Date of Award
Doctor of Philosophy
Environmental Science and Engineering
Sergio A. Luna Fong
Water scarcity has increased substantially in the last decades in many parts of the world, and it is expected to worsen due to the significant increase in global water withdrawals, intensive population growth, and climate change. Water is easily one of the most essential and invaluable global resources due to its many uses, such as drinking, industrial processes, and irrigation. The agriculture sector is one of the most significant water users globally, accounting for nearly 70% of global freshwater withdrawals. Cities and industries compete with the agriculture sector for water sources, producing alarming levels of stress and pollution in the water sources by the increasing numbers of countries and populations. The global population is expected to increase over the following years, reaching 8.6 billion people in 2030 and rising further to 9.8 billion in 2050. To this extent, the agricultural sector will have to increase food production by more than 60 percent. Therefore, increasing water productivity is critical in many countries. The agriculture sector's main challenges are adapting to climate change and water scarcity impacts on developing low-cost, reliable, and efficient irrigation systems that support water conservation practices, mitigate environmental effects and improve food production. However, their selection and spatial placement for land use represent another challenge at the watershed scale. In order to achieve the best possible outcome with limited natural resources, this work proposes an irrigation systems optimization framework that integrates The Soil Water Assessment Tool (SWAT) and Multiple Objective Evolutionary Algorithm (MOEA) to identify the optimal spatial placement of land-use and irrigation systems to reduce tradeoffs between conflicting objectives in irrigated agriculture. Hydrologic simulation models are commonly used as water balance and crop estimators. On the other hand, multiple objective optimization has emerged as a solution to solve many real-life problems. In many situations, evolutionary algorithms can simultaneously optimize conflicting objectives and develop Pareto-optimal sets that decision-makers can use to explore the trade-off between optimal solutions. Furthermore, the findings of this research will provide decision-makers with the best spatial placement configuration of land-use and irrigation systems that will enable them to plan management practices for each hydrological response unit by considering crop yield, energy consumption, and irrigation system costs. This research will also allow decision-makers to explore different management strategies that can inform them about possible outcomes for different scenarios.
Received from ProQuest
Juan Valentin Fernandez
Fernandez, Juan Valentin, "Natural Resources Management Framework For Agricultural Irrigation Systems Optimization" (2022). Open Access Theses & Dissertations. 3671.