Accounting for climate change in the water infrastructure design: evaluating approaches and recommending a hybrid framework

Abstract

A traditional hydrologic water infrastructure design assumes that the climate is stationary, and that historic data reflect future conditions. The traditional approach may no longer be applicable since the earth's climate is not stationary. Thus, there is a need for a new way of designing water infrastructure that accounts for the effects of climate change by shifting the current static design paradigm to a more dynamic paradigm. Researchers have proposed several approaches accounting for climate change. In this paper, we group the approaches into five groups (adaptive management, inverse climate change impact, machine learning, flood frequency analysis, and soft computing approaches), outline each approach's strengths and weaknesses, and assess their applicability to the water infrastructure design. We find that the flood frequency analysis approach is most applicable to the water infrastructure design as it is the least disruptive in terms of standard hydrological analysis methods, is cost-effective, and adaptable to most basins. However, adaptive management approaches are best suited for uncertainty reductions since they provide opportunities to constantly adjust decisions based on improved climate change data. Combining these two approaches could provide an optimal way of accounting for non-stationarity.