0 20 40 60 80 100 Shoreline Position (m)
0.00 0.02 0.04 0.06 0.08 0.10 0.12
0 10 20 30 40 50 60 70 80
Shoreline Position (m) 0.0
0.2 0.4 0.6 0.8 1.0
Cummulative Probability
(a) (b)
Fig. 7.10 Shoreline position PDF (a) and CDF (b) for the 0 MSL contour as calculated using the simulated waves. The median (red line) and the 5th and 95th% confidence limits (shaded) are also shown in the plot.
Callaghan et al. (2013) showed that semi-empirical and process based approaches can replicate this upper limit. However using those models with long-term synthetic wave time series may be challenging.
Seasonal statistics suggest that the wave model accurately describes the origin and direction of wave energy associated with different wave heights. The use of CPs to drive the model is beneficial since they have been shown to accurately delineate directional wave energy sources. Furthermore the CPs are capable of describing bimodal wave energy sources which is beneficial for modelling purposes.
The new dynamic shoreline evolution model developed by Jara et al. (2015) was used to demonstrate the advantage of simulating long continuous wave sequences.
Shoreline positions can be simulated for long periods with relatively low computa- tional effort, which is advantageous for coastal risk assessments. In contrast shoreline positions estimated in Yates et al. (2009) were based on wave data simulated using a spectral wave model, which is not well suited to simulating long sequences of shoreline changes because it is computationally demanding. A simple least squares technique was used to calibrate the shoreline model using observed wave data. The calibrated shoreline model successfully captured the general trends of observed shoreline posi- tions.
A significant advantage of the stochastic simulation technique presented here is that it is well suited to studying future climate scenarios that include climate change effects.
The links to atmospheric CPs should provide a robust framework for evaluating the effects of changes in synoptic scale meteorology obtained from global climate models (GCMs). Such changes have direct effects on surface variables of interest. For example CPs have been used to asses changes in the statistical properties of precipitation (e.g.
Abiodun et al., 2015; Bárdossy & Pegram, 2011) and recently on the European wave climate (e.g. Perez et al., 2015). Therefore the stochastic simulation of waves based on GCM outputs for future climate scenarios provides a promising new methodology for quantifying coastal vulnerability within the context of climate change. This is because the effect of the changing climate on nearshore processes such as longshore and cross shore beach response can be easily quantified through the statistical links between CPs and waves.
Atmospheric classification as a
framework for assessing future
coastal vulnerability.
Recent developments in coastal vulnerability assessment have highlighted the benefits of identifying the synoptic scale atmospheric circulation patterns (CPs) that drive regional wave climates. A useful application of these CPs is to provide a framework to stochastically simulate waves that are conditioned on the weather systems that drive them. This has recently been exploited in the development of a hybrid CP- linked stochastic wave climate simulation technique. The present study is focussed on demonstrating two important properties of the CP classification and wave simulation techniques. Firstly we demonstrate that the outcome of our CP classification algorithm is robust to whether the wave data used is derived from models (with re-analysis) or from direct measurements. Secondly, we show how the new CP-linked wave simulation techniques can be applied to elucidate future wave climates associated with climate change scenarios. To illustrate these methods the future wave climate on the east coast of South Africa was simulated using CPs from HadGEM2-ES GCM simulations for the period 2010 – 2100. Some long-term changes in the occurrence statistics of extreme wave events are found for the case study location. More generally the results suggest broad applicability of the methodology for both current and future coastal vulnerability assessments.
8.1 Introduction
Within the context of climate change the ability to quantify risk in non-stationary en- vironments is of fundamental importance. In coastal environments, coastal engineers and planners require reliable models that can accurately describe regional wave cli- mates and associated risk factors. The application of multivariate statistical models is one method of addressing this issue, but in general they are not directly linked to the meteorological forcing of waves and can sometimes produce unrealistic results (Cor- bella & Stretch, 2013). Furthermore, non-stationary statistics can be complex and
difficult to incorporate into such models. Alternatively, process-based global wave models have become increasingly attractive due to improvements in their performance (Caireset al., 2004; Chawla et al., 2013; Mínguezet al., 2011; Swail & Cox, 1999; Tol- man et al., 2002). The accuracy of these models depends largely upon the wind field inputs that drive them and they currently require re-analysis with data assimilation to produce accurate results. They are therefore unable to accurately predict future wave climate scenarios. Furthermore the ability of global wave models to accurately simulate extreme events depends strongly on the grid resolution. This is particularly important nearshore, an area that is typically poorly resolved by global wave models (Mínguez et al., 2011). The prediction of climate change effects can therefore benefit from hybrid statistical models that retain links to the physical mechanisms that drive regional wave climates.
The use of meteorological features to drive coastal risk assessment models has re- cently attracted increasing attention in research. For example Zouet al. (2013) incor- porated an ensemble modelling framework linking meteorological features to coastal flood risk through regional downscaling. Camus et al. (2014) proposed the use of weather pattern types as a statistical downscaling framework for regional wave cli- mates. Espejo et al. (2014); Pringle et al. (2014) have recently shown how atmo- spheric classification can be a useful tool in coastal vulnerability assessment since it can provide a link between statistical risk assessment models and process-based global wave models. The transition between different atmospheric states or classes is a phys- ically meaningful way to describe wave behaviour. This link was exploited in a recent stochastic wave simulation technique proposed in Pringle & Stretch (2015). Further- more the use of atmospheric classification provides a useful framework within which to assess climate change effects (e.g. Perez et al., 2015).
Following Bárdossy et al. (1995) the atmospheric classification algorithm used herein is based on fuzzy rules with intrinsic links to surface variables, in this case wave characteristics. Pringle et al. (2014) were the first to apply this atmospheric classification technique to elucidate the drivers of regional wave climates. The classes are automatically derived in a supervised manner based on their ability to explain wave characteristics such as wave heights, directions, periods and storm durations.
Pringle et al. (2014) focussed on using the wave height as the variable of interest to classify circulation patterns (CPs).
Wave climate estimation using the latest generation of spectral wave models has become increasingly accurate. While it may never replace the reliability of direct
wave observations it is particularly useful where direct measurements are not available.
Furthermore it provides an alternate dataset to use in an atmospheric classification algorithm. Therefore CP classification using modelled wave data has the potential to describe the drivers of wave climates in regions where no wave observations are available. This approach is also well suited to analysing and quantifying future coastal vulnerability concerns.
The aim of this study is to investigate the robustness of our automated fuzzy rule based classification in locating the drivers of regional wave climates using only modelled wave data. If the algorithm can locate the same CPs as those derived from direct wave measurements then this robustness can be exploited in coastal vulnerability at locations without wave measurements. We also demonstrate an application of a CP- linked stochastically wave simulation model for evaluating future wave characteristics under climate change scenarios. This is based on a supposition that changes in regional CP occurrence frequencies predicted by global climate models (GCMs) will in turn drive significant changes in the wave climates in those regions.