Uncertainty in stormwater drainage adaptation: what matters and how much is too much?
Abstract:Published research continues to report that long-term, local-scale precipitation forecasts are too uncertain to support local-scale adaptation. Numerous studies quantify the range of uncertainty in downscaled model output; compare this with uncertainty from other sources such as hydrological modeling; and propose circumventing uncertainty via “soft” or “low regret” actions, or adaptive management. Yet non-structural adaptations alone are likely insufficient. Structural adaptation requires quantified engineering design specifications. However, the literature does not define a tolerable level of uncertainty. Without such a benchmark, how can we determine whether the climate-change-cognizant design specifications that we are capable of, for example the climate change factors increasingly utilized in European practice, are viable?
The presentation will explore this question, in the context of reporting results and observations from an ongoing ten-year program assessing local-scale stormwater drainage system vulnerabilities, required capacities, and adaptation options and costs. This program has studied stormwater systems of varying complexity in a variety of regions, topographies, and levels of urbanization, in northern-New England and the upper-Midwestern United States. These studies demonstrate the feasibility of local-scale design specifications, and provide tangible information on risk to enable valid cost/benefit decisions. The research program has found that stormwater planners and engineers have routinely accepted, in the normal course of professional practice, a level of uncertainty in hydrological modeling comparable to that in long-term precipitation projections. Moreover, the ability to quantify required capacity and related construction costs for specific climate change scenarios, the insensitivity of capacity and costs to uncertainty, and the percentage of pipes and culverts that never require upsizing, all serve to limit the impact of uncertainty inherent in climate change projections.