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n8hz7_v1 A continental-scale assessment of density, size, distribution, and historical trends of Australian farm dams Farm dams are a cornerstone of modern agriculture and are ubiquitous across rural landscapes, but their intensifying effects on the environment remain unclear. As a striking example, Australia is the second driest continent on Earth, yet most farm dams are unmonitored and unregulated. As a result, there are no nation-wide statistics on how many farm dams exist or their historical trends. In this study, we trained a deep-learning convolutional neural network model to carry out the first continental-scale assessment on density, distribution, and historical trends of Australian farm dams using satellite imagery. We estimated that Australia has 1.765 million farm dams occupying an area of 4,678 Km2 and storing 11,396 GL of water. The State of New South Wales recorded the highest number of farm dams (654,983; 37% of the total) and Victoria the highest overall density (1.73 dams Km-2). We also estimated that 202,119 farm dams (11.5%) remain omitted from any records, especially in South Australia, Western Australia, and the Northern Territory. Three decades of historical records revealed an ongoing decrease in the construction rate of farm dam, from >3% per annum before 2000, to ~1% after 2000, to <0.05% after 2010 – except in the Australian Capital Territory where rates have remained relatively high. To facilitate sharing information with the Government, scientists, managers, and the local community, we developed AusDams.org: a free interactive portal to visualise the distribution of farm dams and generate statistics for any area of Australia. 2020-08-31T22:35:53.734576 2021-02-08T21:35:30.138219 2020-09-01T16:00:34.109739     eartharxiv 0 withdrawn 1 1 https://doi.org/10.31223/osf.io/n8hz7 CC-By Attribution 4.0 International Australian management policies; GIS technology; artificial water bodies; deep learning classification model; freshwater reserves; invasive species; land-use change; spatially explicit dataset; urbanisation rates; water security ["Australian management policies", "GIS technology", "artificial water bodies", "deep learning classification model", "freshwater reserves", "invasive species", "land-use change", "spatially explicit dataset", "urbanisation rates", "water security"] Martino E. Malerba; Nicholas Wright; Peter Macreadie [{"id": "45gct", "name": "Martino E. Malerba", "index": 0, "orcid": "0000-0002-7480-4779", "bibliographic": true}, {"id": "b3rpf", "name": "Nicholas Wright", "index": 1, "orcid": null, "bibliographic": true}, {"id": "3u9yb", "name": "Peter Macreadie", "index": 2, "orcid": null, "bibliographic": true}] Martino E. Malerba Physical Sciences and Mathematics; Environmental Sciences; Environmental Monitoring; Natural Resources Management and Policy; Water Resource Management; Natural Resources and Conservation [{"id": "59ea64a954be8111216c1395", "text": "Physical Sciences and Mathematics"}, {"id": "59ea64aa54be8111216c13c1", "text": "Environmental Sciences"}, {"id": "59ea64aa54be8111216c13c7", "text": "Environmental Monitoring"}, {"id": "59ea64aa54be8111216c13c8", "text": "Natural Resources Management and Policy"}, {"id": "59ea64aa54be8111216c13ca", "text": "Water Resource Management"}, {"id": "59ea64aa54be8111216c13cc", "text": "Natural Resources and Conservation"}]   0   no not_applicable []   2025-04-09T20:04:15.759367