Dynamic Adaptive Approach to Transportation-Infrastructure Planning for Climate Change: San-Francisco-Bay-Area Case Study

Abstract

Adaptation of existing infrastructure is a response to climate change that can ensure a viable, safe, and robust transportation network. However, deep uncertainties associated with climate change pose significant challenges to adaptation planning. Specifically, current transportation planning methods are ill-equipped to address deep uncertainties, as they rely on designing responses to a few predicted futures, none of which will occur exactly as envisioned. In this paper, we propose using dynamic adaptive planning (DAP), an emerging general strategic planning method, to account for deep uncertainties by building flexibility and learning mechanisms into plans that enable continuous adaptation throughout implementation. This paper first reviews uncertainty in general, introduces what is meant by deep uncertainty, and then introduces DAP. Then, DAP is applied to a case study of the Oakland approach to the San Francisco-Oakland Bay Bridge, which was initially assessed under the 2010-2011 FHWA Climate Change Vulnerability Assessment Pilot program, to illustrate how DAP could be applied as a response to climate change in the context of evolving transportation infrastructure adaptation planning practices in the United States. We conclude that DAP is well suited to account for the deep uncertainties of climate change in transportation and infrastructure planning, and provide suggestions for further research to better apply DAP in this field.

The flexibility gamble: challenges for mainstreaming flexible approaches to climate change adaptation

“…Nonetheless, there have been recent repeated calls for the 'mainstreaming' of dynamic-adaptive methodologies in CCA governance, also in relation to more routine spatial development projects, so as to secure their functioning in view of potential adverse effects of climate change (see e.g. Gersonius et al, 2016;Ranger et al, 2013;Wall et al, 2015;Zevenbergen et al, 2018). In view of such an ambition, Carstens et al (2019) nonetheless caution that adaptive methodologies for deciding upon CCA measures can be perceived also as complex, difficult and resource-demanding, which may be less of a problem for mega-projects but more of a concern for smaller, 'run-of-the-mill' spatial development projects.…”

“…However, when such governance mechanisms are deployed within organizational landscapes that are less than perfectly stable it may be called into doubt whether such assumptions are indeed reasonable. These challenges are further exacerbated if the number of flexible solutions that need to be 'carried' and monitored are multiplied, for instance through the 'mainstreaming' of flexible approaches to CCA governance, as has been proposed in recent interventions in the debate (Gersonius et al, 2016;Ranger et al, 2013;Wall et al, 2015;Zevenbergen et al, 2018).…”

Application of Dynamic Adaptive Planning and Risk-Adjusted Decision Trees to Capture the Value of Flexibility in Resilience and Transportation Planning

“…But with the effects of recent disasters and the failures associated with focusing on infrastructural hardening (19), an important distinction is being drawn between engineering resilience and socio-technical resilience in the built systems literature. Walls (18,20). Given that the service provided by transportation systems is the efficient movement of people and goods from one location to another, strictly strengthening a particular structural infrastructure restricts thinking in relation to ideas around multimodal transportation, other alternative modes for the service, and other opportunities that deviate from the norm.…”

“…The use of probability distributions of extreme events such as floods, earthquakes, and so forth has been considered misleading for reasons such as sparse data, the cognitive bias in underestimating the effects of expected events, and future climate factors that are changing and unpredictable in triggering these events (7). Wall et al (18) and Walker et al ( 21) discuss four levels of uncertainty in decision making, ranging from natural variability to lack of knowledge. The highest order or Level-4 uncertainty, also called deep uncertainty in the literature, is considered to be the deepest level of recognized uncertainty: "We know only that we do not know" (18,21,22).…”

“…Extreme events falling under deep uncertainty are also called "black swans." Climate change and the extreme events it triggers are reasonably characterized under the deep uncertainty level (18,23,24).…”