Development of an optimisation framework for the investigation of multiple water stores. (2022)
Type of ContentTheses / Dissertations
Degree NameMaster of Science
This thesis develops a framework to investigate optimal storage distributions of water. This is developed through an investigation of current soil and agricultural models to include accurate details of a soil-crop-atmosphere system combined with farming practices and infrastructure. Irrigation scheduling is combined with the use of on-farm and bulk water storages in a Mixed Integer Non-Linear Program. This is developed in the Python programming language with the use of the pyomo library. The program is solved with the Outer Approximation decomposition technique.
The framework showed good agreement with current rule based decision scheduling, and was able to produce more optimal soil moisture dynamics. These dynamics reduced drainage losses and were able to meet the evapotranspiration demand equally well with less water use in some cases. This showed that with optimal management, more desirable dynamics are achievable. With access to additional storage, the model was able to utilise additional water in a cost efficient manner to improve crop production under varying source water availabilities. There was a high sensitivity to rainfall shown in the ability for soil moisture to hold enough water to provide sufficient supply to the crop. This prompted the conclusion that the model must be used on a case-by-case basis to investigate different strategies with fixed parameter combinations.
With the structure of the framework presented, it is possible to scale to include explicit details of large farms in order to run simulations. In the current set up, simulations work on a per hectare basis but this can be scaled as required to represent larger farming systems. Future work would involve using the pyomo model to add extra detail into the framework to allow larger scales to be represented more accurately.
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