GIS datasets from the colocation analyses. The GIS data could be open in either QGIS or ArcGIS. The files were separated into the folders of CGF and BGF, and DPGF. In addition to the abbreviations of mineral, the abbreviations of FM, FT, and TM in the file name refer to: FM, colocation of faults and neighbouring indicator minerals; FT, colocation maps of faults and neighbouring high temperature; TM, colocation of high temperatures and neighbouring indicator minerals.

This GIS dataset contains the results of wind-wave parameter modelling in the area of the Gulf of Gdańsk (Southern Baltic). For the simulations, a high resolution SWAN model was used. The dataset consists of the significant wave height, the direction of the wave approaching the shore and the wave period during 21 historical, extreme storms (rasters). The storms were selected by an automatic search over the 44-year-long significant wave height time series.

The Temperature station results represent the water quality results for all monitoring stations associated with the Temperature parameter included in the 2022 NJ Integrated Water Quality Monitoring and Assessment Report (Integrated Report). This data represents the assessment results at monitoring stations in NJ's 958 assessment units to determine if the Temperature parameter is attaining. The data reflects which of three sublists each assessment was assigned: Sublist 2- Full Attaining, Sublist 3- Insufficient data available to assess, Sublist 5- Non-Attaining. Because of the number of stations originally assessed, any station where the parameter had insufficient data for an assessment were excluded from this file.

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

GIS-based parameters of the Taz River main steam and tributaries

Basal Area (BA). 30 meter pixel resolution. Data represents forest conditions circa 2002.These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002

This image service is part of a collection of maps for PGA, PGV and spectral accelerations at 0.2 s (SA02), 1.0 s (SA10), and 2.0 s (SA20) to illustrate seismic hazards in California. For each ground motion parameter, maps at two different hazard levels were presented: one with a 2% probability of being exceeded in 50 years (equivalent to 2,475-year recurrence interval) and the other with 10% probability of being exceeded in 50 years (equivalent to 475-year recurrence interval). The ArcGIS Online interface allows users to select any two ground motion hazard maps to compare side by side. Ground motion parameters were calculated using the 2023 update of the U.S. Geological Survey National Seismic Hazard Model. See the “Scientific Background” on MS48 webpage for detailed information.Due to software limitations, symbology cannot be added to this service. To match the symbology used in the MS48 Ground Motion application, use the following configuration:Esri Color Ramp: MagmaMinimum: 4.41 gMaximum: 0.032 gGamma: 1

Compressed file sedPbZn_SourceData_gdb.zip contains the GIS datasets and Python scripts used to calculate the estimated potential and certainty that sediment-hosted Pb-Zn (lead-zinc) deposits might be present in an area in Alaska. The statewide datasets include: Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), lithology layers created from Alaska Geologic Map (SIM3340), and 12-digit HUCs, subwatersheds from the National Watershed Boundary dataset. FGDC metadata for all datasets are included. In addition, files are included for the user to modify the parameters of the analysis. These include two Python scripts, 1) used to score ARDF sites for sediment-hosted Pb-Zn potential, and 2) to evaluate each 12-digit HUC for sediment-hosted Pb-Zn potential and certainty based on queries on AGDB3, ARDF, and lithology. An mxd file and cartography layers are included for viewing the data selections in ArcGIS. Other supporting documents are included. Compressed file sedPbZn_SourceData_shape.zip contains shapefiles versions of all the geodatabase feature classes which are derivatives of published datasets ARDF and SIM3340, and CSV files of the ARDF keyword files. Other supporting documents included are: FGDC metadata for each data source, a pdf showing the query parameters for the analysis, and a README file.

The TDS station results represent the water quality results for all monitoring stations associated with the TDS parameter included in the 2022 NJ Integrated Water Quality Monitoring and Assessment Report (Integrated Report). This data represents the assessment results at monitoring stations in NJ's 958 assessment units to determine if the TDS parameter is attaining. The data reflects which of three sublists each assessment was assigned: Sublist 2- Full Attaining, Sublist 3- Insufficient data available to assess, Sublist 5- Non-Attaining. Because of the number of stations originally assessed, any station where the parameter had insufficient data for an assessment were excluded from this file.

This is a point feature class of environmental monitoring stations maintained in the California Department of Water Resources’ (hereafter the Department) Water Data Library Database (WDL) for discrete “grab” water quality sampling stations. The WDL database contains DWR-collected, current and historical, chemical and physical parameters found in drinking water, groundwater, and surface waters throughout the state. This dataset is comprised of a Stations point feature class and a related “Period of Record by Station and Parameter” table. The Stations point feature class contains basic information about each station including station name, station type, latitude, longitude, and the dates of the first and last sample collection events on record. The related Period of Record Table contains the list of parameters (i.e. chemical analyte or physical parameter) collected at each station along with the start date and end date (period of record) for each parameter and the number of data points collected. The Lab and Field results data associated with this discrete grab water quality stations dataset can be accessed from the California Natural Resources Agencies Open Data Platform at https://data.cnra.ca.gov/dataset/water-quality-data or from DWR’s Water Data Library web application at https://wdl.water.ca.gov/waterdatalibrary/index.cfm.

VIC grid polygons represent the Variable Infiltration Capacity (VIC) model spatial discretization of input forcing parameters (precipitation, temperature, wind speed), physical parameters (land use, soil, elevation, etc.), and simulated hydrologic parameters (snow water equivalent, runoff, baseflow, etc.) on a 1/16th degree (nominal 6km by 6km) spatial resolution. Each polygon represents the location of where a set of computations occur throughout the simulation period. Input parameters and physical parameters are defined by the modeler and used to calculate the simulated hydrologic parameters.

**23 April 2021: A new version of this data set has been published. It includes data on 4 parameters (Ammoniacal nitrogen (adjusted), _Escherichia coli**_**, Macroinvertebrate Community Index and Total Phosphorus) that had been missing from the file that was published as part of the Our freshwater 2020 release in April 16 2020. The updated data set also includes data on DRP for all 593,337 REC segments, since the file from April 16 2020 only had data for 255,860 of these segments.**

16 April 2020: Subsequent to publication in April 2019 we discovered two small errors with this dataset. These included:

• Errors in the coordinates of some sites and their associated metadata (such as landcover and elevation).
• Errors in our calculation of dominant landcover.

In addition, flow data from TopNet has also been updated.

These changes have a minor impact on overall results. These changes have have been corrected, and are republished here, as part of the Our freshwater 2020 release.

​

IMPORTANT INFORMATION

_1) The main (cleaned) dataset is structured by each row having a nzsegment and np_id combination. A large dataset (~ 1 GB) has resulted, due to the inclusion of the ANZG/NOF columns and the 10 different np_id values. There are ~ 6 million rows to this dataset, however a 32-bit version of Microsoft Excel will only display/download ~ 1 million rows. A DBMS, statistical or GIS application is needed to view the entire dataset._

2) A smaller raw dataset (see attachments) is provided which structures each row relating to a river segment and drops the ANZG/NOF columns.

3) The attached metadata/date quality report provides further information on the NOF, ANZG and the "McDowell meet/doesnt meet" attachment.

​

This dataset contains ten parameters of water quality based on measurements made at monitored river sites:

• Nitrate-nitrogen
• Ammoniacal nitrogen
• Ammoniacal nitrogen (adjusted)
• Total nitrogen
• Total phosphorus
• Dissolved reactive phosphorus
• Water clarity
• Turbidity
• Escherichia coli
• Macroinvertebrate community index These parameters are used to measure:
• Modelled median values for all of New Zealand’s river length for the period 2013 to 2017
• For selected indicators, how the modelled values compare to the National Objectives Framework (NOF) (MfE, 2017) bands related to ecosystem health and human health for recreation, and to expected concentrations in natural conditions, as shown by the default guideline values in the Australian and New Zealand guidelines for fresh and marine water quality (ANZG, 2018)

More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Summary report available at http://www.mfe.govt.nz/publications/fresh-water/spatial-modelling-of-river-water-quality-state-incorporating-monitoring

A database has been developed and delivered during the FAIRWAY project. This database was developed as a response to the need to harmonize datasets and assessment methods related to pressure and state indicators for water quality in the EU member states, in order to compare and assess indicators using a harmonized approach.

The dataset that is made available here provides two files:

a public version* of the Excel database, which contains all "tabular" (non-GIS) data related to the 13 case studies that was gathered for the purposes of FAIRWAY's Monitoring & Indicators research theme. It is structured as one "data sheet" and one "summary sheet" per case study. The data sheets contain various parameters (ideally time-dependent data series i.e. time series) that were used, wherever possible, to compute relevant Agri-drinking water quality indicators (ADWIs) such as "nitrogen budget" (a compound Pressure indicator) or "lag time" (a statistically-inferred Link indicator).

a ZIP folder containing all GIS data gathered for the FAIRWAY's Monitoring & Indicators research theme. The GIS files are grouped in subfolders, by case study, and then by keywords describing the nature of the spatial data.

The Excel database contains near 385,000 rows of data from the 13 case study sites, with more than 65 parameters and more than 500 sub-parameters. The dataset also contains spatial information in a GIS-data zipped folder. The spatial mapping information can be made visible using basic QGIS project files (.qgz), so that GIS data from each case study can be explored.

The indicators database can be used in several ways. It may be used to explore data or to calculate additional indicators. Depending of the case studies’ interests, the most commonly available State indicators are about nitrate and pesticides concentrations in water.

From a practical point of view based on its actual content, the database may notably be used to explore statistical relations (or Links) between related Pressure and State indicators. This database can also be used as a spatial mapping portal for other usages.

For more information on the database, follow this link.

• Note that this is a public version of the database, which means that all confidential data was removed from the data sheets.

The California Irrigation Management Information System (CIMIS) currently manages over 145 active weather stations throughout the state. Archived data is also available for 85 additional stations that have been disconnected from the network for various reasons. CIMIS stations provide hourly records of solar radiation, precipitation, air temperature, air humidity, and wind speed. Most of the CIMIS stations produce estimates of reference evapotranspiration (ETo) for the station location and their immediate surroundings, often in agricultural areas. The Department of Water Resources operates CIMIS as a free resource to help California to manage water resources more efficiently. CIMIS weather stations collect weather data on a minute-by-minute basis. Hourly data reflects the previous hour's 60 minutes of readings. Hourly and daily values are calculated and stored in the dataloggers. A computer at the DWR headquarters in Sacramento calls every station starting at midnight Pacific Standard Time (PST) and retrieves data at predetermined time intervals. At the time of this writing, CIMIS data is retrieved from the stations every hour. When there is a communication problem between the polling server and any given station, the server skips that station and calls the next station in the list. After all other stations have reported, the polling server again polls the station with the communication problem. The interrogation continues into the next day until all of the station data have been transmitted. CIMIS data processing involves checking the accuracy of the measured weather data for quality, calculating reference evapotranspiration (ETo/ETr) and other intermediate parameters, flagging measured and calculated parameters, and storing the data in the CIMIS database. Evapotranspiration (ET) is a loss of water to the atmosphere by the combined processes of evaporation from soil and plant surfaces and transpiration from plants. Reference evapotranspiration is ET from standardized grass or alfalfa surfaces over which the weather stations are sitting. The standardization of grass or alfalfa surfaces for a weather station is required because ET varies depending on plant (type, density, height) and soil factors and it is difficult, if not impossible, to measure weather parameters under all sets of conditions. Irrigators have to use crop factors, known as crop coefficients (Kc), to convert ET from the standardized reference surfaces into an actual evapotranspiration (ETc) by a specific crop. For more information go to https://cimis.water.ca.gov/. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.3, dated April 13, 2022. DWR makes no warranties or guarantees —either expressed or implied — as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to GIS@water.ca.gov.

This abstract contains links to public ArcGIS maps that include locations of carbonate springs and some of their characteristics. Information for accessing and navigating through the maps are included in a PowerPoint presentation IN THE FILE UPLOAD SECTION BELOW. Three separate data sets are included in the maps:

1. Geochemistry data from the US Water Quality Portal (WQP), which compiles geochemistry data from the USGS and other federal agencies.
2. Discharge data from WoKaS, a world wide spring discharge data set (Olarinoye et al., 2020).
3. Regional karst data from selected US state agencies.

Several base maps are included in the links. The US carbonate map describes and categorizes carbonates (e.g., depth from surface, overlying geology/ice, climate). The carbonate springs map categorizes springs as being urban, specifically within 1000 ft of a road, or rural. The basis for this categorization was that the heat island effect defines urban as within a 1000 ft of a road. There are other methods for defining urban versus rural to consider. Map links and details of the information they contain are listed below.

Map set 1: The WQP map provides three mapping options separated by the parameters available at each spring site. These maps summarize discrete water quality samples, but not data logger availability. Information at each spring provides links for where users can explore further data.

Option 1: WQP data with urban and rural springs labeled, with highlight of springs with or without NWIS data https://www.arcgis.com/home/item.html?id=2ce914ec01f14c20b58146f5d9702d8a

Options 2: WQP data by major ions and a few other solutes https://www.arcgis.com/home/item.html?id=5a114d2ce24c473ca07ef9625cd834b8

Option 3:WQP data by various carbon species https://www.arcgis.com/home/item.html?id=ae406f1bdcd14f78881905c5e0915b96

Map 2: The worldwide carbonate map in the WoKaS data set (citation below) includes a description of carbonate purity and distribution of urban and rural springs, for which discharge data are available: https://www.arcgis.com/apps/mapviewer/index.html?webmap=5ab43fdb2b784acf8bef85b61d0ebcbe.

Reference: Olarinoye, T., Gleeson, T., Marx, V., Seeger, S., Adinehvand, R., Allocca, V., Andreo, B., Apaéstegui, J., Apolit, C., Arfib, B. and Auler, A., 2020. Global karst springs hydrograph dataset for research and management of the world’s fastest-flowing groundwater. Scientific Data, 7(1), pp.1-9.

Map 3: Karst and spring data from selected states: This map includes sites that members of the RCN have suggested to our group.

https://uageos.maps.arcgis.com/apps/mapviewer/index.html?webmap=28ed22a14bb749e2b22ece82bf8a8177

This data set is incomplete (as of October 13, 2022 it includes Florida and Missouri). We are looking for more information. You can share data links to additional data by typing them into the hydroshare page created for our group. Then new sites will periodically be added to the map: https://www.hydroshare.org/resource/0cf10e9808fa4c5b9e6a7852323e6b11/

Acknowledgements: These maps were created by Michael Jones, University of Arkansas and Shishir Sarker, University of Kentucky with help from Laura Toran and Francesco Navarro, Temple University.

TIPS FOR NAVIGATING THE MAPS ARE IN THE POWERPOINT DOCUMENT IN THE FILE UPLOAD SECTION BELOW.

General Description The High Resolution 30m Land Surface Parameters for Europe dataset is derived from Global Ensemble DTM. Data is computed using GRASS GIS and SAGA GIS. Original DTM data is in projection EPSG:4326, and reprojects to Equi7 (EPSG:27704), computes the parameters, and eventually reprojects to EPSG:3035. High resolution layers are computed in tiles. In order to eliminate boundary effects and reprojection resampling, Below is the list of land-surface parameters. Hillshade and Slope in degree are available to download through this resporitory, others are available to access from public S3 server. All files are in COG. slope in degree (slope): steepness at each cell S3 path: https://s3.eu-central-1.wasabisys.com/arco/slope_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif hillshade: visualizing of terrain determined by a light source and the slope and aspect of the elevation surface S3 path: https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif minimum curvature (minic): valleys in negative value and local convex landform in positive value S3 path: https://s3.eu-central-1.wasabisys.com/arco/minic_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif maximum curvature (maxic): ridges in positive values and local concave landform in negative value S3 path: https://s3.eu-central-1.wasabisys.com/arco/maxic_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif positive openness (pos.openness): the "dominance" of an elevated location over its surroundings S3 path: https://s3.eu-central-1.wasabisys.com/arco/pos.openness_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif negative openness (neg.openness): the "enclosure" of a lower location by elevated surroundings S3 path: https://s3.eu-central-1.wasabisys.com/arco/neg.openness_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif Data Details Time period: January 2000 – December 2022 Type of data: Land surface parameters of geomorphometry How the data was collected or derived: Derived from Global Ensemble DTM in 30m using GRASS GIS and SAGA GIS running in a local HPC. Coordinate reference system: EPSG:3035 Bounding box (Xmin, Ymin, Xmax, Ymax): (900000 899000 7401000 5501000) Spatial resolution: 30m Image size: 216700, 153400 File format: Cloud Optimized Geotiff (COG) format. Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: https://github.com/AI4SoilHealth/SoilHealthDataCube/issues Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: generic variable name: slope = slope in degree variable procedure combination: edtm = Ensemble digital terrain model Position in the probability distribution / variable type: m = measurement Spatial support: 30m Depth reference: s = surface Time reference begin time: 20000101 = 2000-01-01 Time reference end time: 20221231 = 2022-12-31 Bounding box: eu = Europe EPSG code: epsg.3035 = EPSG:3035 Version code: v20240528 = 2024-05-28 (creation date)

Post processed results including levels, depths, velocities, hazard, flood function, flood emergency response classification, flood planning area, pipe capacity assessment, flood planning constraint categories and sensitivity analysis of flood model parameters.

Rauer Group 1:50000 Topographic GIS dataset. Data conforms to SCAR Feature Catalogue which can be searched. 10 metre contour interval on rock, 20 metre contour interval on ice up to 100 metres, 100 metre contour interval on ice above 100 metres.

Displays the NC DEQ 2022 Integrated Report Water Quality Parameter AssessmentsNorth Carolina 2022 Integrated Report polyline layer. This layer contains the NC 2022 EPA Approved 303(d) list and all other water quality assessments. Each polyline feature represents a geographic assessment unit and the water quality assessment(s) attached to it. The assessments are stacked (each individual assessment has a polyline feature). This layer is the primary feature on the 2022 IR Dashboard.The polygon layer paired with this polyline layer can be found here: https://ncdenr.maps.arcgis.com/home/item.html?id=ea2ec54ec4404e04892c1246d3a9741aDownloaded from NCDEQ by Chatham County on 1/13/2025. Data is static.

Abstract Easily understandable thematic maps of geo-scientific parameters are important for land use decision making. If several parameters are relevant and have to be compared, it is important that they are consistent with each other, available at the same spatial range and detail and normed to a common data range. In the current study, geological and topographical data have been used to derive a set of 90 geo-scientific maps for an area of 400 km² in the northern part of the metropolitan area of Belo Horizonte. Each parameter has been transferred to a common data range between 0 and 1 using a Semantic Import Model strategy and afterwards combined to derive new parameters for soil hydrology and hydrogeology. From these, many intermediate geo-scientific parameters, maps of geo-resources (sand/gravel, carbonates, fertile soils) and geo-hazards (erosion, groundwater pollution) have been derived that they can be used as base information for a participatory and sustainable land use planning. The workflow is transparently stored in GIS-tools and can be modified and updated if new information is available.