def concentration_maximum_asset_for_year(year: int) -> HttpAsset:
"""Handle the maximum concentration "off-years"."""
month = conc_max_month(year)
month_abbr = calendar.month_abbr[month]
return HttpAsset(
id=f'maximum_concentration_{year}',
urls=
[('ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/geotiff'
f'/{month:02d}_{month_abbr}/N_{year}{month:02d}_concentration_v3.0.tif'
)],
)
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
seismograph_stations = Dataset(
id='seismograph_stations',
assets=[
HttpAsset(
id='only',
urls=[
'http://www.isc.ac.uk/registries/download/stations.kmz',
],
),
],
metadata={
'title':
'International Registry of Seismograph Stations (IR)',
'abstract':
("""The International Seismograph Station Registry (IR) has been
jointly maintained by the International Seismological Centre (ISC)
and the World Data Center for Seismology (NEIC/USGS) since the
1960s. At present there are over 26000 stations (including those
already closed) with globally unique codes registered in the IR."""
),
'citation': {
'text':
("""International Seismological Centre (2020), International
Seismograph Station Registry (IR),
https://doi.org/10.31905/EL3FQQ40"""),
'url':
'http://www.isc.ac.uk/registries/',
},
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
velocity_mosaic = Dataset(
id='velocity_mosaic',
assets=[
HttpAsset(
id='only',
urls=[
'http://its-live-data.jpl.nasa.gov.s3.amazonaws.com/velocity_mosaic/landsat/v00.0/static/GRE_G0120_0000.nc',
],
),
],
metadata={
'title':
'Regional Glacier and Ice Sheet Surface Velocities',
'abstract':
("""The Inter-mission Time Series of Land Ice Velocity and Elevation
(ITS_LIVE) project facilitates ice sheet, ice shelf and glacier
research by providing a globally comprehensive and temporally dense
multi-sensor record of land ice velocity and elevation with low
latency."""),
'citation': {
'text':
("""Velocity data generated using auto-RIFT (Gardner et al.,
2018) and provided by the NASA MEaSUREs ITS_LIVE project
(Gardner et al., 2019).
Gardner, A. S., M. A. Fahnestock, and T. A. Scambos, 2019
[Accessed on {{date_accessed}}]: ITS_LIVE Regional Glacier and
Ice Sheet Surface Velocities. Data archived at National Snow and
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
undersea_features = Dataset(
id='undersea_features',
assets=[
HttpAsset(
id='only',
urls=[
'https://www.ngdc.noaa.gov/gazetteer/feature/export?aoi=POLYGON%28%28-162.82882+39.14360%2C+120.79574+45.17504%2C+-1.80233+31.69553%2C+-68.42289+32.69612%2C+-162.82882+39.14360%29%29&name=&featureType=&proposerId=&discovererId=&meeting=&status=&format=shapefile',
],
),
],
metadata={
'title':
'IHO-IOC GEBCO Gazetteer of Undersea Feature Names',
'abstract':
("""The General Bathymetric Chart of the Oceans (GEBCO) Sub-Committee
on Undersea Feature Names (SCUFN) maintains and makes available a
digital gazetteer of the names, generic feature type, and geographic
position of features on the seafloor. The gazetteer is available to
view and download (http://www.ngdc.noaa.gov/gazetteer/) via a web
map application, hosted by the International Hydrographic
Organization Data Centre for Digital Bathymetry (IHO DCDB)
co-located with the US National Centers for Environmental
Information (NCEI)."""),
'citation': {
'text': ("""IHO-IOC GEBCO Gazetteer of Undersea Feature Names,
www.gebco.net"""),
'url':
'https://www.gebco.net/data_and_products/undersea_feature_names/',
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
pangaea_ground_temperature = Dataset(
id='pangaea_ground_temperature',
assets=[
HttpAsset(
id='25km',
urls=[
'http://store.pangaea.de/Publications/ObuJ-etal_2018/UiO_PEX_5.0_20181127_2000_2016_25km.nc',
],
),
HttpAsset(
id='10km',
urls=[
'http://store.pangaea.de/Publications/ObuJ-etal_2018/UiO_PEX_5.0_20181127_2000_2016_10km.nc',
],
),
HttpAsset(
id='5km',
urls=[
'http://store.pangaea.de/Publications/ObuJ-etal_2018/UiO_PEX_5.0_20181127_2000_2016_5km.nc',
],
),
],
metadata={
'title':
'Ground Temperature Map, 2000-2016, Northern Hemisphere Permafrost',
'abstract':
("""Original data information: The product provides modeled mean
annual ground temperatures (MAGT) at the top of the permafrost for
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
geothermal_heat_flux = Dataset(
id='geothermal_heat_flux',
assets=[
HttpAsset(
id='only',
urls=[
'https://ads.nipr.ac.jp/api/v1/metadata/A20180227-001/2.00/data/DATA?path=GHF_Greenland_Ver2.0_GridEPSG3413_05km.nc',
],
),
],
metadata={
'title':
'Geothermal heat flux distribution for the Greenland ice sheet, derived by combining a global representation and information from deep ice cores',
'abstract':
("""The data present a distribution of the geothermal heat flux (GHF)
for Greenland, which is an update of two earlier versions by Greve
(2005, Ann. Glaciol. 42) and Greve and Herzfeld (2013, Ann. Glaciol.
54). The GHF distribution is constructed in two steps. First, the
global representation by Pollack et al. (1993, Rev. Geophys. 31) is
scaled for the area of Greenland. Second, by means of a
paleoclimatic simulation carried out with the ice sheet model
SICOPOLIS, the GHF values for five deep ice core locations are
modified such that observed and simulated basal temperatures match
closely. The resulting GHF distribution generally features low
values in the south and the north-west, whereas elevated values
prevail in central North Greenland and towards the north-east. The
original source data are provided as NetCDF files on two different
grids (EPSG:3413 grid, Bamber grid) that have frequently been used
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
BASE_URL = 'https://www.ncei.noaa.gov/thredds-ocean/fileServer/ncei/woa/temperature/decav'
woa2018_temperature = Dataset(
id='woa2018_temperature',
assets=[
HttpAsset(
id='seasonal_winter',
urls=[
f'{BASE_URL}/0.25/woa18_decav_t13_04.nc',
],
),
HttpAsset(
id='seasonal_summer',
urls=[
f'{BASE_URL}/0.25/woa18_decav_t15_04.nc',
],
),
],
metadata={
'title': 'WORLD OCEAN ATLAS 2018 Volume 1: Temperature',
'abstract': (
"""From the World Ocean Atlas: This atlas consists of a description
of data analysis procedures and horizontal maps of climatological
distribution fields of temperature at selected standard depth levels
of the World Ocean on one-degree and quarter-degree
latitude-longitude grids. The aim of the maps is to illustrate
large-scale characteristics of the distribution of ocean
MockOnlineLayerConfig = Layer(**mock_online_layer_cfg)
_mock_http_asset_cfg = {
'id': _mock_asset_id,
'urls': ['https://foo.bar.com/data.zip'],
}
mock_raster_layer_cfg = {
'id': 'example_raster',
'title': 'Example raster',
'description': 'Example layer description.',
'tags': ['foo', 'bar', 'baz'],
'in_package': True,
'input': {
'dataset': {
'id': 'example_dataset',
'assets': [HttpAsset(**_mock_http_asset_cfg)],
'metadata': _mock_metadata,
},
'asset': HttpAsset(**_mock_http_asset_cfg),
},
'steps': [
{
'type': 'command',
'args': ['foo', 'bar'],
},
],
}
MockRasterLayerConfig = Layer(**mock_raster_layer_cfg)
def _layer_node(cfg: Layer) -> LayerGroupNode:
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
utm_zones = Dataset(
id='utm_zones',
assets=[
HttpAsset(
id='only',
urls=[
'http://sandbox.idre.ucla.edu/mapshare/data/world/data/utmzone.zip',
],
),
],
metadata={
'title':
'World UTM Zones',
'abstract':
("""World UTM Zones represents the Universal Transverse Mercator
(UTM) zones of the world. The polygons represent the Universal
Transverse Mercator (UTM) zones, which lie between 84 degrees North
and 80 degrees South latitude. With few exceptions, they divide the
world into sixty zones, each of which is six degrees of longitude
wide. The zones are numbered from 1 through 60 eastward from 180
degrees West longitude. The zone characters designate rows that are
8 degrees of latitude high extending north and south from the
equator with the exception of the northern-most row which is 12
degrees high."""),
# Is this citation good enough? Find another source?
'citation': {
'text': ("""ESRI Data & Maps. 2015."""),
'url': 'https://apps.gis.ucla.edu/geodata/dataset/world_utm_zones',
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
wmm = Dataset(
id='world_magnetic_model',
assets=[
HttpAsset(
id='geomagnetic_north_pole',
urls=[
'https://www.ngdc.noaa.gov/geomag/data/poles/WMM2020_NP.xy',
],
),
HttpAsset(
id='igrf_geomagnetic_north_pole',
urls=[
'https://www.ngdc.noaa.gov/geomag/data/poles/NP.xy',
],
),
HttpAsset(
id='geomagnetic_coordinates',
urls=[
'ftp://ftp.ngdc.noaa.gov/geomag/wmm/wmm2020/shapefiles/WMM2020_geomagnetic_coordinate_shapefiles.zip', # noqa:E501
],
),
HttpAsset(
id='blackout_zones',
urls=[
'ftp://ftp.ngdc.noaa.gov/geomag/wmm/wmm2020/shapefiles/WMM2020-2025_BoZ_Shapefile.zip', # noqa:E501
],
),
*[
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
ice_cores = Dataset(
id='ice_cores',
assets=[
HttpAsset(
id='only',
urls=[
'http://gis.ncdc.noaa.gov/kml/paleo_icecore.kmz',
],
),
],
metadata={
'title':
'Ice Cores',
'abstract':
("""Greenland ice core locations. Ice cores can provide records of
past temperature, precipitation, atmospheric trace gases, and other
aspects of climate and environment. Additional information is
available in the 'description' attribute, including an ice core
dataset URL. Data were accessed using the Google Earth Map Search
Dataset. For details please see:
http://www.ncdc.noaa.gov/paleo/icecore.html."""),
'citation': {
'text': ("""World Data Center (2020). Ice core locations. Download:
http://gis.ncdc.noaa.gov/kml/paleo_icecore.kmz. Date accessed:
{{date_accessed}}."""),
'url':
'http://www.ncdc.noaa.gov/paleo/icecore.html',
},
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
arctic_dem = Dataset(
id='arctic_dem',
assets=[
HttpAsset(
id='1km',
urls=[
'http://data.pgc.umn.edu/elev/dem/setsm/ArcticDEM/mosaic/v3.0/1km/arcticdem_mosaic_1km_v3.0.tif',
],
),
HttpAsset(
id='500m',
urls=[
'http://data.pgc.umn.edu/elev/dem/setsm/ArcticDEM/mosaic/v3.0/500m/arcticdem_mosaic_500m_v3.0.tif',
],
),
HttpAsset(
id='100m',
# This shouldn't be necessary?
verify_tls=False,
urls=[
'https://data.pgc.umn.edu/elev/dem/setsm/ArcticDEM/mosaic/v3.0/100m/arcticdem_mosaic_100m_v3.0.tif',
],
),
],
metadata={
'title': 'Arctic DEM (1km mosaic)',
'abstract': (
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
icesheet_height_and_thickness_change = Dataset(
id='icesheet_height_and_thickness_change',
assets=[
HttpAsset(
id='only',
urls=[
'https://digital.lib.washington.edu/researchworks/bitstream/handle/1773/45388/ICESat1_ICESat2_mass_change.zip',
],
),
],
metadata={
'title':
'Ice-sheet height and thickness changes from ICESat to ICESat-2',
'abstract':
("""These data represent ice-column thickness-change-rate estimates
based on data from NASA's ICESat and ICESat-2 satellites. These data
aided the first estimates of ice-sheet mass change from these two
missions, spanning the 16 years from 2003 to 2019, taking advantage
of the high vertical and horizontal resolution of the two
satellites' laser altimeters."""),
'citation': {
'text':
("""Smith, Ben; Fricker, Helen; Gardner, Alex; Medley, Brooke;
Nilsson, Johan; Paolo, Fernando; Holschuh, Nicholas; Adusumilli,
Susheel; Brunt, Kelly; Csatho, Bea; Harbeck, Kaitlin; Markus,
Thorsten; Neumann, Thomas; Siegfried, Matthew; Zwally, H. Jay;
NASA grant numbers: NNX15AE15G, NNX15AC80G, NNX16AM01G,
NNX17AI03G. NASA Cryospheric Sciences and MEaSUREs programs.
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
geothermal_heat_flow = Dataset(
id='geothermal_heat_flow',
assets=[
# This is interpolated data.
# TODO: is there anything special about the upsampling done here?
HttpAsset(
id='10km_map',
urls=[
'https://dataverse01.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/F9P03L/7WDXNF'
],
),
# This is the native resolution
HttpAsset(
id='55km_map',
urls=[
'https://dataverse01.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/F9P03L/HJ7AIM'
],
),
HttpAsset(
id='heat_flow_measurements',
urls=[
'https://dataverse01.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/F9P03L/JMAXKV'
],
),
],
metadata={
'title':
'Greenland Geothermal Heat Flow Database and Map',
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
land_shape = Dataset(
id='land_shape',
assets=[
HttpAsset(
id='only',
urls=[
'https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/physical/ne_10m_land.zip',
],
),
],
metadata={
'title': 'Natural Earth Land (10m)',
'abstract': (
"""Natural Earth Land (Public Domain)."""
),
'citation': {
'text': (
"""Made with Natural Earth"""
),
'url': 'https://github.com/nvkelso/natural-earth-vector/blob/master/LICENSE.md',
},
},
)
ocean_shape = Dataset(
id='ocean_shape',
assets=[
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
future_icesheet_coverage = Dataset(
id='future_icesheet_coverage',
assets=[
HttpAsset(
id='rcp_26',
urls=[
'https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3A61ff2294-4734-46ba-a0b0-845d69298131',
],
),
HttpAsset(
id='rcp_45',
urls=[
'https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3Aed2d7235-2193-4ba3-a98f-f09d871199a1',
],
),
HttpAsset(
id='rcp_85',
urls=[
'https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3Aecec7b68-a544-4575-8731-47d60b73215f',
],
),
],
metadata={
'title':
'Contribution of the Greenland Ice Sheet to sea-level over the next millennium using Large Ensemble Simulations, spatial time series, 2008-3007.',
'abstract':
("""Citation publication abstract: The Greenland Ice Sheet holds
around 7.2 meters of sea-level equivalent. In recent decades rising
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
monthly_albedo = Dataset(
id='monthly_albedo',
assets=[
HttpAsset(
id='2018_07',
urls=[
'https://dataverse01.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/URJ2VK/XXQUSC'
],
),
HttpAsset(
id='2019_07',
urls=[
'https://dataverse01.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/URJ2VK/6YZNSZ'
],
),
],
metadata={
'title':
'SICE 1 km broadband albedo monthly averages and visualisations',
'abstract':
("""We present a simplified atmospheric correction algorithm for
snow/ice albedo retrievals using single view satellite
measurements. The validation of the technique is performed using
Ocean and Land Colour Instrument (OLCI) on board Copernicus
Sentinel-3 satellite and ground spectral or broadband albedo
measurements from locations on the Greenland ice sheet and in the
French Alps. Through comparison with independent ground
observations, the technique is shown to perform accurately in a
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
bas_coastlines = Dataset(
id='bas_coastlines',
assets=[
HttpAsset(
id='only',
urls=[
'https://ramadda.data.bas.ac.uk/repository/entry/get/Greenland_coast.zip?entryid=synth:8cecde06-8474-4b58-a9cb-b820fa4c9429:L0dyZWVubGFuZF9jb2FzdC56aXA=',
],
),
],
metadata={
'title': (
'The coastline of Kalaallit Nunaat/ Greenland available as a shapefile'
' and geopackage, covering the main land and islands, with glacier'
' fronts updated as of 2017.'
),
'abstract': (
"""A coastline of Kalaallit Nunaat/ Greenland covering all land and
islands, produced in 2017 for the BAS map 'Greenland and the
European Arctic'. The dataset was produced by extracting the land
mask from the Greenland BedMachine dataset and manually editing
anomalous data. Some missing islands were added and glacier fronts
were updated using 2017 satellite imagery. The dataset can be used
for cartography, analysis and as a mask, amongst other uses. At very
large scales, the data will appear angular due to the nature of
being extracted from a raster with 150 m cell size, but the dataset
should be suitable for use at most scales and can be edited by the
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
tectonic_plates = Dataset(
id='tectonic_plates',
assets=[
HttpAsset(
id='only',
urls=[
'https://github.com/fraxen/tectonicplates/archive/339b0c5.zip',
],
),
],
metadata={
'title':
'World tectonic plates and boundaries',
'abstract':
("""As per data source - This dataset is a conversion of the dataset
originally published in the paper 'An updated digital model of plate
boundaries' by Peter Bird (Geochemistry Geophysics Geosystems, 4(3),
1027, doi:10.1029/2001GC000252
[http://scholar.google.se/scholar?cluster=1268723667321132798],
2003). To bring this dataset into the modern age, the original data
has been parsed, cleaned, and verified using ArcGIS 10.2 and
converted to shape files. The dataset presents tectonic plates and
their boundaries, and in addition orogens and information about the
boundaries. The data is useful for geological applications, analysis
and education, and should be easy to use in any modern GIS software
application. For information on the fields and values, please refer
to the [original](http://peterbird.name/oldFTP/PB2002/2001GC000252_r
eadme.txt) documentation and the scientific article.
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
nga_arctic_sea_routes = Dataset(
id='nga_arctic_sea_routes',
assets=[
HttpAsset(
id='only',
urls=[
('https://opendata.arcgis.com/datasets/67760a7f85614902ac19fa6ff643b9fa_0.zip?'
'outSR=%7B%22latestWkid%22%3A102018%2C%22wkid%22%3A102018%7D'
),
],
),
],
metadata={
'title':
'Arctic Sea Routes',
'abstract':
("""Arctic lines of general transportation. In the case of the
Northern Sea Route, the route is based on the actual route used by
Russian icebreakers and cargo ships. The Northwest Passage is based
on the channels that would be able to support large cargo ships. The
transpolar route is a hypothetical route that could be used either
as a result of ice-free summers or the extensive use of icebreakers
and ice-hardened ships."""),
'citation': {
'text': ("""National Geospatial-Intelligence Agency (NGA)"""),
'url':
'https://arctic-nga.opendata.arcgis.com/datasets/67760a7f85614902ac19fa6ff643b9fa_0',
},
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
ne_timezones = Dataset(
id='ne_timezones',
assets=[
HttpAsset(
id='only',
urls=[
'https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_time_zones.zip',
],
),
],
metadata={
'title':
'Timezones',
'abstract':
("""Time zones primarily derive from the Central Intelligence Agency
map of Time Zones, downloaded from the World Factbook website May
2012. Boundaries were adjusted to fit the Natural Earth line work at
a scale of 1:10 million and to follow twelve nautical mile
territorial sea boundary lines when running along coasts. Additional
research was performed based on recent news to update several areas
including the international dateline and time zone adjustments for
Samoa and Tokelau and the discarding of daylight savings time in
Russia.
Data attributes include time offset from Coordinated Universal Time
(UTC, aka “zulu” time) and map color codes for a 6-up and 8-up
styling."""),
'citation': {
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
gshhg_coastlines = Dataset(
id='gshhg_coastlines',
assets=[
HttpAsset(
id='only',
urls=[
'http://www.soest.hawaii.edu/pwessel/gshhg/gshhg-shp-2.3.7.zip',
],
),
],
metadata={
'title':
('GSHHG: A Global Self-consistent, Hierarchical, High-resolution'
' Geography Database'),
'abstract':
("""We present a high-resolution geography data set amalgamated from
three data bases in the public domain:
World Vector Shorelines (WVS).
CIA World Data Bank II (WDBII).
Atlas of the Cryosphere (AC).
The WVS is our basis for shorelines except for Antarctica while the
WDBII is the basis for lakes, although there are instances where
differences in coastline representations necessitated adding WDBII
islands to GSHHG. The WDBII source also provides all political
borders and rivers. The addition of AC since 2.3.0 allows us to
offer two choices for Antarctica coastlines: Ice-front or Grounding
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
gc_net_promice_stations = Dataset(
id='gc_net_promice_stations',
assets=[
HttpAsset(
id='promice',
urls=[
'https://raw.githubusercontent.com/GEUS-PROMICE/map_GC-Net_PROMICE_kml/59455ddb50f7eeb1b8c5a5fdd7f80bfd548a0c92/input_data/PROMICE_info_from_GPS_data_2017-2018.csv',
],
),
HttpAsset(
id='promice_former',
urls=[
'https://raw.githubusercontent.com/GEUS-PROMICE/map_GC-Net_PROMICE_kml/59455ddb50f7eeb1b8c5a5fdd7f80bfd548a0c92/input_data/PROMICE_info_from_GPS_data_2017-2018_former_sites.csv',
],
),
HttpAsset(
id='gc_net',
urls=[
'https://raw.githubusercontent.com/GEUS-PROMICE/map_GC-Net_PROMICE_kml/59455ddb50f7eeb1b8c5a5fdd7f80bfd548a0c92/input_data/GCN%20info%20ca.2000.csv',
],
),
],
metadata={
'title':
'Map of GC-Net and PROMICE locations',
'abstract':
("""GitHub data description (creator: jasonebox): For PROMICE, I use
a 2017-2018 average of station GPS data. I added an updated position
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
# TODO: How to create a layer using this data? The NETCDF files are composed of
# daily runoff amounts predicted by a climate model for each basin in the
# streams_outlets_basins dataset.
promice_runoff = Dataset(
id='promice_runoff',
assets=[
HttpAsset(
id=f'land_mar_{year}',
urls=[
f'https://promice.org/PromiceDataPortal/api/download/0f9dc69b-2e3c-43a2-a928-36fbb88d7433/version_01/runoff/coast/runoff_land_MAR_{year}.nc',
],
) for year in range(2010, 2017 + 1)
],
metadata={
'title':
'Map of GC-Net and PROMICE locations',
'abstract':
("""High resolution map of Greenland hydrologic outlets, basins, and
streams, and a 1979 through 2017 time series of Greenland liquid
water runoff for each outlet."""),
'citation': {
'text': ("""Mankoff et al. - submitted to ESSD."""),
'url':
'https://doi.org/10.22008/promice/data/freshwater_runoff/v01',
},
},
)
Greenland. Web:
https://kort.nunagis.gl/portal/home/item.html?id=b70a43b814e84
78c9514208548ca5f61.
Date accessed: {{date_accessed}}."""),
'url':
'https://kort.nunagis.gl/portal/home/item.html?id=b70a43b814e8478c9514208548ca5f61',
},
},
)
ne_states_provinces = Dataset(
id='ne_states_provinces',
assets=[
HttpAsset(
id='only',
urls=[
'https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_1_states_provinces.zip',
],
),
],
metadata={
'title':
'Admin 1 – States, Provinces',
'abstract':
("""Internal, first-order administrative boundaries and polygons for
all but a few tiny countries. Includes name attributes (including
diacritical marks), name variants, and some statistical codes (FIPS,
ISO, HASC)."""),
'citation': {
'text': ("""Made with Natural Earth"""),
'url':
'https://github.com/nvkelso/natural-earth-vector/blob/master/LICENSE.md',
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
continental_shelf = Dataset(
id='continental_shelf',
assets=[
HttpAsset(
id='north_points',
urls=[
'http://tuvalu.grida.no/ecs/dnk_76_2014_points.zip',
],
),
HttpAsset(
id='north_lines',
urls=[
'http://tuvalu.grida.no/ecs/dnk_76_2014_lines.zip',
],
),
HttpAsset(
id='north_polygons',
urls=[
'http://tuvalu.grida.no/ecs/dnk_76_2014_polygons.zip',
],
),
HttpAsset(
id='northeast_points',
urls=[
'http://tuvalu.grida.no/ecs/dnk_68_2013_points.zip',
],
),
HttpAsset(
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
mineral_and_hydrocarbon_licenses = Dataset(
id='mineral_and_hydrocarbon_licenses',
assets=[
HttpAsset(
id='mcas_mlsa_public_all',
urls=[
'https://gis.govmin.gl/geoserver/MLSA/ows?service=WFS&version=1.0.0&request=GetFeature&outputFormat=shape-zip&typeNames=MLSA:mcas_mlsa_public_all',
],
),
HttpAsset(
id='mcas_mlsa_public_historic',
urls=[
'https://gis.govmin.gl/geoserver/MLSA/ows?service=WFS&version=1.0.0&request=GetFeature&outputFormat=shape-zip&typeNames=MLSA:mcas_mlsa_public_historic',
],
),
],
metadata={
'title': 'Mineral and hydrocarbon licenses',
'abstract': (
"""Mineral and hydrocarbon license data, including historic public
licenses and public licenses. License data is sourced from the
Mineral Resource Authority, Government of Greenland using their
GeoServer version 2.14.1 (https://gis.govmin.gl/geoserver/web/)."""
),
'citation': {
'text': 'Government of Greenland/GINR',
'url': '',
MAX_CONCENTRATION_YEARS,
MIN_CONCENTRATION_YEARS,
concentration_maximum_asset_for_year,
)
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
seaice_index = Dataset(
id='seaice_index',
assets=[
*[
HttpAsset(
id=f'median_extent_line_{month:02d}',
urls=[(
'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/shapefiles/shp_median'
f'/median_extent_N_{month:02d}_1981-2010_polyline_v3.0.zip'
)],
) for month in range(1, 12 + 1)
],
*[
HttpAsset(
id=f'minimum_concentration_{year}',
urls=[(
'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/geotiff'
f'/09_Sep/N_{year}09_concentration_v3.0.tif'
)],
) for year in MIN_CONCENTRATION_YEARS
],
*[
concentration_maximum_asset_for_year(year) for year in MAX_CONCENTRATION_YEARS
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
caff_murre_colonies = Dataset(
id='caff_murre_colonies',
assets=[
HttpAsset(
id='only',
urls=[
'https://abds.is/index.php/publications/the-distribution-of-thick-billed-and-common-murre-colonies-in-the-north/download',
],
),
],
metadata={
'title':
'The distribution of thick-billed and common murre colonies in the North.',
'abstract':
("""Murres are among the most abundant seabirds in the Northern
Hemisphere with a population in excess of ten million adults. No
obvious global trend has been identified but the majority of
regional populations have shown declines over the past three
decades. While they are currently abundant, climate change is
projected to pose problems to murres in the future, especially for
the more northern species, the thick-billed murre, which is strongly
associated with sea ice. Other threats include fisheries
interactions, over-exploitation, contaminants, and oil spills, the
latter becoming more important if climate change expands shipping
and hydrocarbon development in the Arctic."""),
'citation': {
'text':
("""Arctic Biodiversity Trends 2010 – Selected indicators of
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
bathymetric_chart = Dataset(
id='bathymetric_chart',
assets=[
HttpAsset(
id='only',
urls=[
'https://www.bodc.ac.uk/data/open_download/ibcao/ibcao_v4_400m_ice/cfnetcdf/',
],
),
],
metadata={
'title':
'Bathymetric Chart of the Arctic Ocean (IBCAO)',
'abstract':
("""The goal of the IBCAO initiative is to develop a digital database
that contains all available bathymetric data north of 64° North, for
use by mapmakers, researchers, institutions, and others whose work
requires a detailed and accurate knowledge of the depth and the
shape of the Arctic seabed."""),
'citation': {
'text': ("""GEBCO Compilation Group (2020) GEBCO 2020 Grid
(doi:10.5285/a29c5465-b138-234d-e053-6c86abc040b9)"""),
'url':
'https://www.gebco.net/data_and_products/gridded_bathymetry_data/arctic_ocean/',
},
},
)