Infrastructure Engineering - Theses
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ItemTowards improved irrigation scheduling through sensitivity analysis and remote sensing of crop coefficients( 2022)Agriculture faces significant challenges to increase food and fibre production under increasingly variable climate and uncertain supply of resources such as water. At present only 37% of the fresh water delivered to agricultural crops is used by crop in part due to inefficient management of irrigation. Therefore, optimal irrigation scheduling is important for saving water while maintaining crop productivity. To improve irrigation scheduling, it is important to identify the key input factors influencing the efficacy of the scheduling. In this study, the FAO56-based soil water balance irrigation scheduling method has been selected as one of the most widely-applied methods (Allen et al., 1998). Firstly, realistic ranges of the uncertainties of ten selected input factors of the method, including weather, soil, crop, and management factors, have been evaluated to assess their impact on irrigation scheduling. Then, the sensitivity of the irrigation scheduling to the uncertainty of each input factor is calculated using the Sobol’ global sensitivity analysis method. Results show that FAO56 crop coefficient, which has an expected average uncertainty of 20%, has the highest impact on irrigation scheduling. The sensitivity analysis was performed for eight different climates in Australia, which showed that the ranking of the influential factors on irrigation scheduling did not change with climate conditions, making crop coefficient the most sensitive factor in all climates. Since crop coefficient is an important factor in irrigation scheduling, FAO56 and remote-sensing-based Irrisat crop coefficients were compared with measured crop coefficients in one maize field in Australia and two lucerne fields in New Zealand. The Irrisat-derived dynamic crop coefficient values reproduced temporal changes in crop coefficient better than the FAO56 values. FAO56 crop coefficients were within a reasonable range for most periods of crop growth but they could not capture their temporal changes. However, Irrisat underestimated crop coefficients during the mid-season and end-season for maize, likely due to the saturation effect of the Normalised Difference Vegetation Index (NDVI) used to derive the dynamic crop coefficient. This problem was less significant in the lucerne fields since they feature lower biomass ranges. Overall, while FAO56 coefficients captured reasonable values in magnitude, the Irrisat-derived crop coefficient is superior in detecting temporal changes when it is available. Hence, using both of them in consideration of their advantages and disadvantages can help reduce the uncertainty in crop coefficients and ultimately save water in irrigation scheduling. Future research should focus on developing effective assimilation approaches to combine these different sources of crop coefficients, and thus ultimately improve the accuracy while reduce uncertainty of their estimates.
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ItemA Hybrid Framework for Short-term Irrigation Demand Forecasting( 2022)Reliable short-term estimates of irrigation water demand can provide valuable information to help water supply system operators with day-to-day operating decisions. Modeling irrigation demand is a complex task due to the different natural (soil, water, crop, and climate interactions) and behavioral (farmers' decision-making) components of the irrigation process. So far, various approaches have been attempted to estimate irrigation water needs in different contexts. Early studies have used simplified physical models to determine irrigation water needs conceptually. However, many of recent studies have applied data-driven methods to map the relationship between the principal influential factors and the water demand. In this study, a generic hybrid framework has been developed to forecast irrigation water demand by integrating a conceptual model (estimating crop water needs based on existing knowledge of the physical system) and a data-driven model (capturing the remaining input-output relationships that cannot be picked up by the conceptual model). The performance of this hybrid framework is evaluated based on real-world system data in Victoria, Australia, and compared to a benchmarking data-driven model (developed using a similar data-driven approach as the hybrid model). It was found that the proposed hybrid framework is able to estimate, with reasonable accuracy, daily irrigation water demand values up to 7 days ahead of the case study system. The hybrid model performs better than the data-driven benchmarking model for most lead times, and particularly for the high-demand period. The results demonstrate that integrating system understanding with data-driven modeling can lead to improved estimates of irrigation water demand. In addition to better predictive performance, the proposed hybrid framework provides improved system understanding and thus increased capacity to support operational decisions.
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ItemLow-fidelity Hydrodynamic Model-based Method for Efficient Flood Inundation Modelling( 2021)Flood is one of the most devastating natural hazards, as it often causes fatalities and damages to infrastructure. To develop strategies for flood risk mitigation, flood inundation models are often used to provide useful information for assessments of potential impacts of floods. Two-dimensional (2D) hydrodynamic models are commonly used for flood inundation simulations. However, they can be computationally intensive when used to simulate many flood events, for example for uncertainty analysis, or to simulate very large floodplains. To improve computational efficiency, data-driven models based on machine learning techniques and conceptual models based on simplified water-filling concepts have been developed. Data-driven models appear as black-box models and are yet to be used by many practitioners with confidence. Simplified conceptual models are generally not designed to simulate the temporal propagation of floods and are often only applied to estimate maximum/final flood extent and floodwater levels. In Australia, 2D hydrodynamic models have been established for many important catchments. There is potential to build on these existing models and develop methods to speed up flood inundation simulations. In this MPhil thesis, a new modelling method, LoHy+, is proposed based on existing 2D hydrodynamic models, to produce an efficient simulation of flood extent and depth with time. The method first develops a low-fidelity 2D hydrodynamic model (LFM) with coarse mesh based on an existing high-fidelity 2D hydrodynamic model (HFM). The aim of the LFM is to produce reasonably accurate simulation of water levels within the main river channels while tolerating poorer simulation elsewhere in the floodplain. Next, the method develops a Mapping Module by using training data to establish relationships between water levels in both river channels and across the floodplain generated using the HFM and water levels in the river channels generated using this LFM. In subsequent applications, the LFM is run first, and the Mapping Module is applied to estimate flood inundation within the entire model domain. The implementation of the LoHy+ is demonstrated using a real-world catchment located in the southern Murray Darling Basin, Australia. A fully calibrated HF MIKE21 FM hydrodynamic model is available for the catchment. The performance of the LoHy+ method is evaluated against simulation from the high-fidelity hydrodynamic model. There is a good agreement between results from the LoHy+ method and the original high-fidelity 2D hydrodynamic model. The new method is much more efficient and can simulate the spatiotemporal evolution of flood inundation with reasonable accuracy. It is potentially a useful tool for applications that require many model runs or long simulation durations.