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Abstract: Not Available Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: Not Available
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Abstract: Not Available Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: Not Available
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Abstract: This study presents a new in situ method to explore the impact of macrofauna on seafloor microtopography and corresponding microroughness based on underwater laser line scanning. The local microtopography was determined with mm-level accuracy at three stations colonised by the tubeworm Lanice conchilega offshore of the island of Sylt in the German Bight (south-eastern North Sea), covering approximately 0.5 m**2 each. Ground truthing was done using underwater video data. Two stations were populated by tubeworm colonies of different population densities, and one station had a hydrodynamically rippled seafloor. Tubeworms caused an increased skewness of the microtopography height distribution and an increased root mean square roughness at short spatial wavelengths compared with hydrodynamic bedforms. Spectral analysis of the 2D Fourier transformed microtopography showed that the roughness magnitude increased at spatial wavelengths between 0.020 and 0.003 m independently of the tubeworm density. This effect was not detected by commonly used 1D roughness profiles but required consideration of the complete spectrum. Overall, the results reveal that new indicator variables for benthic organisms may be developed based on microtopographic data. An example demonstrates the use of local slope and skewness to detect tubeworms in the measured digital elevation model. Category: geoscientificInformation Source: Supplement to: Schönke, Mischa; Feldens, Peter; Wilken, Dennis; Papenmeier, Svenja; Heinrich, Christoph A; Schneider von Deimling, Jens; Held, Philipp; Krastel, Sebastian (2016): Impact of Lanice conchilega on seafloor microtopography off the island of Sylt (German Bight, SE North Sea). Geo-Marine Letters, 14 pp, https://doi.org/10.1007/s00367-016-0491-1 Supplemental Information: Approx 0.5m square meter digital elevation models measured at three different sites off the island of Sylt (German Bight, SE North Sea) with a sub mm accuracy, recorded by the ULS200 lasersystem by 2G Robotics in 2015. The mat-struct files contains: raw data vector form -> x,y,z raw data matrix form -> X,Y,Z working grid -> Xgrid, Ygrid, Zgrid subspace domains -> bathy_filt 1-4 Coverage: EVENT LABEL: * LATITUDE: 55.013200 * LONGITUDE: 8.264300 * LOCATION: North Sea German Bight * METHOD|DEVICE: Multiple investigations
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Abstract: Not Available Category: geoscientificInformation Source: Not Available Supplemental Information: Maximum density has been selected with a 12h window, the temperature corresponding to this maximum density is presented here. Data were measured with a SBE37 CT probe moored at 344 meters depth. Coverage: EVENT LABEL: * LATITUDE: 35.862000 * LONGITUDE: -5.970000 * DATE/TIME START: 2004-09-30T00:00:00 * DATE/TIME END: 2016-09-21T00:00:00 * ELEVATION: -362.0 m * LOCATION: Strait of Gibraltar * METHOD|DEVICE: Mooring
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Abstract: Potential temperature measured with a SBE37 at 35.862ºN, 5.97ºW at 344 meters Depth. Data expand from September the 30th, 2004 to March the 2nd, 2016. Original measurement frequency was 30 minutes, the data presented here is a subsampling that extract the coldest temperature found each 12 hours. The time vector corresponds with the moment in which this minimun temperature is observed. Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: EVENT LABEL: * LATITUDE: 35.862000 * LONGITUDE: -5.970000 * DATE/TIME START: 2004-09-30T00:00:00 * DATE/TIME END: 2016-09-21T00:00:00 * ELEVATION: -362.0 m * LOCATION: Strait of Gibraltar * METHOD|DEVICE: Mooring
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Abstract: Not Available Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: EVENT LABEL: * LATITUDE: 35.948070 * LONGITUDE: -5.770470 * DATE/TIME START: 2013-06-09T00:00:00 * DATE/TIME END: 2013-09-26T00:00:00 * ELEVATION: -301.0 m * LOCATION: Strait of Gibraltar * METHOD|DEVICE: Mooring
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Abstract: Not Available Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: EVENT LABEL: * LATITUDE: 35.910530 * LONGITUDE: -5.745350 * DATE/TIME START: 2013-06-09T00:00:00 * DATE/TIME END: 2013-09-26T00:00:00 * ELEVATION: -304.0 m * LOCATION: Strait of Gibraltar * METHOD|DEVICE: Mooring
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Abstract: The circulation in the North Indian Ocean (NIO) is one of the most complex systems compared with other regions of global oceans, mostly due to its interactions with the monsoon winds. In recent years, our ability to measure the ocean's mean dynamic topography (MDT) from space has improved immensely with the availability of satellite gravity measurements from Gravity Recovery and Climate Experiment (GRACE) and Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) missions. The present study uses data from GOCE and GRACE satellite gravity missions together with altimeter data in retrieving the geoid, satellite-only MDT, and surface velocities in the NIO. The study estimates geoid heights of the NIO from all five releases of the direct approach and the time-wise GOCE gravity data. The formal error associated with geoid heights at different resolutions is found to be the lowest for the latest release of direct approach GOCE data. In addition, a new satellite-only MDT is estimated from the direct approach GOCE geoid and the CNES_CLS11 mean sea surface. This MDT corrected to a 20-year time reference is used together with the newly reprocessed sea level anomaly data to estimate absolute dynamic topography and surface geostrophic velocities in the NIO. The total surface velocities computed from the Ekman and geostrophic velocity fields reproduce all major surface currents in the NIO, along with their seasonality. Furthermore, total surface velocity estimates computed here are validated using surface drifters and are found to be highly comparable (difference within ± 10 cm s–1) with more than 170,000 individual surface drifter observations. Finally, the total velocities estimated here are used to examine the variability of the East India Coastal Current. Category: geoscientificInformation Source: Supplement to: Raj, Roshin P (2016): Surface velocity estimates of the North Indian Ocean from satellite gravity and altimeter missions. International Journal of Remote Sensing, 38(1), 296-313, https://doi.org/10.1080/01431161.2016.1266106 Supplemental Information: North Indian Ocean: 40E to 120E and 0N to 30 N. A satellite-only Mean Dynamic Topography (MDT) of the North Indian Ocean is estimated from the DIRR5 geoid and CNES_CLS11 mean sea surface (Schaffer et al. 2012). DIRR5 geoid is estimated from the latest release (Release 5) of GOCE gravity data according to previous studies (e.g., Johannessen et al. 2003; Raj, 2014). Note that this MDT estimated is referenced to a time period of 7 years (1993-1999). A correction data obtained from AVISO is later used to convert the MDT to a time reference of 20 years (1993-2012). More details are given in Raj (2016). Coverage: EVENT LABEL: * LATITUDE: 15.000000 * LONGITUDE: 81.000000 * LOCATION: North Indian
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Abstract: River runoff is an essential climate variable as it is directly linked to the terrestrial water balance and controls a wide range of climatological and ecological processes. Despite its scientific and societal importance, there are to date no pan-European observation-based runoff estimates available. Here we employ a recently developed methodology to estimate monthly runoff rates on regular spatial grid in Europe. For this we first assemble an unprecedented collection of river flow observations, combining information from three distinct data bases. Observed monthly runoff rates are first tested for homogeneity and then related to gridded atmospheric variables (E-OBS version 12) using machine learning. The resulting statistical model is then used to estimate monthly runoff rates (December 1950 - December 2015) on a 0.5° x 0.5° grid. The performance of the newly derived runoff estimates is assessed in terms of cross validation. The paper closes with example applications, illustrating the potential of the new runoff estimates for climatological assessments and drought monitoring. Category: geoscientificInformation Source: Supplement to: Gudmundsson, Lukas; Seneviratne, Sonia I (2016): Observation-based gridded runoff estimates for Europe (E-RUN version 1.1). Earth System Science Data, 8(2), 279-295, https://doi.org/10.5194/essd-8-279-2016 Supplemental Information: Monthly mean runoff rates for Europe (December 1950 - December 2015) on a regular 0.5 degree grid. The data are estimated on the basis of streamflow observations from small catchments which are combined with gridded observations of precipitation and temperature using machine learning. The resulting machine learning model allows to predict monthly mean runoff rates at all grid-cells of the considered atmospheric drivers. Coverage: EVENT LABEL: * LATITUDE: 54.000000 * LONGITUDE: 14.800000
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Abstract: River runoff is an essential climate variable as it is directly linked to the terrestrial water balance and controls a wide range of climatological and ecological processes. Despite its scientific and societal importance, there are to date no pan-European observation-based runoff estimates available. Here we employ a recently developed methodology to estimate monthly runoff rates on regular spatial grid in Europe. For this we first collect an unprecedented collection of river flow observations, combining information from three distinct data bases. Observed monthly runoff rates are first tested for homogeneity and then related to gridded atmospheric variables (E-OBS version 11) using machine learning. The resulting statistical model is then used to estimate monthly runoff rates (December 1950-December 2014) on a 0.5° × 0.5° grid. The performance of the newly derived runoff estimates is assessed in terms of cross validation. The paper closes with example applications, illustrating the potential of the new runoff estimates for climatological assessments and drought monitoring. Category: geoscientificInformation Source: Not Available Supplemental Information: Monthly mean runoff rates for Europe (December 1950 - December 2014) on a regular 0.5 degree grid. The data are estimated on the basis of streamflow observations from small catchments which are combined with gridded observations of precipitation and temperature using machine learning. The resulting machine learning model allows to predict monthly mean runoff rates at all grid-cells of the considered atmospheric drivers. Coverage: EVENT LABEL: * LATITUDE: 54.000000 * LONGITUDE: 14.800000
Data Types:
  • Software/Code
  • Dataset
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