Defra Network WMS server provided by the Environment Agency. See full dataset here.The Most Probable Overland Flow Pathway dataset is a polyline GIS vector dataset that describes the likely flow routes of water along with potential accumulations of diffuse pollution and soil erosion features over the land.It is a complete network for the entire country (England) produced from a hydro-enforced LIDAR 1-metre resolution digital terrain model (bare earth DTM) produced from the 2022 LIDAR Composite 1m Digital Terrain Model. Extensive processing on the data using auxiliary datasets (Selected OS Water Network, OS MasterMap features as well as some manual intervention) has resulted in a hydro-enforced DTM that significantly reduces the amount of non-real-world obstructions in the DTM. Although it does not consider infiltration potential of different land surfaces and soil types, it is instructive in broadly identifying potential problem areas in the landscape.The flow network is based upon theoretical one-hectare flow accumulations, meaning that any point along a network feature is likely to have a minimum of one-hectare of land potentially contributing to it. Each segment is attributed with an estimate of the mean slope along it.The product is comprised of 3 vector datasets; Probable Overland Flow Pathways, Detailed Watershed and Ponding and Errors. Where Flow Direction Grids have been derived, the D8 option was applied. All processing was carried out using ARCGIS Pro’s Spatial Analyst Hydrology tools. Outlined below is a description of each of the feature class.Probable Overland Flow Pathways The Probable Overland Flow Pathways layer is a polyline vector dataset that describes the probable locations accumulation of water over the Earth’s surface where it is assumed that there is no absorption of water through the soil. Every point along each of the features predicts an uphill contribution of a minimum of 1 hectare of land. The hydro-enforced LIDAR Digital Terrain Model 1-Metre Composite (2022) has been used to derive this data layer. Every effort has been used to digitally unblock real-world drainage features; however, some blockages remain (e.g. culverts and bridges. In these places the flow pathways should be disregarded. The Ponding field can be used to identify these erroneous pathways. They are flagged in the Ponding field with a “1”. Flow pathways are also attributed with a mean slope value which is calculated from the Length and the difference of the start and end point elevations. The maximum uphill flow accumulation area is also indicated for each flow pathway feature.Detailed Watersheds The Detailed Watersheds layer is a polygon vector dataset that describes theoretical catchment boundaries that have been derived from pour points extracted from every junction or node of a 1km2 Flow Accumulation dataset. The hydro-enforced LIDAR Digital Terrain Model 1-Metre Composite (2022) has been used to derive this data layer.Ponding Errors The Ponding and Errors layer is a polygon vector dataset that describes the presence of depressions in the landscape after the hydro-enforcing routine has been applied to the Digital Terrain Model. The Type field indicates whether the feature is Off-Line or On-Line. Off-Line is indicative of a feature that intersects with a watercourse and is likely to be an error in the Overland Flow pathways. On-line features do not intersect with watercourses and are more likely to be depressions in the landscape where standing water may accumulate. Only features of greater than 100m2 with a depth of greater than 20cm have been included. The layer was derived by filling the hydro-enforced DTM then subtracting the hydro-enforced DTM from the filled hydro-enforced DTM.Please use with caution in very flat areas and areas with highly modified drainage systems (e.g. fenlands of East Anglia and Somerset Levels). There will occasionally be errors associated with bridges, viaducts and culverts that were unable to be resolved with the hydro-enforcement process.

Source DataThe National Agriculture Imagery Program (NAIP) Color Infrared Imagery, captured in 2018 Processing Methodsdownloaded NAIP imagery tiles for all Southern Appalachian sky islands with spruce forest type present. Mosaiced individual imagery tiles by sky island. This step resulted in a single, seamless imagery raster dataset for each sky island.Changed the raster band combination of the mosaiced sky island imagery to visually enhance the spruce forest type from the other forest types. Typically, the band combination was Band 2 for Red, Band 3 for Green, and Band 1 for Blue. Utilizing the ArcGIS Pro Image Analyst extension, performed an image segmentation of the mosaiced sky island imagery. Segmentation is a process in which adjacent pixels with similar multispectral or spatial characteristics are grouped together. These objects represent partial or complete features on the landscape. In this case, it simplified the imagery to be more uniform by forest type present in the imagery, especially for the spruce forest type.Utilizing the segmented mosaiced sky island imagery, training samples were digitized. Training samples are areas in the imagery that contain representative sites of a classification type that are used to train the imagery classification. Adequate training samples were digitized for every classification type required for the imagery classification. The spruce forest type was included for every sky island. Classified the segmented mosaiced sky island imagery utilizing a Support Vector Machine (SVM) classifier. The SVM provides a powerful, supervised classification method that is less susceptible to noise, correlated bands, and an unbalanced number or size of training sites within each class and is widely used among researchers. This step took the segmented mosaiced sky island imagery and created a classified raster dataset based on the training sample classification scheme. Reclassified the classified dataset only retaining the spruce forest type and shadows class.Converted the spruce and shadows raster dataset to polygon.

World Water Bodies provides a detailed basemap layer for the lakes, seas, oceans, large rivers, and dry salt flats of the world.

World Water Bodies represents the open water rivers, lakes, dry salt flats, seas, and oceans of the world.For complete hydrographic coverage, use this dataset in conjunction with the World Linear Water dataset.

Source DataThe National Agriculture Imagery Program (NAIP) Color Infrared Imagery, captured in 2018 Processing Methodsdownloaded NAIP imagery tiles for all Southern Appalachian sky islands with spruce forest type present. Mosaiced individual imagery tiles by sky island. This step resulted in a single, seamless imagery raster dataset for each sky island.Changed the raster band combination of the mosaiced sky island imagery to visually enhance the spruce forest type from the other forest types. Typically, the band combination was Band 2 for Red, Band 3 for Green, and Band 1 for Blue. Utilizing the ArcGIS Pro Image Analyst extension, performed an image segmentation of the mosaiced sky island imagery. Segmentation is a process in which adjacent pixels with similar multispectral or spatial characteristics are grouped together. These objects represent partial or complete features on the landscape. In this case, it simplified the imagery to be more uniform by forest type present in the imagery, especially for the spruce forest type.Utilizing the segmented mosaiced sky island imagery, training samples were digitized. Training samples are areas in the imagery that contain representative sites of a classification type that are used to train the imagery classification. Adequate training samples were digitized for every classification type required for the imagery classification. The spruce forest type was included for every sky island. Classified the segmented mosaiced sky island imagery utilizing a Support Vector Machine (SVM) classifier. The SVM provides a powerful, supervised classification method that is less susceptible to noise, correlated bands, and an unbalanced number or size of training sites within each class and is widely used among researchers. This step took the segmented mosaiced sky island imagery and created a classified raster dataset based on the training sample classification scheme. Reclassified the classified dataset only retaining the spruce forest type and shadows class.Converted the spruce and shadows raster dataset to polygon.

Shows the line feature class built from the Lake Simcoe Protection Act Watershed Boundary (LSPAWB) is owned by the Ontario Ministry of Environment (MOE), in cooperation with Lake Simcoe Region Conservation Authority (LSRCA) and the Ontario Ministry of Natural Resources (MNR), and was created to support the Lake Simcoe Protection Act. The boundary was first created by LSRCA in 2005 as part of a Source Water Protection boundary project using the LSRCA 2002 5-metre Digital Elevation Model (DEM) and the best available data at the time. The portions of the watershed where the boundary shared a border with the Toronto and Region Conservation Authority and the Nottawasga Valley Conservation Authority were manually reviewed and updated though a series of consultations between the agencies. The boundary was created in ArcHydro using following procedures: 1) The updated watercourse was burned into the DEM, which generates Re-conditioned DEM; 2) Sinks in the Re-conditioned DEM were filled; 3) Flow direction grid was generated from the DEM updated in Steps 2); 4) Flow accumulation grid was generated; 5) Stream Definition grid was generated; 6) Stream Definition Segmentation grid was generated from the result in Step 5); 7) Catchment Grid was generated based on the Stream Definition Segmentation grid; 8) Catchment Grid was then converted to catchment polygons; 9) Drainage lines were generated; 10) Then we generated adjoint catchments and drainage points feature classes; 11) Subwatershed outlet points was digitized in a new feature class layer for the all sub watersheds for which we need to delineate boundaries 12) Run subwatershed delineation process; 13) The subwatersheds where dissolved to create one polygon. In 2008-09 in preparation for the Lake Simcoe Protection Plan data collection was performed on the hydrology layers for the Ramara Creeks, Talbot River, Upper Talbot River, and Whites Creek Subwatersheds of the watershed and the watershed boundary in that area was revised. The following steps were used in the delineation of the boundary in the north eastern portion of the watershed. The same Arc Hydro steps were followed as in the original boundary creation except for one additional step in which the culverts were burned into the DEM at the same time the watercourse was. The dataset was in turn converted to geodatabase format and additional attribute information was added to support the Act and the utility of the product for the end-user.