{"ACIS_NRCC_NN":{"api_variable_path":"#acis-nrcc-nn","category":"Climate/Hydrology","citations":"None","coll_desc":"ACIS NRCC NN Daily","coll_name":"projects/climate-engine-pro/assets/noaa-nrcc-acis-nn/daily","dataset_website":"https://www.rcc-acis.org/examples.html","description":"The ACIS Climate Maps are produced daily using data from the Applied Climate Information System (ACIS). Station data in ACIS primarily come from the following networks: National Weather Service Cooperative Observer Program (NWS COOP), Weather-Bureau-Army-Navy/Automated Surface Observing System (WBAN/ASOS), Snow Telemetry (SNOTEL), Community Collaborative Rain, Hail, & Snow (CoCoRaHS) Network, Remote Automatic Weather Stations (RAWS).","ee_asset_path":"https://gee-community-catalog.org/projects/noaa_acis/?h=acis","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"ACIS NRCC NN - 5km - Daily","geographic_coverage":"CONUS","processing_steps":"","product_name":"ACIS NRCC NN - Daily","spatial_resolution":"5km","spatial_resolution_normalized":"5000","start_year":"1951","support_thumbnail":"static/img/support/acis_nrcc_nn_e876c204ef.jpg","caption":"Example 1-month Total Hargreaves Potential Evapotranspiration Map","support_path":"datasets/climatehydrology/acisnrccnn_daily_5000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"Climate products and tools are developed by the Regional Climate Centers to provide users with a means to routinely access recent weather and climate data. While applied research has demonstrated that the use of such data in decision making can reduce risk and lower a user's cost, it must be pointed out that near-real-time data are preliminary in nature and sometimes contain errors. Although care is taken to remove known errors, it is possible that some data will change after receipt of written records and final quality control steps. Users are therefore forewarned to consider any negative impact that errant data might have on their business and use preliminary data and information at their own risk. The Regional Climate Centers are not responsible for the misuse or abuse of data provided through our services.","variable_info":{"precip":{"band":"precip","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"tmax":{"band":"tmax","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"tmin":{"band":"tmin","name":"Minimum Temperature","common_name":"Temperature","units_out":"K"},"spi":{"band":"precip","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"peth":{"band":"peth","name":"Potential ET Hargreaves","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit Hargreaves","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index Hargreaves (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"peth","name":"Evap. Demand Drought Index Hargreaves (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""}}},"ANUSPLIN_DAILY":{"api_variable_path":"#anusplin-gridded-climate-for-canada","category":"Climate/Hydrology","citations":"1) Hutchinson, M. F., McKenney, D.W., Lawrence, K., Pedlar, J.H., Hopkinson, R.F., Milewska, E., Papadopol, P. (2009). Development and testing of Canada-Wide Interpolated Spatial Models of Daily Minimum-Maximum Temperature and Precipitation for 1961-2003. American Meteorological Society(April): 725-741. 2) McKenney, D. W., Hutchinson, M.F., Papadopol, P., Lawrence, K., Pedlar, J., Campbell, K., Milewska, E., Hopkinson, R., Price, D., Owen, T. (2011). Customized spatial climate models for North America. Bulletin of American Meteorological Society-BAMS December: 1612-1622.","coll_desc":"ANUSPLIN Gridded Climate for Canada - Daily","coll_name":"projects/climate-engine-pro/assets/ce-anusplin-daily","dataset_website":"https://cfs.nrcan.gc.ca/projects/3/4","description":"The ANUSPLIN Gridded Climate Dataset for Canada is a station based interpolated dataset produced using the Australian National University Spline (ANUSPLIN) model. It is produced by Agriculture and Agri-Food Canada and covers all of Canada. The dataset is available from 1950-2015 at daily timesteps for maximum temperature, minimum temperature, and total precipitation at 10km resolution.","ee_asset_path":"https://gee-community-catalog.org/projects/anusplin/","ee_source":"ClimateEngine.org","end_year":"2015","full_name":"ANUSPLIN - 10km - Daily","geographic_coverage":"Canada","processing_steps":"","product_name":"ANUSPLIN Gridded Climate for Canada - Daily","spatial_resolution":"10km","spatial_resolution_normalized":"10000","start_year":"1950","support_thumbnail":"static/img/support/anusplin_daily_dea10a6cf0.jpg","caption":"Example Dec - Feb Potential Water Deficit Map","support_path":"datasets/climatehydrology/anusplin_daily_10000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"Find Terms and Conditions here: https://www.canada.ca/en/transparency/terms.html","variable_info":{"pcp":{"band":"pcp","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"maxt":{"band":"maxt","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"mint":{"band":"mint","name":"Minimum Temperature","common_name":"Temperature","units_out":"K"},"spi":{"band":"pcp","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"peth":{"band":"peth","name":"Potential ET Hargreaves","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit Hargreaves","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index Hargreaves (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"peth","name":"Evap. Demand Drought Index Hargreaves (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""}}},"ANUSPLIN_MONTHLY":{"api_variable_path":"#anusplin-gridded-climate-for-canada","category":"Climate/Hydrology","citations":"1) Hutchinson, M. F., McKenney, D.W., Lawrence, K., Pedlar, J.H., Hopkinson, R.F., Milewska, E., Papadopol, P. (2009). Development and testing of Canada-Wide Interpolated Spatial Models of Daily Minimum-Maximum Temperature and Precipitation for 1961-2003. American Meteorological Society(April): 725-741. 2) McKenney, D. W., Hutchinson, M.F., Papadopol, P., Lawrence, K., Pedlar, J., Campbell, K., Milewska, E., Hopkinson, R., Price, D., Owen, T. (2011). Customized spatial climate models for North America. Bulletin of American Meteorological Society-BAMS December: 1612-1622.","coll_desc":"ANUSPLIN Gridded Climate for Canada - Monthly","coll_name":"projects/climate-engine-pro/assets/ce-anusplin-monthly","dataset_website":"https://cfs.nrcan.gc.ca/projects/3/4","description":"The ANUSPLIN Gridded Climate Dataset for Canada is a station based interpolated dataset produced using the Australian National University Spline (ANUSPLIN) model. It is produced by Agriculture and Agri-Food Canada and covers all of Canada. The dataset is available from 1950-2015 at daily timesteps for maximum temperature, minimum temperature, and total precipitation at 10km resolution.","ee_asset_path":"https://gee-community-catalog.org/projects/anusplin/?h=anusp","ee_source":"ClimateEngine.org","end_year":"2015","full_name":"ANUSPLIN - 10km - Monthly","geographic_coverage":"Canada","processing_steps":"","product_name":"ANUSPLIN Gridded Climate for Canada - Monthly","spatial_resolution":"10km","spatial_resolution_normalized":"10000","start_year":"1950","support_thumbnail":"static/img/support/anusplin_monthly_dea10a6cf0.jpg","caption":"Example Dec - Feb POtential Water Deficit Map","support_path":"datasets/climatehydrology/anusplin_monthly_10000","temporal_resolution":"Monthly","temporal_resolution_normalized":"30","terms_of_use":"Find Terms and Conditions here: https://www.canada.ca/en/transparency/terms.html","variable_info":{"pcp":{"band":"pcp","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"maxt":{"band":"maxt","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"mint":{"band":"mint","name":"Minimum Temperature","common_name":"Temperature","units_out":"K"},"spi":{"band":"pcp","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"peth":{"band":"peth","name":"Potential ET Hargreaves","common_name":"Potential Evapotranspiration","units_out":"mm"},"pett":{"band":"pett","name":"Potential ET Thornthwaite","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit Hargreaves","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index Hargreaves (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"speit":{"band":"wbt","name":"Standardized Precipitation Evapotranspiration Index Thornthwaite (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"peth","name":"Evap. Demand Drought Index Hargreaves (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""}}},"BVHP":{"api_variable_path":"#bvhp-4km-daily","category":"Remote Sensing","citations":"None","coll_desc":"Blended Vegetation Health Product","coll_name":"projects/climate-engine-pro/assets/ce-bvhp","dataset_website":"https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_ftp.php","description":"Blended-VHP is a re-processed Vegetation Health data set derived from VIIRS (2013-present) and AVHRR (1981-2012) GAC data. The images are color-coded maps showing vegetation health using Vegetation Health (VH) Indices, ranging from 0 (poor) to 100 (excellent). Green represents fair conditions, with brown and red for worsening and blue for improving. VH measures changes in vegetation based on chlorophyll, moisture, and surface temperature, helping detect drought early. Values below 40 indicate stress, making VH useful for monitoring drought impacts on vegetation and crops.","ee_asset_path":"Not publicly available","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"Blended VHP - 4km - Weekly","geographic_coverage":"Global","processing_steps":"","product_name":"BVHP","spatial_resolution":"4km","spatial_resolution_normalized":"4000","start_year":"1981","support_thumbnail":"static/img/support/bvhp_6b680b9a1c.jpg","caption":"Example June - Aug Vegetation Health Index Percentile Map","support_path":"datasets/remotesensing/blendedvhp_weekly_4000","temporal_resolution":"Weekly","temporal_resolution_normalized":"7","terms_of_use":"NOAA’s publicly released environmental data and products (including most satellite-derived products) are made available under NOAA’s open data principles, which broadly permit reuse, redistribution, derivative work creation, and research and commercial utilization, subject to applicable laws and policies.","variable_info":{"ndvi":{"band":"SMN","name":"Smoothed NDVI","common_name":"Normalized Difference Vegetation Index (NDVI)","units_out":"Dimensionless"},"tmean":{"band":"SMT","name":"Smoothed Brightness Temperature","common_name":"Brightness Temperature","units_out":"Dimensionless"},"vci":{"band":"VCI","name":"Vegetation Condition Index","common_name":"Vegetation Condition Index","units_out":"Dimensionless"},"tci":{"band":"TCI","name":"Temperature Condition Index","common_name":"Temperature Condition Index","units_out":"Dimensionless"},"vhi":{"band":"VHI","name":"Vegetation Health Index","common_name":"Vegetation Health Index","units_out":"Dimensionless"}}},"CDOUTLOOK":{"api_variable_path":"#canadian-drought-outlook","category":"Forecasts","citations":"1) Agriculture and Agri-Food Canada, 2021, Canadian Drought Outlook, Agroclimate, Geomatics and Earth Observation Division, Science and Technology Branch.","coll_desc":"Canadian Drought Outlook dataset","coll_name":"projects/climate-engine-pro/assets/ce-aafc-cdo-monthly","dataset_website":"https://open.canada.ca/data/en/dataset/2c82daab-f6d9-4b19-96b5-238249e09fb9","description":"The Canadian Drought Outlook raster dataset is produced by the Agriculture and Agri-Food Canada. The Canadian Drought Outlook predicts whether drought across Canada will emerge, stay the same or get better over the target month. In calculating the outlook, consideration is given to Agroclimate indices, such as the Standard Precipitation Index (SPI), the Standard Precipitation Evaporation Index (SPEI), and the Palmer Drought Severity Index (PDSI). The drought outlook is issued on the first Thursday of each calendar month and is valid for 32 days from that date.","ee_asset_path":"https://gee-community-catalog.org/projects/can_drought_outlook/?h=drought+ou","ee_source":"ClimateEngine.org","end_year":"Forecast","full_name":"CAN Drought -  800m - Monthly","geographic_coverage":"Canada","processing_steps":"","product_name":"Canadian Drought Outlook","spatial_resolution":"800m","spatial_resolution_normalized":"800","start_year":"Forecast","support_thumbnail":"static/img/support/cdoutlook_5f2cab36c3.jpg","caption":"Example Drought Outlook Forecast Map","support_path":"datasets/forecasts/candrought_monthly_800","temporal_resolution":"Monthly","temporal_resolution_normalized":"30","terms_of_use":"See Open Government License - Canada here: https://open.canada.ca/en/open-government-licence-canada","variable_info":{"drought_outlook_class":{"band":"drought_outlook_class","name":"Canadian Drought Outlook","common_name":"Canadian Drought Outlook","units_out":""}}},"CEMS":{"api_variable_path":"#copernicus-emergency-management-service-fire-danger-indices","category":"Hazards","citations":"1) Vitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel-Ayanz, J., Libertá, G., & Krzeminski, B. (2020). ERA5-based global meteorological wildfire danger maps. Scientific data, 7(1), 1-11. 'Contains modified Copernicus Climate Change Service information [Year]'","coll_desc":"Copernicus Emergency Management Service Fire Danger Indices","coll_name":"projects/climate-engine-pro/assets/ce-cems-fire-daily-4-1","dataset_website":"https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview","description":"Fire danger indices from the ECMWF, calculated using weather forecasts from historical simulations provided by ECMWF ERA5 reanalysis.","ee_asset_path":"https://gee-community-catalog.org/projects/cems_fire/?h=cems","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"CEMS FIRE - 800m - Daily","geographic_coverage":"Global","processing_steps":"","product_name":"CEMS Fire Danger Indices","spatial_resolution":"800m","spatial_resolution_normalized":"800","start_year":"2021","support_thumbnail":"static/img/support/cems_1768097d81.jpg","caption":"Example Burning Index Map","support_path":"datasets/hazards/cemsfire_daily_800","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"Find license information here: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf","variable_info":{"fire_weather_index":{"band":"fire_weather_index","name":"Fire Weather Index","common_name":"Fire Weather Index","units_out":"Dimensionless"},"danger_risk":{"band":"danger_risk","name":"Danger Risk","common_name":"Danger Risk","units_out":"Dimensionless"},"fire_daily_severity_rating":{"band":"fire_daily_severity_rating","name":"Fire Daily Severity Rating","common_name":"Fire Daily Severity Rating","units_out":"Dimensionless"},"burning_index":{"band":"burning_index","name":"Burning Index","common_name":"Burning Index","units_out":"Dimensionless"},"energy_release_component":{"band":"energy_release_component","name":"Energy Release Component","common_name":"Energy Release Component","units_out":"J/m^2"},"ignition_component":{"band":"ignition_component","name":"Ignition Component","common_name":"Ignition Component","units_out":"Percent"},"spread_component":{"band":"spread_component","name":"Ignition Component","common_name":"Spread Component","units_out":"Dimensionless"}}},"CFS_GRIDMET":{"api_variable_path":"#cfs-gridmet-28-day-forecasts","category":"Forecasts","citations":"1) Abatzoglou J. T. 'Development of gridded surface meteorological data for ecological applications and modelling'. International Journal of Climatology. (2011) doi: 10.1002/joc.3413.(Abstract) 2) Saha, Suranjana and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015.1057. doi: 10.1175/2010BAMS3001.1","coll_desc":"CFS-gridMET-EDDI 4-km 28-day forecast dataset (UC Merced)","coll_name":"projects/climate-engine/cfsv2/forecast","dataset_website":"http://www.climatologylab.org/gridmet.html","description":"Surface meteorological forecast dataset of 48-ensemble members of CFS forecasts bias corrected to gridMET statistics.","ee_asset_path":"Not publicly available","ee_source":"ClimateEngine.org","end_year":"Forecast","full_name":"CFS gridMET - 4km - 1to4week","geographic_coverage":"CONUS","processing_steps":"","product_name":"CFS GRIDMET 1-4 Week Drought Forecasts","spatial_resolution":"4km","spatial_resolution_normalized":"4000","start_year":"Forecast","support_thumbnail":"static/img/support/cfs_gridmet_ed74b9bc9e.jpg","caption":"Example 1-week Evaporative Demand Drought Index (EDDI) Map","support_path":"datasets/forecasts/cfsgridmet_1to4week_4000","temporal_resolution":"1 to 4 week","temporal_resolution_normalized":"1","terms_of_use":"CC BY 4.0","variable_info":{"pr":{"band":"pr","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"tmean":{"band":"tmean","name":"Mean Temperature","common_name":"Temperature","units_out":"K"},"tmmx":{"band":"tmmx","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"tmmn":{"band":"tmmn","name":"Minimum Temperature","common_name":"Temperature","units_out":"K"},"eto":{"band":"eto","name":"ASCE Grass Reference Evapotranspiration","common_name":"ASCE Reference Evapotranspiration","units_out":"mm"},"srad":{"band":"srad","name":"Downwelling Shortwave Radiation","common_name":"Downward Shortwave Radiation","units_out":"W/m^2"},"vs":{"band":"vs","name":"Wind Speed","common_name":"Wind Speed","units_out":"m/s"},"sph":{"band":"sph","name":"Specific Humidity","common_name":"Humidity","units_out":"1/1000 kg/kg"},"erc":{"band":"erc","name":"Energy Release Component","common_name":"Energy Release Component","units_out":""},"bi":{"band":"bi","name":"Burning Index","common_name":"Burning Index","units_out":""},"fm100":{"band":"fm100","name":"Fuel Moisture (100-hr)","common_name":"Fuel Moisture","units_out":""},"fm1000":{"band":"fm1000","name":"Fuel Moisture (1000-hr)","common_name":"Fuel Moisture","units_out":""},"vpd":{"band":"vpd","name":"Vapor Pressure Deficit","common_name":"Vapor Pressure Deficit","units_out":"kPa"},"eddi":{"band":"eddi","name":"Evap. Demand Drought Index (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""}}},"CFS_GRIDMET_DAILY":{"api_variable_path":"#cfs-gridmet-28-day-forecasts","category":"Forecasts","citations":"1) Abatzoglou J. T. 'Development of gridded surface meteorological data for ecological applications and modelling'. International Journal of Climatology. (2011) doi: 10.1002/joc.3413.(Abstract) 2) Saha, Suranjana and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. 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Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. 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Note that, in general, the differences in CHIRPS-Prelim and CHIRPS are within acceptable limits, as both data sets share the same climatological mean.","ee_asset_path":"https://gee-community-catalog.org/projects/chirps_prelim/?h=chirp","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"CHIRPS Prelim - 4.8km - Pentad","geographic_coverage":"Global","processing_steps":"","product_name":"CHIRPS Prelim - Pentad Precipitation","spatial_resolution":"4.8km","spatial_resolution_normalized":"4800","start_year":"2015","support_thumbnail":"static/img/support/chirps_prelim_pentad_09f3b40749.jpg","caption":"Example 1-month Precipitation Map","support_path":"","temporal_resolution":"Pentad","temporal_resolution_normalized":"5","terms_of_use":"To the extent possible under the law, Pete Peterson has waived all copyright and related or neighboring rights to CHIRPS. CHIRPS data is in the public domain as registered with Creative Commons. 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CHIRPS is produced by the Climate Hazards Center at the University of California Santa Barbara.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"CHIRPS - 4.8km - Pentad","geographic_coverage":"Global","processing_steps":"","product_name":"CHIRPS - Pentad Precipitation","spatial_resolution":"4.8km","spatial_resolution_normalized":"4800","start_year":"1981","support_thumbnail":"static/img/support/chirps_pentad_09f3b40749.jpg","caption":"Example 1-month Precipitation Map","support_path":"datasets/climatehydrology/chirps_pentad_4800","temporal_resolution":"Pentad","temporal_resolution_normalized":"5","terms_of_use":"To the extent possible under the law, Pete Peterson has waived all copyright and related or neighboring rights to CHIRPS. CHIRPS data is in the public domain as registered with Creative Commons. This work is published from: the United States.","variable_info":{"precipitation":{"band":"precipitation","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"spi":{"band":"precipitation","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""}}},"CHIRPS_DAILY":{"api_variable_path":"#chirps-daily","category":"Climate/Hydrology","citations":"1) Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p., http://dx.doi.org/10.3133/ds832","coll_desc":"CHIRPS 4.8-km (1/20-deg) precipitation dataset (UCSB/CHG)","coll_name":"UCSB-CHG/CHIRPS/DAILY","dataset_website":"https://www.chc.ucsb.edu/data/chirps","description":"The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set. 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CHIRPS is produced by the Climate Hazards Center at the University of California Santa Barbara.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"CHIRPS - 4.8km - Daily","geographic_coverage":"Global","processing_steps":"","product_name":"CHIRPS - Daily Precipitation","spatial_resolution":"4.8km","spatial_resolution_normalized":"4800","start_year":"1981","support_thumbnail":"static/img/support/chirps_daily_09f3b40749.jpg","caption":"Example 1-month Precipitation Map","support_path":"datasets/climatehydrology/chirps_daily_4800","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"To the extent possible under the law, Pete Peterson has waived all copyright and related or neighboring rights to CHIRPS. CHIRPS data is in the public domain as registered with Creative Commons. This work is published from: the United States.","variable_info":{"precipitation":{"band":"precipitation","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"spi":{"band":"precipitation","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""}}},"DAYMET":{"api_variable_path":"#daymet","category":"Climate/Hydrology","citations":"1) Thornton, M.M., R. Shrestha, Y. Wei, P.E. Thornton, S. Kao, and B.E. Wilson. 2020. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1840","coll_desc":"DAYMET 1-km dataset (NASA ORNL DACC)","coll_name":"NASA/ORNL/DAYMET_V4","dataset_website":"https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1840","description":"Daymet V4 provides gridded estimates of daily weather parameters for Continental North America, Hawaii, and Puerto Rico (Data for Puerto Rico is available starting in 1950). 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See NASA's Earth Science Data & Information Policy for additional information.","variable_info":{"ppt":{"band":"prcp","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"tmax":{"band":"tmax","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"tmin":{"band":"tmin","name":"Minimum Temperature","common_name":"Temperature","units_out":"K"},"tmean":{"band":"tmean","name":"Average Temperature","common_name":"Temperature","units_out":"K"},"srad":{"band":"srad","name":"Downwelling Shortwave Radiation","common_name":"Downward Shortwave Radiation","units_out":"W/m^2"},"swe":{"band":"swe","name":"Snow Water Equivalent","common_name":"Snow Water Equivalent","units_out":"mm"},"vp":{"band":"vp","name":"Vapor Pressure","common_name":"Vapor Pressure","units_out":"kPa"},"spi":{"band":"prcp","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"peth":{"band":"peth","name":"Potential ET Hargreaves","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit Hargreaves","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index Hargreaves (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"peth","name":"Evap. 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The dataset is organized around two main variables: 1) the mean radiant temperature (MRT) and 2) the universal thermal climate index (UTCI) These variables describe how the human body experiences atmospheric conditions, specifically air temperature, humidity, ventilation and radiation.","ee_asset_path":"https://gee-community-catalog.org/projects/era5_heat/?h=era5+hea","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"ERA5 Heat - 27.75km - Daily","geographic_coverage":"Global","processing_steps":"","product_name":"ERA5 HEAT","spatial_resolution":"27.75km","spatial_resolution_normalized":"27750","start_year":"1940","support_thumbnail":"static/img/support/era5_heat_f4fe1f0194.jpg","caption":"Example Mean Universal Thermal Climate Index Map","support_path":"datasets/climatehydrology/era5heat_daily_27750","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"Terms of Use","variable_info":{"mrt_mean":{"band":"mrt_mean","name":"MRT daily mean","common_name":"Mean Radiant Temperature (MRT)","units_out":"K"},"mrt_max":{"band":"mrt_max","name":"MRT daily max","common_name":"Mean Radiant Temperature (MRT)","units_out":"K"},"mrt_min":{"band":"mrt_min","name":"MRT daily min","common_name":"Mean Radiant Temperature (MRT)","units_out":"K"},"mrt_median":{"band":"mrt_median","name":"MRT daily median","common_name":"Mean Radiant Temperature (MRT)","units_out":"K"},"utci_mean":{"band":"utci_mean","name":"UTCI daily mean","common_name":"Universal Thermal Climate Index (UTCI)","units_out":"K"},"utci_max":{"band":"utci_max","name":"UTCI daily max","common_name":"Universal Thermal Climate Index (UTCI)","units_out":"K"},"utci_min":{"band":"utci_min","name":"UTCI daily min","common_name":"Universal Thermal Climate Index (UTCI)","units_out":"K"},"utci_median":{"band":"utci_median","name":"UTCI daily median","common_name":"Universal Thermal Climate Index (UTCI)","units_out":"K"}}},"ERA5_AG":{"api_variable_path":"#era5-ag-v20-daily","category":"Climate/Hydrology","citations":"1) Boogaard, H., Schubert, J., De Wit, A., Lazebnik, J., Hutjes, R., Van der Grijn, G., (2020): Agrometeorological indicators from 1979 to present derived from reanalysis. 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Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.","ee_asset_path":"https://gee-community-catalog.org/projects/agera5_datasets/?h=era5+ag","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"ERA5 Ag - 9.6km - Daily","geographic_coverage":"Global","processing_steps":"","product_name":"ERA5 Ag Reanalysis - Daily","spatial_resolution":"9.6km","spatial_resolution_normalized":"9600","start_year":"1979","support_thumbnail":"static/img/support/era5_ag_6fec0b74da.jpg","caption":"Example 1-month Standardized Precipitation Evapotranspiration Index (SPEI) Map","support_path":"datasets/climatehydrology/era5ag_daily_9600","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"Find license information here (see license tab): https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators?tab=overview","variable_info":{"wind_speed":{"band":"Wind_Speed_10m_Mean_24h","name":"Wind speed (10 m)","common_name":"Wind Speed","units_out":"m/s"},"dewpoint_2m_temperature":{"band":"Dew_Point_Temperature_2m_Mean_24h","name":"Dewpoint Temperature (2 m)","common_name":"Dew Point Temperature","units_out":"C"},"maximum_2m_air_temperature":{"band":"Temperature_Air_2m_Max_24h","name":"Maximum Temperature (2 m)","common_name":"Temperature","units_out":"C"},"mean_2m_air_temperature":{"band":"Temperature_Air_2m_Mean_24h","name":"Mean Temperature (2 m)","common_name":"Temperature","units_out":"C"},"minimum_2m_air_temperature":{"band":"Temperature_Air_2m_Min_24h","name":"Minimum Temperature (2 m)","common_name":"Temperature","units_out":"C"},"total_precipitation":{"band":"Precipitation_Flux","name":"Precipitation flux","common_name":"Precipitation","units_out":"mm"},"Snow_Depth":{"band":"Snow_Thickness_Mean_24h","name":"Snow thickness","common_name":"Snow Depth","units_out":"cm"},"SWE":{"band":"Snow_Thickness_LWE_Mean_24h","name":"Snow thickness LWE","common_name":"Snow Water Equivalent","units_out":"cm"},"srad":{"band":"Solar_Radiation_Flux","name":"Downward shortwave radiation","common_name":"Downward Shortwave Radiation","units_out":"W/m2"},"vap":{"band":"Vapour_Pressure_Mean","name":"Vapour Pressure","common_name":"Vapor Pressure","units_out":"kPa"},"vpd":{"band":"Vapour_Pressure_Deficit_at_Maximum_Temperature","name":"Vapor Pressure Deficit","common_name":"Vapor Pressure Deficit","units_out":"kPa"},"eto":{"band":"ReferenceET_PenmanMonteith_FAO56","name":"FAO Reference Evapotranspiration","common_name":"Reference Evapotranspiration","units_out":"mm"},"peth":{"band":"peth","name":"Potential ET Hargreaves","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit Hargreaves","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index Hargreaves (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"peth","name":"Evap. 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Copernicus Climate Change Service Climate Data Store (CDS), (date of access), https://cds.climate.copernicus.eu/cdsapp#!/home","coll_desc":"ERA5 dataset (ECMWF)","coll_name":"ECMWF/ERA5/DAILY","dataset_website":"https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview","description":"ECMWF's Copernicus Climate Change Service produces the (ERA5) which is a 30+ year global climate reanalysis dataset. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset. ERA5 replaces its predecessor, the ERA-Interim reanalysis. Note that the dataset used here is a merger of the Google Earth Engine dataset (up to 7/9/2020) and data to real-time ingested by Climate Engine.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_DAILY","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"ERA5 - 24km - Daily","geographic_coverage":"Global","processing_steps":"","product_name":"ERA5 Reanalysis","spatial_resolution":"24km","spatial_resolution_normalized":"24000","start_year":"1979","support_thumbnail":"static/img/support/era5_fec7442a36.jpg","caption":"Example 1-month Potential Water Deficit Map","support_path":"datasets/climatehydrology/era5_daily_24000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"Please acknowledge the use of ERA5 as stated in the Copernicus C3S/CAMS License agreement: 5.1.2 Where the Licensee communicates or distributes Copernicus Products to the public, the Licensee shall inform the recipients of the source by using the following or any similar notice: \"Generated using Copernicus Climate Change Service information (Year)\". 5.1.3 Where the Licensee makes or contributes to a publication or distribution containing adapted or modified Copernicus Products, the Licensee shall provide the following or any similar notice: \"Contains modified Copernicus Climate Change Service information (Year)\". 5.1.3 Any such publication or distribution covered by clauses 5.1.1 and 5.1.2 shall state that neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or Data it contains.","variable_info":{"mean_2m_air_temperature":{"band":"mean_2m_air_temperature","name":"Mean Temperature (2 m)","common_name":"Temperature","units_out":"K"},"minimum_2m_air_temperature":{"band":"minimum_2m_air_temperature","name":"Minimum Temperature (2 m)","common_name":"Temperature","units_out":"K"},"maximum_2m_air_temperature":{"band":"maximum_2m_air_temperature","name":"Maximum Temperature (2 m)","common_name":"Temperature","units_out":"K"},"dewpoint_2m_temperature":{"band":"dewpoint_2m_temperature","name":"Dew Point Temperature (2 m)","common_name":"Dew Point Temperature","units_out":"K"},"total_precipitation":{"band":"total_precipitation","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"surface_pressure":{"band":"surface_pressure","name":"Surface Pressure","common_name":"Pressure","units_out":"kPa"},"mean_sea_level_pressure":{"band":"mean_sea_level_pressure","name":"Sea Level Pressure","common_name":"Pressure","units_out":"kPa"},"wind_speed":{"band":"wind_speed","name":"Wind Speed","common_name":"Wind Speed","units_out":"m/s"},"u_component_of_wind_10m":{"band":"u_component_of_wind_10m","name":"Eastward Wind Component","common_name":"Wind Component","units_out":"m/s"},"v_component_of_wind_10m":{"band":"v_component_of_wind_10m","name":"Northward Wind Component","common_name":"Wind Component","units_out":"m/s"},"spi":{"band":"total_precipitation","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"peth":{"band":"peth","name":"Potential ET Hargreaves","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit Hargreaves","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index Hargreaves (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"peth","name":"Evap. Demand Drought Index Hargreaves (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""}}},"ERA5_LAND_DAILY":{"api_variable_path":"#era5-land-daily","category":"Climate/Hydrology","citations":"1) Muñoz Sabater, J., (2019): ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (), doi:10.24381/cds.68d2bb30","coll_desc":"ERA5 Land (ECMWF)","coll_name":"ECMWF/ERA5_LAND/DAILY_RAW","dataset_website":"https://www.ecmwf.int/en/era5-land","description":"ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_DAILY_AGGR","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"ERA5 Land - 11.1km - Daily","geographic_coverage":"Global","processing_steps":"","product_name":"ERA5 Land","spatial_resolution":"11.1km","spatial_resolution_normalized":"11100","start_year":"1963","support_thumbnail":"static/img/support/era5_land_daily_c4d4b9a728.jpg","caption":"Example 1-month Snow Water Equivalent Map","support_path":"datasets/climatehydrology/era5land_daily_11100","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"Please acknowledge the use of ERA5-Land as stated in the Copernicus C3S/CAMS License agreement: 5.1.2 Where the Licensee communicates or distributes Copernicus Products to the public, the Licensee shall inform the recipients of the source by using the following or any similar notice: \"Generated using Copernicus Climate Change Service information (Year)\". 5.1.3 Where the Licensee makes or contributes to a publication or distribution containing adapted or modified Copernicus Products, the Licensee shall provide the following or any similar notice: \"Contains modified Copernicus Climate Change Service information (Year)\". 5.1.3 Any such publication or distribution covered by clauses 5.1.1 and 5.1.2 shall state that neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or Data it contains.","variable_info":{"total_precipitation":{"band":"total_precipitation_sum","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"temperature_2m":{"band":"temperature_2m","name":"Temperature (2 m)","common_name":"Temperature","units_out":"K"},"temperature_2m_max":{"band":"temperature_2m_max","name":"Max Temperature (2 m)","common_name":"Temperature","units_out":"K"},"temperature_2m_min":{"band":"temperature_2m_min","name":"Max Temperature (2 m)","common_name":"Temperature","units_out":"K"},"dewpoint_temperature_2m":{"band":"dewpoint_temperature_2m","name":"Dewpoint Temperature (2 m)","common_name":"Dew Point Temperature","units_out":"K"},"wind_speed":{"band":"wind_speed","name":"Wind Speed","common_name":"Wind Speed","units_out":"m/s"},"srad":{"band":"surface_solar_radiation_downwards_sum","name":"Downwelling Shortwave Radiation","common_name":"Downward Shortwave Radiation","units_out":"W/m^2"},"spi":{"band":"total_precipitation_sum","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"peth":{"band":"peth","name":"Potential ET Hargreaves","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit Hargreaves","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index Hargreaves (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"peth","name":"Evap. Demand Drought Index Hargreaves (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""},"Snow_Depth":{"band":"snow_depth","name":"Snow thickness","common_name":"Snow Depth","units_out":"mm"},"SWE":{"band":"snow_depth_water_equivalent","name":"Snow depth water equivalent","common_name":"Snow Water Equivalent","units_out":"mm"},"surface_net_solar_radiation_sum":{"band":"surface_net_solar_radiation_sum","name":"surface_net_solar_radiation_sum","common_name":"Net Shortwave Radiation","units_out":"W/m2"}}},"ERA5_LAND_MONTHLY":{"api_variable_path":"#era5-land-monthly","category":"Climate/Hydrology","citations":"1) Muñoz Sabater, J., (2019): ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (), doi:10.24381/cds.68d2bb30","coll_desc":"ERA5 Land (ECMWF)","coll_name":"ECMWF/ERA5_LAND/MONTHLY_AGGR","dataset_website":"https://www.ecmwf.int/en/era5-land","description":"ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_DAILY_AGGR","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"ERA5 Land - 11.1km - Monthly","geographic_coverage":"Global","processing_steps":"","product_name":"ERA5 Land","spatial_resolution":"11.1km","spatial_resolution_normalized":"11100","start_year":"1950","support_thumbnail":"static/img/support/ERA5_LAND_MONTHLY.jpg","caption":"Example Snow Water Equivalent Map","support_path":"datasets/climatehydrology/era5land_monthly_11100","temporal_resolution":"Monthly","temporal_resolution_normalized":"30","terms_of_use":"Please acknowledge the use of ERA5-Land as stated in the Copernicus C3S/CAMS License agreement: 5.1.2 Where the Licensee communicates or distributes Copernicus Products to the public, the Licensee shall inform the recipients of the source by using the following or any similar notice: \"Generated using Copernicus Climate Change Service information (Year)\". 5.1.3 Where the Licensee makes or contributes to a publication or distribution containing adapted or modified Copernicus Products, the Licensee shall provide the following or any similar notice: \"Contains modified Copernicus Climate Change Service information (Year)\". 5.1.3 Any such publication or distribution covered by clauses 5.1.1 and 5.1.2 shall state that neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or Data it contains.","variable_info":{"total_precipitation":{"band":"total_precipitation_sum","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"temperature_2m":{"band":"temperature_2m","name":"Temperature (2 m)","common_name":"Temperature","units_out":"K"},"temperature_2m_max":{"band":"temperature_2m_max","name":"Max Temperature (2 m)","common_name":"Temperature","units_out":"K"},"temperature_2m_min":{"band":"temperature_2m_min","name":"Max Temperature (2 m)","common_name":"Temperature","units_out":"K"},"dewpoint_temperature_2m":{"band":"dewpoint_temperature_2m","name":"Dewpoint Temperature (2 m)","common_name":"Dew Point Temperature","units_out":"K"},"wind_speed":{"band":"wind_speed","name":"Wind Speed","common_name":"Wind Speed","units_out":"m/s"},"srad":{"band":"surface_solar_radiation_downwards_sum","name":"Downwelling Shortwave Radiation","common_name":"Downward Shortwave Radiation","units_out":"W/m^2"},"spi":{"band":"total_precipitation_sum","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"peth":{"band":"peth","name":"Potential ET Hargreaves","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit Hargreaves","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index Hargreaves (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"peth","name":"Evap. Demand Drought Index Hargreaves (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""},"Snow_Depth":{"band":"snow_depth","name":"Snow thickness","common_name":"Snow Depth","units_out":"mm"},"SWE":{"band":"snow_depth_water_equivalent","name":"Snow depth water equivalent","common_name":"Snow Water Equivalent","units_out":"mm"},"surface_net_solar_radiation_sum":{"band":"surface_net_solar_radiation_sum","name":"surface_net_solar_radiation_sum","common_name":"Net Shortwave Radiation","units_out":"W/m2"}}},"FLDAS":{"api_variable_path":"#fldas","category":"Climate/Hydrology","citations":"1) If you use these data in your research or applications, please include a reference in your publication(s) similar to the following example: Amy McNally NASA/GSFC/HSL (2018), FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS), Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/5NHC22T9375G 2) McNally, A., Arsenault, K., Kumar, S., Shukla, S., Peterson, P., Wang, S., Funk, C., Peters-Lidard, C.D., & Verdin, J. P. (2017). A land data assimilation system for sub-Saharan Africa food and water security applications. Scientific Data, 4, 170012.","coll_desc":"FLDAS 9.6-km dataset (FEWSNET)","coll_name":"NASA/FLDAS/NOAH01/C/GL/M/V001","dataset_website":"https://ldas.gsfc.nasa.gov/fldas/","description":"The FLDAS Global model (McNally et al. 2017) is a custom instance of the NASA Land Information System (LIS; http://lis.gsfc.nasa.gov/) that has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings. Adopting LIS allows FEWS NET to leverage existing land surface models and generate ensembles of soil moisture, ET, and other variables based on multiple meteorological inputs or land surface models. The goal of the FLDAS project is to achieve more effective use of limited available hydroclimatic observations and is designed to be adopted for routine use for FEWS NET decision support.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"FLDAS - 9.6km - Monthly","geographic_coverage":"Global","processing_steps":"","product_name":"FLDAS","spatial_resolution":"9.6km","spatial_resolution_normalized":"9600","start_year":"1982","support_thumbnail":"static/img/support/fldas_ae00c20c6e.jpg","caption":"Example 1-month Total Runoff Map","support_path":"datasets/climatehydrology/fldas_monthly_9600","temporal_resolution":"Monthly","temporal_resolution_normalized":"30","terms_of_use":"Distribution of data from the Goddard Earth Sciences Data and Information Services Center (GES DISC) is funded by NASA's Science Mission Directorate (SMD). Consistent with NASA Earth Science Data and Information Policy, data from the GES DISC archive are available free to the user community. For more information visit the GES DISC Data Policy page.","variable_info":{"Tair_f_tavg":{"band":"Tair_f_tavg","name":"Near Surface Air Temperature","common_name":"Temperature","units_out":"K"},"Evap_tavg":{"band":"Evap_tavg","name":"Evapotranspiration","common_name":"Evapotranspiration","units_out":"mm"},"SoilMoi00_10cm_tavg":{"band":"SoilMoi00_10cm_tavg","name":"Surface Soil Moisture (10cm)","common_name":"Soil Moisture","units_out":"mm"},"Qs_tavg":{"band":"Qs_tavg","name":"Surface Runoff","common_name":"Surface Runoff","units_out":"mm"},"TotalRunoff":{"band":"TotalRunoff","name":"Total Runoff","common_name":"Runoff","units_out":"mm"},"RootZoneSoilMoisture":{"band":"RootZoneSoilMoisture","name":"Root Zone Soil Moisture (0-100cm)","common_name":"Soil Moisture","units_out":"mm"},"SWE_inst":{"band":"SWE_inst","name":"Snow Water Equivalent","common_name":"Snow Water Equivalent","units_out":"mm"},"SnowDepth_inst":{"band":"SnowDepth_inst","name":"Snow Depth","common_name":"Snow Depth","units_out":"mm"}}},"FRET":{"api_variable_path":"#fret-7-day-forecasts","category":"Forecasts","citations":"None","coll_desc":"FRET ?-km 7-day forecast dataset (NWS)","coll_name":"projects/climate-engine/fret/forecast","dataset_website":"https://digital.weather.gov/?zoom=4&lat=37&lon=-96.5&layers=F000BTTTFTT&region=0&element=42&mxmz=false&barbs=false&subl=TFFFF&units=english&wunits=nautical&coords=latlon&tunits=localt","description":"The National Weather Service is now producing Forecast Reference Crop Evapotranspiration (FRET), a forecast estimate of the amount of evapotranspiration for a well- watered reference crop (grass or alfalfa) under prescribed conditions for a 24 hour period. Weekly FRET forecast calculations and NLDAS derived reference crop ET Climatology and departure from normal are available as well. The Forecast Reference Evapotranspiration (FRET) are for a short canopy (or 12cm grasses). The short canopy ET values are calculated using the Penman-Monteith Reference Evapotranspiration Equations, adopted by the Environmental Water Resources Institute - American Society of Civil engineers (ASCE-EWRI, 2004), and the National Weather Service forecast of temperatures, relative humidity, wind, and cloud cover. This product will be issued daily by 8 am local time, year round.","ee_asset_path":"https://gee-community-catalog.org/projects/fret/?h=fret","ee_source":"ClimateEngine.org","end_year":"Forecast","full_name":"FRET - 4km - 1to7day","geographic_coverage":"CONUS","processing_steps":"","product_name":"FRET 1-7 Day Forecasts","spatial_resolution":"4km","spatial_resolution_normalized":"4000","start_year":"Forecast","support_thumbnail":"static/img/support/fret_c0e709a543.jpg","caption":"Example Forecast 48-ensemble mean Grass Reference Evapotranspiration Map","support_path":"datasets/forecasts/fret_1to7days_4000","temporal_resolution":"1 to 7 days","temporal_resolution_normalized":"1","terms_of_use":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.","variable_info":{"eto":{"band":"eto","name":"ASCE Grass Reference Evapotranspiration","common_name":"Reference Evapotranspiration","units_out":"mm"}}},"FORDRI":{"api_variable_path":"tbd","category":"Remote Sensing","citations":"Tadesse, T., Hollinger, D.Y., Bayissa, Y.A., Svoboda, M., Fuchs, B., Zhang, B., Demissie, G., Wardlow, B.D., Bohrer, G., Clark, K.L. and Desai, A.R., 2020. Forest Drought Response Index (ForDRI): a new combined model to monitor forest drought in the eastern United States. Remote Sensing, 12(21), p.3605.","coll_desc":"Forest Drought Response Index (ForDRI)","coll_name":"projects/climate-engine-pro/assets/ce-fordri","dataset_website":"https://fordri.unl.edu/Home.aspx","description":"ForDRI integrates data for 12 different environmental variables, including vegetation health, climate, evaporative demand, ground water and soil moisture, into a single hybrid index to estimate forest-related drought stress.","ee_asset_path":"N/A","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"ForDRI - 4.4km - Weekly","geographic_coverage":"CONUS","processing_steps":"","product_name":"Forest Drought Response Index (ForDRI)","spatial_resolution":"4.4km","spatial_resolution_normalized":"4400","start_year":"2003","support_thumbnail":"static/img/support/fordri.png","caption":"Example Forest Drought Response Index (forDRI) Map","support_path":"datasets/remotesensing/fordri_weekly_1000","temporal_resolution":"Weekly","temporal_resolution_normalized":"7","terms_of_use":"","variable_info":{"fordri":{"band":"fordri","name":"ForDRI","common_name":"Forest Drought Response Index (ForDRI)","units_out":"N/A"}}},"GEPS_2WK":{"api_variable_path":"#global-ensemble-prediction-system-2-week","category":"Forecasts","citations":"None","coll_desc":"Global Ensemble Prediction System 2 week","coll_name":"projects/climate-engine-pro/assets/ce-geps2w-forecast","dataset_website":"https://eccc-msc.github.io/open-data/msc-data/nwp_geps/readme_geps-datamart_en/","description":"The Global Ensemble Prediction System (GEPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the current day out to 16 days into the future (up to 32 days once a week on Thursdays at 00UTC). The GEPS produces different outlooks (scenarios) to estimate the forecast uncertainties due the nonlinear (chaotic) behaviour of the atmosphere. The probabilistic predictions are based on an ensemble of 20 scenarios that differ in their initial conditions, choice of physics parametrization as well as stochastic perturbations (physical tendencies and kinetic energy). A control member that is not perturbed is also available.","ee_asset_path":"Not publicly available","ee_source":"ClimateEngine.org","end_year":"Forecast","full_name":"GEPS - 55km - 2week","geographic_coverage":"Global","processing_steps":"","product_name":"Global Ensemble Prediction System 2-Week","spatial_resolution":"55km","spatial_resolution_normalized":"55000","start_year":"Forecast","support_thumbnail":"static/img/support/geps_2wk_4979e11c4b.jpg","caption":"Example Forecast 20-ensemble mean Precipitation Map","support_path":"datasets/forecasts/geps_2weeks_55000","temporal_resolution":"2-weeks","temporal_resolution_normalized":"1","terms_of_use":"Find license information here: https://eccc-msc.github.io/open-data/licence/readme_en/","variable_info":{"prcp":{"band":"prcp","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"temp_max":{"band":"temp_max","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"temp_min":{"band":"temp_min","name":"Minimum Temperature","common_name":"Temperature","units_out":"K"},"temp_mean":{"band":"temp_mean","name":"Mean Temperature","common_name":"Temperature","units_out":"K"}}},"GEPS_4WK":{"api_variable_path":"#global-ensemble-prediction-system-4-week","category":"Forecasts","citations":"None","coll_desc":"Global Ensemble Prediction System 4 week","coll_name":"projects/climate-engine-pro/assets/ce-geps4w-forecast","dataset_website":"https://eccc-msc.github.io/open-data/msc-data/nwp_geps/readme_geps-datamart_en/","description":"The Global Ensemble Prediction System (GEPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the current day out to 16 days into the future (up to 32 days once a week on Thursdays at 00UTC). The GEPS produces different outlooks (scenarios) to estimate the forecast uncertainties due the nonlinear (chaotic) behaviour of the atmosphere. The probabilistic predictions are based on an ensemble of 20 scenarios that differ in their initial conditions, choice of physics parametrization as well as stochastic perturbations (physical tendencies and kinetic energy). 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The drought indicators describe current wet or dry conditions, expressed as a percentile showing the probability of occurrence for that particular location and time of year, with lower values (warm colors)meaning dryer than normal, and higher values (blues) meaning wetter than normal.","ee_asset_path":"projects/climate-engine-pro/assets/ce-grace-drought/global","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"GRACE Drought - 28km - Weekly","geographic_coverage":"Global","processing_steps":"","product_name":"GRACE Drought","spatial_resolution":"28km","spatial_resolution_normalized":"28000","start_year":"2003","support_thumbnail":"static/img/support/grace_global.png","caption":"Example Shallow Groundwater Percentile Map","support_path":"datasets/remotesensing/gracedrought_weekly_28000","temporal_resolution":"Weekly","temporal_resolution_normalized":"7","terms_of_use":"","variable_info":{"sfsm":{"band":"sfsm","name":"Surface Soil Moisture","common_name":"Surface Soil Moisture","units_out":"percentile"},"rtzsm":{"band":"rtzsm","name":"Root Zone Soil Moisture","common_name":"Soil Moisture","units_out":"percentile"},"gws":{"band":"gws","name":"Shallow Groundwater","common_name":"Shallow Groundwater","units_out":"percentile"}}},"ESI":{"api_variable_path":"#esi-4-week","category":"Remote Sensing","citations":"1) Anderson, M.C., C. 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Here, ET is retrieved via energy balance using remotely sensed land-surface temperature (LST) time-change signals. LST is a fast- response variable, providing proxy information regarding rapidly evolving surface soil moisture and crop stress conditions at relatively high spatial resolution. The ESI also demonstrates capability for capturing early signals of “flash drought”, brought on by extended periods of hot, dry and windy conditions leading to rapid soil moisture depletion.","ee_asset_path":"https://gee-community-catalog.org/projects/global_esi/?h=esi","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"ESI - 4km - 4/12week","geographic_coverage":"Global","processing_steps":"","product_name":"Evaporative Stress Index (ESI)","spatial_resolution":"4km","spatial_resolution_normalized":"4000","start_year":"2001","support_thumbnail":"static/img/support/esi_da8170cd75.jpg","caption":"Example 4-week Evaporative Stress Index (ESI) Map","support_path":"datasets/remotesensing/esi_412week_4000","temporal_resolution":"4/12 Week","temporal_resolution_normalized":"28","terms_of_use":"This dataset is open and there are no restrictions on it's use.","variable_info":{"ESI_4wk":{"band":"ESI","name":"4 week ESI (Evaporative Stress Index)","common_name":"Evaporative Stress Index (ESI)","units_out":""},"ESI_12wk":{"band":"ESI","name":"12 week ESI (Evaporative Stress Index)","common_name":"Evaporative Stress Index (ESI)","units_out":""}}},"GRIDMET":{"api_variable_path":"#gridmet","category":"Climate/Hydrology","citations":"1) Abatzoglou J. 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GridMET blends spatial attributes of gridded climate data from PRISM with desirable temporal attributes (and additional variables) from regional reanalysis (NLDAS-2) using climatically aided interpolation. The resulting product is a spatially and temporally complete, high-resolution (1/24th degree ~4-km) gridded dataset of surface meteorological variables.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"GridMET - 4km - Daily","geographic_coverage":"CONUS","processing_steps":"1) For Minimum/Maximum Temperature, Precipitation, Minimum/Maximum/Specific Humidity variables, a 'day' is defined as the 24 hours ending at 12:00 Greenwich Mean Time (GMT, or 7:00am Eastern Standard Time). 2) For Radiation and Wind variables, a 'day' is defined as 6 UTC to 6 UTC. 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The drought indices computed are for the standardized indices of SPI, SPEI and EDDI and the Palmer Drought Severity Index (PDSI) and Palmer Z-Index.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/GRIDMET_DROUGHT","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"GridMET Drought - 4km - Pentad","geographic_coverage":"CONUS","processing_steps":"","product_name":"gridMet Drought - Pentad","spatial_resolution":"4km","spatial_resolution_normalized":"4000","start_year":"1979","support_thumbnail":"static/img/support/gridmet_drought_0ae5a0c36b.jpg","caption":"Example 90 Day Standardized Precipitation Index (SPI) Map","support_path":"datasets/climatehydrology/gridmetdrought_pentad_4000","temporal_resolution":"Pentad","temporal_resolution_normalized":"5","terms_of_use":"CC BY 4.0","variable_info":{"z":{"band":"z","name":"Palmer Z Index","common_name":"Palmer Z Index","units_out":""},"pdsi":{"band":"pdsi","name":"Palmer Drought Severity Index 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The data they provide is used to unify precipitation measurements made by an international network of partner satellites to quantify when, where, and how much it rains or snows around the world.","ee_asset_path":"Not publicly available","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"GPM - 11km - Daily","geographic_coverage":"Global","processing_steps":"","product_name":"GPM - Daily Precipitation","spatial_resolution":"11km","spatial_resolution_normalized":"11000","start_year":"2000","support_thumbnail":"static/img/support/gpm_daily_ced4df7d7d.jpg","caption":"Example 1-month Precipitation Map","support_path":"datasets/climatehydrology/gpm_daily_11000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"All NASA-produced data from the GPM mission is made freely available for the public to use.","variable_info":{"precipitationCal":{"band":"precipitation","name":"Precipitation Calibrated","common_name":"Precipitation","units_out":"mm"},"spi":{"band":"precipitation","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""}}},"GPM_DAILY_EARLY":{"api_variable_path":"#global-precipitation-measurement-early-daily","category":"Climate/Hydrology","citations":"1) Jackson, Gail & Berg, Wesley & Kidd, Chris & Kirschbaum, Dalia & Petersen, Walter & Huffman, George & Takayabu, Yukari. (2018). Global Precipitation Measurement (GPM): Unified Precipitation Estimation from Space. 10.1007/978-3-319-72583-3_7.","coll_desc":"GPM 11-km (1/10-deg) precipitation dataset (NASA)","coll_name":"projects/climate-engine-pro/assets/ce-gpm-imerg-v07/early-daily","dataset_website":"https://www.nasa.gov/mission_pages/GPM/overview/index.html","description":"Global Precipitation Measurement (GPM) is an international satellite mission to provide next-generation observations of rain and snow worldwide every three hours. NASA and the Japanese Aerospace Exploration Agency (JAXA) launched the GPM Core Observatory satellite on February 27th, 2014, carrying advanced instruments that set a new standard for precipitation measurements from space. The data they provide is used to unify precipitation measurements made by an international network of partner satellites to quantify when, where, and how much it rains or snows around the world.","ee_asset_path":"Not publicly available","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"GPM Early - 11km - Daily","geographic_coverage":"Global","processing_steps":"1) The GPM Daily dataset hosted on Climate Engine is sourced from the 30-min data. These data are summed to daily based on 0 UTC time zone. 2) This collection contains provisional products that are regularly replaced with updated versions when the data become available. This transition typically occurs about 1-2 years out.3) Climate Engine does not do any post processing of data. This product is available on a daily cadence from the site. The GPM Daily collection is the merging of the Late and Final.","product_name":"GPM - Daily Precipitation","spatial_resolution":"11km","spatial_resolution_normalized":"11000","start_year":"2000","support_thumbnail":"static/img/support/gpm_daily_early_ced4df7d7d.jpg","caption":"Example 1-month Precipitation Map","support_path":"","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"All NASA-produced data from the GPM mission is made freely available for the public to use.","variable_info":{"precipitationCal":{"band":"precipitation","name":"Precipitation Calibrated","common_name":"Precipitation","units_out":"mm"},"spi":{"band":"precipitation","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""}}},"GPM_DAILY_LATE":{"api_variable_path":"#global-precipitation-measurement-late-daily","category":"Climate/Hydrology","citations":"1) Jackson, Gail & Berg, Wesley & Kidd, Chris & Kirschbaum, Dalia & Petersen, Walter & Huffman, George & Takayabu, Yukari. (2018). Global Precipitation Measurement (GPM): Unified Precipitation Estimation from Space. 10.1007/978-3-319-72583-3_7.","coll_desc":"GPM 11-km (1/10-deg) precipitation dataset (NASA)","coll_name":"projects/climate-engine-pro/assets/ce-gpm-imerg-v07/late-daily","dataset_website":"https://www.nasa.gov/mission_pages/GPM/overview/index.html","description":"Global Precipitation Measurement (GPM) is an international satellite mission to provide next-generation observations of rain and snow worldwide every three hours. NASA and the Japanese Aerospace Exploration Agency (JAXA) launched the GPM Core Observatory satellite on February 27th, 2014, carrying advanced instruments that set a new standard for precipitation measurements from space. The data they provide is used to unify precipitation measurements made by an international network of partner satellites to quantify when, where, and how much it rains or snows around the world.","ee_asset_path":"Not publicly available","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"GPM Late - 11km - Daily","geographic_coverage":"Global","processing_steps":"1) The GPM Daily dataset hosted on Climate Engine is sourced from the 30-min data. These data are summed to daily based on 0 UTC time zone. 2) This collection contains provisional products that are regularly replaced with updated versions when the data become available. This transition typically occurs about 1-2 years out.3) Climate Engine does not do any post processing of data. This product is available on a daily cadence from the site. The GPM Daily collection is the merging of the Late and Final.","product_name":"GPM - Daily Precipitation","spatial_resolution":"11km","spatial_resolution_normalized":"11000","start_year":"2000","support_thumbnail":"static/img/support/gpm_daily_late_ced4df7d7d.jpg","caption":"Example 1-month Precipitation Map","support_path":"","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"All NASA-produced data from the GPM mission is made freely available for the public to use.","variable_info":{"precipitationCal":{"band":"precipitation","name":"Precipitation Calibrated","common_name":"Precipitation","units_out":"mm"},"spi":{"band":"precipitation","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""}}},"GPM_DAILY_FINAL":{"api_variable_path":"#global-precipitation-measurement-final-daily","category":"Climate/Hydrology","citations":"1) Jackson, Gail & Berg, Wesley & Kidd, Chris & Kirschbaum, Dalia & Petersen, Walter & Huffman, George & Takayabu, Yukari. (2018). Global Precipitation Measurement (GPM): Unified Precipitation Estimation from Space. 10.1007/978-3-319-72583-3_7.","coll_desc":"GPM 11-km (1/10-deg) precipitation dataset (NASA)","coll_name":"projects/climate-engine-pro/assets/ce-gpm-imerg-v07/final-daily","dataset_website":"https://www.nasa.gov/mission_pages/GPM/overview/index.html","description":"Global Precipitation Measurement (GPM) is an international satellite mission to provide next-generation observations of rain and snow worldwide every three hours. NASA and the Japanese Aerospace Exploration Agency (JAXA) launched the GPM Core Observatory satellite on February 27th, 2014, carrying advanced instruments that set a new standard for precipitation measurements from space. The data they provide is used to unify precipitation measurements made by an international network of partner satellites to quantify when, where, and how much it rains or snows around the world.","ee_asset_path":"Not publicly available","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"GPM Final - 11km - Daily","geographic_coverage":"Global","processing_steps":"1) The GPM Daily dataset hosted on Climate Engine is sourced from the 30-min data. These data are summed to daily based on 0 UTC time zone. 2) This collection contains provisional products that are regularly replaced with updated versions when the data become available. This transition typically occurs about 1-2 years out.3) Climate Engine does not do any post processing of data. This product is available on a daily cadence from the site. The GPM Daily collection is the merging of the Late and Final.","product_name":"GPM - Daily Precipitation","spatial_resolution":"11km","spatial_resolution_normalized":"11000","start_year":"2000","support_thumbnail":"static/img/support/gpm_daily_final_ced4df7d7d.jpg","caption":"Example 1-month Precipitation Map","support_path":"","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"All NASA-produced data from the GPM mission is made freely available for the public to use.","variable_info":{"precipitationCal":{"band":"precipitation","name":"Precipitation Calibrated","common_name":"Precipitation","units_out":"mm"},"spi":{"band":"precipitation","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""}}},"HRDPA":{"api_variable_path":"#hrdpa","category":"Climate/Hydrology","citations":"https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/capa_information_leaflet_20141118_en.pdf","coll_desc":"HRDPA 2.5-km (1/24-deg) precipitation dataset (MSC)","coll_name":"projects/climate-engine-pro/assets/ce-hrdpa-daily","dataset_website":"https://open.canada.ca/data/en/dataset/eff69d42-ce81-4672-867f-cc3baaf4157a","description":"The High Resolution Deterministic Precipitation Analysis(HRDPA) is a best estimate of 6 and 24 hour precipitation amounts. This objective estimate integrates data from in situ precipitation gauge measurements, radar QPEs and a trial field generated by a numerical weather prediction system. CaPA produces four analyses of 6 hour amounts per day, valid at synoptic hours (00, 06, 12 and 18 UTC) and two 24 hour analyses valid at 06 and 12 UTC. HRDPA is provided by the Meterological Service of Canada (MSC), a part of Environment and Climate Change Canada (ECCC). The MSC provides weather forecasts and warnings 24 hours a day, 365 days a year.","ee_asset_path":"https://gee-community-catalog.org/projects/hrdpa/?h=hrdp","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"HRDPA - 2.5km - Daily","geographic_coverage":"North America","processing_steps":"","product_name":"HRDPA - Daily Precipitation","spatial_resolution":"2.5km","spatial_resolution_normalized":"2500","start_year":"2018","support_thumbnail":"static/img/support/hrdpa_f03b4d9651.jpg","caption":"Example 1-month Precipitation Percent of Average Map","support_path":"datasets/climatehydrology/hrdpa_daily_2500","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"License information can be found here: https://eccc-msc.github.io/open-data/licence/readme_en/.","variable_info":{"precip":{"band":"precip","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"spi":{"band":"precip","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""}}},"HRDPS":{"api_variable_path":"#hrdps","category":"Climate/Hydrology","citations":"None","coll_desc":"HRDPS 2.5-km (1/24-deg) temperature dataset (MSC)","coll_name":"projects/climate-engine-pro/assets/ce-hrdps-daily","dataset_website":"https://eccc-msc.github.io/open-data/msc-data/nwp_rdps/readme_rdps-datamart_en/","description":"The High Resolution Deterministic Prediction System(HRDPS) provides useful numerical simulations of temperature over large areas. Climate Engine is ingesting only the band containing temperature at 2m above ground level, but HRDPS also produces bands for precipiation, cloud cover, wind speed and direction, humidity, and others. These numerical simulations can be used for air quality modeling and forecasting, climate and wildfire modeling, and extreme weather forecasting. Users who will benefit most from using these new data are those for whom a detailed forecast of surface temperatures and winds is important. The 2.5 km forecasts could add much value especially during the change of seasons and in wintertime when rapid changes in temperature and winds cause phase transitions of precipitation (freezing rain to snow to rain for example). HRDPS is the high resolution counterpart to the RDPS dataset.","ee_asset_path":"https://gee-community-catalog.org/projects/hrdps/?h=hrdp","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"HRDPS - 2.5km - Daily","geographic_coverage":"North America","processing_steps":"","product_name":"HRDPS - Daily Temperature","spatial_resolution":"2.5km","spatial_resolution_normalized":"2500","start_year":"2015","support_thumbnail":"static/img/support/hrdps_036485867e.jpg","caption":"Example 1-year Mean Temperature Percentile Map","support_path":"datasets/climatehydrology/hrdps_daily_2500","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"License information can be found here: https://open.canada.ca/en/open-government-licence-canada","variable_info":{"Tavg":{"band":"Tavg","name":"Mean Temperature","common_name":"Temperature","units_out":"K"}}},"LANDSAT_TOA":{"api_variable_path":"#landsat-5-top-of-atmosphere","category":"Remote Sensing","citations":"None","coll_desc":"Landsat 5/7/8/9, top-of-atmosphere reflectance daily {} (cloud mask applied)","coll_name":["LANDSAT/LT05/C02/T1_TOA","LANDSAT/LE07/C02/T1_TOA","LANDSAT/LC08/C02/T1_RT_TOA","LANDSAT/LC09/C02/T1_TOA"],"dataset_website":"https://landsat.usgs.gov/","description":"Merged Landsat 5/7/8/9 top of atmosphere reflectance data in visible, near, and short wave infrared and thermal bands.","ee_asset_path":"See individual landsat collection paths","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"Landsat 5/7/8/9 TOA - 30m - 16day","geographic_coverage":"Global","processing_steps":"1) The Collection 2 CFMask algorithm is applied to each Collection 2 Surface Reflectance dataset. The cloud masking attempts to remove medium and high confidence snow, shadow and cirrus clouds using the BQA quality band provided in the Landsat GEE collection. You can read more about the mask here. 2) The Collection 2 RADSAT mask is applied to each Collection 2 Surface Reflectance dataset. You can read more about the mask here. 3) Scaling and offset are applied. Band order is changed to align with Landsat 8. Band names are changed to common names. Pan-chromatic band, high gain thermal band, Coastal Band, Cirrus Band are not included.4) NOTE: We do not apply additional harmonization between Landsat sensors. While the need for harmonization coefficients in Landsat Collection 1 was demonstrated, Landsat Collection 2 Tier 1 products, as provided in Climate Engine, are considered appropriate for cross-sensor timeseries analysis without additional harmonization. See our article for more information.","product_name":"Landsat Top of Atmosphere Reflectance","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"1984","support_thumbnail":"static/img/support/landsat_toa_2aa69cbe79.jpg","caption":"Example Dec-Feb Normalized Difference Vegetation Index (NDVI) Map","support_path":"datasets/remotesensing/landsat5789toa_16day_30","temporal_resolution":"16 Day","temporal_resolution_normalized":"16","terms_of_use":"Landsat datasets are federally created data and therefore reside in the public domain and may be used, transferred, or reproduced without copyright restriction. Acknowledgement or credit of the USGS as data source should be provided by including a line of text citation such as the example: (Product, Image, Photograph, or Dataset Name) courtesy of the U.S. Geological Survey. See the USGS Visual Identity System Guidance for further details on proper citation and acknowledgement of USGS products.","variable_info":{"NDVI":{"band":"NDVI","name":"NDVI","common_name":"Normalized Difference Vegetation Index (NDVI)","units_out":""},"EVI":{"band":"EVI","name":"EVI","common_name":"Enhanced Vegetation Index (EVI)","units_out":""},"NBR":{"band":"NBR","name":"NBR","common_name":"Normalized Burn Ratio (NBR)","units_out":""},"SAVI":{"band":"SAVI","name":"SAVI","common_name":"Soil Adjusted Vegetation Index (SAVI)","units_out":""},"MSAVI":{"band":"MSAVI","name":"MSAVI","common_name":"Modified Soil Adjusted Vegetation Index (MSAVI)","units_out":""},"AVI":{"band":"AVI","name":"AVI","common_name":"Advanced Vegetation Index (AVI)","units_out":""},"DVI":{"band":"DVI","name":"DVI","common_name":"Difference Vegetation Index (DVI)","units_out":""},"FCVI":{"band":"FCVI","name":"FCVI","common_name":"Forest Canopy Vegetation Index (FCVI)","units_out":""},"NDSI":{"band":"NDSI","name":"NDSI","common_name":"Normalized Difference Snow Index (NDSI)","units_out":""},"NDWI_NIR_SWIR_Gao":{"band":"NDWI_NIR_SWIR_Gao","name":"NDWI (NIR/SWIR1)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"NDWI_Green_NIR_McFeeters":{"band":"NDWI_Green_NIR_McFeeters","name":"NDWI (Green/NIR)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"NDWI_Green_SWIR_Xu":{"band":"NDWI_Green_SWIR_Xu","name":"NDWI (Green/SWIR1)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"NDWI_Green_SWIR_Hall":{"band":"NDWI_Green_SWIR_Hall","name":"NDWI  (Green/SWIR2)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"NDWI_SWIR_Green_Allen":{"band":"NDWI_SWIR_Green_Allen","name":"NDWI (SWIR1/Green)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"TrueColor":{"band":"TrueColor","name":"True Color","common_name":"Color Composite","units_out":""},"FalseColor":{"band":"FalseColor","name":"False Color","common_name":"Color Composite","units_out":""},"Blue":{"band":"Blue","name":"Blue","common_name":"Surface Reflectance","units_out":""},"Green":{"band":"Green","name":"Green","common_name":"Surface Reflectance","units_out":""},"Red":{"band":"Red","name":"Red","common_name":"Surface Reflectance","units_out":""},"NIR":{"band":"NIR","name":"NIR","common_name":"Surface Reflectance","units_out":""},"SWIR1":{"band":"SWIR1","name":"SWIR1","common_name":"Surface Reflectance","units_out":""},"SWIR2":{"band":"SWIR2","name":"SWIR2","common_name":"Surface Reflectance","units_out":""},"rGreenBlue":{"band":"rGreenBlue","name":"Green/Blue ratio","common_name":"Spectral Ratio","units_out":""},"rRedBlue":{"band":"rRedBlue","name":"Red/Blue ratio","common_name":"Spectral Ratio","units_out":""},"rGreenRed":{"band":"rGreenRed","name":"Green/Red ratio","common_name":"Spectral Ratio","units_out":""},"rBlueNIR":{"band":"rBlueNIR","name":"Blue/NIR ratio","common_name":"Spectral Ratio","units_out":""},"rGreenNIR":{"band":"rGreenNIR","name":"Green/NIR ratio","common_name":"Spectral Ratio","units_out":""},"rRedNIR":{"band":"rRedNIR","name":"Red/NIR ratio","common_name":"Spectral Ratio","units_out":""},"LST":{"band":"lst","name":"Land Surface Temperature","common_name":"Land Surface Temperature","units_out":""},"OC2":{"band":"OC2","name":"Chlorophyll A calculated from Green and Blue bands","common_name":"Chlorophyll Index","units_out":""}}},"LANDSAT5_TOA":{"api_variable_path":"#landsat-5-top-of-atmosphere","category":"Remote Sensing","citations":"None","coll_desc":"Landsat 5, top-of-atmopshere reflectance daily {0} (CFMask cloud mask applied)","coll_name":"LANDSAT/LT05/C02/T1_TOA","dataset_website":"https://landsat.usgs.gov/","description":"Landsat 5 TM Collection 2 Tier 1 calibrated top-of-atmosphere (TOA) reflectance. Calibration coefficients are extracted from the image metadata. See Chander et al. (2009) for details on the TOA computation.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C02_T1_TOA","ee_source":"Earth Engine Catalog","end_year":"2012","full_name":"Landsat 5 TOA - 30m - 16day","geographic_coverage":"Global","processing_steps":"1) The Collection 2 CFMask algorithm is applied to each Collection 2 Surface Reflectance dataset. The cloud masking attempts to remove medium and high confidence snow, shadow and cirrus clouds using the BQA quality band provided in the Landsat GEE collection. You can read more about the mask here. 2) The Collection 2 RADSAT mask is applied to each Collection 2 Surface Reflectance dataset. You can read more about the mask here. 3) Scaling and offset are applied. Band order is changed to align with Landsat 8. Band names are changed to common names. Pan-chromatic band, high gain thermal band, Coastal Band, Cirrus Band are not included.4) NOTE: We do not apply additional harmonization between Landsat sensors. While the need for harmonization coefficients in Landsat Collection 1 was demonstrated, Landsat Collection 2 Tier 1 products, as provided in Climate Engine, are considered appropriate for cross-sensor timeseries analysis without additional harmonization. See our article for more information.","product_name":"Landsat Top of Atmosphere Reflectance","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"1984","support_thumbnail":"static/img/support/landsat5_toa_2aa69cbe79.jpg","caption":"Example Dec-Feb Normalized Difference Vegetation Index (NDVI) Map","support_path":"datasets/remotesensing/landsat5toa_16day_30","temporal_resolution":"16 Day","temporal_resolution_normalized":"16","terms_of_use":"Landsat datasets are federally created data and therefore reside in the public domain and may be used, transferred, or reproduced without copyright restriction. Acknowledgement or credit of the USGS as data source should be provided by including a line of text citation such as the example: (Product, Image, Photograph, or Dataset Name) courtesy of the U.S. Geological Survey. See the USGS Visual Identity System Guidance for further details on proper citation and acknowledgement of USGS products.","variable_info":{"NDVI":{"band":"NDVI","name":"NDVI","common_name":"Normalized Difference Vegetation Index (NDVI)","units_out":""},"EVI":{"band":"EVI","name":"EVI","common_name":"Enhanced Vegetation Index (EVI)","units_out":""},"NBR":{"band":"NBR","name":"NBR","common_name":"Normalized Burn Ratio (NBR)","units_out":""},"SAVI":{"band":"SAVI","name":"SAVI","common_name":"Soil Adjusted Vegetation Index (SAVI)","units_out":""},"MSAVI":{"band":"MSAVI","name":"MSAVI","common_name":"Modified Soil Adjusted Vegetation Index (MSAVI)","units_out":""},"AVI":{"band":"AVI","name":"AVI","common_name":"Advanced Vegetation Index (AVI)","units_out":""},"DVI":{"band":"DVI","name":"DVI","common_name":"Difference Vegetation Index (DVI)","units_out":""},"FCVI":{"band":"FCVI","name":"FCVI","common_name":"Forest Canopy Vegetation Index (FCVI)","units_out":""},"NDSI":{"band":"NDSI","name":"NDSI","common_name":"Normalized Difference Snow Index (NDSI)","units_out":""},"NDWI_NIR_SWIR_Gao":{"band":"NDWI_NIR_SWIR_Gao","name":"NDWI (NIR/SWIR1)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"NDWI_Green_NIR_McFeeters":{"band":"NDWI_Green_NIR_McFeeters","name":"NDWI (Green/NIR)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"NDWI_Green_SWIR_Xu":{"band":"NDWI_Green_SWIR_Xu","name":"NDWI (Green/SWIR1)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"NDWI_Green_SWIR_Hall":{"band":"NDWI_Green_SWIR_Hall","name":"NDWI  (Green/SWIR2)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"NDWI_SWIR_Green_Allen":{"band":"NDWI_SWIR_Green_Allen","name":"NDWI (SWIR1/Green)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"TrueColor":{"band":"TrueColor","name":"True Color","common_name":"Color Composite","units_out":""},"FalseColor":{"band":"FalseColor","name":"False Color","common_name":"Color Composite","units_out":""},"Blue":{"band":"Blue","name":"Blue","common_name":"Surface Reflectance","units_out":""},"Green":{"band":"Green","name":"Green","common_name":"Surface Reflectance","units_out":""},"Red":{"band":"Red","name":"Red","common_name":"Surface Reflectance","units_out":""},"NIR":{"band":"NIR","name":"NIR","common_name":"Surface Reflectance","units_out":""},"SWIR1":{"band":"SWIR1","name":"SWIR1","common_name":"Surface Reflectance","units_out":""},"SWIR2":{"band":"SWIR2","name":"SWIR2","common_name":"Surface Reflectance","units_out":""},"rGreenBlue":{"band":"rGreenBlue","name":"Green/Blue ratio","common_name":"Spectral Ratio","units_out":""},"rRedBlue":{"band":"rRedBlue","name":"Red/Blue ratio","common_name":"Spectral Ratio","units_out":""},"rGreenRed":{"band":"rGreenRed","name":"Green/Red ratio","common_name":"Spectral Ratio","units_out":""},"rBlueNIR":{"band":"rBlueNIR","name":"Blue/NIR ratio","common_name":"Spectral Ratio","units_out":""},"rGreenNIR":{"band":"rGreenNIR","name":"Green/NIR ratio","common_name":"Spectral Ratio","units_out":""},"rRedNIR":{"band":"rRedNIR","name":"Red/NIR ratio","common_name":"Spectral Ratio","units_out":""},"LST":{"band":"lst","name":"Land Surface Temperature","common_name":"Land Surface Temperature","units_out":""},"OC2":{"band":"OC2","name":"Chlorophyll A calculated from Green and Blue bands","common_name":"Chlorophyll Index","units_out":""}}},"LANDSAT7_TOA":{"api_variable_path":"#landsat-7-top-of-atmosphere","category":"Remote Sensing","citations":"None","coll_desc":"Landsat 7, top-of-atmopshere reflectance daily {0} (CFMask cloud mask applied)","coll_name":"LANDSAT/LE07/C02/T1_TOA","dataset_website":"https://landsat.usgs.gov/","description":"Landsat 7 Collection 2 Tier 1 calibrated top-of-atmosphere (TOA) reflectance. 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Band names are changed to common names. Pan-chromatic band, high gain thermal band, Coastal Band, Cirrus Band are not included.4) NOTE: We do not apply additional harmonization between Landsat sensors. While the need for harmonization coefficients in Landsat Collection 1 was demonstrated, Landsat Collection 2 Tier 1 products, as provided in Climate Engine, are considered appropriate for cross-sensor timeseries analysis without additional harmonization. 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Acknowledgement or credit of the USGS as data source should be provided by including a line of text citation such as the example: (Product, Image, Photograph, or Dataset Name) courtesy of the U.S. Geological Survey. 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These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. These data were collected using funding from the U.S. Government and can be used without additional permissions or fees.","variable_info":{"Severity":{"band":"Severity","name":"Thematic Fire Severity","common_name":"Burn Severity","units_out":""}}},"MRRMaid_Mesic":{"api_variable_path":"#mrrmaid-mesic","category":"Remote Sensing","citations":"","coll_desc":"Monthly MRRMaid Mesic Proportion ","coll_name":"","dataset_website":"https://www.boisestate.edu/environment/our-work/mesic-resource-restoration-monitoring-aid/, https://nekolarik.users.earthengine.app/view/monthly-mrrmaid","description":"The Monthly MRRMaid 30m mesic proportions data seeks to expand the temporal extent of the Monthly MRRMaid SF 10m classifications. The MRRMaid team used the 10m classifications and ancillary datasets to train machine learning regressions that estimate proportional estimates of mesic vegetation in Landsat pixels (0-1) for June, July, August, September of each year.","ee_asset_path":"Not publicly available","ee_source":"Private Collection","end_year":"2025","full_name":"MRRMaid Mesic Proportion - 30m - Monthly","geographic_coverage":"Western US","processing_steps":"","product_name":"MRRMAID Mesic Proportions","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"1984","support_thumbnail":"static/img/support/mrrmaid_mesic_proportions.png","caption":"Example Jun-Jul Trend in Mesic Propotion Map","support_path":"datasets/remotesensing/mrrmaidmesic_monthly_30","temporal_resolution":"Monthly","temporal_resolution_normalized":"30","terms_of_use":"Creative Commons Attribution 4.0 International (CC BY 4.0)","variable_info":{"mesic_proportion":{"band":"mesic","name":"mesic_proportion","common_name":"Mesic Proportion","units_out":""}}},"MRRMaid_Monthly_SF":{"api_variable_path":"#mrrmaid-monthly-sf","category":"Remote Sensing","citations":"1) Kolarik, N. E., Roopsind, A., Pickens, A., & Brandt, J. S. (2023). A satellite-based monitoring system for quantifying surface water and mesic vegetation dynamics in a semi-arid region. Ecological Indicators, 147, 109965. 2) Kolarik, N., Brooks, A., Caughlin, T., Jensen, E., Jochems, L., Brandt, J., 2026. Evidence of low watershed resilience across the Western United States. https://doi.org/10.31223/X5CJ23","coll_desc":"MRRMaid Monthly","coll_name":"projects/ee-hd-waterapp/assets/HD_SF","dataset_website":"https://www.boisestate.edu/hes/projects/mrrmaid-mesic-resource-restoration-monitoring-aid/","description":"The Mesic Resource Restoration Monitoring Aid (MRRMaid) toolbox is a set of satellite-based monitoring tools for dryland mesic ecosystems in the US Intermountain West. Mesic ecosystems include river corridors (rivers, floodplains, and riparian zones), wetlands, wet meadows, and other freshwater environments. The web apps in this toolbox have been designed so that a user can zoom in and out of specific areas, draw polygons for their area of interest, and then show maps and plots of mesic ecosystem change over time. The monthly product shows mesic vegetation and surface water during the growing season.","ee_asset_path":"Not publicly available","ee_source":"Private Collection","end_year":"2023","full_name":"MRRMaid Monthly SF - 10m - Monthly","geographic_coverage":"Western US","processing_steps":"","product_name":"MRRMAID Monthly SF","spatial_resolution":"10m","spatial_resolution_normalized":"10","start_year":"2017","support_thumbnail":"static/img/support/mrrmaid_monthly_sf_c98b7fd534.jpg","caption":"Example Mesic, Water, Upland Classification Map","support_path":"datasets/remotesensing/mrrmaidmonthlysf_monthly_10","temporal_resolution":"Monthly","temporal_resolution_normalized":"30","terms_of_use":"Creative Commons Attribution 4.0 International (CC BY 4.0)","variable_info":{"classification":{"band":"classification","name":"classification","common_name":"Landscape Classification","units_out":""},"water_binary":{"band":"water_binary","name":"water_binary","common_name":"Landscape Classification","units_out":""},"mesic_binary":{"band":"mesic_binary","name":"mesic_binary","common_name":"Landscape Classification","units_out":""},"upland_binary":{"band":"upland_binary","name":"upland_binary","common_name":"Landscape Classification","units_out":""}}},"MRRMaid_MVP":{"api_variable_path":"#mrrmaid-mesic-vegetation-persistence","category":"Remote Sensing","citations":"1) Shrestha, N., Kolarik, N., Iskin, E., and Brandt J. (2023). Decision Support Tool – GEE Web App for Mesic Vegetation Persistence. ","coll_desc":"MRRMaid Mesic Vegetation Persistence","coll_name":"COPERNICUS/S2_HARMONIZED","dataset_website":"https://www.boisestate.edu/hes/projects/mrrmaid-mesic-resource-restoration-monitoring-aid/","description":"The Mesic Resource Restoration Monitoring Aid (MRRMaid) toolbox is a set of satellite-based monitoring tools for dryland mesic ecosystems in the US Intermountain West. Mesic ecosystems include river corridors (rivers, floodplains, and riparian zones), wetlands, wet meadows, and other freshwater environments. The web apps in this toolbox have been designed so that a user can zoom in and out of specific areas, draw polygons for their area of interest, and then show maps and plots of mesic ecosystem change over time. The MVP product shows the persistence of mesic vegetation across years, indicating areas where wet riparian vegetation consistently occurs.","ee_asset_path":"Not publicly available","ee_source":"Private Collection","end_year":"Present","full_name":"MRRMaid MVP - 10m - 5day","geographic_coverage":"Western US","processing_steps":"","product_name":"MRRMAID MVP","spatial_resolution":"10m","spatial_resolution_normalized":"10","start_year":"2017","support_thumbnail":"static/img/support/MRRMAID_MVP.jpg","caption":"Example Mesic Persistence Map","support_path":"datasets/remotesensing/mrrmaidmvp_5day_10/","temporal_resolution":"5 Day","temporal_resolution_normalized":"5","terms_of_use":"Creative Commons Attribution 4.0 International (CC BY 4.0)","variable_info":{"mesic_veg":{"band":"mesics_MVsdi","name":"mesic_veg","common_name":"Mesic Vegetation","units_out":""}}},"MRRMaid_Water":{"api_variable_path":"#mrrmaid-water","category":"Remote Sensing","citations":"","coll_desc":"Monthly MRRMaid Water Proportion ","coll_name":"","dataset_website":"https://www.boisestate.edu/environment/our-work/mesic-resource-restoration-monitoring-aid/, https://nekolarik.users.earthengine.app/view/monthly-mrrmaid","description":"The Monthly MRRMaid 30m water proportions data seeks to expand the temporal extent of the Monthly MRRMaid SF 10m classifications. The MRRMaid team used the 10m classifications and ancillary datasets to train machine learning regressions that estimate proportional estimates of water in Landsat pixels (0-1) for June, July, August, September of each year. ","ee_asset_path":"Not publicly available","ee_source":"Private Collection","end_year":"2025","full_name":"MRRMaid Water Proportions - 30m - Monthly","geographic_coverage":"Western US","processing_steps":"","product_name":"MRRMAID Water Proportions","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"1984","support_thumbnail":"static/img/support/mrrmaid_water_proportions.png","caption":"Example Jul Water Propotion Map","support_path":"datasets/remotesensing/mrrmaidwater_monthly_30","temporal_resolution":"Monthly","temporal_resolution_normalized":"30","terms_of_use":"Creative Commons Attribution 4.0 International (CC BY 4.0)","variable_info":{"mesic_proportion":{"band":"water","name":"water_proportion","common_name":"Water Proportion","units_out":""}}},"NLDAS2_DAILY":{"api_variable_path":"#north-american-land-data-assimilation-system-reanalysis-nldas","category":"Climate/Hydrology","citations":"1)Mitchell et al (2004. The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system, J. Geophys. Res., 109, D07S90 https://doi.org/10.1029/2003JD003823 2) Xia et al (2012): Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products, J. Geophys. Res., 117, D03109. https://doi.org/10.1029/2011JD016048","coll_desc":"NLDAS2 DAILY reanalysis","coll_name":"projects/eddi-noaa/nldas/daily","dataset_website":"https://ldas.gsfc.nasa.gov/nldas/v2/models","description":"The Land Data Assimilation System (LDAS) combines multiple sources of observations (such as precipitation gauge data, satellite data, and radar precipitation measurements) to produce estimates of climatological properties at or near the Earth's surface. This dataset is the primary (default) forcing file (File A) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th-degree grid spacing; the temporal resolution is hourly.","ee_asset_path":"Not publicly available","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"NLDAS2 - 12km - Daily","geographic_coverage":"North America","processing_steps":"1) Daily assets are being generated from the hourly assets available in Earth Engine (source collection ID: NASA/NLDAS/FORA0125_H002). 2) Reference ET is being computed directly from the daily aggregations (i.e. NOT as the sum of the hourlies). 3) The 'day' is defined as 6 UTC to 6 UTC. The start date is lagged by one day because of the 6 UTC start time. 4) The average daily wind speed was computed as the average of the hourly wind speed computed from the wind vector components.","product_name":"NLDAS2 Reanalysis - Daily","spatial_resolution":"12km","spatial_resolution_normalized":"12000","start_year":"1979","support_thumbnail":"static/img/support/nldas2_daily_63eda4c922.jpg","caption":"Example Longwave Radiation Map","support_path":"datasets/climatehydrology/nldas2_daily_12000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"Distribution of data from the Goddard Earth Sciences Data and Information Services Center (GES DISC) is funded by NASA's Science Mission Directorate (SMD). Consistent with NASA Earth Science Data and Information Policy, data from the GES DISC archive are available free to the user community. For more information visit the GES DISC Data Policy page.","variable_info":{"tasmean":{"band":"tasmean","name":"Mean Temperature","common_name":"Temperature","units_out":"K"},"tasmin":{"band":"temperature_min","name":"Minimum Temperature","common_name":"Temperature","units_out":"K"},"tasmax":{"band":"temperature_max","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"sph":{"band":"specific_humidity","name":"Specific Humidity","common_name":"Humidity","units_out":"1/1000 g/kg"},"pressure":{"band":"pressure","name":"Pressure","common_name":"Pressure","units_out":"kPa"},"vs":{"band":"wind","name":"Wind","common_name":"Wind Speed","units_out":"m/s"},"precipitation":{"band":"total_precipitation","name":"Precipitation","common_name":"Precipitation","units_out":"m/s"},"longwave_radiation":{"band":"longwave_radiation","name":"Longwave Radiation","common_name":"Longwave Radiation","units_out":"W/m^2"},"shortwave_radiation":{"band":"shortwave_radiation","name":"Shortwave Radiation","common_name":"Downward Shortwave Radiation","units_out":"W/m^2"},"eto":{"band":"eto_asce","name":"Daily grass reference ET","common_name":"Reference Evapotranspiration","units_out":"mm"},"etr":{"band":"etr_asce","name":"Daily alfalfa reference ET","common_name":"Reference Evapotranspiration","units_out":"mm"},"wb":{"band":"wb","name":"Potential Water Deficit","common_name":"Potential Water Deficit","units_out":"mm"},"eddi":{"band":"eto_asce","name":"Evaporative Demand Drought Index (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""},"spi":{"band":"total_precipitation","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"spei":{"band":"wb","name":"Standardized Precipitation Evapotranspiration Index (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"peth":{"band":"peth","name":"Potential ET Hargreaves","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit Hargreaves","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index Hargreaves (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"peth","name":"Evap. Demand Drought Index Hargreaves (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""}}},"OPENET_CONUS":{"api_variable_path":"#openet-conus","category":"Remote Sensing","citations":"1) Melton, F., Huntington, J., Grimm, R., Herring, J., Hall, M., Rollison, D., Erickson, T., Allen, R., Anderson, M., Fisher, J., Kilic, A., Senay, G., volk, J., Hain, C., Johnson, L., Ruhoff, A., Blanenau, P., Bromley, M., Carrara, W., Daudert, B., Doherty, C., Dunkerly, C., Friedrichs, M., Guzman, A., Halverson, G., Hansen, J., Harding, J., Kang, Y., Ketchum, D., Minor, B., Morton, C., Ortega-Salazar, S., Ott, T., Ozdogon, M., Schull, M., Wang, T., Yang, Y., Anderson, R., 2021. OpenET: Filling a Critical Data Gap in Water Management for the Western United States. Journal of the American Water Resources Association, 2021 Nov 2.","coll_desc":"OpenET dataset","coll_name":"OpenET/ENSEMBLE/CONUS/GRIDMET/MONTHLY/v2_0","dataset_website":"https://openetdata.org/","description":"The OpenET dataset includes satellite-based data on the total amount of water that is transferred from the land surface to the atmosphere through the process of evapotranspiration (ET). OpenET provides ET data from multiple satellite-driven models, and also calculates a single \"ensemble value\" from the model ensemble. The models currently included in the OpenET model ensemble are ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop. The OpenET ensemble ET value is calculated as the mean of the ensemble after filtering and removing outliers using the median absolute deviation approach. All models currently use Landsat satellite data to produce ET data at a pixel size of 30 meters by 30 meters (0.22 acres per pixel). The monthly ET dataset provides data on total ET by month as an equivalent depth of water in millimeters.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0#description","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"OpenET - 30m - Monthly","geographic_coverage":"CONUS","processing_steps":"","product_name":"OpenET","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"2000","support_thumbnail":"static/img/support/openet_conus_42628eedda.jpg","caption":"Example Jun-Aug ensemble mean Evapotranspiration Map","support_path":"datasets/remotesensing/openet_monthly_30","temporal_resolution":"Monthly","temporal_resolution_normalized":"30","terms_of_use":"CC-BY-4.0","variable_info":{"et_ensemble_mad":{"band":"et_ensemble_mad","name":"Evapotranspiration Ensemble Mean","common_name":"Evapotranspiration","units_out":"mm"},"et_ensemble_mad_min":{"band":"et_ensemble_mad_min","name":"Evapotranspiration Ensemble Minimum","common_name":"Evapotranspiration","units_out":"mm"},"et_ensemble_mad_max":{"band":"et_ensemble_mad_max","name":"Evapotranspiration Ensemble Maximum","common_name":"Evapotranspiration","units_out":"mm"},"et_geesebal":{"band":"et_geesebal","name":"Evapotranspiration (geeSEBAL)","common_name":"Evapotranspiration","units_out":"mm"},"et_sims":{"band":"et_sims","name":"Evapotranspiration (SIMS)","common_name":"Evapotranspiration","units_out":"mm"},"et_ptjpl":{"band":"et_ptjpl","name":"Evapotranspiration (PT-JPL)","common_name":"Evapotranspiration","units_out":"mm"},"et_disalexi":{"band":"et_disalexi","name":"Evapotranspiration (DISALEXI)","common_name":"Evapotranspiration","units_out":"mm"},"et_ssebop":{"band":"et_ssebop","name":"Evapotranspiration (SSEBop)","common_name":"Evapotranspiration","units_out":"mm"},"et_eemetric":{"band":"et_eemetric","name":"Evapotranspiration (eeMETRIC)","common_name":"Evapotranspiration","units_out":"mm"}}},"PML_ET":{"api_variable_path":"#penman-monteith-leuning-evapotranspiration-v2","category":"Remote Sensing","citations":"1) Zhang, Y., Kong, D., Gan, R., Chiew, F.H.S., McVicar, T.R., Zhang, Q., and Yang, Y., 2019. Coupled estimation of 500m and 8-day resolution global evapotranspiration and gross primary production in 2002-2017. Remote Sens. Environ. 222, 165-182, https://doi.org/10.1016/j.rse.2018.12.031 2) Gan, R., Zhang, Y.Q., Shi, H., Yang, Y.T., Eamus, D., Cheng, L., Chiew, F.H.S., Yu, Q., 2018. Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems. Ecohydrology, https://doi.org/10.1002/eco.1974 3) Zhang, Y., Peña-Arancibia, J.L., McVicar, T.R., Chiew, F.H.S., Vaze, J., Liu, C., Lu, X., Zheng, H., Wang, Y., Liu, Y.Y., Miralles, D.G., Pan, M., 2016. Multi-decadal trends in global terrestrial evapotranspiration and its components. Sci. Rep. 6, 19124. https://doi.org/10.1038/srep19124","coll_desc":"PML_V2 Evapotranspiration dataset","coll_name":"CAS/IGSNRR/PML/V2","dataset_website":"https://github.com/gee-hydro/gee_PML","description":"The Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2) products include evapotranspiration (ET), its three components, and gross primary product (GPP) at 500m and 8-day resolution during 2002-2017 and with spatial range from -60°S to 90°N. The major advantages of the PML_V2 products are: coupled estimates of transpiration and GPP via canopy conductance (Gan et al., 2018; Zhang et al., 2019), partitioning ET into three components: transpiration from vegetation, direct evaporation from the soil and vaporization of intercepted rainfall from vegetation (Zhang et al., 2016).","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PML_V2_v018","ee_source":"Earth Engine Catalog","end_year":"2017","full_name":"MODIS ET PML V2 - 500m - 8day","geographic_coverage":"Global","processing_steps":"","product_name":"PML V2 ET - 8-Day","spatial_resolution":"500m","spatial_resolution_normalized":"500","start_year":"2002","support_thumbnail":"static/img/support/pml_et_d2111b5a57.jpg","caption":"Example Water Year Vegetation Transpiration Trend Map","support_path":"datasets/remotesensing/modisetpmlv2_8day_500","temporal_resolution":"8 Day","temporal_resolution_normalized":"8","terms_of_use":"Acknowledgements - Whenever PML datasets are used in a scientific publication, the given references should be cited. License - The dataset is licensed under the CC-BY 4.0 license.","variable_info":{"et":{"band":"et","name":"Potential Evapotranspiration","common_name":"Potential Evapotranspiration","units_out":"mm/8day"},"ET_water":{"band":"ET_water","name":"Potential Evapotranspiration of water/ice only","common_name":"Potential Evapotranspiration","units_out":"mm/8day"},"Ec":{"band":"Ec","name":"Vegetation transpiration","common_name":"Transpiration","units_out":"mm/8day"},"Es":{"band":"Es","name":"Soil evaporation","common_name":"Soil Evaporation","units_out":"mm/8day"},"Ei":{"band":"Ei","name":"Interception from vegetation canopy","common_name":"Canopy Interception","units_out":"mm/8day"},"GPP":{"band":"GPP","name":"Gross primary product","common_name":"Gross Primary Productivity","units_out":"gC m-2 8 d-1"}}},"PRISM":{"api_variable_path":"#prism-daily","category":"Climate/Hydrology","citations":"1) Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J., and Pasteris, P.A. 2008. Physiographically-sensitive mapping of temperature and precipitation across the conterminous United States. International Journal of Climatology, 28: 2031-2064.(PDF) 2) Daly, C., J.I. Smith, and K.V. Olson. 2015. Mapping atmospheric moisture climatologies across the conterminous United States. PloS ONE 10(10):e0141140. doi:10.1371/journal.pone.0141140. (PDF)","coll_desc":"PRISM 4-km dataset (Oregon State University)","coll_name":"OREGONSTATE/PRISM/ANd","dataset_website":"http://prism.oregonstate.edu/","description":"The PRISM daily and monthly datasets are gridded climate datasets for the conterminous United States, produced by the PRISM Climate Group at Oregon State University. Grids are developed using PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM interpolation routines simulate how weather and climate vary with elevation, and account for coastal effects, temperature inversions, and terrain barriers that can cause rain shadows. Station data are assimilated from many networks across the country.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_ANd","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"PRISM - 4km - Daily","geographic_coverage":"CONUS","processing_steps":"1) A 'day' is defined as the 24 hours ending at 12:00 Greenwich Mean Time (GMT, or 7:00am Eastern Standard Time).","product_name":"PRISM - Daily","spatial_resolution":"4km","spatial_resolution_normalized":"4000","start_year":"1981","support_thumbnail":"static/img/support/PRISM_4000_Monthly.png","caption":"Example Hargreaves Potential Evapotranspiration Map","support_path":"datasets/climatehydrology/prism_daily_4000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"These PRISM datasets are available without restriction on use or distribution. PRISM Climate Group does request that the user give proper attribution and identify PRISM, where applicable, as the source of the data.","variable_info":{"ppt":{"band":"ppt","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"tmax":{"band":"tmax","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"tmin":{"band":"tmin","name":"Minimum Temperature","common_name":"Temperature","units_out":"K"},"tmean":{"band":"tmean","name":"Mean Temperature","common_name":"Temperature","units_out":"K"},"tdmean":{"band":"tdmean","name":"Mean Dew Point Temperature","common_name":"Dew Point Temperature","units_out":"K"},"vpdmax":{"band":"vpdmax","name":"Maximum Vapor Pressure Deficit","common_name":"Vapor Pressure Deficit","units_out":"kPa"},"vpdmin":{"band":"vpdmin","name":"Minimum Vapor Pressure Deficit","common_name":"Vapor Pressure Deficit","units_out":"kPa"},"spi":{"band":"ppt","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"peth":{"band":"peth","name":"Potential ET (Hargreaves)","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"peth","name":"Evap. Demand Drought Index (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""}}},"PRISM_MONTHLY_4KM":{"api_variable_path":"#prism-monthly-4km","category":"Climate/Hydrology","citations":"1) Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J., and Pasteris, P.A. 2008. Physiographically-sensitive mapping of temperature and precipitation across the conterminous United States. International Journal of Climatology, 28: 2031-2064.(PDF) 2) Daly, C., J.I. Smith, and K.V. Olson. 2015. Mapping atmospheric moisture climatologies across the conterminous United States. PloS ONE 10(10):e0141140. doi:10.1371/journal.pone.0141140. (PDF)","coll_desc":"PRISM 4-km dataset (Oregon State University)","coll_name":"projects/climate-engine-pro/assets/ce-prism-monthly","dataset_website":"http://prism.oregonstate.edu/","description":"The PRISM daily and monthly datasets are gridded climate datasets for the conterminous United States, produced by the PRISM Climate Group at Oregon State University. Grids are developed using PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM interpolation routines simulate how weather and climate vary with elevation, and account for coastal effects, temperature inversions, and terrain barriers that can cause rain shadows. Station data are assimilated from many networks across the country.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_ANm?hl=en","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"PRISM - 4km - Monthly","geographic_coverage":"CONUS","processing_steps":"","product_name":"PRISM - Monthly","spatial_resolution":"4km","spatial_resolution_normalized":"4000","start_year":"1895","support_thumbnail":"static/img/support/PRISM_4000_Daily.png","caption":"Example Maximum Vapor Pressure Deficit Map","support_path":"datasets/climatehydrology/prism_monthly_4000","temporal_resolution":"Monthly","temporal_resolution_normalized":"30","terms_of_use":"These PRISM datasets are available without restriction on use or distribution. PRISM Climate Group does request that the user give proper attribution and identify PRISM, where applicable, as the source of the data.","variable_info":{"ppt":{"band":"ppt","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"tmax":{"band":"tmax","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"tmin":{"band":"tmin","name":"Minimum Temperature","common_name":"Temperature","units_out":"K"},"tmean":{"band":"tmean","name":"Mean Temperature","common_name":"Temperature","units_out":"K"},"tdmean":{"band":"tdmean","name":"Mean Dew Point Temperature","common_name":"Dew Point Temperature","units_out":"K"},"vpdmax":{"band":"vpdmax","name":"Maximum Vapor Pressure Deficit","common_name":"Vapor Pressure Deficit","units_out":"kPa"},"vpdmin":{"band":"vpdmin","name":"Minimum Vapor Pressure Deficit","common_name":"Vapor Pressure Deficit","units_out":"kPa"},"spi":{"band":"ppt","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"peth":{"band":"peth","name":"Potential ET (Hargreaves)","common_name":"Potential Evapotranspiration","units_out":"mm"},"pett":{"band":"pett","name":"Potential ET Thornthwaite","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"speit":{"band":"wbt","name":"Standardized Precipitation Evapotranspiration Index Thornthwaite (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""}}},"PRISM_MONTHLY_800M":{"api_variable_path":"#prism-monthly-800m","category":"Climate/Hydrology","citations":"1) Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J., and Pasteris, P.A. 2008. Physiographically-sensitive mapping of temperature and precipitation across the conterminous United States. International Journal of Climatology, 28: 2031-2064.(PDF) 2) Daly, C., J.I. Smith, and K.V. Olson. 2015. Mapping atmospheric moisture climatologies across the conterminous United States. PloS ONE 10(10):e0141140. doi:10.1371/journal.pone.0141140. (PDF)","coll_desc":"PRISM 800-m dataset (Oregon State University)","coll_name":"projects/sat-io/open-datasets/OREGONSTATE/PRISM_800_MONTHLY","dataset_website":"http://prism.oregonstate.edu/","description":"The PRISM daily and monthly datasets are gridded climate datasets for the conterminous United States, produced by the PRISM Climate Group at Oregon State University. Grids are developed using PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM interpolation routines simulate how weather and climate vary with elevation, and account for coastal effects, temperature inversions, and terrain barriers that can cause rain shadows. Station data are assimilated from many networks across the country.","ee_asset_path":"https://gee-community-catalog.org/projects/prism/?h=prism","ee_source":"Awesome GEE Community Catalog","end_year":"Present","full_name":"PRISM - 800m - Monthly","geographic_coverage":"CONUS","processing_steps":"","product_name":"PRISM - Monthly","spatial_resolution":"800m","spatial_resolution_normalized":"800","start_year":"1895","support_thumbnail":"static/img/support/prism_monthly_800m_26ec8ea201.jpg","caption":"Example Water Year Standardized Precipitation Index (SPI) Map","support_path":"datasets/climatehydrology/prism_monthly_800","temporal_resolution":"Monthly","temporal_resolution_normalized":"30","terms_of_use":"These PRISM datasets are available without restriction on use or distribution. PRISM Climate Group does request that the user give proper attribution and identify PRISM, where applicable, as the source of the data.","variable_info":{"ppt":{"band":"ppt","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"tmax":{"band":"tmax","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"tmin":{"band":"tmin","name":"Minimum Temperature","common_name":"Temperature","units_out":"K"},"tmean":{"band":"tmean","name":"Mean Temperature","common_name":"Temperature","units_out":"K"},"tdmean":{"band":"tdmean","name":"Mean Dew Point Temperature","common_name":"Dew Point Temperature","units_out":"K"},"vpdmax":{"band":"vpdmax","name":"Maximum Vapor Pressure Deficit","common_name":"Vapor Pressure Deficit","units_out":"kPa"},"vpdmin":{"band":"vpdmin","name":"Minimum Vapor Pressure Deficit","common_name":"Vapor Pressure Deficit","units_out":"kPa"},"spi":{"band":"ppt","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"peth":{"band":"peth","name":"Potential ET (Hargreaves)","common_name":"Potential Evapotranspiration","units_out":"mm"},"pett":{"band":"pett","name":"Potential ET Thornthwaite","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"speit":{"band":"wbt","name":"Standardized Precipitation Evapotranspiration Index Thornthwaite (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""}}},"PRISM_DAILY_800M":{"api_variable_path":"#prism-daily-800m","category":"Climate/Hydrology","citations":"1) Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J., and Pasteris, P.A. 2008. Physiographically-sensitive mapping of temperature and precipitation across the conterminous United States. International Journal of Climatology, 28: 2031-2064.(PDF) 2) Daly, C., J.I. Smith, and K.V. Olson. 2015. Mapping atmospheric moisture climatologies across the conterminous United States. PloS ONE 10(10):e0141140. doi:10.1371/journal.pone.0141140. (PDF)","coll_desc":"PRISM 800-m dataset (Oregon State University)","coll_name":"projects/sat-io/open-datasets/OREGONSTATE/PRISM_800_DAILY","dataset_website":"http://prism.oregonstate.edu/","description":"The PRISM daily and monthly datasets are gridded climate datasets for the conterminous United States, produced by the PRISM Climate Group at Oregon State University. Grids are developed using PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM interpolation routines simulate how weather and climate vary with elevation, and account for coastal effects, temperature inversions, and terrain barriers that can cause rain shadows. Station data are assimilated from many networks across the country.","ee_asset_path":"https://gee-community-catalog.org/projects/prism/?h=prism","ee_source":"Awesome GEE Community Catalog","end_year":"Present","full_name":"PRISM - 800m - Daily","geographic_coverage":"CONUS","processing_steps":"","product_name":"PRISM - Daily","spatial_resolution":"800m","spatial_resolution_normalized":"800","start_year":"1981","support_thumbnail":"static/img/support/PRISM_800_Daily.png","caption":"Example Potential Water Deficit Map","support_path":"datasets/climatehydrology/prism_daily_800","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"These PRISM datasets are available without restriction on use or distribution. PRISM Climate Group does request that the user give proper attribution and identify PRISM, where applicable, as the source of the data.","variable_info":{"ppt":{"band":"ppt","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"tmax":{"band":"tmax","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"tmin":{"band":"tmin","name":"Minimum Temperature","common_name":"Temperature","units_out":"K"},"tmean":{"band":"tmean","name":"Mean Temperature","common_name":"Temperature","units_out":"K"},"tdmean":{"band":"tdmean","name":"Mean Dew Point Temperature","common_name":"Dew Point Temperature","units_out":"K"},"vpdmax":{"band":"vpdmax","name":"Maximum Vapor Pressure Deficit","common_name":"Vapor Pressure Deficit","units_out":"kPa"},"vpdmin":{"band":"vpdmin","name":"Minimum Vapor Pressure Deficit","common_name":"Vapor Pressure Deficit","units_out":"kPa"},"spi":{"band":"ppt","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"peth":{"band":"peth","name":"Potential ET (Hargreaves)","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"speih","name":"Hargreaves Standardized Precipitation Evapotranspiration Index (SPEI)","common_name":"Hargreaves Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"eddih","name":"Hargreaves Evaporative Demand Drought Index  (EDDI)","common_name":"Hargreaves Evaporative Demand Drought Index (EDDI)","units_out":""}}},"OISST":{"api_variable_path":"#noaa-oisst","category":"Climate/Hydrology","citations":"1) Richard W. Reynolds, Viva F. Banzon, and NOAA CDR Program (2008): NOAA Optimum Interpolation 1/4 Degree Daily Sea Surface Temperature (OISST) Analysis, Version 2. [indicate subset used]. NOAA National Centers for Environmental Information. doi:10.7289/V5SQ8XB5 [access date].","coll_desc":"NOAA Optimum Interpolation Sea Surface Temperature (OISST)","coll_name":"NOAA/CDR/OISST/V2_1","dataset_website":"https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html","description":"The NOAA Optimum Interpolation Sea Surface Temperature is an analysis constructed by combining observations from different platforms (satellites, ships, buoys and Argo floats) on a regular global grid. A spatially complete SST map is produced by interpolating to fill in gaps. The methodology includes bias adjustment of satellite and ship observations (referenced to buoys) to compensate for platform differences and sensor biases.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2_1","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"NOAA OISST - 24km - Daily","geographic_coverage":"Global","processing_steps":"","product_name":"NOAA OISST","spatial_resolution":"24km","spatial_resolution_normalized":"24000","start_year":"1981","support_thumbnail":"static/img/support/oisst_0033f32d10.jpg","caption":"Example 1-month Sea Surface Temperature (SST) Map","support_path":"datasets/climatehydrology/noaaoisst_daily_24000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"The NOAA CDR Program’s official distribution point for CDRs is NOAA’s National Climatic Data Center which provides sustained, open access and active data management of the CDR packages and related information in keeping with the United States’ open data policies and practices as described in the President's Memorandum on \"Open Data Policy\" and pursuant to the Executive Order of May 9, 2013, \"Making Open and Machine Readable the New Default for Government Information\". In line with these policies, the CDR data sets are nonproprietary, publicly available, and no restrictions are placed upon their use. For more information, see the Fair Use of NOAA's CDR Data Sets, Algorithms and Documentation pdf.","variable_info":{"sst":{"band":"sst","name":"Sea Surface Pressure","common_name":"Sea Surface Temperature","units_out":"K"},"ice":{"band":"ice","name":"Sea Ice Concentration","common_name":"Sea Ice Concentration","units_out":"%"}}},"RDPA":{"api_variable_path":"#regional-deterministic-precipitation-analysis","category":"Climate/Hydrology","citations":"None","coll_desc":"RDPA 10.0-km (1/11-deg) precipitation dataset (UCSB/CHG)","coll_name":"projects/climate-engine-pro/assets/ce-rdpa-daily","dataset_website":"https://weather.gc.ca/grib/grib2_RDPA_ps10km_e.html","description":"The Regional Deterministic Precipitation Analysis (RDPA) based on the Canadian Precipitation Analysis (CaPA) system is on a domain that corresponds to that of the operational regional model, i.e. the Regional Deterministic Prediction System (RDPS-LAM3D) except for areas over the Pacific ocean where the western limit of the RDPA domain is slightly shifted eastward with respect to the regional model domain. The resolution of the RDPA analysis is identical to the resolution of the operational regional system RDPS LAM3D.","ee_asset_path":"https://gee-community-catalog.org/projects/rdpa/?h=rdpa#earth-engine-snippet","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"RDPA - 10km - Daily","geographic_coverage":"North America","processing_steps":"","product_name":"RDPA - Daily Precipitation","spatial_resolution":"10km","spatial_resolution_normalized":"10000","start_year":"2003","support_thumbnail":"static/img/support/rdpa_5418e9e738.jpg","caption":"Example Dec - Feb Correlation of Trend in Precipitation Map","support_path":"datasets/climatehydrology/rdpa_daily_10000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"License information can be found here: https://eccc-msc.github.io/open-data/licence/readme_en/","variable_info":{"precip":{"band":"precip","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"spi":{"band":"precip","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""}}},"RDPS":{"api_variable_path":"#regional-deterministic-precipitation-system","category":"Climate/Hydrology","citations":"None","coll_desc":"RDPS 10.0-km (1/11-deg) precipitation dataset (MSC)","coll_name":"projects/climate-engine-pro/assets/ce-rdps-daily","dataset_website":"https://eccc-msc.github.io/open-data/msc-data/nwp_rdps/readme_rdps-datamart_en/","description":"The Regional Deterministic Prediction System (RDPS) carries out physics calculations to arrive at deterministic predictions of atmospheric elements from the current day out to 48 hours into the future. The data for mean temperature covers North America and is provided by the Meterological Service of Canada (MSC), a part of Environment and Climate Change Canada (ECCC). The MSC provides weather forecasts and warnings 24 hours a day, 365 days a year.","ee_asset_path":"https://gee-community-catalog.org/projects/rdps/?h=rdps#earth-engine-snippet","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"RDPS - 10km - Daily","geographic_coverage":"North America","processing_steps":"","product_name":"RDPS - Daily Temperature","spatial_resolution":"10km","spatial_resolution_normalized":"10000","start_year":"2010","support_thumbnail":"static/img/support/rdps_243bfcc15b.jpg","caption":"Example Dec - Feb Mean Temperature Map","support_path":"datasets/climatehydrology/rdps_daily_10000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"License information can be found here: https://open.canada.ca/en/open-government-licence-canada","variable_info":{"Tavg":{"band":"Tavg","name":"Mean Temperature","common_name":"Temperature","units_out":"K"}}},"RTMA":{"api_variable_path":"#regional-deterministic-precipitation-system","category":"Climate/Hydrology","citations":"1) De Pondeca, M. S. F. V., Manikin, G. S., DiMego, G., Benjamin, S. G., Parrish, D. F., Purser, R. J., Wu, W., Horel, J. D., Myrick, D. T., Lin, Y., Aune, R. M., Keyser, D., Colman, B., Mann, G., & Vavra, J. (2011). The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development, Weather and Forecasting, 26(5), 593-612. Retrieved Sep 19, 2022, from https://journals.ametsoc.org/view/journals/wefo/26/5/waf-d-10-05037_1.xml","coll_desc":"Real-Time Mesoscale Analysis","coll_name":"projects/climate-engine-pro/assets/ce-rtma/daily","dataset_website":"https://www.nco.ncep.noaa.gov/pmb/products/rtma/#RTMA2p5","description":"The Real-Time Mesoscale Analysis (RTMA) is a high-spatial and temporal resolution analysis for near-surface weather conditions. This dataset includes hourly analyses at 2.5 km for CONUS.","ee_asset_path":"https://explorer.earthengine.google.com/#detail/NOAA%2FNWS%2FRTMA","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"RTMA - 2.5km - Daily","geographic_coverage":"North America","processing_steps":"","product_name":"Real-Time Mesoscale Analysis","spatial_resolution":"2.5 km","spatial_resolution_normalized":"2500","start_year":"2011","support_thumbnail":"static/img/support/rtma_1ee3fb93e2.jpg","caption":"Example Dec - Feb Total Cloud Cover Map","support_path":"datasets/climatehydrology/rtma_daily_2500","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.","variable_info":{"PRCP":{"band":"PRCP","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"ETo":{"band":"ETo","name":"Grass Reference ET","common_name":"Reference Evapotranspiration","units_out":"mm"},"ETr":{"band":"ETr","name":"Alfalfa Reference ET","common_name":"Reference Evapotranspiration","units_out":"mm"},"TMAX":{"band":"TMAX","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"TMIN":{"band":"TMIN","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"SRAD":{"band":"SRAD","name":"Incoming Shortwave Solar Radiation","common_name":"Downward Shortwave Radiation","units_out":"W/m^2"},"SPH":{"band":"SPH","name":"Specific Humidity","common_name":"Humidity","units_out":"1/1000 kg/kg"},"WIND":{"band":"WIND","name":"Wind Speed","common_name":"Wind Speed","units_out":"m/s"},"WDIR":{"band":"WDIR","name":"Wind Direction (from which blowing)","common_name":"Wind Direction","units_out":""},"TCDC":{"band":"TCDC","name":"Total Cloud Cover","common_name":"Cloud Cover","units_out":"%"},"PRES":{"band":"PRES","name":"Air Pressure","common_name":"Pressure","units_out":"kPa"},"DPT":{"band":"DPT","name":"Dew Point Temperature","common_name":"Dew Point Temperature","units_out":"K"},"wb":{"band":"wb","name":"Climatic Water Balance (PPT-ETo)","common_name":"Potential Water Deficit","units_out":"mm"},"spi":{"band":"PRCP","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"eddi":{"band":"ETo","name":"Evap. 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TRMM delivered a unique 17-year dataset of global tropical rainfall and lightning.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B42","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"TRMM - 28km - Daily","geographic_coverage":"Global","processing_steps":"","product_name":"Tropical Rainfall Measuring Mission","spatial_resolution":"28km","spatial_resolution_normalized":"28000","start_year":"1998","support_thumbnail":"static/img/support/trmm_daily_75978383ae.jpg","caption":"Example 1-month Precipitation Map","support_path":"datasets/climatehydrology/trmm_daily_28000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"This dataset is in the public domain and is available without restriction on use and distribution. See NASA's Earth Science Data & Information Policy for additional information.","variable_info":{"precipitation":{"band":"precipitation","name":"Precipitation","common_name":"Precipitation","units_out":"mm"}}},"URMA":{"api_variable_path":"#unrestricted-mesoscale-analysis","category":"Climate/Hydrology","citations":"","coll_desc":"Unrestricted Mesoscale Analysis(URMA)","coll_name":"projects/climate-engine-pro/assets/noaa-urma/daily","dataset_website":"https://www.nco.ncep.noaa.gov/pmb/products/rtma/","description":"URMA (Unrestricted Mesoscale Analysis) is NOAA's hourly, high-resolution, near-surface “analysis of record.” It reruns the RTMA system approximately six hours after each valid time, assimilating conventional, mesonet, buoy, ship, shelter, and radar-based observations that arrive too late for RTMA. This also includes advanced precipitation QPE from radar and gauge blends, and snowfall updates from NOHRSC, merged via methods like Whittaker blending. URMA uses a two-dimensional variational (2D-Var) assimilation with static background error covariances (gridpoint-statistical-interpolation, GSI) to combine observations and a first-guess model field.","ee_asset_path":"Not publicly available","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"URMA - 2.5km - Daily","geographic_coverage":"CONUS","processing_steps":"This URMA dataset in Climate Engine combines two URMA datasets with ERA5-Land for gap-filling. From 2015 to the present, data are sourced from the public hourly URMA dataset on NOAA's AWS server, which has minor gaps (about 1%). From 1985 to 2014, data are sourced from a non-public, custom hourly URMA dataset produced by the NOAA URMA team for the U.S. Forest Service for fire index modeling, which has significant gaps (about 5%). For 1980 to 1984, no original URMA data exists, so that period was reconstructed using the same ERA5-Land bias-correction methods used elsewhere for gap-filling. 1980 -2014 URMA does not have precipitation. Gap filling was done on the daily. Gaps in the URMA data (caused by missing hours in the source data) are filled using downscaled, bias-corrected ERA5-Land data via quantile mapping (QQ mapping), though not all variables could be filled given ERA5-Land's more limited variable set. TCDC (24 hour mean), GMAX, GMIN, GAVG, and PCP are not bias-corrected, and because ERA5-Land is unavailable over the ocean, gap-filled days do not match the full spatial extent of the original URMA data. Separately, variables labeled [calculated from URMA] were computed by the ClimateEngine.org team from the URMA datasets and stored in the Google Earth Engine asset used by the Climate Engine app and API, with the solar radiation variables among these used as inputs to the ASCE Reference ET calculations.","product_name":"Unrestricted Mesoscale Analysis","spatial_resolution":"2.5km","spatial_resolution_normalized":"2500","start_year":"1985","support_thumbnail":"static/img/support/urma_0f14f5da59.jpg","caption":"Example Jun-Aug Evaporative Demand Drought Index (EDDI) Map","support_path":"datasets/climatehydrology/urma_daily_2500","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"OAA data disseminated through NODD are open to the public and can be used as desired. NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.","variable_info":{"TMAX":{"band":"TMAX","name":"Maximium Temperature","common_name":"Temperature","units_out":"deg K"},"TMIN":{"band":"TMIN","name":"Minimum Temperature","common_name":"Temperature","units_out":"deg K"},"TAVG":{"band":"TAVG","name":"Mean Temperature","common_name":"Temperature","units_out":"deg K"},"DPT":{"band":"DPT","name":"Dew Point Temperature","common_name":"Dew Point Temperature","units_out":"deg K"},"ETo":{"band":"ETo","name":"ASCE Grass Reference ET (summed hourly) [Calculated from URMA]","common_name":"Reference Evapotranspiration"},"ETr":{"band":"ETr","name":"ASCE Alfalfa Reference ET (summed hourly) [Calculated from URMA]","common_name":"Reference Evapotranspiration"},"SPFH":{"band":"SPFH","name":"Specific Humidity","common_name":"Humidity","units_out":"1/1000 kg/kg"},"WIND":{"band":"WIND","name":"Wind Speed","common_name":"Wind Speed"},"WDIR":{"band":"WDIR","name":"Wind Direction","common_name":"Wind Direction"},"GMAX":{"band":"GMAX","name":"Maximum Daily Wind Gust 10m","common_name":"Wind Gust","units_out":""},"GMIN":{"band":"GMIN","name":"Minimum Daily Wind Gust 10m","common_name":"Wind Gust","units_out":""},"GAVG":{"band":"GAVG","name":"Average Daily Wind Gust 10m","common_name":"Wind Gust"},"TCDC":{"band":"TCDC","name":"Total Cloud Cover (24 hour mean)","common_name":"Cloud Cover"},"TCDC_WTD":{"band":"TCDC_WTD","name":"Total Cloud Cover (weighted mean by solar raidation) [Calculated from URMA]","common_name":"Cloud Cover","units_out":"%"},"RA":{"band":"RA","name":"Extraterrestrial Solar Radiation","common_name":"Solar Radiation","units_out":"W/m^2"},"SRAD_TCDC":{"band":"SRAD_TCDC","name":"Downward Surface Short Wave solar radiation","common_name":"Downward Shortwave Radiation","units_out":"W/m^2"},"RSO_SIMPLE":{"band":"RSO_SIMPLE","name":"Clear Sky Surface Short Wave solar radiation (simple method)","common_name":"Downward Shortwave Radiation","units_out":"W/m^2"},"RSO_ASCE":{"band":"RSO_ASCE","name":"Clear Sky Surface Short Wave solar radiation (ASCE method)","common_name":"Downward Shortwave Radiation","units_out":"W/m^2"},"PRES":{"band":"PRES","name":"Atmospheric Pressure","common_name":"Pressure","units_out":"kPa"},"EA":{"band":"EA","name":"Actual Vapor Pressure","common_name":"Vapor Pressure","units_out":"kPa"},"eddi":{"band":"ETo","name":"Evaporative Demand Drought Index (EDDI) [Calculated from URMA]","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""},"eddih":{"band":"peth","name":"Evap. 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It is released on the last day of each month and provides informant on drought outlook for the following month.","ee_asset_path":"https://gee-community-catalog.org/projects/ussdo/?h=drought+outl","ee_source":"ClimateEngine.org","end_year":"Forecast","full_name":"US Drought - 500m - Monthly","geographic_coverage":"US","processing_steps":"","product_name":"US Drought Outlook","spatial_resolution":"500m","spatial_resolution_normalized":"500","start_year":"Forecast","support_thumbnail":"static/img/support/usdoutlook_03a54f0a1c.jpg","caption":"Example Forecast Drought Outlook Map","support_path":"datasets/forecasts/usdrought_monthly_500","temporal_resolution":"Monthly","temporal_resolution_normalized":"30","terms_of_use":"NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site.","variable_info":{"drought_outlook_class":{"band":"drought_outlook_class","name":"US Drought Outlook","common_name":"Drought Outlook","units_out":""}}},"WLDAS":{"api_variable_path":"#western-land-data-assimilation-system-wldas","category":"Climate/Hydrology","citations":"1) Erlingis, J.M., M. Rodell, C.D. Peters-Lidard, B. Li, S.V. Kumar, J.S. Famiglietti, S.L. Granger, J.V. Hurley, P.-W. Liu, and D.M. Mocko. 2021. 'A High-Resolution Land Data Assimilation System Optimized for the Western United States.' Journal of the American Water Resources Association 57(5): 692–710. https://doi.org/10.1111/1752-1688.12910.","coll_desc":"The Western Land Data Assimilation System (WLDAS)","coll_name":"projects/climate-engine-pro/assets/ce-wldas/daily","dataset_website":"https://ldas.gsfc.nasa.gov/wldas","description":"WLDAS uses meteorological observables including precipitation, incoming shortwave and longwave radiation, near surface air temperature, humidity, wind speed, and surface pressure along with parameters such as vegetation class, soil texture, and elevation as inputs to a model that simulates land surface the energy and water budget processes. Outputs of the model include soil moisture, snow depth and snow water equivalent, evapotranspiration, soil temperature, as well as derived quantities such as groundwater recharge and anomalies of the state variables.","ee_asset_path":"Not publicly available","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"WLDAS - 1.1km - Daily","geographic_coverage":"Western US","processing_steps":"","product_name":"WLDAS","spatial_resolution":"1.1km","spatial_resolution_normalized":"1100","start_year":"1980","support_thumbnail":"static/img/support/wldas_dae0ea18d2.jpg","caption":"Example Snow depth Map","support_path":"datasets/climatehydrology/wldas_daily_1100","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"Distribution of data from the Goddard Earth Sciences Data and Information Services Center (GES DISC) is funded by NASA's Science Mission Directorate (SMD). Consistent with NASA Earth Science Data and Information Policy, data from the GES DISC archive are available free to the user community. For more information visit the GES DISC Data Policy page.","variable_info":{"tmean":{"band":"Tair_f_tavg","name":"Mean Temperature","common_name":"Temperature","units_out":"K"},"pr":{"band":"Rainf_tavg","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"eto":{"band":"eto","name":"ASCE Grass Reference Evapotranspiration","common_name":"Reference Evapotranspiration","units_out":"mm"},"etr":{"band":"etr","name":"ASCE Alfalfa Reference Evapotranspiration","common_name":"Reference Evapotranspiration","units_out":"mm"},"aet":{"band":"Evap_tavg","name":"Actual Evapotranspiration","common_name":"Evapotranspiration","units_out":"mm"},"vs":{"band":"Wind_f_tavg","name":"Wind Speed","common_name":"Wind Speed","units_out":"m/s"},"sph":{"band":"Qair_f_tavg","name":"Specific Humidity","common_name":"Humidity","units_out":"1/1000 kg/kg"},"SoilMoist_tavg_0":{"band":"SoilMoist_tavg_0","name":"Soil Moisture 0-10cm","common_name":"Soil Moisture","units_out":"mm"},"SoilMoist_tavg_1":{"band":"SoilMoist_tavg_1","name":"Soil Moisture 10-40cm","common_name":"Soil Moisture","units_out":"mm"},"SoilMoist_tavg_2":{"band":"SoilMoist_tavg_2","name":"Soil Moisture 40-100cm","common_name":"Soil Moisture","units_out":"mm"},"SoilMoist_tavg_3":{"band":"SoilMoist_tavg_3","name":"Soil Moisture 100-200cm","common_name":"Soil Moisture","units_out":"mm"},"SoilTemp_tavg_0":{"band":"SoilTemp_tavg_0","name":"Soil Temperature 0-10cm","common_name":"Soil Temperature","units_out":"K"},"SoilTemp_tavg_1":{"band":"SoilTemp_tavg_1","name":"Soil Temperature 10-40cm","common_name":"Soil Temperature","units_out":"K"},"SoilTemp_tavg_2":{"band":"SoilTemp_tavg_2","name":"Soil Temperature 40-100cm","common_name":"Soil Temperature","units_out":"K"},"SoilTemp_tavg_3":{"band":"SoilTemp_tavg_3","name":"Soil Temperature 100-200cm","common_name":"Soil Temperature","units_out":"K"},"Qsm_tavg":{"band":"Qsm_tavg","name":"Snowmelt","common_name":"Snowmelt","units_out":"mm"},"swe":{"band":"SWE_tavg","name":"Snow Water Equivalent","common_name":"Snow Water Equivalent","units_out":"mm"},"sd":{"band":"SnowDepth_tavg","name":"Snow Depth","common_name":"Snow Depth","units_out":"mm"},"SubSnow_tavg":{"band":"SubSnow_tavg","name":"Snow Sublimation","common_name":"Snow Sublimation","units_out":"mm"},"SnowCover_tavg":{"band":"SnowCover_tavg","name":"Surface Snow Area Fraction as Percent","common_name":"Snow Cover","units_out":"ratio"},"Qs_tavg":{"band":"Qs_tavg","name":"Surface Runoff Amount","common_name":"Surface Runoff","units_out":"mm"},"Qsb_tavg":{"band":"Qsb_tavg","name":"Subsurface Runoff Amount","common_name":"Subsurface Runoff","units_out":"mm"},"Lwnet_tavg":{"band":"Lwnet_tavg","name":"Surface Net Downward Longwave Flux","common_name":"Net Longwave Radiation","units_out":"W m-2"},"Swnet_tavg":{"band":"Swnet_tavg","name":"Surface Net Downward Shortwave Flux","common_name":"Net Shortwave Radiation","units_out":"W/m^2"},"Qle_tavg":{"band":"Qle_tavg","name":"Surface Upward Latent Heat Flux","common_name":"Latent Heat","units_out":"W m-2"},"Qh_tavg":{"band":"Qh_tavg","name":"Surface Upward Sensible Heat Flux","common_name":"Sensible Heat","units_out":"W m-2"},"Qg_tavg":{"band":"Qg_tavg","name":"Downward Heat Flux in Soil","common_name":"Ground Heat Flux","units_out":"W m-2"},"srad":{"band":"SWdown_f_tavg","name":"Surface Downwelling Shortwave Flux in Air","common_name":"Downward Shortwave Radiation","units_out":"W/m^2"},"Snowf_tavg":{"band":"Snowf_tavg","name":"Snowfall Rate","common_name":"Snowfall","units_out":"mm"},"WaterTableD_tavg":{"band":"WaterTableD_tavg","name":"WaterTableD_tavg","common_name":"Water Table Depth","units_out":"mm"},"TWS_tavg":{"band":"TWS_tavg","name":"TWS_tavg","common_name":"Terrestrial Water Storage","units_out":"mm"},"GWS_tavg":{"band":"GWS_tavg","name":"Groundwater Storage","common_name":"Groundwater Storage","units_out":"mm"},"surface_pressure":{"band":"Psurf_f_tavg","name":"Surface Pressure","common_name":"Pressure","units_out":"kPa"},"spi":{"band":"Rainf_tavg","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"eddi":{"band":"eto","name":"Evaporative Demand Drought Index (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""},"wb":{"band":"wb","name":"Climatic Water Balance (PPT-ETo)","common_name":"Potential Water Deficit","units_out":"mm"},"spei":{"band":"wb","name":"Standardized Precipitation Evapotranspiration Index (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""}}},"USGS_ET_MODIS_DEKADAL":{"api_variable_path":"#usgs-modis-eta-dekadal","category":"Remote Sensing","citations":"1) Senay, G.B., Kagone S., Velpuri N.M., 2020, Operational Global Actual Evapotranspiration using the SSEBop model: U.S. Geological Survey data release, https://doi.org/10.5066/P9OUVUUI. Publication: https://www.mdpi.com/1424-8220/20/7/1915 2) Senay, G. B., Budde, M. E., & Verdin, J. P. (2011). Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model. Agricultural Water Management, 98(4), 606-618. 3) Senay, G. B., Budde, M., Verdin, J. P., & Melesse, A. M. (2007). A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields. Sensors, 7(6), 979-1000. 4) Velpuri, N. M., Senay, G. B., Singh, R. K., Bohms, S., and Verdin, J. P. (2013). 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Users assume responsibility to determine the usability of these data. The user is responsible for the results of any application of this data for other than its intended purpose. NOAA and NCEI make no warranty, expressed or implied, regarding these data, nor does the fact of distribution constitute such a warranty. NOAA and NCEI cannot assume liability for any damages caused by any errors or omissions in these data.","variable_info":{"precip":{"band":"precip","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"tmax":{"band":"tmax","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"tmin":{"band":"tmin","name":"Minimum Temperature","common_name":"Temperature","units_out":"K"},"tavg":{"band":"tavg","name":"Mean Temperature","common_name":"Temperature","units_out":"K"},"spi":{"band":"precip","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"peth":{"band":"peth","name":"Potential ET Hargreaves","common_name":"Potential Evapotranspiration","units_out":"mm"},"pett":{"band":"pett","name":"Potential ET Thornthwaite","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit Hargreaves","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index Hargreaves (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"speit":{"band":"wbt","name":"Standardized Precipitation Evapotranspiration Index Thornthwaite (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"peth","name":"Evap. 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Hsu, S. Sorooshian, D. K. Braithwaite, K. R. Knapp, L. D. Cecil, B. R. Nelson, and O. P. Prat, 2015: PERSIANN-CDR: Daily Precipitation Climate Data Record from Multi-Satellite Observations for Hydrological and Climate Studies. Bull. Amer. Meteor. Soc., doi: https://doi.org/10.1175/BAMS-D-13-00068.1. 2) Sorooshian, Soroosh; Hsu, Kuolin; Braithwaite, Dan; Ashouri, Hamed; and NOAA CDR Program (2014): NOAA Climate Data Record (CDR) of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Version 1 Revision 1. [indicate subset used]. NOAA National Centers for Environmental Information. doi:10.7289/V51V5BWQ [access date].","coll_desc":"PERSIANN-CDR","coll_name":"NOAA/PERSIANN-CDR","dataset_website":"https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C00854/html","description":"PERSIANN-CDR is a daily quasi-global precipitation product that spans the period from 1983-01-01 to present. The data is produced quarterly, with a typical lag of three months. The product is developed by the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine (UC-IRVINE/CHRS) using Gridded Satellite (GridSat-B1) IR data that are derived from merging ISCCP B1 IR data, along with GPCP version 2.2.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_PERSIANN-CDR","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"PERSIANN CDR - 24km - Daily","geographic_coverage":"Global","processing_steps":"","product_name":"PERSIANN-CDR - Daily Precipitation","spatial_resolution":"24km","spatial_resolution_normalized":"24000","start_year":"1983","support_thumbnail":"static/img/support/noaa_cdr_persiann_6a783814da.jpg","caption":"Example 1-month Precipitation Map","support_path":"datasets/climatehydrology/persianncdr_daily_24000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"CDR data sets are nonproprietary, publicly available, and no restrictions are placed upon their use. For additional information, see the Fair Use of NOAA's CDR Data Sets, Algorithms and Documentation document.","variable_info":{"precipitation":{"band":"precipitation","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"spi":{"band":"precipitation","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""}}},"NOAA_CPC_CMORPH":{"api_variable_path":"#noaa-satellite-precipitation-cmorph","category":"Climate/Hydrology","citations":"1) Xie, Pingping; Joyce, Robert; Wu, Shaorong; Yoo, S.-H.; Yarosh, Yelena; Sun, Fengying; Lin, Roger, NOAA CDR Program (2019): NOAA Climate Data Record (CDR) of CPC Morphing Technique (CMORPH) High Resolution Global Precipitation Estimates, Version 1 [indicate subset]. NOAA National Centers for Environmental Information. https://doi.org/10.25921/w9va-q159 [access date]","coll_desc":"CPC CMORPH ?-km dataset (NOAA)","coll_name":"projects/climate-engine-pro/assets/noaa-cpc-cmorph/daily","dataset_website":"https://www.ncei.noaa.gov/products/climate-data-records/precipitation-cmorph","description":"The Satellite Precipitation - CMORPH Climate Data Record (CDR) consists of satellite precipitation estimates that have been bias corrected and reprocessed using the the Climate Prediction Center (CPC) Morphing Technique (MORPH) to form a global, high resolution precipitation analysis. Data is reprocessed on a global grid with daily temporal resolution.","ee_asset_path":"https://gee-community-catalog.org/projects/cpc_morph/?h=cmorph","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"CPC CMORPH - 25km - Daily","geographic_coverage":"Global","processing_steps":"","product_name":"NOAA CPC CMORPH - Daily Precipitation","spatial_resolution":"25km","spatial_resolution_normalized":"25000","start_year":"1998","support_thumbnail":"static/img/support/noaa_cpc_cmorph_63eda4c922.jpg","caption":"Example Longwave Radiation Map","support_path":"datasets/climatehydrology/cpccmorph_daily_25000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"NOAA and NCEI cannot provide any warranty as to the accuracy, reliability, or completeness of furnished data. Users assume responsibility to determine the usability of these data. The user is responsible for the results of any application of this data for other than its intended purpose. NOAA and NCEI make no warranty, expressed or implied, regarding these data, nor does the fact of distribution constitute such a warranty. NOAA and NCEI cannot assume liability for any damages caused by any errors or omissions in these data.","variable_info":{"precip":{"band":"precip","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"spi":{"band":"precip","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""}}},"NOAA_CPC_DAILY_CONUS":{"api_variable_path":"#noaa-cpc-conus-unified-gauge-based-analysis","category":"Climate/Hydrology","citations":"","coll_desc":"CPC DAILY CONUS ?-km dataset (NOAA)","coll_name":"projects/climate-engine-pro/assets/noaa-cpc-daily-conus/daily","dataset_website":"","description":"This data set is part of products suite from the CPC Unified Precipitation Project(UPP) that are underway at NOAA Climate Prediction Center (CPC). The primary goal of the project is to create a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique.","ee_asset_path":"https://gee-community-catalog.org/projects/cpc_morph/","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"CPC UPP - 28km - Daily","geographic_coverage":"CONUS","processing_steps":"","product_name":"NOAA CPC CONUS - Daily Precipitation","spatial_resolution":"28km","spatial_resolution_normalized":"28000","start_year":"1948","support_thumbnail":"static/img/support/noaa_cpc_daily_conus_63eda4c922.jpg","caption":"Example Longwave Radiation Map","support_path":"datasets/climatehydrology/cpcupp_daily_28000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"If you acquire CPC Global Unified Gauge-Based Analysis of Daily Precipitation data products from PSL, we ask that you acknowledge us in your use of the data. This may be done by including text such as CPC Global Unified Gauge-Based Analysis of Daily Precipitation data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at https://psl.noaa.gov in any documents or publications using these data.","variable_info":{"precip":{"band":"precip","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"spi":{"band":"precip","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""}}},"NOAA_CPC_DAILY_GLOBAL":{"api_variable_path":"#noaa-cpc-global-unified-gauge-based-analysis","category":"Climate/Hydrology","citations":"1) (Interpolation algorithm) Xie_et_al_2007_JHM_EAG.pdf Xie, P., A. Yatagai, M. Chen, T. Hayasaka, Y. Fukushima, C. Liu, and S. Yang (2007), A gauge-based analysis of daily precipitation over East Asia, J. Hydrometeorol., 8, 607. 626. 2) (Gauge Algorithm Evaluation) Chen_et_al_2008_JGR_Gauge_Algo.pdf Chen, M., W. Shi, P. Xie, V. B. S. Silva, V E. Kousky, R. Wayne Higgins, and J. E. Janowiak (2008), Assessing objective techniques for gauge-based analyses of global daily precipitation, J. Geophys. Res., 113, D04110, doi:10.1029/2007JD009132. 3) (Construction of the Daily Gauge Analysis) Chen_et_al_2008_Daily_Gauge_Anal.pdf Chen, M., P. Xie, and Co-authors (2008), CPC Unified Gauge-based Analysis of Global Daily Precipiation, Western Pacific Geophysics Meeting, Cairns, Australia, 29 July - 1 August, 2008.","coll_desc":"CPC DAILY GLOBAL ?-km dataset (NOAA)","coll_name":"projects/climate-engine-pro/assets/noaa-cpc-daily-global/daily","dataset_website":"https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html","description":"This data set is part of products suite from the CPC Unified Precipitation Project (UPP) that are underway at NOAA Climate Prediction Center (CPC). The primary goal of the project is to create a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique.","ee_asset_path":"https://gee-community-catalog.org/projects/cpc_morph/","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"CPC UPP - 55km - Daily","geographic_coverage":"Global","processing_steps":"","product_name":"NOAA CPC GLOBAL - Daily","spatial_resolution":"55km","spatial_resolution_normalized":"55000","start_year":"1979","support_thumbnail":"static/img/support/noaa_cpc_daily_global_63eda4c922.jpg","caption":"Example Longwave Radiation Map","support_path":"datasets/climatehydrology/cpcupp_daily_55000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"If you acquire CPC Global Unified Gauge-Based Analysis of Daily Precipitation data products from PSL, we ask that you acknowledge us in your use of the data. This may be done by including text such as CPC Global Unified Gauge-Based Analysis of Daily Precipitation data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at https://psl.noaa.gov in any documents or publications using these data. We would also appreciate receiving a copy of the relevant publications. This will help PSL to justify keeping the data freely available online in the future.","variable_info":{"precip":{"band":"precip","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"tmax":{"band":"tmax","name":"Maximum Temperature","common_name":"Temperature","units_out":"K"},"tmin":{"band":"tmin","name":"Minimum Temperature","common_name":"Temperature","units_out":"K"},"spi":{"band":"precip","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"peth":{"band":"peth","name":"Potential ET Hargreaves","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit Hargreaves","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index Hargreaves (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"peth","name":"Evap. Demand Drought Index Hargreaves (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""}}},"WRC":{"api_variable_path":"#wildfire-risk-to-communities","category":"Hazards","citations":"1) Scott, Joe H.; Gilbertson-Day, Julie W.; Moran, Christopher; Dillon, Gregory K.; Short, Karen C.; Vogler, Kevin C. 2020. Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States. Fort Collins, CO: Forest Service Research Data Archive. Updated 25 November 2020. https://doi.org/10.2737/RDS-2020-0016 (Publication Details)","coll_desc":"USDA 30-m static wildfire risk dataset (WRC)","coll_name":"USDA/WRC/v0","dataset_website":"https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0016","description":"The Wildfire Risk to Communities dataset was created by USDA Forest Service to help assess risk to homes, businesses, and other valued resources. The dataset contains nationally-consistent information for the purpose of comparing relative wildfire risk among communities nationally or within a state or county. In situ risk (risk at the location where the adverse effects take place on the landscape) are modeled using the large fire simulation system (FSim) and LANDFIRE fuel loading datasets from 2014. The original data at 250m has been upsampled to 30m for this dataset on Climate Engine.","ee_asset_path":"https://gee-community-catalog.org/projects/wrc/?h=wildfire","ee_source":"Earth Engine Catalog","end_year":"2022","full_name":"WRC - 30m","geographic_coverage":"CONUS","processing_steps":"","product_name":"Wildfire Risk to Communities","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"2022","support_thumbnail":"static/img/support/wrc_229e299c38.jpg","caption":"Example Burn Probability Map","support_path":"datasets/hazards/wrc_static_30","temporal_resolution":"Static","temporal_resolution_normalized":"N/A","terms_of_use":"These data were collected using funding from the U.S. Government and can be used without additional permissions or fees.","variable_info":{"BP":{"band":"BP","name":"Burn Probability","common_name":"Burn Probability","units_out":"probability"},"CFL":{"band":"CFL","name":"Conditional Flame Length","common_name":"Flame Length","units_out":"ft"},"CRPS":{"band":"CRPS","name":"Conditional Risk to Potential Structures","common_name":"Structure Risk","units_out":""},"Exposure":{"band":"Exposure","name":"Exposure Type","common_name":"Exposure Type","units_out":""},"FLEP4":{"band":"FLEP4","name":"Flame Length Exceedance Probability – 4 ft","common_name":"Flame Length Probability","units_out":"probability"},"FLEP8":{"band":"FLEP8","name":"Flame Length Exceedance Probability – 8 ft","common_name":"Flame Length Probability","units_out":"probability"},"RPS":{"band":"RPS","name":"Risk to Potential Structures","common_name":"Structure Risk","units_out":""},"WHP":{"band":"WHP","name":"Wildfire Hazard Potential index","common_name":"Wildfire Hazard Potential","units_out":""}}},"EH":{"api_variable_path":"#usgs-earthquake-damage-risk-dataset","category":"Hazards","citations":"1) Rukstales, K.S., and Petersen, M.D., 2019, Data Release for 2018 Update of the U.S. National Seismic Hazard Model: U.S. Geological Survey data release, https://doi.org/10.5066/P9WT5OVB. 2) Wald, D.J., and Allen, T.J., 2007, Topographic slope as a proxy for seismic site conditions and amplification, Bulletin of the Seismological Society of America, 97(5), 1379-1395","coll_desc":"USGS 1/10-deg static earthquake damage risk dataset","coll_name":"projects/climate-engine-pro/assets/earthquake_hazard/ProbMMI_VI_100Yrs_VariableVs30","dataset_website":"https://www.sciencebase.gov/catalog/item/5cbf47c4e4b0c3b00664fdef","description":"These data represent the chance of experiencing potentially damaging ground shaking for fixed ground shaking levels that corresponds with Modified Mercalli Intensity (MMI) equal to VI, in 100 years. The values are obtained by averaging the probability of experiencing MMI VI based on a peak ground acceleration, and the probability of experiencing MMI VI based on 1.0-second spectral acceleration. Ground motions are amplified using topographic, slope-based Vs30 values (Wald and Allen, 2007). Validity begins in 2018.","ee_asset_path":"Not publicly available","ee_source":"ClimateEngine.org","end_year":"2018","full_name":"Earthquake - 11km - 2018","geographic_coverage":"CONUS","processing_steps":"","product_name":"Earthquake Risk","spatial_resolution":"11km","spatial_resolution_normalized":"11000","start_year":"2018","support_thumbnail":"static/img/support/eh_20350cfc9d.jpg","caption":"Example Percent Chance of Earthquake Damage Map","support_path":"datasets/hazards/earthquake_static_11000","temporal_resolution":"Static","temporal_resolution_normalized":"N/A","terms_of_use":"Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this information product, for the most part, is in the public domain, it also contains copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner whenever applicable. The data have been approved for release and publication by the U.S. Geological Survey (USGS). Although the data have been subjected to rigorous review and are substantially complete, the USGS reserves the right to revise the data pursuant to further analysis and review. Furthermore, the data are released on the condition that neither the USGS nor the U.S. Government may be held liable for any damages resulting from authorized or unauthorized use. Although the data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and/or contained herein. Users of the data are advised to read all metadata and associated documentation thoroughly to understand appropriate use and data limitations.","variable_info":{"ERisk":{"band":"b1","name":"Earthquake damage risk","common_name":"Earthquake Risk","units_out":"%"}}},"RAP_COVER_30m":{"api_variable_path":"#rap-30m-vegetation-cover","category":"Remote Sensing","citations":"1) Allred, B.W., B.T. Bestelmeyer, C.S. Boyd, C. Brown, K.W. Davies, M.C. Duniway, L.M. Ellsworth, T.A. Erickson, S.D. Fuhlendorf, S.D., T.V. Griffiths, V. Jansen, M.O. Jones, J. Karl, A. Knight, J.D. Maestas, J.J. Maynard, S.E. McCord, D.E. Naugle, H.D. Starns, D. Twidwell, and D.R. Uden. Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty. Methods in Ecology and Evolution https://doi.org/10.1111/2041-210X.13564","coll_desc":"USDA-ARS 30 m aerial vegetation cover dataset","coll_name":"projects/rap-data-365417/assets/vegetation-cover-v3","dataset_website":"https://www.rangelands.app/","description":"This datasets consists of gridded fractional estimates of plant functional groups for rangelands in the continental United States. The estimates are produced at 30-meter spatial resolution for each year between 1984–present. The six plant functional groups are Annual Forbs and Grasses, Perennial Forbs and Grasses, Shrubs, Trees, Litter, and Bare Ground. Cover values are reported as percentages on a pixel-by-pixel basis. The estimates were produced using a temporal convolutional network using field measures of plant functional groups collected by the Natural Resources Conservation Service Natural Resources Inventory (NRI) program, the Bureau of Land Management Assessment, Inventory, and Monitoring (AIM) program, and the National Park Service Northern Colorado Plateau Network (NCPN) alongside spatially continuous earth observations from Landsat TM, ETM+, and OLI Collection 2.","ee_asset_path":"https://gee-community-catalog.org/projects/rap/?h=rangela","ee_source":"Awesome GEE Community Catalog","end_year":"Present","full_name":"RAP Cover - 30m - Yearly","geographic_coverage":"CONUS","processing_steps":"","product_name":"RAP 30m Annual Vegetation Cover","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"1986","support_thumbnail":"static/img/support/rap_cover_30m_2294b9666c.jpg","caption":"Example Perennial Forb and Grass Cover Trend Map","support_path":"datasets/remotesensing/rapcover_yearly_30","temporal_resolution":"Yearly","temporal_resolution_normalized":"365","terms_of_use":"Public Domain-CC0","variable_info":{"AFG":{"band":"AFG","name":"Annual Forb and Grass Cover","common_name":"Herbaceous Cover","units_out":"%"},"PFG":{"band":"PFG","name":"Perennial Forb and Grass Cover","common_name":"Herbaceous Cover","units_out":"%"},"SHR":{"band":"SHR","name":"Shrub Cover","common_name":"Shrub Cover","units_out":"%"},"TRE":{"band":"TRE","name":"Tree Cover","common_name":"Tree Cover","units_out":"%"},"BGR":{"band":"BGR","name":"Bare Ground Cover","common_name":"Bare Ground Cover","units_out":"%"},"LTR":{"band":"LTR","name":"Litter Cover","common_name":"Litter Cover","units_out":"%"}}},"RAP_COVER_10m":{"api_variable_path":"#rap-10m-vegetation-cover","category":"Remote Sensing","citations":"1) Allred, B. W., McCord, S. E., Assal, T. J., Bestelmeyer, B. T., Boyd, C. S., Brooks, A. C., Cady, S. M., Fuhlendorf, S. D., Green, S. A., Harrison, G. R., Jensen, E. R., Kachergis, E. J., Mattilio, C. M., Mealor, B. A., Naugle, D. E., O’Leary, D., Olsoy, P. J., Peirce, E. S., Reinhardt, J. R., Shriver, R. K., Smith, J. T., Tack, J. D., Tanner, A. M., Tanner, E. P., Twidwell, D., Webb, N. P., & Morford, S. L. (2025). Estimating rangeland fractional cover and canopy gap size class with Sentinel-2 imagery. bioRxiv. https://doi.org/10.1101/2025.03.13.643073 ","coll_desc":"USDA-ARS 10 m aerial vegetation cover dataset","coll_name":"projects/rap-data-365417/assets/vegetation-cover-10m","dataset_website":"https://www.rangelands.app/","description":"RAP Vegetation Cover 10m provides gridded annual estimates of plant functional group cover classes across rangelands in the western United States. Estimates span 2018–present and are produced at a 10-meter spatial resolution using top-of-atmosphere Sentinel-2 reflectance and a one-dimensional convolutional neural network (1D CNN). The model was trained on 47,833 field plots collected via the Natural Resource Conservation Services’ Natural Resources Inventory (NRI), Bureau of Land Management’s Assessment, Inventory, and Monitoring (AIM) program, National Park Service, and other contributors. The dataset includes fractional cover for 10 plant functional groups and land cover types—Annual Forbs and Grasses, Perennial Forbs and Grasses, Shrubs, Trees, Litter, Bare Ground, Invasive Annual Grasses, Sagebrush, Pinyon-Juniper. Predictions were made using temporally segmented and log-normalized Sentinel-2 reflectance, spatial coordinates, and derived vegetation indices (NDVI, NBR2). The model demonstrated strong predictive performance and showed slight improvements when compared with 30-m Landsat-based vegetation cover estimates by capturing finer-scale spatiotemporal heterogeneity critical to modeling rangeland ecosystems. Output is provided as annual 10 m GeoTIFFs available and as Google Earth Engine assets.","ee_asset_path":"https://gee-community-catalog.org/projects/rap/?h=rangela","ee_source":"Awesome GEE Community Catalog","end_year":"Present","full_name":"RAP Cover - 10m - Yearly","geographic_coverage":"CONUS","processing_steps":"","product_name":"RAP 10m Annual Vegetation Cover","spatial_resolution":"10m","spatial_resolution_normalized":"10","start_year":"2018","support_thumbnail":"static/img/support/rap_cover_10m_4230c462c8.jpg","caption":"Example Pinyon-Juniper Cover Anomaly Map","support_path":"datasets/remotesensing/rapcover_yearly_10","temporal_resolution":"Yearly","temporal_resolution_normalized":"365","terms_of_use":"Public Domain-CC0","variable_info":{"AFG":{"band":"AFG","name":"Annual Forb and Grass Cover","common_name":"Herbaceous Cover","units_out":"%"},"PFG":{"band":"PFG","name":"Perennial Forb and Grass Cover","common_name":"Herbaceous Cover","units_out":"%"},"SHR":{"band":"SHR","name":"Shrub Cover","common_name":"Shrub Cover","units_out":"%"},"TRE":{"band":"TRE","name":"Tree Cover","common_name":"Tree Cover","units_out":"%"},"BGR":{"band":"BGR","name":"Bare Ground Cover","common_name":"Bare Ground Cover","units_out":"%"},"LTR":{"band":"LTR","name":"Litter Cover","common_name":"Litter Cover","units_out":"%"},"IAG":{"band":"IAG","name":"Invasive Annual Grass Cover","common_name":"Herbaceous Cover","units_out":"%"},"ARTE":{"band":"ARTE","name":"Sagebrush Cover","common_name":"Sagebrush Cover","units_out":"%"},"PJ":{"band":"PJ","name":"Pinyon-Juniper Cover","common_name":"Vegetation Cover","units_out":"%"}}},"RAP_PRODUCTION":{"api_variable_path":"#rap-herbaceous-production","category":"Remote Sensing","citations":"1) Jones, M.O., N.P. Robinson, D.E. Naugle, J.D. Maestas, M.C. Reeves, R.W. Lankston, and B.W. Allred. Annual and 16-day rangeland production estimates for the western United States. Rangeland Ecology and Management https://doi.org/10.1016/j.rama.2021.04.003 2) Robinson, N. P., M. O. Jones, A. Moreno, T. A. Erickson, D. E. Naugle, and B. W.Allred. 2019. Rangeland productivity partitioned to sub-pixel plant functional types. Remote Sensing 11:1427. http://dx.doi.org/10.3390/rs11121427","coll_desc":"USDA-ARS 30 m herbaceous vegetation production dataset","coll_name":"projects/rap-data-365417/assets/npp-partitioned-v3","dataset_website":"https://www.rangelands.app/","description":"This dataset consists of gridded estimates of herbaceous aboveground biomass, partitioned into vegetation types for annual forbs and grasses and perennial forbs and grasses. The estimates are produced at 30m spatial resolution from 1986-present. Estimates are provided annually and at 16-day intervals. Values are reported in terms of net primary productivity which can be converted to pounds per acre of new growth of aboveground biomass using the function in the Google Earth Engine script below– estimates do not reflect standing biomass from previous years. Estimates are calculated using a light use efficiency model (to estimate net primary production in terms of carbon) which is then allocated to aboveground and belowground pools (based on mean annual temperature) and further converted to biomass using a carbon-to-dry matter ratio.","ee_asset_path":"https://gee-community-catalog.org/projects/rap/?h=rangela","ee_source":"Awesome GEE Community Catalog","end_year":"Present","full_name":"RAP Production - 30m - Yearly","geographic_coverage":"CONUS","processing_steps":"","product_name":"RAP Herbaceous Production","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"1986","support_thumbnail":"static/img/support/rap_production_2294b9666c.jpg","caption":"Example Perennial Forb and Grass Cover Trend Map","support_path":"datasets/remotesensing/rapproduction_yearly_30","temporal_resolution":"Yearly","temporal_resolution_normalized":"365","terms_of_use":"Public Domain-CC0","variable_info":{"afgAGB":{"band":"afgAGB","name":"Herbaceous production from annual forbs and grasses","common_name":"Biomass Production","units_out":"lbs/acre"},"pfgAGB":{"band":"pfgAGB","name":"Herbaceous production from perennial forbs and grasses","common_name":"Biomass Production","units_out":"lbs/acre"},"shrAGB":{"band":"shrAGB","name":"Production from shrubs","common_name":"Biomass Production","units_out":"lbs/acre"},"herbaceousAGB":{"band":"herbaceousAGB","name":"Total herbaceous production","common_name":"Biomass Production","units_out":"lbs/acre"}}},"RAP_PRODUCTION_16DAY":{"api_variable_path":"#rap-herbaceous-production-16-day","category":"Remote Sensing","citations":"1) Jones, M.O., N.P. Robinson, D.E. Naugle, J.D. Maestas, M.C. Reeves, R.W. Lankston, and B.W. Allred. Annual and 16-day rangeland production estimates for the western United States. Rangeland Ecology and Management https://doi.org/10.1016/j.rama.2021.04.003 2) Robinson, N. P., M. O. Jones, A. Moreno, T. A. Erickson, D. E. Naugle, and B. W.Allred. 2019. Rangeland productivity partitioned to sub-pixel plant functional types. Remote Sensing 11:1427. http://dx.doi.org/10.3390/rs11121427","coll_desc":"USDA-ARS 30 m near real-time herbaceous vegetation production dataset","coll_name":"projects/rap-data-365417/assets/npp-partitioned-16day-v3","dataset_website":"https://www.rangelands.app/","description":"This dataset consists of gridded estimates of herbaceous aboveground biomass, partitioned into vegetation types for annual forbs and grasses and perennial forbs and grasses. The estimates are produced at 30m spatial resolution from 1986-present and are provided annually (available through Climate Engine, as described here) and at 16-day intervals. Values are provided, initially, in terms of net primary productivity which are converted to pounds per acre of new growth of aboveground biomass. Estimates are calculated using a light-use efficiency model (to estimate net primary production in terms of carbon) which is then allocated to aboveground and belowground pools (based on mean annual temperature) and further converted to biomass using a carbon-to-dry matter ratio.","ee_asset_path":"https://gee-community-catalog.org/projects/rap/?h=rangela","ee_source":"Awesome GEE Community Catalog","end_year":"Present","full_name":"RAP Production - 30m - 16day","geographic_coverage":"CONUS","processing_steps":"","product_name":"RAP Production 16-day","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"1986","support_thumbnail":"static/img/support/rap_production_16day_aa54af9e43.jpg","caption":"Example Herbaceous Production Anomaly Map","support_path":"datasets/remotesensing/rapproduction_16day_30","temporal_resolution":"16 Day","temporal_resolution_normalized":"16","terms_of_use":"Public Domain-CC0","variable_info":{"afgAGB":{"band":"afgAGB","name":"Herbaceous production from annual forbs and grasses","common_name":"Biomass Production","units_out":"lbs/acre"},"pfgAGB":{"band":"pfgAGB","name":"Herbaceous production from perennial forbs and grasses","common_name":"Biomass Production","units_out":"lbs/acre"},"shrAGB":{"band":"shrAGB","name":"Production from shrubs","common_name":"Biomass Production","units_out":"lbs/acre"},"herbaceousAGB":{"band":"herbaceousAGB","name":"Total herbaceous production","common_name":"Biomass Production","units_out":"lbs/acre"}}},"RAP_NDVI":{"api_variable_path":"#rap-ndvi-16-day","category":"Remote Sensing","citations":"1) Robinson, N.P., B.W. Allred, M.O. Jones, A. Moreno, J.S. Kimball, D.E. Naugle, T.A. Erickson, and A.D. Richardson. A dynamic Landsat derived normalized difference vegetation index (NDVI) product for the conterminous United States. Remote Sensing 9:863. https://doi.org/10.3390/rs9080863","coll_desc":"USDA-ARS 30 m near real-time NDVI","coll_name":"projects/rap-data-365417/assets/ndvi-composites-v1-conus","dataset_website":"https://www.rangelands.app/","description":"This dataset provides consistent gap-filled normalized differenced vegetation index (NDVI) maps derived from Landsat 5/7/8/9. The images are produced at a 30-meter spatial resolution every 16-days from 1986-present for the conterminous United States and are an input for the RAP Production data that are also available in Climate Engine.","ee_asset_path":"https://gee-community-catalog.org/projects/rap/?h=rangela","ee_source":"Awesome GEE Community Catalog","end_year":"Present","full_name":"RAP NDVI - 30m - 16day","geographic_coverage":"CONUS","processing_steps":"https://www.rangelands.app/","product_name":"RAP NDVI","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"1986","support_thumbnail":"static/img/support/rap_ndvi_0dcd754af0.jpg","caption":"Example Normalized Difference Vegetation Index (NDVI) Anomaly Map","support_path":"datasets/remotesensing/rapndvi_16day_30","temporal_resolution":"16 Day","temporal_resolution_normalized":"16","terms_of_use":"Public Domain-CC0","variable_info":{"NDVI":{"band":"NDVI","name":"NDVI (Vegetation Index)","common_name":"Normalized Difference Vegetation Index (NDVI)","units_out":""}}},"CONUS404_DAILY":{"api_variable_path":"#conus404","category":"Climate/Hydrology","citations":"1) Rasmussen, R.M., Chen, F., Liu, C., Ikeda, K., Prein, A., Kim, J., Schneider, T., Dai, A., Gochis, D., Dugger, A., Zhang, Y., Jaye, A., Dudhia, J., He, C., Harrold, M., Xue, L., Chen, S., Newman, A., Dougherty, E., Abolafia-Rozenzweig, R., Lybarger, N., R. Viger, Dunne, K., Rasmussen, K., Miguez-Macho, G., 2023, Four-kilometer long-term regional hydroclimate reanalysis over the conterminous United States (CONUS), 1979-2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9PHPK4F.","coll_desc":"CONUS404 - 4km Hydroclimate Reanalysis for CONUS","coll_name":"projects/openet/assets/meteorology/conus404/daily","dataset_website":"https://www.sciencebase.gov/catalog/item/6372cd09d34ed907bf6c6ab1","description":"CONUS404 is a unique, high-resolution hydro-climate dataset appropriate for forcing hydrological models and conducting meteorological analysis over the contiguous United States. CONUS404, so named because it covers the CONtiguous United States for 40 years at 4-km resolution, was produced by the Weather Research and Forecasting (WRF) Model simulations run by National Center for Atmospheric Research (NCAR) as part of a collaboration with the U.S. Geological Survey (USGS) Water Mission Area","ee_asset_path":"Not publicly available","ee_source":"ClimateEngine.org","end_year":"2020","full_name":"CONUS404 - 4km - Daily","geographic_coverage":"North America","processing_steps":"","product_name":"CONUS 404","spatial_resolution":"4km","spatial_resolution_normalized":"4000","start_year":"1979","support_thumbnail":"static/img/support/conus404_daily_49341a4281.jpg","caption":"Example 12-month Standardized Precipitation Evapotranspiration Index (SPEI) Map","support_path":"datasets/climatehydrology/conus404_daily_4000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"This work is licensed under  a Creative Commons Attribution 4.0 International License.","variable_info":{"T2_MIN":{"band":"T2_MIN","name":"Minimum Temperature (2 m)","common_name":"Temperature","units_out":"K"},"T2_MAX":{"band":"T2_MAX","name":"Maximum Temperature (2 m)","common_name":"Temperature","units_out":"K"},"TD2":{"band":"TD2","name":"Dewpoint Temperature (2 m)","common_name":"Dew Point Temperature","units_out":"K"},"PREC_ACC_NC":{"band":"PREC_ACC_NC","name":"Precipitation","common_name":"Precipitation","units_out":"mm"},"ETO_ASCE":{"band":"ETO_ASCE","name":"Grass Reference Evapotranspiration (ETo)","common_name":"Reference Evapotranspiration","units_out":"mm"},"ETR_ASCE":{"band":"ETR_ASCE","name":"Alfalfa Reference Evapotranspiration (ETr)","common_name":"Reference Evapotranspiration","units_out":"mm"},"WIND10":{"band":"WIND10","name":"Windspeed (10 m)","common_name":"Wind Speed","units_out":"m/s"},"PSFC":{"band":"PSFC","name":"Surface Pressure","common_name":"Pressure","units_out":"kPa"},"ACSWDNB":{"band":"ACSWDNB","name":"Downwelling Shortwave Radiation","common_name":"Downward Shortwave Radiation","units_out":"J/m^2"},"peth":{"band":"peth","name":"Potential ET Hargreaves","common_name":"Potential Evapotranspiration","units_out":"mm"},"wbh":{"band":"wbh","name":"Potential Water Deficit Hargreaves","common_name":"Potential Water Deficit","units_out":"mm"},"speih":{"band":"wbh","name":"Standardized Precipitation Evapotranspiration Index Hargreaves (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""},"eddih":{"band":"peth","name":"Evap. Demand Drought Index Hargreaves (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""},"spi":{"band":"PREC_ACC_NC","name":"Standardized Precipitation Index (SPI)","common_name":"Standardized Precipitation Index (SPI)","units_out":""},"eddi":{"band":"ETO_ASCE","name":"Evaporative Demand Drought Index (EDDI)","common_name":"Evaporative Demand Drought Index (EDDI)","units_out":""},"wb":{"band":"wb","name":"Climatic Water Balance (PPT-ETo)","common_name":"Potential Water Deficit","units_out":"mm"},"spei":{"band":"wb","name":"Standardized Precipitation Evapotranspiration Index (SPEI)","common_name":"Standardized Precipitation Evapotranspiration Index (SPEI)","units_out":""}}},"RCMAP":{"api_variable_path":"#rcmap-rangeland-component-timeseries","category":"Remote Sensing","citations":"1) Rigge, M.B., Bunde, B., and Postma, K., 2025, Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across Western North America from 1985-2024. U.S. Geological Survey data release, https://doi.org/10.5066/P13QF8HT","coll_desc":"RCMAP Rangeland Component Timeseries v7 (1985-2025)","coll_name":"projects/sat-io/open-datasets/USGS/RCMAP/V8/TIME_SERIES/COVER","dataset_website":"https://www.mrlc.gov/data/type/rcmap-time-series-trends","description":"The Rangeland Condition Monitoring Assessment and Projection (RCMAP) product suite includes ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, and shrub, rule-based error maps, and the temporal trends of each component. Data characterize the percentage of each 30-meter pixel in the Western United States covered by each component for each year from 1985-2025 - providing change information for 41 years (imagery for 2012 was unavailable).","ee_asset_path":"https://gee-community-catalog.org/projects/rcmap","ee_source":"Awesome GEE Community Catalog","end_year":"Present","full_name":"RCMAP - 30m - Yearly","geographic_coverage":"Western US","processing_steps":"","product_name":"RCMAP","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"1985","support_thumbnail":"static/img/support/rcmap.jpg","caption":"Example Tree Cover Trend Map","support_path":"datasets/remotesensing/rcmap_yearly_30","temporal_resolution":"Yearly","temporal_resolution_normalized":"365","terms_of_use":"This work was authored as part of the Contributor's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law. This is an Open Access article that has been identified as being free of known restrictions under copyright law, including all related and neighboring rights (https://creativecommons.org/publicdomain/mark/1.0/). You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.","variable_info":{"annual_herbaceous":{"band":"annual_herbaceous","name":"Annual Herbaceous Cover","common_name":"Herbaceous Cover","units_out":"%"},"bare_ground":{"band":"bare_ground","name":"Bare Ground Cover","common_name":"Bare Ground Cover","units_out":"%"},"non_sagebrush_shrub":{"band":"non_sagebrush_shrub","name":"Non-sagebrush Shrub Cover","common_name":"Shrub Cover","units_out":"%"},"herbaceous":{"band":"herbaceous","name":"Herbaceous Cover","common_name":"Herbaceous Cover","units_out":"%"},"litter":{"band":"litter","name":"Litter Cover","common_name":"Litter Cover","units_out":"%"},"sagebrush":{"band":"sagebrush","name":"Sagebrush Cover","common_name":"Sagebrush Cover","units_out":"%"},"shrub":{"band":"shrub","name":"Shrub Cover","common_name":"Shrub Cover","units_out":"%"},"shrub_height":{"band":"shrub_height","name":"Shrub Height","common_name":"Shrub Height","units_out":"cm"},"perennial_herbaceous":{"band":"perennial_herbaceous","name":"Perennial Herbaceous Cover","common_name":"Herbaceous Cover","units_out":"%"},"tree":{"band":"tree","name":"Tree Cover","common_name":"Tree Cover","units_out":"%"}}},"RCMAP_EAG":{"api_variable_path":"#rcmap-exotic-annual-grasses-rcmap-eag","category":"Remote Sensing","citations":"1) Rigge, M., Postma, K., Dahal, D., Dornbierer, J., Megard, L., Benedict, T., and Bunde, B., 2026, Weekly Herbaceous and Exotic Annual Grass (EAG) Cover for western North America 2016 - 2026: U.S. Geological Survey database, https://doi.org/10.5066/P13QWBFH.","coll_desc":"RCMAP Weekly Herbaceous and Exotic Annual Grass (RCMAP-EAG)","coll_name":"projects/sat-io/open-datasets/USGS/RCMAP/EAG/TIME_SERIES/COVER","dataset_website":"https://www.usgs.gov/data/weekly-herbaceous-and-exotic-annual-grass-eag-cover-western-north-america-2016-2026","description":"The RCMAP Weekly Herbaceous and Exotic Annual Grass (EAG) dataset provides 30-meter estimates of percent cover for eight herbaceous and grass components across western North America. Weekly products are available from 2016 to present and include total herbaceous, green herbaceous, senesced herbaceous, combined EAG, cheatgrass, medusahead, field brome, and Sandberg's bluegrass cover. The finer temporal resolution supports land-management decisions related to invasive grasses, fire, grazing, and treatment effectiveness.","ee_asset_path":"https://gee-community-catalog.org/projects/rcmap-eag/","ee_source":"Awesome GEE Community Catalog","end_year":"Present","full_name":"RCMAP EAG - 30m - Weekly","geographic_coverage":"Western US","processing_steps":"","product_name":"RCMAP_EAG","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"2016","support_thumbnail":"static/img/support/rcmap_eag.jpg","caption":"Example Total Herbaceous Cover Anomaly Map","support_path":"datasets/remotesensing/rcmapeag_weekly_30","temporal_resolution":"Weekly","temporal_resolution_normalized":"7","terms_of_use":"This work was authored as part of the Contributor's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law. This is an Open Access article that has been identified as being free of known restrictions under copyright law, including all related and neighboring rights (https://creativecommons.org/publicdomain/mark/1.0/). You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.","variable_info":{"total_herbaceous":{"band":"total_herbaceous","name":"Total Herbaceous Cover","common_name":"Total Herbaceous Cover","units_out":"%"},"green_herbaceous":{"band":"green_herbaceous","name":"Green Herbaceous Cover","common_name":"Green Herbaceous Cover","units_out":"%"},"senesced_herbaceous":{"band":"senesced_herbaceous","name":"Senesced Herbaceous Cover","common_name":"Senesced Herbaceous Cover","units_out":"%"},"eag":{"band":"eag","name":"Exotic Annual Grass Cover","common_name":"Exotic Annual Grass Cover","units_out":"%"},"cheatgrass":{"band":"cheatgrass","name":"Cheatgrass Cover","common_name":"Cheatgrass Cover","units_out":"%"},"medusahead":{"band":"medusahead","name":"Medusahead Cover","common_name":"Medusahead Cover","units_out":"%"},"fieldbrome":{"band":"fieldbrome","name":"Field Brome Cover","common_name":"Field Brome Cover","units_out":"%"},"sandbergs_bluegrass":{"band":"sandbergs_bluegrass","name":"Sandberg's Bluegrass Cover","common_name":"Sandberg's Bluegrass Cover","units_out":"%"}}},"LANDCART":{"api_variable_path":"#landcart","category":"Remote Sensing","citations":"None","coll_desc":"Landscape Cover Analysis and Reporting Tools (LandCART) v2025","coll_name":"projects/blm-gee-landcart/assets/LandCART_v2025","dataset_website":"https://eros.usgs.gov/doi-remote-sensing-activities/2022/blm/landcart-landscape-cover-analysis-and-reporting-tools","description":"The Landscape Cover Analysis and Reporting Tools (LandCART) product suite includes seasonal estimates ten fractional cover types: annual forbs, annual grasses, perennial forbs, perennial grasses, sagebrush, total foliar cover, woody vegetation, canopy gaps ≥ 25 cm, canopy gaps ≥ 100 cm, and canopy gaps ≥ 200 cm. Data characterize the percentage of each 30-meter pixel in the western United States covered by each component from 1985-2024 for four seasonal periods (December 22- March 21, March 22 - June 21, June 22- September 21, and September 22 - December 21), characterizing plant phenology and other seasonal patterns.","ee_asset_path":"Not publicly available","ee_source":"Private Collection","end_year":"Present","full_name":"LandCART - 30m - Seasonal","geographic_coverage":"Western US","processing_steps":"","product_name":"LandCART","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"1985","support_thumbnail":"static/img/support/landcart_27e56781af.jpg","caption":"Example Canopy Gaps >25cm Trend Map","support_path":"datasets/remotesensing/landcart_3month_30","temporal_resolution":"3 Month","temporal_resolution_normalized":"90","terms_of_use":"This work was authored as part of the Contributor's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law. This is an Open Access article that has been identified as being free of known restrictions under copyright law, including all related and neighboring rights (https://creativecommons.org/publicdomain/mark/1.0/). You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.","variable_info":{"annual_grass":{"band":"annual_grass","name":"Annual Herbaceous Cover","common_name":"Herbaceous Cover","units_out":"%"},"annual_forb":{"band":"annual_forb","name":"Annual Forb Cover","common_name":"Herbaceous Cover","units_out":"%"},"perennial_forb":{"band":"perennial_forb","name":"Perennial Forb Cover","common_name":"Herbaceous Cover","units_out":"%"},"perennial_grass":{"band":"perennial_grass","name":"Perennial Grass Cover","common_name":"Herbaceous Cover","units_out":"%"},"sagebrush":{"band":"sagebrush","name":"Sagebrush Cover","common_name":"Sagebrush Cover","units_out":"%"},"total_foliar":{"band":"total_foliar","name":"Total Foliar Cover","common_name":"Vegetation Cover","units_out":"%"},"woody":{"band":"woody","name":"Woody Cover","common_name":"Woody Cover","units_out":"%"},"canopy_gaps_25cm_plus":{"band":"canopy_gaps_25cm_plus","name":"Canopy Gaps >25cm","common_name":"Canopy Gap Fraction","units_out":"%"},"canopy_gaps_100cm_plus":{"band":"canopy_gaps_100cm_plus","name":"Canopy Gaps >100cm","common_name":"Canopy Gap Fraction","units_out":"%"},"canopy_gaps_200cm_plus":{"band":"canopy_gaps_200cm_plus","name":"Canopy Gaps >200cm","common_name":"Canopy Gap Fraction","units_out":"%"}}},"USFS_TCC_V5":{"api_variable_path":"#usfs-tree-canopy-cover","category":"Remote Sensing","citations":"1) USDA Forest Service. 2023. USFS Tree Canopy Cover v2021.4 (Conterminous United States and Southeastern Alaska). Salt Lake City, Utah.","coll_desc":"USFS Tree Canopy Cover (TCC) 2023","coll_name":"USGS/NLCD_RELEASES/2023_REL/TCC/v2023-5","dataset_website":"https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/","description":"Part of the Tree Canopy Cover (TCC) data suite, it includes modeled TCC, standard error (SE), and National Land Cover Database's (NLCD) TCC data for each year. TCC data produced by the the United States Department of Agriculture, Forest Service (USFS) are included in the Multi-Resolution Land Characteristics (MRLC) consortium that is part of the National Land Cover Database (NLCD) project managed by the United States (US) Geological Survey (USGS).","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2023_REL_TCC_v2023-5","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"USFS TCC - 30m - Yearly","geographic_coverage":"CONUS","processing_steps":"","product_name":"USFS Tree Canopy Cover","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"1985","support_thumbnail":"static/img/support/usfs_tcc_v5_ee8161239c.jpg","caption":"Example Tree Canopy Cover Trend Map","support_path":"datasets/remotesensing/usfstcc_yearly_30","temporal_resolution":"Yearly","temporal_resolution_normalized":"365","terms_of_use":"These data were collected using funding from the U.S. Government and can be used without additional permissions or fees.","variable_info":{"Science_Percent_Tree_Canopy_Cover":{"band":"Science_Percent_Tree_Canopy_Cover","name":"Tree Canopy Cover","common_name":"Tree Canopy Cover","units_out":"%"},"NLCD_Percent_Tree_Canopy_Cover":{"band":"NLCD_Percent_Tree_Canopy_Cover","name":"Tree Canopy Cover (NLCD)","common_name":"Tree Canopy Cover","units_out":"%"}}},"ABOVE_COVER":{"api_variable_path":"#above-biomeshift-alaska-yukon-vegetation-cover","category":"Remote Sensing","citations":"1) Macander, M.J., and P.R. Nelson. 2022. ABoVE: Modeled Top Cover by Plant Functional Type over Alaska and Yukon, 1985-2020. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2032","coll_desc":"ABoVE BiomeShift Alaska-Yukon Vegetation Cover","coll_name":"projects/foreststructure/ABoVE/BiomeShift/Alaska_Yukon_PFT_202207_Filled","dataset_website":"https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2032","description":"Data files of modeled top cover estimates by plant functional type (PFT) for the Arctic and Boreal Alaska and Yukon regions. Estimates are presented for each year from 1985 to 2020. Plant functional types include conifer trees, broadleaf trees, deciduous shrubs, evergreen shrubs, graminoids, forbs, and light macrolichens. Estimates were derived through the combination of two stochastic gradient-boosting models that used environmental and spectral covariates.","ee_asset_path":"Not publicly available","ee_source":"Private Collection","end_year":"2020","full_name":"ABoVE BiomeShift - 30m - Yearly","geographic_coverage":"Alaska","processing_steps":"","product_name":"ABoVE BiomeShift","spatial_resolution":"30m","spatial_resolution_normalized":"30","start_year":"1985","support_thumbnail":"static/img/support/above_cover_506a6debf2.jpg","caption":"Example Broadleaf Tree Cover Map","support_path":"datasets/remotesensing/abovebiomeshift_yearly_30","temporal_resolution":"Yearly","temporal_resolution_normalized":"365","terms_of_use":"This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use Policy. See the Data Use and Citation Policy for more information.","variable_info":{"allDecShrub":{"band":"allDecShrub","name":"Deciduous shrubs","common_name":"Shrub Cover","units_out":"%"},"allEvShrub":{"band":"allEvShrub","name":"Evergreen shrubs","common_name":"Shrub Cover","units_out":"%"},"allForb":{"band":"allForb","name":"Forbs","common_name":"Herbaceous Cover","units_out":"%"},"bTree":{"band":"bTree","name":"Broadleaf trees","common_name":"Vegetation Cover","units_out":"%"},"cTree":{"band":"cTree","name":"Conifer trees","common_name":"Vegetation Cover","units_out":"%"},"graminoid":{"band":"graminoid","name":"Graminoids","common_name":"Herbaceous Cover","units_out":"%"},"tmlichenLight2":{"band":"tmlichenLight2","name":"Light microlichen","common_name":"Vegetation Cover","units_out":"%"}}},"VEGDRI":{"api_variable_path":"#vegetation-drought-response-index","category":"Remote Sensing","citations":"1) Brown, J. F., Wardlow, B. D., Tadesse, T., Hayes, M. J., & Reed, B. C. (2008). The Vegetation Drought Response Index (VegDRI): A New Integrated Approach for Monitoring Drought Stress in Vegetation. GIScience & Remote Sensing, 45(1), 16–46. https://doi.org/10.2747/1548-1603.45.1.16","coll_desc":"Vegetation Drought Response Index (VegDRI)","coll_name":"projects/climate-engine-pro/assets/ce-veg-dri","dataset_website":"https://vegdri.unl.edu/Home.aspx","description":"The Vegetation Drought Response Index (VegDRI) is a weekly geospatial model that depicts drought stress on vegetation within the conterminous United States. The development of the VegDRI drought-monitoring tool was a collaborative effort by scientists at the USGS EROS Center, the National Drought Mitigation Center (NDMC) at the University of Nebraska, and the High Plains Regional Climate Center (HPRCC).","ee_asset_path":"https://gee-community-catalog.org/projects/veg_dri/?h=vegdr","ee_source":"ClimateEngine.org","end_year":"Present","full_name":"VegDRI - 1km - Weekly","geographic_coverage":"CONUS","processing_steps":"","product_name":"Vegetation Drought Response Index (VegDRI)","spatial_resolution":"1km","spatial_resolution_normalized":"1000","start_year":"2009","support_thumbnail":"static/img/support/vegdri_962dd9457c.jpg","caption":"Example Vegetation Drought Response Index (vegDRI) Map","support_path":"datasets/remotesensing/vegdri_weekly_1000","temporal_resolution":"Weekly","temporal_resolution_normalized":"7","terms_of_use":"","variable_info":{"vegdri":{"band":"vegdri","name":"VegDRI","common_name":"Vegetation Drought Response Index (VegDRI)","units_out":"N/A"}}},"VIIRS_16DAY":{"api_variable_path":"#viirs-vegetation-indices-16-day","category":"Remote Sensing","citations":"None","coll_desc":"VIIRS Vegetation Indices 16-day 500m","coll_name":"NASA/VIIRS/002/VNP13A1","dataset_website":"https://lpdaac.usgs.gov/products/vnp13a1v002/","description":"The Suomi National Polar-Orbiting Partnership (S-NPP) NASA Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A1) data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 500 meter resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP13A1","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"VIIRS - 500m - 16day","geographic_coverage":"Global","processing_steps":"","product_name":"VIIRS 16-Day Vegetation Indice","spatial_resolution":"500m","spatial_resolution_normalized":"500","start_year":"2012","support_thumbnail":"static/img/support/viirs_16day_48b441bd6f.jpg","caption":"Example Normalized Difference Vegetation Index (NDVI) Map","support_path":"datasets/remotesensing/viirs_16day_500","temporal_resolution":"16 Day","temporal_resolution_normalized":"16","terms_of_use":"All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge.","variable_info":{"NDVI":{"band":"NDVI","name":"NDVI (Vegetation Index)","common_name":"Normalized Difference Vegetation Index (NDVI)","units_out":""},"EVI":{"band":"EVI","name":"EVI (Enhanced Vegetation Index)","common_name":"Enhanced Vegetation Index (EVI)","units_out":""},"EVI2":{"band":"EVI2","name":"EVI (Enhanced Vegetation Index (2-band))","common_name":"Enhanced Vegetation Index (EVI)","units_out":""},"NDSI":{"band":"NDSI","name":"NDSI (Snow Index)","common_name":"Normalized Difference Snow Index (NDSI)","units_out":""},"TrueColor":{"band":"TrueColor","name":"True Color","common_name":"Color Composite","units_out":""},"FalseColor":{"band":"FalseColor","name":"False Color","common_name":"Color Composite","units_out":""},"NDWI_NIR_SWIR_Gao":{"band":"NDWI_NIR_SWIR_Gao","name":"NDWI (NIR/SWIR1)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"NDWI_Green_NIR_McFeeters":{"band":"NDWI_Green_NIR_McFeeters","name":"NDWI (Green/NIR)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"NDWI_Green_SWIR_Xu":{"band":"NDWI_Green_SWIR_Xu","name":"NDWI (Green/SWIR1)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"NDWI_Green_SWIR_Hall":{"band":"NDWI_Green_SWIR_Hall","name":"NDWI (Green/SWIR2)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""},"NDWI_SWIR_Green_Allen":{"band":"NDWI_SWIR_Green_Allen","name":"NDWI (SWIR1/Green)","common_name":"Normalized Difference Water Index (NDWI)","units_out":""}}},"VIIRS_DAILY":{"api_variable_path":"#viirs-day-land-surface-temperature-and-emissivity-daily","category":"Remote Sensing","citations":"None","coll_desc":"Day Land Surface Temperature and Emissivity Daily 1km","coll_name":"NASA/VIIRS/002/VNP21A1D","dataset_website":"https://lpdaac.usgs.gov/products/vnp21a1dv002/","description":"The NASA Suomi National Polar-Orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 1 product (VNP21A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily VNP21 swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging.","ee_asset_path":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP21A1D","ee_source":"Earth Engine Catalog","end_year":"Present","full_name":"VIIRS - 1km - Daily","geographic_coverage":"Global","processing_steps":"","product_name":"VIIRS Land Surface Temperature","spatial_resolution":"1km","spatial_resolution_normalized":"1000","start_year":"2012","support_thumbnail":"static/img/support/viirs_daily_72e829214d.jpg","caption":"Example Jun-Aug Land Surface Temperature Map","support_path":"datasets/remotesensing/viirs_daily_1000","temporal_resolution":"Daily","temporal_resolution_normalized":"1","terms_of_use":"All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge.","variable_info":{"LST_Day_1km":{"band":"LST_Day_1km","name":"Land Surface Temperature (Day)","common_name":"Land Surface Temperature","units_out":""}}}}