr Emmanuel PAQUET - EDF/DTG emmanuel.paquet@edf.fr. EDF group – a brief presentation. Some of the 2005 key figures Customers 40.2 million in the world 28 million in France

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EDF group – a brief presentation

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  1. Snow Water Equivalent measurement withCosmic Ray Snow GaugesMarie-Thérèse LAVAL – EDF/DTGmarie-therese.laval@edf.frEmmanuel PAQUET - EDF/DTGemmanuel.paquet@edf.fr

  2. EDF group – a brief presentation • Some of the 2005 key figures • Customers 40.2 million in the world 28 million in France • Employees 161,000 in the world 108,000 in France • Installed capacity All energies : 131,000 MW worldwide Hydropower : 24,000 MW worldwide 100,000 MW in France 21,000 MW in France • Power generation All energies : 640 TWh worldwide Hydropower : 50 TWh worldwide 494 TWh in France 38 TWh in France

  3. EDF group – a brief presentation EDF energy mix : 2005 power generation (France) 445 hydropower plants 17 thermal powerplants 58 reactors on 19 sites

  4. Water Ressources management at EDF • For EDF, water is… • Driving force for hydropower • Cooling source for nuclear and fossil-fired thermal plants • A ressource shared with other uses • Irrigation, water consumption, fishing, leisure activities • Water ressources management at EDF : • 7 bm3 of total storage capacity in our reservoirs • 78% of France total storage in reservoirs • 150 « big dams » (more than 20m height) operated • A dedicated departement to provide help to plant operators • Complete hydrometeorological measurement network • On duty forecast center with hours to months ahead forecast • Study, assessment and R&D team

  5. Water Ressources management at EDF • A complete hydrometeorological measurement network • Designed for safety and optimization of EDF powerplants • 1200 measurements points • River streamflow and powerplant discharge • Lake level • Rainfall • Snow height and water equivalent (SWE) • Air and water temperature • Water quality • 700 real-time stations  Real-time river streamflow measurement network

  6. Water Ressources management at EDF • A complete hydrometeorological measurement network • Designed for safety and optimization of EDF powerplants 320 real-time transmitted rain gages Mostly in mountaineous areas : Half of the gages located above 800m (92% heated) Some stations are co-operated with Météo-France EDF PG 2000 rain gage 2000 cm² captation area Tipping bucket, heated  Real-time automated rain-gages network

  7. Importance of Snow Water Equivalent data • Why measuring SWE ? • Snow-melt inflows of mountain catchments are a significant part of EDF reservoirs inflows • Snow-melts inflows have a huge interannual variability • Estimate of snow stock allows an inflow forecast up to several months ahead • Snow-melt inflows forecast tools • Regressive models • Combination of rainfall and SWE measurements • Linear relations established on long historical series • Hydrological models • Example : April, 1st snow stock (cm SWE) on the Durance catchment (south Alps, 3600 km²) •  37% interannual variability

  8. Snow Water Equivalent measurement Measurement network A snow measurement network operating since the 50’s More than 200 measurement points in 2006 (600 points at the beginning of the 70’s) At the more representative points, more than 50 years of continuous data record Cosmic Ray Snow Gauges Continuous SWE measurement using cosmic rays attenuation Since 1998 Manual core samplings Weekly or monthly measurement of height and SWE along snow courses « Télénivomètres » SWE measurement with radioactive source Late 70’s to 2003

  9. The Cosmic-Ray Snow Gauge Principle of measure • Initial developments by Kodama et al. (1979) • The Cosmic-Ray snow gauge uses a detector sensible to the natural cosmic radiation (CR) • Energetic particles emitted by luminous objects of the universe • This radiation interacts with any material passed through, emitting « thermal » neutrons • Including the water held in the snowpack •  A CR counting above and below the snowcover can lead to the Snow Water Equivalent (SWE)

  10. The Cosmic-Ray Snow Gauge Operational device • Designed by EDF between 1995 and 2000 • Main steps of the development : • Choice and design of the CR sensor (3He detector) • Set up of the SWE=f(CR) law • Analysis of influencing factors (altitude, Patm) • Prototype testing • Installed on 40 sites between 1998 and 2004 • 37 sensors in operation for the 2005-06 season • more than 62 year.station of SWE data stored • good reliability of the sensor (2 failures since 1998)

  11. The Cosmic-Ray Snow Gauge The CR sensor Steel case 100 cm 31 cm Signal outlet 3He neutron detector tubes Polyethylene filling

  12. The Cosmic-Ray Snow Gauge Installation pictures Electronic racks and solar panels Supporting pole carried by helicopter Isoard Pass (2280 m) snow gauge CR sensor buried flush with the ground

  13. The Cosmic-Ray Snow Gauge The Cosmic-Ray Snow Gauge network in 2006  Alps and Jura Pyrenées  • Measurement sites chosen according to : • Operational needs • Hydrological representativity of the site • Availability of historical record

  14. The Cosmic-Ray Snow Gauge Calibration of the CR sensor The CR count =f(SWE) law had to be established experimentaly • The sensor is buried under a pool • 6m diameter pool to minimize side effects • Pool filled with variable water level • up to 2500 mm SWE simulation SWE sensor calibration curve

  15. The Cosmic-Ray Snow Gauge Natural variations of the incidental cosmic radiation • The natural CR can vary considerably over various periods of time • Solar flares, Forbush decrease… • A variation of the incidental CR can be wrongly interpreted as snowfall or melting Moscow neutron monitor (1998-2006) October 2003 Forbush decrease : 18% drop of the CR With a 500mm SWE, equivalent to a 270 mm snowfall

  16. The Cosmic-Ray Snow Gauge Natural variations of the incidental cosmic radiation • The on-site CR signal has to be corrected according to a snow-free reference signal • Using local snow-free CR sensor or data from worldwide network of neutron monitors Example of the Isoard Pass CR snow gauge, 2003-04 season :

  17. The Cosmic-Ray Snow Gauge On-site calibration • An on-site calibration by snow core sampling accounts for local effects • Type and moisture of the ground around the sensor, metrologic variability of the CR sensor… • 4 to 5 on-site core samples during the first season, less afterward if calibration seems stable • Comparision between on-site core samplings and CR snow gauge values • 2002 to 2006 datas (320 SWE values) • R²=0.98, no bias • Above 200 mm SWE, 90% of errors are less than 10%

  18. SWE data collection and processing Real-time data collection • The data transmission mode is adapted to local conditions : • Switched telephone network (7) • Low altitude sites • GSM cellular phone network (12) • Local radio loop (5) • Allows to reach a GSM or switched telephone connection point • INMARSAT satellite (13) • For remote or high altitude sites • Much more expensive and energy consuming than switched tel. or GSM transmission • Data collection can be made up to an hourly timestep • Depending on the forecast/monitoring needs, the transmission mode, the energy availability of the remote system

  19. SWE data collection and processing Real-time monitoring Data processing scheme Centralized data collection Hydromet forecasters Network monitoring Real-time SWE data processing First level quality control Data transmission CR, snow height, air temperature, Patm Historical data validation CR snow gauges Hydrometeorological historical database SWE data validation tool Snow core-sampling report

  20. B SWE data collection and processing CR count data processing • 3 corrections to be made : • Local atmospheric pressure variations (A), • Incidental CR global variations (B), • Local altitude correction (C). Data Loggin Raw count, Raw atmospheric pressure Pressure correction range About  20% A Pressure-corrected count Variation range of Moscow CR variations signal : About 20% (1998-2006 period) Pressure and global CR variations corrected count Example of altitude correction (snow free counts) : Petite Gouille (2410 m ASL) : 3100 tops / hour Varces (200m ASL) : 721 tops / hour C Pressure, global CR variations and altitude corrected count SWE calculated with calibration curve

  21. SWE data collection and processing • Example of real-time data check • 2005-2006 winter – Gaougeta site (Pyrénées, 2040m) CR count raw signal Current site Neighbouring site Inconsistent values to be smoothed Real-time hourly SWE

  22. Core samplings SWE data collection and processing • Examples of end-of-season data quality control • 2005-2006 season – Gaougeta site (Pyrénées, 2040m)

  23. Core samplings SWE data collection and processing • Examples of end-of-season data quality control • 2005-2006 season – Roselend site (Beaufortin range – North Alps, 1950 m)

  24. Core samplings SWE data collection and processing • Examples of end-of-season data quality control • 2005-2006 season – Cezanne site (Pelvoux range – South Alps, 1870m)

  25. Core samplings SWE data collection and processing • Examples of end-of-season data quality control • 2005-2006 season – Prapic site (Ecrins range – South Alps, 2450 m)

  26. SWE data collection and processing • Future applications • Assimilation of SWE measurement by hydrological models Example : measured SWE v/s modelized snowpack MORDOR lumped hydrological model, altitude distributed snow model

  27. Conclusion • The Cosmic-Ray Snow Gauge : a valuable water ressource management tool • Providing an acurate real-time SWE data on remote alpine sites • Robust and reliable sensor • No impact on environement • Chosen or being tested by other operators (in Spain, Canada, Italy) • As most of environmental measurements, this data has to be assessed • On-site calibration necessary to account for local effects • Real-time monitoring to control the operation of the gauge • End-of-season data validation to perform a global data checking • Future prospects • Large-scale spatialization of SWE data • Assimilation of SWE real-time data by hydrological models

  28. Conclusion Благодарю за внимание !

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