Scenario driven optimal sequencing under deep uncertainty

Abstract

After the embargo period  via non-commercial hosting platforms such as their institutional repository  via commercial sites with which Elsevier has an agreement In all cases accepted manuscripts should: link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe optimal sequencing / scheduling of activities is vital in many areas of environmental and water resources planning and management. In order to account for deep uncertainty surrounding future conditions, a new optimal scheduling approach is introduced in this paper, which consists of three stages. Firstly, a portfolio of diverse sequences that are optimal under a range of plausible future conditions is generated. Next, global sensitivity analysis is used to assess the robustness of these sequences and to determine the relative contribution of future uncertain variables to this robustness. Finally, an optimal sequence is selected for implementation. The approach is applied to the optimal sequencing of additional potential water supply sources, such as desalinated-, stormand rain-water, for the southern Adelaide water supply system, over a 40 year planning horizon at 10-year intervals. The results indicate that the proposed approach is useful in identifying optimal sequences under deep uncertainty.

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

“…Furthermore an evaluation of robustness measures from different DMMs discovered each DMM ranked solutions to differing performance levels (Herman et al, 2015). Numerous more individual and comparative DMMs studies have been conducted within the context of WRM adaptive planning with specific attention to a measure of robustness (Ghile et al 2014;Haasnoot et al 2013;Jeuland and Whittington 2014;Kwakkel et al 2014;Lempert and Groves 2010;Li, et al 2009;Moody and Brown 2013;Paton et al 2014a;Tingstad et al 2013;Turner et al 2014a;Whateley et al 2014), including investigations into risk-based metrics for analysing adaptation strategy performance (Borgomeo et al 2014;Brown and Baroang 2011;Hall et al 2012a;Kasprzyk et al 2012;Turner et al 2014b) and various new scenario-based methods for ordering and mapping the deep uncertainties within modern WRM problems (Beh et al 2015a;Kang and Lansey 2013;2014;Nazemi et al 2013;Singh et al 2014;Weng et al 2010). However further testing and comparison of DMMs on real world case studies could be highly beneficial especially in regard to evaluating alternative definitions and calculations of system robustness to uncertainty, the methods of scenario generation and the process of adaptation strategy selection and evaluation.…”

Robustness Metrics: How Are They Calculated, When Should They Be Used and Why Do They Give Different Results?

“…This urban water supply augmentation case study models the southern region of the Adelaide water supply system, as it existed in 2010 (Beh et al, , , , ; Paton et al, , , ). Adelaide has a population of approximately 1.3 million people and is the capital city of the state of South Australia.…”

“…There are a number of options for augmentation including the construction of desalination plants, stormwater harvesting schemes, and household rainwater tanks. A previous study (Beh et al, ) generated 72 optimal decision alternatives for this case study using a multiobjective evolutionary algorithm, which will be used in this article. Greenhouse gas emissions and cost were used as objectives, and the vulnerability and reliability of each decision alternative was used to further analyze each optimal decision alternative.…”

A bottom‐up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate

“…Therefore, knowledge of this failure boundary provides operators with the full extent of climate change that their system could withstand, thereby potentially avoiding unnecessary or premature infrastructure upgrades and reducing the vulnerability of the system [Mastrandrea et al, 2010]. A better understanding of the maximum operational adaptive capacity and of the associated failure boundary can be embedded in existing methods of sequential decision making [Haasnoot et al, 2013;Beh et al, 2015b;Kwakkel et al, 2016;Maier et al, 2016] to determine when adaptation options through changes in management are no longer available.…”