Evolution in Mapping Coastal Climate Change Impacts
Climate change vulnerability studies are increasingly reliant on deterministic numerical modelling to represent future climate scenarios in order to identify areas potentially subject to a future hazard. These models generally simulate surface water hydraulics (e.g. flooding, storm-tide inundation), shoreline response (e.g. coastal erosion and recession), groundwater hydraulics and geotechnical stability.
More than 10 years ago it was common for coastal erosion modelling to produce just three scenarios: present day, 2050 and 2100 conditions (see Fig. 21.1). While simple to interpret, the significant number of assumptions that were intrinsic to the modelling meant that actual shoreline response could feasibly be vastly different to the modelling conditions. Yet the significant uncertainty in the modelling was rarely discussed or challenged.
As engineers became more aware of some of these limitations, such as potential slope adjustment after coastal erosion events, the response was simply ‘more lines on the map’ (see Fig. 21.2 for example). While this was convenient for the
Fig. 21.1 Typical definition of coastal climate change erosion hazards (Source ESC 2016)
Fig. 21.2 Nine hazard lines reflecting different modelling assumptions and different timeframes for consideration (Source Horton and Britton 2015)
modellers, it still did not address the underlying uncertainty in the outputs. In fact, the additional lines implied a false sense of greater certainty.
Over the past 5 years or so there has been a paradigm shift towards risk-based management when considering coastal climate change impacts (Rollason et al. 2010; Rollason and Haines 2011). A risk-based approach allows for greater consideration of uncertainty within modelled outputs. Thus, instead of just one line representing a predicted shoreline position at 2100 (as per shown in Fig. 21.1), the risk-based approach enabled the use of multiple lines, each with a different ‘likelihood’ or chance of occurrence (typically ranging from an almost certain chance to a very rare chance). Figure 21.3 shows three likelihood lines for shoreline recession at Coffs Harbour, NSW for the current timeframe (BMT WBM 2011). Lines were also produced for future timeframes, including 2050 and 2100.
Probabilistic approaches are common for floodplain mapping, and this has been extended to coastal tsunami hazard mapping as well, as shown in Fig. 21.4 for an assessment in the Choiseul Province of the Solomon Islands (BMT WBM 2014a). Again, mapping can be presented at varying timeframes based on underlying assumptions associated with future sea level conditions.
Indeed, most probabilistic inundation mapping of coastal floodplains (see Fig. 21.5 for example) requires basic assumptions on future sea level conditions, as well as other factors such as coincident timing of sea and rain events, tide conditions, and barometric set-up levels.
A risk-based approach requires consideration of the ‘likelihood’ of impact within a particular area, as well as the ‘consequence’ of impact if it is to occur. NGIS (2013) applied this approach, in a simple manner, when evaluating the impacts of sea level rise in Nuku’alofa, Tonga (Fig. 21.6). The plot highlights the vulnerability
Fig. 21.3 Example of shoreline mapping showing different likelihoods of occurrence (for current timeframe) (Source BMT WBM 2011)
Fig. 21.4 Example coastal inundation mapping based on probability of occurrence (future timeframe) (Source BMT WBM 2014a)
Fig. 21.5 Example of flood inundation depths for fixed probability (for future timeframe) (Source BMT WBM 2016)
Fig. 21.6 Example of asset inundation mapping for variable probability (for current and future timeframe) (Source NGIS 2013)
Fig. 21.7 Example risk matrix that defines the level of risk based on both likelihood and consequence
of existing buildings to future sea inundation when adopting future climate change (sea level rise) conditions, although no scale of impact was specifically ascribed to the building affectation. High range conservative estimates were used when adopting sea level conditions for the future timeframe, while historical events were used as the basis for inundation likelihood.
The most contemporary and sophisticated risk maps involve integration of spatially represented ‘likelihood’ and ‘consequence’. BMT WBM (2014b) applied
Fig. 21.8 Risk mapping based on spatially represented likelihood and consequence (for 2100) (Source BMT WBM 2014b) this approach at Stockton Beach, NSW, wherein an agreed risk matrix (Fig. 21.7 for example) of likelihood and consequence was used to derive overall risk levels for areas subject to future hazard (Fig. 21.8). In this example, levels of consequence were related to land use zoning as a proxy for value of the land and assets at risk. Similar risk maps were generated for different timeframes demonstrating a dynamic risk profile for the coastal fringe with time.