Description

This dissertation presents a new methodology for the sustainable and optimal allocation of water for a river basin management area that maximizes sustainable net economic benefit over the long-term planning

This dissertation presents a new methodology for the sustainable and optimal allocation of water for a river basin management area that maximizes sustainable net economic benefit over the long-term planning horizon. The model distinguishes between short and long-term planning horizons and goals using a short-term modeling component (STM) and a long term modeling component (LTM) respectively. An STM optimizes a monthly allocation schedule on an annual basis in terms of maximum net economic benefit. A cost of depletion based upon Hotelling’s exhaustible resource theory is included in the STM net benefit calculation to address the non-use value of groundwater. An LTM consists of an STM for every year of the long-term planning horizon. Net economic benefits for both use and non-use values are generated by the series of STMs. In addition output from the STMs is measured in terms of sustainability which is quantified using a sustainability index (SI) with two groups of performance criteria. The first group measures risk to supply and is based on demand-supply deficits. The second group measures deviations from a target flow regime and uses a modified Hydrologic Alteration (HA) factor in the Range of Variability Approach (RVA). The STM is a linear programming (LP) model formulated in the General Algebraic Modeling System (GAMS) and the LTM is a nonlinear programming problem (NLP) solved using a genetic algorithm. The model is applied to the Prescott Active Management Area in north-central Arizona. Results suggest that the maximum sustainable net benefit is realized with a residential population and consumption rate increase in some areas, and a reduction in others.

Reuse Permissions
  • Included in this item (2)



    Citation and reuse

    Statement of Responsibility

    by Robert Louis Oxley

    Machine-readable links