Quantitative modelling for assessing system trade-offs in environmental flow management

Date

2015

Authors

Barbour, Emily

Journal Title

Journal ISSN

Volume Title

Publisher

Canberra, ACT : The Australian National University

Abstract

This research aims to better enable the management of environmental flows through exploring the opportunities and challenges in using quantitative models for decision making. It examines the development and application of ecological response models, river system models, and multi-objective optimisation for improved ecological outcomes and the identification of trade-offs. In doing so, the thesis endeavours to capture a deeper and more holistic understanding of uncertainty in the application of quantitative models, to assist in making more informed decisions in water resource management. The thesis includes three main components. Firstly, an ecological response model is developed to advance previous methods by: (1) adopting a systems approach to representing water availability for floodplain vegetation, considering rainfall and groundwater in addition to riverine flooding; (2) including antecedent conditions in estimating current ecological condition; and (3) including uncertainty in modelling ecological response through the use of upper and lower prediction bounds and multiple conceptual models derived through expert elicitation. Secondly, the ecological response model is evaluated using sensitivity and uncertainty analysis. Global sensitivity analysis was used to identify model components that are both uncertain and have critical impact on results, and demonstrated that conceptualisation of ecological response had the greatest impact on predicted ecological condition. A novel application of Bayesian analysis was then used to evaluate different expert derived models against observed data, considering multiple sources of uncertainty. The analysis demonstrates a number of remaining challenges in modelling ecological systems, where model performance depends upon assumptions that are highly uncertain. The third and final component evaluates opportunities and challenges in using multi-objective optimisation, to assist in water resource management and the improvement of ecological outcomes. This component begins with a synthesis of previous studies drawing upon literature from hydrology, ecology, optimisation and decision science, and identifies a number of strategies for improvement. The synthesis is followed by a case study on the Lachlan catchment of the Murray-Darling Basin, Australia. The case study uses multi-objective optimisation to explore different environmental flow rules using a river system model combined with the expert-based ecological models. In doing so, it addresses the challenges of objective setting and problem framing in the context of significant uncertainty. The case study evaluates results generated using the optimisation framework in terms of likely actual decision outcomes. The research identifies a need to revisit fundamental questions regarding system understanding and objective framing in the light of rapidly improving computational capacity and sophistication. This is particularly relevant in the case of ecological management, where objectives form an interplay between ecological science and social values. Modelling tools provide valuable pathways to system learning and communication, yet a deeper understanding and evaluation of model behaviour in the context of actual decisions is needed. The methods presented in this thesis aim to provide a step toward addressing the challenges of working with uncertain information, incomplete knowledge, and integration across multiple disciplines within a decision-making environment. Through the methods developed here, the research seeks to advance the science of model development and application.

Description

Keywords

Ecosystem management, optimisation AND optimization, water resources, uncertainty, decision making, system trade-offs, environmental flow management, quantitative modelling AND modeling, Lachlan, Murray-Darling Basin, sensitivity analysis, Bayesian analysis, expert elicitation

Citation

Source

Type

Thesis (PhD)

Book Title

Entity type

Access Statement

License Rights

DOI

10.25911/5d51468f46740

Restricted until