- Title
- Monitoring irrigation volumes using climate data and remote sensing observations
- Creator
- Bretreger, David; Quijano, Juan; Awad, John
- Relation
- 38th Hydrology and Water Resources Symposium (HWRS 2018). Hydrology and Water Resources Symposium (HWRS 2018): Water and Communities (Melbourne 3-6 December, 2018) p. 112-123
- Publisher
- Engineers Australia
- Resource Type
- conference paper
- Date
- 2018
- Description
- Agriculture is the largest user of water resources in Australia, in 2015-2016: it accounted for 70% of the total water extracted. In order to distribute the available resource, water sharing plans have been implemented to allow sustainable distribution in areas of high demand, particularly the Murray Darling Basin (MDB). Enforcing these water sharing plans is an expensive task due to the need of field staff and pump meters. This research attempts to simulate agricultural irrigation Events to estimate actual irrigation water use using remote sensing and climate data. Estimated irrigation is calculated using precipitation and potential/reference evapotranspiration products over Australia in combination with crop coefficients derived from the Australian Geoscience Data Cube's (AGDC) Landsat data. Various combinations of crop coefficients and potential/reference evapotranspiration were tested which provide 12 possible combinations. The model was applied in four monoculture farm scale sites (a cotton farm, two vineyards and an almond orchard) and in the two areas that make up the Murray Irrigation area of operations. Optimal results returned approximate errors of between 1-30% for individual farms and 20-50% for multiple crop irrigated areas. The PET/ETo product used was found to be the sensitive input for the model, which is mainly driven by crop type, either short or tall. Short crops (e.g. cotton) returned better results using the FAO56 method while taller crops (e.g. almonds) showed better results using the ASCE method, agreeing with assumptions made in derivation of these products. Methods to derive the crop coefficient shows signs of being effective for some crops; effectively showing typical growing seasons including winter dormancies, whereas it was ineffective for others; which was mainly attributed to less comprehensive crop canopy cover and localised mangement conditions identified in reports describing the areas. Certain crops that are grown within Murray Irrigation such as rice are likely causing errors as it is commonly irrigated with flood irrigation which can disrupt remote sensing reflectances. Land use misclassification in the published maps used and anthropogenic impacts on localised hydrology were evident in effecting the accuracy of the simulations. This method is aimed to provide a tool for state and federal government agencies to aid in monitoring water use in unregulated catchments and identifying areas or properties that are potentially breaching water allocation volumes. In addition to this, it contributes to the understading and quantification of anthropogenic hydrology fluxes.
- Subject
- irrigation; water resources; climate data; remote sensing observations
- Identifier
- http://hdl.handle.net/1959.13/1403188
- Identifier
- uon:35122
- Identifier
- ISBN:9781925627183
- Language
- eng
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