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Linking climate projections to performance: A yield‐based decision scaling assessment of a large urban water resources system
Author(s) -
Turner Sean W. D.,
Marlow David,
Ekström Marie,
Rhodes Bruce G.,
Kularathna Udaya,
Jeffrey Paul J.
Publication year - 2014
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2013wr015156
Subject(s) - water resources , environmental science , streamflow , climate change , scope (computer science) , computer science , environmental resource management , metric (unit) , scaling , climate model , decision support system , operations research , business , mathematics , geography , drainage basin , data mining , marketing , ecology , cartography , geometry , biology , programming language
Abstract Despite a decade of research into climate change impacts on water resources, the scientific community has delivered relatively few practical methodological developments for integrating uncertainty into water resources system design. This paper presents an application of the “decision scaling” methodology for assessing climate change impacts on water resources system performance and asks how such an approach might inform planning decisions. The decision scaling method reverses the conventional ethos of climate impact assessment by first establishing the climate conditions that would compel planners to intervene. Climate model projections are introduced at the end of the process to characterize climate risk in such a way that avoids the process of propagating those projections through hydrological models. Here we simulated 1000 multisite synthetic monthly streamflow traces in a model of the Melbourne bulk supply system to test the sensitivity of system performance to variations in streamflow statistics. An empirical relation was derived to convert decision‐critical flow statistics to climatic units, against which 138 alternative climate projections were plotted and compared. We defined the decision threshold in terms of a system yield metric constrained by multiple performance criteria. Our approach allows for fast and simple incorporation of demand forecast uncertainty and demonstrates the reach of the decision scaling method through successful execution in a large and complex water resources system. Scope for wider application in urban water resources planning is discussed.