Decision scaling sets boundaries with stakeholders to guide the problem solving process, where climate is simply a stressor of (potential) major concern. In other words, future climate states are not forecast or projected to define the problem statement, since such projections have a strong tendency to limit metrics to those that can be visualized through downscaling climate models rather than the management goals as defined by stakeholders and decision makers. Decision scaling asks the planner to confront the full spectrum of uncertainty provided by climate models and scenarios, though other forms of climate and non-climate data can also be included, such as paleohydrological records, actual climate records, and other types of model output. Performance indicators defined by stakeholders and decision makers can be “stress-tested” against climate data in order to define “breaking points,” which can then be compared with the tolerance for risk and failure held by decision makers (Garcia et al. 2014).

Decision scaling as a technique implies that projected climate conditions should not be part of the problem statement. Instead, we should maintain traditional engineering practices where the problem statement is defined by the critical performance in service provision or risk reduction (USACE 2000). All climate states that violate these critical thresholds of performance or risk reduction can be identified through decision scaling, which are overlaid as added stressors to the planning and design process. Climate science and analysis are used at this stage to determine the plausibility of these critical climate states to inform the evaluation of climate robust engineering solutions (Weaver et al. 2012). The different levels of confidence for a specific climate state, as well as institutional capabilities and levels of consequence, provide a decision framework for climate adaptation designs that can be geared towards flexibility, robustness, efficiency, or some combination of evaluation techniques.

Brown, C. (2010). The end of reliability. Journal of Water Resources Planning and Management, 136(2), 143–145. DOI: 10.1061/(ASCE)WR.1943-5452.65

Brown, C., Werick, W., Leger, W., & Fay, D. (2011). A Decision-Analytic Approach to Managing Climate Risks: Application to the Upper Great Lakes. Journal of the American Water Resources Association, 47(3), 524–534. DOI: 10.1111/j.1752-1688.2011.00552.x

Brown, C., Ghile, Y., Laverty, M., & Li, K. (2012). Decision scaling: Linking bottom-up vulnerability analysis with climate projections in the water sector. Water Resources Research, 48(9), n/a–n/a. DOI: 10.1029/2011WR011212

Brown, C., & Wilby, R. (2012). An Alternate Approach to Assessing Climate Risks. Eos, Transactions, American Geophysical Union, 93(41), 401–402. DOI: 10.1038/nclimate1454

García, L. E., Matthews, J., Rodriguez, D. J., Wijnen, M., DiFrancesco, K. N., & Ray, P. (2014). Beyond Downscaling: A Bottom-Up Approach to Climate Adaptation for Water Resources Management. Washington, DC: World Bank.

USACE 2000. Engineering Regulation 1105-2-100: Planning Guidance Notebook, US Army Corps of Engineers, Washington DC 20314.

Weaver, C.P., Lempert, R.J., Brown, C., Hall, J.A. Revell, D., & Sarewitz, D. (2013). Improving the contribution of climate model information to decision making: The value and demands of robust decision frameworks, Wiley Interdisciplinary Rev.: Clim. Change, 4(1), 39–60. DOI: 10.1002/wcc.202

Wilby, R. L. (2011). Adaptation: Wells of wisdom. Nature Climate Change, 1(6), 302–303. DOI: 10.1038/nclimate1203