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2016

536 record(s)
 
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From 1 - 10 / 536
  • Auteur(s): Cha Lucie , Analyse des paysages de méga évènements sur des sites internationaux. Historique de l'évolution de expositions géantes. Projet d'aménagement de la ville de Bordeaux qui a posé sa candidature pour l'Exposition universelle de 2025

  • Data from FerryBoxes on ships of opportunity going on permanent routes are stored inside this database (ferrydata.hzg.de). Parameters are temperature, salinity, chlorophyll-a fluorescence, oxygen and different others. The data model is transect oriented. A data portal to access and visualise the data is also provided.

  • Specifications of the desirable and recommended product attributes for generating spatial layers of sea level trend for the last 10 years for the Mediterranean basin and for each NUTS3 region along the coast.

  • Businesses, policymakers, and local communities need to access reliable weather and climate information to safeguard human health, wellbeing, economic growth, and environmental sustainability. However, important changes in climate variability and extreme weather events are difficult to pinpoint and account for in existing modelling and forecasting tools. Moreover, many changes in the global climate are linked to the Arctic, where climate change is occurring rapidly, making weather and climate prediction a considerable challenge. Blue-Action evaluated the impact of Arctic warming on the northern hemisphere and developed new techniques to improve forecast accuracy at sub-seasonal to decadal scales. Blue-Action specifically worked to understand and simulate the linkages between the Arctic and the global climate system, and the Arctic’s role in generating weather patterns associated with hazardous conditions and climatic extremes. In doing so, Blue-Action aimed to improve the safety and wellbeing of people in the Arctic and across the Northern Hemisphere, reduce the risks associated with Arctic operations and resource exploitation, and support evidence-based decision-making by policymakers worldwide.

  • GO-SHIP, the Global Ocean Ship-Based Hydrographic Investigations Program, is conducting repeat hydrography with high accuracy high precision reference measurements of a variety of EOVs through the whole water column. A selection of continent-to-continent full depth sections are repeated at roughly decadal intervals. The data archive for CTD data and bottle data is currently at CCHDO, although the CTD data from European cruises are available at Seadatanet as well.

  • Présentation des entreprises, cartographie du risque et consignes en cas d'alerte pour les populations des communes de la Presqu'île d'Ambès. Plaquettes 4 ou 8 pages (avec cartographie)

  • Moving 10-years analysis of nitrate plus nitrite at Northeast Atlantic Ocean for each season: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Every year of the time dimension corresponds to the 10-year centred average of each season. Decades span : - from 1984-1993 until 2005-2014 (winter) - from 1979-1988 until 2005-2014 (spring) - from 1982-1991 until 2005-2014 (summer) - from 1972-1981 until 2005-2014 (autumn) Observational data span from 1962 to 2014. Depth range (IODE standard depths): -3000.0, -2500.0, -2000.0, -1750, -1500.0, -1400.0, -1300.0, -1200.0, -1100.0, -1000.0, -900.0, -800.0, -700.0, -600.0, -500.0, -400.0, -300.0, -250.0, -200.0, -150.0, -125.0, -100.0, -75.0, -50.0,-40.0, -30.0, -20.0, -10.0, -5.0, -0.0 Data Sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVA (Data-Interpolating Variational Analysis) tool. GEBCO 1min topography is used for the contouring preparation. Analyzed filed masked using relative error threshold 0.3 and 0.5 DIVA settings. Signal to noise ratio and correlation length were optimized and filtered vertically and a seasonally-averaged profile was used. Logarithmic transformation applied to the data prior to the analysis. Background field: the data mean value is subtracted from the data. Detrending of data: no, Advection consraint applied: no. Units: umol/l

  • Specification of the desirable and recommended products attributes for generating spatial layers of sea mid-water and sea-bottom temperature for the last 10, 50 and 100 years for the Mediterranean basin and for each NUTS3 region along the coast.

  • The main aim of this product was to define the suitability of offshore sites in the area between the borders of France-Spain-Italy for wind farm development. The adopted approach classifies wind speed data by their level of suitability, ranging from a grade 5 for exclusion zones, to a grade 1 for areas deemed appropriate for wind farm development. The quality indexes adopted were based on mean and variation statistical measures taking into consideration both the expected energy potential and the corresponding variability.

  • Output of the 2016 EUSeaMap broad-scale predictive model, produced by EMODnet Seabed Habitats and aggregated into the predominant habitats of the Marine Strategy Framework Directive. The extent of the mapped area includes the Mediterranean Sea, Black Sea, Baltic Sea, and areas of the North Eastern Atlantic extending from the Canary Islands in the south to Norway in the North. The map was produced using a "top-down" modelling approach using classified habitat descriptors to determine a final output habitat. Habitat descriptors differ per region but include: Biological zone Energy class Oxygen regime Salinity regime Seabed Substrate Riverine input Habitat descriptors (excepting Substrate) are calculated using underlying physical data and thresholds derived from statistical analyses or expert judgement on known conditions. The model is produced in Arc Model Builder (10.1). For more information on the modelling process please read the EMODnet Seabed Habitats The model was created using raster input layers with a cell size of 0.002dd (roughly 250 meters). The model includes the sublittoral zone only; due to the high variability of the littoral zone, a lack of detailed substrate data and the resolution of the model, it is difficult to predict littoral habitats at this scale.