CEFAS5f614dc5-be0c-4379-8dad-33dce560813b
English
dataset
Centre for Environment, Fisheries & Aquaculture Science
Data Manager
+44 (0)1502 562244
Cefas Lowestoft Laboratory
Pakefield Road
Lowestoft
Suffolk
NR33 0HT
UK
data.manager@cefas.co.uk
pointOfContact
2020-12-18T10:41:07
MEDIN Discovery Metadata Standard
Version 2.3.7
urn:ogc:def:crs:EPSG::4936
OGP
1992 - 2019 Centre for Environment, Fisheries & Aquaculture Science (Cefas) Predicted sedimentation rates data for the Baltic Sea derived from samples from 1992 - 2019
2020-12-18
publication
CEFAS5f614dc5-be0c-4379-8dad-33dce560813b
http://www.cefas.co.uk/
Spatial prediction of the sediment accumulation rate, provided as a .geotif
file (as an average linear rate in cm yr <sup>...</sup> since 1986) for the
Baltic Sea created using a machine learning approach. Predicted rates of
sediment accumulation vary from 0.08-1.67 cm yr <sup>...</sup> . Field
measurements were acquired between 1992 and 2012 as compiled by EMODnet
Geology. Predictor variables were derived from 2003 to 2018 from various
sources including EMODnet Bathymetry, Copernicus and Cefas staff. Modelling
subsequently occurred in 2019 by Cefas. Further detail on the data sources are
provided in the associated manuscript.
To access and download the data contact `data.manager@cefas.co.uk`_
For additional details regarding how this layer was generated see the
publication associated with this data:
`https://doi.org/10.1016/j.csr.2020.104325`_
.. _`data.manager@cefas.co.uk`:
mailto:data.manager@cefas.co.uk%20
.. _`https://doi.org/10.1016/j.csr.2020.104325`:
https://doi.org/10.1016/j.csr.2020.104325
Centre for Environment, Fisheries & Aquaculture Science
Data Manager
+44 (0)1502 562244
Cefas Lowestoft Laboratory
Pakefield Road
Lowestoft
Suffolk
NR33 0HT
UK
data.manager@cefas.co.uk
originator
Centre for Environment, Fisheries & Aquaculture Science
Data Manager
+44 (0)1502 562244
Cefas Lowestoft Laboratory
Pakefield Road
Lowestoft
Suffolk
NR33 0HT
UK
data.manager@cefas.co.uk
custodian
notPlanned
Geographic Information System
NDGO0005
Sediment
GEMET, version 1.0
2008-06-01
publication
Geology
GEMET - INSPIRE themes, version 1.0
2008-06-01
publication
Public data (Crown Copyright) - Open Government Licence Terms and Conditions apply
otherRestrictions
Public data (Crown Copyright) - Open Government Licence Terms and Conditions apply
95
English
geoscientificInformation
SeaVoX Vertical Co-ordinate Coverages
2010-05-18
revision
Sediment
10
35
50
70
1992-01-01T00:00:00.000Z
2019-03-31T00:00:00.000Z
dataset
The spatial prediction of sediment accumulation rates were generated for the
Baltic Sea using a machine learning approach. In short, a range of predictor
variables that were thought to relate to either marine dispersal or the
proximity to a sediment source. These full coverage predictor variables
included: derivatives of the bathymetry, estimates of sediment turbidity,
distance from river mouths, estimates of the energy of the Baltic Sea and a
description of the substrate type. A random forest model was fit to the
predictor variables using field observations. The model was then used to
predict sediment accumulation rate for the Baltic Sea.