Timeline / Data.gov — Climate Datasets
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Evidence
| Source | Data.gov — Climate Datasets |
|---|---|
| Agency | Data.gov |
| URL | https://api.gsa.gov/technology/datagov/v4/search?q=climate&sort=last_harvested_date&per_page=100&api_key=${DATAGOV_API_KEY} |
| Observed by | Civic Memory, directly, on 2026-09-12T00:29:34+00:00 |
| Content type | application/json |
| Current object |
a0b3cb2e20dac08c2e90140cb80ae986f15ee681ea6734b9ee5a97de73363bfa
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metadata
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| Previous object |
415ce02ef91ebef47d2ed806aa5587744cb8e03c57875f5464fb8f9392f16013
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metadata
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What changed derived
This diff is not evidence. It was produced by
civic-memory.diff_engine 1.1.0 at
2026-09-12T00:29:34+00:00 by normalizing the two archived objects above. The
objects are authoritative; this reading of them can be regenerated or deleted
without loss. 4071 line(s) added, 4445 line(s) removed.
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The goal of the survey was to understand the extent to which the workshops, and any subsequent follow-up with the USGS and TNC researchers, provided useful information to those decision makers and to use that information to help the USGS and TNC teams reflect on their project outcomes. The survey asked participants for feedback about the utility of the workshops and their experiences receiving additional data products from USGS and TNC hosts. The survey was sent to the 111 people who participated in one of the workshops. 25 people completed the survey. This data pertains only to these four workshops and should not be generalized to other Gulf Coast communities or other USGS or TNC research projects. 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We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). 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Both cover the northwestern United States and part of southern British Columbia (N of about 38 degrees N and W of about 105 degrees W) at 1/16th (0.0625) degree resolution. Climate and hydrologic variables (21 total) in each are as follows: precipitation, temperature (avg./max./min.), outgoing longwave radiation, incoming shortwave radiation, relative humidity, vapor pressure deficit, evapotranspiration, runoff, baseflow, soil moisture (3-layers), snow water equivalent, snow depth, and potential evapotranspiration (5 vegetation references).\nThe first dataset, \"Western US Hydroclimate Scenarios Project Dynamically Downscaled Data\", contains daily dynamically downscaled climate projections and simulated land surface water and energy fluxes. The downscaling is based on the Weather Research and Forecasting (WRF) regional model. WRF was run using boundary conditions from the ECHAM5 global model and the SRES A1B emissions scenario, one of the models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Climate simulations were performed using an inner grid resolution of 12-km over the region and a 100-year (1970-2070) simulation.\nThe second dataset, \"Western US Hydroclimate Scenarios Project Observations and Statistically Downscaled Data\", contains daily statistically downscaled climate projections and simulated land surface water and energy fluxes for the western United States and southern British Columbia at 1/16th (0.0625) degree resolution. The downscaling used is the Modified Delta approach (see Littell et al. 2011), based on 10 models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Note that time-stamps on these data are not in the future.", + "distribution": [ + { + "@type": "dcat:Distribution", + "description": "The metadata original format", + "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.55e4c25ce4b05561fa208552.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_55e4c25ce4b05561fa208552", + "keyword": [ + "Pacific Northwest", + "USGS:55e4c25ce4b05561fa208552", + "Western United States", + "change impacts", + "climate change", + "climatologyMeteorologyAtmosphere", + "dynamical downscaling", + "extremes", + "geospatial datasets", + "hydrologic change", + "statistical downscaling" + ], + "modified": "2026-09-09T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-126.5625, 33.1376, -102.6563, 48.4584", + "theme": [ + "geospatial" + ], + "title": "Western US Hydroclimate Scenarios Project Datasets" + }, + "description": "This archive contains two datasets. Both cover the northwestern United States and part of southern British Columbia (N of about 38 degrees N and W of about 105 degrees W) at 1/16th (0.0625) degree resolution. Climate and hydrologic variables (21 total) in each are as follows: precipitation, temperature (avg./max./min.), outgoing longwave radiation, incoming shortwave radiation, relative humidity, vapor pressure deficit, evapotranspiration, runoff, baseflow, soil moisture (3-layers), snow water equivalent, snow depth, and potential evapotranspiration (5 vegetation references).\nThe first dataset, \"Western US Hydroclimate Scenarios Project Dynamically Downscaled Data\", contains daily dynamically downscaled climate projections and simulated land surface water and energy fluxes. The downscaling is based on the Weather Research and Forecasting (WRF) regional model. WRF was run using boundary conditions from the ECHAM5 global model and the SRES A1B emissions scenario, one of the models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Climate simulations were performed using an inner grid resolution of 12-km over the region and a 100-year (1970-2070) simulation.\nThe second dataset, \"Western US Hydroclimate Scenarios Project Observations and Statistically Downscaled Data\", contains daily statistically downscaled climate projections and simulated land surface water and energy fluxes for the western United States and southern British Columbia at 1/16th (0.0625) degree resolution. The downscaling used is the Modified Delta approach (see Littell et al. 2011), based on 10 models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). 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In this study, we used LPJ at a 30-second (~1-km) spatial resolution to simulate potential vegetation changes for 2070–2099. LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. The fine spatial-scale vegetation simulations resolve patterns of vegetation change that are not visible at coarser resolutions and these fine-scale patterns are particularly important for understanding potential future vegetation changes in topographically complex areas.", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "http://doi.org/10.5066/F73X84PH", + "description": "Landing page for access to the data", + "format": "XML", + "mediaType": "application/http", + "title": "Digital Data" + }, + { + "@type": "dcat:Distribution", + "description": "The metadata original format", + "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.283f3ce9-6b5d-46db-8057-1c74b96e58ee.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_283f3ce9-6b5d-46db-8057-1c74b96e58ee", + "keyword": [ + "British Columbia", + "Climate change", + "Ecosystems", + "Forests", + "Grasses", + "Grasslands", + "Idaho", + "Montana", + "Paleoclimatology", + "Shrubs", + "Simulation and modeling", + "USGS:283f3ce9-6b5d-46db-8057-1c74b96e58ee", + "Washington", + "climate change", + "geospatial datasets", + "modeling", + "vegetation" + ], + "modified": "2026-09-09T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-125.5042, 46.4958, -113.9958, 53.0042", + "theme": [ + "geospatial" + ], + "title": "Projected Future Vegetation Changes for the Northwest United States and Southwest Canada at a Fine Spatial Resolution Using a Dynamic Global Vegetation Model" + }, + "description": "Future climate change may significantly alter the distributions of many plant taxa. 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LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. 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We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). We offer suggestions for improving the management relevance of future studies based on our own insights and those of managers and biologists who participated in this assessment and provided critical review of this report.", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.5066/F7VM49FN", + "description": "Landing page for access to the data", + "format": "XML", + "mediaType": "application/http", + "title": "Digital Data" + }, + { + "@type": "dcat:Distribution", + "description": "The metadata original format", + "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5867e23fe4b0cd2dabe7c76e.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5867e23fe4b0cd2dabe7c76e", + "keyword": [ + "Connectivity", + "Environment and Conservation", + "Idaho", + "Montana", + "Rocky Mountains", + "USGS:5867e23fe4b0cd2dabe7c76e", + "United States", + "Wolverine", + "Wyoming", + "climate change", + "environment", + "natural resource management" + ], + "modified": "2026-09-09T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-117.049289, 41.894474, -108.528169, 49.005950", + "theme": [ + "geospatial" + ], + "title": "Potential climate change impacts on wolverine connectivity in the U.S. Northern Rockies" + }, + "description": "Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. 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We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). 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We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). 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We offer suggestions for improving the management relevance of future studies based on our own insights and those of managers and biologists who participated in this assessment and provided critical review of this report.", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "http://dx.doi.org/10.5066/F7VM49FN", + "description": "Landing page for access to the data", + "format": "XML", + "mediaType": "application/http", + "title": "Digital Data" + }, + { + "@type": "dcat:Distribution", + "description": "The metadata original format", + "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5866933ae4b0cd2dabe7c57f.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5866933ae4b0cd2dabe7c57f", + "keyword": [ + "Bighorn sheep", + "Connectivity", + "Environment and Conservation", + "Idaho", + "Montana", + "Rocky Mountains", + "USGS:5866933ae4b0cd2dabe7c57f", + "United States", + "Wyoming", + "climate change", + "environment", + "natural resource management" + ], + "modified": "2026-09-09T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-117.049289, 41.894474, -108.528169, 49.005950", + "theme": [ + "geospatial" + ], + "title": "Potential climate change impacts on bighorn sheep connectivity in the U.S. Northern Rockies" + }, + "description": "Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. 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Data included here are the summaries of the notes provided by growers.\nThe goal of this data collection was to provide Arizona Cooperative Extension researchers with information about how to provide climate and weather data tailored to the needs of the Arizona viticulture industry.\nThese data represent the experiences of a non-random sample of viticulturalists for one year of time. The data have been de-identified so as to refer only to the county in which a vineyard exists. These data cannot be generalized to apply to other states, years, or vineyards. They provide some insight into the kinds of climate and weather events that affect wine grape production in the state of Arizona.", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.21429/gb4t-y589", + "description": "Landing page for access to the data", + "format": "XML", + "mediaType": "application/http", + "title": "Digital Data" + }, + { + "@type": "dcat:Distribution", + "description": "The metadata original format", + "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.62e2d417d34e394b65364f3d.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_62e2d417d34e394b65364f3d", + "keyword": [ + "USGS:62e2d417d34e394b65364f3d", + "agriculture", + "biota", + "botany", + "climate", + "pest management", + "viticulture", + "weather" + ], + "modified": "2026-09-09T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-114.82, 31.33, -109.04, 37.0", + "theme": [ + "geospatial" + ], + "title": "Climate and Weather Impacts to Wine Grapes in Arizona in 2018-2019 growing season as described by growers" + }, + "description": "In October 2019, as part of our collaboration with a project focused on nexus of climate and viticulture in Arizona, we helped hold two workshops focused on reviewing the 2018-2019 wine grape growing season in Arizona. Workshops were held in two of the main viticulture regions in Arizona: the Verde Valley and Cochise County.\nTwenty-four people attended the Yavapai County workshop; 9 vineyards were represented but a number of workshop participants were students not representing a vineyard. Those participants did not contribute to the climate and weather data. Six people representing 6 vineyards participated in the Cochise County workshop.\nAt each workshop, growers were asked to list various climate- and weather-related events that had affected their vineyards over the past year. Participants made notes about climate and weather events as well as crop quality on large paper timelines taped to the wall of the meeting room. Data included here are the summaries of the notes provided by growers.\nThe goal of this data collection was to provide Arizona Cooperative Extension researchers with information about how to provide climate and weather data tailored to the needs of the Arizona viticulture industry.\nThese data represent the experiences of a non-random sample of viticulturalists for one year of time. The data have been de-identified so as to refer only to the county in which a vineyard exists. These data cannot be generalized to apply to other states, years, or vineyards. They provide some insight into the kinds of climate and weather events that affect wine grape production in the state of Arizona.", + "distribution_titles": [ + "Digital Data", + "Original Metadata" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/8b455ec2-f091-4737-9f75-bd1d0a603e63", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/8b455ec2-f091-4737-9f75-bd1d0a603e63/raw", + "has_download": true, + "has_spatial": true, + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_62e2d417d34e394b65364f3d", + "keyword": [ + "USGS:62e2d417d34e394b65364f3d", + "agriculture", + "biota", + "botany", + "climate", + "pest management", + "viticulture", + "weather" + ], + "last_harvested_date": "2026-09-11T22:56:45.556126", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "143529f7-2eef-4a07-b227-93ac9e84fad8", + "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png", + "name": "Department of the Interior", + "organization_type": "Federal Government", + "slug": "doi" + }, + "parent_identifier": null, + "popularity": 0, + "publisher": "U.S. Geological Survey", + "slug": "climate-and-weather-impacts-to-wine-grapes-in-arizona-in-2018-2019-growing-season-as-descr", + "spatial_centroid": { + "lat": 33.598, + "lon": -112.508 + }, + "spatial_shape": { + "coordinates": [ + [ + [ + -114.82, + 31.33 + ], + [ + -114.82, + 37.0 + ], + [ + -109.04, + 37.0 + ], + [ + -109.04, + 31.33 + ], + [ + -114.82, + 31.33 + ] + ] + ], + "type": "Polygon" + }, + "theme": [ + "geospatial" + ], + "title": "Climate and Weather Impacts to Wine Grapes in Arizona in 2018-2019 growing season as described by growers", + "type": "dataset" + }, + { + "_score": 19.807682, "_sort": [ 1789080653194, - 19.580505, + 19.807682, 1, "51f71287-6f47-45c2-a294-54e061b91aac" ], @@ -166,10 +4215,10 @@ "type": "dataset" }, { - "_score": 8.779156, + "_score": 8.762259, "_sort": [ 1789080651201, - 8.779156, + 8.762259, 2, "44910ba5-b6c4-4494-9313-ae768358536c" ], @@ -468,10 +4517,10 @@ "type": "dataset" }, { - "_score": 19.752262, + "_score": 20.234915, "_sort": [ 1789080649035, - 19.752262, + 20.234915, 2, "45796c70-5520-47f3-b95f-6d0411e385b3" ], @@ -632,10 +4681,10 @@ "type": "dataset" }, { - "_score": 24.623737, + "_score": 24.616894, "_sort": [ 1789080648397, - 24.623737, + 24.616894, 2, "e7b29e91-9d7b-4959-bb36-1ee555d655d6" ], @@ -794,10 +4843,10 @@ "type": "dataset" }, { - "_score": 8.588516, + "_score": 8.566754, "_sort": [ 1789080646634, - 8.588516, + 8.566754, 2, "2b6eccf6-5c1c-426e-9506-bbbace4c9619" ], @@ -948,10 +4997,10 @@ "type": "dataset" }, { - "_score": 42.3414, + "_score": 42.624702, "_sort": [ 1789080646420, - 42.3414, + 42.624702, 0, "81731bf1-de09-4834-909a-b7410311a566" ], @@ -1086,10 +5135,10 @@ "type": "dataset" }, { - "_score": 14.188661, + "_score": 14.134474, "_sort": [ 1789080646190, - 14.188661, + 14.134474, 1, "867f55dd-d860-4bf2-9b7a-e85bbb4db39d" ], @@ -1278,10 +5327,10 @@ "type": "dataset" }, { - "_score": 3.4760838, + "_score": 3.4792871, "_sort": [ 1789080640137, - 3.4760838, + 3.4792871, 26, "ed051ee0-26f4-4f64-8f4e-813d3bddef87" ], @@ -1598,10 +5647,10 @@ "type": "dataset" }, { - "_score": 30.773697, + "_score": 30.89657, "_sort": [ 1789080634405, - 30.773697, + 30.89657, 4, "dda12070-8ac5-4af6-818c-3cdf506cb347" ], @@ -1812,10 +5861,10 @@ "type": "dataset" }, { - "_score": 52.41357, + "_score": 52.724564, "_sort": [ 1789080633051, - 52.41357, + 52.724564, 1, "14e8b9ec-253a-4937-9192-34458291c757" ], @@ -1870,4427 +5919,4 @@ ], "title": "Downscaled Climate Projections for the Edwards Aquifer Region (EAR) using CMIP5 for the years 2006 – 2100 and CMIP6 for the years 2015 – 2100" }, - "description": "Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 7 sets of SD-processed CMIP5 projections and 12 sets of SD-processed CMIP6 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using four GCMs from the CMIP5 archive (CMCC-CM, HadGEM2-CC, inmcm4, MRI-ESM1) and six GCMs from the CMIP6 archive (EC-Earth3, INM-CM-4-8, INM-CM-5-0, KACE-1-0-G, KIOST-ESM, and MPI-ESM1-2-HR), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP5 GCMs simulated response under the representative concentration pathways (RCPs) 4.5 and 8.5. The CMIP6 GCMs simulated response under the shared socioeconomic pathways (SSPs) 2-4.5 and 5-8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2005 for CMIP5 and 1980-2014 for CMIP6) and future projections (2006-2100 for CMIP5 and 2015-2100 for CMIP6) are provided.\nApplied researchers may explore aspects of potential changes in the EAR using these high resolution projections, including as inputs to additional modelling (e.g. hydrology modeling, crop modeling, etc.). This collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the EAR. These climate projection data products are provided as is without any warranty and no agreement to support subsequent projects based on this dataset, beyond providing the data to public domain.", - "distribution_titles": [ - "Digital Data", - "Original Metadata" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/d48921b7-7713-4fd1-a326-3d506ed555c3", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/d48921b7-7713-4fd1-a326-3d506ed555c3/raw", - "has_download": true, - "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_66bb5ff6d34e0338828136e0", - "keyword": [ - "Edwards Aquifer", - "San Antonio", - "Texas", - "USGS:66bb5ff6d34e0338828136e0", - "atmospheric and climatic processes", - "climate change", - "climatologyMeteorologyAtmosphere", - "downscaling" - ], - "last_harvested_date": "2026-09-10T22:50:33.051165", - "organization": { - "aliases": [ - "dept" - ], - "code_repo_exempt": false, - "code_repo_url": null, - "description": null, - "id": "143529f7-2eef-4a07-b227-93ac9e84fad8", - "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png", - "name": "Department of the Interior", - "organization_type": "Federal Government", - "slug": "doi" - }, - "parent_identifier": null, - "popularity": 1, - "publisher": "U.S. Geological Survey", - "slug": "downscaled-climate-projections-for-the-edwards-aquifer-region-ear-using-cmip5-fo-2015-2100", - "spatial_centroid": { - "lat": 30.010859999999997, - "lon": -100.59085999999999 - }, - "spatial_shape": { - "coordinates": [ - [ - [ - -104.8975, - 27.0983 - ], - [ - -104.8975, - 34.3797 - ], - [ - -94.1309, - 34.3797 - ], - [ - -94.1309, - 27.0983 - ], - [ - -104.8975, - 27.0983 - ] - ] - ], - "type": "Polygon" - }, - "theme": [ - "geospatial" - ], - "title": "Downscaled Climate Projections for the Edwards Aquifer Region (EAR) using CMIP5 for the years 2006 – 2100 and CMIP6 for the years 2015 – 2100", - "type": "dataset" - }, - { - "_score": 19.643, - "_sort": [ - 1789080631706, - 19.643, - 1, - "4a1507fe-0d0b-4f09-906e-6705c1299f6e" - ], - "dcat": { - "accessLevel": "public", - "bureauCode": [ - "010:12" - ], - "contactPoint": { - "@type": "vcard:Contact", - "fn": "Adam J Terando", - "hasEmail": "mailto:aterando@usgs.gov" - }, - "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.", - "distribution": [ - { - "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P95BV7GE", - "description": "Landing page for access to the data", - "format": "XML", - "mediaType": "application/http", - "title": "Digital Data" - }, - { - "@type": "dcat:Distribution", - "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.609c874cd34ea221ce3ac1e0.xml", - "format": "XML", - "mediaType": "text/xml", - "title": "Original Metadata" - } - ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c874cd34ea221ce3ac1e0", - "keyword": [ - "Alabama", - "Arkansas", - "Florida", - "Georgia", - "Kentucky", - "Louisiana", - "Mississippi", - "Missouri", - "North Carolina", - "Oklahoma", - "South Carolina", - "Southeast United states", - "Tennessee", - "Texas", - "USGS:609c874cd34ea221ce3ac1e0", - "Virginia", - "West Virginia", - "farming", - "fires", - "geoscientificInformation", - "managed fire regimes", - "statistical downscaling", - "wildfires" - ], - "modified": "2021-09-20T00:00:00Z", - "publisher": { - "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-102.1475, 25.0631, -73.6063, 43.1045", - "theme": [ - "geospatial" - ], - "title": "INMCM Historical Prescribed Burn Windows for the Southeast United States 1950-1999" - }, - "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.", - "distribution_titles": [ - "Digital Data", - "Original Metadata" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/57c6aef2-5486-4321-8320-105e3efdd78d", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/57c6aef2-5486-4321-8320-105e3efdd78d/raw", - "has_download": true, - "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c874cd34ea221ce3ac1e0", - "keyword": [ - "Alabama", - "Arkansas", - "Florida", - "Georgia", - "Kentucky", - "Louisiana", - "Mississippi", - "Missouri", - "North Carolina", - "Oklahoma", - "South Carolina", - "Southeast United states", - "Tennessee", - "Texas", - "USGS:609c874cd34ea221ce3ac1e0", - "Virginia", - "West Virginia", - "farming", - "fires", - "geoscientificInformation", - "managed fire regimes", - "statistical downscaling", - "wildfires" - ], - "last_harvested_date": "2026-09-10T22:50:31.706960", - "organization": { - "aliases": [ - "dept" - ], - "code_repo_exempt": false, - "code_repo_url": null, - "description": null, - "id": "143529f7-2eef-4a07-b227-93ac9e84fad8", - "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png", - "name": "Department of the Interior", - "organization_type": "Federal Government", - "slug": "doi" - }, - "parent_identifier": null, - "popularity": 1, - "publisher": "U.S. Geological Survey", - "slug": "inmcm-historical-prescribed-burn-windows-for-the-southeast-united-states-1950-1999", - "spatial_centroid": { - "lat": 32.27966, - "lon": -90.73102 - }, - "spatial_shape": { - "coordinates": [ - [ - [ - -102.1475, - 25.0631 - ], - [ - -102.1475, - 43.1045 - ], - [ - -73.6063, - 43.1045 - ], - [ - -73.6063, - 25.0631 - ], - [ - -102.1475, - 25.0631 - ] - ] - ], - "type": "Polygon" - }, - "theme": [ - "geospatial" - ], - "title": "INMCM Historical Prescribed Burn Windows for the Southeast United States 1950-1999", - "type": "dataset" - }, - { - "_score": 10.532225, - "_sort": [ - 1789080630380, - 10.532225, - 1, - "94327df7-4187-40c9-a568-57f9fb932e1b" - ], - "dcat": { - "accessLevel": "public", - "bureauCode": [ - "010:12" - ], - "contactPoint": { - "@type": "vcard:Contact", - "fn": "Melanie Gogol-Prokurat", - "hasEmail": "mailto:Melanie.Gogol-Prokurat@wildlife.ca.gov" - }, - "description": "The East Tehama herd is the largest migratory population of mule deer in California (Hill and Figura, 2020). Population numbers peaked in the 1960s, but have declined in recent decades (Ramsey and others, 1981; California Department of Fish and Wildlife unpublished data). These mule deer migrate from a lower elevation winter range in the foothills east of the Sacramento Valley to upper elevation summer ranges in the southern Cascades and northern Sierra Nevada. Although portions of the herd winter on the California Department of Fish and Wildlife’s Tehama Wildlife Area and other public lands, the winter range also comprises many private ranchlands. The herd’s summer range includes significant portions of Lassen National Forest as well as Lassen Volcanic National Park and private timberlands. Primarily oak woodlands and annual grasslands characterize the winter range, while the summer range consists of conifer forests, montane meadows, and montane chaparral. Potential threats to the herd include habitat changes resulting from fire management (including fire suppression and catastrophic wildfires), forest succession, vegetation management, and climate change. A small percentage of the herd are residents, inhabiting areas along the Sacramento River and areas of irrigated agriculture.\nThese mapping layers show the location of the migration routes for mule deer (Odocoileus hemionus) in the East Tehama population in California. They were developed from 63 migration sequences collected from a sample size of 33 animals comprising GPS locations collected every 1-23 hours.", - "distribution": [ - { - "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P9LSKEZQ", - "description": "Landing page for access to the data", - "format": "XML", - "mediaType": "application/http", - "title": "Digital Data" - }, - { - "@type": "dcat:Distribution", - "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.63650bafd34ebe442507ce71.xml", - "format": "XML", - "mediaType": "text/xml", - "title": "Original Metadata" - } - ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_63650bafd34ebe442507ce71", - "keyword": [ - "California", - "Lassen National Forest", - "USGS:63650bafd34ebe442507ce71", - "United States", - "animal behavior", - "biota", - "migration (organisms)", - "migration route", - "migratory species" - ], - "modified": "2023-10-04T00:00:00Z", - "publisher": { - "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-122.2265, 39.7926, -120.6566, 40.7181", - "theme": [ - "geospatial" - ], - "title": "California Mule Deer East Tehama Routes" - }, - "description": "The East Tehama herd is the largest migratory population of mule deer in California (Hill and Figura, 2020). Population numbers peaked in the 1960s, but have declined in recent decades (Ramsey and others, 1981; California Department of Fish and Wildlife unpublished data). These mule deer migrate from a lower elevation winter range in the foothills east of the Sacramento Valley to upper elevation summer ranges in the southern Cascades and northern Sierra Nevada. Although portions of the herd winter on the California Department of Fish and Wildlife’s Tehama Wildlife Area and other public lands, the winter range also comprises many private ranchlands. The herd’s summer range includes significant portions of Lassen National Forest as well as Lassen Volcanic National Park and private timberlands. Primarily oak woodlands and annual grasslands characterize the winter range, while the summer range consists of conifer forests, montane meadows, and montane chaparral. Potential threats to the herd include habitat changes resulting from fire management (including fire suppression and catastrophic wildfires), forest succession, vegetation management, and climate change. A small percentage of the herd are residents, inhabiting areas along the Sacramento River and areas of irrigated agriculture.\nThese mapping layers show the location of the migration routes for mule deer (Odocoileus hemionus) in the East Tehama population in California. They were developed from 63 migration sequences collected from a sample size of 33 animals comprising GPS locations collected every 1-23 hours.", - "distribution_titles": [ - "Digital Data", - "Original Metadata" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/acefb6f8-998f-4b8c-9ee0-5be91c64d43b", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/acefb6f8-998f-4b8c-9ee0-5be91c64d43b/raw", - "has_download": true, - "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_63650bafd34ebe442507ce71", - "keyword": [ - "California", - "Lassen National Forest", - "USGS:63650bafd34ebe442507ce71", - "United States", - "animal behavior", - "biota", - "migration (organisms)", - "migration route", - "migratory species" - ], - "last_harvested_date": "2026-09-10T22:50:30.380896", - "organization": { - "aliases": [ - "dept" - ], - "code_repo_exempt": false, - "code_repo_url": null, - "description": null, - "id": "143529f7-2eef-4a07-b227-93ac9e84fad8", - "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png", - "name": "Department of the Interior", - "organization_type": "Federal Government", - "slug": "doi" - }, - "parent_identifier": null, - "popularity": 1, - "publisher": "U.S. Geological Survey", - "slug": "california-mule-deer-east-tehama-routes", - "spatial_centroid": { - "lat": 40.1628, - "lon": -121.59854 - }, - "spatial_shape": { - "coordinates": [ - [ - [ - -122.2265, - 39.7926 - ], - [ - -122.2265, - 40.7181 - ], - [ - -120.6566, - 40.7181 - ], - [ - -120.6566, - 39.7926 - ], - [ - -122.2265, - 39.7926 - ] - ] - ], - "type": "Polygon" - }, - "theme": [ - "geospatial" - ], - "title": "California Mule Deer East Tehama Routes", - "type": "dataset" - }, - { - "_score": 19.321571, - "_sort": [ - 1789080625632, - 19.321571, - 3, - "cdc0088e-dc4e-4856-a6fd-3a31b3141478" - ], - "dcat": { - "accessLevel": "public", - "bureauCode": [ - "010:12" - ], - "contactPoint": { - "@type": "vcard:Contact", - "fn": "Melinda Martinez", - "hasEmail": "mailto:melindamartinez@usgs.gov" - }, - "description": "Data shows CH4 fluxes from the upper portion of cypress knees across various climate and flooding gradients of the North American Baldcypress Swamp Network in the Mississippi River Alluvial Valley. Climate data in the form of temperature, relative humidity, barometric pressure, and precipitation 3-days leading up to sampling date are also included. There various forms to calculate fluxes using cone and frustrum shapes that were compared to LiDAR scans fromi the field, therefore surface area and volume for each geometric shape is also included in the dataset.", - "distribution": [ - { - "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P164M78X", - "description": "Landing page for access to the data", - "format": "XML", - "mediaType": "application/http", - "title": "Digital Data" - }, - { - "@type": "dcat:Distribution", - "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.6647ad62d34e1955f5a4418e.xml", - "format": "XML", - "mediaType": "text/xml", - "title": "Original Metadata" - } - ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6647ad62d34e1955f5a4418e", - "keyword": [ - "Arkansas", - "Illinois", - "Louisiana", - "USGS:6647ad62d34e1955f5a4418e", - "carbon", - "cypress knees", - "cypress swamp", - "environment", - "freshwater wetlands", - "methane" - ], - "modified": "2024-06-18T00:00:00Z", - "publisher": { - "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-92.6367, 29.7644, -87.9785, 37.9269", - "theme": [ - "geospatial" - ], - "title": "Methane emissions associated with bald cypress knees across the Mississippi River Alluvial Valley" - }, - "description": "Data shows CH4 fluxes from the upper portion of cypress knees across various climate and flooding gradients of the North American Baldcypress Swamp Network in the Mississippi River Alluvial Valley. Climate data in the form of temperature, relative humidity, barometric pressure, and precipitation 3-days leading up to sampling date are also included. 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Robust validation through space and time is needed to quantify product accuracy. We leverage field data observed concurrently with HRS imagery over multiple years and locations in the Western U.S. to dramatically expand the spatial extent and sample size of validation analysis relative to a direct comparison to field observations and to previous work. We compare HRS and BIT data in the corresponding space and time. Our objectives were to evaluate the temporal and spatio-temporal relationships between HRS and BIT data, and to compare their response to spatio-temporal variation in climate. We hypothesize that strong temporal and spatio-temporal relationships will exist between HRS and BIT data and that they will exhibit similar climate response. We evaluated a total of 42 HRS sites across the western U.S. with 32 sites in Wyoming, and 5 sites each in Nevada and Montana. HRS sites span a broad range of vegetation, biophysical, climatic, and disturbance regimes. Our HRS sites were strategically located to collectively capture the range of biophysical conditions within a region. Field data were used to train 2-m predictions of fractional component cover at each HRS site and year. The 2-m predictions were degraded to 30-m, and some were used to train regional Landsat-scale, 30-m, “base” maps of fractional component cover representing circa 2016 conditions. A Landsat-imagery time-series spanning 1985-2018, excluding 2012, was analyzed for change through time. Pixels and times identified as changed from the base were trained using the base fractional component cover from the pixels identified as unchanged. Changed pixels were labeled with the updated predictions, while the base was maintained in the unchanged pixels. The resulting BIT suite includes the fractional cover of the six components described above for 1985-2018. We compare the two datasets, HRS and BIT, in space and time.\nTwo tabular data presented here correspond to a temporal and spatio-temporal validation of the BIT data. First, the temporal data are HRS and BIT component cover and climate variable means by site by year. Second, the spatio-temporal data are HRS and BIT component cover and associated climate variables at individual pixels in a site-year.", - "distribution": [ - { - "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P90Q8BCP", - "description": "Landing page for access to the data", - "format": "XML", - "mediaType": "application/http", - "title": "Digital Data" - }, - { - "@type": "dcat:Distribution", - "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5de7de8fe4b02caea0eb9917.xml", - "format": "XML", - "mediaType": "text/xml", - "title": "Original Metadata" - } - ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5de7de8fe4b02caea0eb9917", - "keyword": [ - "Central Basin and Range", - "MT", - "Montana", - "NV", - "Nevada", - "Northern Basin and Range", - "Northwestern Great Plains", - "Southern Rockies", - "USGS:5de7de8fe4b02caea0eb9917", - "United States", - "WY", - "Wyoming", - "Wyoming Basin", - "annual herbaceous", - "back-in-time", - "bare ground", - "big sagebrush", - "biota", - "farming", - "fractional components", - "geoscientificInformation", - "grass", - "herbaceous", - "litter", - "rangeland", - "rangelands", - "remote sensing", - "sagebrush", - "shrub", - "shrubland", - "shrubland ecosystems", - "shrublands", - "terrestrial ecosystems", - "time-series", - "validation", - "vegetation" - ], - "modified": "2020-08-18T00:00:00Z", - "publisher": { - "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-119.9472, 39.7848, -104.2109, 46.8470", - "theme": [ - "geospatial" - ], - "title": "Temporal and Spatio-Temporal High-Resolution Satellite Data for the Validation of a Landsat Time-Series of Fractional Component Cover Across Western United States (U.S.) Rangelands" - }, - "description": "Western U.S. rangelands have been quantified as six fractional cover (0-100%) components over the Landsat archive (1985-2018) at 30-m resolution, termed the “Back-in-Time” (BIT) dataset. 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Our HRS sites were strategically located to collectively capture the range of biophysical conditions within a region. Field data were used to train 2-m predictions of fractional component cover at each HRS site and year. The 2-m predictions were degraded to 30-m, and some were used to train regional Landsat-scale, 30-m, “base” maps of fractional component cover representing circa 2016 conditions. A Landsat-imagery time-series spanning 1985-2018, excluding 2012, was analyzed for change through time. Pixels and times identified as changed from the base were trained using the base fractional component cover from the pixels identified as unchanged. Changed pixels were labeled with the updated predictions, while the base was maintained in the unchanged pixels. The resulting BIT suite includes the fractional cover of the six components described above for 1985-2018. We compare the two datasets, HRS and BIT, in space and time.\nTwo tabular data presented here correspond to a temporal and spatio-temporal validation of the BIT data. First, the temporal data are HRS and BIT component cover and climate variable means by site by year. Second, the spatio-temporal data are HRS and BIT component cover and associated climate variables at individual pixels in a site-year.", - "distribution_titles": [ - "Digital Data", - "Original Metadata" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/7bde2c73-e23a-43a1-a93e-16ff8e4185ad", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/7bde2c73-e23a-43a1-a93e-16ff8e4185ad/raw", - "has_download": true, - "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5de7de8fe4b02caea0eb9917", - "keyword": [ - "Central Basin and Range", - "MT", - "Montana", - "NV", - "Nevada", - "Northern Basin and Range", - "Northwestern Great Plains", - "Southern Rockies", - "USGS:5de7de8fe4b02caea0eb9917", - "United States", - "WY", - "Wyoming", - "Wyoming Basin", - "annual herbaceous", - "back-in-time", - "bare ground", - "big sagebrush", - "biota", - "farming", - "fractional components", - "geoscientificInformation", - "grass", - "herbaceous", - "litter", - "rangeland", - "rangelands", - "remote sensing", - "sagebrush", - "shrub", - "shrubland", - "shrubland ecosystems", - "shrublands", - "terrestrial ecosystems", - "time-series", - "validation", - "vegetation" - ], - "last_harvested_date": "2026-09-10T22:50:25.159524", - "organization": { - "aliases": [ - "dept" - ], - "code_repo_exempt": false, - "code_repo_url": null, - "description": null, - "id": "143529f7-2eef-4a07-b227-93ac9e84fad8", - "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png", - "name": "Department of the Interior", - "organization_type": "Federal Government", - "slug": "doi" - }, - "parent_identifier": null, - "popularity": 4, - "publisher": "U.S. Geological Survey", - "slug": "temporal-and-spatio-temporal-high-resolution-satellite-data-for-the-validation-of-a-landsa", - "spatial_centroid": { - "lat": 42.60968, - "lon": -113.65267999999999 - }, - "spatial_shape": { - "coordinates": [ - [ - [ - -119.9472, - 39.7848 - ], - [ - -119.9472, - 46.847 - ], - [ - -104.2109, - 46.847 - ], - [ - -104.2109, - 39.7848 - ], - [ - -119.9472, - 39.7848 - ] - ] - ], - "type": "Polygon" - }, - "theme": [ - "geospatial" - ], - "title": "Temporal and Spatio-Temporal High-Resolution Satellite Data for the Validation of a Landsat Time-Series of Fractional Component Cover Across Western United States (U.S.) Rangelands", - "type": "dataset" - }, - { - "_score": 26.95806, - "_sort": [ - 1789080624715, - 26.95806, - 4, - "df046ac9-912d-45b8-bf74-2d4f5ec1bc76" - ], - "dcat": { - "accessLevel": "public", - "bureauCode": [ - "010:12" - ], - "contactPoint": { - "@type": "vcard:Contact", - "fn": "Kathi Jo Jankowski", - "hasEmail": "mailto:kjankowski@usgs.gov" - }, - "description": "This dataset includes average, and annual average (e.g., average of 2020) watershed characteristics and environmental driver data for 189 rivers across the Northern Hemisphere. Average data includes lithology (e.g., percent of watershed covered by volcanics), land use (e.g., percent of watershed covered by cropland), maximum day length, median nitrogen and phosphorus concentrations, maximum watershed proportion of snow covered area, precipitation, temperature, evapotranspiration, green-up day, net primary productivity, 5th percentile discharge, 95th percentile discharge, day of minimum discharge, day of maximum discharge, and coefficient of variation of discharge. Average data includes maximum watershed proportion of snow covered area, precipitation, temperature, evapotranspiration, green-up day, net primary productivity, 5th percentile discharge, 95th percentile discharge, day of minimum discharge, day of maximum discharge, and coefficient of variation of discharge. Land use, lithology, snow covered area, precipitation, temperature, evapotranspiration, green-up day, and net primary productivity were sourced from public, globally available spatial datasets. Nitrogen, phosphorus, and discharge data were sourced from public and/or published datasets. Watershed characteristics and environmental variables were used in a series of random forest models to assess the drivers of 1) average fluvial silicon concentration regime; 2) annual fluvial silicon concentration regime; and 3) the minimum and maximum silicon concentrations within a given regime.", - "distribution": [ - { - "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P14NBAYZ", - "description": "Landing page for access to the data", - "format": "XML", - "mediaType": "application/http", - "title": "Digital Data" - }, - { - "@type": "dcat:Distribution", - "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.6684087fd34e0f592272b3da.xml", - "format": "XML", - "mediaType": "text/xml", - "title": "Original Metadata" - } - ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6684087fd34e0f592272b3da", - "keyword": [ - "Canada", - "Finland", - "Norway", - "Russia", - "Sweden", - "USGS:6684087fd34e0f592272b3da", - "United States", - "biogeochemistry", - "regime", - "river", - "silicon", - "spatial datasets", - "watershed characteristics" - ], - "modified": "2024-08-29T00:00:00Z", - "publisher": { - "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-180.0000, 15.9613, 177.8906, 76.5168", - "theme": [ - "geospatial" - ], - "title": "Average and annual watershed climate, hydrology, and productivity data for rivers across the Northern Hemisphere" - }, - "description": "This dataset includes average, and annual average (e.g., average of 2020) watershed characteristics and environmental driver data for 189 rivers across the Northern Hemisphere. Average data includes lithology (e.g., percent of watershed covered by volcanics), land use (e.g., percent of watershed covered by cropland), maximum day length, median nitrogen and phosphorus concentrations, maximum watershed proportion of snow covered area, precipitation, temperature, evapotranspiration, green-up day, net primary productivity, 5th percentile discharge, 95th percentile discharge, day of minimum discharge, day of maximum discharge, and coefficient of variation of discharge. Average data includes maximum watershed proportion of snow covered area, precipitation, temperature, evapotranspiration, green-up day, net primary productivity, 5th percentile discharge, 95th percentile discharge, day of minimum discharge, day of maximum discharge, and coefficient of variation of discharge. Land use, lithology, snow covered area, precipitation, temperature, evapotranspiration, green-up day, and net primary productivity were sourced from public, globally available spatial datasets. Nitrogen, phosphorus, and discharge data were sourced from public and/or published datasets. Watershed characteristics and environmental variables were used in a series of random forest models to assess the drivers of 1) average fluvial silicon concentration regime; 2) annual fluvial silicon concentration regime; and 3) the minimum and maximum silicon concentrations within a given regime.", - "distribution_titles": [ - "Digital Data", - "Original Metadata" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/5c3947aa-4271-484c-97fc-0bd95a5ee7d5", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/5c3947aa-4271-484c-97fc-0bd95a5ee7d5/raw", - "has_download": true, - "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6684087fd34e0f592272b3da", - "keyword": [ - "Canada", - "Finland", - "Norway", - "Russia", - "Sweden", - "USGS:6684087fd34e0f592272b3da", - "United States", - "biogeochemistry", - "regime", - "river", - "silicon", - "spatial datasets", - "watershed characteristics" - ], - "last_harvested_date": "2026-09-10T22:50:24.715318", - "organization": { - "aliases": [ - "dept" - ], - "code_repo_exempt": false, - "code_repo_url": null, - "description": null, - "id": "143529f7-2eef-4a07-b227-93ac9e84fad8", - "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png", - "name": "Department of the Interior", - "organization_type": "Federal Government", - "slug": "doi" - }, - "parent_identifier": null, - "popularity": 4, - "publisher": "U.S. Geological Survey", - "slug": "average-and-annual-watershed-climate-hydrology-and-productivity-data-for-rivers-across-the", - "spatial_centroid": { - "lat": 40.1835, - "lon": -36.843759999999996 - }, - "spatial_shape": { - "coordinates": [ - [ - [ - -180.0, - 15.9613 - ], - [ - -180.0, - 76.5168 - ], - [ - 177.8906, - 76.5168 - ], - [ - 177.8906, - 15.9613 - ], - [ - -180.0, - 15.9613 - ] - ] - ], - "type": "Polygon" - }, - "theme": [ - "geospatial" - ], - "title": "Average and annual watershed climate, hydrology, and productivity data for rivers across the Northern Hemisphere", - "type": "dataset" - }, - { - "_score": 9.048394, - "_sort": [ - 1789080622725, - 9.048394, - 1, - "b616e77f-77c3-43b2-8b2a-5f814d655755" - ], - "dcat": { - "accessLevel": "public", - "bureauCode": [ - "010:12" - ], - "contactPoint": { - "@type": "vcard:Contact", - "fn": "PCMSC Science Data Coordinator", - "hasEmail": "mailto:pcmsc_data@usgs.gov" - }, - "description": "This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated.\nThe Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.\n ", - "distribution": [ - { - "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P9NUO62B", - "description": "Landing page for access to the data", - "format": "XML", - "mediaType": "application/http", - "title": "Digital Data" - }, - { - "@type": "dcat:Distribution", - "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5d0412ebe4b0e3d3115807a2.xml", - "format": "XML", - "mediaType": "text/xml", - "title": "Original Metadata" - } - ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5d0412ebe4b0e3d3115807a2", - "keyword": [ - "Beaches", - "Central California", - "Central California Coast", - "Climate Change", - "ClimatologyMeteorologyAtmosphere", - "Erosion", - "Extreme Weather", - "Floods", - "Hazards Planning", - "Ocean Waves", - "Ocean Winds", - "Oceans", - "Physical Habitats and Geomorphology", - "San Luis Obispo County", - "Sea Level Rise", - "Sea-level Change", - "State of California", - "Storm Surge", - "Storms", - "USGS:5d0412ebe4b0e3d3115807a2", - "Water Depth", - "Wind", - "coastal erosion", - "earth sciences", - "effects of climate change", - "floods", - "mathematical modeling", - "sea level change", - "waves" - ], - "modified": "2026-03-26T00:00:00Z", - "publisher": { - "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-122.641953027, 34.403744888, -120.444512138, 37.819520138", - "theme": [ - "geospatial" - ], - "title": "San Luis Obispo County: CoSMoS v3.1 Central California flood hazard projections: 20-year storm" - }, - "description": "This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated.\nThe Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. 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Raw counts of pollen data are provided. These data represent new counts from a core collected by Donald R. 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This dataset has been collecting data every 15 minutes with the goal to provide context for ripple and dune migration at an active dune field site.", - "distribution": [ - { - "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P93T2LUL", - "description": "Landing page for access to the data", - "format": "XML", - "mediaType": "application/http", - "title": "Digital Data" - }, - { - "@type": "dcat:Distribution", - "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.62fe7a6cd34e3a4442875b5b.xml", - "format": "XML", - "mediaType": "text/xml", - "title": "Original Metadata" - } - ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_62fe7a6cd34e3a4442875b5b", - "keyword": [ - "Arizona", - "Grand Falls", - "Navajo Indian Reservation", - "USGS:62fe7a6cd34e3a4442875b5b", - "Wind", - "air temperature", - "climate data", - "climatologyMeteorologyAtmosphere", - "geoscientificInformation", - "meteorological data", - "relative humidity" - ], - "modified": "2022-08-23T00:00:00Z", - "publisher": { - "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-111.1681, 35.4345, -111.1680, 35.4346", - "theme": [ - "geospatial" - ], - "title": "Meteorological data at Grand Falls dune field, Arizona, collected from April 2021 to December 2021." - }, - "description": "A meteorological station equipped with a rain gauge, atmospheric pressure sensor, temperature and relative humidity sensor, soil moisture sensor, and an anemometer (measuring wind speed, gust speed, and direction) was deployed at Grand Falls dune field, Arizona. 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The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the periods 2020-59 (centered in the year 2040) and 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. \nAn areal reduction factor (ARF) is computed to convert rainfall statistics of a point, such as at a weather station, to an area, such as a watershed or model grid cell. Regions considered for the development of change factors as part of this study study are taken from NOAA National Center for Environmental Information (NCEI) U.S. Climate Divisions for the state of Florida with some modifications in south Florida. A Microsoft Excel workbook is provided which tabulates areal reduction factors (ARF) by ARF region, event duration, and model grid-cell area. The ARF were developed for each ARF region based on the PRISM gridded precipitation dataset for Florida. 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A Microsoft Excel workbook is provided which tabulates areal reduction factors (ARF) by ARF region, event duration, and model grid-cell area. The ARF were developed for each ARF region based on the PRISM gridded precipitation dataset for Florida. 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Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.\n ", - "distribution": [ - { - "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P9NUO62B", - "description": "Landing page for access to the data", - "format": "XML", - "mediaType": "application/http", - "title": "Digital Data" - }, - { - "@type": "dcat:Distribution", - "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5d8a6478e4b0c4f70d0ae750.xml", - "format": "XML", - "mediaType": "text/xml", - "title": "Original Metadata" - } - ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5d8a6478e4b0c4f70d0ae750", - "keyword": [ - "Beaches", - "CMHRP", - "Central California", - "Central California Coast", - "Climate Change", - "ClimatologyMeteorologyAtmosphere", - "Coastal and Marine Hazards and Resources Program", - "Erosion", - "Extreme Weather", - "Floods", - "Hazards Planning", - "Ocean Waves", - "Ocean Winds", - "Oceans", - "PCMSC", - "Pacific Coastal and Marine Science Center", - "Physical Habitats and Geomorphology", - "San Francisco County", - "Sea Level Rise", - "Sea-level Change", - "State of California", - "Storm Surge", - "Storms", - "U.S. Geological Survey", - "USGS", - "USGS:5d8a6478e4b0c4f70d0ae750", - "Water Depth", - "Wind", - "coastal erosion", - "earth sciences", - "effects of