A decision support system for matching irrigation demand and supply in a near real time environment

In Australia, recent drought conditions and climate change concerns have highlighted the need to manage water resources more sustainably especially in the Murray Darling Basin (MDB), which utilizes more than 70% of water for food production. Typically, improving water management in irrigated areas requires the analysis of real-time water demand to determine the options available to improve efficiencies in irrigation water’s distribution and use whilst enhancing its utility. Real-time water demand information in irrigated areas is a key for planning about sustainable use of irrigation water as it informs decision making. These activities are needed not only to improve water productivity, but also to increase the sustainability of irrigated agriculture by reducing irrigation water losses and the environmental footprint of irrigation activities. This paper presents an application of a holistic systematic approach of water accounting coupled with remote sensing and GIS technique at multiple scales (farms to 22 sub-irrigation systems and irrigation system) to evaluate actual water use efficiency and productivity in Coleambally Irrigation Area ‘CIA’ (world first gravity channel irrigation system with an area of 79,000 ha to achieve more than 90% delivery efficiency) located in the Murrumbidgee river basin, a major food bowl of the MDB. All hydrological data of inflow (i.e. surface water supplies, tube wells pumping, rainfall and capillary upflow) and outflow components (i.e. actual evapotranspiration, deep drainage, and surface outflow) were measured for all established sub-systems of the CIA. Mapping of actual water consumption from various agriculture crops was carried out using a remote sensing based algorithm (Surface Energy Balance System SEBS) and the output season actual evapotranspiration was validated with on-ground instrumentations (eddy Covariance flux tower and Large aperture scintillometers) in the study area. This paper also presents merits and demerits of using different innovative approaches (data mining, artificial neural network and remote sensing) for estimating irrigation demand and supply. Water accounting technique was applied to measure water accounting indicators across all spatial scales (farms, 22-sub-irrigation systems and irrigation systems) for two summer seasons in a Coleambally irrigation area. Overall demand forecast is more closely matched (85%) to actual water diverted during the summer seasons. Lastly, it presented an intelligent web-based decision support system using smart technology solutions (remote sensing, drones and information communication technology) to monitor and predict crop yield and water supply-demand balance for the irrigation area in a near real-time environment. The main features of the Coleambally decision support system are the introduction of various user categories with different access rights,