Farm dam properties

Welcome to – a free interactive portal to share data on Australian farm dams

Australia is the second driest continent on Earth and freshwater is therefore a critical policy concern. Freshwater supplies are likely to worsen under anthropogenic climate change, with increasing droughts, declining rainfalls and greater evaporation. Population growth will nearly double food consumptions by 2050 and Australia will struggle to meet future freshwater demands.

Our mission is to improve water security in Australia by bringing together the latest scientific knowledge, tools and applications on precious freshwater stored in farm dams.

Farm dams are ubiquitous and drive AU$17.7 billion of agricultural value in Australia. Yet, we don’t even know how many they are! There has never been a census of Australian farm dams, with only ballpark estimates ranging from “half a million” to “several millions” reported by Federal documents and scientific articles.

We designed to meet the need for a nation-wide database on density, distribution, water capacity, and historical trends of artificial dams in Australia. Just navigate on our map to any area of Australia to access data and generate statistics, plots and tables on various aspects of farm dams in the specific region.

Farm dams are key for several environmental and ecological processes – they support biodiversity, regulate greenhouse gas emissions, and cycle nutrients – and we hope that will facilitate the communication of basic information to help turning research into management practice, both in Australia and worldwide. We are committed to keep expanding the data in as they become available.

Martino Malerba –,
Nicholas Wright –
Peter Macreadie –

Please get in touch with any comments or questions.

Our farm dam database is now published in Remote Sensing! The PDF is freely available at: See the article for all details of our methods.

Malerba, Martino E., Nicholas Wright, and Peter I. Macreadie. 2021. "A Continental-Scale Assessment of Density, Size, Distribution and Historical Trends of Farm Dams Using Deep Learning Convolutional Neural Networks." Remote Sensing 13 (2):319.



1 2 3