All hydrological data of inflow (i.e. surface water supplies, tubewells pumping, rainfall and capillary upflow) and outflow components (i.e. actual ET, deep drainage, and surface outflow) were estimated for last two years for all established nodes of CIA for the development of field application loss functions. High spatial resolution satellite images (ALOS/AVNIR) and Landsat 5 TM satellite images were used for land use and land cover classification and estimation of actual ET respectively. This estimated actual ET and forecasted meteorological data was used for demand forecasting for next seven days. The results were compared with the data obtained for irrigation supplies. Initial results for forecasted demand are quite promising and provide a deep insight about the spatial variation in crop water demand for each node within the CIA and provide a practical way for water saving at node scale by matching demand and supply.Irrigated agriculture is major consumer of fresh water, but a large part of the water devour for irrigation is wasted due to poor management of irrigation systems. Improving water management in irrigated areas require the analysis of real time water demand in order to determine the possibilities in which it may be modified and rationalised. Real time water demand information in irrigated areas is a key for planning about sustainable use of irrigation water. These activities are needed not only to improve water productivity, but also to increase the sustainability of irrigated agriculture by saving irrigation water. Demand forecasting entail the complete understanding of spatial and expected temporal variability of metrological parameters and evapotranspiration (ET). ET is the overriding aspect for irrigation demand forecasting at farm to catchment scale. Many models have been used to measure the ET rate, either empirical or functional. The major disadvantage of this approach is that most methods generate only point values, resulting in estimates that are not representative of large areas. These methods are based on crop factors under ideal conditions and cannot therefore represent actual crop ET. Satellite remote sensing is a powerful mean to estimate ET over various spatial and temporal scales. For improved irrigation system management and operation, a holistic approach of integrating remote sensing derived ET from SAM-ET (spatial algorithm for mapping ET) algorithm, for Australian agro-ecosystem, with forecasted meteorological data and field application loss functions for major crops were used to forecast actual water demand in Coleambally Irrigation Area (CIA), New South Wales, Australia. It covers approximately 79,000 ha of intensive irrigation and comprise of number of secondary and tertiary canals. In order to capture the spatial variability, CIA has been divided into 22 nodes based on direction of flow and connectivity.
|Title of host publication||American Geophysical Union, Fall Meeting 2010|
|Place of Publication||USA|
|Publisher||American Geophysical Union|
|Number of pages||1|
|Publication status||Published - 2010|
|Event||American Geophysical Union, Fall Meeting 2010 - San Francisco, USA, New Zealand|
Duration: 13 Dec 2010 → 17 Dec 2010
|Conference||American Geophysical Union, Fall Meeting 2010|
|Period||13/12/10 → 17/12/10|
Ullah, M., Hafeez, M., Chemin, Y., Faux, R., & Sixsmith, J. (2010). Integration of remote sensing derived actual evapotranspiration with meteorological data for real time demand forecasting in semi-arid regions. In American Geophysical Union, Fall Meeting 2010 (pp. 1). American Geophysical Union.