Abstract
Adaptation requires planning strategies that consider the combined effect of climatic and non-climatic drivers, which are deeply uncertain. This uncertainty arises from many sources, cascades and accumulates in risk estimates. A prominent trend to incorporate this uncertainty in adaptation planning is through adaptive approaches such as the dynamic adaptive policy pathways (DAPP). We present a quantitative DAPP application for coastal erosion management to increase its utilisation in this field. We adopt an approach in which adaptation objectives and actions have continuous quantitative metrics that evolve over time as conditions change. The approach hinges on an adaptation information system that comprises hazard and impact modelling and systematic monitoring to assess changing risks and adaptation signals in the light of adaptation pathway choices. Using an elaborated case study, we force a shoreline evolution model with waves and storm surges generated by means of stochastic modelling from 2010 to 2100, considering uncertainty in extreme weather events, climate variability and mean sea-level rise. We produce a new type of adaptation pathways map showing a set of 90-year probabilistic trajectories that link changing objectives (e.g., no adaptation, limit risk increase, avoid risk increase) and nourishment placement over time. This DAPP approach could be applied to other domains of climate change adaptation bringing a new perspective in adaptive planning under deep uncertainty.
Highlights
Climate change is posing significant risks from rising temperatures, droughts, increasing flooding and storm damage, shoreline recession, and saltwater intrusion (IPCC, 2014)
The approach we adopt considers changing objectives and implementing actions over time within the erosion modelling itself, providing probabilistic shoreline evolutions over the twenty-first century, which consider uncertainty in climate forcing conditions and incorporate adaptation. We combine this modelling system with the systematic monitoring of signpost variables relevant for coastal erosion management that allow the timely detection of adaptation signals, an essential feature given the late emergence of a low or high mean sea-level rise pathway, and the long planning and implementation times for some adaptation measures
We provide a new type of adaptation pathways map showing a continuous outcome variable on the Y-axis and pathways composed of time-varying sequences of objectives and actions
Summary
Climate change is posing significant risks from rising temperatures, droughts, increasing flooding and storm damage, shoreline recession, and saltwater intrusion (IPCC, 2014). Projections of impacts are highly influenced by uncertainties that arise from different sources (scenarios, climate models, downscaling, and impact models), cascade through the modelling process and accumulate in the outcome, as shown for coastal erosion for instance (Ranasinghe, 2016; Toimil et al, 2020, 2021) In this context, it has been argued that the best way to incorporate uncertainty in adaptation decision making is through robust (http://creativecommons.org/licenses/by/4.0/). Dynamic planning, which can be robust, aims at identifying adaptation policies that respond to new observations over time (Herman et al, 2020), acknowledging that adaptation can be rarely solved with a single action but is a dynamic process of adjusting changes as they unfold through multiple actions managed over time (Barnett et al, 2014) This means to take the necessary actions and monitor to see when further action is required to address a new situation (Dewar et al, 1993; Haasnoot et al, 2018). Adaptation pathways (AP) (Haasnoot et al, 2012), and their combination with adaptive policymaking (Kwakkel et al, 2010) that includes monitoring and contingency actions, namely dynamic adaptive policy pathways (DAPP) (Haasnoot et al, 2013; Walker et al, 2013), are decision-making tools that implement this idea
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