This World Elevation TopoBathy service combines topography (land elevation) and bathymetry (water depths) from various authoritative sources from across the globe. Heights are orthometric (sea level = 0), and bathymetric values are negative downward from sea level. The source data of land elevation in this service is same as in the Terrain layer. When possible, the water areas are represented by the best available bathymetry. Height/Depth units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select additional functions, applied on the server, that return rendered data. For visualizations such as hillshade or elevation tinted hillshade, consider using the appropriate server-side function defined on this service. Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns. NOTE: This image services combine data from different sources and resample the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the max extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percentage Hillshade Multi-Directional Hillshade Elevation Tinted HillshadeSlope MapMosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 is included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request. This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks. Disclaimer: Bathymetry data sources are not to be used for navigation/safety at sea.

Digital Elevation Models (DEMs) for the Murray-Darling Basin at 1 arc second, 25 metre and 5 metre resolution. Elevation for the whole MDB sourced from LIDAR where available at June 2021 and backfilled with hydrologically enforced 1 second SRTM. Developed as part of the Murray-Darling Water and Environment Research Program and Murray-Darling Ecosystems Function Project. Lineage:

LIDAR Lidar data was source from existing CSIRO past project LIDAR holdings as at June 2021. These holdings comprised of datasets of varying projections resolutions (mostly 5m).

LIDAR that was not already at 5m resolution were transformed to 5m. 1m LIDAR was aggregated to 5m (ArcGIS->SpatialAnalyst->Generalization->Aggregate, 5x5cell, MEAN) and 2m LIDAR was resampled to 5m (ArcGIS->DataManagement->RasterProcessing->Resample, bilinear). Once all input layers existed at 5m resolution they were merged (ArcGIS->DataManagement->RasterDataset->Mosiac) into a single target 5m raster in the GA Albers GCS (EPSG 3577).

Overlapping values of input layers were resolved by taking the MINIMUM of the overlapping cell values. Minimum was used to ensure that LIDAR images with actual in-channel elevations (due to being water free at the time flown) or those which had bathymetry enforced, were not overridden by hydro-flatttened in-channel values from an overlapping image. Visual inspection of the result found there to be little edge effect, with any visible “seams” between LIDAR edges deemed to be insignificant given the subsequent aggregation of the data to 25m that would occur for blending with the SRTM. The available LIDAR provided at continuous high resolution coverage along the length of the basin's major rivers save for a gap along the Namoi River between Wee Waa and Narrabri. This was augented with 5m photogrammetry elevation data.

Further details are given in: Teng J, Penton D, Marvanek S, Mateo C, Khanam F, Ticehurst C and Vaze J (2022) Description and metadata for a composite dataset used for development and validation of a predictive flood inundation and volume model. CSIRO, Australia.

This dataset includes conditional Net Value Change (cNVC) values summed by Fireshed Project Areas that intersect National Forest administrative boundaries in the Intermountain Region. Project Areas are approxiamately 25,000 acre accounting units nested within Firesheds. Values are classified in quartiles and given adjective ratings: Very High (>75th Percentile Loss)High (50th-75th Percentile Loss)Moderate (25th-50th Percentile Loss)Low (0-25th Percentile Loss).Housing Density and Critical Infrastructure (power lines and communication sites only) HVRA datasets were obtained from the Risk Management Assistance National Wildfire Risk Assessment (RMA-NWRA). The Surface Water HVRA uses the Forest to Faucets 2.0 (F2F) dataset with response functions from the RMA-NWRA. The analysis area used for modeling these three HVRAs includes the Firesheds that intersect the National Forest administrative boundaries and Wildfire Crisis Strategy Landscapes in the Intermountain Region.HVRA datasets used in the 2023 Assessment.HVRADESCRIPTIONHousing Density (HD)Housing Unit Density from Wildfire Risk to Communities (RMA-NWRA).Critical Infrastructure (CI)Combined infrastructure datasets, transmission lines and communication sites only, from national HIFLD open data (RMA-NWRA).Surface Water (SW)Forests to Faucets 2.0 data with Existing Vegetation Type (EVT) and slope covariates and response functions used in the RMA-NWRA. Only used IMP levels 6-10 (or top 50%).All HVRAs used annual burn probability (BP) data from 2020. The BP data are from the large-fire simulator (FSim) modeling, using a LANDFIRE 2020 fuelscape at 270m resolution. The flame length probability grids use WildEST modeling with a 2023 fuelscape incorporating disturbances through 2022. For risk calculations, the BP data were ‘up-sampled’ using the Pyrologix methodology (running two low-pass (3x3 pixels) focal-mean filters and a bilinear resample to 30m) on the BP raster to give pixels that are burnable at 30m resolution, but non-burnable at 270m resolution, a BP value.For details on the quantitative wildfire risk assessment process, refer to:Scott, Joe H.; Thompson, Matthew P.; Calkin, David E. 2013. A wildfire risk assessment framework for land and resource management. Gen. Tech. Rep. RMRS-GTR-315. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 83 p.

Housing Density and Critical Infrastructure (power lines and communication sites only) HVRA datasets were obtained from the Risk Management Assistance National Wildfire Risk Assessment (RMA-NWRA). The Surface Water HVRA uses the Forest to Faucets 2.0 (F2F) dataset with response functions from the RMA-NWRA. The analysis area used for modeling these three HVRAs includes the Sub-Firesheds that intersect the National Forest administrative boundaries in the Intermountain Region.HVRA datasets used in the 2025 Assessment.HVRADESCRIPTIONHousing Density (HD)Housing Unit Density from Wildfire Risk to Communities (RMA-NWRA).Critical Infrastructure (CI)Combined infrastructure datasets, transmission lines and communication sites only, from national HIFLD open data (RMA-NWRA).Surface Water (SW)Forests to Faucets 2.0 data with Existing Vegetation Type (EVT) and slope covariates and response functions used in the RMA-NWRA. Only used IMP levels 6-10 (or top 50%).The updated burn probability (BP) data represent 2023 conditions in the WCSL’s (disturbances through 2022) and 2020 conditions outside the WCSL’s. The Fire Modeling Institute (FMI) created this updated fuelscape within the WCSLs by updating LANDFIRE 2020 data with 2021-2022 disturbances using the LANDFIRE disturbance datasets and Fuel Rulesets Database. The BP data are from the large-fire simulator (FSim) modeling, at 270m resolution. For risk calculations, the BP data were ‘up-sampled’ using the Pyrologix methodology (running two low-pass (3x3 pixels) focal-mean filters and a bilinear resample to 30m) on the BP raster to give pixels that are burnable at 30m resolution, but non-burnable at 270m resolution, a BP value.The flame length probability grids were updated using WildEST modeling with a 2023 fuelscape at 30m resolution. The 2023 fuelscape was developed by FMI, similar to the one used for BP except it represents 2023 conditions for the entire analysis areas and not just the WCSL’s.For details on the quantitative wildfire risk assessment process, refer to:Scott, Joe H.; Thompson, Matthew P.; Calkin, David E. 2013. A wildfire risk assessment framework for land and resource management. Gen. Tech. Rep. RMRS-GTR-315. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 83 p.