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Updated: Apr 23, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Exploring critical pathways for urban water management to identify robust strategies under deep uncertainties
Christian Urich1, Wolfgang Rauch2
1Unit of Environmental Engineering, Institute of Infrastructure, University of Innsbruck, Technikerstr. 13, 6020 Innsbruck, Austria; Monash University Water for Liveability Centre, Cooperative Research Centre for Water Sensitive Cities, Department of Civil Engineering, Monash University, Clayton, VIC 3800, Australia.
Abstract:
Long-term projections for key drivers needed in urban water infrastructure planning such as climate change, population growth, and socio-economic changes are deeply uncertain. Traditional planning approaches heavily rely on these projections, which, if a projection stays unfulfilled, can lead to problematic infrastructure decisions causing high operational costs and/or lock-in effects. New approaches based on exploratory modelling take a fundamentally different view. Aim of these is, to identify an adaptation strategy that performs well under many future scenarios, instead of optimising a strategy for a handful. However, a modelling tool to support strategic planning to test the implication of adaptation strategies under deeply uncertain conditions for urban water management does not exist yet. This paper presents a first step towards a new generation of such strategic planning tools, by combing innovative modelling tools, which coevolve the urban environment and urban water infrastructure under many different future scenarios, with robust decision making. The developed approach is applied to the city of Innsbruck, Austria, which is spatially explicitly evolved 20 years into the future under 1000 scenarios to test the robustness of different adaptation strategies. Key findings of this paper show that: (1) Such an approach can be used to successfully identify parameter ranges of key drivers in which a desired performance criterion is not fulfilled, which is an important indicator for the robustness of an adaptation strategy; and (2) Analysis of the rich dataset gives new insights into the adaptive responses of agents to key drivers in the urban system by modifying a strategy.
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