# components.parameters.bbox.description
Only features that have a geometry that intersects the bounding box are selected.The bounding box is provided as four or six numbers, depending on whether the coordinate reference system includes a vertical axis (height or depth).

# components.parameters.bbox-crs.description
Indicates the coordinate reference system for the given bbox coordinates.

# components.parameters.bbox-crs-epsg.description
Indicates the EPSG for the given bbox coordinates.

# components.parameters.crs.description
Indicates the coordinate reference system for the results.

# components.parameters.f.description
The optional f parameter indicates the output format which the server shall provide as part of the response document.  The default format is GeoJSON.

# components.parameters.lang.description
The optional lang parameter instructs the server return a response in a certain language, if supported.  If the language is not among the available values, the Accept-Language header language will be used if it is supported. If the header is missing, the default server language is used. Note that providers may only support a single language (or often no language at all), that can be different from the server language.  Language strings can be written in a complex (e.g. "fr-CA,fr;q=0.9,en-US;q=0.8,en;q=0.7"), simple (e.g. "de") or locale-like (e.g. "de-CH" or "fr_BE") fashion.

# components.parameters.offset.description
The optional offset parameter indicates the index within the result set from which the server shall begin presenting results in the response document.  The first element has an index of 0 (default).

# components.parameters.skipGeometry.description
This option can be used to skip response geometries for each feature.

# components.parameters.vendorSpecificParameters.description
Additional "free-form" parameters that are not explicitly defined

# components.responses.Tiles.description
Retrieves the tiles description for this collection

# components.schemas.queryable.properties.description.description
a human-readable narrative describing the queryable

# components.schemas.queryable.properties.language.description
the language used for the title and description

# components.schemas.queryable.properties.queryable.description
the token that may be used in a CQL predicate

# components.schemas.queryable.properties.title.description
a human readable title for the queryable

# components.schemas.queryable.properties.type-ref.description
a reference to the formal definition of the type

# info.contact.x-ogc-serviceContact.contactInstructions
During hours of service. Off on weekends.

# info.description
pygeoapi provides an API to geospatial data

# paths./collections/canada-metadata.get.description
Sample metadata records from open.canada.ca

# paths./collections/canada-metadata/items.get.description
Sample metadata records from open.canada.ca

# paths./collections/canada-metadata/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/canada-metadata/items.options.description
Sample metadata records from open.canada.ca

# paths./collections/canada-metadata/items.options.summary
Options for Open Canada sample data items

# paths./collections/canada-metadata/items/{featureId}.get.description
Sample metadata records from open.canada.ca

# paths./collections/canada-metadata/items/{featureId}.options.description
Sample metadata records from open.canada.ca

# paths./collections/canada-metadata/items/{featureId}.options.summary
Options for Open Canada sample data item by id

# paths./collections/canada-metadata/queryables.get.description
Sample metadata records from open.canada.ca

# paths./collections/canada-metadata/schema.get.description
Sample metadata records from open.canada.ca

# paths./collections/gdps-temperature.get.description
Global Deterministic Prediction System sample

# paths./collections/gdps-temperature.get.summary
Get Global Deterministic Prediction System sample metadata

# paths./collections/gdps-temperature/coverage.get.description
Global Deterministic Prediction System sample

# paths./collections/gdps-temperature/coverage.get.summary
Get Global Deterministic Prediction System sample coverage

# paths./collections/lakes/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/mapserver_world_map.get.summary
Get MapServer demo WMS world map metadata

# paths./collections/mapserver_world_map/map.get.parameters[].description
Background transparency of map (default=true).

# paths./collections/mapserver_world_map/map.get.parameters[].description
The optional f parameter indicates the output format which the server shall provide as part of the response document.  The default format is GeoJSON.

# paths./collections/obs/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./processes/attach-legend-to-corineCLC.get.description
Attaches the official CORINE Land Cover legend (class codes, labels, and colors) to a CORINE CLC raster, and derives the list of urban CLC codes (R function 'get_raster_for_geometry' / D2K wrapper 'attach_legend_to_corineCLC')

# paths./processes/attach-legend-to-corineCLC/execution.post.description
Attaches the official CORINE Land Cover legend (class codes, labels, and colors) to a CORINE CLC raster, and derives the list of urban CLC codes (R function 'get_raster_for_geometry' / D2K wrapper 'attach_legend_to_corineCLC')

# paths./processes/attach-legend-to-corineCLC/execution.post.summary
Process Attach legend to CORINE CLC raster execution

# paths./processes/barplot-trend-results.get.description
This process visualizes statistical analysis results, where the test value (stat.value) is plotted on the Y-axis and id are plotted on the X-axis. Bar color is determined by groups and is displayed in the legend. Bar opacity is controlled by the p-value threshold, with translucent bars representing insignificant results based on the provided p_value_threshold (R function 'barplot_trend_results').

# paths./processes/barplot-trend-results/execution.post.description
This process visualizes statistical analysis results, where the test value (stat.value) is plotted on the Y-axis and id are plotted on the X-axis. Bar color is determined by groups and is displayed in the legend. Bar opacity is controlled by the p-value threshold, with translucent bars representing insignificant results based on the provided p_value_threshold (R function 'barplot_trend_results').

# paths./processes/barplot-trend-results/execution.post.summary
Process Visualisation of statistical analysis results execution

# paths./processes/calculate-weighting.get.description
Calculates population-density-based weights per CORINE Land Cover class by overlapping the Eurostat census grid (human population) with the CORINE CLC 2018 raster, treating continuous urban fabric (111) and discontinuous urban fabric (112) classes separately from all other CORINE Land Cover classes (R function 'calculate_weighting')

# paths./processes/calculate-weighting/execution.post.description
Calculates population-density-based weights per CORINE Land Cover class by overlapping the Eurostat census grid (human population) with the CORINE CLC 2018 raster, treating continuous urban fabric (111) and discontinuous urban fabric (112) classes separately from all other CORINE Land Cover classes (R function 'calculate_weighting')

# paths./processes/calculate-weighting/execution.post.summary
Process Calculate dasymetric weighting table execution

# paths./processes/check-names.get.description
This process XYZ HERE PLEASE, from the sources GBIF, iNaturalist and VertNet, with data provided by the user or other sources. For more details, please ask BOKU.

# paths./processes/check-names/execution.post.description
This process XYZ HERE PLEASE, from the sources GBIF, iNaturalist and VertNet, with data provided by the user or other sources. For more details, please ask BOKU.

# paths./processes/check-names/execution.post.summary
Process Check species names in biodiversity data from separate sources execution

# paths./processes/clean-catchment-geometry.get.description
Validate geometries and save them as multi-polygons (R function 'preprocess_catchment_geometry')

# paths./processes/clean-catchment-geometry/execution.post.description
Validate geometries and save them as multi-polygons (R function 'preprocess_catchment_geometry')

# paths./processes/clean-catchment-geometry/execution.post.summary
Process Validate geometries (e.g. river catchments) execution

# paths./processes/combine-eurostat-data.get.description
Attach EU human population statistics for chosen year to EU NUTS3 regions using temporally matching NUTS3 version.

# paths./processes/combine-eurostat-data/execution.post.description
Attach EU human population statistics for chosen year to EU NUTS3 regions using temporally matching NUTS3 version.

# paths./processes/combine-eurostat-data/execution.post.summary
Process Attach human population statistics to EU NUTS3 regions. execution

# paths./processes/corine-stats-real-areas-europe.get.description
This process computes the size of the area in hectares (ha) covered by CORINE Land Cover (CLC) classes within user-defined polygonal regions within Europe. The analysis is based on harmonized European CORINE raster datasets for the years 2000, 2006, 2012, and 2018 with a spatial resolution of 100 meters. The harmonization aligns the class definitions across different CLC versions to ensure temporal consistency and comparability. This enables robust detection and quantification of land use and land cover changes over time. The harmonization includes reclassification of original classes to a common schema, enabling reliable time-series analysis. The output supports various environmental assessments, such as tracking urban expansion, deforestation, or agricultural change. Please contact the Finnish Environment Institute (Syke, Sampsa.Koponen@syke.fi) for detailed documentation and support in applying this service.

# paths./processes/corine-stats-real-areas-europe/execution.post.description
This process computes the size of the area in hectares (ha) covered by CORINE Land Cover (CLC) classes within user-defined polygonal regions within Europe. The analysis is based on harmonized European CORINE raster datasets for the years 2000, 2006, 2012, and 2018 with a spatial resolution of 100 meters. The harmonization aligns the class definitions across different CLC versions to ensure temporal consistency and comparability. This enables robust detection and quantification of land use and land cover changes over time. The harmonization includes reclassification of original classes to a common schema, enabling reliable time-series analysis. The output supports various environmental assessments, such as tracking urban expansion, deforestation, or agricultural change. Please contact the Finnish Environment Institute (Syke, Sampsa.Koponen@syke.fi) for detailed documentation and support in applying this service.

# paths./processes/corine-stats-real-areas-europe/execution.post.summary
Process Compute areas of CORINE classes within polygons (Europe version) execution

# paths./processes/corine-stats-real-areas-finland.get.description
This process computes the size of the area in hectares (ha) covered by CORINE Land Cover (CLC) classes within user-defined polygonal regions. The analysis is based on harmonized Finnish CORINE raster datasets for the years 2000, 2006, 2012, and 2018. The harmonization aligns the class definitions across different CLC versions to ensure temporal consistency and comparability. This enables robust detection and quantification of land use and land cover changes over time. The Finnish CLC datasets are an enhanced national version with higher resolution and accuracy than the pan-European CLC data. The harmonization includes reclassification of original classes to a common schema, enabling reliable time-series analysis. The output supports various environmental assessments, such as tracking urban expansion, deforestation, or agricultural change. Please contact the Finnish Environment Institute (Syke, Sampsa.Koponen@syke.fi) for detailed documentation and support in applying this service.

# paths./processes/corine-stats-real-areas-finland/execution.post.description
This process computes the size of the area in hectares (ha) covered by CORINE Land Cover (CLC) classes within user-defined polygonal regions. The analysis is based on harmonized Finnish CORINE raster datasets for the years 2000, 2006, 2012, and 2018. The harmonization aligns the class definitions across different CLC versions to ensure temporal consistency and comparability. This enables robust detection and quantification of land use and land cover changes over time. The Finnish CLC datasets are an enhanced national version with higher resolution and accuracy than the pan-European CLC data. The harmonization includes reclassification of original classes to a common schema, enabling reliable time-series analysis. The output supports various environmental assessments, such as tracking urban expansion, deforestation, or agricultural change. Please contact the Finnish Environment Institute (Syke, Sampsa.Koponen@syke.fi) for detailed documentation and support in applying this service.

# paths./processes/corine-stats-real-areas-finland/execution.post.summary
Process Compute areas of CORINE classes within polygons (Finland version) execution

# paths./processes/create-visualisations.get.description
Creates ten interactive HTML visualisations (histograms and maps) evaluating the dasymetric refinement results against observed census grid and LAU population data, including weight distribution, CORINE class coverage, observed/estimated population maps, absolute and percentage error maps, and accuracy metrics (R function set in 'create_visualisations.R')

# paths./processes/create-visualisations/execution.post.description
Creates ten interactive HTML visualisations (histograms and maps) evaluating the dasymetric refinement results against observed census grid and LAU population data, including weight distribution, CORINE class coverage, observed/estimated population maps, absolute and percentage error maps, and accuracy metrics (R function set in 'create_visualisations.R')

# paths./processes/create-visualisations/execution.post.summary
Process Create evaluation visualisations for dasymetric population mapping execution

# paths./processes/crop-and-mask-raster.get.description
Crops and masks an existing CORINE CLC raster to a given spatial analysis extent (e.g. a catchment or country boundary), returning only the raster cells within that geometry (R function 'get_raster_for_geometry')

# paths./processes/crop-and-mask-raster/execution.post.description
Crops and masks an existing CORINE CLC raster to a given spatial analysis extent (e.g. a catchment or country boundary), returning only the raster cells within that geometry (R function 'get_raster_for_geometry')

# paths./processes/crop-and-mask-raster/execution.post.summary
Process Crop and mask raster to analysis extent execution

# paths./processes/dasymetric-refinement.get.description
Performs dasymetric refinement of LAU (Local Administrative Unit) human population for a chosen focus year, redistributing population to finer CORINE Land Cover raster cells (approx. 1 km2) within a catchment. With refinement type 'weighted', population is distributed across urban CORINE classes according to the supplied weight table; if EUBUCCO building footprints are also supplied, raster cells whose building count exceeds a threshold (default 1) are additionally treated as populated. With refinement type 'simple', a binary distribution restricted to urbanised CORINE classes 111 and 112 is used instead, and building footprints and the building count threshold are not used at all (R function 'dasymetric_refinement_raster')

# paths./processes/dasymetric-refinement/execution.post.description
Performs dasymetric refinement of LAU (Local Administrative Unit) human population for a chosen focus year, redistributing population to finer CORINE Land Cover raster cells (approx. 1 km2) within a catchment. With refinement type 'weighted', population is distributed across urban CORINE classes according to the supplied weight table; if EUBUCCO building footprints are also supplied, raster cells whose building count exceeds a threshold (default 1) are additionally treated as populated. With refinement type 'simple', a binary distribution restricted to urbanised CORINE classes 111 and 112 is used instead, and building footprints and the building count threshold are not used at all (R function 'dasymetric_refinement_raster')

# paths./processes/dasymetric-refinement/execution.post.summary
Process Dasymetric refinement of LAU population to raster cells execution

# paths./processes/data-intersect.get.description
Calculates a spatial intersection between an input dataset (e.g. LAU polygons, census grid) and a spatial analysis extent (e.g. a catchment boundary), keeping only the features that intersect the extent. Reprojects the input to match the extent's CRS if needed (R function 'intersect_with_analysis_extent')

# paths./processes/data-intersect/execution.post.description
Calculates a spatial intersection between an input dataset (e.g. LAU polygons, census grid) and a spatial analysis extent (e.g. a catchment boundary), keeping only the features that intersect the extent. Reprojects the input to match the extent's CRS if needed (R function 'intersect_with_analysis_extent')

# paths./processes/data-intersect/execution.post.summary
Process Intersect data with spatial analysis extent execution

# paths./processes/evaluate-refinement.get.description
Evaluates dasymetric refinement results by comparing weighted and simple refinement rasters against observed census grid population (reference year 2021, treated as ground truth). Produces two evaluation datasets (one per refinement type) with absolute and percentage differences, accuracy metrics (bias, MAE, MdAE, MSE, RMSE, MAPE) for each, and a CORINE CLC raster restricted to cells overlapping census grid cells with positive observed population (R functions 'add_evaluations_to_censusgrid' and 'get_only_corineCLC_overlapping_positive_pop')

# paths./processes/evaluate-refinement/execution.post.description
Evaluates dasymetric refinement results by comparing weighted and simple refinement rasters against observed census grid population (reference year 2021, treated as ground truth). Produces two evaluation datasets (one per refinement type) with absolute and percentage differences, accuracy metrics (bias, MAE, MdAE, MSE, RMSE, MAPE) for each, and a CORINE CLC raster restricted to cells overlapping census grid cells with positive observed population (R functions 'add_evaluations_to_censusgrid' and 'get_only_corineCLC_overlapping_positive_pop')

# paths./processes/evaluate-refinement/execution.post.summary
Process Evaluate dasymetric refinement results execution

# paths./processes/ferrybox-river-eo-plot.get.description
This tool takes EO, ferrybox and river discharge data as input and generates plots where the values can be compared. The period defined for input1 (disharge data) defines the year that is plotted (use 2023 or 2024 since ferrybox data is available only from those years).

# paths./processes/ferrybox-river-eo-plot/execution.post.description
This tool takes EO, ferrybox and river discharge data as input and generates plots where the values can be compared. The period defined for input1 (disharge data) defines the year that is plotted (use 2023 or 2024 since ferrybox data is available only from those years).

# paths./processes/ferrybox-river-eo-plot/execution.post.summary
Process Plot EO, ferrybox and river discharge data execution

# paths./processes/filter-clip-clean-extent.get.description
Clip three overlapping datasets (D1=NUTS3 regions; D2=LAU regions; D3=river catchments) to the same analysis extent defined as the overlap of D1 and D3 and validate the geometries of the clipped D2.

# paths./processes/filter-clip-clean-extent/execution.post.description
Clip three overlapping datasets (D1=NUTS3 regions; D2=LAU regions; D3=river catchments) to the same analysis extent defined as the overlap of D1 and D3 and validate the geometries of the clipped D2.

# paths./processes/filter-clip-clean-extent/execution.post.summary
Process Align extents of three datasets (D1=NUTS3 regions; D2=LAU regions; D3=river catchments) including validation steps. execution

# paths./processes/get-analysis-extent.get.description
Calculates the overall spatial analysis extent as the union of LAU (Local Administrative Unit) polygons selected within the catchment for the focus year. If no focus-year LAU polygons are found, the union of the reference-year LAU polygons is used instead (R script 'get_analysis_extent.R')

# paths./processes/get-analysis-extent/execution.post.description
Calculates the overall spatial analysis extent as the union of LAU (Local Administrative Unit) polygons selected within the catchment for the focus year. If no focus-year LAU polygons are found, the union of the reference-year LAU polygons is used instead (R script 'get_analysis_extent.R')

# paths./processes/get-analysis-extent/execution.post.summary
Process Get spatial analysis extent execution

# paths./processes/get-census-grid.get.description
Fetches the Eurostat/GISCO 1 km population census grid for the given countries directly from the GISCO web service, builds 1 km x 1 km grid cell polygons (EPSG:3035) from the cell corner coordinates, and validates the resulting geometries (R function 'get_census_grid')

# paths./processes/get-census-grid/execution.post.description
Fetches the Eurostat/GISCO 1 km population census grid for the given countries directly from the GISCO web service, builds 1 km x 1 km grid cell polygons (EPSG:3035) from the cell corner coordinates, and validates the resulting geometries (R function 'get_census_grid')

# paths./processes/get-census-grid/execution.post.summary
Process Get Eurostat census grid execution

# paths./processes/get-corineCLC.get.description
Selects and fetches the CORINE Land Cover (CLC) raster covering the European Union for the CORINE release year closest to the given population focus year, loading it directly from a remote Cloud-Optimized GeoTIFF (COG). Focus years 2015-2024 resolve to CORINE 2018; focus years 2011-2014 resolve to CORINE 2012. Returns both the raster and the resolved CORINE year

# paths./processes/get-corineCLC/execution.post.description
Selects and fetches the CORINE Land Cover (CLC) raster covering the European Union for the CORINE release year closest to the given population focus year, loading it directly from a remote Cloud-Optimized GeoTIFF (COG). Focus years 2015-2024 resolve to CORINE 2018; focus years 2011-2014 resolve to CORINE 2012. Returns both the raster and the resolved CORINE year

# paths./processes/get-corineCLC/execution.post.summary
Process Get CORINE CLC raster for focus year execution

# paths./processes/get-ecrins-catchment.get.description
Queries the EEA ECRINS (European Catchments and Rivers Network System) ArcGIS REST service for a specific functional elementary catchment by ID, fixes invalid geometries, reprojects to EPSG:3035, and looks up the list of countries that catchment overlaps from a fixed catchment-to-country mapping (R function 'get_specific_catchment'). Valid catchment IDs range from 1 to 118

# paths./processes/get-ecrins-catchment/execution.post.description
Queries the EEA ECRINS (European Catchments and Rivers Network System) ArcGIS REST service for a specific functional elementary catchment by ID, fixes invalid geometries, reprojects to EPSG:3035, and looks up the list of countries that catchment overlaps from a fixed catchment-to-country mapping (R function 'get_specific_catchment'). Valid catchment IDs range from 1 to 118

# paths./processes/get-eubucco-buildings.get.description
Fetches building footprint data from the EUBUCCO v0.2 dataset (https://eubucco.com) for all NUTS2 regions overlapping a given catchment, combines and deduplicates results, and returns them as a single spatial object with attributes including construction year, type, height, and floors (R function 'get_eubucco_buildings')

# paths./processes/get-eubucco-buildings/execution.post.description
Fetches building footprint data from the EUBUCCO v0.2 dataset (https://eubucco.com) for all NUTS2 regions overlapping a given catchment, combines and deduplicates results, and returns them as a single spatial object with attributes including construction year, type, height, and floors (R function 'get_eubucco_buildings')

# paths./processes/get-eubucco-buildings/execution.post.summary
Process Get EUBUCCO building footprints for catchment execution

# paths./processes/get-hydro90m-catchment-by-id-gisco.get.description
Queries the aqua.igb-berlin.de pygeoapi 'get-basin-polygon' process (Hydrography90m / GeoFRESH) for a single drainage basin by basin_id, fixes invalid geometries, reprojects to EPSG:3035, and determines the list of countries that catchment overlaps by intersecting it live against GISCO (Eurostat) country boundaries (R function 'get_specific_catchment') - no precomputed catchment-to-country mapping is used, unlike get-ecrins-catchment and get-hydrobasins-l7-catchment. basin_id is not restricted to a fixed range; any valid Hydrography90m basin_id can be requested.

# paths./processes/get-hydro90m-catchment-by-id-gisco/execution.post.description
Queries the aqua.igb-berlin.de pygeoapi 'get-basin-polygon' process (Hydrography90m / GeoFRESH) for a single drainage basin by basin_id, fixes invalid geometries, reprojects to EPSG:3035, and determines the list of countries that catchment overlaps by intersecting it live against GISCO (Eurostat) country boundaries (R function 'get_specific_catchment') - no precomputed catchment-to-country mapping is used, unlike get-ecrins-catchment and get-hydrobasins-l7-catchment. basin_id is not restricted to a fixed range; any valid Hydrography90m basin_id can be requested.

# paths./processes/get-hydro90m-catchment-by-id-gisco/execution.post.summary
Process Get Hydrography90m basin catchment by ID (live GISCO country lookup) execution

# paths./processes/get-hydrobasins-l7-catchment.get.description
Queries the Ramsar Sites Information Service (RSIS) WFS for a single HydroBASINS (HydroSHEDS) level 07 catchment by HYBAS_ID, fixes invalid geometries, reprojects to EPSG:3035, and looks up the list of EU countries that catchment overlaps from a fixed catchment-to-country mapping (R function 'get_hydrobasinsL7_catchment'). Only catchments with at least some EU territory are available; no bulk dataset download is required, only the requested catchment is fetched live from the WFS.

# paths./processes/get-hydrobasins-l7-catchment/execution.post.description
Queries the Ramsar Sites Information Service (RSIS) WFS for a single HydroBASINS (HydroSHEDS) level 07 catchment by HYBAS_ID, fixes invalid geometries, reprojects to EPSG:3035, and looks up the list of EU countries that catchment overlaps from a fixed catchment-to-country mapping (R function 'get_hydrobasinsL7_catchment'). Only catchments with at least some EU territory are available; no bulk dataset download is required, only the requested catchment is fetched live from the WFS.

# paths./processes/get-hydrobasins-l7-catchment/execution.post.summary
Process Get HydroBASINS level 07 catchment (via Ramsar WFS) execution

# paths./processes/get-lau-data.get.description
Fetches Eurostat LAU (Local Administrative Unit) human population data for the given countries and focus year via GISCO, reprojected to EPSG:3035 (R function 'get_lau_population'). This process is used for both the population focus year and the reference year by supplying a different year value. GISCO's LAU boundaries don't cover every country for every year (e.g. the UK is missing from the 2021 vintage); if a requested country has no LAU data for focus_year, the nearest year (by proximity) that does have data for that country is used instead, and that country's population columns are relabeled to focus_year so the output always has a consistent 'POP_<focus_year>' column. A message is printed whenever this substitution happens.

# paths./processes/get-lau-data/execution.post.description
Fetches Eurostat LAU (Local Administrative Unit) human population data for the given countries and focus year via GISCO, reprojected to EPSG:3035 (R function 'get_lau_population'). This process is used for both the population focus year and the reference year by supplying a different year value. GISCO's LAU boundaries don't cover every country for every year (e.g. the UK is missing from the 2021 vintage); if a requested country has no LAU data for focus_year, the nearest year (by proximity) that does have data for that country is used instead, and that country's population columns are relabeled to focus_year so the output always has a consistent 'POP_<focus_year>' column. A message is printed whenever this substitution happens.

# paths./processes/get-lau-data/execution.post.summary
Process Get LAU population data execution

# paths./processes/heat1.get.description
Generate the gridded assessment units to be used for reproducing the HOLAS assessment using HELCOM's HEAT assessment tool, covering the Baltic Sea, for a selected HOLAS assessment period. The area is gridded, the grid cells have different sizes. For every period, spatial units and configuration (e.g. grid size) are predefined, so this tool just recreates the units used by HOLAS for the various periods. This tool represents the first part of the original HEAT assessment computation script.

# paths./processes/heat1/execution.post.description
Generate the gridded assessment units to be used for reproducing the HOLAS assessment using HELCOM's HEAT assessment tool, covering the Baltic Sea, for a selected HOLAS assessment period. The area is gridded, the grid cells have different sizes. For every period, spatial units and configuration (e.g. grid size) are predefined, so this tool just recreates the units used by HOLAS for the various periods. This tool represents the first part of the original HEAT assessment computation script.

# paths./processes/heat1/execution.post.summary
Process Reproducing HEAT HOLAS results: Generate the Assessment Units execution

# paths./processes/heat1advanced.get.description
Generate the gridded assessment units to be used for the eutrophication assessment using HELCOM's HEAT assessment tool. The area is gridded, the grid cells have different sizes. The spatial units and configuration (e.g. grid size) have to be provided. This tool extends the first part of the original HEAT assessment computation script.

# paths./processes/heat1advanced/execution.post.description
Generate the gridded assessment units to be used for the eutrophication assessment using HELCOM's HEAT assessment tool. The area is gridded, the grid cells have different sizes. The spatial units and configuration (e.g. grid size) have to be provided. This tool extends the first part of the original HEAT assessment computation script.

# paths./processes/heat1advanced/execution.post.summary
Process Assessing Eutrophication: Generate the Assessment Units execution

# paths./processes/heat2.get.description
Combine the various types of samples to be used for reproducing the HOLAS assessment using HELCOM's HEAT assessment tool, for a selected HOLAS assessment period. For every assessment period, the configation and the spatial grid are predefined. Default data is included for all three sample types (bottle, pump/ferrybox, CTD). You can specify whether to use the default data or leave it out. You can also provide custom bottle samples, which will then be used instead of the default bottle data. Currently, usage of custom pump/ferrybox and CTD data are not envisaged, but can be enabled, if required. Please make sure the data you provide matches the selected assessment period. This tool represents the second part of the original HEAT assessment computation script.

# paths./processes/heat2/execution.post.description
Combine the various types of samples to be used for reproducing the HOLAS assessment using HELCOM's HEAT assessment tool, for a selected HOLAS assessment period. For every assessment period, the configation and the spatial grid are predefined. Default data is included for all three sample types (bottle, pump/ferrybox, CTD). You can specify whether to use the default data or leave it out. You can also provide custom bottle samples, which will then be used instead of the default bottle data. Currently, usage of custom pump/ferrybox and CTD data are not envisaged, but can be enabled, if required. Please make sure the data you provide matches the selected assessment period. This tool represents the second part of the original HEAT assessment computation script.

# paths./processes/heat2/execution.post.summary
Process Reproducing HEAT HOLAS results: Combine the samples execution

# paths./processes/heat2advanced.get.description
Combine the various types of samples to be used for the eutrophication assessment using HELCOM's HEAT assessment tool. Please provide input data for at least one of the three sample types (bottle, pump/ferrybox, CTD). Of course you can also provide all three types. This tool extends the second part of the original HEAT assessment computation script.

# paths./processes/heat2advanced/execution.post.description
Combine the various types of samples to be used for the eutrophication assessment using HELCOM's HEAT assessment tool. Please provide input data for at least one of the three sample types (bottle, pump/ferrybox, CTD). Of course you can also provide all three types. This tool extends the second part of the original HEAT assessment computation script.

# paths./processes/heat2advanced/execution.post.summary
Process Assessing Eutrophication: Combine the samples execution

# paths./processes/heat3.get.description
Compute the Annual Indicators (i.e. the calculated HEAT EQRS per indicator per year per assessment unit) using HELCOM's HEAT assessment code, for a selected HOLAS assessment period. For every assessment period, the configation and the spatial grid are predefined but you can specify how Chlorophyll-A is handled. Please provide a file containing combined and filtered samples (CTD, bottle and pump/ferrybox combined in one file) and please make sure the data you provide matches the selected period (assessment period). This tool represents the third part of the original HEAT assessment computation script.

# paths./processes/heat3/execution.post.description
Compute the Annual Indicators (i.e. the calculated HEAT EQRS per indicator per year per assessment unit) using HELCOM's HEAT assessment code, for a selected HOLAS assessment period. For every assessment period, the configation and the spatial grid are predefined but you can specify how Chlorophyll-A is handled. Please provide a file containing combined and filtered samples (CTD, bottle and pump/ferrybox combined in one file) and please make sure the data you provide matches the selected period (assessment period). This tool represents the third part of the original HEAT assessment computation script.

# paths./processes/heat3/execution.post.summary
Process Reproducing HEAT HOLAS results: Compute Annual Indicators execution

# paths./processes/heat3advanced.get.description
Compute the Annual Indicators (i.e. the calculated HEAT EQRS per indicator per year per assessment unit), as part the eutrophication assessment using HELCOM's HEAT assessment tool. As input, please provide a file containing combined and filtered station samples (CTD, bottle and pump/ferrybox combined in one file). Besides your custom station samples, you may define your own custom configuration files to set your own indicator thresholds etc. In addition, please specify how Chlorophyll-A should be handled.  This tool extends the third part of the original HEAT assessment computation script.

# paths./processes/heat3advanced/execution.post.description
Compute the Annual Indicators (i.e. the calculated HEAT EQRS per indicator per year per assessment unit), as part the eutrophication assessment using HELCOM's HEAT assessment tool. As input, please provide a file containing combined and filtered station samples (CTD, bottle and pump/ferrybox combined in one file). Besides your custom station samples, you may define your own custom configuration files to set your own indicator thresholds etc. In addition, please specify how Chlorophyll-A should be handled.  This tool extends the third part of the original HEAT assessment computation script.

# paths./processes/heat3advanced/execution.post.summary
Process Assessing Eutrophication: Compute Annual Indicators execution

# paths./processes/heat4.get.description
Compute the Assessment Indicators (i.e. EQRS per assessment period per assessment unit) using HELCOM's HEAT assessment code, for a selected HOLAS assessment period. The calculation is based on the Annual Indicators (i.e. the calculated HEAT EQRS per indicator per year per assessment unit) which you have to provide as a CSV file. This tool represents the fourth part of the original HEAT assessment computation script.

# paths./processes/heat4/execution.post.description
Compute the Assessment Indicators (i.e. EQRS per assessment period per assessment unit) using HELCOM's HEAT assessment code, for a selected HOLAS assessment period. The calculation is based on the Annual Indicators (i.e. the calculated HEAT EQRS per indicator per year per assessment unit) which you have to provide as a CSV file. This tool represents the fourth part of the original HEAT assessment computation script.

# paths./processes/heat4/execution.post.summary
Process Reproducing HEAT HOLAS results: Compute Assessment Indicators execution

# paths./processes/heat4advanced.get.description
Compute the Assessment Indicators (i.e. EQRS per assessment period per assessment unit), as part the eutrophication assessment using HELCOM's HEAT assessment tool. The calculation is based on the Annual Indicators (i.e. the calculated HEAT EQRS per indicator per year per assessment unit) which you have to provide as a CSV file. In addition to that ,you may define your own custom configuration files to set your own indicator thresholds etc. This tool represents the fourth part of the original HEAT assessment computation script.

# paths./processes/heat4advanced/execution.post.description
Compute the Assessment Indicators (i.e. EQRS per assessment period per assessment unit), as part the eutrophication assessment using HELCOM's HEAT assessment tool. The calculation is based on the Annual Indicators (i.e. the calculated HEAT EQRS per indicator per year per assessment unit) which you have to provide as a CSV file. In addition to that ,you may define your own custom configuration files to set your own indicator thresholds etc. This tool represents the fourth part of the original HEAT assessment computation script.

# paths./processes/heat5.get.description
Compute the Assessment (i.e. EQRS per assessment unit, grouped by overall and criterial level indicators) using HELCOM's HEAT assessment code, for a selected HOLAS assessment period. The calculation is based on the Assessment Indicators (i.e. EQRS per assessment period per assessment unit) which you have to provide as a CSV file. This tool represents the fifth and last part of the original HEAT assessment computation script.

# paths./processes/heat5/execution.post.description
Compute the Assessment (i.e. EQRS per assessment unit, grouped by overall and criterial level indicators) using HELCOM's HEAT assessment code, for a selected HOLAS assessment period. The calculation is based on the Assessment Indicators (i.e. EQRS per assessment period per assessment unit) which you have to provide as a CSV file. This tool represents the fifth and last part of the original HEAT assessment computation script.

# paths./processes/heat5/execution.post.summary
Process Reproducing HEAT HOLAS results: Compute Assessment execution

# paths./processes/heat5advanced.get.description
Compute the Assessment (i.e. EQRS per assessment unit, grouped by overall and criterial level indicators), as part the eutrophication assessment using HELCOM's HEAT assessment tool. The calculation is based on the Assessment Indicators (i.e. EQRS per assessment period per assessment unit) which you have to provide as a CSV file. In addition to that ,you may define your own custom configuration files to set your own indicator thresholds etc. This tool represents the fifth and last part of the original HEAT assessment computation script.

# paths./processes/heat5advanced/execution.post.description
Compute the Assessment (i.e. EQRS per assessment unit, grouped by overall and criterial level indicators), as part the eutrophication assessment using HELCOM's HEAT assessment tool. The calculation is based on the Assessment Indicators (i.e. EQRS per assessment period per assessment unit) which you have to provide as a CSV file. In addition to that ,you may define your own custom configuration files to set your own indicator thresholds etc. This tool represents the fifth and last part of the original HEAT assessment computation script.

# paths./processes/hello-world.get.description
An example process that takes a name as input, and echoes it back as output. Intended to demonstrate a simple process with a single literal input.

# paths./processes/hello-world/execution.post.description
An example process that takes a name as input, and echoes it back as output. Intended to demonstrate a simple process with a single literal input.

# paths./processes/keep-only-valid-corineCLCclasses.get.description
Restricts a CORINE CLC raster to only the urbanised classes present in the supplied weight table, masking out (setting to NA) any raster cells whose CORINE class code does not have an assigned weight (R function 'get_only_valid_corine_categories_raster')

# paths./processes/keep-only-valid-corineCLCclasses/execution.post.description
Restricts a CORINE CLC raster to only the urbanised classes present in the supplied weight table, masking out (setting to NA) any raster cells whose CORINE class code does not have an assigned weight (R function 'get_only_valid_corine_categories_raster')

# paths./processes/keep-only-valid-corineCLCclasses/execution.post.summary
Process Keep only valid CORINE CLC classes execution

# paths./processes/malta-groundwater.get.description
This process runs a 3D variable-density groundwater flow and transport model (SEAWAT) for the Malta aquifer system. It is configured to simulate the effects of sea-level changes and groundwater recharge on aquifer salinity over a 30-year period. Users can provide custom sea-level projections and recharge rates to test different scenarios.

# paths./processes/malta-groundwater/execution.post.description
This process runs a 3D variable-density groundwater flow and transport model (SEAWAT) for the Malta aquifer system. It is configured to simulate the effects of sea-level changes and groundwater recharge on aquifer salinity over a 30-year period. Users can provide custom sea-level projections and recharge rates to test different scenarios.

# paths./processes/malta-groundwater/execution.post.summary
Process Malta Groundwater Model (SEAWAT) execution

# paths./processes/map-shapefile-points.get.description
This process maps the study region and data point locations on OpenStreetMap, providing a visual representation of the geographic distribution of in-situ data points within the defined study area (R function 'map_shapefile_points').

# paths./processes/map-shapefile-points/execution.post.description
This process maps the study region and data point locations on OpenStreetMap, providing a visual representation of the geographic distribution of in-situ data points within the defined study area (R function 'map_shapefile_points').

# paths./processes/map-shapefile-points/execution.post.summary
Process Spatial visualisation of regions and data points  execution

# paths./processes/map-trends-static.get.description
This process maps the trend analysis results onto a study region map, visually representing each region according to the statistical results (significant increase vs. significant decrease vs. insignificant trend). The map helps in understanding the spatial distribution of trends across different areas (R function 'map_trends_static').

# paths./processes/map-trends-static/execution.post.description
This process maps the trend analysis results onto a study region map, visually representing each region according to the statistical results (significant increase vs. significant decrease vs. insignificant trend). The map helps in understanding the spatial distribution of trends across different areas (R function 'map_trends_static').

# paths./processes/map-trends-static/execution.post.summary
Process Spatial visualisation of regions and data points  execution

# paths./processes/match-data.get.description
This process combines and matches biodiveryity data, i.e. occurrences for various species, from the sources GBIF, iNaturalist and VertNet, with data provided by the user or other sources. For more details, please ask BOKU.

# paths./processes/match-data/execution.post.description
This process combines and matches biodiveryity data, i.e. occurrences for various species, from the sources GBIF, iNaturalist and VertNet, with data provided by the user or other sources. For more details, please ask BOKU.

# paths./processes/match-data/execution.post.summary
Process Combine and match biodiversity data from separate sources execution

# paths./processes/mean-by-group.get.description
description: This function calculates and returns the average value for each defined group (for example per site, per year, per season and per HELCOM_ID) (R function 'mean_by_group').

# paths./processes/mean-by-group/execution.post.description
description: This function calculates and returns the average value for each defined group (for example per site, per year, per season and per HELCOM_ID) (R function 'mean_by_group').

# paths./processes/mitgcm-prep.get.description
Preprocessing phase of the AquaINFRA marine modeling chain. Initial conditions, boundary conditions and atmospheric forcing files are generated from Copernicus data to be used as input to the MITgcm model in the Model Run phase.

# paths./processes/mitgcm-prep/execution.post.description
Preprocessing phase of the AquaINFRA marine modeling chain. Initial conditions, boundary conditions and atmospheric forcing files are generated from Copernicus data to be used as input to the MITgcm model in the Model Run phase.

# paths./processes/mitgcm-resultplot.get.description
Plot the results of the MITgcm runs. The plots are lat lon maps of the selected variable for the selected time and depth. This tool will only work with NetCDF files that were configured in a specific way (variables, ...), for example, with the output files of the AquaINFRA MITgcm tool..

# paths./processes/mitgcm-resultplot/execution.post.description
Plot the results of the MITgcm runs. The plots are lat lon maps of the selected variable for the selected time and depth. This tool will only work with NetCDF files that were configured in a specific way (variables, ...), for example, with the output files of the AquaINFRA MITgcm tool..

# paths./processes/mitgcm-run-model.get.description
Model Run phase of the AquaINFRA marine modeling chain. A simulation for the Catalan marine model is run, using the MITgcm modeling code, for the selected time period. Inputs are initial and boundary conditions files and atmospheric forcing files (output from the Preprocessing phase), bathymetry and configured namelists. Outputs are 3D timeseries of sea temperature, salinity, currents and sea surface height.

# paths./processes/mitgcm-run-model/execution.post.description
Model Run phase of the AquaINFRA marine modeling chain. A simulation for the Catalan marine model is run, using the MITgcm modeling code, for the selected time period. Inputs are initial and boundary conditions files and atmospheric forcing files (output from the Preprocessing phase), bathymetry and configured namelists. Outputs are 3D timeseries of sea temperature, salinity, currents and sea surface height.

# paths./processes/multidetect-and-clean.get.description
This process detects outliers using various methods and cleans the data: Ensemble multiple outlier detection methods to ably compare the outliers flagged by each method; then extract final clean data using either absolute or best method generated outliers. For more details, please ask BOKU.

# paths./processes/multidetect-and-clean/execution.post.description
This process detects outliers using various methods and cleans the data: Ensemble multiple outlier detection methods to ably compare the outliers flagged by each method; then extract final clean data using either absolute or best method generated outliers. For more details, please ask BOKU.

# paths./processes/multidetect-and-clean/execution.post.requestBody.content.application/json.example.inputs.methods
mixediqr, logboxplot, iqr, distboxplot, jknife, semiqr, hampel

# paths./processes/multidetect-and-clean/execution.post.summary
Process Run multidetect and clean the data execution

# paths./processes/netcdf-assessment-area.get.description
Plot FerryBox and Logger location on world map. Supports optional input with waterbodies of assessment area.

# paths./processes/netcdf-assessment-area/execution.post.description
Plot FerryBox and Logger location on world map. Supports optional input with waterbodies of assessment area.

# paths./processes/netcdf-extract-fb-data.get.description
Extract FerryBox environmental monitoring data from a THREDDS-hosted NetCDF file. Supports optional filtering by date interval and bounding box. Outputs a CSV containing datetime, latitude, longitude, value, unit, parameter, and derived time fields.

# paths./processes/netcdf-extract-fb-data/execution.post.description
Extract FerryBox environmental monitoring data from a THREDDS-hosted NetCDF file. Supports optional filtering by date interval and bounding box. Outputs a CSV containing datetime, latitude, longitude, value, unit, parameter, and derived time fields.

# paths./processes/netcdf-extract-fb-data/execution.post.summary
Process Extract FerryBox data from NetCDF (THREDDS) to CSV execution

# paths./processes/netcdf-join-dataframes.get.description
Join FerryBox and logger dataframe by date and specified parameters. Contains information for each latitudal sampling point.

# paths./processes/netcdf-join-dataframes/execution.post.description
Join FerryBox and logger dataframe by date and specified parameters. Contains information for each latitudal sampling point.

# paths./processes/netcdf-logger-extract.get.description
Extract river logger environmental monitoring data from a THREDDS-hosted NetCDF file. Supports optional filtering by date. Outputs a CSV containing datetime, value, unit, parameter, and derived time fields.

# paths./processes/netcdf-logger-extract/execution.post.description
Extract river logger environmental monitoring data from a THREDDS-hosted NetCDF file. Supports optional filtering by date. Outputs a CSV containing datetime, value, unit, parameter, and derived time fields.

# paths./processes/netcdf-logger-extract/execution.post.summary
Process Extract river logger data from Baterod from NetCDF (THREDDS) to CSV execution

# paths./processes/netcdf-scatter-datax-vs-datay.get.description
Scatterplot FerryBox against logger based on the filtered parameter in joined dataframe. Option to summarise across specific waterbodies or latitudal transect of the FerryBox.

# paths./processes/netcdf-scatter-datax-vs-datay/execution.post.description
Scatterplot FerryBox against logger based on the filtered parameter in joined dataframe. Option to summarise across specific waterbodies or latitudal transect of the FerryBox.

# paths./processes/netcdf-scatter-datax-vs-datay/execution.post.summary
Process Scatterplot FerryBox against logger parameter execution

# paths./processes/netcdf-scatter-plot.get.description
Reads FerryBox-style CSV and generates a scatterplot between two selected parameters, faceted by month. Outputs a PNG.

# paths./processes/netcdf-scatter-plot/execution.post.description
Reads FerryBox-style CSV and generates a scatterplot between two selected parameters, faceted by month. Outputs a PNG.

# paths./processes/netcdf-scatter-plot/execution.post.summary
Process Scatterplot between two parameters (faceted by month) execution

# paths./processes/netcdf-tile-plot.get.description
Reads a FerryBox CSV and generates one or more Hovmöller-style tile plots over time and latitude for selected parameters. Optional filters: date interval, latitude bounds, storm date marker. Outputs a PNG.

# paths./processes/netcdf-tile-plot/execution.post.description
Reads a FerryBox CSV and generates one or more Hovmöller-style tile plots over time and latitude for selected parameters. Optional filters: date interval, latitude bounds, storm date marker. Outputs a PNG.

# paths./processes/netcdf-tile-plot/execution.post.summary
Process FerryBox tile plot (Hovmöller-style) for one or more parameters execution

# paths./processes/owt-classification.get.description
Optical Water Type classification for ocean, coastal and inland waters.

# paths./processes/owt-classification/execution.post.description
Optical Water Type classification for ocean, coastal and inland waters.

# paths./processes/peri-conv.get.description
This process groups the data into groups based on the date. If requested (by setting "year_starts_at_Dec1"), it adds December to the next year (i.e. all winter months together). In that case, in the result, every year starts at Dec-01 and ends on Nov-30. The process generates the numeric variable "Year_adj_generated" to show this change. It generates the character variable "group_labels" to allow grouping the data based on the provided periods, and the column "period_label" which contains the period definition, e.g. "Jun-01:Aug-30" (R function 'peri_conv').

# paths./processes/peri-conv/execution.post.description
This process groups the data into groups based on the date. If requested (by setting "year_starts_at_Dec1"), it adds December to the next year (i.e. all winter months together). In that case, in the result, every year starts at Dec-01 and ends on Nov-30. The process generates the numeric variable "Year_adj_generated" to show this change. It generates the character variable "group_labels" to allow grouping the data based on the provided periods, and the column "period_label" which contains the period definition, e.g. "Jun-01:Aug-30" (R function 'peri_conv').

# paths./processes/peri-conv/execution.post.summary
Process Group data to groups based on date execution

# paths./processes/points-att-polygon.get.description
Merge data points with the attributes of the study region the points fall into (R function 'points_att_polygon').

# paths./processes/points-att-polygon/execution.post.description
Merge data points with the attributes of the study region the points fall into (R function 'points_att_polygon').

# paths./processes/points-att-polygon/execution.post.summary
Process Group points by region execution

# paths./processes/pred-extract.get.description
pred_extract. For extraction of environmental data used for species distribution modeling.

# paths./processes/pred-extract/execution.post.description
pred_extract. For extraction of environmental data used for species distribution modeling.

# paths./processes/pred-extract/execution.post.summary
Process pred extract for extraction of environmnetal data execution

# paths./processes/process-create-visualizations.get.description
Visualising final urban significant CLC-class specific dasymetric refinement input weights in table (output 1), error map of dasymetric refinement process at LAU (Local Areal Unit) level (output 2), and estimated human population density for target zones (output 3).

# paths./processes/process-create-visualizations/execution.post.description
Visualising final urban significant CLC-class specific dasymetric refinement input weights in table (output 1), error map of dasymetric refinement process at LAU (Local Areal Unit) level (output 2), and estimated human population density for target zones (output 3).

# paths./processes/process-create-visualizations/execution.post.summary
Process Visualising final outputs of dasymetric refinement process. execution

# paths./processes/process-dasymetric-refinement.get.description
Dasymetric refinement of human population from NUTS3 regions to significant urban CORINE CLC polygons using CLC-class-specific input weights.

# paths./processes/process-dasymetric-refinement/execution.post.description
Dasymetric refinement of human population from NUTS3 regions to significant urban CORINE CLC polygons using CLC-class-specific input weights.

# paths./processes/process-dasymetric-refinement/execution.post.summary
Process Dasymetric refinement of human population from NUTS3 regions to significant urban CORINE CLC polygons. execution

# paths./processes/process-interpolate-lau.get.description
Estimate annual human population for Eurostat LAU (Local Areal Units) regions based on area-weighted ancillary data and compare with observed LAU human population values.

# paths./processes/process-interpolate-lau/execution.post.description
Estimate annual human population for Eurostat LAU (Local Areal Units) regions based on area-weighted ancillary data and compare with observed LAU human population values.

# paths./processes/process-interpolate-lau/execution.post.summary
Process Measure precision of human population dasymetric refinement by comparing observed and estimated human population for EU LAU regions. execution

# paths./processes/process-interpolate-subbasins.get.description
Area-weighted interpolation of ancillary data to calculate human population in target regions. The ancillary dataset should contain a polygon ID column called 'nuts_cor_int_id' and an estimated human population numeric column called 'estPopCor'. The target dataset should contain a polygon ID column called 'OBJECTID'.

# paths./processes/process-interpolate-subbasins/execution.post.description
Area-weighted interpolation of ancillary data to calculate human population in target regions. The ancillary dataset should contain a polygon ID column called 'nuts_cor_int_id' and an estimated human population numeric column called 'estPopCor'. The target dataset should contain a polygon ID column called 'OBJECTID'.

# paths./processes/process-interpolate-subbasins/execution.post.summary
Process Area-weighted interpolation of ancillary data to calculate human population in target regions execution

# paths./processes/retrieve-biodiversity-data.get.description
This process retrieves biodiveryity data, i.e. occurrences for various species, from the sources GBIF, iNaturalist and VertNet. For more details, please ask BOKU.

# paths./processes/retrieve-biodiversity-data/execution.post.description
This process retrieves biodiveryity data, i.e. occurrences for various species, from the sources GBIF, iNaturalist and VertNet. For more details, please ask BOKU.

# paths./processes/retrieve-biodiversity-data/execution.post.requestBody.content.application/json.example.inputs.input_data
Squalius cephalus, Salmo trutta, Thymallus thymallus, Anguilla anguilla

# paths./processes/retrieve-biodiversity-data/execution.post.summary
Process Retrieve biodiversity data from the web execution

# paths./processes/riverload.get.description
This process computes daily river loading values from input data which consists of: 
((1)) TOC (Total Organic Carbon, mg/l) concentration values based on laboratory samples (in Syke's case coming from the VESLA database)
 ((2)) Daily discharge values (m3/s) observed by automated stations (in this example a station from River Vantaanjoki, HYDRO database). 

The process interpolates the TOC lab values into daily data series and then multiplies those with the discharge values to obtain daily loading (kg/day). 

The Syke interface providing the discharge values provides 500 data lines with a single service request. Thus, the number of input URLs can be more than one for longer time periods. 

For more details, please ask Syke. Please note that the values of conc. and load may be missing from the first and last lines of the output data. This is due to interpolation of concentration which was carried out between the first and last observations.

# paths./processes/riverload-plot.get.description
Analyse and plot results from the CORINE and Compute River Load -tools

# paths./processes/riverload-plot/execution.post.description
Analyse and plot results from the CORINE and Compute River Load -tools

# paths./processes/riverload/execution.post.description
This process computes daily river loading values from input data which consists of: 
((1)) TOC (Total Organic Carbon, mg/l) concentration values based on laboratory samples (in Syke's case coming from the VESLA database)
 ((2)) Daily discharge values (m3/s) observed by automated stations (in this example a station from River Vantaanjoki, HYDRO database). 

The process interpolates the TOC lab values into daily data series and then multiplies those with the discharge values to obtain daily loading (kg/day). 

The Syke interface providing the discharge values provides 500 data lines with a single service request. Thus, the number of input URLs can be more than one for longer time periods. 

For more details, please ask Syke. Please note that the values of conc. and load may be missing from the first and last lines of the output data. This is due to interpolation of concentration which was carried out between the first and last observations.

# paths./processes/swat-marine-connection.get.description
This code converts SWAT+ output (https://swat.tamu.edu/) of water quantity (Flow out and Temperature) in sqlite format to date input for the Marine Modelling System (based in MITgcm) in txt format. The tool uses R code and libraries DBI, RSQLite, and dplyr to correct the date format and dataframe structure.

# paths./processes/swat-marine-connection/execution.post.description
This code converts SWAT+ output (https://swat.tamu.edu/) of water quantity (Flow out and Temperature) in sqlite format to date input for the Marine Modelling System (based in MITgcm) in txt format. The tool uses R code and libraries DBI, RSQLite, and dplyr to correct the date format and dataframe structure.

# paths./processes/swat-marine-connection/execution.post.summary
Process SWAT+ output to Marine Modelling System (AquaINFRA) input connection tool execution

# paths./processes/swat-simulation.get.description
The Soil and Water Assessment Tool (SWAT) is a hydrological model used to simulate processes such as surface runoff, groundwater flow, and water quality across watersheds of varying scales. It evaluates the effects of land use, management practices, and climate scenarios on water resources. SWAT is widely used to study soil erosion, nonpoint source pollution, and watershed management (https://swat.tamu.edu/). The tool allows users to upload custom TxtInOut folders to model any watershed of interest in SWAT+ or SWAT2012 model version format. Users can define the simulation time range, calibration settings, output variables, and temporal resolution. This tool is using SWATrunR functions ( https://github.com/chrisschuerz/SWATplusR ) with executables rev60.5.7_64rel_linux (for SWAT+) and rev688_64rel_linux (for SWAT2012).

# paths./processes/swat-simulation/execution.post.description
The Soil and Water Assessment Tool (SWAT) is a hydrological model used to simulate processes such as surface runoff, groundwater flow, and water quality across watersheds of varying scales. It evaluates the effects of land use, management practices, and climate scenarios on water resources. SWAT is widely used to study soil erosion, nonpoint source pollution, and watershed management (https://swat.tamu.edu/). The tool allows users to upload custom TxtInOut folders to model any watershed of interest in SWAT+ or SWAT2012 model version format. Users can define the simulation time range, calibration settings, output variables, and temporal resolution. This tool is using SWATrunR functions ( https://github.com/chrisschuerz/SWATplusR ) with executables rev60.5.7_64rel_linux (for SWAT+) and rev688_64rel_linux (for SWAT2012).

# paths./processes/swat-simulation/execution.post.summary
Process SWAT, Soil and Water Assessment Tool execution

# paths./processes/swatplus-lulc-change.get.description
A lightweight tool for rapidly reassigning land use to selected Hydrological Response Units (HRUs) in a SWAT+ model. The user provides a CSV file specifying the target HRUs and their new land cover classes; the tool then updates the land_use field (lu_mgt) in the hru_data.hru file within the provided TxtInOut directory, and returns an updated TxtInOut ready to run on a remote server or download for local execution. Designed as a first approximation for exploring land use/land cover (LULC) change scenarios in watershed modelling, this tool offers a quick and accessible entry point before committing to more complex LULC change workflows.

# paths./processes/swatplus-lulc-change/execution.post.description
A lightweight tool for rapidly reassigning land use to selected Hydrological Response Units (HRUs) in a SWAT+ model. The user provides a CSV file specifying the target HRUs and their new land cover classes; the tool then updates the land_use field (lu_mgt) in the hru_data.hru file within the provided TxtInOut directory, and returns an updated TxtInOut ready to run on a remote server or download for local execution. Designed as a first approximation for exploring land use/land cover (LULC) change scenarios in watershed modelling, this tool offers a quick and accessible entry point before committing to more complex LULC change workflows.

# paths./processes/swatplus-lulc-change/execution.post.summary
Process Land Use Land Cover Change for SWAT+ Models execution

# paths./processes/trend-analysis-mk.get.description
This process performs a Man-Kendall trend analysis to identify the presence or absence of significant trends in time series data. It returns statistical analysis coefficients (e.g., p-values, tau, and slope) for each defined group, allowing for group-wise trend identification (R function 'trend_analysis_mk').

# paths./processes/trend-analysis-mk/execution.post.description
This process performs a Man-Kendall trend analysis to identify the presence or absence of significant trends in time series data. It returns statistical analysis coefficients (e.g., p-values, tau, and slope) for each defined group, allowing for group-wise trend identification (R function 'trend_analysis_mk').

# paths./processes/trend-analysis-mk/execution.post.summary
Process Man-Kendall Trend Analysis on Time Series execution

# paths./processes/ts-selection-interpolation.get.description
This process selects time series with sufficient data for interpolation and returns continuous time series where missing values (NAs) are replaced using linear interpolation (R function zoo::na.approx). The result is a complete time series without gaps. Timeseries with insufficient data are removed. / Split data into sub-tables for each season and HELCOM_ID separately. Create a list to store sub-tables of transparency (R function 'ts_selection_interpolation').

# paths./processes/ts-selection-interpolation/execution.post.description
This process selects time series with sufficient data for interpolation and returns continuous time series where missing values (NAs) are replaced using linear interpolation (R function zoo::na.approx). The result is a complete time series without gaps. Timeseries with insufficient data are removed. / Split data into sub-tables for each season and HELCOM_ID separately. Create a list to store sub-tables of transparency (R function 'ts_selection_interpolation').

# paths./processes/ts-selection-interpolation/execution.post.summary
Process Select and Interpolate Time Series execution

# paths./processes/weighting-functions.get.description
Mapping CORINE CLC raster to Eurostat 2021 Census Grid to calculate human population density-derived weights for significant urban CLC classes.

# paths./processes/weighting-functions/execution.post.description
Mapping CORINE CLC raster to Eurostat 2021 Census Grid to calculate human population density-derived weights for significant urban CLC classes.

# paths./processes/weighting-functions/execution.post.summary
Process Calculating human population density-derived weights for CORINE CLC classes. execution

# servers[].description
pygeoapi provides an API to geospatial data

# tags[].description
pygeoapi provides an API to geospatial data

# tags[].description
Global Deterministic Prediction System sample

# tags[].description
Sample metadata records from open.canada.ca