<div>A document outlining how geoscience data can be useful for natural resource managers and engagement tool for geoscientists interacting with these people.</div><div><br></div>

<div>The Groundwater Dependent Ecosystem (GDE) Atlas (Bureau of Meteorology, 2019) is a well-known national product that has been utilised for a wide range of applications including environmental impact statements, water planning and research. A complementary GDE dataset, Groundwater Dependent Waterbodies (GDW), has been produced from Digital Earth Australia (DEA) national data products. This new GDW ArcGIS dataset is spatially aligned with Landsat satellite-derived products, enabling ready integration with other spatial data to map and characterise GDEs across the continent.</div><div><br></div><div>The DEA Water Observations Multi Year Statistics (Mueller et al. 2016; DEA 2019) and the DEA Waterbodies (version 2) data product (Kraus et al., 2021; DEA Waterbodies, 2022) have been combined with the national GDE Atlas to produce the GDW dataset which delineates surface waterbodies that are known and/or high potential aquatic GDEs. The potential of a GDE relates to the confidence that the mapped feature is a GDE, where known GDEs have been mapped from regional studies and high potential GDEs identified from regional or national studies (Nation et al., 2017). The GDW dataset are aquatic GDE waterbodies, including springs, rivers, lakes and wetlands, which rely on a surface expression of groundwater to meet some or all of their water requirements. </div><div><br></div><div>The DEA Water Observation Multi Year Statistics, based on Collection 3 Landsat satellite imagery, shows the percentage of wet observations in the landscape relative to the total number of clear observations since 1986. DEA Waterbodies identifies the locations of waterbodies across Australia that are present for greater than 10% of the time and are larger than 2700m2 (3 Landsat pixels) in size. These waterbodies include GDEs and non-GDEs (e.g. surface water features not reliant on groundwater, such as dams). Where known/high potential GDEs in the GDE Atlas intersected a DEA waterbody, the entire waterbody polygon was assigned as a potential GDW, resulting in 55,799 waterbodies in the GDW dataset. Conversely, any GDEs not classified as known/high potential GDEs in the Atlas, due to a lack of data, are not included in the GDW product. Even though this method should remove dams from the GDW dataset (assuming they have been assigned appropriately in the GDE Atlas), due to spatial misalignment some may still be included that are not potential GDEs. Furthermore, surface water features that are too small to be detected by Landsat satellite data will be excluded from the GDW dataset.</div><div><br></div><div>The GDW polygons were attributed with the spatial summary of maximum, median, mean and minimum percentages for pixels within each GDW, derived from the DEA Water Observation Multi Year Statistics i.e. maximum/minimum pixel value or median/mean across all pixels in the GDW. This attribute enables comparison between GDWs of the proportion of time they have surface water observed. An additional attribute was added to the GDW dataset to indicate amount of overlap between waterbodies and aquatic GDEs in the GDE Atlas.&nbsp;</div><div><br></div><div>An ESRI dataset, AquaticGDW.gdb, and a variety of national ArcGIS layer files have been produced using the spatial summary statistics in the GDW dataset.</div><div>These provide a first-pass representation of known/high potential aquatic GDEs and their surface water persistence, derived consistently from Landsat satellite imagery across Australia.</div><div><br></div><div><strong>References:</strong></div><div> </div><div>Bureau of Meteorology, 2019. <em>Groundwater Dependent Ecosystems Atlas</em>. http://www.bom.gov.au/water/groundwater/gde/index.shtml </div><div>&nbsp;</div><div>DEA Water Observations Statistics, 2019. https://cmi.ga.gov.au/data-products/dea/686/dea-water-observations-statistics-landsat</div><div><br></div><div>DEA Waterbodies, 2022. https://www.dea.ga.gov.au/products/dea-waterbodies</div><div><br></div><div>Krause, C.E., Newey, V., Alger, M.J., and Lymburner, L., 2021. Mapping and Monitoring the Multi-Decadal Dynamics of Australia’s Open Waterbodies Using Landsat, <em>Remote Sensing</em>, 13(8), 1437. https://doi.org/10.3390/rs13081437</div><div><br></div><div>Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S. and Ip, A., 2016. Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia. <em>Remote Sensing of Environment</em>, 174, 341-352, ISSN 0034-4257.</div><div><br></div><div>Nation, E.R., Elsum, L., Glanville, K., Carrara, E. and Elmahdi, A., 2017. Updating the Atlas of Groundwater Dependent Ecosystems in response to user demand, 22nd International Congress on Modelling and Simulation, Hobart, Tasmania, mssanz.org.au/modsim2017</div>