- Title
- Comparing remote sensing and tabulated crop coefficients to assess irrigation water use
- Creator
- Bretreger, David; Warner, Alexander; Yeo, In-Young
- Relation
- 23rd International Congress on Modelling and Simulation (MODSIM2019). Proceedings of MODSIM2019, 23rd International Congress on Modelling and Simulation (Canberra, ACT, Australia 1-6 December, 2019) p. 379-385
- Publisher Link
- http://dx.doi.org/10.36334/modsim.2019.c1.bretreger
- Publisher
- Modelling and Simulation Society of Australia and New Zealand
- Resource Type
- conference paper
- Date
- 2019
- Description
- Agriculture accounts for approximately 72% of water use in Australia. This high volume of water use over many years has led to water sharing plans being implemented in regions of high agricultural activity, such as the Murray Darling Basin. Theoretically, the implementation of water sharing plans provides a sustainable solution for sharing this resource between irrigators, communities and the environment. Although, currently there are minimal options to choose from to monitor water use without visiting sites, which can be time consuming and cost prohibitive. There is a need to monitor the water used by the various stakeholders within the system, to monitor, and regulate, for its long-term success and sustainability. This paper compares a previously developed approach for monitoring irrigation water use via remote sensing of crop coefficients (𝐾𝐾𝑐𝑐), and gridded climate data with a repurposed irrigation scheduling approach, recommended by State Governments in Australia using tabulated 𝐾𝐾𝑐𝑐 and rain gauges (using Food and Agriculture Organisation Paper 56 (FAO56) methodology). The tabulated values have been locally derived, which is often important in accounting for environmental factors which may not occur elsewhere in the world. The remote sensing relationships used were derived in North America over a range of crops, which may introduce errors. Although the remote sensing methodology possesses many benefits as it does not require knowledge of seasonal growth and soil characteristics. The comparisons were performed over an almond orchard in the Northern Adelaide Plains and a vineyard located in the McLaren Vale wine region, both in South Australia. This study found that the remote sensing approach provided better results for the almond plantation, which is thought to be due to the management of almonds agreeing with hydrological assumptions made during the methodology derivation. Conversely, the vineyard returned better results using the localised tabulated 𝐾𝐾𝑐𝑐; thought to be due to an induced water stress, a common farming practice used to produce quality fruit and wine products. It is evident that the remote sensing relationships are unable to monitor these management strategies. The sparse canopy cover of wine grape vines may also be contributing to the limitation of the remote sensing methodology. The remote sensing method has definite advantages compared to using tabulated values as it shows an actual 𝐾𝐾𝑐𝑐, as opposed to a theoretical 𝐾𝐾𝑐𝑐, removing the possibility of disease and other non-typical conditions going unnoticed in the tabulated method. The remote sensing method removes the need for modellers to obtain data on planting dates, soil texture/hydraulic characteristics and detailed knowledge of crop type. A tabulated method is likely more difficult from a technical perspective to scale up to a catchment/basin scale extent that covers multiple crop/land use types. Overall, this paper demonstrates that the tabulated 𝐾𝐾𝑐𝑐 method can be used to monitor irrigation on farm scale sites. Although, despite its shortcomings, the use of the remote sensing method allows the simulation to be performed in remote areas where there is little to no in-situ measurements and shows greater ability and potential to be scaled to larger regions.
- Subject
- irrigation; remote sensing; water accounting; water management; SDG 6; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1474271
- Identifier
- uon:49251
- Identifier
- ISBN:9780975840092
- Language
- eng
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