Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data

Attribute table for merged rasters

Technical validation data

Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

Ecological modeling requires sufficient spatial resolution and a careful selection of environmental variables to achieve good predictive performance. Although national and international administrations offer fine-scale environmental data, they usually have limited spatial coverage (country or continent). Alternatively, optical and radar satellite imagery is available with high resolutions, global coverage and frequent revisit intervals. Here, we compared the performance of ecological models trained with free satellite data with models fitted using regionally restricted spatial datasets. We developed brown bear habitat suitability and connectivity models from three datasets with different spatial coverage and accessibility. These datasets comprised (1) a Sentinel-1 and 2 land cover map (global coverage); (2) pan-European vegetation and land cover layers (continental coverage); and (3) LiDAR data and the Forest Map of Spain (national coverage). Results show that Sentinel imagery and pan-European datasets are powerful sources to estimate vegetation variables for habitat and connectivity modeling. However, Sentinel data could be limited for understanding precise habitat–species associations if the derived discrete variables do not distinguish a wide range of vegetation types. Therefore, more effort should be taken to improving the thematic resolution of satellite-derived vegetation variables. Our findings support the application of ecological modeling worldwide and can help select spatial datasets according to their coverage and resolution for habitat suitability and connectivity modeling.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

Available Layers: Reference Grid, Global Land Cover, Climate and Ecological Zones, Soil Default C-Stocks, Land Use System Factor(FLUSYS), IFPRI Global Regions, Crop Surface Area . The data made available on these pages are referred to in the Guide for Calculation of Carbon Stock Changes in Soil and Above and Below Ground Vegetation due to Land Use Conversion, which was prepared in support to Commission Decision C(2010) 3751). The thematic spatial layers complement the data already published on the reference climate region and soil type classification. 1. BASE DATA The basic data cover the layers needed to set the framework for the spatial analysis of the land use change analysis. Details on the characteristics of the spatial layers are provided in Table 1. Table 1: Technical Specifications of Spatial Data Layers FEATURE VALUE Data type 16-bit integer File type binary No. of columns 4320 No of rows 2160 Reference system ETRS89 Reference units Degrees Min. x coordinate -180.00 Max. x coordinate 180.00 Min. y coordinate -90.00 Max. y coordinate 90.00 1.1 Reference Grid The reference grid defines the common spatial layer specifications and specifies for position of a grid cell on land a unique identifier (ID) between 330053 and 7561355. The grid resolution was set to a regular size of 5 arc minutes (0.083333 deg). This grid spacing corresponds to approx. 10km at the equator. 1.2 Grid Cell Area The reference grid layer is also used to define the land-sea mask applied to all layers of the series. In all layers the area covered by Antarctica was not included. For each grid cell the surface area is represented in a separate layer. Since the layers are not projected the surface area changes with latitude. This uneven weight in computations related to areas with latitude has to be considered in calculations for changes in land use classes. For convenience the area layer is therefore provided. 2. THEMATIC DATA The data forming the set of thematic layers define the input information for the computation of the default values to calculate GHG emissions from changes in soil C-stocks according to the factors defined in the Guide (Carré, et al., 2010) . 2.1 Land Cover The land cover layer compromises a merge between data from the GlobCover project (Version 2.2, released 10.12.2008; Bicheron et al., 2008), and the McGill University M3-Cropland data (Ramankutty et al., 2008. The land cover classes were aligned to correspond to the specifications of the RED: Open Forest, < 30% cover Closed Forest, >= 30 % cover Cropland Grassland Shrub Sparse vegetation Wetland Artificial areas Other land areas The layers contain for each grid the relative proportion of the land cover type at the resolution of 5 arc min.. 2.2 Ecological Zones In addition to the climatic regions a layer containing ecological zones was defined. The definition of the ecological zones is described in Chapter 4 – Forest Land of the 2006 IPCC Guidelines for National Greenhouse Gas Inventories rather than in Chapter 3 – Consistent Representation of Lands. The map of global ecological zones given in Figure 4.1 of the report originates from Global Forests Resources Assessment 2000 (FAO, 2001), FRA2000. Spatial layers of ecological zones and domains can be downloaded from the FAO GeoNetwork server. The definition of the ecological zones is described in Table 4.1 (IPCC, 2006). To maintain compatibility with the Climate Region map a spatial layer of Ecological Zones was generated with the minimum of modifications. The Ecological Zone data is therefore only an approximation of the FAO map on Global Ecological Zones. The main difference in the definition of the ecological zones between the two maps is the use of only climatic data to guide the classification in the study data and not incorporate information on the vegetation pattern. This difference is of some significance because the layer is employed to map the carbon estimates in above and below ground vegetation by land cover type. This leads to some of the ecological zones not being present in the layer. 2.3 Soil Default C-Stocks From the combination of the soil classes with the climate regions the default reference soil organic C-stocks can be generated. The corresponding layer provides the soil organic C-stocks in a depth of 0-30 cm in t C ha-1 for mineral soil types. The C-stocks were calculated from the Harmonized World Soil Database (HWSD) V. 1.1, using information from all typological units. Areas missing in the HWSD were substituted from the FAO-UNESCO Soil Map of the World. 2.4 Land Use System Factor In the approach used the variation of soil C-stocks from the default value are governed by the Land Use System Factor (FLUSYS). The FLUSYS is a combination of the land use type, (FLU) management system (FMGM) and input (FI). A FLUSYS of 1 is applied to all native ecosystems and non-degraded grassland with nominal management. For cultivated areas, including areas of set-aside, the nominal value may deviate from 1, depending on the management practice and input factors. Spatial layers of the FLUSYS were thus generated for to following land use types: Cultivated, annual Cultivated, perennial Rice, paddy Set-aside Grassland Where cropland expands to areas previously without cropland the FLUSYS of the neighbouring land can be applied. The potential FLUSYS of these areas was estimated from the reference data using an expansion function based on the inverse distance. Since the land use types “rice, paddy” and “set-aside” only have a single value for the FLUSYS the expansion was only performed for the annual and perennial land use type layers. 3. PROJECT DATA Data needed to evaluate the output from an economic model for GHG emissions from ILUC can be specific to that model. The main areas of variations concern the definition of the economic regions and the crops or groups of crops used. It should be noted that the composition of the groups of crops used in the economic models are not necessarily identical to those of the crop groups defined in the ancillary spatial data. Where needed, crop groups of sugar or oil crops can be generated from corresponding individual crops. 3.1 IFPRI Global Regions The International Food Policy Research Institute, Washington (IFPRI) evaluates the area needs for biofuels based on a set of economic scenarios. Estimates are provided by global economic region. The attribution of countries to an economic region as used in the project is given in this layer. There can be areas with a different attribution in the layer from the one used by IFPRI, such as French Guyana or some of the disputed regions in Asia and Africa. In Europe the attribution of the areas covered by the Former Republic of Yugoslavia were assigned to the rest of the World (RoWorld). 3.2 Crop Surface Area The proportional surface area of crops used in the economic models was derived from the harvested area of the McGill University M3-Crops data (Monfreda, et al., 2008). The conversion of harvested to proportional surface area was based on the estimation of multi-cropping systems. Spatial layers for the following crops were generated: Wheat Grain Maize Rice Sugar beet Sugarcane Oil palm Rapeseed Soybean Sunflower Vegetables & Fruit Other crops Rest The crop "Other Crops" includes any crops not otherwise covered by a specific crop type. The layer “Rest” accounts for the difference in area between the M3-Cropland data (Ramakutty, et al., 2008) and the sum of the crop group area. Bibliography Bicheron P., P. Defourny, C. Brockmann, L. Schouten, C. Vancutsem, M. Huc, S. Bontemps, M. Leroy, F. Achard, M. Herold, F. Ranera and O. Arino (2008) CLOBCOVER: Products Description and Validation Report. MEDIAS France, 18, avenue E. Belin, bpi 2102, 31401 Toulouse Cedex 9, France. 47pp GLOBCOVER_Products_Description_Validation_Report_I2.1.pdf FAO (2001) Global Forest Resources Assessment 2000. FAO, Rome. 479pp. FAO/IIASA/ISRIC/ISSCAS/JRC (2009) Harmonized World Soil Database (version 1.1). FAO, Rome, Italy and IIASA, Laxenburg, Austria. Intergovernmental Panel on Climate Change (IPCC) (2006) 2006 Guidelines for National Greenhouse Gas Inventories. Eggelstone, S., L. Buemdia, K. Miwa, T. Ngara and K. Tanabe (Eds.). IPCC/OECD/IEA/IGES, Hayama, Japan Monfreda C., N. Ramankutty and J. Foley (2008) Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biochemical Cycles, Vol. 22, GB1022, doi : 10.1029/ 2007GB002952 Official Journal L140, 05.06.2009, p. 16-62. Official Journal L151, 17.06.2010, p. 19-41. Ramankutty, N., A. T. Evan, C. Monfreda, and J. A. Foley, 2008. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochemical Cycles, Vol. 22, GB1003, doi:10.1029/2007GB002952.

In June 2002, 200 1:20,000 scale conventional-color metric film diapositives for Long Island, New York were collected as part of an effort to map submerged aquatic vegetation (SAV) in Long Island's South Shore bays. They were provided by New York State Department of State's Division of Coastal Resources. Photographs were taken at low tide and during times that the growth stage of the SAV allowed for clear identification. Care was taken to minimize the effects of turbidity, sun glint, wind, and haze on the photos. The photos were scanned at a resolution of 15 microns. Ground control points were collected primarily from NYSDS 2 ft orthophotos. Additional control points were collected from USGS DOQQs where coverage from the primary source was lacking. All elevations were derived from USGS digital elevation models. A bundle block adjustment was performed using Albany and exterior orientation parameters were calculated. Boeing/Autometric's Softplotter was used to orthorectify the photos. The images were then dodged and mosaicked using Z/I's Orthopro. No additional color-balancing was performed as the mosaic's intended purpose was the delineation of benthic habitats. The mosaic was then output into 1000m by 1000m tiles with a 0.5m pixel resolution. The naming convention uses the first 3 numbers of the UTM x coordinate followed by the first 4 numbers in the UTM y coordinate of the southwest corner. Stereo digital images were created and the habitat features were interpreted and digitized on screen using softplotter microstation resulting in accurate and efficient 3D extraction of the data. Habitats were delineated with a high level of detail with the minimum mapping unit (MMU) being 0.01 hectares (approx.10m x 10m).The digitized linework have the following specifications: Vertex Distance less than 1.0 m Node Snap Distance less than 4.0 m Arc Snap Distance less than 4.0 m In some cases differences in color and texture between adjacent features tend to be more subtle and boundaries more difficult to detect. In these areas the boundary was delineated using the best possible line between points where the edge can be reliably determined. This line was attributed as confidence level "LOW" in the line coverage. The "low confidence" lines were found mainly in the deep water areas of the data set although they were found occasionally in areas of high turbidity. In cases where a clear determination of a habitat boundary was made with confidence, that line was given the attribute "HIGH". The items are stored in the attribute table under 'Confidence'. This data was delivered as '2002 Long Island South Shore Estuary Benthic Habitat Line Data Set '. The collected data was converted to an ARCGIS format for quality control and delivery. The data was assessed for horizontal spatial accuracy and thematic agreement during 2003. This is a companion dataset to the polygon coverage '2002 Long Island South Shore Estuary Benthic Habitat Polygon Data Set '.

This dataset is the first 1: 100,000 desert spatial database in China based on the graphic data of desert thematic maps. It mainly reflects the geographical distribution, area size, and mobility of sand dunes in China. According to the system design requirements and relevant standards, the input data is standardized and uniformly converted into a standard format for various types of data input. Build a library to run the delivery system. This project uses the TM image in 2000 as the information source, and interprets, extracts, and edits the coverage of the national land use map and TM digital image information in 2000. It uses remote sensing and geographic information system technology to 1: 100,000 Thematic mapping requirements for scale bar maps were made on the desert, sandy land and gravel Gobi in China. The 1: 100,000 desert map across the country can save users a lot of data entry and editing work when they are engaged in research on resources and the environment. Digital maps can be easily converted into layout maps The dataset properties are as follows: Divided into two folders e00 and shp: Desert map name and province comparison table in each folder 01 Ahsm Anhui 02 Bjsm Beijing 03 Fjsm Fujian 04 Gdsm Guangdong 05 Gssm Gansu 06 Gxsm Guangxi Zhuang Autonomous Region 07 Gzsm Guizhou 08 Hebsm Hebei 09 Hensm Henan 10 Hljsm Heilongjiang 11 Hndsm Hainan 12 Hubsm Hubei 13 Jlsm Jilin Province 14 Jssm Jiangsu 15 Jxsm Jiangxi 16 Lnsm Liaoning 17 Nmsm Inner Mongolia Gu Autonomous Region 18 Nxsm Ningxia Hui Autonomous Region 19 Qhsm Qinghai 20 Scsm Sichuan 21 Sdsm Shandong 22 Sxsm Shaanxi Province 23 Tjsm Tianjin 24 Twsm Taiwan Province 25 Xjsm Xinjiang Uygur Autonomous Region 26 Xzsm Tibet Autonomous Region 27 Zjsm Zhejiang 28 Shxsm Shanxi 1. Data projection: Projection: Albers False_Easting: 0.000000 False_Northing: 0.000000 Central_Meridian: 105.000000 Standard_Parallel_1: 25.000000 Standard_Parallel_2: 47.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) 2. Data attribute table: area (area) perimeter ashm_ (sequence code) class (desert encoding) ashm_id (desert encoding) 3. Desert coding: mobile sandy land 2341010 Semi-mobile sandy land Semi-fixed sandy land 2341030 Gobi 2342000 Saline land 2343000 4: File format: National, sub-provincial and county-level desert map data types are vector shapefiles and E00 5: File naming: Data organization based on the National Basic Resources and Environmental Remote Sensing Dynamic Information Service System is performed on the file management layer of Windows NT. The file and directory names are compound names of English characters and numbers. Pinyin + SM composition, such as the desert map of Gansu Province is GSSM. The flag and county desert map is the pinyin + xxxx of the province name, and xxxx is the last four digits of the flag and county code. The division of provinces, districts, flags and counties is based on the administrative division data files in the national basic resources and environmental remote sensing dynamic information service operation system.

Overview: era5.copernicus: air temperature daily averages from 2000 to 2020 resampled with CHELSA to 1 km resolution

Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies.

Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA.

Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.

Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields.

Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021).

Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset.

Consistency: 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.

Positional accuracy: 1 km spatial resolution

Temporal accuracy: Daily maps for the years 2020-2020.

Thematic accuracy: The raster values represent minimum, mean, and maximum daily air temperature 2m above ground in degrees Celsius x 10.

This data release contains records from research focused on understanding social vulnerability to water insecurity, resiliency demonstrated by institutions, and conflict or crisis around water resource management. This data release focuses on social vulnerability to water insecurity. The data is derived from a meta-analysis of studies in the empirical literature which measured factors of social vulnerability associated with conditions of water insecurity. In the water security context this data and associated study identify the indicators used to measure social vulnerability, the frequency at which indicators are used, and the uncertainty associated with measurements based on those indictors. Assessed studies were published between 2000 and 2022 and covered states of the conterminous U.S. located west of the Mississippi River. This meta-analysis is published as ‘Social vulnerability and water insecurity in the western US: A systematic review of framings, indicators, and uncertainty’. It is part of the Social and Economic Drivers Program’s ‘Measuring Intended and Unintended Effects of Water Management Decisions’ study. The data was gathered to provide baseline metrics supporting the development of a set of indicators describing vulnerability of key water-use sectors (agricultural and municipal) to conditions of water insecurity (including concerns of water quality, quantity, and access to the resource). This includes understanding the inherent vulnerabilities of populations dependent on these water-use sectors as well as those decision-making processes that can exacerbate vulnerabilities. This data may further be used to validate social vulnerability metrics, provide the basis from which sociodemographic data can be integrated into models of water use and demand, and improve models of susceptibility to water-related hazards including drought and floods. The data release contains six (6) related datasets and their associated metadata: Papers: Contains bibliographic data and abstract for each scientific paper included in the meta-analysis. Each entry represents a unique model of social vulnerability to water insecurity. In cases where a scientific paper included multiple models that produced different associations between social vulnerability and water insecurity, the paper is recorded separately for each unique model. Literature Results Summary of Indicators of social vulnerability to water insecurity in the Western US 2000-2022: Contains a high-level overview showing how each paper was classified. The table identifies the water-use sector of focus, thematic issue of water security covered, study location, spatial scale, dimension (thematic category) of social vulnerability covered, the determinants (attributes) of social vulnerability measured, and a count of the number of times each social vulnerability determinant (attribute) was measured. Aggregated indicators of social vulnerability to water insecurity in the Western US 2000-2022: For each model studied this table records: the dimensions (thematic category) of social vulnerability covered, the determinants (attributes) of social vulnerability assessed, aggregated indicators (variables) used to measure individual components of each determinant, and a count of the number of individual variables used to measure each aggregated indicator (e.g., the aggregated indicator ‘Dependents’ may be measured by specific indicators for the population aged below 18 years as well as the population above 65 years). Sector Summary of social vulnerability to water insecurity in the Western US 2000-2022: For each determinant (attribute) of social vulnerability assessed, this table presents a summary of the number of indicators measured and number of papers (studies) including those indicators in both the agricultural and municipal water-use sectors. Uncertainty Summary by Determinant of social vulnerability to water insecurity in the Western US 2000-2022: Provides a high-level summary of the amount of evidence available and agreement in the literature for the direction of influence associated with each determinant of social vulnerability found in the meta-analysis. Uncertainty Summary of social vulnerability to water insecurity in the Western US 2000-2022: For each aggregated indicator assessed, this table provides counts of the number of models in the meta-analysis for which specific relationships (positive, negative, no relationship or for which the directionality could not be determined) to conditions of water insecurity were identified. The strength of these relationships is indicated by a count of the number of models recording them. The table also provides an indication of the levels of evidence and agreement between models.

The Map based index (GeoIndex) provides a map based index to datasets that BGS have collected or have obtained from a wide variety of sources. The site allows users to search for information about BGS data collections covering the UK and other areas worldwide. Access is free, the interface is easy to use, and it has been developed to enable users to check coverage of different types of data and find out some background information about the data. The index shows the extents of available dataset coverage in Great Britain and the surrounding seas, drawn against a topographical map background. The spatial data are grouped into themes holding related data together in manageable-sized units for sensible querying by any end-users. The current onshore themes are Boreholes, Geophysics, Civil Engineering, Geochemistry, Collections, Earthquakes, Landsat, Local Government, Map products and Minerals. Data is also available for the UK offshore. The map themes were created after discussion with customers and specialists in these particular sectors to ensure they encompass data (data layers) required for meaningful querying of the BGS data holdings by that sector. Data is also available for Northern Ireland using the Geological Survey of Northern Ireland GeoIndex.

Overview: era5.copernicus: precipitation daily sums from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: 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. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent cumulative daily precipitation in mm x 10.

New version 2.0.0 with majors change

For free and complete informations concerning CASSMIR datasets, please visit our website (in French).

The CASSMIR database (Contribution to the Spatial and Sociological Analysis of Residential Real Estate Markets) is a spatial and population datasets on housing property market of the Parisian metropolitan area, from 1996 to 2018. The indicators in the CASSMIR database cover four "thematic areas of investigation" : prices, socio-demographic profile of buyers and sellers, purchasing regimes and types of property transfers and types of real estate. These indicators characterize spatial units at three scales (communal level, 1km grid and 200m grid) and population groups of buyers and sellers declined according to social, generational and gender criteria. The delivery of the database follows a series of matching and aggregation of individual data from two original databases : a database on real estate transactions (BIEN database) and a database on first-time buyer investments (PTZ database). CASSMIR delivers aggregated data (with nearly 350 variables) in open access for non-commercial use.

This repository consists of sevenfiles.

"CASSMIR_SpatialDataBase" is a Geopackage file, it lists all the data aggregated to spatial units of reference. It is composed of three layers that correspond to the geographical scale of aggregation: at a communal level, a grid of one kilometer on each side and a grid of two hundred meters on each side.

"CASSMIR_GroupesPopDataBase" is a .csv file, it lists all the data aggregated to population groups of reference. There are three types of population groups : groups referenced by the social position of the buyers/sellers (social group), groups referenced by the age group to which the buyers/sellers belong (generational group), groups referenced by the sex of the buyers/sellers (gender group).

Two metadata files (.csv) lists the metadata of the indicators made available. They are systematically structured as follows :

• Id_var: the identifier of the variable contained in "CASSMIR_SpatialDataBase" or "CASSMIR_GroupesPopDataBase"
• Unite d'observation des variables descriptives : descriptive units of observation (Prices, buyers, sellers...)
• Type d'information : precision on the type of information
• Label : Description of the contents of the variable
• Indicator_Group: The group of indicators to which the variable relates (prices, socio-demographics indicators of buyers and sellers...)
• Unit : The unit of measurement of the variable
• Spatial_Availability : A precision on the availability of the variable in the spatial database (communes, 1 km grid and 200m grid)
• GroupesPop_Availability : A precision on the availability of the variable in the population groupes database (Social, generational , gender)
• Data_Source: The main origin of the data (INSEE, BIEN and/or PTZ)
• Remarques : possible remarks on the construction of the variable

"BIENSampleForTest" and "PTZSampleForTest" are two .txt files which restore a sample of individual data from each of the original databases. All data were anonymized and the values randomized. These two files are specifically dedicated to reproducing the different stages of processing that lead to the production of the CASSMIR files ("CASSMIR_SpatialDataBase" or "CASSMIR_GroupesPopDataBase") and cannot be used in any other way.

"LEXIQUE" is a glossary of terms used to name the variables (.csv).

The creation of the database was funded by the National Reseach Agency (ANR WIsDHoM https://anr.fr/Projet-ANR-18-CE41-0004).

All CASSMIR documentation (in French) and R codes are accessible via the Gitlab repository at the following address : https://gitlab.huma-num.fr/tlecorre/cassmir.git

METADATA :

• Licence

This dataset is registered under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You are free to copy, distribute, transmit, and adapt the data, provided that you give credit to the CASSMIR data base and specify the original source of the data. If you modify or use the data in other derivative works, you may distribute them only under the same license. You may not make commercial use of this database, nor may you use it for any purpose other than scientific research.

• Citation standard

- Figures: (CC - CASSMIR database, indicator(s) constructed from XXX data)

- Bibliography : Productions that use the CASSMIR database must reference the dataset and the data paper.

Dataset: Le Corre T., 2020, CASSMIR (Version 2.0.0) [Data set], Zenodo. http://doi.org/10.5281/zenodo.4497219

Data paper: Thibault Le Corre, « Une base de données pour étudier vingt années de dynamiques du marché immobilier résidentiel en Île-de-France », Cybergeo: European Journal of Geography [En ligne], Data papers, article No.992, mis en ligne le 09 août 2021. URL : http://journals.openedition.org/cybergeo/37430 ; DOI : https://doi.org/10.4000/cybergeo.37430

• Data paper title

"Une base de données pour étudier vingt années de dynamiques du marché immobilier en Île-de-France"

• Author

Thibault Le Corre

• Keywords

Housing market, data base, Île-de-France, spatio-temporal dynamics

• Related Publication

DOI : https://doi.org/10.4000/cybergeo.37430

• Language

French

• Time Period Covered

The time period covered by the indicators in the database depends on the data sources used, respectively:
For data from BIEN: 1996, 1999, 2003-2012, 2015, 2018
For data from PTZ: 1996-2016

• Kind of data

Nature of data submitted

• vector: Vector data

• grid: Data mesh

• code: programming code (see the website or GitLab of the project)

• Data Sources

Base BIEN

Base PTZ

• Geographical Coverage

Île-de-France region

• Geographical Unit

Municipalities and grid mesh elements (1km side grid and 200 side grid) concerned by real estate transactions

• Geographic Bounding Box

Reference Coordinate System (RCS): EPSG 2154 RGF93/Lambert 93.

- Xmin : 586421.7
- Xmax : 741205.6
- Ymin : 6780020
- Ymax : 6905324

• Type of article

Data Paper

The lake map for the State of Alaska was generated from selected Landsat acquired during summer seasons of circa 2000. Nearly 400 30-m resolution Enhanced Thematic Mapper Plus (ETM+) images were used to produce the lake map. The database contains over 38,000 lakes larger than 0.1 km2. The spatial coverage of the product is nearly the entire state except the Aleutian islands. The lake product is released at three different levels in response to lake size classes:

Level 1: large lakes greater than 10 km2;

Level 2: medium-sized lakes between 1 and 10 km2;

Level 3: small lakes between 0.1 and 1 km2. The Alaskan lake products are released in ArcView shapefile format.

The LUISA Base Map 2018 is a high-resolution land use/land cover map developed and produced by the Joint Research Centre of the European Commission. It corresponds to a modified and improved version of the CORINE Land Cover 2018 map. Compared to CORINE, the LUISA Base Map delivers a higher overall spatial detail and finer thematic breakdown of artificial land use/cover categories (17 categories instead of 11 in CORINE). The LUISA Base Map can be used for multiple purposes and it is more suitable than CORINE for applications requiring fine spatial and/or thematic detail of land use/land cover consistently across Europe, such as land use/cover accounting and modelling. Coverage: EU27, Albania, Bosnia and Herzegovina, Iceland, Kosovo, Liechtenstein, Montenegro, North Macedonia, Norway, Serbia, Switzerland, Turkey, United Kingdom. RESOLUTION: MMU = 1ha (artificial surfaces); MMU = 5ha (non artificial surfaces); pixel resolution = 50m, 100m LINEAGE: The 2018 edition of the LUISA Base Map is constructed by refining the original thematic and spatial detail of the CORINE Land Cover (CLC) 2018. The methodology consists of a structured, automated and reproducible geospatial data fusion approach that integrates disparate but highly detailed land use information from a series of trusted, off-the-shelf datasets onto the CLC 2018 map, relying on data from the reporting year 2018 whenever possible. The main sources include the CLC Change Maps, the Copernicus High Resolution Layers (forest, water, wetlands, and imperviousness layers), the Copernicus Urban Atlas and Coastal Zones, the Global Human Settlement Layer from the Joint Research Centre, as well as the TomTom Multinet and OpenStreeMap. The use of various European-wide remotely sensed imagery as input and a uniform and automated methodology yields high comparability of the map across countries. The LUISA Base Maps 2018 and 2012 were produced using the same method and data sources. However, input data from 2012 and 2018 may not be always comparable. This is especially the case of the Copernicus High Resolution Layers whose sensors and algorithms changed between 2012 and 2018. For this reason, the LUISA Base Maps are not suitable for change detection. For what concerns the accounting of changes in urban fabric for larger geographical units (e.g. NUTS), the effect of differences in input data is limited because the LUISA Base Map uses the Copernicus Imperviousness change layers to detect meaningful changes of urban fabric backwards, using 2018 as the reference period. COMPLETENESS: 100%

We present a concurrent series of 144 monthly reanalyses of Super Dual Auroral Radar Network (SuperDARN) plasma velocity measurements, using the method of data-interpolating Empirical Orthogonal Functions (EOFs). For each monthly reanalysis, the 5-minute median values of the northern polar region''s radar-measured line-of-sight Doppler plasma velocities are binned in an equal-area grid defined in quasi-dipole latitude and quasi-dipole magnetic local time (MLT). The grid cells each have an area of approximately 110,000km2, and the grid extends to 30 degrees colatitude. Within this spatial grid, the sparse binned data are infilled to provide a measurement at every spatial and temporal location via two different EOF analysis models: one tailored to instances of low data coverage, the other tailored to higher data coverage. These two models each comprise 144 monthly sets of orthogonal modes of variability (spatial and temporal patterns), along with the timestamps of each epoch, and the spatial coordinate information of all bin locations. A companion dataset determines which of the two models should be chosen in each location for each month, in order to ensure the best accuracy of the infill solution. We also provide the temporal mean of the data in each spatial bin, which is removed prior to the EOF analysis. Collectively, the reanalysis delivers the SuperDARN data in terms of cardinal north and east vector components (in the quasi-dipole coordinate frame), without its usual extreme sparseness, for studies of ionospheric electrodynamics for the period 1997.0 to 2009.0.

Funding was provided by NERC Standard grant NE/N01099X/1, titled ''Thermospheric Heating Modes and Effects on Satellites'' (THeMES) and the NERC grant NE/V002732/1, titled ''Space Weather Instrumentation, Measurement, Modelling, and Risk: Thermosphere'' (SWIMMR-T).

1) The data includes the soil erosion modulus of 11 watersheds with a resolution of 5 m in the year of 2017 in Tibet. 2）Based on the surface layer of rainfall erosivity R, soil erodibility K, slope length factor LS, vegetation coverage FVC, and rotation sampling survey unit, the Chinese soil erosion model (CSLE) was used to calculate soil erosin modulus in 11 watersheds respectively. Through spatial data processing (including chart linking and transformation, vector-grid conversion, and resampling), R, K, LS factors were calculated from the regional thematic map of rainfall erosivity, soil erodibility, and DEM. By half-month FVC, NPV, half-month rainfall erosivity data, we calculated the value of B factors in each sampling watershed. The value of E factor was calculated based on the remote sensing interpretation results and engineering measure factor table. The value of tillage factor T was obtained from tillage zoning map and tillage measure table. And then the soil erosion modulus in each sampling watershed was calculated by the equation: A=R•K•LS•B•E•T. The selection of 11 watersheds was based on the layout of sampling survey in pan-third polar region. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion modulus is of great significance for studying the present situation of soil erosion in Pan third polar region, and it is also crucial for the implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.

The LANDSAT Thematic Mapper, a mechanical scanning radiometer, operates in 7 channels of electro-magnetic spectra, including visual, near infrared and thermal infrared.

Data collected by the Earth Observation Center, NASDA, cover a circular area about 5000 km diameter. The Earth Observation Center receives TM data weekdays and every other Saturday. It performs both radiometric and geometric correction and distributes products in the form of magnetic tape and imagery.

CORINE Land Cover (CLC) was specified to standardize data collection on land in Europe to support environmental policy development. The reference year of first CLC inventory was 1990 (CLC1990), and the first update created in 2000. Later the update cycle has become 6 years. The number of participating countries has increased over time − currently includes 33 European Environment Agency (EEA) member countries and six cooperating countries (EEA39) with a total area of over 5.8 Mkm2. Ortho-corrected high spatial resolution satellite images provide the geometrical and thematic basis for mapping. In-situ data (topographic maps, ortho-photos and ground survey data) are essential ancillary information. The project is coordinated by the EEA in the frame of the EU Copernicus programme and implemented by national teams under the management and quality control (QC) of the EEA. The basic technical parameters of CLC (i.e. 44 classes in nomenclature, 25 hectares minimum mapping unit (MMU) and 100 meters minimum mapping width) have not changed since the beginning, therefore the results of the different inventories are comparable. The layer of CORINE Land Cover Changes (CHA) is produced since the second CLC inventory (CLC2000). CHA is derived from satellite imagery by direct mapping of changes taken place between two consecutive inventories, based on image-to-image comparison. Change mapping applies a 5 ha MMU to pick up more details in CHA layer than in CLC status layer. Integration of national CLC and CHA data includes some harmonization along national borders. Two European validation studies have shown that the achieved thematic accuracy is above the specified minimum (85 %). Primary CLC and CHA data are in vector format with polygon topology. Derived products in raster format are also available. The seamless European CLC and CHA time series data (CLC1990, CLC2000, CLC2006, CLC2012, CLC2018 and related CHA data) are distributed in the standard European Coordinate Reference System defined by the European Terrestrial Reference System 1989 (ETRS89) datum and Lambert Azimuthal Equal Area (LAEA) projection (EPSG: 3035). Results of the CLC inventories can be downloaded from Copernicus Land site free of charge for all users. CLC data can contribute to a wide range of studies with European coverage, e.g.: ecosystem mapping, modelling the impacts of climate change, landscape fragmentation by roads, abandonment of farm land and major structural changes in agriculture, urban sprawl, water management.

Twenty-seven 10-day synthesis products of VEGETATON sensor (VGT-S10) on SPOT 4 satellite were acquired for land cover characterization in temperate East Asia. The 10-day VGT-S10 composites have a spatial resolution of 1 km and are generated based on the maximum NDVI values during the 10-day period (see SPOT website, "http://www.spot.com", for more info). The primary emphasis of this project was to develop a dataset which describes the spatial distribution and seasonal dynamics of snow and ice cover in the alpine regions of east Asia. These data are critically needed by the climate and hydrological modeling communities. A Snow/Ice Cover data set is currently available in this collection. The Snow/Ice Cover data set contains two variables: 1) A normalized difference snow and ice index (NDSII) and 2) a snow and ice cover thematic map. The NDSII was calculated from band reflectance data from each of the 27 10-day composites acquired during the time period. Snow and ice coverage maps were generated for each of the 27 composites by assigning snow/ice cover to pixels which had an NDSII >= 0.40 and a Band 3 value (NIR reflectance) > 0.11. Each of the two variables has 27 holdings representing images for each 10-day composite period between March 1, 1999 and November 30, 1999. These data are provided in the Lambert Azimuthal Equal Area projection at a 1 km resolution.