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Updated: Mar 17, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
A structured elicitation method to identify key direct risk factors for the management of natural resources
Michael Smith1, Ken Wallace2, Loretta Lewis3
1Science Division, Department of Parks and Wildlife, 17 Dick Perry Avenue, Technology Park, Western Precinct, Kensington, WA 6151; Australian Wildlife Conservancy, PO Box 8070 Subiaco East, WA 6008, Australia.
Abstract:
The high level of uncertainty inherent in natural resource management requires planners to apply comprehensive risk analyses, often in situations where there are few resources. In this paper, we demonstrate a broadly applicable, novel and structured elicitation approach to identify important direct risk factors. This new approach combines expert calibration and fuzzy based mathematics to capture and aggregate subjective expert estimates of the likelihood that a set of direct risk factors will cause management failure. A specific case study is used to demonstrate the approach; however, the described methods are widely applicable in risk analysis. For the case study, the management target was to retain all species that characterise a set of natural biological elements. The analysis was bounded by the spatial distribution of the biological elements under consideration and a 20-year time frame. Fourteen biological elements were expected to be at risk. Eleven important direct risk factors were identified that related to surrounding land use practices, climate change, problem species (e.g., feral predators), fire and hydrological change. In terms of their overall influence, the two most important risk factors were salinisation and a lack of water which together pose a considerable threat to the survival of nine biological elements. The described approach successfully overcame two concerns arising from previous risk analysis work: (1) the lack of an intuitive, yet comprehensive scoring method enabling the detection and clarification of expert agreement and associated levels of uncertainty; and (2) the ease with which results can be interpreted and communicated while preserving a rich level of detail essential for informed decision making.
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