The U.S. Geological Survey (USGS), prepared geographic information systems (GIS) layers for use in the West Virginia StreamStats application. The Digital Elevation Model and associated data were hydrologically conditioned, which is the process of burning in single line streams at the 1:24,000 scale into a digital elevation model to produce flow direction and flow accumulation grids. This data includes geotif images for a 10 meter digital elevation model, a flow direction, and a flow accumulation raster/grid image for the WV Streamstats area. The 34 HUCs represented by this dataset are 02070001, 02070002, 02070003, 02070004, 02070005, 02070006, 02070007, 05020001, 05020002,05020003, 05020004, 05020005, 05020006, 05030101, 05030106, 05030201, 05030202, 05030203, 05050001, 05050002, 05050003, 05050004,05050005, 05050006, 05050007, 05050008, 05050009, 05070101, 05070102, 05070201, 05070202, 05070204, 05090101, and 05090102.

The U.S. Geological Survey (USGS), in cooperation with the New Jersey Department of Environmental Protection (NJDEP), prepared hydro-conditioned geographic information systems (GIS) data layers for use in the updated New Jersey StreamStats 2022 application (U.S. Geological Survey, 2022). This update features improvements in base-elevation resolution from 10 meters to 10 feet and stream centerline hydrography from 1:24,000 to 1:2,400 scale. Hydro conditioning is the process of burning single-line stream centerlines at the 1:2,400 scale into a digital elevation model to produce flow direction and flow accumulation grids. This data release contains raster digital datasets for a 10-foot digital elevation model, a flow direction grid, and a flow accumulation grid for the updated New Jersey Streamstats 2022 application. The eleven 8-digit Hydrologic Unit Codes (HUCs) represented by this dataset are 02020007, 02030103, 02030104, 02030105, 02040104, 02040105, 02040201, 02040202, 02040206, 02040301, and 02040302 (U.S. Geological Survey, 2016). The updated New Jersey StreamStats 2022 application provides access to spatial analytical tools that are useful for water-resources planning and management, as well as engineering and design purposes. The map-based user interface can be used to delineate drainage areas, determine basin characteristics, and estimate flow statistics, including instantaneous flood discharge, monthly flow-duration, and monthly low-flow frequency statistics for ungaged streams. References cited: U.S. Geological Survey, 2016, National Hydrography: U.S. Geological Survey, accessed February 4, 2022, at https://www.usgs.gov/national-hydrography. U.S. Geological Survey, 2022, StreamStats v4.6.2: U.S. Geological Survey, accessed February 4, 2022, at https://streamstats.usgs.gov/ss/.

A raster model with a cell size of 1 with values representing the weight of all cells flowing into each downslope cell. Higher weights represent areas with more water accumulation. In this raster, the cells have been recalculated to select only the centerlines of weighed cells (the highest values).

The U.S. Geological Survey (USGS), in cooperation with the Illinois Center for Transportation and the Illinois Department of Transportation, prepared hydro-conditioned geographic information systems (GIS) layers for use in the Illinois StreamStats application. These data were used to delineate drainage basins and compute basin characteristics for updated peak flow and flow duration regression equations for Illinois. This dataset consists of raster grid files for elevation (dem), flow accumulation (fac), flow direction (fdr), and stream definition (str900) for each 8-digit Hydrologic Unit Code (HUC) area in Illinois merged into a single dataset. There are 51 full or partial HUC 8s represented by this data set: 04040002, 05120108, 05120109, 05120111, 05120112, 05120113, 05120114, 05120115, 05140202, 05140203, 05140204, 05140206, 07060005, 07080101, 07080104, 07090001, 07090002, 07090003, 07090004, 07090005, 07090006, 07090007, 07110001, 07110004, 07110009, 07120001, 07120002, 071200 ...

USGS delineation: Rivers and Streams. Originally harvested from Inside Idaho (https://insideidaho.org), and clipped to the RCEW watershed. Lauer delineations: Streams were delineated from a 1m DEM derived from the 2014 LiDAR. First, the DEM was prepared for hydrologic processing by smoothing the model with a low-pass filter, filling NODATA holes with FocalStatistics, and filling sinks with the Fill tool. The cleaned DEM was then used to produce flow direction and accumulation maps using their respective tools in ArcMap. The flow accumulation raster was reduced to areas of accumulation greater than 0.1km^2 to avoid delineating small drainages without likely surficial flow. Then stream links and stream order maps were produced from the reduced flow map and converted to polylines using the Stream to Feature tool. Finally, lines were smoothed with a sensitivity of 3m using the PAEK algorithm in the Smooth Line tool.For the stream features, two stream networks were created with differing minimum accumulation areas, 0.1km^2 (this layer) and 1km^2 . The 1km^2 stream network likely has the closest accuracy to consistently flowing streams, but a careful evaluation by researchers more familiar with field work in the area is prudent to eliminate or label intermittent or ephemeral stream segments.

The flow accumulation tool calculates the number of all cells flowing into each downslope cell in the output raster. A flow accumulation raster was generated in GIS for Baltimore County using a 2014 Digital Elevation Model (cell size equal to 2.5 ft by 2.5). To generate the shown flow accumulation lines, the flow accumulation raster was filtered to only include cells that receive flows from 500 or more other cells.

Flow accumulation grid generated from 10 meter DEM, Andrews Experimental Forest. This grid is useful for determining the area of land that drains to a point. The user selects a point on the grid, and the value of that point represents the area (in 100 square meters) that drain to the point. This grid can also be used for generating watershed boundaries and stream networks.

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.

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. On-Line is indicative of a feature that intersects with a watercourse and is likely to be an error in the Overland Flow pathways. Off-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. Attribution statement: © Environment Agency copyright and/or database right 2023. All rights reserved.

A new data set of watersheds and river networks is available, being derived primarily from the TerrainBase Global DTM (Digital Terrain Model) with additional information from the CIA World Data Bank II. These data are useful in hydrologic modeling, and can serve as a basemap for routing continental runoff to the appropriate coast, and therefore into the appropriate ocean or inland sea in a variety of methods. Using this data set, the runoff produced in any grid cell, when coupled with a routing algorithm, can easily be transported to the appropriate water body and distributed across that water body as desired. The data set includes watershed and flow direction information, as well as supporting hydrologic data at 5 minute, 1/2 degree, and 1 degree resolutions globally.

The dataset is composed of nine spatial layers, each delineated at 3 resolutions. These layers include:

Land/Sea Mask -- Determined from TerrainBase DTM elevation data after conversion to GIS format. Some manual correction was also performed in land areas with elevations below sea level.

Flow Direction Data -- Derived from the filled digital elevation model produced from the land/sea mask and the TerrainBase DTM using GIS. Elevation data conditioned before filling by burning in rivers from the CIA World Data Bank II with subsequent manual corrections for discrepancies between model coastlines. Flow direction data in Antarctica altered to ensure valid flow direction information.

Flow Accumulation Data -- Derived from the flow direction data in GIS after correction.

Rivers Delineation -- Created from flow accumulation data based on threshold values using GIS. Greenland and Antarctica removed from river delineation.

55 Large Watersheds Delineation -- Derived from flow direction and flow accumulation data using GIS. Watershed selection conducted as combination of largest watersheds and those rivers suggested by Russell and Miller [1990] for use in global climate modeling studies.

Internally Draining Regions -- Defined as those large regions of Africa, central Eurasia, and Australia which were internally draining. Derived using GIS based on the original elevation data and closed depressions.

19 Large-Scale Drainage Regions -- Derived as watersheds from the flow direction data in conjunction with coastal cells in GIS. The 19 basins were originally selected as land-water body pairings for use in the National Center for Atmospheric Research (NCAR) Climate System Model (CSM).

19 Large-Scale Drainage Regions Including Water Bodies -- 19 large-scale drainage regions including water bodies were derived by computing nearest neighbor data values for grid cells without data values in GIS. Artificial divisions have been included in some areas as they are common geographic modeling divisions, or otherwise create a better separation of drainage regions.

Lakes Delineation -- Created in GIS by gridding water bodies from the CIA World Data Bank II.

Runoff Data -- Runoff data was taken from Perry et al. [1996] and UNESCO [1974] for the 55 rivers selected in this study. ASCII data file data (tabular) with associated geographic and political reference information.

Technical reports describing data analysis and integration are online at [http://www.ngdc.noaa.gov/seg/cdroms/graham/graham/graham.htm].

Coweeta LTER researchers sampled fifty-eight stream sites in the Upper Little Tennessee River Basin in February and June of 2009. Sites were selected to represent the range of land cover and land use within the basin. Samples were taken over three days of stable weather and discharge during periods of baseflow. They were used to characterize conditions across the basin during the growing and the non-growing seasons without the influence of elevated discharge. The GIS data presented was used to both help in selecting the 58 sites. The GIS files are split between raster and vector files. The files are in .zip archives. There are text files with information about each GIS in the .zip files.

Pour Point, LV Watershed, raster, 2000 Reference Information and Units: GCS: WGS 1984 Projection: SR-ORG:7483 (http://spatialreference.org/) Pixel Size: ~90 meters File Naming Convention: Snapped_Pour.tif Data Origin: Developed at Salisbury University Sensor: SRTM Code: NA Data Development/Processing: Data was developed using a Flow Accumulation dataset that was developed with 90m SRTM data within ArcGIS. The Snap Pour Point tool was used within ArcGIS to create this data.

Abstract

The dataset was derived by the Bioregional Assessment Programme from Geodata: 9 second DEM and the National surface water sites Hydstra. The source datasets for this dataset are identified in the Lineage field of the metadata statement. The history field in the metadata describes the processes undertaken to produce this dataset.

Contributing areas (residual catchments) for selected stream gauges in the Galilee subregion.

Dataset History

Gauge locations were snapped to GA DEM 9s derived flow accumulation lines representing their respective rivers. ArcGIS WATERSHED tool was run on the GA 9s flow direction raster using the snapped gauge locations as pour points to produce catchments areas for the selected gauges. Output rasters were converted to vector polygon shapefiles to delineate catchment boundaries for individual gauges.

Dataset Citation

Bioregional Assessment Programme (2014) Galilee gauge contributing area. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/2e01c3cf-8aa6-45a6-8c77-b25f025fe629.

Dataset Ancestors

• Derived From National Surface Water sites Hydstra

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

Dataset is a section of the South Fork Eel River in northern California near its headwaters. It is a shapefile of the river network derived from LiDAR DEM using a standard flow accumulation algorithm. Each reach has drainage area in square kilometers and a reach-averaged slope.

Malaysia, particularly Pahang, experiences devastating floods annually, causing significant damage. The objective of the research was to create a flood susceptibility map for the designated area by employing an Ensemble Machine Learning (EML) algorithm based on geographic information system (GIS). By analyzing nine key factors from a geospatial database, flood susceptibility map was created with the ArcGIS software (ESRI ArcGIS Pro v3.0.1 x64). The Random Forest (RF) model was employed in this study to categorize the study area into distinct flood susceptibility classes. The Feature selection (FS) method was used to ranking the flood influencing factors. To validate the flood susceptibility models, standard statistical measures and the Area Under the Curve (AUC) were employed. The FS ranking demonstrated that the primary attributes to flooding in the study region are rainfall and elevation, with slope, geology, curvature, flow accumulation, flow direction, distance from the river, and land use/land cover (LULC) patterns ranking subsequently. The categories of ’very high’ and ’high’ class collectively made up 37.1% and 26.3% of the total area, respectively. The flood vulnerability assessment of Pahang found that the Eastern, Southern, and central regions were at high risk of flooding due to intense precipitation, low-lying topography with steep inclines, proximity to the shoreline and rivers, and abundant flooded vegetation, crops, urban areas, bare ground, and rangeland. Conversely, areas with dense tree canopies or forests were less susceptible to flooding in this research area. The ROC analysis demonstrated strong performance on the validation datasets, with an AUC value of >0.73 and accuracy scores exceeding 0.71. Research on flood susceptibility mapping can enhance risk reduction strategies and improve flood management in vulnerable areas. Technological advancements and expertise provide opportunities for more sophisticated methods, leading to better prepared and resilient communities.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset includes files for catchment boundaries and SILO cell percentages.

The catchment boundaries were delineated using 1 sec Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) dataset with gauge locations as pour points. Catchments were then intersected with SILO climate cells for use in gridded AWRA hydrological modelling. The new boundaries are better then those delineated using the Bureau of Meteorology's (BOM) Geofabric for all catchments, except for the catchment 209003 where a flaw in the SRTM derived flowpath excluded a valley when compared with the Geofabric. This catchment was manually edited to conform with the Geofabric representation of 209003.

The grid cell percentage was calculated for all 5km resolution SILO grid cells located within each catchment. The percentage information is used for gridded AWRA hydrological modelling and for weighting average for estimating streamflow aggregated at each gauge outlet.

Purpose

This dataset is intermediate, delineated using 1-sec SRTM DEM dataset, and is used for AWRA hydrological modelling.

Dataset History

A point feature dataset was created from the gauge location spreadsheet. This was compared with SRTM 1 sec derived flow accumulation and ArcMap Basemap imagery and point features manually snapped to the SRTM flowpaths reperesenting the river names in the gauge name.

Contributing catchment rasters were then generated using the ArcGIS 10.1 Spatial Analyst Tool "WATERSHED with snapped gauge location pour points and the SRTM derived flow direction grid as inputs.

Resultant catchment rasters were then exported as shapefiles and compared with the coarser Geofabric catchments. All but one catchment conformed in extent. SRTM derived catchment for gauge 209003 excluded a large flat valley (when compared to with the geogfabric due to a misdirected flowpath. This was manually corrected.

Catchment shapefiles were then intersected with BILO Climate model cells to obtain 1) percentage of cell in the catchment and 2) proportion of catchment made up by the cell. Both these determinations were based on intersected areas calculated on the Albers equal area projection.

These cell to catchments ratios were then exported to database file (dbf).

Dataset Citation

Bioregional Assessment Programme (2014) Selected catchment boundaries and their SILO cell percentages for AWRA modelling for the Gloucester subregion. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/38f3a272-66cc-4fe2-bef9-ff4aedf6fc24.

Dataset Ancestors

• Derived From GLO SW Model Calibration Gauges v01

• Derived From Gloucester Surface Water Discharge & Quality extract v1 060314

• Derived From Geofabric Surface Catchments - V2.1

• Derived From BA SYD 1 sec SRTM (h) DEM and hydrological derivatives

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM)

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From Climate model 0.05x0.05 cells and cell centroids

Runoff Attenuation Features Potential is our best estimate of locations of high flow accumulation across the land surface or in smaller channels, where it may be possible to temporarily store water and attenuate flooding during high flows. The dataset is designed to support signposting of areas where to target enhanced storage. It is based upon the Risk of Flooding from Surface Water datasets and identifies areas of high flow accumulations for the 3.3% Annual Exceedance Probability surface water maps. The areas of ponding or accumulation are between 100 and 5000 metres squared, and have been tagged where they fall on an area of slope steeper than 6% as gully blocking opportunities. All the potential areas have been constrained so that they are not in urban areas or on roads, rails or canals.The data does not does not provide information on design, which may need to consider issues such as drain-down between flood events. It is important to note that land ownership and change to flood risk have not been considered. Locations identified may have more recent building or land use than available.A GIS tool developed by JBA, called JRAFF (JBA Runoff Attenuation Feature Finder) was used to analyse potential for Runoff Attenuation Features. This identifies areas of high flow accumulation from the Risk of Flooding from Surface Water Depth 3.3 percent annual chance map that could be targeted as opportunities for enhanced temporary storage. OS Terrain 50 (2016) was used to determine gully blocking potential, based on a threshold of > 6 degrees (10%). A constraint layer was applied based on CORINE Land Cover Urban layer (2012), OS Open Map Local – Roads, Rail and Building layers (2016) and OS Open Rivers – Canal layer (2016).

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement.

The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Contributing areas (residual catchments) for 61 node catchments in the Galilee subregion.

Dataset History

Node locations were snapped to GA DEM 9s derived flow accumulation lines representing their respective rivers. ArcGIS WATERSHED tool was run on the GA 9s flow direction raster using the snapped node locations as pour points to produce catchments areas for the nodes. Raster outputs were converted to vector polygon shapefiles to delineate node catchment boundary.

Dataset Citation

Bioregional Assessment Programme (2014) Node catchment for Galilee surface water modelling. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/2907c473-8824-42a1-9b6e-3ed4ba2aedc6.

Dataset Ancestors

• Derived From Onsite and offsite mine infrastructure for the Carmichael Coal Mine and Rail Project, Adani Mining Pty Ltd 2012

• Derived From Alpha Coal Project Environmental Impact Statement

• Derived From QLD Exploration and Production Tenements (20140728)

• Derived From China Stone Coal Project initial advice statement

• Derived From Kevin's Corner Project Environmental Impact Statement

• Derived From Galilee surface water modelling nodes

• Derived From Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale

• Derived From China First Galilee Coal Project Environmental Impact Assessment

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From Seven coal mines included in Galilee surface water modelling

The main objective for this component of the study was to improve the extent and locational accuracy of 1st and 2nd order watercourses, which are often difficult to locate and map when working at a large scale such as the Muskoka River Watershed. The DTM used was 50cm bare-earth terrain derived from a classified lidar point cloud, which provided a much higher resolution picture of the study area’s topography than previously available. To ensure the watercourse modelling was as detailed as possible the DTM was not resampled and was kept at the original 50cm resolution. Flow Direction was determined first using the D8 algorithm, followed by flow accumulation. A flow accumulation cut-off of 175000 pixels was used after careful comparison with known watercourses. Watercourses were the extracted from the flow accumulation raster, simplified and connected across the watershed.The flow direction has been set to match the start/end geometry of the line. Flow direction has been set for the geometric network using the source/sinks option. Flow direction can be viewed by selecting the "display arrows" button on the Utility Network Analyst toolbar in ArcMap.Aquatic Ecosystem Classification mapping processes and techniques were applied to the Muskoka River Watershed LiDAR watercourses to quantify general attributes using a combination of existing data and modeling. Primary attributes were calculated including:  Reach, Reach Slope, Reach Catchment Area, Velocity Class, Network Line Type. For full methods on the application of the AEC reference Muskoka River Watershed IWM Natural Capital Inventory - Addendum 5: Aquatic Ecosystem Classification.

This data set provides estimates of watershed deforestation, as a proportion of the total area of watersheds, in Rondonia, Brazil for 1999. Deforestation maps were determined for the main agricultural and surrounding forested areas of Rondonia using multiple Landsat TM scenes (Biggs et al. 2008). Cumulative deforestation estimates were derived from this time series of Landsat scenes from 1975 to 1999. To obtain watershed-level estimates of deforestation, watershed boundaries and stream networks were delineated by a flow accumulation algorithm using a 90-m resolution digital elevation model (DEM) from NASA's Shuttle Radar Topography Mission (SRTM). The results were watersheds of seven Strahler stream orders (1-7) with stream networks that closely matched those of the 1:100,000 topographic maps for the area. The watershed boundaries, classified by stream order, were overlain on the time series of deforestation maps to determine the cumulative deforestation extent in 1999.

This data set contains six ESRI ArcGIS shapefiles of the watershed boundaries for streams orders 2-7, the smallest watershed (second order) to the largest inclusive watershed (seventh order). The cumulative deforestation estimates, as a proportion of total area for each watershed, are available as a comma-delimited text file that can be related to the individual watershed boundary shapefiles. Cumulative deforestation data are available for first order streams, although not as a shapefile.

There are six zipped ESRI ArcGIS shapefiles (*.zip) and one ASCII comma separated file with this data set.