ID,Resource Type,Data Type,Data Level,Atmosphere,Biosphere,Cryosphere,Hydrosphere,Lithosphere,Society & Governance,Economic sector,Natural Hazards,Continent,Name,Version,Provider,URL,Contact,Spatial resolution,Start temporal coverage,End temporal coverage,Temporal frequency,Variable(s),DOI,Paleo(Y/N),Mountain Focus (Y/N),Comment(s),
1,Data Portal,Multiple,1,x,x,,x,x,,x,,Africa,Africa GeoPortal,,Regional Centre for Mapping of Resources for Development; Digital Earth Africa; NASA Earth Science; others,https://www.africageoportal.com/,africageoportal@esri.com,,,,,,,No,No,The datasets extend beyond mountains but some may be relevant for mountainous areas.,
2,Data Portal,Multiple,1,x,x,x,x,x,,,,Europe,AlpEnDAC,,Bavarian State Ministry of the Environment and Consumer Protection,https://www.alpendac.eu/,info@vao.bayern.de,,,,,,,No,Yes,See also: https://www.vao.bayern.de/vao.htm/. This dataportal focus on the Alps. ,
3,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,,Europe,Alpine Convention Atlas,,Permanent Secretariat of the Alpine Convention,https://www.atlas.alpconv.org/,research@alpconv.org,,,,,,,No,Yes,"The Alpine Convention Atlas is the repository for the data collected in the framework of the Alpine Convention activities, its Working Groups and its network. The Atlas is mostly meant to display GIS information, but it contains much more.",
4,Data Portal,Multiple,Multiple,x,x,x,x,x,,,,Europe,Alpine Environmental Data Analysis Center (AlpEnDAC) Data Explorer,,Virtual Alpine Observatory project (VAO),https://www.alpendac.eu/spa#!/products/ ,support@alpendac.eu,,,,,Cloud cover/fraction; Snow depth; Snow Water Equivalent (SWE); Stratospheric CH4; Tropospheric column CH4; Tropospheric column CO2; Near-surface air temperature; Near-surface wind speed and direction; Total precipitation,,No,Yes,See also: https://www.alpendac.eu/landkreis-tool/ ; https://www.alpendac.eu/node/34/,
5,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Global,AmeriGEO Datahub,,AmeriGEO,https://data.amerigeoss.org/,,,,,,,,No,No,"Contains many datasets, some of which may be useful for mountain applications.",
6,Data Portal,Multiple,Multiple,,,,x,,,,,Global,AQUASTAT,,Food and Agriculture Organization of the United Nations (FAO),https://www.fao.org/aquastat/en/geospatial-information/ ,FAO-HQ@fao.org,,,,,,,No,No,"Not mountain specific, but may be useful for mountain applications.",
7,Data Portal,In Situ,1,,x,,,,,,,Europe,BioREGIO WebGIS,,EURAC Research,https://webgis.eurac.edu/bioregio/ ,,,,,,Land cover; Habitats; ecological corridors for species; Species distribution and abundance,,No,No,,
8,Data Portal,In Situ,1,,,,,,x,,,North America,California Climate Data Archive,,Western Regional Climate Center (WRCC); Scripps Institution of Oceanography,https://calclim.dri.edu/,wrcc@dri.edu,,,,,,,No,No,"Climate monitoring and data access website for the state of California. The datasets extend beyond mountains, but may be relevant for the mountainous areas. See also: https://raws.dri.edu/cgi-bin/rawMAIN.pl?caCSRS",
9,Data Portal,Multiple,Multiple,x,,x,x,,,,,North America,California Data Exchange Center,,California Department of Water Resources (DWR),https://cdec.water.ca.gov/,,,,,,,,No,No,"Data for the state of California. Not mountain specific, but could be useful for mountain applications.",
10,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,North America,California State Geoportal,,California Department of Technology,https://gis.data.ca.gov/,opendata@state.ca.gov,,,,,,,No,No,,
11,Data Portal,Multiple,1,x,,,,,,,,Europe,CARPATCLIM,,Climate of the Carpathian Region (CARPATCLIM),http://www.carpatclim-eu.org/pages/metadata/ ,szalai.sandor@mkk.szie.hu,,,,,Near-surface air temperature,,No,Yes,Related publication: https://doi.org/10.1002/joc.6952/ ; See also: https://www.carpatclim-eu.org/pages/download/ ; https://www.carpatclim-eu.org/pages/atlas/,
12,Data Portal,In Situ,1,,x,,,,,,,Asia,Caucasus Barcode of Life Data Portal,,German Federal Ministry of Education and Research,https://ggbc.eu/data/ ,shota.japarashvili.1@iliauni.edu.ge; n.hein@leibniz-zfmk.de,,,,,Vegetation species abundancies and extents,,No,Yes,"Resources concerning the Caucasus region, containing many datasets.",
13,Data Portal,Multiple,1,,x,,,,,,,Europe,CCIBIS Geoportal,,Carpathian Convention; UNEP Vienna,https://gis.vm.stuba.sk/portal/apps/webappviewer/index.html/ ,office@wwfdcp.org,,,,,Forest extent; Land cover; Vegetation species abundancies and extents; Protected areas; Natural Parks,,No,No,See also: https://library.spectra-perseus.org/ ; https://webgis.eurac.edu/carpathianportal/,
14,Data Portal,Multiple,Multiple,x,x,x,x,,x,x,,Asia,Central Asia Climate Portal,,The World Bank; Central Asia Regional Environmental Center (CAREC); International Center for Agricultural Research in the Dry Areas (ICARDA),https://geonode.centralasiaclimateportal.org/,contact@centralasiaclimateportal.org,,,,,,,No,No,See also: https://mapviewer.centralasiaclimateportal.org/,
15,Data Portal,Multiple,Multiple,x,,,,,,,,North America,Climate and Weather Tools AKCASC,,University of Alaska; USGS; UAF International Arctic Research Center,https://akcasc.org/climate-weather-tools/ ,,,,,,,,No,No,"The datasets extend beyond mountains, but may be relevant for mountainous areas.",
16,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Global,Climate Attribution,,Sabin Center for Climate Change Law; Lamont-Doherty Earth Observatory; High Tide Foundation,https://climateattribution.org/resources/ ,climateattribution@law.columbia.edu,,,,,,,No,No,"Does not link to many datasets at present, and those it does may not be mountain specific.",
17,Data Portal,Multiple,Multiple,x,,,x,,,,,Global,Climate Change Knowledge Portal (CCKP),,The World Bank Group,https://climateknowledgeportal.worldbank.org/download-data/ ,climateportal@worldbank.org,,,,,,,No,No,"The datasets extend beyond mountains, but some may be relevant for mountainous areas.",
18,Data Portal,Multiple,2,x,,,,,,,,Global,Climate Data Store (CDS),,European Centre for Medium-Range Weather Forecasts (ECMWF),https://cds.climate.copernicus.eu/#!/home/ ,https://cds.climate.copernicus.eu/cdsapp#!/usersupport ,,,,,,,No,No,"The datasets extend beyond mountains, however they might be relevant for mountainous areas.",
19,Data Portal,Multiple,1,x,,,,,,,,North America,Climate Summaries WRCC,,Western Regional Climate Center (WRCC),https://wrcc.dri.edu/Climate/summaries.php/ ,wrcc@dri.edu,,,,,,,No,No,See also: https://wrcc.dri.edu/Climate/comp_tables.php/,
20,Data Portal,In Situ,1,x,,,,,,,,North America,Community Environmental Monitoring Program (CEMP),,U.S. Department of Energy; Desert Research Institute (DRI); Western Regional Climate Center (WRCC),https://cemp.dri.edu/,,,,,,,,No,Yes,Environmental monitoring around the the Nevada National Security Site (NNSS).,
21,Data Portal,Multiple,Multiple,,,x,,,,,,Global,Community snow Observations - Snow Data,,Community Snow Observations (CSO),https://www.mountainsnow.org/home/ ; https://communitysnowobs.org,communitysnowobs@gmail.com,,,,,Snow covered area / fraction (SCA/F); Snow depth; Snow Water Equivalent (SWE),,No,Yes,,
22,Data Portal,Multiple,Multiple,x,,x,x,,,,,Europe,Copernicus Reference Data Access (CORDA),,Copernicus,https://corda.eea.europa.eu/SitePages/Statistics.aspx/ ,https://corda.eea.europa.eu/SitePages/Feedback.aspx/ ,,,,,,,No,No,"The datasets extend beyond mountains, however they might be relevant for mountainous areas.",
23,Data Portal,Modelled,2,x,,,,,,,,Global,CORDEX regional climate model data,,EU; Copernicus; ECMWF; Climate Change Service,https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.bc91edc3?tab=overview/ ,,,,,,Cloud cover/fraction; Near-surface air temperature; Near-surface water vapor; Near-surface wind speed and direction; Total precipitation; Surface ERB Longwave; Surface ERB Shortwave; Tropospheric temperature profile,https://doi.org/10.24381/cds.bc91edc3/ ,No,Yes,,
24,Data Portal,Remotely sensed,Multiple,,,x,,,,,,Global,Cryo2Ice,,European Space Agency (ESA); NASA,https://cs2eo.org/cryo2ice,info@earthwave.co.uk,,,,,,,No,No,"Cite as: Alford, J., Ewart, M., Bizon, J., Easthope, R., Gourmelen, N. Parrinello, T., Bouffard, J., Michael, C., Meloni, M. 2021. #CRYO2ICE Coincident Data Explorer, Version 1, https://cryo2ice.org. European Space Agency. 1/9/2022 ; The datasets extend beyond mountains, but may be relevant for mountainous areas.",
25,Data Portal,In Situ,1,,,x,,,,,,Global,Cryobs-Clim BD,,Observatoire des Sciences de l'UniversitÈ de Grenoble (OSUG); Institut des Geosciences de l'Environnement (IGE),https://data.cryobsclim.fr/main.jsf/ ,,,,,,,,No,Yes,,
26,Data Portal,Multiple,1,,,,x,,x,,,Global,Dartmouth Flood Observatory,,University of Colorado; INSTAAR; CSDMS,https://floodobservatory.colorado.edu/index.html/ ,,,,,,,,No,No,"Not specific to mountains, but may be useful for mountain applications.",
27,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Europe,Data and maps from EEA,,European Environment Agency (EEA),https://www.eea.europa.eu/data-and-maps/ ,,,,,,,,No,No,"The datasets extend beyond mountains, however they might be relevant for mountainous areas. See also: https://www.eea.europa.eu/data-and-maps/data#c0=5&c11=&c5=all&b_start=0",
28,Data Portal,Multiple,Multiple,x,x,x,x,,x,x,x,Oceania,Data and publications - Australian Government,,Australian Government,https://www.ga.gov.au/data-pubs/ ,clientservices@ga.gov.au,,,,,,,No,No,"The datasets extend beyond mountains, but they might be relevant for mountainous areas.",
29,Data Portal,In Situ,1,x,,x,,,,,,Europe,Data Browser Matsch / Mazia,,Institute for Alpine Environment; EURAC Research; LTSER (Long-term socio-ecological research site Matschertal / Val di Mazia),https://browser.lter.eurac.edu/,Veronika.Fontana@eurac.edu,,,,,,,No,Yes,"An app providing a user-friendly interface to download meteorological and biophysical variables of the long-term socio-ecological research LT(S)ER site Matschertal / Val di Mazia, South Tyrol, Italy.",
30,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Global,Data Catalog US,,U.S. General Services Administration; Technology Transformation Service,https://catalog.data.gov/dataset/ ,datagov@gsa.gov,,,,,,,No,No,"The datasets extend beyond mountains, however some may be relevant for mountainous areas.",
31,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,North America,Data Catalogue (British Columbia),,Government of British Columbia,https://catalogue.data.gov.bc.ca/dataset?sector=Natural Resources/ ,data@gov.bc.ca,,,,,,,No,No,"The datasets extend beyond mountains, however some may be relevant for mountainous areas.",
32,Data Portal,Multiple,Multiple,x,x,,x,,x,,x,Global,Data integration analyses system (DIAS),,The University of Tokyo,https://diasjp.net/en/ ,dias-office@diasjp.net,,,,,,,No,No,Not mountain specific.,
33,Data Portal,Multiple,Multiple,,,,x,,,,,Global,Data Portals,,CUAHSI,https://www.cuahsi.org/data-models/portals/ ,help@cuahsi.org,,,,,,,No,No,Really just links to other data portals. Not mountain specific.,
34,Data Portal,Remotely sensed,Multiple,x,x,,x,x,x,,,Africa,DE Africa Metadata Explorer,,Digital Earth Africa,https://explorer.digitalearth.africa/products,,,,,,,,No,No,"See also: https://maps.digitalearth.africa/ ; https://www.africageoportal.com/search?collection=Dataset/ ; https://www.digitalearthafrica.org/platform-resources/platform/; https://www.data4sdgs.org/ARDC/ ; The datasets extend beyond mountains, however they might be relevant for mountainous areas.",
35,Data Portal,Multiple,1,,,,,,x,,x,Global,DesInventar Sendai,,UNDRR,https://www.desinventar.net/,,,,,,,,No,No,"Disaster loss data. Not specific to mountains, but may be useful for mountain applications.",
36,Data Portal,Multiple,Multiple,,,,x,,,,,Global,Digital Data & Tools - IWMI,,International Water Management Institute (IWMI),https://www.iwmi.cgiar.org/resources/data-and-tools/ ,,,,,,,,No,No,"Not mountain specific, but may be useful for mountain applications.",
37,Data Portal,Remotely sensed,Multiple,x,x,x,x,x,x,x,x,Oceania,Digital Earth Australia,,Australian Government; Digital Earth Australia,https://www.dea.ga.gov.au/,earth.observation@ga.gov.au,,,,,,,No,No,"The datasets extend beyond mountains, but may be relevant for mountainous areas.",
38,Data Portal,,,,,,,,,,,Global,Digital Earth Knowledge Platform,,International Society for Digital Earth,http://www.digitalearth-isde.org/list-41-1.html/ ,isde@radi.ac.cn,,,,,,,No,No,Not sure how much data can be accessed.,
39,Data Portal,Remotely sensed,Multiple,x,,,,,,,,Global,Earth Observation Portal - EUMETSAT,,European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT),https://eoportal.eumetsat.int/userMgmt/confirmed.faces/ ,,,,,,,,No,No,"Login required. The datasets extend beyond mountains, however they might be relevant for mountainous areas.",
40,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Global,EarthCube,,US National Science Foundation (NSF),https://www.earthcube.org/,ec-info@earthcube.org,,,,,,,No,No,Lots of material; not mountain specific but some may be relevant.,
41,Data Portal,Multiple,Multiple,x,x,,x,,x,,,Global,EarthEnv,,NCEAS; NASA; NSF; Yale University,https://www.earthenv.org/,,1 km,,,,,,No,Yes,"General page containing links to many datasets such as habitat heterogeneity, consensus land cover, cloud cover climatology, freshwater environmental variables, etc.",
42,Data Portal,Multiple,Multiple,,x,,,,x,x,,Africa,East African Community Data Portal,,African Development Bank,https://eac.opendataforafrica.org/,eac@eachq.org,,,,,,,No,No,Seems to be mostly statistical data. See also other resources here: https://www.eac.int,
43,Data Portal,Multiple,Multiple,,x,,,,,,,Global,EBV Data Portal,,GEO BON,https://portal.geobon.org/datasets,,,,,,,,No,No,See also the portal: https://portal.geobon.org/map/#/,
44,Data Portal,In Situ,2,x,,,,,,,,Europe,ECA&D gridded observational dataset (E-OBS),,European Climate Assessment & Dataset project; EUMETNET; European Commission,https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php#/ ,eca@knmi.nl,,1950,2021,Daily,Total precipitation; Temperature; Sea level Pressure; Relative Humidity; Wind speed; Global radiation,,No,No,"Provides information on changes in weather and climate extremes, as well as the daily dataset needed to monitor and analyse these extremes. See also: https://www.ecad.eu ; https://www.ecad.eu/download/ensembles/download.php ; Related publication: https://doi.org/10.1029/2017JD028200",
45,Data Portal,Multiple,1,,x,,,,,,,South America,ECOANDES Geoportal,,Consortium for the Sustainable Development of the Andean Ecoregion (CONDESAN),https://condesan-ecoandes.org/geoportales/#1550523850230-9687cf30-ab54/ ,condesan@condesan.org,,,,,,,No,No,"The datasets may extend beyond mountains, however they might be relevant for mountainous areas. In Spanish.",
46,Data Portal,Multiple,1,,,,,,,,,Europe,EIONET Central Data Repository,,European Environment Information and Observation Network,https://cdr.eionet.europa.eu/,helpdesk@eionet.europa.eu,,,,,,,No,No,"Note, contains only data reports on the environment.",
47,Data Portal,Multiple,1,,,,,,x,,x,Global,EM-DAT,,UniversitÈ Catholique de Louvain (UCLouvain),https://public.emdat.be/data/ ,,,,,,,,No,No,"International Disaster Database. See also: https://www.emdat.be/ ; The datasets extend beyond mountains, however they might be relevant for mountainous areas.",
48,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Global,EnviDat,,WSL,https://www.envidat.ch/#/,envidat@wsl.ch,,,,,Snow melt / runoff; Snow Water Equivalent (SWE); Forest extent; Land cover; Land surface temperature; Past natural hazard event extents and hazard intensities; Total precipitation,,No,Yes,"A joint project of the Swiss Confederation, cantons, communes and other organizations. Makes open government data available to the general public in a central catalogue. Selected entries also appear in the inventory as 'Datasets'.",
49,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Europe,Environmental data and geodata of the FOEN,,Swiss Federal Office for the Environment (FOEN),https://www.bafu.admin.ch/bafu/en/home/state/data/environmental-data.html/ ,info@bafu.admin.ch,,,,,,,No,No,,
50,Data Portal,Multiple,1,,,,,,,,,Global,Environmental Data Explorer,,United Nations Environment Programme (UNEP),https://geodata.grid.unep.ch/,stefan.schwarzer@unep.org,,,,,,,No,No,"The datasets extend beyond mountains, however they might be relevant for mountainous areas.",
51,Data Portal,Remotely sensed,1,x,x,x,,x,x,,x,Global,EO Browser,,Sentinel Hub; Euro Data Cube,https://apps.sentinel-hub.com/eo-browser/ ,,,,,,,,No,No,"This page seems similar to the following, but please as there might be some differences: https://browser.eurodatacube.com/?zoom=9&lat=41.85422&lng=12.29233&fromTime=1970-01-01T00:00:00.000Z&toTime=2021-11-22T11:33:59.255Z ; To access Custom Processing Scripts visit : https://www.sentinel-hub.com/develop/custom-scripts/ ; Check out this webinar: https://www.youtube.com/watch?v=eK0OMn5H-kY&t=1s ; An interesting tool is the Terrain Viewer in EO Browser (explore the world in 3D with any satellite visualization).",
52,Data Portal,Remotely sensed,1,x,x,x,x,x,,,,Global,Euro Data Cube,,European Space Agency (ESA),https://browser.eurodatacube.com/,hub@eox.at,,,,,,,No,No,Log in is required. Visit: https://eurodatacube.com/marketplace/ ; to access all the products (for free or to purchase),
53,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Global,FAO Map Catalog,,Food and Agriculture Organization of the United Nations (FAO),https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/home/ ,,,,,,,,No,No,"The datasets extend beyond mountains, however some might be relevant for mountainous areas.",
54,Data Portal,In Situ,1,,x,,,,,,,Global,FLUXNET Datasets,,ORNL DAAC; NASA,https://daac.ornl.gov/cgi-bin/dataset_lister.pl?p=9/ ,support@earthdata.nasa.gov,,,,,,,No,No,"FLUXNET is a global network of micrometeorological tower sites that use eddy covariance methods to measure the exchanges of carbon dioxide, water vapor, and energy between terrestrial ecosystems and the atmosphere. More than 500 long-term tower sites globally. Overarching goal to provide information for validating remote sensing products for net primary productivity, evaporation, and energy absorption. See also: https://asiaflux.net/ ; Some sites might be relevant for mountainous applications.",
55,Data Portal,In Situ,1,,x,,,,,,,Global,ForestPlots.net,,ForestPlots,https://www.forestplots.net/en/ ,admin@forestplots.net,,,,,,,No,No,"The datasets extend beyond mountains, however some might be relevant for mountainous areas.",
56,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Global,Free GIS Data,,Robin Wilson,https://freegisdata.rtwilson.com/,robin@rtwilson.com,,,,,,,No,No,The datasets extend beyond mountains but some may be useful for mountainous applications.,
57,Data Portal,Multiple,1,,x,,x,,,,,Global,Freshwater Information Platform,,Multiple organisations,https://www.freshwaterplatform.eu/,,,,,,,,No,No,"Focus is on Europe. See links provided to various data portals, sources, and repositories. Not mountain specific, but may be useful for mountain applications.",
58,Data Portal,In Situ,1,,,x,,,,,,Global,GCW Data Portal,,WMO; Global Cryosphere Watch (GCW),https://gcw.met.no/metsis/search/ ,https://gcw.met.no/contact ,,,,,,,No,No,"The datasets extend beyond mountains, but may be relevant for mountainous areas. Also not clear that all data can be accessed at present. See also a corresponding inventory of in situ and remotely sensed snow data: https://globalcryospherewatch.org/reference/snow_inventory.php",
59,Data Portal,Multiple,1,x,x,x,x,,x,x,x,Europe,Geoportail OPCC,,Communaute de Travail des Pyrénées (CTP),https://www.opcc-ctp.org/fr/geoportal/ ,info_opcc@ctp.org,,,,,,,No,Yes,A computer application developed by the Pyrenean Observatory of Climate Change within the framework of the OPCC2 project to make scientific and cartographic information available to the public on the effects of climate change in the Pyrenees.,
60,Data Portal,,,,,x,x,,x,,,Global,Glacier Lake Outburst Flood Database (GLOFs),,Multiple organisations,http://glofs.geoecology.uni-potsdam.de/,georg.veh@uni-potsdam.de,,,,,,,No,Yes,Not mountain specific but may be useful.,
61,Data Portal,Multiple,1,,,x,,,x,,,Europe,Glacier Risks Database,,Multiple organisations,http://www.nimbus.it/glaciorisk/gridabasemainmenu.asp,,,,,,,,No,Yes,,
62,Data Portal,Multiple,1,,x,,,,,,,Global,Global Biodiversity Information Facility (GBIF),,GBIF Secretariat,https://www.gbif.org/,,,,,,,,No,No,See also: https://www.gbif.org/dataset/search/,
63,Data Portal,Remotely sensed,1,,,,,,,,,Global,Global Climate Observing System,,World Meteorological Organization (WMO),https://gcos.wmo.int/,gcos@wmo.int,,,,,,,No,No,Not necessarily any links to datasets per se.,
64,Data Portal,Remotely sensed,2,,x,,,,,,,Global,Global Ecosystem Viewer,,USGS,https://rmgsc.cr.usgs.gov/ecosystems/dataviewer.shtml,,,,,,,,No,No,See also: https://www.usgs.gov/products/data-and-tools/overview/ ; https://www.usgs.gov/products/maps/overview/ ; https://rmgsc.cr.usgs.gov/ecosystems/datadownload.shtml/; https://rmgsc.cr.usgs.gov/outgoing/ecosystems/ ; https://www.usgs.gov/centers/geosciences-and-environmental-change-science-center/science/global-ecosystems/,
65,Data Portal,Multiple,1,,x,,,,,,,Global,Global Forest Resources Assessments,,FAO,https://fra-data.fao.org/,FAO-HQ@fao.org,,,,,Forest extent,,No,No,"Provides information for understanding the extent of forest resources, their condition, management and uses. See also: https://www.fao.org/forest-resources-assessment/fra-2020/maps/en/ ; https://www.fao.org/forest-resources-assessment/en/ ; The datasets extend beyond mountains, but some might be relevant for mountainous areas.",
66,Data Portal,In Situ,1,,,,x,,,,,Global,Global Groundwater Monitoring Network (GGMN),,International Groundwater Resources Assessment Centre (IGRAC); World Meteorological Organization (WMO); UNESCO,https://ggis.un-igrac.org/,,,,,,,,No,No,"An online platform supporting the sharing of groundwater data and information worldwide. Created to advance the assessment of groundwater resources and make knowledge accessible to those who depend on groundwater or are engaged in groundwater management, development or protection. See also: https://ggis.un-igrac.org/maps/?limit=5&offset=0",
67,Data Portal,Remotely sensed,2,,,,,x,,,,Global,Global Mountain Explorer,v2,USGS; Others,https://rmgsc.cr.usgs.gov/gme/ ,rsayre@usgs.gov,,,,,Mountain extent,,No,Yes,Provides three commonly used mountain delineations. Useful for clipping more extensive datasets.,
68,Data Portal,Multiple,1,,,,x,,,,,Global,Global Terrestrial Network - Hydrology,,GCOS,https://www.gtn-h.info/,gtn-h@bafg.de,,,,,,,No,No,Really a 'network of networks' providing links to other data portals. Not mountain specific.,
69,Data Portal,In Situ,1,,,x,,,,,,Global,Global Terrestrial Network for Permafrost GTN-P,,International Permafrost Association (IPA); Global Climate observing System (GCOS); Global Terrestrial Observing Network (GTOS),https://gtnp.arcticportal.org/data/ ,,,,,,Active layer thickness; Ground temperature,,No,No,See also: https://gtnp.arcticportal.org/index.php/resources/maps ; https://gtnp.arcticportal.org/resources/useful-downloads,
70,Data Portal,In Situ,1,,,,x,,,,,Global,Global Terrestrial Network for River Discharge (GTN-R),,Global Terrestrial Network - Hydrology (GTN-H); Global Climate Observing system (GCOS); Hydrology and Water Resources Programme of the WMO (HWRP),https://www.bafg.de/GRDC/EN/01_GRDC/grdc_node.html/ ,,,,,,River discharge,,No,No,"The datasets extend beyond mountains, however some might be relevant for mountainous areas.",
71,Data Portal,Multiple,1,,,,x,,,,,North America,Global Water Futures Data,,Global Water Futures (GWF); University of Saskatchewan,https://gwf.usask.ca/outputs-data/data.php#Datamanagement/ ,laleh.moradi@usask.ca,,,,,,,No,No,"The datasets extend beyond mountains, but many are relevant for mountainous areas.",
72,Data Portal,Multiple,2,x,,,,,,,,Global,Gridded Climate Datasets,,NOAA,https://psl.noaa.gov/data/gridded/ ,webmaster.psl@noaa.gov; psl.data@noaa.gov,,,,,,,No,No,"The datasets extend beyond mountains, but some may be relevant for mountainous areas.",
73,Data Portal,Remotely sensed,1,,x,,x,,,,,Europe,High Resolution Layers - Land cover characteristics,,Copernicus; European Commission; EEA,https://land.copernicus.eu/pan-european/high-resolution-layers/ ,copernicus@eea.europa.eu,,,,,Forest extent; Land cover,,No,No,"Provides access to many different datasets e.g. imperviousness, forests, grassland, water and wetness etc. Not mountain specific. Visit also: https://land.copernicus.eu",
74,Data Portal,Multiple,1,,,,x,,,,,Europe,Hydrological Atlas of Switzerland (HADES),,University of Bern; Swiss Federal Office for the Environment (FOEN),https://hydrologicalatlas.ch/,hades@giub.unibe.ch,,,,,,,No,No,"The result of a collaborative effort by Swiss hydrologists which has provided basic hydrological information, specialist knowledge and didactic materials to a wide range of users for over 30 years. Digital data now available. See also: https://hydromapscc.ch/#en/8/46.830/8.193/bl_hds",
75,Data Portal,Multiple,1,,,,x,,,,,North America,HydroShare,,"Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI); US National Science Foundation (NSF)",https://www.hydroshare.org/search/ ,,,,,,,,No,No,"Contains many different datasets, some of which will be relevant for mountainous areas. See also: https://hiscentral.cuahsi.org/pub_services.aspx ; https://data.cuahsi.org",
76,Data Portal,Remotely sensed,1,,,x,,,,,,Global,ICESat-2 Data Sets,,National Snow & Ice Data Center (NSIDC); NASA,https://nsidc.org/data/explore-data,,,,,,Glacier extent; Snow covered area / fraction (SCA/F); Snow depth; Lake ice cover; Land cover; Vegetation species abundancies and extents,,No,No,"See also: https://nsidc.org/data/icesat-2/products/ ; Not mountain specific, but may be useful for mountain applications.",
77,Data Portal,Multiple,1,,x,,,,x,,,Global,IFRI Database,,International Forestry Resources and Institutions (IFRI),https://ifri.forgov.org/resources/data/ ,ifri@isb.edu ; ifridatahelp@umich.edu,,,,,,,No,No,"The IFRI network is in the process of developing a data-sharing policy that will set the conditions whereby IFRI data will be made available for public use. The datasets extend beyond mountains, however some might be relevant for mountainous areas.",
78,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Europe,INSPIRE Geoportal,,European Commission,https://inspire-geoportal.ec.europa.eu/,ENV-INSPIRE@ec.europa.eu,,,,,,,No,No,"The datasets extend beyond mountains, however some might be relevant for mountainous areas.",
79,Data Portal,In Situ,1,,x,,,,,,,Global,International Soil Moisture Network (ISMN),,Multiple organisations,https://www.geo.tuwien.ac.at/insitu/data_viewer/ ,ismn@geo.tuwien.ac.at,,,,,Snow depth; Near-surface air temperature; Ground temperature; Total precipitation; Root-zone soil moisture; Near-surface soil moisture,https://doi.org/10.5194/hess-25-5749-2021/ ,No,No,"Citation: https://doi.org/10.5194/hess-15-1675-2011/ ; The datasets extend beyond mountains, but may be relevant for mountainous areas. There don't appear to be that many sites at present. See also: https://ismn.geo.tuwien.ac.at/en/ ; https://github.com/TUW-GEO/ismn",
80,Data Portal,Multiple,2,x,x,x,x,,,,,Global,IPCC WGI Interactive Atlas,,WHO; UNEP,https://interactive-atlas.ipcc.ch/,ipcc-ddc@ifca.unican.es,,,,,Snow depth; Near-surface air temperature; Near-surface wind speed and direction; Tropospheric ozone concentration; Total precipitation,,No,No,Datasets can masked to mountain regions.,
81,Data Portal,Multiple,1,,x,,,,,,,Global,IUCN Red List of Threatened Species,,UCN Global Species Programme Red List Unit,https://www.iucnredlist.org/resources/grid/ ,redlist@iucn.org,,,,,Vegetation species abundancies and extents,,No,No,"The datasets extend beyond mountains, however some might be relevant for mountainous areas.",
82,Data Portal,Multiple,1,,,,,,,,,North America,iUTAH Survey Data Viewer,,US National Science Foundation (NSF),http://data.iutahepscor.org/surveys/ ,,,,,,,,No,No,Survey results,
83,Data Portal,In Situ,1,,x,,,,,,,Asia,JaLTER Data Catalog,,Center for Global Environmental Research; JaLTER Network,https://db.cger.nies.go.jp/JaLTER/metacat/style/skins/jalter-en/index.jsp/ ,yuuri.sakai@db.soc.i.kyoto-u.ac.jp,,,,,,,No,No,A local network of ecological monitoring sites for facilitating long-term ecological studies.,
84,Data Portal,In Situ,1,,x,,,,,,,Africa,Kilimanjaro ecosystems under global change,,German Science Foundation; Universit‰t W¸rzburg,https://www.kilimanjaro.biozentrum.uni-wuerzburg.de/Data/Data.aspx/ ,kili@biozentrum.uni-wuerzburg.de,,,,,,,No,Yes,Datasets concern Mt. Kilimanjaro (Tanzania).,
85,Data Portal,Remotely sensed,1,x,x,x,x,x,,,,Global,Land Processes Distributed Active Archive Center (LP DAAC),,USGS,https://lpdaac.usgs.gov/product_search/?status=Operational,LPDAAC@usgs.gov,,,,,,,No,No,"Not mountain specific, but may be useful for mountain applications.",
86,Data Portal,Multiple,1,x,x,x,x,x,,,x,Global,Land Product Validation (LPV) subgroup,,CEOS; NASA,https://lpvs.gsfc.nasa.gov/index.html/ ,,,,,,,,No,No,"An effort around the validation of remotely sensed products. Links to some data are provided. The datasets extend beyond mountains, but some may be relevant for mountainous applications.",
87,Data Portal,In Situ,1,,x,,,,,,,Europe,Links4Soils Geonetwork Node,,Interreg Alpine Space; The Alpine Soil Partnership (AlpSP),https://alpinesoils.eu/geonetwork-node/ ,,,,,,,,No,Yes,The Alpine WebGIS viewer does not contain data itself. It should be used in combination with Link4Soils Geonetwork node: https://alpinesoils.eu/webgis_app/index.html ; See also: https://alpinesoils.eu/soil-info/national-gis/,
88,Data Portal,Multiple,1,,x,,,,,,,Global,Long Term Ecological Research (LTER) Network EDI Data Portal,,US National Science Foundation (NSF),https://portal.edirepository.org/nis/home.jsp#/ ,frank.davis@nceas.ucsb.edu,,,,,,,No,No,"The datasets extend beyond mountains, however some might be relevant for mountainous areas. See also: https://environmentaldatainitiative.org/# ; https://portal.edirepository.org/nis/browse.jsp ; https://nwt.lternet.edu/data-catalog",
89,Data Portal,In Situ,1,,x,,,,,,,Oceania,Long Term Ecological Research Network (LTERN),,Australian Government; National Collaborative Research Infrastructure Strategy; Super Science Initiative,https://openresearch-repository.anu.edu.au/handle/1885/130861/ ,repository.admin@anu.edu.au,,,,,Vegetation species abundancies and extents,,No,No,"Integrates key established plot networks across Australia to tackle critical questions associated with the impacts of disturbance on Australian ecosystems. Formally established in 2012. Visit also: https://www.ltern.org.au; https://openresearch-repository.anu.edu.au/handle/1885/130861 ; The datasets extend beyond mountains, however some might be relevant for mountainous areas.",
90,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Europe,Maps for Europe,,EuroGeographics,https://eurogeographics.org/maps-for-europe/ ,contact@eurogeographics.org,,,,,Topographic data,,No,No,"The datasets extend beyond mountains, however some might be relevant for mountainous areas.",
91,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Europe,Maps of Switzerland,,Swiss Confederation,https://map.geo.admin.ch/,,,,,,,,No,No,See also: https://www.bafu.admin.ch/bafu/en/home/state/data/geodata.html/ ; https://www.geo.admin.ch/en/geo-information-switzerland/geodata-index-inspire.html/,
92,Data Portal,In Situ,2,x,,,,,,,,Global,Meteostat,,National Oceanic and Atmospheric Administration (NOAA); Deutscher Wetterdienst; Environment Canada,https://meteostat.net/en/ ,,,,,,,,No,No,The mountainous subset of stations would have to be identified.,
93,Data Portal,Remotely sensed,,x,x,x,x,,,,x,Global,MODIS Data (Moderate Resolution Imaging Spectroradiometer),,NASA,https://modis.gsfc.nasa.gov/data/ ,,,,,,,,No,No,Some related tools that could also be useful: https://modis.gsfc.nasa.gov/tools/,
94,Data Portal,,,,,,,,,,x,Europe,Montclima Geoportal,,European Regional Development Fund (ERDF); Interreg SUDOE programme,http://www.montclima.eu/en/geoportal/ ,montclima@montclima.eu,,,,,,,No,Yes,"Page concerning the SUDOE regions (Spanish Autonomous Communities), the Southwestern regions of France (Auvergne, Nouvelle Aquitaine, Occitanie), all continental regions of Portugal, United Kingdom (Gibraltar) and the Principality of Andorra).",
95,Data Portal,Multiple,1,x,,,x,,,,,North America,National Water and Climate Center,,National Water and Climate Center (NWCC); United States Department of Agriculture,https://www.nrcs.usda.gov/wps/portal/wcc/home/quicklinks/ ,,,,,,Snow depth; Snow Water Equivalent (SWE); Near-surface air temperature; Total precipitation; River discharge; Near-surface soil moisture,,No,No,Data for the United States. See also: https://wwa.colorado.edu/sites/default/files/2021-10/Snowpack_Monitoring_in_the_Rocky_Mountain_West_A_User_Guide.pdf ; https://www.nrcs.usda.gov/wps/portal/nrcs/main/co/snow/products/ ; https://www.cbrfc.noaa.gov/station/sweplot/sweplot2.cgi???open/ ; https://www.ncdc.noaa.gov/snow-and-ice/daily-snow/AL/snowfall/20211225/ ; https://data.cocorahs.org/cocorahs/maps/ ; https://www.nohrsc.noaa.gov/interactive/html/map.html/ ; https://snodas.cdss.state.co.us/app/index.html/ ; https://climate.arizona.edu/snowview/ ; https://www.cbrfc.noaa.gov/lmap/lmap.php?interface=snow/ ; https://www.nrcs.usda.gov/wps/portal/wcc/home/dataAccessHelp/,
96,Data Portal,In Situ,1,,,,x,,,,,North America,National Water Information System,,USGS,https://waterdata.usgs.gov/nwis/ ,,,,,,,,No,No,See also GAUGES-II: https://www.sciencebase.gov/catalog/item/59692a64e4b0d1f9f05fbd39/,
97,Data Portal,Multiple,1,,,,,,,,,Europe,Naturgefahren,,"Federal Ministry, Republic of Austria",https://www.naturgefahren.at/,,,,,,,,No,No,Austrial dataset. Various information on natural hazards,
98,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Global,NCEI environmental data,,National Centers for Environmental Information (NCEI); NOAA,https://www.ncei.noaa.gov/access/ ,ncei.info@noaa.gov,,,,,,,No,No,"Provides access to NCEI's land-based (in situ) datasets that are developed from data collected across the United States and globally. Data availability varies by data type and station, and some have periods of record of more than a century. Of course, the mountainous subset of stations would have to be identified. See also: https://www.ncei.noaa.gov/products/land-based-station",
99,Data Portal,In Situ,1,,x,,,,,,,North America,NEON Data Products,,US National Science Foundation (NSF),https://data.neonscience.org/data-products/explore/ ,,,,,,,,No,No,"The datasets extend beyond mountains, however some might be relevant for mountainous areas.",
100,Data Portal,In Situ,2,,x,,,,,,,Global,Neotoma Paleoecology Database,,National Science Foundation NSF; World Data System,https://apps.neotomadb.org/explorer/ ,neotoma-contact@googlegroups.com,,,,,,,Yes,No,,
101,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Global,NextGEOSS DataHub,,Global Earth Observation System of Systems (GEOSS),https://catalogue.nextgeoss.eu/,,,,,,,,Yes,No,"Not all data are mountain specific, but much is. Also, some of the resources relate to paleoscience.",
102,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,North America,NOAA Western Region Climate Service Providers Database,,WRCC; NOAA; Others,https://wrcc.dri.edu/ClimSvcProviders/ ,wrcc@dri.edu,,,,,,,No,No,"The datasets extend beyond mountains, but may be relevant for mountainous areas.",
103,Data Portal,Remotely sensed,1,x,x,x,x,,x,x,x,Global,NoR Discovery Portal,,European Space Agency (ESA),https://nor-discover.cloudeo.group/,nor-support@cloudeo.group,,,,,,,No,No,"The datasets extend beyond mountains, but some may be relevant for mountainous areas.",
104,Data Portal,Multiple,1,,,,,x,,,,North America,Northwest Territories Geological Survey (NTGS) Database,,Government of Northwest Territories,https://app.nwtgeoscience.ca/,NTGS@gov.nt.ca,,,,,,,No,No,See also: http://c94000.eos-intl.net/C94000/OPAC/Search/AdvancedSearch.aspx/,
105,Data Portal,Multiple,1,x,x,x,,x,,,,Global,Open Data Portal,,European Space Agency (ESA),https://climate.esa.int/en/odp/#/dashboard/ ,,,,,,,,No,No,,
106,Data Portal,Remotely sensed,1,x,x,x,x,,,,x,Global,Open Digital Elevation Model (OpenDEM),,Open DEM Project,https://www.opendem.info/index.html/ ,contact@OpenDEMData.info,,,,,Topographic data,,No,No,"The datasets extend beyond mountains, but may be relevant for mountainous areas.",
107,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,North America,Open Government Portal - Statistics Canada,,Government of Canada,https://open.canada.ca/en/open-data/ ,open-ouvert@tbs-sct.gc.ca,,,,,,,No,No,See also: https://open.canada.ca/en/search/inventory/ ; https://open.canada.ca/en/open-maps/ ; https://www150.statcan.gc.ca/n1/en/type/data?MM=1/ ; https://www150.statcan.gc.ca/n1/en/type/analysis?MM=1/,
108,Data Portal,Multiple,1,,,,,,x,x,,Global,Statistics of the Department of Economic and Social Affairs,,United Nations Statistics Division,https://unstats.un.org/UNSDWebsite/,statistics@un.org,,,,,,,No,No,"The datasets extend beyond mountains, however some might be relevant for mountainous areas.",
109,Data Portal,Multiple,1,x,x,x,x,x,,,,Global,Open Topography - Data Catalog,,US National Science Foundation (NSF),https://portal.opentopography.org/datasets/ ,,,,,,Topographic data,,No,No,"See also: https://portal.opentopography.org/dataCatalog; https://opentopography.org/otsoftware ; Not mountain specific, but may be useful for mountain applications.",
110,Data Portal,Multiple,1,,,,,,x,x,,Europe,opendata.swiss,,Swiss Federal Statistical Office,https://opendata.swiss/en/dataset/ ,opendata@bfs.admin.ch,,,,,,,No,No,,
111,Data Portal,In Situ,1,x,,,,,,,,Global,OSCAR - Surface,,WMO (WIGOS),https://oscar.wmo.int/surface/#/ ,,,,,,,,No,No,"The datasets extend beyond mountains, however some may be relevant for mountainous areas.",
112,Data Portal,Multiple,2,x,,,,,,,,Global,PaleoClim,,Leeds University; The City College of New York; Southern Illinois University-Carbondale,https://www.paleoclim.org/,paleoclim@gmail.com,,,,,,,Yes,No,"A source of free, high-resolution gridded paleoclimate data for use in biological modelling and GIS. Citation: https://doi.org/10.1038/sdata.2018.254 ; Visit also: https://www.paleoclim.org/links/",
113,Data Portal,In Situ,2,x,x,x,x,x,,,,Global,Paleoclimatology / Paleo Data,,"National Centers for Environmental Information (NCEI), National Oceanic and Atmosphere Administration (NOAA)",https://www.ncei.noaa.gov/products/paleoclimatology/ ; https://www.ncei.noaa.gov/access/paleo-search/?dataTypeId=18/ ,paleo@noaa.gov,,,,,,,Yes,No,"Offers search and download of Paleoclimatic proxy data and Paleoclimate Reconstructions from the NOAA/World Data Service for Paleoclimatology archives. Over 10,000 data sets are available, derived from natural sources such as tree rings, ice cores, corals, and ocean and lake sediments. Many filtering options. Data files for the entire result set, or a subset of it, can be downloaded as a single compressed file.",
114,Data Portal,In Situ,1,,,x,,,,,,Europe,PERMOS Data Portal,,MeteoSwiss; FOEN; SCNAT (Swiss Academy of Sciences),https://www.permos.ch/data.html/ ; https://newshinypermos.geo.uzh.ch/app/DataBrowser/ ,office@permos.ch,,,,,Active layer thickness; Ground temperature,https://dx.doi.org/10.13093/permos-2021-01/ ,No,Yes,Swiss permafrost data.,
115,Data Portal,Remotely sensed,1,x,x,x,x,x,,,,Global,PRISMA mission - Hyperspectral Precursor of the Application Mission,,Italian Space Agency (ASI),https://prismauserregistration.asi.it/,,,,,,,,No,No,"Not mountain specific, but may be useful for mountain applications.",
116,Data Portal,Multiple,1,x,,,,,,,,Global,PSL (Physical Sciences Laboratory) Data and Imagery,,NOAA Physical Sciences Laboratory,https://psl.noaa.gov/data/ ,webmaster.psl@noaa.gov,,,,,,,No,No,,
117,Data Portal,In Situ,2,x,x,x,x,x,,,,Global,Query Datasets on LinkedEarth,,Linked Paleo Data (LiPD),https://lipd.net/query,,,,,,,,Yes,No,"Related publication: https://doi.org/10.5194/cp-12-1093-2016/ ; The datasets extend beyond mountains, but some may be relevant for mountainous areas.",
118,Data Portal,Modelled,2,x,,,,,,,,Global,Reanalyses.org,,Atmospheric Circulation Reconstructions over the Earth initiative; Global Climate Observing System (GCOS) Working Group on Surface Pressure; World Climate Research Programme; NOAA,https://reanalyses.org/,,,,,,,,No,No,"Visit also: https://reanalyses.org/observations/international-surface-pressure-databank/ ; These resources and tools extend beyond mountains, but might be relevant for mountainous areas.",
119,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Asia,Regional Database System,,ICIMOD,https://rds.icimod.org/,rds@icimod.org,,,,,,,No,Yes,"See also: https://rds.icimod.org/dataexplorer/ ; https://rds.icimod.org/TemporalTIff?id=snow/ ; https://rds.icimod.org/clim/ ; https://www.icimod.org/initiative/mountain-geoportal/ ; General repository for documents, reports, journal articles, theses, photos, multimedia, etc.: https://lib.icimod.org;",
120,Data Portal,Multiple,1,,,,x,,,,,Global,Resource watch datasets,,World Resources Institute (WRI),https://resourcewatch.org/data/explore/ ,,,,,,,,No,No,"The datasets are not mountain specific, but may be useful for mountainous applications. Visit also: https://resourcewatch.org/data/pulse/",
121,Data Portal,In Situ,1,x,x,x,x,x,x,x,x,Africa,SAEON Data Portal,,South African Environmental Observation Network (SAEON); National Research Foundation (NRS),https://catalogue.saeon.ac.za/,tim@saeon.ac.za,,,,,,,No,No,"Resources concern South Africa. See also: https://www.sasdi.net/search.aspx?guid=5bb81c7b-15e1-1be1-df7a-0eb437683c76&noframe=true&tab=Analysis/ ; The datasets extend beyond mountains, but may be relevant for mountainous areas.",
122,Data Portal,In Situ,1,,x,,,,,,,Global,SAPFLUXNET: A global database of sap flow measurements,version 0.1.5,CREAF; others,https://sapfluxnet.creaf.cat/,sapfluxnet@creaf.uab.cat,,,,,,https://doi.org/10.5281/zenodo.3971689/ ,No,No,"The dataset is global (extends beyond mountains), however it might be relevant for mountainous areas.",
123,Data Portal,Multiple,1,x,,x,x,,,,,North America,SCENIC (Southwest Climate and ENvironmental Information Collaborative),,Western Regional Climate Center (WRCC); Southwest Climate Science Center (SWCSC),https://wrcc.dri.edu/csc/scenic/data/ ,scenic@dri.edu,,,,,,,No,No,,
124,Data Portal,Multiple,1,x,x,x,x,x,x,,,Europe,Scotland's environment,,Scottish Environment Protection Agency (SEPA),https://www.environment.gov.scot/data/data-analysis/ ,seweb.administrator@sepa.org.uk,,,,,,,No,No,"Data analysis applications present data in an interactive format of graphs, tables and maps. Export data views as images, CSV files and PDF documents for use in reports and presentations. The datasets extend beyond mountains, however they might be relevant for mountainous areas.",
125,Data Portal,In Situ,1,,,x,,,,,,Asia,Service d'Observation GLACIOCLIM,,Observatoire des Sciences de l'UniversitÈ de Grenoble (OSUG); Institut des GÈosciences de l'Environnement (IGE),https://glacioclim.osug.fr/-Descriptif-des-mesures,,,,,,Glacier extent; Glacier mass balance; Glacier surface elevation; Snow depth; Near-surface air temperature; Near-surface wind speed and direction; Precipitation partitioning (solid vs. liquid); Total precipitation; River discharge,,No,Yes,,
126,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Asia,SERVIR Hindu Kush Himalaya,,ICIMOD,https://servir.icimod.org/,,,,,,,,No,Yes,Focus on Hindu Kush Himalaya.,
127,Data Portal,Multiple,1,,,,,,x,,,Global,Socioeconomic Data and Applications Center (SEDAC),,NASA's Earth Observing System Data and Information System; CIESIN at Columbia University,https://sedac.ciesin.columbia.edu/data/sets/browse/ ,,,,,,,,No,No,"Not mountain specific, but may be useful for mountain applications. This page also provides some mapping tools: https://sedac.ciesin.columbia.edu/maps/tools",
128,Data Portal,In Situ,1,,x,,x,,,,,Global,SoilGrids 2.0,,International Soil Reference and Information Centre,https://www.isric.org/explore/soilgrids/ ,,250 m,,,,,https://doi.org/10.5194/soil-7-217-2021/ ,No,No,"Outputs are soil properties at six standard depths. The datasets are global (extend beyond mountains), but may be relevant for mountainous areas.",
129,Data Portal,In Situ,1,,x,,,,,,,Global,SoilTemp database,,Mountain Invasion Research Network (MIREN),https://onlinelibrary.wiley.com/doi/10.1111/gcb.15123/ ,jonas.lembrechts@uantwerpen.be,,,,,Near-surface air temperature,https://doi.org/10.6084/m9.figshare.12126516/ ,No,No,Citation: https://doi.org/10.1111/gcb.15123/; See also: https://soiltemp.weebly.com/data-products.html/ ; https://www.mountaininvasions.org/publications/,
130,Data Portal,In Situ,1,x,x,,x,x,,,,Europe,Sonnblick Observatory (SBO) Data Portal,,Sonnblick Observatorium,https://data.sonnblick.net/,,,,,,,,No,Yes,A remote measuring and research station in the Austrian Alps established in 1886. See corresponding entry in the GEO Mountains In Situ Inventory. Visit also: https://www.sonnblick.net/en/data/download-portal/sbo-data-portal/,
131,Data Portal,In Situ,1,x,,,,,,,,Europe,Spatial Climate Analyses,,MeteoSwiss,https://www.meteoswiss.admin.ch/home/climate/swiss-climate-in-detail/raeumliche-klimaanalysen.html/ ,,,,,,Cloud cover/fraction; Near-surface air temperature; Total precipitation; Surface ERB Longwave; Surface ERB Shortwave,,No,No,Resource concerns Switzerland.,
132,Data Portal,Multiple,1,x,x,x,,x,x,,,Europe,Swiss Data Cube - GeoServer,GeoServer Version 2.18.1,UNEP/GRID-Geneva; University of Geneva; Swiss Federal Office for the Environment (FOEN),https://geoserver.swissdatacube.org/geoserver/web/wicket/bookmarkable/org.geoserver.web.demo.MapPreviewPage?1&filter=false,,,,,,,,No,No,"The GeoServer is the main page, see also: https://www.swissdatacube.org/viewer/ ; https://www.swissdatacube.org/index.php/products/",
133,Data Portal,Remotely sensed,1,,x,,,,,,,Europe,Swiss EnvEO,,Swiss Datacube,https://geonetwork.swissdatacube.org/geonetwork/srv/eng/catalog.search#/home/ ,,,,,,Topographic data; Vegetation species abundancies and extents,,No,No,"Provides access to many datasets e.g. NDWI, NDVI (annual and seasonal means), Sentinel 1, 2 Analysis Ready Data, Landsat 5, 7, 8 Analysis Ready Data for Switzerland.",
134,Data Portal,Remotely sensed,1,x,x,x,x,,,,,Global,The Essential Climate Variables (ECV) Inventory,v4.0,Commitee on Earth Observation Satellites (CEOS); Coordination Group for Meteorological Satellites (CGMS),https://climatemonitoring.info/ecvinventory/ ,ecv_inventory@eumetsat.int,,,,,Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for adaptation; Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for modeling; Aerosol extinction coefficient; Aerosol Optical Depth (AOD); Single-scattering albedo; Aerosol layer height; BHR albedo for adaptation; DHR albedo for modeling; Cloud cover/fraction; Cloud top pressure; Effective particle radius (liquid and ice); Cloud water path (liquid and ice); Glacier extent; Glacier surface elevation; Snow covered area / fraction (SCA/F); Snow Water Equivalent (SWE); Top of atmosphere ERB longwave; Top of atmosphere ERB shortwave (reflected); Latent heat flux; Sensible heat flux; Tropospheric column CH4; Tropospheric column CO2; Groundwater levels; Water surface temperature; Land cover; Land surface temperature; Near-surface wind speed and direction; Total column ozone; Precipitation partitioning (solid vs. liquid); Total precipitation; CO tropospheric column (supporting the Aerosol and Ozone EMCVs); CO tropospheric profile (supporting the Aerosol and Ozone EMCVs); Near-surface soil moisture; Surface ERB Longwave; Surface ERB Shortwave; Downward longwave radiation flux; Downward shortwave radiation flux; Upward longwave radiation flux; Upward shortwave radiation flux; Tropospheric temperature profile; Total column water vapor; Tropospheric and lower stratospheric profiles of water vapor; Upper tropospheric humidity,,No,No,"Not mountain specific, but may be useful for mountain applications. See our work on Essential Mountain Climate Variables: https://doi.org/10.1016/j.oneear.2021.05.005",
135,Data Portal,Multiple,1,,x,,,,,,,Global,The Freshwater Ecosystems Explorer,,United Nations Environment Programme (UNEP),https://www.sdg661.app/downloads/ ,sdg661@un.org,,,,,,,No,No,"Provides the ability to download available time series data for all administrative areas and basins level 6, including five-year rolling annual averages which can be used to track long-term change in water-related ecosystems. Data are provided as both shapefile and GeoTiff formats.",
136,Data Portal,Other,1,,,,,,x,,,Global,The GDELT Project,,The GDELT Project,https://www.gdeltproject.org/,kalev.leetaru5@gmail.com,,,,,,,No,No,"Extends beyond mountains, but could be useful in some way to mountain research, policy, and practice.",
137,Data Portal,Multiple,1,,,,,,x,x,,Global,The Global Atlas for Renewable energy,,International Renewable Energy Agency (IRENA),https://globalatlas.irena.org/workspace/ ,GARE@irena.org,,,,,,,No,No,See also: https://irena.org/Statistics/,
138,Data Portal,Multiple,1,x,x,,x,,x,x,x,Global,The Inter-Sectoral Impact Model Intercomparison Project,,Potsdam Institute for Climate Impact Research (PIK); International Institute for Applied Systems Analysis (IIASA),https://www.isimip.org/,info@isimip.org,,,,,,,No,No,,
139,Data Portal,In Situ,2,,x,,,,,,,Global,The Paleobiology Database,,NSF; EAR; ICER; DUE,https://paleobiodb.org/,info@paleobiodb.org,,,,,,,Yes,No,"A public database of paleontological data maintained by an international non-governmental group of paleontologists. Users can filter fossil occurrences by time, space, and taxonomy, and display their modern and paleogeographic locations. There are also download options. The data extend beyond mountains but some records may be relevant for mountainous areas.",
140,Data Portal,Multiple,1,,,x,,,,,,North America,The Permafrost Information Network (PIN),,Natural Resources Canada,https://pin.geosciences.ca/,,,,,,,,No,No,,
141,Data Portal,Multiple,1,,,,,,,,,Asia,The Third Pole,,Earth Journalism Network (EJN); The Third Pole; ICIMOD; IWMI; others,https://data.thethirdpole.net/,info@chinadialogue.net,,,,,,,No,No,See also: https://www.thethirdpole.net/en/ ; https://earthjournalism.net/program-updates/open-data-in-asias-water-tower-datathethirdpolenet/,
142,Data Portal,Remotely sensed,1,x,,,,,,,,Global,The World Data Center for Remote Sensing of the Atmosphere,,WMO; GAW,https://wdc.dlr.de/data_products/VIEWER/ ,,,,,,,,No,No,Not mountain specific but may be useful.,
143,Data Portal,Multiple,1,x,,,,,,,,Global,The World Data Centre for Aerosols (WDCA),,WMO,https://www.gaw-wdca.org/,,,,,,,,No,No,Not mountain specific but may be useful.,
144,Data Portal,Multiple,1,x,,,,,,,,Global,The World Data Centre for Greenhouse Gases (WDCGG),,WMO,https://gaw.kishou.go.jp/,,,,,,,,No,No,Not mountain specific but may be useful.,
145,Data Portal,Multiple,1,x,,,,,,,,Global,The World Radiation Data Centre (WRDC),,WMO,http://wrdc.mgo.rssi.ru/wrdc_en_new.htm/ ,,,,,,,,No,No,Not mountain specific but may be useful.,
146,Data Portal,Multiple,1,,,,,,,,,Asia,Tibetan Plateau Data Center (TPDC),,"Institute of Tibetan Plateau Research, CAS",https://data.tpdc.ac.cn/en/ ,data@itpcas.ac.cn,,,,,,,No,No,,
147,Data Portal,Multiple,1,,,,,,x,,,North America,TNM Download (National Maps),v2.0,USGS,https://apps.nationalmap.gov/downloader/ ,,,,,,Topographic data,,No,No,"The datasets extend beyond mountains, but may be relevant for mountainous areas.",
148,Data Portal,Multiple,1,x,,x,,,x,x,,Europe,Trentino-South Tyrol Open Data,,Provincia Autonoma di Bolzano; CIVIS,https://opendatahub.bz.it/,opendata@retecivica.bz.it,,,,,,,No,Yes,Resources concerning the region of Trentino. See also: https://dati.retecivica.bz.it/it/dataset?_groups_limit=0/ ; https://weather.provinz.bz.it/download-data.asp/,
149,Data Portal,Remotely sensed,1,,,,,x,,,,North America,U.S. Interagency Elevation Inventory,,NOAA; USGS,https://coast.noaa.gov/inventory/ ,,,,,,,,No,No,"US Elevation data. The datasets extend beyond mountains, however they might be relevant for mountainous areas.",
150,Data Portal,Multiple,1,,x,,,,,,,North America,UC Natural Reserve System Data Registry,,University of California; National Center for Ecological Analysis and Synthesis (NCEAS),https://knb.ecoinformatics.org/knb/style/skins/nrs/index.jsp/ ,,,,,,Vegetation species abundancies and extents,,No,No,"See also: https://knb.ecoinformatics.org/about/ ; https://ucnrs.org/use-data/ ; Not mountain specific, but may be useful for mountain applications.",
151,Data Portal,Multiple,1,,x,,x,,,,,Europe,UK Centre for Ecology & Hydrology,,UK Centre for Ecology & Hydrology (CEH),https://www.ceh.ac.uk/data/ ,,,,,,,,No,No,,
152,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Global,UN-SPIDER Knowledge Portal (Data Sources),,United Nations,https://www.un-spider.org/links-and-resources/ ,,,,,,,,No,No,"Contains many datasets, some of which may be useful for mountain applications. Note that not all datasets are free / open.",
153,Data Portal,Remotely sensed,1,x,x,x,x,x,,,,Global,USGS Earth Explorer,,USGS,https://earthexplorer.usgs.gov/,,,,,,,,No,No,"The datasets extend beyond mountains, however they might be relevant for mountainous areas. See also: https://www.usgs.gov/sitemap",
154,Data Portal,Remotely sensed,2,x,x,,x,,,,,North America,USGS Geo Data Portal (GDP),,USGS,https://cida.usgs.gov/gdp/ ,gdp@usgs.gov,,,,,,,No,No,"Citation: Blodgett, David L., Nathaniel L. Booth, Thomas C. Kunicki, Jordan I. Walker, and Roland J. Viger. Description and testing of the Geo Data Portal: Data integration framework and Web processing services for environmental science collaboration. No. 2011-1157. US Geological Survey, 2011. See also: https://cida.usgs.gov/gdp/how-to-gdp/ ; Not mountain specific, but could be useful for mountainous applications.",
155,Data Portal,In Situ,1,,,,x,,,,,North America,USGS Water Data for USA,,U.S. Department of the Interior; USGS,https://waterdata.usgs.gov/nwis/ ; https://dashboard.waterdata.usgs.gov/app/nwd/?region=lower48&aoi=default/ ,,,,,,,,No,No,"The datasets extend beyond mountains, but may be relevant for mountainous areas.",
156,Data Portal,Multiple,1,,,,x,,,,,Europe,Vallon de Nant - environmental research catchment,N/A,University of Lausanne; WSL; Others,https://zenodo.org/communities/vdn/ ,bettina.schaefli@giub.unibe.ch,,,,,,,No,Yes,Multiple datasets from a research catchment in the western Swiss Alps.,
157,Data Portal,Multiple,1,x,x,x,x,x,x,x,x,Europe,Visualize Swiss Open Government Data,,Swiss Federal Office for the Environment (FOEN),https://www.visualize.admin.ch/en/ ,,,,,,,,No,No,Swiss Datasets. This tool is still in the test phase. Datasets will be updated and added periodically. Many datasets can be accessed.,
158,Data Portal,Multiple,1,,,,,,x,,,Global,WOCAT SLM Database,,United Nations Convention to Combat Desertification (UNCCD),https://qcat.wocat.net/en/wocat/ ,wocat@cde.unibe.ch,,,,,,,No,No,Global database on sustinable land management,
159,Data Portal,In Situ,1,x,,,,,,,,Global,World Data Centre for Precipitation Chemistry (WDCPC),,WMO,http://wdcpc.org/,manager@qasac-americas.org,,,,,,,No,No,Not mountain specific but may be useful.,
160,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Global,World Environment Situation Room (WESR),,UN Environment Programme (UNEP),https://wesr.unep.org/downloader/ ,,,,,,,,No,No,See also: https://app.mapx.org/?project=MX-5Z8-45E-K4I-SKH-75H&lockProject=true&language=en/ ; https://wesr.unep.org/sdgs/ ; https://www.informea.org/en/,
161,Data Portal,Multiple,1,,x,,,,,,,Global,World Environment Situation Room (WESR): Mountains,,UNEP; UNDP; CBD,https://wesr.unepgrid.ch/?project=MX-IXP-F6R-H5U-GA8-DTT&language=en/ ,Yaniss.Guigoz@unepgrid.ch,,,,,Forest extent; Land cover,,No,Yes,,
162,Data Portal,Multiple,1,,,x,,,,,,Global,World Glacier Monitoring Service (WGMS),,ISC (WDS); IUGG (IACS); UNEP; UNESCO; WMO,https://wgms.ch/data_exploration/ ,wgms@geo.uzh.ch,,,,,Glacier extent; Glacier surface elevation,,No,Yes,"Provides standardized observations on changes in mass, volume, area and length of glaciers with time (Fluctuations of Glaciers), as well as statistical information on the distribution of perennial surface ice in space (World Glacier Inventory). Use requires acknowledgement of the WGMS and/or the original investigators and sponsoring agencies according to the available meta-information.",
163,Data Portal,Multiple,2,x,,,,,,,,Global,WorldClim,Version 2,FAO; WorldClim,https://worldclim.org/,info@worldclim.org,,,,,Near-surface air temperature; Near-surface water vapor; Near-surface wind speed and direction; Total precipitation,,No,No,"Not mountain specific, but may be useful for mountain applications. Citation: https://doi.org/10.1002/joc.5086",
164,Data Portal,Multiple,1,,,x,,,,,,North America,Yukon Permafrost Database,,Government of Yukon; Yukon Geological Survey,https://service.yukon.ca/permafrost/index.html/ ,YGS-surficial@yukon.ca,,,,,,,No,No,,
165,Dataset,Modelled,2,x,,,,,,,,Global,20th Century Reanalysis V3 (20CRv3),V3,NOAA-CIRES-DOE,https://www.psl.noaa.gov/data/gridded/data.20thC_ReanV3.html/ ,psl.data@noaa.gov,,,,,,,No,No,Related publication: https://doi.org/10.1002/qj.776/,
166,Dataset,Modelled,2,,,,,x,,,,Europe,A 3D geological model of a structurally complex Alpine region as a basis for interdisciplinary research,1,University of Neuchatel; University of Lausanne,https://doi.org/10.6084/m9.figshare.c.4130759,james.thornton@unibe.ch,,,,,,https://doi.org/10.6084/m9.figshare.c.4130759.v1/ ,No,Yes,Associated publication: https://doi.org/10.1038/sdata.2018.238/,
167,Dataset,Remotely sensed,1,,,x,,,,,,North America,A cloud-free MODIS snow cover dataset for the contiguous United States,,"University of California, Irvine",https://doi.org/10.6084/m9.figshare.5902381.v4,,,2000,2017,,Snow covered area / fraction (SCA/F),,No,No,Citation: https://doi.org/10.1038/sdata.2018.300/,
168,Dataset,Remotely sensed,1,,,x,,,,,,North America,A comprehensive inventory of perennial snow and ice in Glacier National Park in 2005,,USGS,https://www.sciencebase.gov/catalog/item/5c8152d5e4b09388244762be,dan_fagre@usgs.gov,,2005,2005,,Glacier extent; Snow covered area / fraction (SCA/F),https://doi.org/10.5066/P90F4G50/ ,No,Yes,"Inventory of perennial snow and ice of Glacier National Park, US, in 2005. Citation: https://doi.org/10.5066/P90F4G50/",
169,Dataset,In Situ,2,x,,,,,,,,Global,A global database of Holocene paleotemperature records,,US National Science Foundation; Swiss National Science Foundation; NOAA,https://www.ncei.noaa.gov/access/paleo-search/study/27330/ ,darrell.kaufman@nau.edu,,1981,2016,,,https://doi.org/10.25921/4RY2-G808/ ,Yes,No,"Related publication: https://doi.org/10.1038/s41597-020-0445-3 ; The dataset extends beyond mountains, but some records might be relevant for mountainous areas.",
170,Dataset,Modelled,2,,,,x,,,,,North America,A hydrological simulation dataset of the Upper Colorado River Basin from 1983 to 2019,N/A,Princeton University,https://doi.org/10.1038/s41597-022-01123-w,hoangtran@Princeton.edu,,1983,2019,,,https://doi.org/10.25739/nv2q-ct31/ ,No,Yes,ParFlow model outputs.,
171,Dataset,Modelled,1,x,,x,,,,,,North America,"A long-term, 1-km resolution daily meteorological dataset for modeling and mapping permafrost in Canada",,Natural Resources Canada,https://geoscan.nrcan.gc.ca/starweb/geoscan/servlet.starweb?path=geoscan/fulle.web&search1=R=328114/ ,yu.zhang@canada.ca,,,,,,,No,No,"A blend of several other gridded products (CRU JRA, Princeton, NRCANmet, and PNWAmet). The data is constructed by adding interpolated anomalies (with respect to long-term climatology) from the coarse resolution gridded daily datasets overtop the high-resolution monthly 1km climatologies from WorldClim2. There can be issues with discontinuities at month boundaries using this approach unless the monthly climatologies are properly smoothed to day-of-year climatologies. Another concern is related to the temporal non-stationarity due to the stitching together of different gridded daily datasets in different parts of the 1901- period, and also potential loss of coherence between variables stemming from drawing different variables from different datasets within given blocks of time. Related publication: https://doi.org/10.3390/atmos11121363",
172,Dataset,Multiple,2,x,,,x,,,,,South America,ACToday ENACTS COL Precip Daily merged,v1,IRI   Colombian Met Service (IDEAM),https://iridl.ldeo.columbia.edu/home/.xchourio/.ACToday/.ENACTS/.COL/.Precip/.Daily/.merged_1981-2016/index.html#info/ ,xchourio@iri.columbia.edu,,,,Daily,Precipitation,,No,No,,
173,Dataset,Remotely sensed,1,,,,,,,,,Global,Advanced Microwave Scanning Radiometer - Earth Observing System sensor (AMSR-E),,NSDIC,https://nsidc.org/data/explore-data,,,,,,,,No,No,"This datasets extend beyond mountains, however it can be relevant for mountainous areas.",
174,Dataset,Remotely sensed,Multiple,x,x,x,x,x,x,,x,Global,Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 3 (GDEM 003),,NASA,https://www.earthdata.nasa.gov/topics,,,,,,Topographic data,,No,No,"This site also provides links to many other datasets. Not mountain specific, but may be relevant for mountainous areas. See also: ASTER Water Body Dataset (ASTWBD): https://lpdaac.usgs.gov/products/astwbdv001/",
175,Dataset,Modelled,1,x,,,,,,,,Global,AgCFSR Climate Forcing Dataset for Agricultural Modeling,,NASA,https://data.giss.nasa.gov/impacts/agmipcf/agcfsr/ ,,,,,,,,No,No,Related publication: https://doi.org/10.1016/j.agrformet.2014.09.016/ ; See also: https://data.giss.nasa.gov/impacts/agmipcf/,
176,Dataset,Modelled,2,x,,,,,,,,Global,AgERA5 - Agrometeorological indicators derived from reanalysis,,Copernicus Climate Change Service; ECMWF,https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators?tab=overview/ ,,0.1∞,1979,Present,Daily,,,No,No,Provides daily surface meteorological data as input for agriculture and agro-ecological studies. Is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1∞ spatial resolution.,
177,Dataset,Modelled,1,x,,,,,,,,Global,AgMERRA Climate Forcing Dataset for Agricultural Modeling,,NASA,https://data.giss.nasa.gov/impacts/agmipcf/agmerra/ ,,,,,,,,No,No,Related publication: https://doi.org/10.1016/j.agrformet.2014.09.016/ ; See also: https://data.giss.nasa.gov/impacts/agmipcf/,
178,Dataset,Remotely sensed,1,,,,,x,,,,Global,Open Topography Data Catalog,V3.2,Japan Aerospace Exploration Agency (JAXA),https://portal.opentopography.org/dataCatalog,,30 m,,,,Topographic data,https://doi.org/10.5069/G94M92HB/ ,No,No,"Citation: https://doi.org/10.5069/G94M92HB/ ; The dataset is global (extends beyond mountains), but may be useful for mountainous applications.",
179,Dataset,In Situ,2,x,,,x,,,,,Europe,Alpine gridded monthly precipitation data since 1871 derived from in-situ observations,version 1.1,EU; Copernicus; ECMWF; Climate Change Service,https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-alpine-precipitation?tab=overview/ ,,,,,,Total precipitation,https://doi.org/10.24381/cds.6a6d1bc3/ ,No,Yes,"Dataset is also known as the Long-term Alpine Precipitation Reconstruction (LAPrec). Derived from station observations and provides gridded fields of monthly precipitation for the Alpine region (eight countries), and is provided in two issues. Citation: Copernicus Climate Change Service (C3S) (2021): Alpine gridded monthly precipitation data since 1871 derived from in-situ observations, v1.1, Copernicus Climate Change Service (C3S) Climate Data Store (CDS).",
180,Dataset,In Situ,1,,x,,,,,,,North America,"Alpine vegetation trends in Glacier National Park, Montana 2003-2018",,USGS; Northern Rocky Mountain Science Center,https://www.sciencebase.gov/catalog/item/get/5cb8af43e4b0c3b0065f50dd?files.metadataFirst=true#attached-files-section/ ,,,2003,2018,,Near-surface air temperature; Vegetation species abundancies and extents,https://doi.org/10.5066/P9PX9UVT/ ,No,Yes,Citation: https://doi.org/10.5066/P9PX9UVT/ ; See also: https://www.usgs.gov/centers/norock/science/climate-change-mountain-ecosystems-ccme?qt-science_center_objects=3#qt-science_center_objects/,
181,Dataset,Remotely sensed,1,,,x,,,,,,North America,"An inventory of rock glaciers in the central British Columbia Coast Mountains, Canada, from high resolution Google Earth imagery",,University of Victoria,https://www.tandfonline.com/doi/full/10.1080/15230430.2018.1489026,smith@uvic.ca,,,,,,,No,Yes,,
182,Dataset,In Situ,1,,x,,,,,,,South America,Andean Forest Network (Red de Bosques Andinos),,Consortium for the Sustainable Development of the Andean Ecoregion (CONDESAN); Swiss Agency for Development and Cooperation (SDC),https://redbosques.condesan.org/ ; https://redbosques.condesan.org/categoria-producto/datos/,condesan@condesan.org,,,,,Forest extent; Vegetation species abundancies and extents,,No,Yes,Related publication: https://doi.org/10.1371/journal.pone.0231553/ ; Note that most of the data seem to be only available via the paper.,
183,Dataset,Modelled,1,x,,,,,,,,North America,ANUSPLIN (McKenney),,CFS; Natural Resources Canada,https://cfs.nrcan.gc.ca/projects/3/ ; https://osf.io/uzac9/ ,,,1955,2017,Daily; Pentad; Monthly; 30-year average,,,No,No,"Provides snow depth over Canada. It was generated using ANUSPLIN with a 60 arc-second (approximately 2 km) and with a 300 arc-second (approximately 10 km) Digital Elevation Model (DEM; Lawrence et al, 2008) and station observations from Environment and Climate Change Canada (ECCC) and the National Oceanic and Atmospheric Administration (NOAA).",
184,Dataset,Modelled,2,x,,,,,,,,North America,Arctic CORDEX,,World Climate Research Program (WCRP),https://cordex.org/domains/region-11-arctic/ ,john.cassano@colorado.edu; Annette.Rinke@awi.de,,,,,,,No,No,This page provides an ensemble of RCMs driven by GCM simulations from CMIP5. A table showing the CORDEX data available on the ESGFòis available here. Arctic CORDEX simulations are noted as ARC-22 (for 0.22∞ x 0.22∞ spatial resolution) and ARC-44 (for 0.44∞ x 0.44∞ spatial resolution): http://htmlpreview.github.io/?http://is-enes-data.github.io/CORDEX_status.html,
185,Dataset,In Situ,1,,,,x,x,x,,,Europe,Austrian torrential event catalog,,,https://link.springer.com/article/10.1007/s10346-019-01218-3#Sec14/ ,,,,,,,,No,Yes,"Citation: https://doi.org/10.1007/s10346-019-01218-3/ ; Note that the link to the actual catalogue is not completely clear, but see also: https://doi.org/10.1594/PANGAEA.927584/ ; https://doi.org/10.1016/j.crm.2021.100294/",
186,Dataset,Other,2,,x,,,,,,,Global,Biodiversity Hotspots,version 2016.1,Critical Ecosystem Partnership Fund (CEPF),https://zenodo.org/record/3261807#.YcRduS-B1mD/ ,,,,,,Vegetation species abundancies and extents,https://doi.org/10.5281/zenodo.3261807/ ,No,No,See also: https://www.cepf.net/node/1996 ; https://zenodo.org/record/4311850#.YcRhgC-B1mB ; https://zenodo.org/record/4311831#.YcRh8S-B1mB,
187,Dataset,Multiple,Multiple,,,,x,,,,,North America,CAMELS (Catchment Attributes and Meteorology for Large-sample Studies),,"National Center for Atmospheric Research (NCAR), National Science Foundation, US Army Corps of Engineers Climate Preparedness and Resilience programs",https://ral.ucar.edu/solutions/products/camels/ ,ziady@ucar.edu ; naddor@ucar.edu,,,,,Evapotranspiration; Land cover; Mineralogy; Near-surface air temperature; Precipitation partitioning (solid vs. liquid); Total precipitation; River discharge; Topographic data,https://doi.org/10.5065/D6G73C3Q/ ; https://doi.org/10.5065/D6MW2F4D/ ,No,No,"Two data sets: hydrometeorological time series and catchment attributes. Available for download separately. The datasets extend beyond mountains, but may be useful for mountain applications.",
188,Dataset,Multiple,2,,,,x,,,,,South America,CAMELS-CL,,Center for Climate and Resilience Research (CR2),http://camels.cr2.cl/,camila.alvarez@uach.cl,,,,,,,No,Yes,Concerns Chile. Can be visualised here: http://camels.cr2.cl/ ; Citation: https://doi.org/10.5194/hess-22-5817-2018/,
189,Dataset,Multiple,2,,,,x,,,,,Europe,CAMELS-GB,,University of Bristol,https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9 ,gemma.coxon@bristol.ac.uk,,,,,,,No,No,"Concerns Great Britain. Of course, not all catchments are mountainous. Citation: https://doi.org/10.5194/essd-12-2459-2020",
190,Dataset,Modelled,2,x,,,,,,,,North America,Canadian Downscaled Climate Scenarios - Univariate (CMIP5): CanDCS-U5,,ECCC; Pacific Climate Impacts Consortium (PCIC),https://data.pacificclimate.org/portal/downscaled_gcms/map/ ,,,,,,,,No,No,"CMIP5 simulations statistically downscaled with the BCCAQv2 method over Canada against ANUSPLIN gridded observations. ClimateData (https://climatedata.ca) permits the download of daily data (for each of the 24 climate models) for specific locations in netCDF or CSV format. The site offers also climate indices (annul, seasonal and monthly) based on the daily BCCAQv2 data, as well as the possibility to compute online indices with specific thresholds (the ensemble for indices is represented by the 5th, 25th, 50th, 75th and 95th percentiles). Data for a single point can be downloaded as a CSV or JSON. 24 simulations with 24 models (list of the models): https://climate-scenarios.canada.ca/?page=downscaled-indices-notes#table2 ; See also: https://climate-scenarios.canada.ca/?page=downscaled-indices-data/ ; https://climate-change.canada.ca/climate-data/#/downscaled-data/ ; https://www.canada.ca/en/environment-climate-change/services/climate-change/canadian-centre-climate-services/display-download/technical-documentation-downscaled-climate-scenarios.html ",
191,Dataset,Multiple,2,x,,,,,,,,North America,Canadian Gridded Temperature and Precipitation Anomalies (CANGRD),,CRD; ECCC,https://open.canada.ca/data/en/dataset/3d4b68a5-13bc-48bb-ad10-801128aa6604/ ,ec.ccds.info-info.dscc.ec@canada.ca,,,,,,,No,No,Related publication: https://doi.org/10.1080/07055900.2020.1765728/,
192,Dataset,In Situ,1,,,x,,,,,,North America,Canadian historical Snow Water Equivalent dataset (CanSWE),version 3,Multiple organisations,https://zenodo.org/record/5889352#.YgoVMy-B1mA/ ,vincent.vionnet@ec.gc.ca,,1928,2020,,Snow Water Equivalent (SWE),https://doi.org/10.5281/zenodo.4734371/ ,No,No,Citation: https://doi.org/10.5194/essd-13-4603-2021/,
193,Dataset,Modelled,2,x,,,,,,,,North America,Canadian Regional Deterministic Reforecast System (RDRSv2.1),,CCMEP; ECCC,https://github.com/julemai/CaSPAr/wiki/How-to-get-started-and-download-your-first-data/ ; https://open.canada.ca/data/en/dataset/a9f2828c-0d78-5eb6-a4c7-1fc1219f1e3d/ ,ECWeather-Meteo@ec.gc.ca,,,,,,,No,No,"Related publication: https://doi.org/10.5194/hess-25-4917-2021 ; Selected output variables from RDRS-15 and RDRS-10 are available through the Canadian Surface Prediction Archive (CaSPAr, 2021, https://caspar-data.ca, Mai et al., 2020). The RDRS-15 and RDRS-10 products are called RDRS and RDRS_v2 respectively. Once its production is complete, the version of RDRS-10 covering years 1980?2018 and containing the bug fix for maximum snow density will be available under the name RDRS_v2.1. More details on how to retrieve data from CaSPAr can be found at: https://github.com/julemai/CaSPAr/wiki/How-to-get-started-and-download-your-first-data/ ; (Mai, 2021). The list of variables available can also be found under Mai (2021). Additional variables, including upper-air fields, are available from CCMEP upon demand (dps-client@ec.gc.ca).",
194,Dataset,Modelled,2,x,,,,,,,,North America,CanLEAD-CanESM2 from Canadian Centre for Climate Modelling and Analysis (ECCC),,ECCC; Canadian Sea Ice and Snow Evolution Network (CanSISE) Climate Change and Atmospheric Research (CCAR) Network project,http://crd-data-donnees-rdc.ec.gc.ca/CDAS/products/CanLEADv1/ ,open-ouvert@tbs-sct.gc.ca,,,,,,,No,No,"Canadian Earth System Model Large Ensembles (CanESM2 LE) is statistically downscaled over North America using a multivariate bias adjusted (MBCn method) against two target datasets: S14FD (Iizumi et al., 2017) and EWEMBI, (Lange, 2018). Two ensembles, each of them with 50 simulations with one GCM (CanESM2); The difference between the ensembles is the target dataset used in bias correction. See also: https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c/ ; Related publication: https://doi.org/10.5194/tc-12-1137-2018/",
195,Dataset,Modelled,2,x,,,,,,,,North America,CanLEAD-CanRCM4 from Canadian Centre for Climate Modelling and Analysis (ECCC),,ECCC; Canadian Sea Ice and Snow Evolution Network (CanSISE) Climate Change and Atmospheric Research (CCAR) Network project,http://crd-data-donnees-rdc.ec.gc.ca/CDAS/products/CanLEADv1/ ,open-ouvert@tbs-sct.gc.ca,,,,,,,No,No,"Canadian Earth System Model Large Ensembles (CanESM2 LE) is dynamically downscaled over North America using the Canadian Regional Climate (CanRCM4 LE), which is next statistically downscaled using a multivariate bias adjusted (MBCn method) against two target datasets: S14FD (Iizumi et al., 2017) and EWEMBI, (Lange, 2018). Two ensembles, each of them with 50 simulations with one RCP driven by a different member of CanESM2 LE; The difference between the ensembles is the target dataset used in bias correction. See also: https://open.canada.ca/data/en/dataset/83aa1b18-6616-405e-9bce-af7ef8c2031c/ ; https://doi.org/10.1007/s00382-017-3580-6/",
196,Dataset,Multiple,Multiple,,,x,,,,,,Global,CanSISE Observation-Based Ensemble of Northern Hemisphere Terrestrial Snow Water Equivalent,version 2,US National Snow and Ice Data Center (NSIDC); NASA,https://nsidc.org/data/NSIDC-0668/ ,,1∞,1981,2010,,Snow Water Equivalent (SWE),https://doi.org/10.5067/96ltniikJ7vd/ ,No,No,Daily gridded terrestrial snow water equivalent (SWE) dataset based on five component SWE products (Northern Hemisphere). Related publication: https://doi.org/10.1175/JCLI-D-15-0229.1/,
197,Dataset,Multiple,1,,,x,,,x,,,Europe,Carta storica della Valanghe,,Regione Abruzzo,http://opendata.regione.abruzzo.it/content/carta-storica-della-valanghe/ ,,,1957,2013,,,,No,Yes,Italian avalanche inventory (Abruzzo region).,
198,Dataset,Multiple,2,,,x,,,,,,Asia,Caucasus glacier mass balance and thickness changes in 2000-2019,version 2,DEGLaciation dans le grAnd Caucase DEGLAC Project,https://zenodo.org/record/5817232#.YgqeQS-B1mB/ ,tielidzelevan@gmail.com,,2000,2019,,Glacier mass balance; Glacier surface elevation,https://doi.org/10.5281/zenodo.5816997/ ,No,Yes,Related publication: https://doi.org/10.3390/atmos13020256/,
199,Dataset,Remotely sensed,2,,,x,,,,,,Asia,Central and Eastern Himalaya glacier velocities 2017-2019,Version 1,"Bell Edwards Geographic Data Institute (BEGIN), University of St. Andrews",https://zenodo.org/record/4537289#.Ye02iy-B1mA/ ; https://risweb.st-andrews.ac.uk/portal/en/datasets/central-and-eastern-himalaya-glacier-velocities-20172019-sentinel-2(4f84263e-3dd8-46b3-b55b-1aec2d5a6ff4).html/ ,research-data@st-andrews.ac.uk,,2017,2019,,Glacier extent; Median glacier surface velocity,https://doi.org/10.5281/zenodo.4537289/ ,No,Yes,"Citation: https://doi.org/10.5281/zenodo.4537289/ ; For other resources data from St. Andrews University, see: https://risweb.st-andrews.ac.uk/portal/en/persons/tobias-bolch(9338aeea-7cb7-48a1-957b-14b3cca14852)/datasets.html",
200,Dataset,Remotely sensed,2,x,,,,,,,,Global,CEOS Analysis-Ready Datasets,,Committee on Earth Observation Satellites (CEOS),https://ceos.org/ard/index.html#datasets,,,,,,,,No,No,"The dataset is global (extends beyond mountains), however it might be relevant for mountainous areas.",
201,Dataset,Modelled,2,x,,,,,,,,Global,CFSR - Climate Forecast System Reanalysis   CFSv2,,NOAA; NCEP,https://www.ncei.noaa.gov/products/weather-climate-models/climate-forecast-system#pagetop/ ,ncei.info@noaa.gov,,,,,,,No,No,Related publication: https://doi.org/10.1175/JCLI-D-12-00823.1/,
202,Dataset,Multiple,2,x,,,,,,,,Global,CHELSA,Version 2.1,,https://chelsa-climate.org/,,1 km,,,Daily; Monthly; Annual,,,No,No,Download from here: https://chelsa-climate.org/downloads/,
203,Dataset,Multiple,2,,,x,,,,,,North America,Circum-Arctic Map of Permafrost and Ground-Ice Conditions,Version 2,National Snow and Ice Data Center (NSIDC),https://nsidc.org/data/ggd318/ ,nsidc@nsidc.org,,,,,,,No,No,Spatial extent: Northern Hemisphere (20N to 90N),
204,Dataset,Multiple,2,,,x,,,x,,,Global,Circumpolar raster grids of permafrost extent and geohazard potential for near-future climate scenarios,,"Academy of Finland, NSF USA",https://www.nature.com/articles/sdata201937/ ,olli.karjalainen@oulu.fi,,,,,,,No,No,Related article: https://doi.org/10.1038/sdata.2019.37/ ; See also: https://doi.org/10.1038/s41467-018-07557-4/,
205,Dataset,In Situ,2,x,,,,,,,,Global,Climatic Research Unit (CRU): Time-series (TS) datasets of variations in climate with variations in other phenomena v3,,University of East Anglia Climatic Research Unit (CRU),https://catalogue.ceda.ac.uk/uuid/3f8944800cc48e1cbc29a5ee12d8542d/ ,,0.5∞,,,,,,No,No,"Monthly gridded climate data based on weather station data. Citation: University of East Anglia Climatic Research Unit. Jones, P.D. & Harris, I.C. (2008): Climatic Research Unit (CRU): Time-series (TS) datasets of variations in climate with variations in other phenomena v3. NCAS British Atmospheric Data Centre.",
206,Dataset,Remotely sensed,1,x,,,x,,,,,Global,CloudSat,,Colorado State University,https://www.cloudsat.cira.colostate.edu/data-products,haynes@atmos.colostate.edu,,,,Monthly,,,No,No,"Related paper: https://doi.org/10.1029/2008JD009973/ ; Sensitive to low intensity rain and snowfall events. Good high latitude coverage. Only reports rainfall rate over ocean, but reports precipitation occurrence everywhere (land and ocean).",
207,Dataset,Multiple,2,x,,,x,,,,,Global,CMAP - CPC Merged Analysis of Precipitation / Analysis and Observations,,NOAA Climate Prediction Center (CPC),https://psl.noaa.gov/data/gridded/data.cmap.html#detail/ ,,,,,,,,No,No,Related paper: https://doi.org/10.1007/978-1-4020-5835-6_25/,
208,Dataset,Modelled,2,x,,,,,,,,North America,CMIP5 1∞ x 1∞ gridded data,,ECCC,https://dd.weather.gc.ca/climate/cmip5/; https://climate-change.canada.ca/climate-data/#/cmip5-data/ ,,1∞,,,,,,No,No,"A sub ensemble of 29 CMIP5 simulations have been regridded onto a common 1∞ x 1∞ global grid. Provides projected changes and absolute values. 29 simulations with 29 models (list of models): https://www.canada.ca/en/environment-climate-change/services/climate-change/canadian-centre-climate-services/display-download/technical-documentation-coupled-model-intercomparison-phase5.html/ ; ECCC provide access to a subset of variables in netCDF and GeoTIFF formats at monthly, seasonal and annual timescale: surface air temperature, total precipitation, sea ice thickness, sea ice concentration, snow depth, and near-surface wind speed. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles (the 5th, 25th, 50th, 75th and 95th percentiles) across the multi-model ensemble are available for download on https://dd.weather.gc.ca/climate/cmip5/ ; and https://climate-scenarios.canada.ca/?page=gridded-data/ ; (also individual models).",
209,Dataset,Remotely sensed,1,,x,,,,x,,,Global,Copernicus DEM - Global Digital Elevation Model (COP-DEM),,European Space Agency (ESA),https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198/ ,eosupport@copernicus.esa.int,10 m; 30 m; 90 m,,,,Topographic data,,No,No,"A Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. Provided in 3 different resolutions.",
210,Dataset,Modelled,2,x,,,,,,,,Global,Coupled Model Intercomparison Project 5 (CMIP5),,Earth System Grid Federation (ESGF),http://esgf-node.llnl.gov/,,,,,,,,No,No,Ensemble of coupled atmosphere-ocean general circulation models. More than 60 models (the number of models varies with the scenarios and the variable). All of the CMIP5 model output can be accessed through any one of the following Earth System Grid Federation (ESGF) gateways: PCMDI:http://esgf-node.llnl.gov/ ; BADC:http://esgf-index1.ceda.ac.uk/ ; DKRZ:http://esgf-data.dkrz.de/ ; NCI:http://esgf.nci.org.au/ ; http://www.ipcc-data.org/sim/gcm_monthly/AR5/Reference-Archive.html/ ; See also: https://cmip-esmvaltool.dkrz.de/history/result-browser/,
211,Dataset,Modelled,1,x,,,,,,,,Global,CRU CL v.2.0 - A gridded climatology of 1961-1990 monthly means,version 2.0,University of East Anglia Climatic Research Unit (CRU),https://crudata.uea.ac.uk/cru/data/hrg/ ,,,,,,,,No,No,,
212,Dataset,Modelled,2,x,,,,,,,,Global,CRU JRA: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data,version 2.2,University of East Anglia Climatic Research Unit (CRU),https://catalogue.ceda.ac.uk/uuid/4bdf41fc10af4caaa489b14745c665a6/ ,,,,,,,,No,No,"Citable as: University of East Anglia Climatic Research Unit; Harris, I.C. (2021): CRU JRA v2.2: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2020. NERC EDS Centre for Environmental Data Analysis.",
213,Dataset,Modelled,1,x,,,,,,,,Global,CRU TS4.05 - Time-Series (TS) of high-resolution gridded data of month-by-month variation in climate,version 4.05,University of East Anglia Climatic Research Unit (CRU),https://catalogue.ceda.ac.uk/uuid/c26a65020a5e4b80b20018f148556681?search_url=/%3Fq%3DCRU+TS+Version+4+Climatic+Research+Unit&sort_by%3Drelevance&results_per_page%3D20/ ,i.harris@uea.ac.uk,,,,,,,No,No,Related publication: https://doi.org/10.1038/s41597-020-0453-3/,
214,Dataset,Remotely sensed,2,,,x,x,,,,,Global,CryoLand,,Copernicus,http://cryoland.eu/,,,,,,Glacier extent; Snow covered area / fraction (SCA/F),,No,No,See also: https://neso1.cryoland.enveo.at/cryoclient/,
215,Dataset,Remotely sensed,2,,,x,,,,,,Global,C-SNOW,,BELSPO; KU Leuven; Cryosphere Geophysics and Remote Sensing (CRYOGARS),https://ees.kuleuven.be/apps/project-c-snow-data/ ,hans.lievens@kuleuven.be,1 km,,,,,,No,Yes,Short-term project started in 2019 around observing snow depth / mass in the Northern Hemisphere mountain ranges. Related publications: https://doi.org/10.1038/s41467-019-12566-y/ ; https://doi.org/10.5194/tc-16-159-2022/,
216,Dataset,Multiple,2,,,,,x,,,,South America,Database of active and potentially-active continental faults in Chile,,"Millennium Scientific Initiative (ICM), Chilean Government",https://www.nature.com/articles/s41597-021-00802-4/ ,daniel.melnick@uach.cl,,,,,,https://doi.org/10.6084/m9.figshare.13268993.v1/ ,No,Yes,Resource concerns Chile.,
217,Dataset,In Situ,2,x,,,,,,,,Europe,Dataset of daily temperature and precipitation records for Trentino-South Tyrol,,Hydrographic Office of the Autonomous Province of Bolzano and Meteotrentino; DPS4ESLAB,https://essd.copernicus.org/articles/13/2801/2021/essd-13-2801-2021.html/ ,alice.crespi@eurac.edu,,1980,2018,,Near-surface air temperature; Total precipitation,https://doi.org/10.1594/PANGAEA.924502/ ,No,Yes,Citation: https://doi.org/10.5194/essd-13-2801-2021/,
218,Dataset,In Situ,2,x,,,,,,,,North America,Daymet,version 4,NASA; Oak Ridge National Laboratory,https://daymet.ornl.gov/,uso@daac.ornl.gov,1 km,1980,Present,Daily,Snow depth; Near-surface air temperature; Near-surface water vapor; Total precipitation,,No,No,"A multi-variate meteorological dataset for North America, Hawaii, and Puerto Rico. Includes an objective quantification of uncertainty based on strict cross-validation analysis for temperature and precipitation results. Related publication: https://doi.org/10.1038/s41597-021-00973-0/ ; See also: https://daac.ornl.gov/get_data/",
219,Dataset,Modelled,2,x,,,,,,,,Global,ERA5-Land hourly data,,European Centre for Medium-range (ECMWF); Copernicus,https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview/ ,joaquin.munoz@ecmwf.int,,,,,,,No,No,Citation: https://doi.org/10.5194/essd-13-4349-2021/,
220,Dataset,Remotely sensed,1,,x,,,x,,,,Global,ESA WorldCover Data,,European Space Agency (ESA),https://esa-worldcover.org/en/data-access/ ,remotesensing@vito.be,,,,,Land cover; Topographic data,https://doi.org/10.5281/zenodo.5571936/ ,No,No,"See also the World Cover viewer: https://viewer.esa-worldcover.org/worldcover/?language=en/ ; Citation: Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, Linlin, Tsendbazar, N.E., Ramoino, F., Arino, O., 2021. ESA WorldCover 10 m 2020. The datasets extend beyond mountains, but may be relevant for mountainous areas.",
221,Dataset,Remotely sensed,1,,x,,,,,,,Global,Esri 2020 Land Cover,,ESRI,https://www.arcgis.com/home/item.html?id=d6642f8a4f6d4685a24ae2dc0c73d4ac/ ,,10 m,2020,2020,,Land cover,,No,No,"10 classes. Citation: Karra, Kontgis, et al. Global land use/land cover with Sentinel-2 and deep learning. IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021",
222,Dataset,Remotely sensed,1,x,x,x,x,,x,,,Global,Essential Climate Variables,,GCOS; WMO; others,https://gcos.wmo.int/en/essential-climate-variables/table/ ,,,,,,,,No,No,"Links to various individual data sources can be found in this page. Note that where not in situ data, most of these datasets are global. Also see the recent GEO Mountains effort to develop a mountain specific set of ECVs: https://doi.org/10.1016/j.oneear.2021.05.005/",
223,Dataset,Remotely sensed,2,,,,,,x,,,Europe,European Settlement Map,,European Commission Joint Research Centre,https://land.copernicus.eu/pan-european/GHSL/european-settlement-map/ ,,2.5 m,2012,2015,,,,No,No,Human settlement mapping based on SPOT5 and SPOT6 satellite imagery.,
224,Dataset,Remotely sensed,1,x,,,,x,,,,North America,Extremes of summer climate trigger thousands of thermokarst landslides in a High Arctic environment,,University of Ottawa,https://www.nature.com/articles/s41467-019-09314-7/ ,alewkowi@uottawa.ca,,,,,,,No,Yes,Citation: https://doi.org/10.1038/s41467-019-09314-7/,
225,Dataset,Multiple,1,,,,,,,,,Europe,Geoportal of Slovakia,,European Union,https://www.geoportal.sk/en/ ,gku@skgeodesy.sk,,,,,,,No,No,"Provision of reference spatial data, spatial data services and information about spatial reference data.",
226,Dataset,Multiple,1,,,x,,,,,,Europe,Glacier Monitoring in Switzerland (GLAMOS),,Swiss Federal Office for the Environment (FOEN); MeteoSwiss within the framework of GCOS Switzerland; Swiss Academy of Sciences (SCNAT); Swiss Federal Office of Topography swisstopo,https://www.glamos.ch/en/ ,office@glamos.ch,,,,,Glacier extent; Glacier mass balance,,No,Yes,See also: https://www.glamos.ch/en/publications#/B22-01/ ; https://doi.glamos.ch/data/inventory/inventory_sgi2016_r2020.html/,
227,Dataset,Multiple,2,,,x,,,,,,Global,Glacier Thickness Database (GlaThiDa),36528,World Glacier Monitoring Service (WGMS),https://www.gtn-g.ch/data_catalogue_glathida/ ,mail@gtn-g.org,,,,,Glacier extent; Glacier surface elevation,https://dx.doi.org/10.5904/wgms-glathida-2020-10/ ,No,Yes,Links to various other datasets provided on this site.,
228,Dataset,Remotely sensed,1,,,x,,,,,,Global,Glaciers CCI Database,,European Space Agency (ESA),https://glaciers-cci.enveo.at/crdp2/index.html/ ,frank.paul@geo.uzh.ch,,,,,Glacier extent; Glacier surface elevation,,No,Yes,,
229,Dataset,Remotely sensed,1,,,x,,,,,,Global,GLIMS Glacier Database,,US National Snow and Ice Data Center,http://glims.colorado.edu/glacierdata/ ,,,,,,,,No,Yes,,
230,Dataset,Multiple,2,x,x,,,,,,,Global,Global 1-km climate classification maps,,Princeton University,http://www.gloh2o.org/koppen/ ,,1 km,,,,,,No,No,"Global maps of the Kˆppen-Geiger climate classification for the present day (1980-2016) and for projected future conditions (2071-2100). Citation: https://doi.org/10.1038/sdata.2018.214/ ; Not mountin specific, but may be useful in mountain areas.",
231,Dataset,Remotely sensed,1,,x,,,,,,,Global,Global distribution and bioclimatic characterization of alpine biomes,version 2,Marie Curie Clar≠n-COFUND program of the Principality of Asturias-EU; NASA; Google,https://figshare.com/articles/dataset/Global_distribution_and_bioclimatic_characterization_of_alpine_biomes/11710002/2?file=21304323/ ,riccardo.testolin@uniroma1.it,30 m,,,,Vegetation species abundancies and extents,https://doi.org/10.6084/m9.figshare.11710002.v2/ ,No,Yes,A global map of alpine areas above the treeline by modelling regional treeline elevation using global forest cover data and quantile regression. Related publication: https://doi.org/10.1111/ecog.05012/,
232,Dataset,Multiple,1,x,,,,,x,x,,Global,Global Fuel Exploitation Inventory (GFEI),version 2,Harvard University,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HH4EUM,,0.1∞,2010,2019,,,,No,No,Gridded methane emissions data for 2019. Emission grids are also available for 2010-2018. The dataset is extends beyond mountains but might be relevent for some mountain applications.,
233,Dataset,In Situ,1,x,,,,,,,,Global,Global Historical Climatology Network monthly (GHCNm),,National Oceanic and Atmosphere Administration (NOAA); National Centrers for Environmental Information (NCEI),https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-monthly/ ,NCDC.GHCNM@noaa.gov,,,,,Near-surface air temperature; Total precipitation,,No,No,Global climate station data. Citation: https://doi.org/10.1175/JCLI-D-18-0094.1/,
234,Dataset,Remotely sensed,1,,,x,,,,,,Global,Global Land Ice Measurements from Space (GLIMS) Glacier Inventory,,US National Snow and Ice Data Center (NSIDC),https://www.gtn-g.org/data_catalogue_glims/ ,,,,,,Glacier extent; Glacier mass balance; Glacier surface elevation,,No,Yes,See also: https://www.gtn-g.ch/ ; https://glims.colorado.edu/glacierdata/ ; https://www.glims.org/RGI/ ; https://www.gtn-g.ch/data_catalogue/,
235,Dataset,In Situ,1,x,,,,,,,,Global,Global Land Surface Temperature Databank,36526,International Surface Temperature Initiative (ISTI); National Centers for Environmental Information (NCEI); National Oceanic and Atmospheric Administration (NOAA),https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00849 ,ncei.orders@noaa.gov,,,,,,https://doi.org/10.7289/V5PK0D3G/ ,No,No,"Citation: Jared Rennie, Byron E. Gleason, Jay H. Lawrimore, Matthew J. Menne, Claude N. Williams, Peter W. Thorne, and Colin Morice. 2014. International Surface Temperature Initiative (ISTI) Global Land Surface Temperature Databank - Stage 3 Monthly. v1.0.0. NOAA National Centers for Environmental Information. doi:10.7289/V5PK0D3G ; See also: https://www.surfacetemperatures.org/databank ; https://doi.org/10.1002/gdj3.8",
236,Dataset,Multiple,1,,,,,x,x,,,Global,Global Landslide Catalog,,NASA,https://svs.gsfc.nasa.gov/4710/ ,,,2007,2019,,,,No,Yes,Reported landslides triggered by rainfall,
237,Dataset,Multiple,2,,,x,,,,,,Global,Global Permafrost Zonation Index Map,,University of Zurich,http://www.geo.uzh.ch/microsite/cryodata/pf_global/ ,stephan.gruber@carleton.ca,,,,,,,No,No,Citation: https://doi.org/10.5194/tc-6-221-2012/,
238,Dataset,In Situ,1,x,,,x,,,,,Global,Global Precipitation Climatology Center (GPCC),,WMO,http://gpcc.dwd.de/,climate-data@wmo.int,,,,,,,No,No,See also: http://climexp.knmi.nl/select.cgi?gpccall_10/ ; https://opendata.dwd.de/climate_environment/GPCC/PDF/GPCC_intro_products_lastversion.pdf,
239,Dataset,Multiple,2,x,,,x,,,,,Global,Global Precipitation Climatology Project (GPCP) Climate Data Record (CDR),Version 2.3,NASA (GSFC); NOAA,https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00979/ ,ncei.info@noaa.gov,,,,Monthly,,,No,No,"Current version (V2.3) product has been used to assess global precipitation, as a reference dataset for Arctic precipitation, and to study decadal variabiliaty of Arctic precipitation. Considered to be a Climate Data Record. Related publication: https://doi.org/10.3390/atmos9040138/",
240,Dataset,Remotely sensed,2,,,,x,,,,,Global,Global River Classification GloRiC,1,Canadian Network for Aquatic Ecosystem Services CNAES; McGill University,https://hydrosheds.org/page/gloric/ ,bernhard.lehner@mcgill.ca,,,,,,https://doi.org/10.1088/1748-9326/aad8e9/ ,No,No,"Provides hydrologic, physio-climatic, geomorphic classification of river reaches. The dataset comprises 8.5 million river reaches with a total length of 35.9 m km. Related publication: https://doi.org/10.1088/1748-9326/aad8e9/ ; See also HydroBASINS, HydroATLAS etc. at this site. These dataset extends beyond mountains, but might be relevant for mountainous areas.",
241,Dataset,Remotely sensed,2,,,,x,,,,,Global,Global river network dataset accounting for variable drainage density,,NASA; U.S. Army Corps of Engineers' International Center for Integrated Water Resources Management (ICIWaRM),https://springernature.figshare.com/articles/dataset/Metadata_record_for_A_new_vector-based_global_river_network_dataset_accounting_for_variable_drainage_density/13377329/ ,peirongl@princeton.edu,,,,,,https://doi.org/10.6084/m9.figshare.13377329.v1/ ,No,No,"Related publication: https://doi.org/10.1038/s41597-021-00819-9/ ; Not mountain specific, but could be useful for mountainous applications.",
242,Dataset,In Situ,1,x,x,,,,,,,Global,Global Soil Temperature,,Multiple organisations,https://zenodo.org/record/4558663#.YgOUdi-B1mA/ ,Jonas.lembrechts@uantwerpen.be,,,,,Ground temperature,https://zenodo.org/record/4558663#.YgOTnS-B1mA/ ,No,No,Related publication: https://doi.org/10.1111/gcb.16060/ ; Soil bioclim layers SBIO1-11 are also directly available in Google Earth Engine under: projects/crowtherlab/soil_bioclim/soil_bioclim_0_5cm projects/crowtherlab/soil_bioclim/soil_bioclim_5_15cm,
243,Dataset,Remotely sensed,2,,,,,,x,,,Global,Global terrestrial Human Footprint maps for 1993 and 2009,,Wildlife Conservation Society; James Cook University; Australian Research Council,https://datadryad.org/stash/dataset/doi:10.5061/dryad.052q5/ ,,,1993,2009,,,https://doi.org/10.5061/dryad.052q5/ ,No,No,"Citation: https://doi.org/10.1038/sdata.2016.67/ ; Not mountain specific, but may be useful for mountain applications.",
244,Dataset,,,,,,,x,,,,Global,GMBA Mountain Inventory,1.2,Global Mountain Biodiversity Assessment (GMBA),https://ilias.unibe.ch/goto_ilias3_unibe_cat_1000515.html/ ,gmba@ips.unibe.ch,,,,,,,No,Yes,Citation: https://doi.org/10.1007/s00035-016-0182-6/,
245,Dataset,Modelled,1,x,,,,,,,,Global,GMFD - Global Meteorological Forcing Dataset for Land Surface Modeling,,Princeton University,https://rda.ucar.edu/datasets/ds314.0/ ,,,,,,,,No,No,Related publication: https://doi.org/10.1175/JCLI3790.1/,
246,Dataset,,,,,,x,,,x,,Global,GOODD,,King's College London,https://doi.org/10.6084/m9.figshare.c.4648214/ ,mark.mulligan@kcl.ac.uk,,,,,,,No,No,"A global dataset of more than 38,000 georeferenced dams Not mountain specific but may be useful. Citation: https://doi.org/10.1038/s41597-020-0362-5/ ; See also: http://www.globaldamwatch.org/",
247,Dataset,Multiple,1,x,,,x,,,,,Global,GPCP (Daily) - Global Precipitation Climatology Project,,NASA; NOAA,https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-daily/access/ ,,,,,Daily,,,No,No,Daily precipitation product at higher resolution than its companion GPCP Monthly product. Considered to be a Climate Data Record. May have some limitations in the North due to coverage of various datasets. Product is adjusted using GPCP Monthly product to agree climatologically. Reference: https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2/,
248,Dataset,Multiple,2,x,,,,,,,,Global,GPCP (Monthly) - Global Precipitation Climatology Project,Version 3.1,NASA; NOAA,https://measures.gesdisc.eosdis.nasa.gov/data/GPCP/GPCPMON.3.1/ ,,,,,Monthly,,,No,No,"In V3.1, the CloudSat, TRMM, and GPM combined climatology is used to adjust precipitation values, with CloudSat dominating at high latitudes (65N-82N). This may be an improvement of the representation of precipitation in the Canadian North, but there are some inhomogeneities in the record. This is not considered a Climate Data Record like V2.3. Use with some caution. See also: https://docserver.gesdisc.eosdis.nasa.gov/public/project/MEaSUREs/GPCP/GPCP_ATBD_V3.1.pdf",
249,Dataset,Modelled,2,x,,,,,,,,North America,Gridded daily weather data for North america with comprehensive uncertainty quantification,V4,Energy Exascale Earth System Model (E3SM) project; U.S. Department of Energy (DOE); Office of Science; Office of Biological and Environmental Research (BER),https://www.nature.com/articles/s41597-021-00973-0 ,thorntonpe@ornl.gov,1 km,1980,2019,,Snow Water Equivalent (SWE); Near-surface air temperature; Near-surface water vapor; Total precipitation; Surface ERB Shortwave,https://doi.org/10.3334/ORNLDAAC/1840/ ,No,No,"See also: https://daymet.ornl.gov ; The dataset extends beyond mountains, but may be relevant for mountainous areas.",
250,Dataset,Modelled,2,,,,,,x,x,,Global,Gridded global datasets for Gross Domestic Product and Human Development Index,,Aalto University,https://datadryad.org/stash/dataset/doi:10.5061/dryad.dk1j0/ ,matti.kummu@aalto.fi,,1990,2015,,,https://doi.org/10.5061/dryad.dk1j0/ ,No,No,"Citation: https://doi.org/10.5061/dryad.dk1j0/ ; The dataset is global (extends beyond mountains), but it might be relevant for mountainous areas.",
251,Dataset,Remotely sensed,1,,,,,,x,x,,Global,GRIP global roads database,,PBL Netherlands Environmental Assessment Agency,https://www.globio.info/download-grip-dataset/ ,,,,,,,,No,No,Current and future (projected) road infrastructure. Citation: https://doi.org/10.1088/1748-9326/aabd42/,
252,Dataset,Multiple,2,,,x,,,,,,North America,Ground ice map of Canada,,Natural Resources Canada,https://geoscan.nrcan.gc.ca/starweb/geoscan/servlet.starweb?path=geoscan/fulle.web&search1=R=326885/ ,hughbrendan.oneill@canada.ca,,,,,,,No,No,Citation: https://doi.org/10.4095/326885/ ; See also: https://tc.copernicus.org/articles/13/753/2019/,
253,Dataset,Modelled,2,x,,,,,,,,Global,HadISDH - Gridded global surface humidity dataset,,Met Office Hadley Centre,https://www.metoffice.gov.uk/hadobs/hadisdh/ ,,,,,,,,No,No,"This dataset extends beyond mountains, however it can be relevant for mountainous areas.",
254,Dataset,In Situ,1,,,,,,x,,,Global,healthsites,2.0.23-4-g208a40d,healthsites,https://healthsites.io/,mark@healthsites.io,,,,,,,No,No,"A free, curated, global, canonical source of healthcare location data. See also: https://github.com/healthsites/",
255,Dataset,Remotely sensed,1,,,x,,,,,,Asia,High Mountain Asia 8-meter DEM Mosaics Derived from Optical Imagery,Version 1,NASA; National Snow and Ice Data Center (NSIDC),https://nsidc.org/data/HMA_DEM8m_MOS/versions/1/ ,,,2002,2016,,Topographic data,https://doi.org/10.5067/KXOVQ9L172S2/ ,No,Yes,Citation: https://doi.org/10.5067/KXOVQ9L172S2/,
256,Dataset,Multiple,1,,,,,x,,,,Global,High Mountain Asia Landslide Catalog,Version 1.1,NASA; National Snow and Ice Data Center Distributed Active Archive Center,https://nsidc.org/data/HMA_LS_Cat/versions/1/ ,nsidc@nsidc.org,,,,,,https://doi.org/10.5067/5ST0TZCD9RQ3/ ,No,Yes,"An open global landslide inventory with input from citizen scientists. Data include the time and location of various landslide events, as well as event characteristics, such as triggers, the number of fatalities, country of occurrence, and the length and area of the slide.",
257,Dataset,Remotely sensed,1,,,,x,,,,,Global,High Mountain Asia Near-Global Multi-Decadal Glacial Lake Inventory,Version 1,National Snow and Ice Data Center (NSIDC); NASA,https://nsidc.org/data/HMA_GLI/versions/1/ ,,30 m,1990,2018,,Glacial lake extent,https://doi.org/10.5067/UO20NYM3YQB4/ ,No,Yes,"Data provided as ESRI Shapefiles. Associated publication: https://doi.org/10.1038/s41558-020-0855-4/ ; Also see the main page of the NSIDC for many more datasets related to glaciers, snow, ice sheets, permafrost, etc.: https://nsidc.org/data/",
258,Dataset,Remotely sensed,1,,,x,,,,,,Europe,High Resolution Snow & Ice Monitoring,,"European Environment Agency (EEA), Copernicus; European Commission (EC)",https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-snow-and-ice-monitoring/ ,,,,,,Glacier extent; Snow covered area / fraction (SCA/F),,No,No,Provides access to many cryospheric datasets. See also: https://cryo.land.copernicus.eu,
259,Dataset,Multiple,2,,,,x,,,,,Global,HiHydroSoil,v2.0,Future Water,https://www.futurewater.eu/projects/hihydrosoil/ ,g.simons@futurewater.nl,250 m,,,,,,No,No,"See also: https://www.futurewater.nl/wp-content/uploads/2020/10/HiHydroSoil-v2.0-High-Resolution-Soil-Maps-of-Global-Hydraulic-Properties_v2.pdf ; Not mountain specific, but may be useful for mountain applications (reasonable spatial resolution).",
260,Dataset,In Situ,1,,,,x,,,,,North America,HYDAT,,Water Survey of Canada,https://doi.pangaea.de/10.1594/PANGAEA.894885/ ,,,,,,River discharge,,No,Yes,"Canadan dataset. Use in conjunction with HYDEX, which provides information about the stations themselves e.g. location, equipment, and type(s) of data collected. Of course, not all sites drain mountainous areas.",
261,Dataset,Remotely sensed,2,,,,x,,,,,Global,Hydrography90m,,Yale University,https://public.igb-berlin.de/index.php/s/od7neyLcYgi5qRp/ ,giuseppe.amatulli@yale.edu,,,,,,,No,No,"Citation: https://doi.org/10.5194/essd-2022-9/ (pre-print). Not mountain specific, but may do a better job of representing low order streams than other products. See also: https://doi.org/10.5446/56343",
262,Dataset,Multiple,1,x,,x,x,,,,,North America,"Hydrometeorological, glaciological and geospatial research data from the Peyto Glacier Research Basin",Version 02,University of Saskatchewan,https://essd.copernicus.org/articles/13/2875/2021/ ,,,,,,Glacier mass balance; Snow depth; Land cover; Near-surface air temperature; Near-surface wind speed and direction; Total precipitation; Downward longwave radiation flux; Downward shortwave radiation flux; Topographic data,https://doi.org/10.20383/101.0259/ ,No,Yes,Associated publication: https://doi.org/10.5194/essd-13-2875-2021/,
263,Dataset,Remotely sensed,1,,,x,,,,,,Global,Ice velocity and thickness of the world's glaciers,,French Centre National d'Etudes Spatiales (CNES); ESA; Copernicus; NASA; Deutsches Zentrum fÅr Luft- und Raumfahrt e.V.,https://www.nature.com/articles/s41561-021-00885-z#/ ,romain.millan@univ-grenoble-alpes.fr,,,,,Glacier extent,https://doi.org/10.6096/1007/ ,No,Yes,Related publication: https://doi.org/10.1038/s41561-021-00885-z/,
264,Dataset,Remotely sensed,2,x,,,x,,,,,Global,IMERG: Integrated Multi-satellitE Retrievals for GPM,,NASA,https://gpm.nasa.gov/data/imerg ; https://gpm.nasa.gov/data/directory/ ,,0.1∞,2000,Present,30 min,Total precipitation,https://dx.doi.org/10.5067/GPM/IMERG/3B-HH/05/ ; https://dx.doi.org/10.5067/GPM/IMERG/3B-MONTH/05/ ,No,No,"The dataset is global (extends beyond mountains), but may be relevant for mountainous areas.",
265,Dataset,Multiple,2,x,,,x,,,,,South America,INPE CPTEC latam CoSch daily rainfall,,Instituto Nacional de Pesquisas Espaciais - INPE (Brazil),https://iridl.ldeo.columbia.edu/SOURCES/.INPE/.CPTEC/.latam/.CoSch/.daily/.rainfall/ ,xchourio@iri.columbia.edu,,2000,Present,Daily,Total precipitation,,No,No,Served from: https://opendap.ccst.inpe.br/,
266,Dataset,In Situ,1,,x,,,,,,,Global,International Long Term Ecological Research (ILTER),,ILTER,https://www.ilter.network/network/global-coverage/ ,,,,,,Vegetation species abundancies and extents,,No,No,General site of the ILTER network. Links to the websites of the 39 member networks.,
267,Dataset,In Situ,1,x,,,,,,,,Global,International Surface Pressure Databank,version 4.7,Multiple organisations,https://rda.ucar.edu/datasets/ds132.2/ ,,,,,,Surface atmospheric pressure,https://doi.org/10.5065/9EYR-TY90/ ,No,No,"Citation: https://doi.org/10.5065/9EYR-TY90/ ; The dataset is global (extends beyond mountains), but may be relevant for mountainous areas.",
268,Dataset,Remotely sensed,1,,,x,,,,,,North America,"Inventory of active retrogressive thaw slumps in the Peel Plateau, Northwest Territories",,GNWT - Northwest Territories Geological Survey,https://nwtdiscoveryportal.enr.gov.nt.ca/geoportal/catalog/search/resource/details.page?uuid={4B819ED8-A9C4-44C9-944F-8C17BFB4C48A}/ ,donnamarie_ouellette@gov.nt.ca,,,,,,,No,Yes,,
269,Dataset,Remotely sensed,1,,,x,,,,,,North America,Inventory of active retrogressive thaw slumps on eastern Banks Island,,GNWT - Northwest Territories Geological Survey,https://nwtdiscoveryportal.enr.gov.nt.ca/geoportal/rest/document?f=html&showRelativeUrl=true&id={56ED5C84-AD5C-41BC-AE88-643F19EE37EF}/ ,,,,,,,,No,Yes,,
270,Dataset,Remotely sensed,1,,,x,,,,,,North America,"Inventory of active retrogressive thaw slumps on eastern Banks Island, Northwest Territories",,GNWT - Northwest Territories Geological Survey,https://nwtdiscoveryportal.enr.gov.nt.ca/geoportal/catalog/search/resource/details.page?uuid={7AF49DD1-E64B-4F36-B7F8-6D66298731D5}/ ,donnamarie_ouellette@gov.nt.ca,,,,,,,No,Yes,,
271,Dataset,Remotely sensed,1,,,x,,,,,,Europe,Jamtalferner glacier mass balance,,IGF/OAW,https://doi.pangaea.de/10.1594/PANGAEA.917610,andrea.fischer@oeaw.ac.at,,,,,,https://doi.org/10.5194/tc-15-4637-2021/ ; https://doi.org/10.3389/fenvs.2021.683397/ ; https://doi.org/10.1038/s41598-019-50273-2/ ,Yes,Yes,,
272,Dataset,Modelled,2,x,,,,,,,,Global,JRA55,,Japan Meteorological Agency (JMA),https://rda.ucar.edu/datasets/ds628.0/ ,,,,,,,,No,No,Related paper: https://doi.org/10.2151/jmsj.2015-001/,
273,Dataset,Remotely sensed,2,x,,,,,,,,Global,Kˆppen-Geiger climate classification (KGClim),,Department of Geographical Sciences (GEOG); University of Maryland,https://glass.umd.edu/KGClim/ ,sliang@umd.edu,1 km,,,,Total precipitation; Vegetation species abundancies and extents,For historical climate: https://doi.org/10.5281/zenodo.5347837/ ; For future climate: https://doi.org/10.5281/zenodo.4542076/ ,No,No,KGClim is a new global dataset of historical and future Kˆppen-Geiger climate classification maps and bioclimatic variables. Citation: https://doi.org/10.5194/essd-2021-186/,
274,Dataset,Multiple,1,,,x,,,,,,Europe,L'EnquÍte Permanente sur les Avalanches (EPA),,INRAE,https://www.avalanches.fr/,,,,,,,,No,Yes,Concerns France. Historical chronicle of avlanche events observed at selected sites. See also winter precipitation data here: https://www.avalanches.fr/precipitations-avalanches/,
275,Dataset,In Situ,1,,,,x,,,,,North America,Lake O'Hara alpine hydrological observatory,,Federated Research Data Repository (FRDR),https://essd.copernicus.org/articles/11/111/2019/ ,jehe@ucalgary.ca,,2004,2017,,Snow depth; Snow melt / runoff; Near-surface air temperature; Near-surface wind speed and direction; Total precipitation; Water level (supporting River Discharge); River discharge,https://doi.org/10.20383/101.035/ ,No,Yes,"Citation: https://doi.org/10.5194/essd-11-111-2019, 2019/",
276,Dataset,Remotely sensed,1,,,,x,,,,,Global,Lakes Climate Change Initiative (Lakes_cci),Version 1.1,European Space Agency (ESA),https://catalogue.ceda.ac.uk/uuid/3c324bb4ee394d0d876fe2e1db217378,,,,,,Water color (Lake Water-Leaving Reflectance); Water surface temperature,https://dx.doi.org/10.5285/ef1627f523764eae8bbb6b81bf1f7a0a/ ,No,No,"Citation: https://dx.doi.org/10.5285/ef1627f523764eae8bbb6b81bf1f7a0a/ ; Not mountain specific, but may be useful for mountain applications.",
277,Dataset,Remotely sensed,1,,x,,,,x,,,Global,Land Cover Map,,European Space Agency (ESA),https://maps.elie.ucl.ac.be/CCI/viewer/index.php/ ,,,1992,2019,,Land cover,,No,No,"Not mountain specific, but may be useful for mountain applications.",
278,Dataset,In Situ,1,,x,,,,x,,,Europe,Land Use/Cover Area frame Survey (LUCAS),version 4,European Commission Joint Research Centre (JRC),https://essd.copernicus.org/articles/13/1119/2021/#abstract/ ,raphael.dandrimont@ec.europa.eu,,,,,Land cover,https://doi.org/10.6084/m9.figshare.12382667.v4/ ,No,No,"Citation: https://doi.org/10.5194/essd-13-1119-2021/ ; The dataset is global (extends beyond mountains), but may be relevant for mountainous areas.",
279,Dataset,Remotely sensed,2,,,x,,,,,,North America,Landsat Fractional Snow Covered Area Science Products,,USGS,https://www.usgs.gov/landsat-missions/landsat-fractional-snow-covered-area-science-products/ ,,30 m,,,,Snow covered area / fraction (SCA/F),,No,No,Seems to have only been processed for western US and Alaska at present.,
280,Dataset,In Situ,1,,,x,,,,,,Asia,"Mass balances of Yala and Rikha Samba glaciers, Nepal",,Government of Norway; ICIMOD; Nepal Department of Hydrology and Meteorology; Kathmandu University; Tribhuvan University; others,https://essd.copernicus.org/articles/13/3791/2021/ ; https://lib.icimod.org/record/35285?utm_content=bufferff421&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer/ ,stummd@gmail.com,,2000,2017,,Glacier mass balance,https://doi.org/10.5904/wgms-fog-2021-05/ ,No,Yes,Citation: https://doi.org/10.5194/essd-13-3791-2021/,
281,Dataset,Modelled,2,x,,,,,,,,Global,MERRA-2,Version 2,NASA; Global Modeling and Assimilation Office (GMAO),https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/ ,,,,,,,,No,No,Related publication: https://doi.org/10.1175/JCLI-D-16-0758.1/,
282,Dataset,Multiple,2,x,,,,,,,,Europe,MeteoNet,,MÈtÈo-France,https://meteonet.umr-cnrm.fr/,,,,,,,,No,No,"Goal is to provide a clean and ready-to-use dataset for those who want to practice working with weather data. Sample dataset spans three years and two geographical areas: the north-western and south-eastern quarters of France. Contains station observations, land-sea and relief masks, radar observation, weather models forecasts and satellite data. Not mountain specific, but could provide useful training. Citation: Larvor et al. MeteoNet, an open reference weather dataset by METEO FRANCE, 2020. See also: https://github.com/meteofrance/meteonet/ ; Not mountain specific, but could provide useful training.",
283,Dataset,In Situ,2,x,,,,,,,,North America,Meteorological observations collected during the Storms and Precipitation Across the continental Divide Experiment (SPADE),,Global Water Futures (GWF); Others,https://essd.copernicus.org/articles/13/1233/2021/ ,theriault.julie@uqam.ca,,2019,2019,,Surface atmospheric pressure; Snow depth; Near-surface air temperature; Near-surface water vapor; Near-surface wind speed and direction; Precipitation partitioning (solid vs. liquid); Total precipitation,https://doi.org/10.20383/101.0221/ ,No,Yes,Associated publication: https://doi.org/10.5194/essd-13-1233-2021/,
284,Dataset,Modelled,2,,,,x,,,,,North America,Modelled streamflow data,,PCIC,https://data.pacificclimate.org/portal/hydro_stn_cmip5/map/ ,,,,,Daily,,,No,Yes,The dataset (Observation driven station VIC hydrologic model output) is produced by driving the VIC-GL model with PNWNAmet (Werner et al. 2019) gridded climate data. Detailed description is available in Schoeneberg and Schnorbus (2021). Temporal coverage: historical 1945-2012. GCM driven gridded VIC hydrologic model output is produced by driving the VIC-GL model with BCCAQ statistically downscaled GCMs (Cannon et al. 2015). Detailed description is available in Schoeneberg and Schnorbus (2021). Temporal coverage: historical 1950-2005; Future 2006-2100 for RCP4.5 and RCP8.5 scenarios.,
285,Dataset,Modelled,2,,,x,,,,,,Europe,Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections,,EU; Copernicus; ECMWF; Climate Change Service,https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-tourism-snow-indicators?tab=overview/ ,,,1950,2100,,Snow depth; Snow Water Equivalent (SWE); Near-surface air temperature; Total precipitation,,No,Yes,See also: https://datastore.copernicus-climate.eu/documents/sis-european-tourism/SIS_Tourism_Mountain_Tourism_dataset_description_v2.1.pdf,
286,Dataset,Multiple,2,x,,,x,,,,,Global,MSWEP,v2.2,Princeton University,http://www.gloh2o.org/mswep/ ,,0.1∞,1979,Present,3 hourly,Total precipitation,,No,No,"Not corrected for gauge undercatch. For a corrected dataset, see PBCOR. Citation: https://doi.org/10.1175/BAMS-D-17-0138.1/",
287,Dataset,Multiple,2,x,,,,,,,,Global,Multi-Source Weather (MSWX),,Princeton University,http://www.gloh2o.org/mswx/ ,,0.1∞,1979,Future,3 hourly,Total precipitation; Near-surface air temperature; Surface pressure; Relative Humidity; Specific humidity; Wind speed; Downward shortwave radiation; Downward longwave radiation,,No,No,Includes forecasts 7 months into the future. Is compatible with MSWEP.,
288,Dataset,Modelled,2,x,x,x,x,,,,,Global,NASA Global Land Data Assimilation System (GLDAS-2),version 2,NASA,https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH10_M_2.0/summary/ ,gsfc-dl-help-disc@mail.nasa.gov,,,,,,,No,No,"This dataset extends beyond mountains, however it can be useful for mountainous areas.",
289,Dataset,Multiple,1,,x,,x,,,,,Global,Near-global environmental information for freshwater ecosystems in 1km resolution,,NCEAS; NASA; NSF; Yale University,https://www.earthenv.org/streams/ ,sami.domisch@yale.edu,,,,,Land cover; Near-surface air temperature; Total precipitation; Near-surface soil moisture; Topographic data,https://doi.org/10.1038/sdata.2015.73/ ,No,No,"Associated publication: https://doi.org/10.1038/sdata.2015.73 ; See also: https://data.earthenv.org/streams/ReadMe.txt ; The data providers recommend R to load and process the variables, and provide example code; see also the tutorials at: https://spatial-ecology.net/ ; The dataset is global (extends beyond mountains), but may be relevant for mountainous areas.",
290,Dataset,In Situ,1,,x,,x,,,,,North America,NevCAN: The Nevada Climate-ecohydrological Assessment Network,,NSF; EPSCoR,https://nevcan.dri.edu/data_download.html/ ,scotty@dayhike.net; Greg.McCurdy@dri.edu,,,,,Snow depth; Near-surface air temperature; Near-surface wind speed and direction; Total precipitation; Near-surface soil moisture; Vegetation species abundancies and extents,,No,Yes,"Basin-to-mountain top transect monitoring stations are located in the Snake Range (east central NV along the UT border; approximately 335 km NNE of Las Vegas) and in the Sheep Range (located approximately 35 km NNW of Las Vegas), Nevada. See also corresponding entries in the GEO Mountains In Situ Inventory.",
291,Dataset,Modelled,2,x,x,x,x,,,,,North America,NLDAS (North American Land Data Assimilation System) LSM,,NASA,https://ldas.gsfc.nasa.gov/nldas/NLDAS2model_download.php/ ,,,,,,,,No,No,Multiple disciplines.,
292,Dataset,Modelled,1,x,,,,,,,,Global,NOAA Climate Data Record (CDR) of Northern Hemisphere (NH) Snow Cover Extent (SCE),Version 1,NECI (NOAA Centers for Environmental Information); Rutgers University,https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00756/ ,ncei.info@noaa.gov,,,,,,,No,No,Spatial extent: Northern Hemisphere. Related paper: https://essd.copernicus.org/articles/7/137/2015/essd-7-137-2015.pdf ; See also: http://climate.rutgers.edu/snowcover/,
293,Dataset,Modelled,2,x,,,,,,,,North America,North America CORDEX,,World Climate Research Program (WCRP),https://www.earthsystemgrid.org/search/cordexsearch.html/ ,mcginnis@ucar.edu,,,,,,,No,No,See also: https://na-cordex.org/index.html ; A table showing the CORDEX data available on the ESGF is available here. North America CORDEX simulations are noted as NAM-22 (for 0.22∞ x 0.22∞ spatial resolution) and NAM-44 (for 0.44 x 0.44 deg spatial resolution) : http://htmlpreview.github.io/?http://is-enes-data.github.io/CORDEX_status.html/ ; Citation: https://doi.org/10.5065/D6SJ1JCH/,
294,Dataset,Modelled,2,x,,,,,,,,North America,North America CORDEX Bias-adjusted data against Daymet,,National Center for Atmospheric Research NCAR; NSF,https://www.earthsystemgrid.org/search/cordexsearch.html/ ,,,,,,,,No,No,CORDEX simulations over Norther America bias adjusted using Cannon's MBCn algorithm against Daymet gridded observations. Data in netCDF format can be accessed from the NA-CORDEX search page by selecting mbcn-Daymet in Bias Correction section. CAUTION:òThere is a problem with bias-corrected data in the NA CORDEX archive. Work is in progress to fix it. Details about bias-adjustment methods applied to the CORDEX simulations are provided here: http://is-enes-data.github.io/CORDEX_adjust_add.html/,
295,Dataset,Modelled,2,x,,,,,,,,North America,North American Regional Reanalysis: NARR,,NOAA's National Center for Atmospheric Prediction (NCEP),https://psl.noaa.gov/data/gridded/data.narr.html#detail/ ,psl.data@noaa.gov,,,,,,,No,No,Related publication: https://doi.org/10.1175/BAMS-87-3-343/,
296,Dataset,In Situ,1,,,x,,,,,,South America,Observatorio de Nieve en los Andes de Argentina y Chile,,"Instituto Argentino de Nivolog≠a, Glaciolog≠a y Ciencias Ambientales (IANIGLA-CONICET); Centro de Investigaciõn del Clima y la Resiliencia de Chile",https://observatorioandino.com/estaciones/,ianigla@mendoza-conicet.gob.ar,,,,,Snow covered area / fraction (SCA/F); Snow depth,,No,Yes,"The data come from MODIS (NASA / NSIDC), processed for the Observatory of Snow of the Andes, Argentina and Chile. Provides a visualisation of snow cover in the main watersheds of the subtropical Andes of Argentina and Chile (27-37S) from 2000 onwards. Visit also: https://estaciones.ianigla.mendoza-conicet.gob.ar/",
297,Dataset,In Situ,1,,x,,,,,,,Europe,÷denwinkel: an Alpine platform for research on the emergence of multidiversity and ecosystem complexity,,Austrian Science Fund (FWF),https://we.copernicus.org/articles/20/95/2020/#/ ,robert.junker@uni-marburg.de,,2019,2019,,Vegetation species abundancies and extents,https://doi.org/10.5194/we-20-95-2020-supplement/ ,No,Yes,"Dataset for ÷denwinkelkees glacier (Hohe Tauern National Park, Austria). See also: https://deims.org/activity/fefd07db-2f16-46eb-8883-f10fbc9d13a3",
298,Dataset,Modelled,2,x,,,,,,,,North America,Ouranos ensemble of statistically downscaled CMIP5 data,,Ouranos Inc.,https://pavics.ouranos.ca/datasets.html#a/ ,pavics@ouranos.ca,,,,,,,No,No,"CMIP5 simulations statistically downscaled for North America against ANUSPLIN and Livneh (2015) gridded observations, using a quantile-mapping method. Contains a total of 22 simulations comprising RCP4.5 and RCP8.5 from 11 models. Daily data in netCDF format. See also: https://www.ouranos.ca/wp-content/uploads/FicheLoganGauvin2016_EN.pdf ; https://pavics.ouranos.ca/",
299,Dataset,In Situ,1,x,,,,,x,,,Global,p3k14c,,Past Global Changes (PAGES) project; Projekt DEAL,https://core.tdar.org/collection/70213/p3k14c-data/ ,darcy.bird@wsu.edu,,,,,,,Yes,No,Related publication: https://doi.org/10.1038/s41597-022-01118-7 ; This dataset extends beyond mountains but some records likely relate to mountainous areas.,
300,Dataset,Remotely sensed,1,,,x,,,,,,Europe,Pan-European High-Resolution Snow & Ice products (HR-S&I),,Copernicus,https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-high-4/ ,,20 m,2016,Present,5 days,,,No,No,,
301,Dataset,Multiple,2,x,,,x,,,,,Global,PBCOR,,Princeton University,http://www.gloh2o.org/pbcor/ ,,0.05∞,,,Monthly; Annual,Total precipitation,,No,No,"Bias corrected precipitation, with a specific focus on mountain areas. Citation: https://doi.org/10.1175/JCLI-D-19-0332.1/",
302,Dataset,Multiple,2,,,x,,,,,,Global,Permafrost extent for the Northern Hemisphere,v3.0,European Space Agency (ESA),https://catalogue.ceda.ac.uk/uuid/6e2091cb0c8b4106921b63cd5357c97c/ ,,,,,,,,No,Yes,Citation: http://dx.doi.org/10.5285/6e2091cb0c8b4106921b63cd5357c97c/,
303,Dataset,Multiple,2,,,x,,,,,,North America,Permafrost Map of Canada,,Natural Resources Canada,https://open.canada.ca/data/en/dataset/d1e2048b-ccff-5852-aaa5-b861bd55c367/ ,NRCan.geogratis-geogratis.RNCan@canada.ca,,,,,,,No,No,,
304,Dataset,Multiple,1,x,,,,,,,,North America,PNWNAmet dataset,,Pacific Climate Impacts Consortium (PCIC),https://www.pacificclimate.org/data/daily-gridded-meteorological-datasets/ ,mschnorb@uvic.ca,,,,,,,No,No,Related publication: https://doi.org/10.1038/sdata.2018.299/,
305,Dataset,Multiple,2,x,,,x,,,,,Asia,Rainfall Dataset over the Southern Tibetan Plateau,,National Tibetan Plateau Data Center,https://essd.copernicus.org/articles/13/5455/2021/essd-13-5455-2021.html/ ,tianfq@mail.tsinghua.edu.cn,,2014,2019,Daily,Total precipitation,https://doi.org/10.11888/Hydro.tpdc.271303/ ,No,No,,
306,Dataset,In Situ,1,x,,,,,,,,North America,RAWS USA Climate Archive,,Western Regional Climate Center (WRCC),https://raws.dri.edu/wraws/ ,wrcc@dri.edu,,,,,Near-surface air temperature; Near-surface water vapor; Near-surface wind speed and direction; Total precipitation; Near-surface soil moisture,,No,Yes,Users can access a large amount of station data. For more information see: https://raws.dri.edu/wraws/documents/RAWS.pdf,
307,Dataset,Multiple,2,,,,x,,,,,Europe,Rivers and Catchments of Europe,,European Commission Joint Research Centre,https://data.jrc.ec.europa.eu/dataset/fe1878e8-7541-4c66-8453-afdae7469221/ ,juergen.vogt@ec.europa.eu,,,,,,,No,No,Of course not all catchments are mountainous.,
308,Dataset,Remotely sensed,1,,,,,x,,,x,North America,Rock Avalanches in the Saint Elias Mountains of Alaska,,University of Utah; USGS,https://www.frontiersin.org/articles/10.3389/feart.2020.00293/full#supplementary-material/ ,,,1984,2019,,,,No,No,Citation: https://doi.org/10.3389/feart.2020.00293/ ; See also: https://www.sciencebase.gov/catalog/item/5ec349de82ce476925e8a155/,
309,Dataset,Modelled,1,,,x,,,,,,Global,Rutgers Northern Hemisphere 24 km Weekly Snow Cover Extent,Version 1,Rutgers University (housed at NSIDC),https://nsidc.org/data/G10035/versions/1/ ,nsidc@nsidc.org,,,,,,,No,Yes,Spatial extent: Northern Hemisphere.,
310,Dataset,Modelled,1,x,,,,,,,,Global,S14FD global meteorological forcing dataset,,Data Integration and Analysis System (DIAS),http://search.diasjp.net/en/dataset/S14FD/ ,iizumit@affrc.go.jp,,,,,,https://doi.org/10.20783/DIAS.523/ ,No,No,Related publication: https://doi.org/10.1002/2017JD026613/,
311,Dataset,Multiple,1,,,x,,,x,,,Europe,Satellite avalanche mapping validation data,,SLF,https://www.envidat.ch/dataset/satellite-avalanche-mapping-validation/ ,,,,,,,https://doi.org/10.16904/envidat.202/ ,No,Yes,Swiss dataset (Davos region). Citation: https://doi.org/10.5194/tc-2020-272/,
312,Dataset,Multiple,2,x,,,,,,,,Global,SC-Earth,,Global Water Futures (GWF),https://journals.ametsoc.org/view/journals/clim/34/16/JCLI-D-21-0067.1.xml/ ; https://zenodo.org/record/4762586/ ,guoqiang.tang@usask.ca,,1950,2009,Daily,Near-surface air temperature; Near-surface water vapor; Near-surface wind speed and direction; Total precipitation,,No,No,"Temporally gap-filled station data. Not mountain specific, but may be useful for mountain applications.",
313,Dataset,In Situ,1,,,x,,,x,,,Europe,Selected wet snow avalanche activity data,,,https://www.envidat.ch/#/metadata/selected-wet-snow-avalanche-activity-data-davos-switzerland-2011-2014 ,,,2011,2014,,,10.16904/envidat.39/,No,Yes,Swiss dataset (Davos region). Citation: https://doi.org/10.1029/2017JF004515/,
314,Dataset,In Situ,1,,,,,x,,,,Europe,SGID - Maps and Data,,European Union,https://www.geology.sk/maps-and-data/?lang=en/ ,secretary@geology.sk,,,,,,,No,No,See also: https://www.geology.sk/maps-and-data/?lang=en/,
315,Dataset,In Situ,1,x,,x,,,,,,Europe,"Snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France)",,MÈtÈo-France,https://doi.org/10.5194/essd-11-71-2019/ ,col_de_porte@meteo.fr,,1960,2017,Daily,Snow depth; Snow melt / runoff; Snow Water Equivalent (SWE); Latent heat flux; Sensible heat flux; Near-surface air temperature; Near-surface water vapor; Near-surface wind speed and direction; Precipitation partitioning (solid vs. liquid),http://doi.osug.fr/r/CRYOBSCLIM.CDP.2018.SnowProfile/ ,No,Yes,See also: https://doi.org/10.5194/essd-4-13-2012/ ; Plus. the corresponding entry in the GEO Mountains In Situ Inventory.,
316,Dataset,In Situ,1,x,x,x,,,,,,Europe,"Snow depth, canopy structure and meterorological datasets from the Davos area, Switzerland",,Swiss National Science Foundation SNSF; SLF,https://www.envidat.ch/#/metadata/forest-snow-modelling-davos-2012-2015/ ,giulia.mazzotti@slf.ch,,2012,2015,,Snow depth; Forest extent; Vegetation species abundancies and extents,https://www.doi.org/10.16904/envidat.125/ ,No,Yes,Related publication: https://doi.org/10.1029/2019WR026129/,
317,Dataset,Multiple,1,x,,x,x,,,,,North America,SnowClim Model and Dataset,version 1.0,"Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI); National Science Foundation NSF",https://www.hydroshare.org/resource/acc4f39ad6924a78811750043d59e5d0/ ,aby.lute@gmail.com,,,,,Snow covered area / fraction (SCA/F); Snow depth; Snow Water Equivalent (SWE); Near-surface air temperature; Near-surface water vapor; Total precipitation,,No,No,"Citation: Lute, A. C., J. Abatzoglou, T. Link (2021). SnowClim Model and Dataset, HydroShare, https://www.hydroshare.org/resource/acc4f39ad6924a78811750043d59e5d0/ ; Note that model code is also provided. Related paper: https://doi.org/10.5194/gmd-2021-407/ (pre-print)",
318,Dataset,Modelled,2,,,x,,,,,,Global,SnowModel Pan-Arctic Data,,UCAR/NCAR,https://data.eol.ucar.edu/dataset/106.309/ ,eol-datahelp@ucar.edu,,1979,2009,,,,No,No,Spatial extent: NH land area north of ~55N.,
319,Dataset,Remotely sensed,1,,x,,x,,,,,Global,Soil Moisture CCI,CCI SM v06.1,European Space Agency (ESA); others,https://www.esa-soilmoisture-cci.org/node/93/ ,cci_sm_contact@eodc.eu,,,,,Near-surface soil moisture,,No,No,"Not mountain specific, but may be useful for mountain applications.",
320,Dataset,Remotely sensed,1,,,x,,,x,,,Europe,SPOT6 Avalanche outlines 24 January 2018,,,https://www.envidat.ch/#/metadata/spot6-avalanche-outlines-24-january-2018/ ,,,2018,2018,,,10.16904/envidat.77/,No,Yes,Swiss dataset. Citation: https://doi.org/10.5194/tc-13-3225-2019/,
321,Dataset,Multiple,2,,x,,x,,,,,Global,Synthesis of global actual evapotranspiration,,,https://doi.org/10.7910/DVN/ZGOUED,,1 km,1982,2019,Monthly,Actual evapotranspiration,https://doi.org/10.5194/essd-13-447-2021/ ,No,No,Blended dataset,
322,Dataset,Modelled,2,x,,,,,,,,Global,The Arctic System Reanalysis (ASR),ASR version 2,Byrd Polar Research Center; The Ohio State University; Multiple,https://rda.ucar.edu/datasets/ds631.1/ ,,,,,,,,No,No,"Related publication: https://doi.org/10.1029/2010EO020001/ ; Domain is the Arctic, but may be useful for mountain applications.",
323,Dataset,Multiple,1,,,,,,x,,,Global,The Geocoded Disasters (GDIS) Dataset,,NASA Socioeconomic Data and Applications Center (SEDAC),https://sedac.ciesin.columbia.edu/data/set/pend-gdis-1960-2018/ ,ciesin.info@ciesin.columbia.edu,,1960,2018,,,https://doi.org/10.7927/zz3b-8y61/ ,No,No,"A geocoded extension of a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters' (CRED) Emergency Events Database (EM-DAT). The highest spatial resolution corresponds to administrative level 3 (usually district/commune/village) in the Global Administrative Areas database (GADM, 2018). The vast majority of the locations are administrative level 1 (typically state/province/region). Related publication: https://doi.org/10.1038/s41597-021-00846-6 ; The dataset extends beyond mountains, however it might be relevant for mountainous areas.",
324,Dataset,In Situ,1,,,x,,,,,,Global,The GTN-P global mean annual ground temperature data,,International Permafrost Association,https://pubmed.ncbi.nlm.nih.gov/30651568/ ,boris.biskaborn@awi.de,,,,,Ground temperature,https://doi.org/10.1594/PANGAEA.884711/ ,No,No,Related publication: https://doi.org/10.1038/s41467-018-08240-4/,
325,Dataset,Multiple,2,x,,,,,,,,Asia,The High Asia Refined analysis,v2,TU Berlin,https://www.klima.tu-berlin.de/index.php?show=daten_har2&lan='.$language.'/ ,xun.wang@tu-berlin.de,10 km,1980,2020,Hourly; Daily; Monthly; Yearly,,https://doi.org/10.1002/joc.6686/ ,No,Yes,"Focus on Asia Mountain Ranges, Hindu Kush Himalaya, Tibetan Plateau",
326,Dataset,In Situ,1,x,,,,,,,,Global,The Integrated Surface Dataset (ISD),,National Centers for Environmental Information (NCEI); National Oceanic and Atmospheric Administration (NOAA),https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00532/ ,ncei.info@noaa.gov,,,,Hourly,,,No,No,"This hourly dataset is global (extends beyond mountains), but may relevant for mountainous areas.",
327,Dataset,Remotely sensed,1,,,x,,,,,,Global,The RGI-TOPO dataset,Beta,International Association of Cryospheric Sciences (IACS),https://rgitools.readthedocs.io/en/latest/dems.html#/ ,Matthias.Dusch@uibk.ac.at,,,,,,10.5194/gmd-12-909-2019/,No,Yes,"Should not be used for glacier change assessment: the aim is to provide a baseline for glacier modelling and other data analysis efforts. RGI-TOPO does not generate any new topography data, but rather uses freely available data and interpolates it to a local glacier map. If you make use of these data for a publication, presentation or website, refer to the original data provider as given in the dem_source.txt file found in each DEM folder. Citation: https://doi.org/10.5194/gmd-12-909-2019",
328,Dataset,Remotely sensed,1,,,x,,,,,,Europe,Theia Snow collection,,University of Toulouse,https://doi.org/10.5194/essd-11-493-2019/ ,,,,,,,https://doi.org/10.24400/329360/F7Q52MNK/ ,No,No,,
329,Dataset,Remotely sensed,1,,,,,,x,,,Global,World Settlement Footprint 2015,,German Aerospace Center (DLR); Google; European Space Agency (ESA); MindEarth,https://figshare.com/articles/dataset/World_Settlement_Footprint_WSF_2015/10048412/ ,,10 m,,,,Settlement extent and Urbanisation,https://doi.org/10.6084/m9.figshare.10048412.v1/ ,No,No,Binary mask outlining the 2015 global settlement extent derived by jointly exploiting multitemporal Sentinel-1 radar and Landsat-8 optical satellite imagery. Settlements are associated with value 255; all other pixels are associated with value 0. Citation: https://doi.org/10.1038/s41597-020-00580-5/ ; Likely useful for many mountain applications (high resolution).,
330,Other,Multiple,Multiple,,,,,,,,,Europe,Data for Science,,ENVRIplus,https://www.envriplus.eu/themes/t2/ ,z.zhao@uva.nl,,,,,,,No,No,Not mountain specific. Not clear whether much data can actually be obtained.,
331,Other,Multiple,1,x,,,,,,,,Europe,EUMETNET,,EUMETNET,https://www.eumetnet.eu/,info@eumetnet.eu,,,,,,,No,No,"The datasets extend beyond mountains, however they might be relevant for mountainous areas.",
332,Other,Multiple,1,,,,x,x,,,,Europe,European Geological Data Infrastructure,,EuroGeoSurveys,https://www.europe-geology.eu/,info@eurogeosurveys.org,,,,,,,No,No,"The datasets extend beyond mountains, however some might be relevant for mountainous applications",
333,Other,In Situ,1,,,,,,,,,Europe,European Observatory of Earth Observation Networks,ENEON,European Union,https://www.eneon.net/,eneon@creaf.uab.cat,,,,,,,No,No,"See also: https://www.eneon.net/graph/index.htm/ ; Not immdiately clear how much data can be directly accessed from this page. Also, not mountain specific.",
334,Other,,,,x,x,x,x,,,,Global,Linked Paleo Data (LiPD),,Linked Paleo Data (LiPD),https://lipd.net/,,,,,,,,Yes,No,Provides / aims to provide a framework for interacting with the LiPD data.,
335,Other,Multiple,1,,,,,,x,,x,Global,reliefweb,,United Nations Office for the Coordination of Humanitarian Affairs,https://reliefweb.int/disasters/ ,,,,,,,,No,No,"Summary of ongoing global disaster events, plus associated reports etc.",
336,Other,Remotely sensed,1,,,x,,,,,,North America,Snow Today,,NSIDCM CIRES; INSTAAR; NASA,https://nsidc.org/reports/snow-today/daily_image_updates/ ,,,,,,,,No,No,Provides a snapshot and interpretation of snow conditions in near-real time across the Western United States according to a combination of satellite data and surface observations.,
337,Other,Multiple,1,x,,,,,,,,Global,The KNMI Climate Explorer,,WMO; KNMI,https://climexp.knmi.nl/start.cgi/ ,,,,,,,,No,No,"A research tool to investigate the climate, including a web application to analyse climate data statistically. Lots of climate data and analysis tools provided. See also: https://gitlab.com/KNMI-OSS/climexp/",
338,Software,,,,,,x,,,,,North America,Automated Water Supply Model (AWSM),,USDA Agricultural Research Service,https://awsm.readthedocs.io/en/latest/ ,micah.johnson150@gmail.com ; micah.sandusky@ars.usda.gov,,,,,,https://doi.org/10.5281/ZENODO.3455762/ ,No,No,Visit also: https://www.sciencedirect.com/science/article/pii/S0098300420305598#sec5/ ; https://github.com/USDA-ARS-NWRC/awsm/ ; Test cases are available at: https://doi.org/10.5281/zenodo.3381607/,
339,Software,,,x,,,x,,,,,Global,CLIMADA,,ETH Zurich,https://wcr.ethz.ch/research/climada.html/ ,sarah.spitzauer@usys.ethz.ch,4 km,,,,,,No,No,"Allows users to estimate expected economic damage as a measure of risk today, as well as potential incremental increases due to economic growth and climate change. Provides global coverage of major climate-related extreme-weather hazards at high resolution via a data API, namely (i) tropical cyclones, (ii) river flood, (iii) agro drought and (iv) European winter storms, all at 4km spatial resolution. For all hazards, historic and probabilistic event sets exist, some also under select climate forcing scenarios (RCPs) at distinct time horizons (e.g. 2040). Might be relevant for some mountain applications (but beware coarse spatial resolution).",
340,Software,Modelled,2,x,,,,,,,,Global,CMIP6 - Coupled Model Intercomparison Project Phase 6,Phase 6,Working Group on Coupled Modelling (WGCM),https://pcmdi.llnl.gov/CMIP6/Guide/dataUsers.html,,,,,,,,No,No,"Related publication: Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: https://doi.org/10.5194/gmd-9-1937-2016",
341,Software,,,,,x,x,,,,,North America,Cold Region Hydrological Model (CRHM),,The University of Saskatchewan,https://research-groups.usask.ca/hydrology/modelling/crhm.php#TechnicalDetails/ ,centre.hydrology@usask.ca,,,,,Glacier melt / runoff; Snow depth; Snow melt / runoff; Evapotranspiration; Dynamic groundwater storage; Near-surface soil moisture,,No,No,"Physics and HRU-based hydrological modelling framework, with emphasis on cold region processes. The manual can be found at: https://research-groups.usask.ca/hydrology/modelling/crhm_manual_march_15_2013.pdf",
342,Software,,,,,x,,,,,,Global,Cryotools,,Geo.X; GFZ Postdam,https://cryo-tools.org/,post@cryo-tools.org,,,,,,,No,No,"A community effort to build a collection of open source software tools for scientific analysis. The focus is on cryospheric investigations, including glaciers, permafrost, climate change, sensitivity analysis. Most of the tools are, however, also applicable in a variety of other contexts. This website focuses on information and documentation. Code is stored and developed on GitHub: https://github.com/cryotools",
343,Software,,,,,,x,x,,,,Global,DebrisFlow Predictor,,European Geosciences Union; Stantec,https://www.stantec.com/en/ideas/debris-flow-predictor-an-agent-based-runout-program-for-shallow-landslides/ ,rick.guthrie@stantec.com ; andrew.befus@stantec.com,,,,,,,No,Yes,Related publication: https://www.stantec.com/content/dam/stantec/files/PDFAssets/technical/001/debrisflow-predictor-nhess-1029-2021-whitepaper.pdf,
344,Software,,,,x,,x,,,,,Global,Distributed Hydrology Soil Vegetation Model (DHSVM),,Pacific Northwest National Laboratory (PNNL); University of Washington; US Department of Energy,https://www.pnnl.gov/projects/distributed-hydrology-soil-vegetation-model/ ,ning.sun@pnnl.gov,,,,,,,No,No,,
345,Software,,,,,x,,,,,,North America,Distributed Snow-Evolution Modeling System (SnowModel),,USDA-ARS Northwest Watershed Research Center; NASA,https://github.com/jupflug/SnowModel/ ,liston@cira.colostate.edu,,,,,,,No,No,Related publication: https://doi.org/10.1175/JHM548.1/,
346,Software,,,,,,,,,,,,Docker,,Multiple organisations,https://www.docker.com/,https://www.docker.com/company/contact ,,,,,,,,No,,
347,Software,,,,x,,,,,,,Europe,FORCLIM,version 3.0,ETH Zurich,https://ites-fe.ethz.ch/openaccess/products/forclim/ ,,,,,,Forest extent,,No,No,"A climate-sensitive forest succession model, developed to simulate forest stand dynamics over a wide range of environmental conditions. Currently parameterized for ca. 180 tree species that are dominant in temperate forests worldwide. Tested comprehensively for the representation of natural temperate forest dynamics of the Northern Hemisphere, with an emphasis on European forests.",
348,Software,,,,,,,,,,,,GDAL/OGR,36984,OSGeo Project,https://gdal.org/,,,,,,,https://doi.org/10.5281/zenodo.5884351/ ,,No,Command line tools for processing and converting spatial data (raster and vector).,
349,Software,,,,,,,x,,,,,GemPy,v2.2.10,RWTH Aachen University,https://www.gempy.org/,varga@cgre.rwth-aachen.de,,,,,,,No,No,Script-based 3D geological modelling software. See also: https://github.com/cgre-aachen/gempy/ ; https://doi.org/10.5194/gmd-12-1-2019/,
350,Software,Modelled,2,,,,,x,,,,North America,Geomorphologic feature mapping methodology developed for the Dempster Highway and Inuvik to Tuktoyaktuk Highway corridors,,Natural Resources Canada,https://geoscan.nrcan.gc.ca/starweb/geoscan/servlet.starweb?path=geoscan/downloade.web&search1=R=328181/ ,,,,,,,,No,No,This dataset is a manual delineation of geomorphologic features in Dempster Highway and Inuvik to Tuktoyaktuk highway corridors. Citation: https://doi.org/10.4095/328181,
351,Software,,,,,,,,,,,,GRASS GIS,,GRASS Development Team,https://grass.osgeo.org/,grass-web@lists.osgeo.org,,,,,,,,No,"Also ships with QGIS, SAGA, GDAL etc. via: https://trac.osgeo.org/osgeo4w/",
352,Software,,,,,,x,,,,,,HydroGeoSphere (HGS),,Aquanty Inc.,https://www.aquanty.com/hydrogeosphere/ ,support@aquanty.com,,,,,,,,No,An online toolbox of open source and satellite information that provides farm producers across Canada with essential farm data. Visit also: https://agsat.ca/,
353,Software,,,,,x,,,,,,North America,iSnobal,,USDA Agricultural Research Service,https://data.nal.usda.gov/dataset/isnobal/ ,,,,,,,,No,No,Related publication: https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/133.0.CO;2-C/,
354,Software,In Situ,1,,x,,,,,,,Asia,JapanFlux,,JapanFlux,https://www.japanflux.org/?page_id=29#_30/ ,jimukyoku@japanflux.org,,,,,,,No,No,Also acts as a network and provides a map of in situ monitoring sites: https://www.japanflux.org/?page_id=28)/,
355,Software,,,,,,,,,,,,Jupyter,,Project Jupyter,https://jupyter.org/,N/A,,,,,,,,No,"JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning.",
356,Software,,,,x,,,,,,,,LandClim,,ETH ZÅurich,https://ites-fe.ethz.ch/openaccess/products/landclim/ ,,,,,,,,No,No,A stochastic process-based model designed to study spatially explicit forest dynamics at the landscape scale over long time periods with a fine spatial resolution.,
357,Software,,,,,,,,,,,,LaTeX,,The LaTeX Project,https://www.latex-project.org/,,,,,,,,,No,Scientific (and other) document preparation and publishing.,
358,Software,,,,,,x,,,,,Europe,LISFLOOD-FP flood inundation model,,UK Engineering and Physical Sciences Research Council: University of Bristol,https://gmd.copernicus.org/articles/14/3577/2021/#/ ,js102@zepler.net ; g.kesserwani@sheffield.ac.uk,,,,,,https://doi.org/10.5281/zenodo.4073011/ ; https://doi.org/10.5281/zenodo.4066823/ ,No,No,"Citation: https://doi.org/10.5194/gmd-14-3577-2021/ ; See also: https://www.bristol.ac.uk/geography/research/hydrology/models/lisflood/#:~:text=LISFLOOD-FP is a two,efficient manner over complex topography.&text=as part of the RASP,England and Wales and DEFRA/ ; https://www.seamlesswave.com/LISFLOOD8.0/",
359,Software,,,,,,x,,,,,North America,MODFLOW 6: USGS Modular Hydrologic Model,version 6,USGS,https://www.usgs.gov/software/modflow-6-usgs-modular-hydrologic-model/ ,modflow@usgs.gov,,,,,,https://doi.org/10.5066/F76Q1VQV ,No,No,"Citation: Langevin, C.D., Hughes, J.D., Banta, E.R., Provost, A.M., Niswonger, R.G., and Panday, Sorab, 2021, MODFLOW 6 Modular Hydrologic Model version 6.2.2: U.S. Geological Survey Software Release, 30 July 2021",
360,Software,,,,,,,,,,,,Open Data Cube,,Open Data Cube Project,https://www.opendatacube.org/,info@opendatacube.org,,,,,,,No,No,A set of Python libraries and PostgreSQL database that help you work with geospatial raster data. See also: https://github.com/opendatacube/,
361,Software,,,,,x,,,,,,,Open Global Glacier Model (OGGM),,OGGM e.V.,https://oggm.org/oggmev/ ,info@oggm.org,,,,,,https://zenodo.org/badge/latestdoi/43965645/ ,No,No,"An open source modelling framework for glaciers accounting for glacier geometry (including contributory branches) and including an explicit ice dynamics module and a calving parametrization. Can simulate past and future mass-balance, volume and geometry of (almost) any glacier in the world in a fully automated and extensible workflow. Relies exclusively on publicly available data for calibration and validation. Is modular and supports novel modelling workflows. Related publication: https://doi.org/10.5194/gmd-12-909-2019/",
362,Software,,,,,,x,,,,,Global,OpenFOAM,,OpenCFD Ltd,https://www.openfoam.com/,,,,,,,,No,No,"Free, open-source CFD software with a large user base across most areas of engineering and science and an extensive range of features to solve anything from complex fluid flows involving chemical reactions, turbulence and heat transfer, to acoustics, solid mechanics and electromagnetics.",
363,Software,,,,,,,,,,,,ParaView,version 5.10.0,Kitware Inc.; Los Alamos National Laboratory: US Department of Energy,https://www.paraview.org/,,,,,,,,,No,"An open-source, multi-platform data analysis and visualization application. Data exploration can be done interactively in 3D or programmatically using ParaView's batch processing capabilities. Can be run on supercomputers to analyze datasets of petascale size.",
364,Software,,,,,,x,,,,,,ParFlow,version 3.9.0,Multiple organisations,https://parflow.org/#about/ ,,,,,,,,No,No,,
365,Software,,,,,,x,,,,,North America,Penn State Integrated Hydrologic Model (PIHM),,Pennsylvania State University; NSF; NOAA: NASA,https://www.pihm.psu.edu/,,,,,,,,No,No,,
366,Software,,,,,,,,,,,,PEST: Model-Independent Parameter Estimation and Uncertainty Analysis,,GMDSI; others,https://pesthomepage.org/,,,,,,,,,No,"A software package and suite of utility programs which support it. Collectively, these are essential tools in decision-support environmental modelling. The software package automates calibration and calibration-constrained uncertainty analysis of any numerical model.",
367,Software,,,,,,,,,,,,"PEST  , a Software Suite for Parameter Estimation, Uncertainty Analysis, Management Optimization and Sensitivity Analysis",,USGS,https://www.usgs.gov/software/pest-software-suite-parameter-estimation-uncertainty-analysis-management-optimization-and/ ,rjhunt@usgs.gov ; mnfienen@usgs.gov,,,,,,https://doi.org/10.3133/tm7C26/ ,,No,"Provides environmental modeling practitioners access to tools to support decision making with environmental models, including tools for global sensitivity analysis (PESTPP-SEN); least-squares parameter estimation with integrated first-order, second-moment parameter and forecast uncertainty estimation (PESTPP-GLM); an iterative, localized ensemble smoother (PESTPP-IES); and a tool for management optimization under uncertainty (PESTPP-OPT). Citation: White, J.T., Hunt, R.J., Fienen, M.N., and Doherty, J.E., 2020, Approaches to Highly Parameterized Inversion: PEST   Version 5, a Software Suite for Parameter Estimation, Uncertainty Analysis, Management Optimization and Sensitivity Analysis: U.S. Geological Survey Techniques and Methods 7C26, 51 p.",
368,Software,,,,,,,,,,,,PostGIS,,PostGIS Project Steering Committee (PSC),https://postgis.net/,,,,,,,,,No,See also non-spatial component: https://www.postgresql.org/,
369,Software,,,,,,x,,,,,North America,Precipitation-Runoff Modeling System (PRMS),version 5.2.0,USGS,https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms/ ,markstro@usgs.gov,,,,,,https://doi.org/10.5066/P9HJ5TKZ/ ,No,No,"Software/Code citation: Regan, R.S., Markstrom, S.L., LaFontaine, J.H., 2020, PRMS version 5.2.0: Precipitation-Runoff Modeling System (PRMS): U.S. Geological Survey Software Release, 01/20/2021/",
370,Software,,,,,,,,,,,,Python,,The Python Software Foundation,https://www.python.org/,,,,,,,,,No,"See list of modules (extentions), e.g.: https://catswhocode.com/python-modules-list/",
371,Software,,,,,,,,,,,,QGIS,,QGIS.ORG Association,https://www.qgis.org/en/site/ ,,,,,,,,,No,"Also ships with GRASS, SAGA, GDAL etc. via: https://trac.osgeo.org/osgeo4w/",
372,Software,,,,,,,,,,,,R Project,,R Development Core Team,https://www.r-project.org ; https://www.rstudio.com/,,,,,,,,,No,See list of packages (extensions): https://cran.rproject.org/web/packages/available_packages_by_name.html/ ; Use in conjunction with: https://www.rstudio.com/,
373,Software,,,,,,x,x,,,,Global,r.avaflow,version 2.4,"University of Natural Resources and Life Sciences (BOKU), Vienna; others",https://www.landslidemodels.org/r.avaflow/ ,martin.mergili@uni-graz.at,,,,,,,No,No,"Cite as: Mergili, M., Pudasaini, S.P., 2014-2021. r.avaflow - The mass flow simulation tool. https://www.avaflow.org ; Related publications: https://doi.org/10.1126/science.abh4455 ; https://doi.org/10.1007/s10346-021-01670-0/",
374,Software,,,,,,x,x,,,,Global,r.landslide,,"Federal University of Fronteira Sul, Brazil",https://github.com/UFFSEnvModelling/r.landslide/ ,lucimarabragagnolo@hotmail.com,,,,,,,,No,A free and open source add-on to the open source Geographic Information System (GIS) GRASS software for landslide susceptibility mapping. Written in Python language and works on the top of an Artificial Neural Network (ANN) fed with environmental parameters and landslide databases. Citation: https://doi.org/10.1016/j.envsoft.2019.104565/,
375,Software,,,,,x,,,,,,,RAMMS rapid mass movements,,WSL Institute for Snow and Avalanche Research (SLF),https://ramms.slf.ch/ramms/ ,ramms@slf.ch,,,,,,,,No,"Note, may not be fully open source; RAMMS licenses may be purchased here: https://ramms.slf.ch/ramms/index.php?option=com_content&view=article&id=49&Itemid=71/",
376,Software,,,,,,x,,,,,North America,River Analysis System (HEC-RAS),,U.S. Army Corps of Engineers (USACE),https://www.hec.usace.army.mil/software/hec-ras/ ,,,,,,,,No,No,"Allows the user to perform one-dimensional steady flow, one and two-dimensional unsteady flow calculations, sediment transport/mobile bed computations, and water temperature/water quality modeling.",
377,Software,,,,,,,,,,,,SAGA GIS,version 2.1.4,University of Hamburg,https://saga-gis.sourceforge.io/en/index.html,,,,,,,,,No,"Citation: https://doi.org/10.5194/gmd-8-1991-2015/ ; Also ships with QGIS, GRASS, GDAL etc. via: https://trac.osgeo.org/osgeo4w/",
378,Software,,,,,,,,,,,,The Oasis Loss Modelling Framework,,Oasis Loss Modelling Framework Ltd,https://oasislmf.org/,support@oasislmf.org,,,,,,,No,No,Might be relevant for some mountain applications.,
379,Software,,,,,,x,x,,,,Europe,The sedFlow modelling tool,version 1.0,WSL Institute for Snow and Avalanche Research SLF,https://www.wsl.ch/de/services-und-produkte/software-websites-und-apps/sedflow.html#/ ,dieter.rickenmann@wsl.ch,,,,,,,No,No,,
380,Software,,,,x,,,,,,,Europe,TreeMig,,WSL Institute for Snow and Avalanche Research SLF,https://www.wsl.ch/de/projekte/treemig-1.html/ ,heike.lischke@wsl.ch,,,,,Forest extent; Vegetation species abundancies and extents,,,No,Code can be requested from Dr. Heike Lischke.,
381,Software,,,,,,x,,,,,Global,Water Flow and Balance Simulation Model (WaSiM),,ETH Zurich,https://www.wasim.ch/en/ ,j.schulla@wasim.ch,,,,,,,No,No,,
382,Software,,,x,,,,,,,,Global,Weather Research and Forecasting (WRF) Model,Version 4.1,"National Center for Atmospheric Research (NCAR), NOAA; U.S. Air Force; Naval Research Laboratory; University of Oklahoma; Federal Aviation Administration (FAA)",https://www.mmm.ucar.edu/wrf-release-information/ ,,,,,,,,No,No,A mesoscale numerical weather prediction system designed for both atmospheric research and operational forecasting applications. Serves a wide range of meteorological applications across scales from tens of meters to thousands of kilometers.,
383,Software,,,,,,,,,,,,WhiteboxTools,,Whitebox Geospatial Inc.,https://www.whiteboxgeo.com/,support@whiteboxgeo.com,,,,,,,,No,An advanced open-source geospatial data analysis platform.,
384,Software,,,x,,,x,,,,,North America,WRF-HYDRO Model,version 5.2,NSF; NCAR; NASA; NOAA,https://ral.ucar.edu/projects/wrf_hydro/model-code/ ,wrfhydro@ucar.edu,,,,,Snow depth; Snow Water Equivalent (SWE); Latent heat flux; Sensible heat flux; Evapotranspiration; Dynamic groundwater storage; Groundwater levels; Near-surface air temperature; Ground temperature; River discharge; Near-surface soil moisture,,No,No,"Citation: Gochis, D.J., M. Barlage, R. Cabell, M. Casali, A. Dugger, K. FitzGerald, M. McAllister, J. McCreight, A. RafieeiNasab, L. Read, K. Sampson, D. Yates, Y. Zhang (2020). The WRF-Hydro modeling system technical description (Version 5.2). NCAR Technical Note.108 pages. Available at: https://ral.ucar.edu/sites/default/files/public/projects/wrf-hydro/technical-description-user-guide/wrf-hydrov5.2technicaldescription.pdf",
385,Software,,,,,x,,,,,,Europe,WSL/SLF snow cover models,,WSL Institute for Snow and Avalanche Research (SLF),https://models.slf.ch/,bavay@slf.ch; fierz@slf.ch,,,,,Snow covered area / fraction (SCA/F); Snow depth; Snow Water Equivalent (SWE),,No,No,Links to and information on several snow cover models. See also: https://www.envidat.ch/dataset/10-16904-1/ ; https://www.slf.ch/de/services-und-produkte/alpine-3d.html/,
386,Software,,,,x,,,,,,,Global,Predictive Ecosystem Analyzer (PEcAn),,National Center for Supercomputing Applications (NCSA); University of Illinois Urbana-Champaign,https://pecanproject.github.io/index.html/ ,pecanproj@gmail.com,,,,,,,No,No,"An integrated ecological bioinformatics toolbox which consists of a scientific workflow system to manage the immense amounts of publicly-available environmental data, and a Bayesian data assimilation system to synthesize this information within state-of-the-art ecosystems models. Visit the demo page: https://pecan.ncsa.illinois.edu/pecan/01-introduction.php/ ; Not mountain specific, but may be useful for mountain applications.",
387,Tool,,,,,,,,,,,,Arc2Meters Converter,,Open DEM Project,https://www.opendem.info/arc2meters.html,contact@OpenDEMData.info,,,,,Topographic data,,No,No,A tool that converts arc seconds to meters or meters to arc seconds at a specified latitude.,
388,Tool,,,x,,,,,,,,,Climate Data Operators,2.0.4,Max Planck Institute for Meteorology (MPI-M),https://code.mpimet.mpg.de/projects/cdo/ ,,,,,,,,No,No,A collection of command line operators to manipulate and analyse climate and NWP model data.,
389,Tool,Remotely sensed,2,,x,,,,x,x,,Global,Crop Assessment Tool,,GEOGLAM,https://data.nal.usda.gov/dataset/geoglam-geo-global-agricultural-monitoring-crop-assessment-tool/ ,ijarvis@geosec.org,,,,,,,No,No,Not mountain specific but may be useful.,
390,Tool,Multiple,1,,x,,,,,,,Global,ECOPOTENTIAL Virtual Laboratory (VLab),,European Union,http://www.ecopotential-project.eu/products/vlab.html/ ,,,,,,,,No,No,A tool for facilitating the publication and invocation of scientific workflows supporting evidence-based decision-making. Final data and products have been archived in the community ECOPOTENTIAL: the legacy on Zenodo: https://zenodo.org/communities/ecopotentialh2020/?page=1&size=20/ ; Also search for The Earth Observation Data for Ecosystem Monitoring (EODESM) in VLab.,
391,Tool,,,,,,,x,,,,,FSLAM,Software version 1.0,UPC BarcelonaTECH,https://github.com/EnGeoModels/fslam_plugin/ ; https://github.com/EnGeoModels/fslam/ ,cuggzz@cug.edu.cn,,,,,,,No,No,A QGIS plugin to delineate areas where landslides are prone to occur within a region. Integrates a simplified hydrological model and a geotechnical model based on the infinite slope theory. Publication: https://doi.org/10.1016/j.envsoft.2022.105354,
392,Tool,Multiple,1,,,,,,,,,,Geofolio,,Multiple organisations,https://geofolio.org/,info@geofolio.org,,,,,,,No,No,"Automagically generated thematic factsheets with information on land cover, topography, climate, soils, hydrology, and agriculture, all based on open data. Accessible from the browser, with written summaries, charts, references, and interactive maps with legends.",
393,Tool,Modelled,2,,,,x,,,,,Global,GEOGloWS ECMWF Streamflow Hydroviewer,,GEOGloWS; ECMWF; ESRI,https://tethys.byu.edu/apps/geoglows-hydroviewer/ ,,,,,,,,No,No,River discharge forecasts for major rivers. Not mountain specific. See also: https://earthobservations.org/geo_blog_obs.php?id=478/,
394,Tool,,,,,,,,,,,,GFDRR Online Tools,,GFDRR,https://www.gfdrr.org/en/onlinetools/ ,,,,,,,,No,No,"The datasets extend beyond mountains, however some might be relevant for mountainous areas.",
395,Tool,,,x,,,,,,,,Global,Global Climate Monitor,,University of Seville Climate Research Group; European Commission (EC),https://www.globalclimatemonitor.org/,globalclimatemonitor@us.es,,,,,,,No,No,Publication: https://doi.org/10.1080/17538947.2018.1429502/ ; Not mountain specific.,
396,Tool,Multiple,2,,,,x,,,,,Global,Global Flood Awareness System (GloFAS),,Copernicus Programme,https://www.globalfloods.eu/general-information/data-access/ ,,,,,,,,No,No,"Predominantly a web-mapping tool for riverine floods, but also provides links to data: https://www.globalfloods.eu/general-information/data-and-services/ ; See also: https://hepex.inrae.fr/glofas-era5-reanalysis/ ; The datasets are global (extends beyond mountains), but may be relevant for mountainous areas.",
397,Tool,Remotely sensed,,,,,,,,,,,Google Earth Engine,,Google,https://earthengine.google.com,,,,,,,,No,No,"To access the code editor, the explorer and libraries, visit: https://earthengine.google.com/platform/ ; Datasets: https://developers.google.com/earth-engine/datasets/",
398,Tool,,,,,,,,,,,,rayshader,0.27.2,Tyler Morgan-Wall,https://www.rayshader.com/,,,,,,,,No,No,Open source package for producing 2D and 3D data visualizations in R. Citation: Morgan-Wall T (2022). rayshader: Create Maps and Visualize Data in 2D and 3D.,
399,Tool,,,,,,,,,,,,Scientific Toolboxes (ESA),,European Space Agency,https://eo4society.esa.int/scientific-toolboxes/ ,eo4society@esa.int,,,,,,,No,No,See also: https://eo4society.esa.int/virtual-labs/,
400,Tool,Remotely sensed,2,,,,,,x,,,Global,SEDAC POPGRID Viewer,,POPGRID Data Collaborative; others,https://sedac.ciesin.columbia.edu/mapping/popgrid/comparison-view/#/ ,,,,,,,,No,No,"The datasets shown extend beyond mountains, but may be relevant for mountainous areas.",
401,Tool,,,,,,,x,,,,,Visible Geology,Beta,Seequent (Bentley Systems),https://app.visiblegeology.com/,support@seequent.com,,,,,,,No,No,Visualisation tool for teaching / understanding geological concepts.,
402,Dataset,In Situ,1,x,,,x,,,,,North America,"Hydrometeorological data from Marmot Creek Research Basin, Canadian Rockies",,Federated Research Data Repository,https://www.frdr-dfdr.ca/repo/dataset/cde76a76-735c-3983-7a30-52fa5c78fb24,,,,,,,,No,Yes,Dataset on Canadian Rockies. See also the linked article: https://essd.copernicus.org/articles/11/455/2019/,
403,Dataset,In Situ,1,x,,x,,,,,,North America,"Glaciers and climate of the upper Susitna Basin, Alaska; supporting data",,Alaska Division of Geological & Geophysical Surveys,https://dggs.alaska.gov/pubs/id/30138,,,,,,,,No,Yes,See also article:  https://essd.copernicus.org/articles/12/403/2020/,
404,Dataset,Modelled,2,,x,x,x,x,x,,,Global,Protected Areas (WDPA),,Protected Planet,https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA,protectedareas@unep-wcmc.org,,,,,Protected Areas,,No,No,"Not mountain focus, but can be easily joined. All protected areas in  the world  and other datasets available.",
405,Dataset,Remotely sensed,2,,,x,,x,,,x,Global,Database of glacier and permafrost disasters,2,"GAPHAZ, UCCS, IPA",https://www.gaphaz.org/database,a.m.kaab@geo.uio.no,,,,,Cryosphere Natural Disasters,,No,Yes,"This database is a selection and overview of typical glacier and permafrost disasters in mountains. When using this database, we would be grateful if you cite the
""Working group on glacier and permafrost hazards in mountains by UCCS and IPA"", ""http://jern.uio.no/remotesensing/gaphaz"" and/or
Geophysical Research Abstracts, Vol. 9, 04374, 2007",
406,Data Portal,Other,3,,x,,,,,,,Global,World Network of Mountain Biosphere Reserves (WNMBR),,World Network of Mountain Biosphere Reserves (WNMBR),https://www.mountainbiosphere.org/en/web-resources/,hello@mountainbiosphere.org,,,,,Natural Reserve,,No,Yes,,
407,Dataset,Remotely sensed,3,,,,,,x,x,,Global,World Heritage Interactive Map,,UNESCO,https://whc.unesco.org/en/interactive-map/,,,,,,,,No,No,Map of all World Heritage sites. Could be linked to mountain areas. Also downloadable in other formats.,
408,Data Portal,Remotely sensed,2,,,,,,,,x,Global,Global Fire Emissions Database,,Global Fire Emissions Database,https://www.globalfiredata.org/data.html,https://www.globalfiredata.org/contact.html,,,,,Fire Emissins,,No,No,"The data is divided into 3 main datasets: (1) Burned area, (2) Monthly emissions and fractional contributions of different fire types, (3) Daily / 3-hourly fields to scale the monthly emissions to higher temporal resolutionsThe data is divided into 3 main datasets.

Burned area
Monthly emissions and fractional contributions of different fire types
Daily / 3-hourly fields to scale the monthly emissions to higher temporal resolutions",
409,Tool,,,,,,,,,,,,OpenDA: Integrating models and observations,,OpenDA,https://openda.org/,,,,,,,,No,No,OpenDA is an open interface standard for (and free implementation of) a set of tools to quickly implement data-assimilation and calibration for arbitrary numerical models. OpenDA wants to stimulate the use of data-assimilation and calibration by lowering the implementation costs and enhancing the exchange of software among researchers and end-users.,
410,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Europe,EURO-CLIMHIST DATABASE,,"Euro-Climhist, Unibern",https://www.euroclimhist.unibe.ch/en/search-database/,,,,,,,,No,No,Focus on Switzerland.,
411,Dataset,Remotely sensed,3,,x,,,x,,,,Asia,Regional Land Cover Monitoring System for the Hindu Kush Himalaya,,"SERVIR, ICIMOD",https://servir.icimod.org/science-applications/regional-land-cover-monitoring-system-for-the-hindu-kush-himalaya/,,,,,,Land Cover,,No,Yes,Focus on Hindu Kush Himalaya. The Regional Land Cover Monitoring System (RLCMS) for the Hindu Kush Himalaya (HKH) region is an operational service that provides annual land cover mapping and change analysis services. The system produces consistent annual land cover data using a robust method and a harmonized classification scheme to enable monitoring and change analysis.,
412,Dataset,Remotely sensed,3,,,,x,,,,,Asia,Streamflow Prediction Tool – HKH river basins,,"SERVIR, ICIMOD",https://servir.icimod.org/science-applications/streamflow-prediction-tool-hkh-river-basins/,,,,,,Streamflow,,No,Yes,"Focus on Hindu Kush Himalaya. The Streamflow Prediction Tool for the HKH river basins provides 10-day streamflow forecasts for major rivers within the Amu Darya, Brahmaputra, Ganges, and Indus basins in the Hindu Kush Himalaya (HKH) region. Each river segment displayed in the map is assigned a unique identifier. Users can click on a particular river segment to display 10-day streamflow forecasts for the",
413,Dataset,Remotely sensed,2,,x,,,,,,,Asia,Land cover of Nepal,,ICIMOD,http://rds.icimod.org/Home/DataDetail?metadataId=1972729,raja.aryal@nepal.gov.np,,,,,Land Cover,,No,Yes,,
414,Dataset,In Situ,1,x,,x,,,,,,Europe,The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021),,AERIS,https://www.aeris-data.fr/en/landing-page/?uuid=865730e8-edeb-4c6b-ae58-80f95166509b#v2020.2,,,,,,Snow,,No,Yes,"This file takes part from a 60-years reanalysis of meteorological and snow conditions in the French Alps, Pyrenees and Corsica from 1958 to 2022. The simulations are performed over relatively homogeneous units designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). ",
415,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Global,Data Terra,,Data Terra,https://www.data-terra.org/en/access-to-data-catalogues/,,,,,,,,No,No,Not mountain specific,
416,Other,Other,,,,,,,,,,Global,Space for Climate Observatory (SCO) Projects - Mountains,,Space for Climate Observatory (SCO),https://www.spaceclimateobservatory.org/projects,,,,,,,,No,Yes,Contains a map with specific mountain-related projects,
417,Tool,,,,,,,,,,,,openEO,,openEO,https://openeo.org/,,,,,,,,No,No,"openEO develops an open API to connect R, Python, JavaScript and other clients to big Earth observation cloud back-ends in a simple and unified way.",
418,Data Portal,Remotely sensed,2,,,,,,x,,x,Global,GLOBAL EXPOSURE DATABASE FOR ALL: GED4ALL,,Humanitarian OpenStreetMap Team (HOT),https://www.hotosm.org/tools-and-data,https://www.hotosm.org/people/mhairi-ohara/,,,,,,,No,No,https://github.com/orgs/hotosm/repositories,
419,Data Portal,Remotely sensed,3,,,,,,x,,x,,Global Earthquake Model Products,,Global Earthquake Model (GEM),https://www.globalquakemodel.org/products?type=Dataset,info@globalquakemodel.org,,,,,"Natural Disaster, Vulnerability, Risk",,No,No,"Not mountain specific but contain a lot of dataset regarding natural hazards, risk, vulnerability, exposure, etc.",
420,Data Portal,Remotely sensed,Multiple,,,,x,,x,x,x,Global,space4water Portal,,space4water,https://www.space4water.org/geoss,,,,,,Water,,No,No,,
421,Dataset,Modelled,3,,,x,,,,,,Global,A new global dataset of mountain glacier centerline and length,,Science Data Bank,https://www.scidb.cn/en/detail?dataSetId=3a0a90a72b824dce854308db6c7da212,,,,,,Glacier lengh,,No,Yes,See also related article : https://essd.copernicus.org/articles/14/3889/2022/,
422,Dataset,Remotely sensed,2,,,,,,x,,,Global,Global Building Footprint,,GitHub,https://github.com/microsoft/GlobalMLBuildingFootprints,,,,,,Buildings footprint,,No,No,"Bing Maps is releasing open building footprints around the world. We have detected 1.2B buildings from Bing Maps imagery between 2014 and 2023 including Maxar, Airbus, and IGN France imagery. The data is freely available for download and use under ODbL. This dataset includes our other releases.",
423,Dataset,Remotely sensed,2,,x,,,,x,x,,Europe,Open Soil Data Cube for Europe,,OpenGeo Hub,https://medium.com/mlearning-ai/dynamic-soil-information-at-farm-scale-based-on-machine-learning-and-eo-data-building-an-open-1a6ffe282162,,,,,,Land use,,No,No,https://ecodatacube.eu/?base=OpenStreetMap%20(grayscale)&layer=Land%20Cover%20&zoom=4&eye=5000000&center=53.7139,17.0066&opacity=45&time=2019
424,Dataset,Remotely sensed,1,,x,,,x,x,,,Global,Restor Map,,RESTOR,https://beta.restor.eco/map,,,,,,Restoration,,No,No,"Restoration projects around the world, not mountain specific. Restor is the largest network of restoration and conservation sites across the globe.",
425,Dataset,Multiple,Multiple,,,,,,x,,,Global,GeoNames,,GeoNames,https://www.geonames.org/,,,,,,,,No,No,The GeoNames geographical database covers all countries and contains over eleven million placenames that are available for download free of charge.The GeoNames geographical database is available for download free of charge under a creative commons attribution license. It contains over 25 million geographical names and consists of over 11 million unique features whereof 4.8 million populated places and 13 million alternate names.,
426,Data Portal,Multiple,Multiple,x,x,,,x,,,,Europe,GeoSphere Austria,,GeoSphere Austria,https://www.zamg.ac.at/cms/en/products,,,,,,,,No,No,,
427,Tool,,,,,,,,,,,,Flow-Py,,GitHub,https://github.com/avaframe/FlowPy,,,,,,,,,No,"Flow-Py is an open source tool to compute gravitational mass flows (GMF) run out and intensity. The main objective is to compute the spatial extent of GMF, which consists of the starting, transit and runout zones of GMF on the surface of a three dimensional terrain. The resulting run out is mainly dependent on the terrain and the location of the starting/release point. No time-dependent equations are solved in the model. Flow-Py uses existing statistical-data-based approaches for solving the routing and stopping of GMF.",
428,Dataset,Remotely sensed,2,,,,,,x,,,Global,GHSL - Global Human Settlement Layer (New release 2023),,"GHSL, European Comission",https://ghsl.jrc.ec.europa.eu/p2022Release.php,,,,,,Settlements,,No,No,,
429,Dataset,In Situ,2,,,,,,,,x,Asia,EMCA Landslide catalog Central Asia,,GFZ Data Services,https://dataservices.gfz-potsdam.de/panmetaworks/showshort.php?id=escidoc:3657915,,,,,,Landslides,,No,Yes,"The EMCA landslide catalog of Central Asia covers mostly western and northern Kyrgyzstan as well as Tajikistan's Region of Republican Subordination. The catalog is a summary (point locations) of the documented landslides between 1954 and 2009, which are collected by the Central Asian Institute for Applied Geosciences through geological surveys (field campaigns) on single sites close to urban areas in order to mitigate landslide risk. The catalog is presented in identical .csv and NetCDF (.nc) formats. Both the formats include the point locations of the landslides (variables: latitude [WGS 84], longitude [WGS 84]), and the dates of about 5% of the landslides (variable: date). The remaining %95 of the data is undated and marked as NaT (dating not possible).",
430,Dataset,In Situ,2,,,,,,,,x,Asia,Multi-temporal landslide inventory for a study area in Southern Kyrgyzstan derived from multi-sensor optical satellite time series data (1986 – 2013),,GFZ Data Services,https://dataservices.gfz-potsdam.de/panmetaworks/showshort.php?id=escidoc:5085890,,,,,,Landslides,,No,Yes,"Multi-temporal landslide inventories are important information for the understanding of landslide dynamics and related predisposing and triggering factors, and thus a crucial prerequisite for probabilistic hazard and risk assessment. Despite the great importance of these inventories, they do not exist for many landslide prone regions in the world. In this context, the recently evolving global-scale availability of high temporal and spatial resolution optical satellite imagery (RapidEye, Sentinel-2A/B, planet) has opened up new opportunities for the creation of these multi-temporal inventories.",
431,Data Portal,Multiple,Multiple,x,,,,,,,,Global,ClimateSERV,2,ClimateSERV,https://climateserv.servirglobal.net/,,,,,,,,No,No,"ClimateSERV enables users to easily visualize and download 180-day rainfall and temperature forecasts, as well as historic rainfall and vegetation conditions. Whether you’re a development practitioner, scientist, or another type of decision-maker, ClimateSERV can provide critical information for applications ranging from agriculture to water availability.",
432,Data Portal,Multiple,Multiple,,,,x,,,,,Global,Global Groundwater Information System (GGIS),,International Groundwater Resources Assessment Centre (IGRAC); World Meteorological Organization (WMO); UNESCO,un-igrac.org/global-groundwater-information-system-ggis,,,,,,Groundwater,,No,No,"The GGIS is the Global Groundwater Information System, an online platform supporting the sharing of groundwater data and information worldwide. Since 2004, it provides groundwater users, professionals and managers with data and information on this hidden yet crucial resource.",
433,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Global,Joint Research Center Data Catalogue,,JRC,https://data.jrc.ec.europa.eu/dataset,,,,,,,,No,No,,
434,Data Portal,Multiple,Multiple,,,,,,,,,Global,The European Open Science Cloud,,European Open Science Cloud,https://eosc-portal.eu/,,,,,,,,No,No,"unified access to the European hub of research data,
tools and services for innovation and education",
435,Data Portal,Multiple,Multiple,,,,,,,,,Global,STAC SpatioTemporal Asset Catalogs,,STAC,https://stacspec.org/en/,,,,,,,,,No,"At its core, the SpatioTemporal Asset Catalog (STAC) specification provides a common structure for describing and cataloging spatiotemporal assets.  plus associated datasets: https://stacspec.org/en/about/datasets/ ; https://eoapi.dev/

A spatiotemporal asset is any file that represents information about the earth captured in a certain space and time.",
436,Dataset,Multiple,Multiple,x,,,,,,,,Europe,ClimateEU: historical and projected climate data for Europe,,Andreas Hamann,https://sites.ualberta.ca/~ahamann/data/climateeu.html,,,,,,,,No,No,,
437,Dataset,Remotely sensed,2,,,x,,,,,,Global,Global Permafrost Zonation Index Map,,NCAR Climate Data Guide,https://climatedataguide.ucar.edu/climate-data/global-permafrost-zonation-index-map,,30 arc-seconds; 0.008333333; 60S-90N; 43200x18000,,,,Permafrost,,No,No,Relative publication: https://tc.copernicus.org/articles/6/221/2012/,
438,Dataset,Multiple,Multiple,x,,x,,,,,,North America,"A Meteorology and Snow Data Set From Adjacent Forested and Meadow Sites at Crested Butte, CO, USA",,AGU,https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022WR033006,hannah.bonner@colorado.edu,,,,,"Snow, Meteorology",,No,Yes,"We present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021).",
439,Data Portal,Multiple,Multiple,,,,,,,,,Global,Source Cooperative,,Radiant Earth,https://beta.source.coop/,,,,,,,,No,No,Source Cooperative is a data publishing utility that allows trusted organizations and individuals to share data products using standard HTTP methods,
440,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Global,Planetary Computer,,Microsoft,https://planetarycomputer.microsoft.com/,,,,,,,,No,No,"The Planetary Computer combines a multi-petabyte catalog of global environmental data with intuitive APIs, a flexible scientific environment that allows users to answer global questions about that data, and applications that put those answers in the hands of conservation stakeholders.",
441,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Global,Google Earth Engine,,Google,https://earthengine.google.com/,,,,,,,,No,No,"A planetary-scale platform for Earth science data & analysis. Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. Scientists, researchers, and developers use Earth Engine to detect changes, map trends, and quantify differences on the Earth's surface. Earth Engine is now available for commercial use, and remains free for academic and research use.",
442,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Global,Earth on AWS,,Amazon,https://aws.amazon.com/earth/,,,,,,,,No,No,,
443,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Global,Climate Change Knowledge Portal,,World Bank Group,https://climateknowledgeportal.worldbank.org/,,,,,,,,No,No,"The Climate Change Knowledge Portal (CCKP) provides global data on historical and future climate, vulnerabilities, and impacts. ",
444,Other,Multiple,Multiple,,,,x,,,,,Global,Plateforme Eau du Canton du Valais,,Canton du Valais,https://www.vs.ch/web/plateforme-eau,,,,,,,,No,No,"Focus on Valais, Switzerland",
445,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Africa,openAFRICA,,openAFRICA,https://open.africa/dataset,,,,,,,,No,No,"openAFRICA aims to be the largest independent repository of open data on the African continent.

openAFRICA is not a government portal. Instead, it’s a grassroots initiative, maintained by Code for Africa, as a public service. The platform is available as a free resource for ordinary citizens, civil society organisations, civic activists, the media, and government agencies.",
446,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Europe,REDIAM Portal Ambiental de Andalucía,,Junta de Andalucia,https://www.juntadeandalucia.es/medioambiente/portal/acceso-rediam,,,,,,,,No,No,"Focus on Andalusia, Spain",
447,Data Portal,Multiple,Multiple,x,,,,,,,,Europe,HITSALP Datasets,,"(HITSALP) HISTORICAL INSTRUMENTAL CLIMATOLOGICAL
SURFACE TIME SERIES OF THE GREATER ALPINE REGION",,,,,,,,,No,Yes,"Focus on Alpine Region, Europe. Alps",
448,Dataset,Multiple,Multiple,,,,x,,,,,Europe,Catchment attributes and hydro-meteorological time series for large-sample studies across hydrologic Switzerland (CAMELS-CH),,CAMELS-CH,https://zenodo.org/record/7957061,,,,,,,,No,No,Focus on Switzerland. Might have a lot of data on Swiss Alps.,
449,Dataset,In Situ,2,,,x,,,,,,Global,NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in situ snow depth time series,,Earth System Science Data,https://essd.copernicus.org/articles/15/2577/2023/essd-15-2577-2023-discussion.html,axf984@bham.ac.uk,,,,,"Snow Depth, SWE",,No,No,,
450,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,South America,Observational Datasets in South America,,NCAR RESEARCH APPLICATIONS LABORATORY,https://ral.ucar.edu/projects/south-america-affinity-group-saag/observational-datasets,,,,,,,,No,No,,
451,Dataset,Multiple,Multiple,,x,,,,,,,South America,Expert range maps of global mammal distributions harmonised to three taxonomic authorities,,Journal of Biogeography,https://onlinelibrary.wiley.com/doi/full/10.1111/jbi.14330,yanina.sica@yale.edu,,,,,Biodiversity,,No,No,"Comprehensive, global information on species' occurrences is an essential biodiversity variable and central to a range of applications in ecology, evolution, biogeography and conservation. ",
452,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Global,awesome-gee-community-catalog,,awesome-gee-community-catalog,https://gee-community-catalog.org/,,,,,,,,No,No,The awesome-gee-community-catalog consists of community sourced geospatial datasets made available for use by the larger Google Earth Engine community and shared publicly as Earth Engine assets.,
453,Data Portal,Multiple,Multiple,,,,,,x,,,Global,POPGRID Data Collaborative,,The Center For International Earth Science Information Network (CIESIN),https://www.popgrid.org/ciesin, ciesin.info@ciesin.columbia.edu,,,,,"Population, Settlements",,No,No,"CIESIN manages the NASA Socioeconomic Data and Applications Center (SEDAC), which supports the development of a number of population, settlement, and infrastructure data products.",
454,Data Portal,Multiple,Multiple,x,,,,,,,,Global,CHELSA-W5E5,,"CHELSEA, Climatologies at high resolution for the earth’s land surface areas",https://chelsa-climate.org/chelsa-w5e5-v1-0-daily-climate-data-at-1km-resolution/,,1km,,,,,,Yes,No,"The CHELSA-W5E5 dataset was created to serve as observational climate input data for the impact assessments carried out in phase 3a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a).

Version 1.0 of the CHELSA-W5E5 dataset covers the entire globe at 30 arcsec horizontal and daily temporal resolution from 1979 to 2016. Data sources of CHELSA-W5E5 are version 1.0 of WFDE5 over land merged with ERA5 over the ocean (W5E5; Lange, 2019; Cucchi et al., 2020), the ERA5 global reanalysis (Hersbach et al. 2020) and the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010; Danielson and Gersch, 2011).
The CHELSA-W5E5 dataset was created to serve as observational climate input data for the impact assessments carried out in phase 3a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a).

Version 1.0 of the CHELSA-W5E5 dataset covers the entire globe at 30 arcsec horizontal and daily temporal resolution from 1979 to 2016. Data sources of CHELSA-W5E5 are version 1.0 of WFDE5 over land merged with ERA5 over the ocean (W5E5; Lange, 2019; Cucchi et al., 2020), the ERA5 global reanalysis (Hersbach et al. 2020) and the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010; Danielson and Gersch, 2011).
The CHELSA-W5E5 dataset was created to serve as observational climate input data for the impact assessments carried out in phase 3a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a).

Version 1.0 of the CHELSA-W5E5 dataset covers the entire globe at 30 arcsec horizontal and daily temporal resolution from 1979 to 2016. Data sources of CHELSA-W5E5 are version 1.0 of WFDE5 over land merged with ERA5 over the ocean (W5E5; Lange, 2019; Cucchi et al., 2020), the ERA5 global reanalysis (Hersbach et al. 2020) and the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010; Danielson and Gersch, 2011).
The CHELSA-W5E5 dataset was created to serve as observational climate input data for the impact assessments carried out in phase 3a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a).

Version 1.0 of the CHELSA-W5E5 dataset covers the entire globe at 30 arcsec horizontal and daily temporal resolution from 1979 to 2016. Data sources of CHELSA-W5E5 are version 1.0 of WFDE5 over land merged with ERA5 over the ocean (W5E5; Lange, 2019; Cucchi et al., 2020), the ERA5 global reanalysis (Hersbach et al. 2020) and the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010; Danielson and Gersch, 2011).
The CHELSA-W5E5 dataset was created to serve as observational climate input data for the impact assessments carried out in phase 3a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a).

Version 1.0 of the CHELSA-W5E5 dataset covers the entire globe at 30 arcsec horizontal and daily temporal resolution from 1979 to 2016. Data sources of CHELSA-W5E5 are version 1.0 of WFDE5 over land merged with ERA5 over the ocean (W5E5; Lange, 2019; Cucchi et al., 2020), the ERA5 global reanalysis (Hersbach et al. 2020) and the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010; Danielson and Gersch, 2011).",
455,Dataset,Remotely sensed,2,,,,,,x,,,Global,GADM maps and data,,GADM,https://gadm.org/index.html,,,,,,,,No,No,"GADM wants to map the administrative areas of all countries, at all levels of sub-division. We provide data at high spatial resolutions that includes an extensive set of attributes. This is a never ending project, but we are happy to share what we have. You can write us with questions and suggestions, using this contact form.",
456,Dataset,Remotely sensed,1,,,,,,x,,,Global,Cartes de densité de population en haute résolution,,Meta,https://dataforgood.facebook.com/dfg/tools/high-resolution-population-density-maps#accessdata,,,,,,Population density,,No,No,,
457,Dataset,Remotely sensed,Multiple,,,,,,x,,,Global,Open Buildings,,Google,https://sites.research.google/open-buildings/,,,,,,Buildings,,No,No,"The dataset contains 1.8 billion building detections, across an inference area of 58M km2 within Africa, South Asia, South-East Asia, Latin America and the Caribbean.

The current dataset is in its 3rd version (v3). V1 covered Africa, in v2 we expanded to South and South-East Asia and in the current version v3 detections from Latin America and the Caribbean are also included.

For each building in this dataset we include the polygon describing its footprint on the ground, a confidence score indicating how sure we are that this is a building, and a Plus Code corresponding to the centre of the building. There is no information about the type of building, its street address, or any details other than its geometry.",
458,Software,Other,Other,,,,,,,,,Global,SDSM Statistical Downscaling Model,,SDSM,https://www.sdsm.org.uk/sdsmmain.html,,,,,,,,,No,"SDSM (Statistical DownScaling Model) is a decision support tool for assessing local climate change impacts using a robust statistical downscaling technique.
SDSM facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future regional climate forcing.
Additionally, the software performs ancillary tasks of predictor variable pre-screening, model calibration, basic diagnostic testing, statistical analyses and graphing of climate data.",
459,Dataset,Multiple,Multiple,,,,,,x,x,x,Global,"Collection of global datasets for the study of floods, droughts and their interactions with human societies",,Zenodo,https://zenodo.org/record/3690826,,,,,,"Floods, droughts",,No,No,"This is a collection of 134 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to Lindersson et al. (2020) for further information about review methodology.",
460,Data Portal,Remotely sensed,Multiple,,,,x,,,,,Global,HydroSHEDS database,,HydroSHEDS,https://www.hydrosheds.org/products,,,,,,,,No,No,"‍The HydroSHEDS database offers a suite of global digital data layers in support of hydro-ecological research and applications worldwide. Its various hydrographic data products include catchment boundaries, river networks, and lakes at multiple resolutions and scales.
HydroSHEDS data are freely available in standard GIS formats and form the geospatial framework for a broad range of assessments including hydrological, environmental, conservation, socioeconomic, and human health applications.",
461,Data Portal,Multiple,Multiple,,,x,,,,,,Europe,EBIBALPIN,,UNIL (Université de Lausanne),http://ebibalpin.unil.ch,,,,,,,,No,Yes,"Portal with several resources, focus on Valais, Switzerland (Val d'Hérens, Val d'Arolla, Lac des Dix, etc.). Swiss Alps.",
462,Data Portal,Multiple,Multiple,x,,,,,,,,North America,Canadian Surface Prediction Archive (CASPAr),,Canadian Surface Prediction Archive (CASPAr),https://caspar-data.ca/,,,,,,,,No,No,"Canadian data portal, extends beyond mountains",
463,Dataset,Multiple,Multiple,,x,,,,,,,Global,Alpine Treelines Online,,Alpine Treelines Online,https://alpine-treelines.de/map-metadata.html,,,,,,Alpine treeline,,No,Yes,A COMMUNITY-BASED INFORMATION FACILITY FOR ALPINE TREELINE RESEARCH,
464,Dataset,Modelled,2,,,,,,,,x,Global,The Global Waveform Catalogue (gWFM),,"Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics",https://comet.nerc.ac.uk/the-global-waveform-catalogue/,,,,,,Earthquakes,,No,No, Database of point-source fault-plane solutions and focal depths for moderate-magnitude earthquakes,
465,Data Portal,Modelled,2,,,,x,,,,,Global,LIMNADES,,LIMNADES,https://limnades.stir.ac.uk/Limnades_login/info_pages/About.php,,,,,,,,No,No,database of ground bio-optical measurements of worldwide lakes,
466,Data Portal,Multiple,Multiple,,x,,,,,,,Europe,National forest inventory (NFI) ,,National Forest Inventory (NFI),https://www.lfi.ch/dienstleist/katalog-en.php?lang=en,,,,,,Forests,,No,No,data catalog of Swiss forests. Only in german though,
467,Data Portal,In Situ,Multiple,x,,,,,,,,South America,BDHI | Base de Datos Hidrológica Integrada,,BDHI | Base de Datos Hidrológica Integrada,http://bdhi.hidricosargentina.gob.ar/,,,,,,,,No,No,registration required. Website in spanish. Wheather stations in Argentina. Paper https://www.frontiersin.org/articles/10.3389/feart.2020.00328/full,
468,Dataset,Modelled,Multiple,,,x,x,,,,,South America,"Columbia River headwaters - hydroclimate and glacier change, 1977-2017",,Zenodo,https://zenodo.org/record/3779279#.Yin6wS8w3q0,,,,,,,,No,Yes,paper: https://www.frontiersin.org/articles/10.3389/feart.2020.00136/full#h7,
469,Dataset,Multiple,Multiple,,x,,,,,,,Europe,"Dataset of Global Change, altitudinal range shift and colonization of degraded habitats in mediterranean mountains (MIGRAME)",,"Andalusian Environmental Center, University of Granada, Regional Government of Andalusia",https://doi.org/10.15470/orboj4,,,,,,,,No,Yes,"dataset about tree catalogue in Sierra Nevada, Spain. article https://scholar.google.com/scholar_url?url=https://www.mdpi.com/1999-4907/12/11/1584/pdf&hl=en&sa=X&d=2931129160752868005&ei=CU6cYdr6BtmP6rQP-5qHqAM&scisig=AAGBfm1mjm_zBYCSsxarRAuvMedXVYdzxA&oi=scholaralrt&hist=WFKmLbgAAAAJ:13938909646747700291:AAGBfm2mMW1IuBpd4CcRPLR4zQv19HMgBA&html=&folt=kw",
470,Dataset,Multiple,Multiple,,x,,,,,,,Europe,"Dataset of Passerine bird communities in a mediterranean high mountain (Sierra Nevada, Spain)",,"Andalusian Environmental Center, University of Granada, Regional Government of Andalusia",https://www.gbif.org/dataset/bb1c7420-fbb5-46e2-87ad-658081360694#description; (doi.org/10.15468/ow9noo),,,,,,,,No,Yes,dataset about bird communities in Sierra Nevada high mountain region. article https://scholar.google.com/scholar_url?url=https://www.mdpi.com/1999-4907/12/11/1584/pdf&hl=en&sa=X&d=2931129160752868005&ei=CU6cYdr6BtmP6rQP-5qHqAM&scisig=AAGBfm1mjm_zBYCSsxarRAuvMedXVYdzxA&oi=scholaralrt&hist=WFKmLbgAAAAJ:13938909646747700291:AAGBfm2mMW1IuBpd4CcRPLR4zQv19HMgBA&html=&folt=kw,
471,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Global,GEOSS portal,,GEOSS,https://www.geoportal.org/,,,,,,,,No,No,data portal with many different themes + global extent. Extends beyond mountains,
472,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Europe,TARKKA Service for Satellite Data,,Finnish Environment Institute,https://tarkka.syke.fi/eo-tarkka/?lang=fi,,,,,,,,No,No,"data portal, extend beyond mountains. Check also https://www.copernicus-user-uptake.eu/resources/data-portals",
473,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Global,ArcGIS Hub,,ArcGIS,https://hub.arcgis.com/search?collection=Dataset,,,,,,,,No,No,"ArcGIS Hub is an easy-to-configure cloud platform that organizes people, data, and tools to accomplish Initiatives and goals.",
474,Dataset,In Situ,2,,x,,,,,,,Asia,Pteridophyte of the Urals and adjacent areas,,"Komarov Botanical Institute, Russian Academy of Sciences, St. Petersburg",https://www.gbif.org/dataset/d2875a50-0304-469d-b19e-78dc08007931#description,,,,,,,,No,Yes,Article about Pteridophyte of the Urals and adjacent areas. rekated paper: https://scholar.google.com/scholar_url?url=https://bdj.pensoft.net/article/76680/download/pdf/&hl=en&sa=X&d=15812948067416192505&ei=m3alYcz3HOaTy9YP8feA4AQ&scisig=AAGBfm2qgsdMvIVSfp0fEYBeSuJ-Lrj7wA&oi=scholaralrt&hist=WFKmLbgAAAAJ:14317015567059880161:AAGBfm0b8VXF1kw9gNDlu6oWzOWq7b0wcg&html=&folt=kw,
475,Data Portal,Multiple,Multiple,x,x,x,x,x,,,,Global,Polar Data Catalogue,,Polar Data Catalogue (PDC),https://polardata.ca/pdcsearch/,,,,,,,,No,No,"Extends beyond mountains but can still be relevant. Focus on Poles, Artic and Antarctica. ",
476,Data Portal,Multiple,Multiple,x,x,x,x,x,,,,Global,Artic Data Center ,,Arctic Data Center,https://adc.met.no,,,,,,,,No,No,Arctic Data Centre is a WMO Information System Data Collection and Production Centre building on the IPY legacy. extends beyond mountains but can still be relevant. Data portal,
477,Data Portal,Multiple,Multiple,,,,,,,,x,Europe,GIN - Common Information Platform for Natural Hazard,,GIN - Common Information Platform for Natural Hazard,https://www.gin5.admin.ch,,,,,,,,No,No,"Plenty of data related to meterology and climate of Switzerland, from swiss organisations such as meteosuisse, WSL. Login required",
478,Data Portal,Multiple,Multiple,x,,x,x,,,,,Global,Physical Sciences Laboratory - Data and Immagery,,Physical Sciences Laboratory,https://psl.noaa.gov/data/index.html,,,,,,,,No,No,,
479,Data Portal,Multiple,Multiple,,x,,,,,,,North America,USDA Agricultural Research Service  - Ag Data Commons,,USDA Agricultural Research Service,  https://data.nal.usda.gov/search/type/dataset ,,,,,,,,No,No,,
480,Data Portal,Multiple,Multiple,,x,,,,,,,North America,LTAR The Long-Term Agroecosystem Research Network,,USDA Agricultural Research Service,https://ltarnetwork.org ,,,,,,,,No,No,,
481,Data Portal,Multiple,Multiple,x,x,x,x,x,,,,Global,DEIMS-SDR (Dynamic Ecological Information Management System - Site and dataset registry) ,,DEIMS-SDR (Dynamic Ecological Information Management System - Site and dataset registry) , https://deims.org,,,,,,,,No,No,"DEIMS-SDR (Dynamic Ecological Information Management System - Site and dataset registry) is an information management system powered by eLTER. It allows you to discover long-term ecosystem research sites around the globe, along with the data gathered at those sites and the people and networks associated with them. DEIMS-SDR describes a wide range of sites, providing a wealth of information, including each site’s location, ecosystems, facilities, parameters measured and research themes. It is also possible to access a growing number of datasets and data products associated with the sites",
482,Data Portal,Multiple,Multiple,x,,,,,,,,Global,ICOS - integrated Carbon observation system - Data Portal,,ICOS - integrated Carbon observation system,"https://data.icos-cp.eu/portal/#%7B%22filterCategories%22:%7B%22project%22:%5B%22icos%22%5D,%22level%22:%5B1,2%5D,%22stationclass%22:%5B%22ICOS%22%5D%7D%7D",,,,,,,,No,No,,
483,Dataset,Multiple,Multiple,,,,,x,,,,Europe,Glacial and postglacial sedimentary infill in Slovakian High Tatra Mts. lakes: Acoustic survey and lithological data,,ScienceDirect,https://www.sciencedirect.com/science/article/pii/S2352340921009197#afn001,pipik@savbb.sk,,,,,,,No,Yes,Article,
484,Data Portal,Multiple,Multiple,x,x,x,x,x,,,,Europe,Environmental Information Platform,,UK Centre for Ecology and Hydrology, https://eip.ceh.ac.uk/ ,,,,,,,,No,No,"The Environmental Information Platform provides enhanced access to UKCEH's key data holdings via web-based tools, programming interfaces and a data catalogue. It enables you to visualise and interrogate some of the diverse environmental datasets held by UKCEH.",
485,Data Portal,Multiple,Multiple,x,x,,,x,,,,North America, THE FOREST ECOSYSTEM MONITORING COOPERATIVE (formerly the Vermont Monitoring Cooperative),, THE FOREST ECOSYSTEM MONITORING COOPERATIVE (formerly the Vermont Monitoring Cooperative),https://www.uvm.edu/femc/data,,,,,,,,No,No,,
486,Data Portal,Multiple,Multiple,x,,,,,,,,Global,Network for the Detection of Atmospheric Composition Change,,Network for the Detection of Atmospheric Composition Change,https://ndacc.larc.nasa.gov/data,,,,,,,,No,No,,
487,Data Portal,Multiple,Multiple,,x,,,,,,,Global,NSII: National Specimen Information Infrastructure,,National Science & Technology Infrastructure,http://www.nsii.org.cn/2017/home-en.php ,,,,,,,,No,No,,
488,Dataset,In Situ,1,,,,,x,,,,Africa,"An hourly ground temperature dataset for 16 high-elevation sites (3493–4377 m a.s.l.) in the Bale Mountains, Ethiopia (2017–2020)",,Zenodo,https://zenodo.org/record/6047457,,,,,,,,No ,Yes,"Groos, A. R., Niederhauser, J., Lemma, B., Fekadu, M., Zech, W., Hänsel, F., Wraase, L., Akçar, N., and Veit, H.: An hourly ground temperature dataset for 16 high-elevation sites (3493–4377 m a.s.l.) in the Bale Mountains, Ethiopia (2017–2020), Earth Syst. Sci. Data, 14, 1043–1062, https://doi.org/10.5194/essd-14-1043-2022, 2022. article: https://essd.copernicus.org/articles/14/1043/2022/",
489,Data Portal,Multiple,Multiple,,x,,,,,,,North America,USDA Forest Service Dataset,,USDA,https://www.fs.usda.gov/research/srs/products/dataandtools/datasets,,,,,,,,No,No,,
490,Data Portal,Multiple,Multiple,,x,,,,,,,Global,"PhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000-2018",2,PhenoCam, https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1674 ; https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1689,,,,,,,,No,No,,
491,Data Portal,Multiple,Multiple,,x,,,,,,,Asia,Georgian Biodiversity Database,,Georgian Biodiversity Database,http://biodiversity-georgia.net,,,,,,,,No,No,Extend beyond mountains but relevant for Caucasus. ,
492,Dataset,In Situ,1,,x,,,,,,,Global,MIREN survey of plant species in mountains,,Mountain Invasion Research Network (MIREN),https://zenodo.org/record/5529072#.Yjrhui8w3q,miren.data@gmail.com,,,,,,,No,Yes,related paper: https://onlinelibrary.wiley.com/doi/epdf/10.1002/ece3.8590,
493,Dataset,Multiple,Multiple,x,,x,,,,,,,Cryobs-Clim-CDP (2018): Cryobs-Clim-CDP / Col de Porte : a meterological and snow observatory,,Cryobs-Clim-CDP,https://doi.osug.fr/public/CRYOBSCLIM_CDP/CRYOBSCLIM.CDP.2018.html,,,,,,,,No,Yes,The Col de Porte observatory is located near Grenoble in Chartreuse massif and is dedicated to study interactions between the cryosphere and the atmosphere at medium altitude. The site is managed by Météo-France (CNRM/CEN). related paper : https://essd.copernicus.org/articles/11/71/2019/,
494,Data Portal,Multiple,Multiple,x,,,,,,,,Global,ACTRiS Data Centre - an atmospheric data portal,,"ACTRIS - The Aerosol, Clouds and Trace Gases Research Infrastructure ",,,,,,,,,No,No,,
495,Data Portal,Multiple,Multiple,x,,,,,,,,Global,The Advanced Global Atmospheric Gases Experiment - AGAGE,,The Advanced Global Atmospheric Gases Experiment - AGAGE,https://agage.mit.edu/data/agage-data,,,,,,,,No,No,,
496,Data Portal,Multiple,Multiple,,,,x,,,,,Global,IS OLA (Observatory on LAkes),,IS OLA (Observatory on LAkes),https://si-ola.inrae.fr/si_lacs/login.jsf,,,,,,,,No,No,"The database of the Observatory include various types of data from monitored lakes, including biological, physical and chemical parameters (phytoplankton, zooplankton, fish, water chemical analyses, physical characteristics, etc..). The data are, on the one hand, data obtained from direct in situ measurements, as those collected from probe sensors (vertical depth profiles for pH, T°, turbidity, transparency, fluorescence, etc..), and, on the other hand, data obtained from laboratory and microscopy analyses (plankton composition, nutrients concentrations in water, ?). The OLA SI provides long-term data on 4 deep peri-alpine lakes (Lake Geneva, Lake Annecy, Lake Bourget and Lake Aiguebelette), and more recent dataset (from 2015) for several high altitude alpine lakes (sentinel lakes). Not exclusively for mountain areas, but can be applied to mountains. Some data are available now for alpine lakes. ",
497,Data Portal,Multiple,Multiple,,,,,,x,x,,Global,"UNECE United Nations Economic Commission for Europe, statistics",,"UNECE United Nations Economic Commission for Europe, statistics",https://w3.unece.org/PXWeb/en,,,,,,,,No,No,,
498,Data Portal,Multiple,Multiple,,x,,,,,,,Global,Avian Knowledge Network,,Avian Knowledge Network,https://avianknowledge.net/index.php/discover-and-download-data/,,,,,,,,No,No,,
499,Data Portal,Multiple,Multiple,,x,,,,,,,Europe,vogelwarte.ch Open Repository and Archive,,Swiss Ornithological Institute,https://zenodo.org/communities/vora?page=1&size=20;%20https:%2F%2Fwww.vogelwarte.ch%2Fen%2Fvogelwarte%2Flibrary%2Fopen-access-and-repository,,,,,,Birds,,No,No,Focus on Switzerland. ,
500,Data Portal,Multiple,Multiple,x,x,x,x,x,,,,Africa,BIOTA Data Portal,,BIOTA,https://www.biota-africa.org/reg_disciplines_main_ba.php?Page_ID=L600,,,,,,,,No,No,,
501,Data Portal,Multiple,Multiple,,,,x,,,,,North America,"USGS Water, Energy, and Biogeochemical Budgets (WEBB)",,USGS,https://water.usgs.gov/webb/software.html,,,,,,,,No,Yes,"The U.S. Geological Survey initiated the Water, Energy, and Biogeochemical Budgets (WEBB) program in 1991 to understand the processes controlling water, energy, and biogeochemical fluxes over a range of temporal and spatial scales and to understand the interactions of these processes, including the effect of atmospheric and climatic variables. Five small research watersheds were selected, in part because they had existing long-term research data sets on which the WEBB program could build, and in part to be geographically and ecologically diverse and represent a range of hydrologic and climatic conditions. Loch Vale Watershed, in the mountains of Colorado, has alpine, subalpine, and montane basins that are typical of the Rocky Mountains. Lying within Rocky Mountain National Park and administered by the National Park Service, the watershed has been part of the interagency National Acid Precipitation Assessment Program (NAPAP), and is a UNESCO- designated International Biosphere Reserve.

Luquillo Experimental Forest in eastern Puerto Rico is a tropical rainforest. It is administered by the U.S. Forest Service and has been designated a long-term ecological research (LTER) site by NSF and an International Biosphere Reserve by UNESCO. WEBB research is also being conducted in the Río Grande de Loiza basin, an urbanized and agriculturally developed watershed near the Experimental Forest.

Panola Mountain Watershed, Georgia, is a forested watershed located southeast of Atlanta. It is in the Panola Mountain State Conservation Park, and has been part of NAPAP.

Sleepers River Watershed in northeastern Vermont is about 2/3 forest with the remaining land primarily pasture. The watershed was administered by the Department of Agriculture's Agricultural Research Service from 1957 to 1966; by the National Weather Service's Office of Hydrology from 1966 to 1979; and by the U.S. Army's Cold Regions Research and Engineering Laboratory (CRREL). The site has also been used as a comparison site to the Hubbard Brook LTER site.

Trout Lake Watershed in northcentral Wisconsin is in a highlands lakes area. It is the North Temperate Lakes LTER which is operated through the University of Wisconsin's Center for Limnology.",
502,Dataset,In Situ,1,,,,x,,,,,Global,ICP Waters,,ICP Waters,https://www.icp-waters.no/data/,,,,,,,,No,No,"ICP Waters is a collaborative programme involving circa 20 countries in Europe, plus the USA and Canada. The core dataset comprises around 250 river and lake monitoring stations, some of which have more than 30 years’ worth of surface water data.

The map below shows the locations of the main ICP Waters sites, together with basic catchment properties. A more comprehensive overview map, including summary statistics and trend analyses for key surface water parameters, can be found on the data exploration page.
ICP Waters is a collaborative programme involving circa 20 countries in Europe, plus the USA and Canada. The core dataset comprises around 250 river and lake monitoring stations, some of which have more than 30 years’ worth of surface water data.

The map below shows the locations of the main ICP Waters sites, together with basic catchment properties. A more comprehensive overview map, including summary statistics and trend analyses for key surface water parameters, can be found on the data exploration page.
ICP Waters is a collaborative programme involving circa 20 countries in Europe, plus the USA and Canada. The core dataset comprises around 250 river and lake monitoring stations, some of which have more than 30 years’ worth of surface water data.

The map below shows the locations of the main ICP Waters sites, together with basic catchment properties. A more comprehensive overview map, including summary statistics and trend analyses for key surface water parameters, can be found on the data exploration page.
ICP Waters is a collaborative programme involving circa 20 countries in Europe, plus the USA and Canada. The core dataset comprises around 250 river and lake monitoring stations, some of which have more than 30 years’ worth of surface water data.

The map below shows the locations of the main ICP Waters sites, together with basic catchment properties. A more comprehensive overview map, including summary statistics and trend analyses for key surface water parameters, can be found on the data exploration page.
ICP Waters is a collaborative programme involving circa 20 countries in Europe, plus the USA and Canada. The core dataset comprises around 250 river and lake monitoring stations, some of which have more than 30 years’ worth of surface water data.

The map below shows the locations of the main ICP Waters sites, together with basic catchment properties. A more comprehensive overview map, including summary statistics and trend analyses for key surface water parameters, can be found on the data exploration page.",
503,Data Portal,Multiple,Multiple,x,x,x,x,x,x,,,,The Arctic Data archive System (ADS),,The Arctic Data archive System (ADS),https://ads.nipr.ac.jp,,,,,,,,No,No,,
504,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,,Global,Norwegian Polar Data Centre,,Norwegian Polar Data Centre,https://data.npolar.no/home/,,,,,,,,No,No,,
505,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,,Europe,Norwegian Polar Institute Map Data and Services,,Norwegian Polar Institute,https://geodata.npolar.no,,,,,,,,No,No,,
506,Data Portal,Multiple,Multiple,x,x,x,x,,,,,Europe,Svalbard Integrated Arctic Earth Observing System,,Svalbard Integrated Arctic Earth Observing System,https://www.sios-svalbard.org,,,,,,,,No,No,,
507,Data Portal,Multiple,Multiple,x,,,,,,,,Europe, EBAS database - by the Norwegian Institute for Air Research (NILU) ,,Norwegian Institute for Air Research (NILU) ,https://ebas.nilu.no,,,,,,,,No,No,"EBAS is a database with atmospheric measurement data.
EBAS objective is to handle, store and disseminate atmospheric composition data generated by international and national frameworks like long-term monitoring programmes and research projects.",
508,Data Portal,Multiple,Multiple,,x,,x,,,,,Global,Arctic Biodiversity Data Service (ABDS) - Circumpolar Biodiversity Monitoring Programme (CBMP),,Conservation of Artic Fauna and Flora (CAFF),https://abds.is/index.php,,,,,,,,No,No,"The ABDS is the online, interoperable data management system for biodiversity data generated via the activities of CAFF, including its Circumpolar Biodiversity Monitoring Programme (CBMP). ",
509,Dataset,Multiple,2,,,,x,,,,,Europe,"Physical Features of Waterbodies from Sierra Nevada, Spain",Version 1,Instituto Andaluz de Investigacion y Formacion Agraria Pesquera Alimentaria y de la Produccion Ecologica,https://data.mendeley.com/datasets/m9wzjwhffk/2,,,,,,,,No,Yes,"This dataset was obtained over repeated field-trips to the massif in question and contains the physical parameters of its recognised water-bodies. It therefore defines the general cartography of the area, with data on individual features regarding the geographical coordinates (x, y, z), dimensions (length, width, depth), flooded surface area, stored water volume, shoreline length, as well as the area of associated green fringes and the length of their borders. https://doi.org/10.3390/hydrology6030059",
510,Data Portal,Multiple,Multiple,,x,,x,,,,,,freshwaterecology.info database,,freshwaterecology.info,https://www.freshwaterecology.info,,,,,,,,No,No,"Here you can find autecological characteristics, ecological preferences and biological traits as well as distribution patterns of more than 20,000 European freshwater organisms belonging to fish, macro-invertebrates, macrophytes, phytobenthos and phytoplankton.

The ecology data feature occurrence related parameters (e.g. ecoregional distribution or endemism, etc.), region related parameters (e.g. stream zonation or altitudinal preference, etc.), habitat related parameters (e.g. temperature or substrate preference, etc.) or life and body related parameters (e.g. feeding type or life duration, etc.) and others. All biological traits and ecological parameters can be individually combined and queried.",
511,Dataset,Other,Multiple,,x,,,,,,,South America,The Andes through time and the evolution and distribution of Andean floras,,"Figshare, Oscar Perez",https://figshare.com/articles/dataset/The_Andes_through_time_and_the_evolution_and_distribution_of_Andean_floras/16540173,,,,,,,,No,No,"The Andes are the world’s most biodiverse mountain chain, encompassing a complex array of ecosystems from tropical rainforests to alpine habitats. Here, we provide a synthesis of Andean Tracheophyte plant diversity by estimating a list of all species with publicly available records, which we integrate with a phylogenetic dataset of 14,501 Neotropical plant species in 194 clades. We find that (i) the Andean flora comprises at least 28,691 georeferenced species documented to date, (ii) Northern Andean mid-elevation cloud forests are the most species-rich Andean ecosystems, (iii) the Andes are a key source and sink of Neotropical plant diversity, and (iv) the Andes, Amazonia and other Neotropical biomes have had a considerable amount of biotic interchange through time.
The Andes are the world’s most biodiverse mountain chain, encompassing a complex array of ecosystems from tropical rainforests to alpine habitats. Here, we provide a synthesis of Andean Tracheophyte plant diversity by estimating a list of all species with publicly available records, which we integrate with a phylogenetic dataset of 14,501 Neotropical plant species in 194 clades. We find that (i) the Andean flora comprises at least 28,691 georeferenced species documented to date, (ii) Northern Andean mid-elevation cloud forests are the most species-rich Andean ecosystems, (iii) the Andes are a key source and sink of Neotropical plant diversity, and (iv) the Andes, Amazonia and other Neotropical biomes have had a considerable amount of biotic interchange through time.
Here, we provide a synthesis of Andean Tracheophyte plant diversity by estimating a list of all species with publicly available records, which we integrate with a phylogenetic dataset of 14,501 Neotropical plant species in 194 clades. We find that (i) the Andean flora comprises at least 28,691 georeferenced species documented to date, (ii) Northern Andean mid-elevation cloud forests are the most species-rich Andean ecosystems, (iii) the Andes are a key source and sink of Neotropical plant diversity, and (iv) the Andes, Amazonia and other Neotropical biomes have had a considerable amount of biotic interchange through time.",
512,Dataset,Modelled,2,,,,,,,,x,Asia,"Reconstruction and Characterisation of Past and the Most Recent Slope Failure Events at the 2021 Rock-Ice Avalanche Site in Chamoli, Indian Himalaya",,"School of Geosciences, University of Aberdeen, Meston Building, King’s College",https://www.mdpi.com/2072-4292/14/4/949,"Anshuman Bhardwaj
anshuman.bhardwaj@abdn.ac.uk",,,,,,,No,Yes,"On the 7 February 2021, a flash flood triggered by a rock-ice avalanche with an unusually long runout distance, caused significant damage of life and property in the Tapovan region of the Indian Himalaya. Using multi-temporal satellite datasets, digital terrain models (DTMs) and simulations, here we report the pre-event and during-event flow characteristics of two large-scale avalanches within a 5-year interval at the slope failure site.",
513,Dataset,Modelled,3,,,x,,,,,,Asia,Caucasus glacier mass balance and thickness changes in 2000-2019,,Zenodo,https://doi.org/10.5281/zenodo.5816997,,,,,,,,No,Yes,"In this study, we used glacier mass balance and thickness data obtained from geodetic method by Hugonnet et al. (2021) to clarify the response of region wide glacier mass balance to climate change in the Greater Caucasus over the last two decades (2000‒2019). We also analyze the long-term spatio-temporal glacier surface elevation and specific mass change for the regional, river basin, and individual glaciers. ",
514,Dataset,In Situ,1,,x,,,,,,,Asia,"MHA Herbarium: Collections of mosses from Yana-Indigirka Region, Yakutia, Russia",,Tsitsin Main Botanical Garden Russian Academy of Sciences,https://www.gbif.org/dataset/39d71489-2029-4272-95ce-eec6fb8bb5fb,,,,,,,,No,No,"The Skvortsov Herbarium of the Main Botanical Garden, Russian Academy of Sciences (MHA) in 1945-1980s dealt with vascular plants and only scatterred occasional collections of bryophytes and lichens accumulated there without special arrangement. Since late 1980s, the bryophyte accessions in the MHA Herbarium became permanent and several project were started since then, inclcuding currently conducted ""Moss Flora of Russia"".",
515,Dataset,Multiple,Multiple,,,x,,,,,,Asia,High Mountain Asia UCLA Daily Snow Reanalysis,Version 1,National Snow and Ice Data Center (NSIDC),https://doi.org/10.5067/HNAUGJQXSCVU,,,,,,,,No,No,"This HMA snow reanalysis data set contains daily estimates of posterior snow water equivalent (SWE), fractional snow covered area (fSCA), snow depth (SD), etc.",
516,Dataset,Multiple,Multiple,,x,,x,x,,,,North America,Qinayan/Soil-thickness: Soil thickness estimation,,Zenodo,https://zenodo.org/record/4445383#.YkLE2i8Rp-U,,,,,,,,No,Yes,"related article: A hybrid data–model approach to map soil thickness in mountain hillslopes - https://esurf.copernicus.org/articles/9/1347/2021/. This release includes Python codes and associated field sampling and remote sensing data for the estimation of the spatial distribution of soil thickness in two hillslopes in the Pump House area in the East River Watershed in the CO., the U.S.",
517,Dataset,Multiple,Multiple,,,x,,,,,,Global,The retreat of mountain glaciers since the Little Ice Age: a spatially explicit global database,,figshare,https://figshare.com/articles/dataset/The_retreat_of_mountain_glaciers_since_the_Little_Ice_Age_a_spatially_explicit_global_database/13700215,,,,,,,,No,Yes,"Here, we present a spatially explicit dataset showing positions of glacier fronts since the Little Ice Age (LIA) maxima. The dataset is based on multiple historical archival records including topographical maps; repeated photographs, paintings and aerial or satellite images with supplement of geochronology and our own field data. We provide ESRI shapefiles showing 728 past positions of 93 glacier fronts from all continents, except Antarctica, covering the period between the Little Ice Age maxima and the present. On average, the time series span the past 190 years.",
518,Dataset,Multiple,Multiple,x,,,x,,,,,Asia,"Deformation and meteorological data of the Khoko landslide, Enguri, Republic of Georgia (2016-2020)",,"Tibaldi, Alessandro (Università degli Studi di Milano-Bicocca)",https://www.unidata.unimib.it/?indagine=deformation-and-meteorological-data-of-the-khoko-landslide-enguri-republic-of-georgia-2016-2020,,,,,,,,No,Yes,"In particular data deal with measurement time series taken over the period 2016-2020. Data include information on slope deformation, meteorological factors and man-induced perturbations of the water level variations of the reservoir. ",
519,Dataset,Multiple,Multiple,,,x,,,,,,Asia,"Glacier Inventories in the Chhombo Chhu Watershed of Tista basin, Sikkim Himalaya, India",,Zenodo,https://doi.org/10.5281/zenodo.4457183,,,,,,,,No,Yes,"Multi-temporal inventory of glaciers compiled for the Chhombo Chhu Watershed (CCW) of Tista basin, Sikkim Himalaya, India. ",
520,Dataset,Multiple,Multiple,,,x,x,,,,,North America,"Hydrometeorological, glaciological and geospatial research data from the Peyto Glacier Research Basin in the Canadian Rockies",,Federated Research Data Repository / dépôt fédéré de données de recherche,https://doi.org/10.20383/101.0259,,,,,,,,No,Yes,"Hydrological, meteorological, glaciological, and geospatial data of the Peyto Glacier Research Basin (PGRB) in the Canadian Rockies are presented",
521,Data Portal,Remotely sensed,Multiple,x,x,x,x,x,x,x,x,Global,Chinese High-resolution Satellite Data Resources (CSDR) ,,Natural ResourcesSatellite Remote Sensing Cloud Service Platform,http://www.sasclouds.com/english/home/,,,,,,,,No,No,,
522,Data Portal,Multiple,Multiple,,x,,,,,,,Global,DATA.GEO-TREES,,DATA.GEO-TREES,https://data.geo-trees.org/,,,,,,,,No,No,"DATA.GEO-TREES, formerly named Forest Observation System is an international cooperation to establish a global in-situ forest biomass database to support earth observation and to encourage investment in relevant field-based observations and science.

DATA.GEO-TREES provides well curated biomass plot data in a unified format, that is aggregated from tree level data consistently across different networks.",
523,Data Portal,Multiple,Multiple,x,x,x,x,x,x,,x,Global,Office for Outer Space Affairs UN-SPIDER Knowledge Portal Data Sources,,Office for Outer Space Affairs UN-SPIDER Knowledge Portal,https://www.un-spider.org/links-and-resources/data-sources,,,,,,,,No,No,"This database provides descriptions of a large variety of satellite imagery, elevation models, land use and land cover maps as well as near real-time data products for different hazard types. You can search the database by data type or hazard as well as by other relevant factors including costs, temporal or spatial coverage, satellite or file types.",
524,Data Portal,Multiple,Multiple,x,,,,,,,,Global,TIGGE Archive,,TIGGE Archive,https://confluence.ecmwf.int/display/TIGGE,,,,,,,,No,No,"The TIGGE dataset consists of ensemble forecast data from thirteen global NWP centres, starting from October 2006. TIGGE was established as a key component of THORPEX: a World Weather Research Programme to accelerate the improvements in the accuracy of 1-day to 2 week high-impact weather forecasts for the benefit of humanity. Although the 10-year THORPEX program ended at the end of 2014, TIGGE continued for another 5 years. Currently another stage of TIGGE archive has already been confirmed for further 4 years until the end of 2023.",
525,Data Portal,Multiple,Multiple,x,x,x,x,x,,x,x,Global,GEO-CRADLE Regional Data Hub (GCRDH) ,,GEO-CRADLE,http://geocradle.eu/en/tools/regional-data-hub/,,,,,,,,No,No,"The GEO-CRADLE Regional Data Hub (GCRDH) provides access to both region-related datasets, portals and services developed by a regional network of raw data providers, intermediate users/service providers, end-users from Industry, Academic and Public Sector from the Region of Interest, and, also, datasets and services directly fed from the GEOSS-portal. ",
526,Data Portal,Multiple,Multiple,,,,,,,,x,Global,Geohazard Supersites and Natural Laboratories GSNL,,Geohazard Supersites and Natural Laboratories GSNL,https://geo-gsnl.org/supersites/permanent-supersites/,,,,,,,,No,No,"Geohazard Permanent Supersites (Supersites) are single sites or extended areas of high priority to the geohazard community, in which single or multiple geological hazards pose a threat to human population and/or critical facilities.",
527,Other,Multiple,Multiple,,,,x,,,,,Global,Global Drought Information System GDIS,,Global Drought Information System GDIS,https://gdis-noaa.hub.arcgis.com,,,,,,,,No,No,,
528,Other,Multiple,Multiple,x,,,x,,x,,,Global,Stockholm Convention Global Monitoring Plan Data Warehouse,,Stockholm Convention Global Monitoring Plan Data Warehouse,https://www.pops-gmp.org/,,,,,,,,No,No,"GMP Data Warehouse provides on-line software tools supporting the implementation of the Stockholm Convention on Persistent Organic Pollutants, especially the effectiveness evaluation by providing comparable, harmonised and reliable information on persistent organic pollutants levels globally in core environmental matrices: air, human tissues (breast milk, blood), and water.",
529,Other,Multiple,Multiple,,,,,,,,x,Global,Global Wildfire Information System GWIS,,Global Wildfire Information System GWIS,https://gwis.jrc.ec.europa.eu,,,,,,,,No,No,,
530,Data Portal,Multiple,Multiple,,x,,,,,,,Global,NPS DataStore ,,NPS DataStore ,https://irma.nps.gov/DataStore/,,10m-100m,,,Annual,"Locations, areas, and morphological attributes for each water body",,No,No,"The DataStore application is a digital repository for the National Park Service that provides tools for adding, organizing, and discovering documents and datasets relevant to park resources.",
531,Dataset,Multiple,Multiple,,,,x,,,,,North America,High resolution inland surface water dataset for the tundra and boreal in North America,,National Tibetan Plateau Data Center,https://doi.org/10.5281/zenodo.4537289,,,,,,,https://doi.org/10.5281/zenodo.4537289,No,No,"The main contributions of this dataset are as follows: 1) the first investigation of water bodies in the tundra and boreal biomes in North America. Nearly 6.5 million water bodies were identified, about 90% of which were smaller than 0.1 km2; 2) this dataset provides the locations, areas, and morphological attributes for each water body, enabling the analysis of water body distribution, types, and more, which is expected to support studies in the water cycle, carbon emission, and permafrost in high latitudes under global climate change.",
532,Dataset,Remotely sensed,2,,,x,,,,,,Asia,Central and Eastern Himalaya glacier velocities 2017-2019 (Sentinel 2),,Zenodo,https://doi.org/10.5281/zenodo.4537289,,80m,,,,Velocity,https://doi.org/10.5281/zenodo.4537289,No,Yes,This dataset contains the median glacier surface velocity for the Central and Eastern Himalaya glacier velocities 2017-2019 (Sentinel 2). The velocities have been obtained by feature-tracking of November Sentinel 2 images spaced 1 year apart.,
533,Dataset,Multiple,Multiple,x,,x,,,,,,Asia,"Permafrost, active layer, and meteorological data (2010–2020) at the Mahan Mountain relict permafrost site of northeastern Qinghai–Tibet Plateau",,National Tibetan Plateau Data Center,https://data.tpdc.ac.cn/en/data/c0a65170-d7cc-4a10-b3fd-39f813cd1387/,,1m-10m,,,Hourly,"air temperature, air relative humidity, wind speed and direction, shortwave and longwave downwards and upwards radiation, atmospheric pressure, precipitation, and ground surface temperature ",https://doi.org/10.11888/Cryos.tpdc.271838,No,Yes,"We present 11 years of such data for a relict permafrost site at Mahan Mountain on the northeast of Qinghai-Tibet Plateau. The meteorological data are comprised of air temperature, air relative humidity, wind speed and direction, shortwave and longwave downwards and upwards radiation, atmospheric pressure, precipitation, and ground surface temperature on half-an-hour timescale. ",
534,Dataset,Modelled,2,x,,,,,,,,Europe,"Two decades of distributed global radiation time series across a mountainous semiarid area (Sierra Nevada, Spain)",,PANGAEA,https://doi.pangaea.de/10.1594/PANGAEA.921012,,30mx30m,,,"Daily, Monthly, Annually",Global Radiation,https://doi.org/10.5194/essd-2020-250,No,Yes,"This work presents a time series of distributed global radiation data in a mountainous area in southern Europe: Sierra Nevada Mountain Range (Spain). The range is representative of semiarid high elevation regions with significant topographic gradients, high insolation rates, and the coexistence of Alpine and Mediterranean conditions. The datasets consist of nineteen years (2000-2018) of high resolution (30m x 30m) daily, monthly and annual global radiation maps derived using the model proposed by Aguilar et al. (2009), driven by in situ daily global radiation measurements, from sixteen weather stations with historical records in the area, and a high resolution digital elevation model.",
535,Dataset,Multiple,Multiple,,,x,,,,,,Asia,Improved daily MODIS TERRA/AQUA Snow and Randolph Glacier Inventory (RGI6.0) data for High Mountain Asia (2002-2019),,PANGAEA,https://doi.org/10.1594/PANGAEA.918198,,500 m,,,,Snow cover,https://doi.org/10.1594/PANGAEA.918198,No,Yes,"The data contains improved daily MODIS Terra/Aqua combined snow-cover merged with Randolph Glacier Inventory (RGI6.0) product. This product is generated using MODIS Terra and Aqua daily snow cover products MOD10A1 and MYD10A1 collection 6 (C6), respectively. The data covers High Mountain Asia (HMA) covering latitude 24.32− 49.19 N and Longitude 58.22 - 122.48 E with temporal coverage between 2002 and 2019. The data has daily temporal resolution and 500 m spatial resolution.",
536,Dataset,Remotely sensed,2,,,,x,,,,,Asia,Annual 30 m dataset for glacial lakes in High Mountain Asia from 2008 to 2017,,Zenodo,https://doi.org/10.5281/zenodo.4275164,,30 m,,,,Glacial Lakes,https://doi.org/10.5281/zenodo.4275164,No,Yes,We developed a High Mountain Asia (HMA) Glacial Lake Inventory (Hi-MAG) database to characterize the annual coverage of glacial lakes from 2008 to 2017 at 30 m resolution. ,
537,Dataset,Remotely sensed,1,,,,x,,,,,Asia,Glacial lake inventory of high-mountain Asia in 1990 and 2018 derived from Landsat images,,National Cryosphere Desert Data Centre,https://doi.org/10.12072/casnw.064.2019.db,,,,,,Glacial Lakes,,No,Yes,"This dataset is based on glacier catalogue data and 668 landscape Landsat TM/ETM+/OLI images, combined with ArcGIS and ENVI software, NDWI and Google Earth images, and extracted by manual visual interpretation in a buffer zone 10 km away from the boundary of the glacier Icy lake boundary.",
538,Dataset,Multiple,Multiple,x,,,x,x,,,,North America,"Co-located contemporaneous mapping of morphological, hydrological, chemical, and biological conditions in a 5th-order mountain stream network, Oregon, USA",,HYDROSHARE,https://doi.org/10.4211/hs.f4484e0703f743c696c2e1f209abb842,,,,,,,,No,Yes,Related article: Gridded maps of geological methane emissions and their isotopic signature; https://essd.copernicus.org/articles/11/1/2019/,
539,Dataset,In Situ,2,x,,,,,,,,Asia,A high-resolution air temperature data set for the Chinese Tianshan Mountains in 1979-2016,,,https://doi.org/10.1594/PANGAEA.887700,,1 km,1976,2016,6 hours,Air temperature,,No,Yes,"This study presents a unique high-resolution (1km, 6h) air temperature data set for the Chinese Tianshan Mountains from 1979 to 2016 based on a robust statistical downscaling framework. The coverage of data set is 41.1814-45.9945 °N, 77.3484-96.9989 °E. The grid point is derived from SRTM DEM, which is resampled from 90m to 1km. The total number of grid point is 818126. The time step is 6 hour at 00, 06, 12, and 18 Uhr. The data set was validated by 24 meteorological stations at daily scale. ",
540,Dataset,Modelled,2,,,,x,x,,,,Europe,"Water and sediment fluxes in Mediterranean mountainous regions: comprehensive dataset for hydro-sedimentological analyses and modelling in a mesoscale catchment (River Isábena, NE Spain)",,GFZ Data Services,http://doi.org/10.5880/fidgeo.2018.011,,,,,,,,No,Yes,"A comprehensive hydro-sedimentological dataset for the Isábena catchment, NE Spain, for the period 2010-2018 is presented to analyse water and sediment fluxes in a Mediterranean meso-scale catchment. The dataset includes rainfall data from twelve rain gauges distributed within the study area complemented by meteorological data of twelve official meteo-stations. It comprises discharge data derived from water stage measurements as well as suspended sediment concentrations (SSC) at six gauging stations of the Isábena river and its sub-catchments. Soil spectroscopic data from 351 suspended sediment samples and 152 soil samples were collected to characterize sediment source regions and sediment properties via fingerprinting analyses.",
541,Dataset,Multiple,Multiple,x,x,,x,x,,,,North America,"Soil, snow, weather, and sub-surface storage data from a mountain catchment in the rain–snow transition zone",,PANGAEA,http://dx.doi.org/10.1594/PANGAEA.819837,,,,,,,https://essd.copernicus.org/articles/6/165/2014/,No,Yes,"A comprehensive hydroclimatic data set is presented for the 2011 water year to improve understanding of hydrologic processes in the rain-snow transition zone. This type of dataset is extremely rare in scientific literature because of the quality and quantity of soil depth, soil texture, soil moisture, and soil temperature data. Standard meteorological and snow cover data for the entire 2011 water year are included, which include several rain-on-snow events. Surface soil textures and soil depths from 57 points are presented as well as soil texture profiles from 14 points. Meteorological data include continuous hourly shielded, unshielded, and wind corrected precipitation, wind speed, air temperature, relative humidity, dew point temperature, and incoming solar and thermal radiation data. Sub-surface data included are hourly soil moisture data from multiple depths from 7 soil profiles within the catchment, and soil temperatures from multiple depths from 2 soil profiles. Hydrologic response data include hourly stream discharge from the catchment outlet weir, continuous snow depths from one location, intermittent snow depths from 5 locations, and snow depth and density data from ten weekly snow surveys. Though it represents only a single water year, the presentation of both above and below ground hydrologic condition makes it one of the most detailed and complete hydro-climatic datasets from the climatically sensitive rain-snow transition zone for a wide range of modeling and descriptive studies.",
542,Dataset,Multiple,Multiple,x,,x,,,,,,Europe,"An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models",,PANGAEA,http://dx.doi.org/10.1594/PANGAEA.774249,,,,,,,https://doi.org/10.5194/essd-4-13-2012,No,Yes,"Article: https://essd.copernicus.org/articles/13/3525/2021/. A quality-controlled snow and meteorological dataset spanning the period 1 August 1993-31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France). Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist of measurements of air temperature, relative humidity, windspeed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September), when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN), in order to provide a continuous and consistent gap-free record. Data relevant to snowpack properties are provided at the daily (snow depth, snow water equivalent, runoff and albedo) and hourly (snow depth, albedo, runoff, surface temperature, soil temperature) time resolution. Internal snowpack information is provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles) and from a hourly record of temperature and height of vertically free ''settling'' disks. This dataset has been partially used in the past to assist in developing snowpack models and is presented here comprehensively for the purpose of multi-year model performance assessment. Related article : Supplement to: Morin, S et al. (2012): A 18-yr long (1993-2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models. Earth System Science Data, 4(1), 13-21, https://doi.org/10.5194/essd-4-13-2012",
543,Dataset,Multiple,Multiple,,x,,x,x,,,,Asia,Lake surface sediment pollen dataset for the alpine meadow vegetation type from the eastern Tibetan Plateau and its potential in past climate reconstructions,,National Tibetan Plateau Data Center,https://doi.org/10.11888/Paleoenv.tpdc.271191,,,,,,,,No,Yes,,
544,Dataset,Multiple,Multiple,x,,,,,,,,Asia,A long term hourly eddy covariance dataset of consistently processed CO2 and H2O Fluxes from the Tibetan Alpine Steppe at Nam Co (2005 - 2019),,Zenodo,https://doi.org/10.17026/dans-zfb-qegy,,,,,,,,No,Yes,The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau. The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. ,
545,Dataset,Multiple,Multiple,,x,,,,,,,Global,A nearly complete database on the records and ecology of the rarest boreal tiger moth from 1840s to 2020,,figshare,https://figshare.com/articles/dataset/The_Menetries_Tiger_Moth_Range_and_Ecology_Database_1840s-2020_/15000399,https://figshare.com/authors/Ivan_Bolotov/11150274,,,,,,https://doi.org/10.1038/s41597-022-01230-8,No,No,"This database presents nearly all available information on the extremely rare Menetries’ Tiger Moth Arctia menetriesii (Eversmann, 1846) (Insecta: Lepidoptera: Erebidae) collected since its original description in 1846. Totally, it contains geographic, environmental, and temporal information on 78 occurrences. ",
546,Dataset,Multiple,Multiple,,x,,,,,,,Europe,A massive coverage experiment of cosmic ray neutron sensors for soil moisture observation in a pre-alpine catchment in SE-Germany (part II: thermal imagery) [post-review version],,EUDAT Collaborative Data Infrastructure,https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b,,,,,,,https://doi.org/10.23728/B2SHARE.BD89F066C26A4507AD654E994153358B,No,Yes,"The publication contains records of cosmic ray neutron sensing (CRNS - stationary, roving and Neutron flux), meteo data,
soil moisture (SoilNet, FDR, profile probes, gravimetric), soil properties (bulk density, organic matter),
vegetation / biomass surveys, groundwater levels and discharge",
547,Dataset,Other,Other,x,,,,,,,,Europe,A dataset of tracer concentrations and meteorological observations from the Bolzano Tracer EXperiment (BTEX) to characterize pollutant dispersion processes in an Alpine valley,,PANGAEA,https://doi.org/10.1594/PANGAEA.898761,,,,,,,,No,Yes,"The data set contains ground concentrations and related meteorological measurements collected during the field campaign of the Bolzano Tracer EXperiment (BTEX).
The experiment was performed to characterize the dispersion of pollutants emitted by a waste incinerator in the basin of the city of Bolzano in the Alps.",
548,Dataset,Multiple,Multiple,,,,,x,,,,Europe,Present-day surface deformation of the Alpine region inferred from geodetic techniques,,PANGAEA,https://doi.pangaea.de/10.1594/PANGAEA.886889,,,,,,Surface deformation,,No,Yes,"We provide a present-day surface-kinematics model for the Alpine region and surroundings based on a high-level data analysis of a network of about 300 continuously operating GNSS (GPS+GLONASS) stations with observations collected over 12.4 years. Based on the network station velocities, a continuous kinematic field is derived using a geodetic least-squares collocation approach with empirically determined covariance functions.",
549,Dataset,In Situ,1,,,,x,,,,,Europe,Lake surface water temperatures of European Alpine lakes (1989–2013) based on the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set,,PANGAEA,http://dx.doi.org/10.1594/PANGAEA.831007,,,,,,Water temperature,,No,Yes,"Lake water temperature (LWT) is an important driver of lake ecosystems and it has been identified as an indicator of climate change. Consequently, the Global Climate Observing System (GCOS) lists LWT as an essential climate variable. Although for some European lakes long in situ time series of LWT do exist, many lakes are not observed or only on a non-regular basis making these observations insufficient for climate monitoring. Satellite data can provide the information needed. However, only few satellite sensors offer the possibility to analyse time series which cover 25 years or more. The Advanced Very High Resolution Radiometer (AVHRR) is among these and has been flown as a heritage instrument for almost 35 years.",
550,Dataset,Multiple,Multiple,x,x,,,x,,,,Asia,Plio-Pleistocene climate record on the Tibetan plateau,,figshare,https://figshare.com/articles/dataset/Plio-Pleistocene_climate_record_on_the_Tibetan_plateau/19033130/2,,,,,,,,Yes,Yes,"Supplement to Cheng F. et al., Alpine permafrost could account for a quarter of thawed carbon based on Plio-Pleistocene paleoclimate analogue, Nature Communications.",
551,Dataset,In Situ,Modelled,,,,,x,,,,Europe,The contamination legacy of a decommissioned iron smelter in the Italian Alps,,Zenodo,https://zenodo.org/record/1117922#.YkRygy8Rrq0,,,,,,,,No,Yes,"Supplementary material from: The contamination legacy of a decommissioned iron smelter in the Italian Alps

Journal of Geochemical Exploration, GEXPLO6064 (PII:S0375-6742(17)30432-6). https://linkinghub.elsevier.com/retrieve/pii/S0375674217304326",
552,Dataset,Multiple,Multiple,,,,,x,,,,Europe,"Geomorphology model (ArcMap version), input datasets and legend symbology files",,Research Foundation for Alpine and Subalpine Environments (RFASE),https://zenodo.org/record/4716261,,,,,,,,No,Yes,"Hierarchical geomorphological mapping in mountainous areas. 2021. Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Journal of Maps

This model creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail. The input dataset needed to create this 'three-tier-legend' is a geomorphological map of Vorarlberg with a Tier 3 category (e.g. 1111, for glacially eroded bedrock). The model then automatically adds Tier 1, Tier 2 and Tier 3 categories based on the Tier 3 code in the 'Geomorph' field. The model replaces the input file with an updated shapefile of the geomorphology of Vorarlberg, now including three tiers of geomorphological features. Python script files and .lyr symbology files are also provided here.",
553,Dataset,Multiple,Multiple,,x,,,,,,,Asia,Ecological and evolutionary processes shape belowground springtail communities along an elevational gradient,v2.0,Zenodo,https://zenodo.org/record/5815170#.YkSACS8RpmA,,,,,,,,No,Yes,"Changbai Mountain, north-east China. Here, we investigated how environmental gradients across elevation may affect species divergence in the past and act as filters of contemporary assembly of soil detritivores via traits. Full article: https://onlinelibrary.wiley.com/doi/full/10.1111/jbi.14317",
554,Dataset,Multiple,Multiple,,,x,,,,,,Asia,"A comprehensive dataset of microbial abundance, dissolved organic carbon, and nitrogen in Tibetan Plateau glaciers ",,National Tibetan Plateau Data Center,https://doi.org/10.11888/Cryos.tpdc.271841 ,,,,,,,,No,Yes,"This is a comprehensive dataset on microbial abundance, dissolved organic carbon (DOC), and total nitrogen (TN) for glaciers on the TP based on extensive field sampling from 2010. The dataset comprises 5,409 microbial abundance records of ice cores and snow pits from 12 glaciers and 2,532 DOC and TN records of five habitats, including ice core, snow pit, surface ice, surface snow, and proglacial runoff, from 38 glaciers. These glaciers covered broad areas and diverse climate conditions with a multiyear average temperature ranging from -13.4 ℃ (the Guliya glacier) to 2.9 ℃ (the Zhuxigou glacier) and multiyear average precipitation ranging from 76.9 mm (the No.15 glacier) to 927.8 mm (the 24K glacier). The data is stored in Excel format, and the contents of each file are: 1. Glacier-info.xlsx: glacier name and location 2. Icecore-info.xlsx：information about ice-core sampling points 3. Snowpit-info.xlsx：information about snow-pit sampling points 4. Microbial abundance-ice core.xlsx: microbial abundance in ice cores 5. Microbial abundance-snow pit.xlsx: microbial abundance in snow pits 6. DOC-TN-ice core.xlsx: DOC and TN concentrations in ice cores 7. DOC-TN-sonw pit.xlsx: DOC and TN concentrations in snow pits 8. DOC-TN-surface snow ice.xlsx: DOC and TN concentrations in surface ice and snow 9. DOC-TN-runoff.xlsx: DOC and TN concentrations in proglacial runoff",
555,Dataset,Remotely sensed,Multiple,,,x,,,,,,Asia,Annual 30-meter Dataset for Glacial Lakes in High Mountain Asia from 2008 to 2017,,Zenodo,https://doi.org/10.5281/zenodo.4275164 ,,30m,,,,,,No,Yes,"We developed a High Mountain Asia (HMA) Glacial Lake Inventory (Hi-MAG) database to characterize the annual coverage of glacial lakes from 2008 to 2017 at 30 m resolution. For the development of the Hi-MAG database, a total of 40,481 satellite images including Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI were used, and a systematic glacial lake detection method that comprised the automated processing using GEE and subsequent manual refinement of these lake mapping results were applied. This is the first glacial lake inventory across the HMA with annual temporal resolution, it can provide details for different types of glacial lakes and evolution patterns.",
556,Dataset,Remotely sensed,Multiple,,x,,,,,,,Global,A global map of terrestrial habitat types,,GitHub,https://github.com/Martin-Jung/Habitatmapping,,,,,,,,No,No,Article: https://zenodo.org/record/4058819,
557,Dataset,Remotely sensed,Multiple,,,,,x,,,,Global,World Karst Spring Hydrograph Database (WoKaS),,GitHub,https://github.com/KarstHub/WoKaS,tunde.olarinoye@hydmod.uni-freiburg.de,,,,,,,No,No,,
558,Dataset,In Situ,Multiple,,,,,x,,,,Global,SoilKsatDB: global soil saturated hydraulic conductivity measurements for geoscience applications ,,GitHub,https://doi.org/10.5281/zenodo.3752721,,,,,,,,No,No,"A total of 13,258 Ksat measurements from 1,908 sites were assembled from the published literature and other sources, standardized, and quality-checked in order to obtain a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB covers most global regions, with the highest data density from North America, followed by Europe, Asia, South America, Africa, and Australia. In addition to Ksat, other soil variables such as soil texture (11,584 measurements), bulk density (11,262 measurements), soil organic carbon (9,787 measurements), field capacity (7,382) and wilting point (7,411) are also included in the data set.",
559,Dataset,In Situ,Multiple,x,,,,,,,,North America,SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 ,,GitHub,https://doi.org/10.5281/zenodo.3735534 ,,,,,,,,No,No,"Station-based serially complete datasets (SCDs) of precipitation and temperature observations are important for hydrometeorological studies. We developed a SCD for North America (SCDNA) of precipitation, minimum temperature, and maximum temperature from 1979 to 2018. Raw meteorological station data were obtained from the Global Historical Climate Network Daily (GHCN-D), the Global Surface Summary of the Day (GSOD), Environment and Climate Change Canada (ECCC), and a compiled station database in Mexico (Livneh et al. 2015).",
560,Dataset,In Situ,Multiple,,,x,x,,,,,North America,A Canadian River Ice Database from National Hydrometric Program Archives,,Government of Canada; Environment and Climate Change Canada (ECCC Data catalogue),https://doi.org/10.18164/c21e1852-ba8e-44af-bc13-48eeedfcf2f4 ,,,,,,,,No,No,"As a result, this work has delivered an original research data set: the Canadian River Ice Database (CRID). The CRID includes near 71,000 river ice variables from a network of 196 NHP sites throughout Canada in operation within the period 1894 to 2015. The task of compiling this database involved manual extraction, data entry and input of more than 460,000 information fields on water level, discharge, date, time and data quality rating. In excess of 100,000 paper and digital files were reviewed with the network representing over 10,000 station years of active operation. ",
561,Dataset,Remotely sensed,Multiple,,,x,,,,,,Asia,"Temporal inventory of glaciers in the Suru sub-basin, western Himalaya: impacts of regional climate variability ",,PANGAEA,https://doi.org/10.1594/PANGAEA.904131 ,,,,,,,,No,Yes,"In this study we have created an inventory of the Suru sub-basin, western Himalaya for year 2017 using Landsat OLI data. Changes in glacier parameters have also been monitored from 1971 to 2017 using temporal satellite remote sensing data and limited field observations. Inventory data shows that the sub-basin has 252 glaciers covering 11% of the basin, having an average slope of 25 ±6° and dominantly north orientation. The average snow line altitude (SLA) of the basin is 5011 ±54 masl with smaller (47%) and cleaner (43%) glaciers occupying the bulk area. Longterm climate data (1901-2017) shows an increase in the mean annual temperature (Tmin & Tmax) by 0.77 ºC (0.25 & 1.3 ºC) in the sub-basin, driving the overall glacier variability in the region. Temporal analysis reveals a glacier shrinkage of ~6 ±0.02 %, an average rate of 4.3 ±1.02 ma-1, debris increase of 62% and 22 ±60 m SLA rise in past 46 years. This confirms their transitional response between the Karakoram and the Greater Himalayan Range (GHR) glaciers. Besides, glaciers in the sub-basin occupy two major ranges, i.e., GHR and Ladakh range (LR) and experience local climate variability, with the GHR glaciers exhibiting a warmer and wetter climate as compared to the LR glaciers. ",
562,Dataset,Remotely sensed,Multiple,x,,,x,,,,,Africa,High resolution Standardized Precipitation Evapotranspiration Index (SPEI) dataset for Africa,,CEDA Archive,https://catalogue.ceda.ac.uk/uuid/bbdfd09a04304158b366777eba0d2aeb,,,,,,,,No,No,"This dataset consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM).

The SPEI dataset covers the whole of the African continent for a 36-year-long period (1981–2016) at a horizontal resolution of 5 km (0.05 deg) and a monthly time resolution. The dataset is provided in NetCDF format with in a Geographic Lat/Lon projection. ",
563,Dataset,Remotely sensed,Multiple,,x,,,,,,,Africa,Global land use for 2015–2100 at 0.05° resolution under diverse socioeconomic and climate scenarios,v4.3,Zenodo,https://doi.org/10.5281/zenodo.3713432,,,,,,,,No,No,,
564,Data Portal,Multiple,Multiple,x,,x,x,x,x,x,,South America,Several databases for Chile,,Centre for Climate abd Reslience Research (CR2),https://www.cr2.cl/eng/#,,,,,,,,No,No,,
565,Data Portal,Multiple,Multiple,,,,,,,,x,Global,FEWS Data Downloads,,USGS,https://earlywarning.usgs.gov/fews/datadownloads/Global/CHIRPS%202.0,,,,,,,,No,No,"The USGS FEWS NET Data Portal provides access to geo-spatial data, satellite image products, and derived data products in support of FEWS NET drought monitoring efforts throughout the world. This portal is provided by the USGS FEWS NET Project, part of the Early Warning Focus Area at the USGS Earth Resources Observation and Science (EROS) Center.",
566,Dataset,Multiple,Multiple,,x,,,,,,,Global,Global Land Evaporation Amsterdam Model (GLEAM) v3,v3.7,GLEAM,https://www.gleam.eu,,,,,,,,No,No,"The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms that separately estimate the different components of land evaporation (often referred to as 'evapotranspiration’): transpiration, bare-soil evaporation, interception loss, open-water evaporation and sublimation. Additionally, GLEAM provides surface and root-zone soil moisture, potential evaporation and evaporative stress conditions",
567,Dataset,Multiple,Multiple,x,x,x,x,,,,,Global,Euro-Climhist database,,University of Bern,https://www.euroclimhist.unibe.ch/en/search-database/,,,,,,,,Yes,No,,
568,Dataset,Remotely sensed,Multiple,,x,,,,,,,Asia,Regional Land Cover Monitoring System for the Hindu Kush Himalaya,,"SERVIR Hindu Kush Himalaya, ICIMOD",https://servir.icimod.org/science-applications/regional-land-cover-monitoring-system-for-the-hindu-kush-himalaya/,,,,,,Land Cover,,No,Yes,,
569,Dataset,Remotely sensed,Multiple,,,,x,,,,,Asia,Streamflow Prediction Tool – HKH river basins,,"SERVIR Hindu Kush Himalaya, ICIMOD",https://servir.icimod.org/science-applications/streamflow-prediction-tool-hkh-river-basins/,,,,,,Streamflow,,No,Yes,"The Streamflow Prediction Tool for the HKH river basins provides 10-day streamflow forecasts for major rivers within the Amu Darya, Brahmaputra, Ganges, and Indus basins in the Hindu Kush Himalaya (HKH) region. Each river segment displayed in the map is assigned a unique identifier. Users can click on a particular river segment to display 10-day streamflow forecasts for the river stretch.
The Streamflow Prediction Tool for the HKH river basins provides 10-day streamflow forecasts for major rivers within the Amu Darya, Brahmaputra, Ganges, and Indus basins in the Hindu Kush Himalaya (HKH) region. Each river segment displayed in the map is assigned a unique identifier. Users can click on a particular river segment to display 10-day streamflow forecasts for the river stretch.
The Streamflow Prediction Tool for the HKH river basins provides 10-day streamflow forecasts for major rivers within the Amu Darya, Brahmaputra, Ganges, and Indus basins in the Hindu Kush Himalaya (HKH) region. Each river segment displayed in the map is assigned a unique identifier. Users can click on a particular river segment to display 10-day streamflow forecasts for the river stretch.
The Streamflow Prediction Tool for the HKH river basins provides 10-day streamflow forecasts for major rivers within the Amu Darya, Brahmaputra, Ganges, and Indus basins in the Hindu Kush Himalaya (HKH) region. Each river segment displayed in the map is assigned a unique identifier. Users can click on a particular river segment to display 10-day streamflow forecasts for the river stretch.
The Streamflow Prediction Tool for the HKH river basins provides 10-day streamflow forecasts for major rivers within the Amu Darya, Brahmaputra, Ganges, and Indus basins in the Hindu Kush Himalaya (HKH) region. Each river segment displayed in the map is assigned a unique identifier. Users can click on a particular river segment to display 10-day streamflow forecasts for the river stretch.",
570,Data Portal,Multiple,Multiple,,,,,,x,,,Global,Dispositif de Recherche Interdisciplinaire sur les Interactions Hommes-Milieux,,LabEx DRIIHM,https://www.driihm.fr/services-et-outils/ids-et-metadonnees,,,,,,,,No,No,Géocatalogue du LabEx DRIIHM regroupant sous forme de portails les catalogues des OHM. GeoDRIIHM permet d'afficher les données cartographiques éventuellement associées aux fiches de métadonnées et d'interroger les données cartographiques.,
571,Dataset,Multiple,Multiple,,x,,,,,,,Global,Geopark Map,,Global Geopark Network,http://www.globalgeopark.org/GeoparkMap/index.htm,,,,,,,,No,No,"UNESCO (not only biosphere reserves, but also world heritage sites, ""global geoparks"", etc. ",
572,Data Portal,Multiple,Multiple,x,x,x,x,x,,,,Global,GEWEX Data Portal,,GEWEX,https://www.gewex.org/resources/data-sets/,,,,,,,,No,No,GEWEX Panels coordinate the production of data sets from measurements taken from satellites and in situ observation networks around the globe. Links to data sets or the projects that generated them are below.,
573,Data Portal,Multiple,Multiple,,,,x,,,,,North America,Water Resources of the United States,,USGS,https://water.usgs.gov/index.html,,,,,,,,No,No,"The U.S. Geological Survey (USGS) collects information needed to understand the Nation's water resources, and provides access to water data, publications, and maps, as well as to recent water projects and events.",
574,Dataset,In Situ,1,,,,,x,,,,Europe,IGM95 NETWORK - ITALY,,ESERCITO,https://www.igmi.org/en/descrizione-prodotti/elementi-geodetici-1/rete-igm95,,,,,,,,No,No,"At the end of the last century, the Geodetic Division completed an important strategic project concerning the setting up and the determination of a new fundamental geodetic network, covering uniformly all Italy and called IGM95. This new network was entirely determined by using GPS differential measurements and it is based on the ETR89 European system by means of EUREF vertexes located in Italy; it is also connected to the traditional networks of triangulation and leveling. The IGM95 network consists of 2000 points covering all Italy and located every 20 km and of 3000 other points, located every 5 km. This increasing of vertex number is carried out only in few regions. All IGM95 points have a MSE of about 5 cm. Some points have a second materialization (i.e. an associated point) near the main materialization (CT-GPS).",
575,Dataset,In Situ,2,,x,,,,,,,South America,"Integrative overview of the herpetofauna from Serra da Mocidade, a granitic mountain range in northern Brazil - Supplementary Material. ",,Biodiversity Literature Repository,https://zenodo.org/record/1153738,,,,,,,,No,Yes,"Related Article: https://doi.org/10.3897/zookeys.715.20288.  Integrative overview of the herpetofauna from Serra da Mocidade, a granitic mountain range in northern BrazilHere the amphibian and reptile species diversity of the remote Serra da Mocidade mountain range, located in extreme northern Brazil, is reported upon, and biogeographical affinities and taxonomic highlights are discussed. A 22-days expedition to this mountain range was undertaken during which specimens were sampled at four distinct altitudinal levels (600, 960, 1,060 and 1,365 m above sea level) using six complementary methods. Specimens were identified through an integrated approach that considered morphological, bioacoustical, and molecular analyses. Fifty-one species (23 amphibians and 28 reptiles) were found, a comparable richness to other mountain ranges in the region.",
576,Dataset,Modelled,2,x,x,,,,,,,Global,"Data from: Mountain building, climate cooling and the richness of cold-adapted plants in the northern hemisphere",,Dryad,https://zenodo.org/record/4998963,,,,,,,,Yes,Yes,We mapped the cold climate in the Northern Hemisphere for most of the Cenozoic (60 Ma until present) based on paleoclimate proxies coupled with paleoelevations. We generated species distribution maps from occurrences and regional atlases for 5464 cold-adapted plant species from 756 genera occupying cold climates.,
577,Dataset,Other,Other,,,,,x,,,,Europe,"ArcGIS Map packages with geomorphological and geographical datasets used to generate maps for Au West study area in Vorarlberg, Austria",,Zenodo,https://zenodo.org/record/4718359,,,,,,,,No,Yes,"Map packages for use in ArcGIS Pro or ArcMap containing three-tiered geomorphological data and geographical datasets such as rivers and hillshading. Datasets were used to generate figures for publication: Hierarchical geomorphological mapping in mountainous areas. 2021. Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Journal of Maps. All data is in MGI Austria GK West projected coordinate system (EPSG: 31254) and was clipped to the study area.",
578,Dataset,Multiple,Multiple,,x,,,,,,,Europe,Data from: Warm temperatures during cold season can negatively affect adult survival in an alpine bird,,Dryad,https://zenodo.org/record/4069050,,,,,,Bird,,No,Yes,"This study investigated the influence of environmental covariates on the demography of a corvid species, the alpine chough Pyrrhocorax graculus, in the highly seasonal environment of the Mont Blanc region. In two steps, we estimated: 1) the seasonal survival of categories of individuals based on their age, sex, etc., 2) the effect of environmental covariates on seasonal survival. Location: Mont Blanc Massif, French Alps",
579,Dataset,Multiple,Multiple,,x,,,,,,,Europe,Data from: Differential effects of soil chemistry on the foliar resorption of nitrogen and phosphorus across altitudinal gradients,,Dryad,https://zenodo.org/record/4960754,,,,,,,,No,Yes,"Location: Dolomites, Italy. How nutrient resorption varied across the gradients through the adaptation of individual species to changing environmental conditions rather than through changes in species composition. ",
580,Dataset,In Situ,1,,x,,,,,,,Europe,"Supplementary material 2 from: Kachamakova M, Antonova V, Koshev Y (2019) The role of ant nests in European ground squirrel's (Spermophilus citellus) post-reintroduction adaptation in two Bulgarian mountains. Biodiversity Data Journal 7: e38292. https://doi.org/10.3897/BDJ.7.e38292",,Biodiversity Literature Repository,https://zenodo.org/record/3489820,,,,,,Bird,,No,Yes,"Related article: https://doi.org/10.3897/BDJ.7.e38292. ere, we present two reintroduced ground squirrel colonies, where the vast majority of the burrows are located in the base of anthills, mainly of yellow meadow ant (Lasius flavus).",
581,Dataset,Other,Other,,,,,x,,,,Europe,"ArcGIS Map packages with geomorphological and geographical datasets used to generate maps for Dunza-Tschengla study area in Vorarlberg, Austria",,Zenodo,https://zenodo.org/record/4716300,,,,,,,,No,Yes,"Map packages for use in ArcGIS Pro or ArcMap containing three-tiered geomorphological data and geographical datasets such as rivers and hillshading. Datasets were used to generate figures for publication: Hierarchical geomorphological mapping in mountainous areas. 2021. Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Journal of Maps. All data is in MGI Austria GK West projected coordinate system (EPSG: 31254) and was clipped to the study area.",
582,Dataset,Modelled,2,,x,,,,,,,North America,Data from: Limited alpine climatic warming and modeled phenology advancement for three alpine species in the Northeast United States,,Dryad,https://zenodo.org/record/4935575,,,,,,,,No,Yes,"Methods: Logistic phenology models for three northeastern US alpine species (Diapensia lapponica, Carex bigelowii and Vaccinium vitis-idaea) were developed from 4 yr (2008–2011) of phenology and air temperature measurements from 12 plots proximate to Mount Washington's long-term summit meteorological station. Plot-level air temperature, the logistic phenology models, and Mount Washington's climate data were used to hindcast model yearly (1935–2011) floral phenology and frost damage risk for the focal species. ",
583,Dataset,Multiple,Multiple,,,x,,,,,,Asia,"Rapid glacier shrinkage and glacial lake expansion of a Chi-na-Nepal transboundary catchment in the Central Himalaya, between 1964 and 2020",,OpenAIRE,https://zenodo.org/record/5496959,,,,,,"glacier, glacier lake",,No,Yes,"Glacial data set of Tama Koshi Basin (1964-2020), including glacier, glacial lake, and code.",
584,Dataset,Multiple,Multiple,,x,,,,,,,Europe,Data from: The individual and combined effects of snowmelt timing and frost exposure on the reproductive success of montane forbs,,OpenAIRE,https://zenodo.org/record/4970401,,,,,,,,No,Yes,"We explored how climate change can alter plant reproduction using an experiment in which we manipulated the individual and combined effects of snowmelt timing and frost exposure, and measured subsequent effects on flowering phenology, peak flower density, frost damage, pollinator visitation, and reproduction of four subalpine wildflowers. Additionally, we conducted a pollen supplementation experiment to test whether the plants in our snowmelt and frost treatments were pollen limited for reproduction. ",
585,Dataset,Multiple,Multiple,,x,,,,,,,Global,Recent changes in mountain birch forest structure and understory vegetation depend on the seasonal timing of reindeer grazing,,Dryad,https://zenodo.org/record/4515734,,,,,,,,No,Yes,Related article: https://doi.org/10.5061/dryad.4qrfj6q91,
586,Dataset,Modelled,2,,,x,,,,,,Asia,"Modeled Ice thickness Distribution of Glaciers in Chandra Basin, Western Himalayas, India",,OpenAIRE,https://zenodo.org/record/3694001,,,,,,ice thickness,,No,Yes,"The glacier ice thickness distribution data provided here was generated using an optimally parameterized GlabTop2_IITB [Glacier Bed Topography Indian Institute of Technology Bombay (IITB) version] model with high-resolution DEM as an input. This research work is under publication in the Journal of Mountain Science. The study reports modeled ice thickness distribution and total ice volume of selected 65 glaciers (>0.5 km2) of Chandra basin, located in Western Himalayas. ",
587,Dataset,Modelled,2,,,x,,,,,,North America,"Data from: Historical retreat of alpine glaciers in the Ahklun Mountains, western Alaska",,Dryad,https://zenodo.org/record/5001848,,,,,,glacier extent,,No,Yes,"The Ahklun Mountains support the only extant glaciers in western Alaska. The glaciers were originally mapped by the U.S. Geological Survey using photogrammetry methods based on 1972 - 1973 aerial photos. We surveyed for presence or absence of the glaciers by fixed-wing aircraft in 2006. Of 109 glaciers originally mapped, 10 (9%) had disappeared. Using aerial imagery of a subset of 76 glaciers at three time steps between 1957 - 2009, we determined the average rate of areal loss was 45% over 52 years. At this rate, it is likely that all Ahklun Mountain glaciers will be extinguished by the end of the current century.",
588,Dataset,Multiple,Multiple,,,,x,,,,x,Europe,Hydrogeological data of groundwater and precipitation monitored in the Vögelsberg landslide catchment,,OpenAIRE,https://zenodo.org/record/5817141,,,,,,,,No,Yes,"Data contains hydrogeological data of precipitation and groundwater within the catchment of the Vögelsberg landslide (Tyrol, Austria) monitored between 2017-11-22 and 2021-07-05. The dataset provides time series of discharge, temperature, electrical conductivity and stable isotope ratios in groundwater and precipitation. Dataset is associated to following preprint: “Pfeiffer, J.; Zieher, T.; Schmieder, J.; Bogaard, T.; Rutzinger, M. and Spötl, C. (2021) Spatial assessment of probable recharge areas - Investigating the hydrogeological controls of an active deep-seated gravitational slope deformation, Natural Hazards and Earth System Sciences Discussions, Vol. 2021, p. 1-29, https://doi.org/10.5194/nhess-2021-388”. Accompanying readme file gives a detailed description of data fields contained in the published data.",
589,Dataset,Modelled,2,,,x,,,,,,North America,"Glaciers and climate of the Upper Susitna basin, Alaska",,"The Great State of Alaska, Department of Natural Resources - Geological and Geophysical Surveys",https://dggs.alaska.gov/pubs/id/30138,,,,,,,,No,Yes,Related article: https://essd.copernicus.org/articles/12/403/2020/,
590,Dataset,Multiple,Multiple,,,x,x,,,,,Asia,"Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China",,Earth System Science Data,https://essd.copernicus.org/articles/11/1483/2019/,,,,,,,,No,Yes,,
591,Dataset,Multiple,Multiple,x,,,x,,,,,Asia,"A long-term hydrometeorological dataset (1993–2014) of a northern mountain basin: Wolf Creek Research Basin, Yukon Territory, Canada",,Earth System Science Data,https://essd.copernicus.org/articles/11/89/2019/,,,,,,,,No,Yes,"A set of hydrometeorological data is presented in this paper, which can be used to characterize the hydrometeorology and climate of a subarctic mountain basin and has proven particularly useful for forcing hydrological models and assessing their performance in capturing hydrological processes in subarctic alpine environments. ",
592,Dataset,Multiple,Multiple,x,,x,x,,,,,Asia,"Djankuat glacier station in the North Caucasus, Russia: a database of glaciological, hydrological, and meteorological observations and stable isotope sampling results during 2007–2017",,Earth System Science Data,https://essd.copernicus.org/articles/11/1463/2019/,,,,,,,,No,Yes,"This study presents a dataset on long-term multidisciplinary glaciological, hydrological, and meteorological observations and isotope sampling in a sparsely monitored alpine zone of the North Caucasus in the Djankuat research basin. The Djankuat glacier, which is the largest in the basin, was chosen as representative of the central North Caucasus during the International Hydrological Decade and is one of 30 “reference” glaciers in the world that have annual mass balance series longer than 50 years (Zemp et al., 2009). The dataset features a comprehensive set of observations from 2007 to 2017 and contains yearly",
593,Dataset,Multiple,Multiple,x,,,,,,,,North America,"Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA",,Earth System Science Data,https://essd.copernicus.org/articles/11/101/2019/,,,,,,,,No,Yes,"We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. ",
594,Dataset,Multiple,Multiple,x,,x,,,,,,Europe,"57 years (1960–2017) of snow and meteorological observations from a mid-altitude mountain site (Col de Porte, France, 1325 m of altitude)",,Earth System Science Data,https://essd.copernicus.org/articles/11/71/2019/,,,,,,,,No,Yes," In this paper, we introduce and provide access to daily (1960–2017) and hourly (1993–2017) datasets of snow and meteorological data measured at the Col de Porte site, 1325 m a.s.l., Chartreuse, France. ",
595,Dataset,Multiple,Multiple,x,,x,,,,,,Europe,"A meteorological and blowing snow data set (2000–2016) from a high-elevation alpine site (Col du Lac Blanc, France, 2720 m a.s.l.)",,Earth System Science Data,https://essd.copernicus.org/articles/11/57/2019/,,,,,,,,No,Yes,"A meteorological and blowing snow data set from the high-elevation experimental site of Col du Lac Blanc (2720 m a.s.l., Grandes Rousses mountain range, French Alps) is presented and detailed in this paper.",
596,Dataset,Multiple,Multiple,x,,,x,,,,,North America,"Spatially distributed water-balance and meteorological data from the Wolverton catchment, Sequoia National Park, California",,Earth System Science Data,https://essd.copernicus.org/articles/10/2115/2018/,,,,,,,,No,Yes,"This 2006–2016 record of snow depth, soil moisture and soil temperature, and meteorological data quantifies the hydrologic inputs and storage in a mostly undeveloped catchment. ",
597,Dataset,Multiple,Multiple,x,,,x,,,,,North America,"Spatially distributed water-balance and meteorological data from the rain–snow transition, southern Sierra Nevada, California",,Earth System Science Data,https://essd.copernicus.org/articles/10/1795/2018/,,,,,,,,No,Yes,"We present 8 years of hourly snow-depth, soil-moisture, and soil-temperature data, as well as 14 years of quarter-hourly streamflow and meteorological data that detail water-balance processes at Providence Creek, the upper part of which is at the current 50 % rain versus snow transition of the southern Sierra Nevada, California.",
598,Dataset,Multiple,Multiple,x,,,x,,,,,North America,The Cariboo Alpine Mesonet: sub-hourly hydrometeorological observations of British Columbia's Cariboo Mountains and surrounding area since 2006,,Earth System Science Data,https://essd.copernicus.org/articles/10/1655/2018/,,,,,,,,No,Yes,"The automatic weather stations typically measure air and soil temperature, relative humidity, atmospheric pressure, wind speed and direction, rainfall and snow depth at 15 min intervals. Additional measurements at some stations include shortwave and longwave radiation, near-surface air, skin, snow, or water temperature, and soil moisture, among others. Details on deployment sites, the instrumentation used and its precision, the collection and quality control process are provided. Instructions on how to access the database at Zenodo, an online public data repository, are also furnished (https://doi.org/10.5281/zenodo.1195043).",
599,Dataset,Multiple,Multiple,x,,,x,,,,,Asia,"Water balance and hydrology research in a mountainous permafrost watershed in upland streams of the Kolyma River, Russia: a database from the Kolyma Water-Balance Station, 1948–1997",,Earth System Science Data,https://essd.copernicus.org/articles/10/689/2018/,,,,,,,,No,Yes,"This paper describes the dataset containing the series of daily runoff from 10 watersheds with an area from 0.27 to 21.3 km2, precipitation, meteorological observations, evaporation from soil and snow, snow surveys, soil thaw and freeze depths, and soil temperature for the period 1948–1997.",
600,Dataset,Multiple,Multiple,x,,x,x,x,,,,North America,"Eleven years of mountain weather, snow, soil moisture and streamflow data from the rain–snow transition zone – the Johnston Draw catchment, Reynolds Creek Experimental Watershed and Critical Zone Observatory, USA",,Earth System Science Data,https://essd.copernicus.org/articles/10/1207/2018/,,,,,,,,No,Yes,We present a complete hydrometeorological dataset for water years 2004 through 2014 for a watershed that spans the rain-to-snow transition zone (https://doi.org/10.15482/usda.adc/1402076). ,
601,Dataset,Multiple,Multiple,,,x,,,,,,Europe,Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014,,Earth System Science Data,https://essd.copernicus.org/articles/10/303/2018/,,,2004,2014,,,,No,Yes,"We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area.",
602,Dataset,Multiple,Multiple,,,x,,,,,,North America,"Hourly mass and snow energy balance measurements from Mammoth Mountain, CA USA, 2011–2017",,Earth System Science Data,https://essd.copernicus.org/articles/10/549/2018/,,,2011,2017,Hourly,,,No,Yes,"For this dataset, we present a clean and continuous hourly record of selected measurements from the three sites covering the 2011–2017 water years. Then, we model the snow mass balance at CUES and compare model runs to snow pillow measurements. ",
603,Dataset,Multiple,Multiple,,,x,x,,,,,Europe,The Rofental: a high Alpine research basin (1890–3770 m a.s.l.) in the Ötztal Alps (Austria) with over 150 years of hydrometeorological and glaciological observations,,Earth System Science Data,https://essd.copernicus.org/articles/10/151/2018/,,,,,,,,No,Yes,"A comprehensive hydrometeorological and glaciological data set is presented, originating from a multitude of glaciological, meteorological, hydrological and laser scanning recordings at research sites in the Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria). The data sets span a period of 150 years and hence represent a unique time series of rich high-altitude mountain observations. ",
604,Dataset,In Situ,1,x,,,,,,,,North America,"31 years of hourly spatially distributed air temperature, humidity, and precipitation amount and phase from Reynolds Critical Zone Observatory",,Earth System Science Data,https://essd.copernicus.org/articles/10/1197/2018/,,,,,,,,No,Yes,"Thirty-one years of spatially distributed air temperature, relative humidity, dew point temperature, precipitation amount, and precipitation phase data are presented for the Reynolds Creek Experimental Watershed, which is part of the Critical Zone Observatory network. The air temperature, relative humidity, and precipitation amount data are spatially distributed over a 10 m lidar-derived digital elevation model at an hourly time step using a detrended kriging algorithm.",
605,Dataset,In Situ,1,x,,x,,,,,,Europe,Meteorological and snow distribution data in the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017,,Earth System Science Data,https://essd.copernicus.org/articles/9/993/2017/,,,2011,2017,,,,No,Yes,"This work describes the snow and meteorological data set available for the Izas Experimental Catchment in the Central Spanish Pyrenees, from the 2011 to 2017 snow seasons. The experimental site is located on the southern side of the Pyrenees between 2000 and 2300 m above sea level, covering an area of 55 ha.",
606,Dataset,Multiple,Multiple,x,,x,,,,,,Asia,Snow observations in Mount Lebanon (2011–2016),,Earth System Science Data,https://essd.copernicus.org/articles/9/573/2017/,,,,,,,,No,Yes,"We present a unique meteorological and snow observational dataset in Mount Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km2 and an elevation up to 3088 m). The dataset consists of (1) continuous meteorological and snow height observations, (2) snowpack field measurements, and (3) medium-resolution satellite snow cover data. ",
607,Dataset,Multiple,Multiple,x,x,x,x,x,,,,North America,"Meteorological, snow, streamflow, topographic, and vegetation height data from four western juniper-dominated experimental catchments in southwestern Idaho, USA",,Earth System Science Data,https://essd.copernicus.org/articles/9/91/2017/,,,,,,,,No,Yes,"Meteorological, snow, streamflow, topographic, and vegetation height data are presented from the South Mountain experimental catchments. The data provide detailed information on the weather and hydrologic response from four highly instrumented catchments in the late stages of woodland encroachment in a sagebrush steppe landscape. ",
608,Dataset,Multiple,Multiple,x,,,,,,,,North America,A Bias-Corrected 3-hourly 0.125 Gridded Meteorological Forcing Data Set (1979 – 2016) for Land Surface Modeling in North America,,FRDR,https://www.frdr-dfdr.ca/repo/dataset/0eb7926c-6e9b-8a97-57d4-9b06c988d5d3,,,1979,2016,Hourly,,,No,Yes,Relatedarticle: https://essd.copernicus.org/articles/12/629/2020/  -High-resolution meteorological forcing data for hydrological modelling and climate change impact analysis in the Mackenzie River Basin- This paper aims to provide an improved set of forcing data for large-scale hydrological models for climate change impact assessment.,
609,Dataset,Multiple,Multiple,,,,x,,,,,North America,Paleo-hydrologic reconstruction of 400 years of past flows at a weekly time step for major rivers of Western Canada,,Earth System Science Data,https://essd.copernicus.org/articles/12/231/2020/,,,,,,,,Yes,Yes,This study presents a novel method of generating weekly time step flows based on tree-ring chronology data.,
610,Dataset,Multiple,Multiple,,,,x,,,,,North America,"Fifty years of recorded hillslope runoff on seasonally frozen ground: the Swift Current, Saskatchewan, Canada, dataset",,Earth System Science Data,https://essd.copernicus.org/articles/11/1375/2019/,,,,,,"Runoff, nutrient concentrations",,No,Yes,"Runoff, runoff nutrient concentration, snowpack depth, density and water equivalent, soil moisture, and soil nutrient concentration were monitored on the three 5 ha hillslopes over a 50-year period (1962–2011).",
611,Dataset,Multiple,Multiple,,x,,x,,,,,North America,"An 11-year (2007–2017) soil moisture and precipitation dataset from the Kenaston Network in the Brightwater Creek basin, Saskatchewan, Canada",,Earth System Science Data,https://essd.copernicus.org/articles/11/787/2019/,,,2007,2017,,"Soil moisture, precipitation",,No,Yes,"Soil moisture and precipitation have been monitored in a hydrometeorological network situated within the Brightwater Creek basin, east of Kenaston, Saskatchewan, Canada, since 2007.",
612,Dataset,Multiple,Multiple,x,,,x,,,,,North America,"Meteorological, soil moisture, surface water, and groundwater data from the St. Denis National Wildlife Area, Saskatchewan, Canada",,Earth System Science Data,https://essd.copernicus.org/articles/11/553/2019/,,,,,,,,No,Yes,"The site was established as a research area in 1968 and has long-term records of hydrological observations, including meteorological, snow, soil moisture, surface water (ponds) and groundwater data. ",
613,Dataset,Multiple,Multiple,x,,,x,,,,,North America,"The Environment and Climate Change Canada solid precipitation intercomparison data from Bratt's Lake and Caribou Creek, Saskatchewan",,Earth System Science Data,https://essd.copernicus.org/articles/11/1337/2019/,,,,,,,,No,Yes," Two precipitation measurement intercomparison sites were established in Saskatchewan to help assess the systematic bias in the automated gauge measurement of solid precipitation and the impact of wind on the undercatch of snow. Caribou Creek, located in the southern boreal forest, and Bratt's Lake, located in the southern plains, are a contribution to the international SPICE project.",
614,Dataset,Multiple,Multiple,x,,,,,,,,North America,"Daily measurements of near-surface humidity from a mesonet in the foothills of the Canadian Rocky Mountains, 2005–2010",,Earth System Science Data,https://essd.copernicus.org/articles/11/23/2019/,,,2005,2010,Hourly,"Near surface humidity, temperature",,No,Yes,"Hourly near-surface relative humidity and temperature were monitored from 2005 to 2010 in a mesoscale network of 232 sites in the foothills of the Rocky Mountains in southwestern Alberta, Canada. The monitoring network covers a range of elevations from 890 to 2880 m above sea level and an area of about 18 000 km2, sampling a variety of topographic settings and surface environments with an average spatial density of one station per 78 km2. ",
615,Dataset,Multiple,Multiple,,,x,,,,,,North America,"A synthesis dataset of permafrost-affected soil thermal conditions for Alaska, USA",,Earth System Science Data,https://essd.copernicus.org/articles/10/2311/2018/,,,1997,2016,,"Permafrost, temperature",,No,Yes,"We compiled a soil temperature dataset from 72 monitoring stations in Alaska using data collected by the U.S. Geological Survey, the National Park Service, and the University of Alaska Fairbanks permafrost monitoring networks. The array of monitoring stations spans a large range of latitudes from 60.9 to 71.3∘ N and elevations from near sea level to ∼1300 m, comprising tundra and boreal forest regions. This dataset consists of monthly ground temperatures at depths up to 1 m, volumetric soil water content, snow depth, and air temperature during 1997–2016. ",
616,Dataset,In Situ,1,x,,,,,,,,North America,"Daily temperature records from a mesonet in the foothills of the Canadian Rocky Mountains, 2005–2010",,Earth System Science Data,https://essd.copernicus.org/articles/10/595/2018/,,,2005,2010,,Near-surface air temperature,,No,Yes,"Near-surface air temperatures were monitored from 2005 to 2010 in a mesoscale network of 230 sites in the foothills of the Rocky Mountains in southwestern Alberta, Canada.",
617,Dataset,Multiple,Multiple,x,,,x,,,,,North America,"Hydrometeorological data from Baker Creek Research Watershed, Northwest Territories, Canada",,Earth System Science Data,https://essd.copernicus.org/articles/10/1753/2018/,,,2003,2016,,,,No,Yes,"This dataset documents physiographic, hydrometeorological and hydrological conditions in the Baker Creek Research Watershed from 2003 to 2016.",
618,Dataset,Multiple,Multiple,x,,x,x,,,,,Europe,"The Guadalfeo Monitoring Network (Sierra Nevada, Spain): 14 years of measurements to understand the complexity of snow dynamics in semiarid regions",,Earth System Science Data,https://essd.copernicus.org/articles/11/393/2019/,,,,,,,,No,Yes,"The data sets consist of continuous meteorological high-frequency records at five automatic weather stations located at different altitudes ranging from 1300 to 2600 m a.s.l. that include precipitation, air temperature, wind speed, air relative humidity and the short- and longwave components of the incoming radiation, dating from 2004 for the oldest station (2510 m a.s.l.) (https://doi.org/10.1594/PANGAEA.895236); additionally, daily data sets of the imagery from two time-lapse cameras are presented, with different scene area (30 m × 30 m, and 2 km2, respectively) and spatial resolution, that consist of fractional snow cover area and snow depth from 2009 (https://doi.org/10.1594/PANGAEA.871706) and snow cover maps for selected dates from 2011 (https://doi.org/10.1594/PANGAEA.898374). ",
619,Tool,Remotely sensed,Multiple,,x,,,,,,,Global,Crop Monitor Tool,,GEOGLAM,https://cropmonitor.org/index.php/eodatatools/cmet/,,,,,,,,No,No,"The Crop Monitors were designed to provide a public good of open, timely, science-driven information on crop conditions in support of market transparency for the G20 Agricultural Market Information System (AMIS).",
620,Data Portal,Remotely sensed,Multiple,,x,,,,,,,Global,Global Forest Watch Map & Data,,GFOI,https://www.globalforestwatch.org/map/,,,,,,,,No,No,"Global Forest Watch (GFW) is an online platform that provides data and tools for monitoring forests. By harnessing cutting-edge technology, GFW allows anyone to access near real-time information about where and how forests are changing around the world.


SUBSCRIBE TO THE GFW NEWSLETTER
",
621,Data Portal,Remotely sensed,Multiple,,x,,,x,,,,Global,"iGOS4M - Global Observation System for Mercury Isotopes
An online database for mercury stable isotope observations in support of the Minamata Convention on mercury.",,iGOS4M - Global Observation System for Mercury Isotopes,https://sites.google.com/view/igos4m/home,,,,,,,,No,No,"iGOS4M is the mercury isotope chapter of GOS4M (Global Observation System for Mercury), which is aimed to support the MC Secretariat, the UN Environment and all Nations in the implementation of the Minamata Convention on Mercury and the activity related to the Effectiveness Evaluation and Global Monitoring framework. ",
622,Data Portal,Remotely sensed,Multiple,,x,,,,,,,Global,Proportion of land that is degraded over total land area (%),,GEO-LDN,https://landportal.org/book/indicator/un-aglnddgrd,,,,,,,,No,No,"The data series presented here measures proportion of land that is degraded over total land area (%).

This data series contributes to the measurement of SDG Indicator 15.3.1, classified as Tier II in September 2018(link is external)",
623,Data Portal,Remotely sensed,Multiple,,x,,,,,,,Global,Trends.Earth - tracking land change,,GEO-LDN,https://docs.trends.earth/en/latest/for_users/datasets/index.html,,,,,,,,No,No,Trends.Earth is a free and open source tool to understand land change: the how and why behind changes on the ground. Trends.Earth allows users to draw on the best available information from across a range of sources - from globally available data to customized local maps. ,
624,Data Portal,Multiple,Multiple,,,,x,,,,,Global,Water Quality Database Inventory,,GEO AquaWatch,https://www.geoaquawatch.org/water-quality-database-inventory/,,,,,,,,No,No,"GEO AquaWatch is pleased to offer you three Water Quality Inventories:
The General Water Quality Project Inventory is a list of known projects offering water quality data or data products.  Click here to download a copy of the most current general water quality project inventory from around the globe and here to find out what has changed since the last version. This list is not comprehensive, and will be updated from time to time.  

This Citizen Science Water Quality Project Inventory, compiled by Justine Spore (University of Wisconsin) in 2021, is a list of known CitSci projects targeting water quality information from around the globe.  This list is not comprehensive, and will be updated from time to time.


The Water Quality Database Inventory presented below was initially compiled by Brooklyn Poutra (NASA intern) in Fall 2022 and points users to useful water quality databases. This list is not comprehensive, and will be updated from time to time.",
625,Data Portal,Multiple,Multiple,x,x,x,x,x,x,x,x,Global,Datasets available in DIAS,,DIAS,https://search.diasjp.net/en/list,,,,,,,,No,No,List of datasets that can be downloaded directly from DIAS.,
626,Tool,Remotely sensed,Multiple,,,,x,,,,,Global,GEOGloWS Global Streamflow Forecasting,,GEOGloWS,https://www.geoglows.org/pages/geoglows-streamflow-forecasting,,,,,,,,No,No,Using streamflow forecasting technology to prepare and protect communities around the world from future threats.,
627,Tool,Remotely sensed,Multiple,,,,,,,,x,Global,The Global Drought Monitor,,Global Drought Information System (GDIS),https://gdis-noaa.hub.arcgis.com/pages/drought-monitoring,,,,,,,,No,No,The Global Drought Monitor depicts current drought conditions across the globe using a “bottom-up” approach. This means that the drought conditions on each continent are assessed by the Nations of that continent. These “continental Drought Monitors” are then provided to NCEI and merged here into this Global Drought Monitor product. Global drought indices and indicators are also provided as a supplement to help show current conditions.,
628,Tool,Remotely sensed,Multiple,,,,,,,,x,Global,The Global Drought Forecasting,,Global Drought Information System (GDIS),"
https://gdis-noaa.hub.arcgis.com/pages/drought-forecasting",,,,,,,,No,No,"There currently is not a drought forecast that covers the entire globe. However, there are several national weather and climate centers which produce global seasonal weather and climate forecasts. The World Meteorological Organization (WMO) designates these as WMO Global Producing Centres for Long-Range Forecasts (GPCLRFs). They form an integral part of the WMO Global Data-Processing and Forecasting System (GDPFS). Some of the national centers produce national drought outlooks.",
629,Data Portal,Multiple,Multiple,x,x,x,x,x,,,,Global,Global Ecosystems and Environment Observation Analysis  Research Cooperation (GEOARC) Annual DataSet,,Global Ecosystems and Environment Observation Analysis  Research Cooperation (GEOARC),http://www.chinageoss.cn/geoarc/en/news/DataSet.html,,,,,,,,No,No,,
630,Data Portal,Other,,x,,x,x,,x,,x,Europe,Met European Research Observatory,,Met European Research Observatory,https://www.globe.gov/globe-data,,,2008,,,,,No,No,"Papers on refereed Journals regarding interdisciplinary data in near my mountain regions: 

1)	Diodato N., Lanfredi M., Bellocchi G., 2023. Long-range, time-varying statistical prediction of annual precipitation in a Mediterranean remote site. Environmental Research: Climate, 2, https://iopscience.iop.org/article/10.1088/2752-5295/acffe9
2)	Diodato N., Ljungqvist F.C., Fiorillo F., … 2022. Climatic fingerprint of spring discharge depletion in the southern Italian Apennines from 1601 to 2020 CE. Environmental Research Communications, 4 125011 https://iopscience.iop.org/article/10.1088/2515-7620/acae23/meta
3)	Diodato N., Ljungqvist F.C., Bellocchi G., 2021. Empirical modelling of snow cover duration patterns in complex terrains of Italy. Theoretical and Applied Climatology https://doi.org/10.1007/s00704-021-03867-8
4)	Diodato, N., Fratianni., S., Bellocchi, G., 2020. Reconstruction of snow days based on monthly climate indicators in the Swiss pre-alpine region. Regional Environmental Change 20: 55. https://link.springer.com/article/10.1007/s10113-020-01639-0
5)	Diodato N., Mao L., Borrelli P., Panagos P., Fiorillo F., Bellocchi G., 2018. Climate-scale modelling of suspended sediment load in an Alpine catchment debris-flow (Rio Cordon-Northeastern Italy).  Geomorphology 309, 20-28. https://tinyurl.com/ut4257h
6)	Diodato N., Bellocchi G., Fiorillo F., Ventafridda, 2017. Case study for investigating groundwater and the future of mountain spring discharges in Southern Italy. Journal of Mountain Science 14, 1791-1800. https://link.springer.com/article/10.1007/s11629-017-4445-5",
631,Dataset,In Situ,1,,x,,,,,,,Asia,Plot-level estimates of aboveground biomass and soil organic carbon stocks from Nepal’s forest inventory,1,Ministry of Forest and Environment of Nepal,https://www.nature.com/articles/s41597-023-02314-9,1khanalshiva@gmail.com,,,,,,https://doi.org/10.6084/m9.figshare.21959636.v1,No,Yes,,
632,Dataset,Modelled,2,,x,,,,,,,Asia,Spatial map of soil organic carbon stocks in Nepal's forests,1,Ministry of Forest and Environment of Nepal,https://www.nature.com/articles/s41598-023-34247-z,1khanalshiva@gmail.com,,,,,,,No,Yes,,
633,Dataset,Modelled,1,x,,,x,,,,,South America,Reference Evapotranspiration,1,Servicio Nacional de Meteorología e Hidrología del Perú,https://doi.org/10.6084/m9.figshare.15215106,adrhuerta@gmail.com; lgutierrez@senamhi.gob.pe; wlavado@senamhi.gob.pe,,,,,,,No,No,Gridded reference evapotranspiration dataset based on FAO Penman-Monteith during 1981-2016 (daily) at 0.01° for Peru; https://doi.org/10.6084/m9.figshare.15215106,
634,Dataset,Modelled,2,x,,,x,,,,,South America,Rainfall erosivity,1,Servicio Nacional de Meteorología e Hidrología del Perú,https://doi.org/10.6084/m9.figshare.24416923,adrhuerta@gmail.com; lgutierrez@senamhi.gob.pe; wlavado@senamhi.gob.pe,,,,,,,No,No,,
635,Dataset,Modelled,2,x,,,x,,,,,South America,Precipitation,1,Servicio Nacional de Meteorología e Hidrología del Perú,https://iridl.ldeo.columbia.edu/SOURCES/.SENAMHI/.HSR/.PISCO/.Prec/.v2p1/.unstable/.monthly/,sendara@senamhi.gob.pe; wlavado@senamhi.gob.pe,,,,,,,No,No,,
636,Dataset,Modelled,2,x,,,x,,,,,South America,PET (Potential Evapotranspiration),1,Servicio Nacional de Meteorología e Hidrología del Perú,https://iridl.ldeo.columbia.edu/SOURCES/.SENAMHI/.HSR/.PISCO/.PET/.v1p1/?Set-Language=es,sendara@senamhi.gob.pe; wlavado@senamhi.gob.pe,,,,,,,No,No,estimation of potential evapotranspiration with the Oudin method,
637,Dataset,Other,2,x,,,x,,,,,South America,Precipitation,1,Servicio Nacional de Meteorología e Hidrología del Perú,https://iridl.ldeo.columbia.edu/SOURCES/.SENAMHI/.HSR/.PISCO/.Prec/.v2p1/.stable/.daily/?Set-Language=es,wlavado@senamhi.gob.pe; sendara@senamhi.gob.pe,,,,,,,No,No,,
638,Data Portal,Modelled,3,,x,,,,,,,Global,Biodiversity and Protected Areas Management Reference Information System (BIOPAMA RIS),,Joint Research Centre,https://rris.biopama.org/,,,,,,,,No,No,The data portals collects data from various sources and at different scale with a focus on protected areas and biodiversity conservation. There is some degree of geographical and thematic overlap with mountain regions,
639,Dataset,Remotely sensed,2,,,x,,,,,,Global,Glacier mass change gridded data from 1976 to present derived from the Fluctuations of Glaciers Database,,"World Glacier Monitoring Service (WGMS), Copernicus",https://cds.climate.copernicus.eu/cdsapp#!/dataset/derived-gridded-glacier-mass-change?tab=overview,,0.5° x 0.5°,1975,,Annual,,10.24381/cds.ba597449,No,Yes,"The dataset provides annual glacier mass changes distributed on a global regular grid at 0.5° resolution (latitude, longitude).",
640,Tool,Other,Other,,,,,,,,,,OpenEO,,OpenEO,https://openeo.org/,,,,,,,,No,No,OpenEO develops an open API to connect various clients to big EO cloud back-ends in a simple and unified way.,
641,Tool,Other,Other,,,,,,,,,,Shiny,,Shiny,https://shiny.posit.co/,,,,,,,,No,No,Web apps for data science,
642,Tool,Other,Other,,,,,,,,,,4DModeller,,4DModeller,https://4dmodeller.github.io/fdmr/index.html,,,,,,,,No,No,"4DModeller is a spatio-temporal modelling package that can be applied to problems at any scale from micro to processes that operate at a global scale. It includes data visualization tools, finite element mesh building tools, Bayesian hierarchical modelling based on Bayesian inference packages INLA and inlabru, and model evaluation and assessment tools.",