Distributed process modeling for regional assessment ... - Springer Link

Environmental Modeling and Assessment 6: 57–65, 2001.  2001 Kluwer Academic Publishers. Printed in the Netherlands.

Distributed process modeling for regional assessment of coastal vulnerability to sea-level rise Brett Bryan a , Nick Harvey b , Tony Belperio c and Bob Bourman b a Department of Geographical and Environmental Studies and GISCA, National Key Centre for Social Applications of GIS,

University of Adelaide, South Australia, Australia b Department of Geographical and Environmental Studies, University of Adelaide, South Australia, Australia c Minotaur Gold, South Australia, Australia

Sea-level rise involves increases in the coastal processes of inundation and erosion which are affected by a complex interplay of physical environmental parameters at the coast. Many assessments of coastal vulnerability to sea-level rise have been detailed and localised in extent. There is a need for regional assessment techniques which identify areas vulnerable to sea-level rise. Four physical environmental parameters – elevation, exposure, aspect and slope, are modeled on a regional scale for the Northern Spencer Gulf (NSG) study area using commonly available low-resolution elevation data of 10 m contour interval and GIS-based spatial modeling techniques. For comparison, the same parameters are modeled on a fine-scale for the False Bay area within the NSG using high-resolution elevation data. Physical environmental parameters on the two scales are statistically compared to coastal vulnerability classes as identified by Harvey et al. [1] using the Spearman rank-correlation test and stepwise linear regression. Coastal vulnerability is strongly correlated with elevation and exposure at both scales and this relationship is only slightly stronger for the high resolution False Bay data. The results of this study suggest that regional scale distributed coastal process modeling may be suitable as a “first cut” in assessing coastal vulnerability to sea-level rise in tide-dominated, sedimentary coastal regions. Distributed coastal process modeling provides a suitable basis for the assessment of coastal vulnerability to sea-level rise of sufficient accuracy for on-ground management and priority-setting on a regional scale. Keywords: coastal vulnerability assessment, environmental modeling, GIS, regional, sea-level rise

1. Introduction Potential accelerated sea-level rise (hereafter referred to as sea-level rise) is a ubiquitous hazard facing coastal areas and is of great economic and ecological significance considering the intensive nature of both biological and human activity in the coastal zone. The necessity of assessment of the vulnerability of coastal areas to sea-level rise has been recognised by the Intergovernmental Panel on Climate Change [2]. The impact of sea-level rise involves increases in the coastal processes of inundation, and wave attack and erosion [3,4]. Coastal vulnerability assessment must incorporate the complex interaction of physical environmental factors at the coast which affect these coastal processes and hence, coastal vulnerability to sea-level rise. The best estimate for projected sea-level rise is 49 cm to the year 2100 [5]. Assessing the vulnerability of coastal areas to sea-level rise of such magnitude and even earlier projections which were closer to 1 m [6] has demanded the use of specialised techniques and data. Many different techniques have been employed in quantifying coastal vulnerability to sea-level rise including ground survey, engineering and high-resolution topographic mapping. Several studies have also assessed coastal vulnerability to sea-level rise using Geographic Information Systems (GIS) [4,7–14]. Many of these have relied upon detailed and precise data such as storm-flood data [13], high-resolution topographic data [4], or detailed coastal profiles and long-term beach monitoring data [14]. As a result, these studies are generally expensive,

time consuming, and only cover localised areas often of high economic importance [4,13,14]. Whilst the importance of localised and detailed studies remains for areas of particular significance, there is a real need for techniques suitable for assessing regional-scale (coastlines in the order of hundreds of kilometres) coastal vulnerability to sea-level rise that do not have intensive data requirements. Regional assessments are required so that areas vulnerable to sea-level rise can be identified within coastal regions and priorities can be set for fine-scale studies and management. Harvey et al. [1] propose a method of coastal geologic mapping in combination with ground survey that is suitable for this kind of regional assessment of coastal vulnerability to sea-level rise. This technique involves the mapping of homogeneous coastal geological units from aerial photography and extensive field verification on a regional scale and was implemented in the Northern Spencer Gulf, South Australia. The units are used as surrogate indicators of the complex interaction of the coastal processes affecting vulnerability to sea-level rise. Vulnerability classes are based on the elevation of the geological units derived from two coast-normal transect surveys [1]. In this study, we assess the potential of GIS-based distributed coastal process modeling for providing an alternative yet compatible technique for the regional assessment of coastal vulnerability to sea-level rise. Distributed coastal process modeling involves modeling the spatial distribution of physical environmental parameters which influ-

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B. Bryan et al. / Modeling coastal vulnerability

ence coastal processes such as inundation and exposure to wave attack. The spatial distribution of these physical environmental parameters including elevation, exposure, aspect and slope of the coast, are modeled for the Northern Spencer Gulf at two scales using GIS. Distributed coastal process modeling at a regional scale relies on commonly available, low resolution (10-metre contour interval) contour data. Fine scale modeling utilises high-resolution elevation data and is used to check the accuracy of regional scale modeling. Statistical relationships between the physical environmental parameters and the coastal vulnerability classes identified by the geologic mapping technique of Harvey et al. [1] are assessed. The parameters are then used to estimate coastal vulnerability to sea-level rise in the Northern Spencer Gulf region in South Australia.

2. Northern Spencer Gulf The Northern Spencer Gulf (NSG) region of South Australia is one of Australia’s few inverse estuaries and is bordered by the industrial towns of Whyalla, Port Augusta and Port Pirie (figure 1). In addition to the assessment of Harvey et al. [1], the NSG has been the subject of several other flooding risk and coastal vulnerability studies [15,16]. The popularity of the NSG for vulnerability assessments is due to the existence of several townships and associated industries

located in low-lying areas which are potentially vulnerable to sea-level rise. In addition, the coast exhibits many holiday shacks also located in vulnerable areas. 2.1. Coastal geology and ecology The Northern Spencer Gulf exhibits a broad, welldeveloped coastal plain particularly along its eastern side. The plain has evolved as a result of regressive peritidal sedimentation over the past 7,000 years [17–19] with rapid rates of bioclastic accumulation in a range of subtidal and intertidal environments. Cores taken along a number of transects across this coastal plain have revealed a common upward shoaling and seaward prograding development of sediment facies that mimic a contemporary peritidal zonation of vegetation, biota and sediment facies [20]. The coastal environment displays strong, contemporary ecological and morphological zonations which are determined by water depth, degree of subaerial exposure (intertidal level) and local wave climate [1]. For any particular coastal sector of given orientation and local wave climate, inter-tidal levels become the prime determinant of ecological zonation. Importantly, the respective intertidal zonation can be used as a surrogate measure of tidal level and reflects the complex interplay of physical environmental parameters [1]. Harvey et al. [1] assert that these homogeneous geoecological zones are manifestations of homogenous physical environmental regimes and vulnerability to sea-level rise. 2.2. Flood risk and coastal vulnerability analyses in the region

Figure 1. Location map of the Northern Spencer Gulf region and the False Bay study area.

The Northern Spencer Gulf is a prime example of the need for regional coastal vulnerability assessment as a “first cut” to identify areas of regional priority. Previous flood risk and coastal vulnerability studies have occurred at Pt. Pirie [15,16], Pt. Augusta, Blanche Harbour and False Bay [15,16]. The methodologies of these assessments have involved expensive and data-intensive techniques, predominantly photogrammetric derivation of elevation data from purpose-flown, high-resolution aerial photography, and ground surveying. These methods are local in coverage and are not suitable for regional assessment. Data from the False Bay study [15,16] is used in this study for fine-scale comparison and verification of regional models. A regional coastal vulnerability assessment by Harvey et al. [1] involved the mapping of the Holocene coastal geology of the Northern Spencer Gulf from large-scale (1 : 15,000) aerial photographs [1,15] and extensive field work. Sixteen homogeneous coastal geological units were identified in the NSG. These units were classified and ranked according to their vulnerability which was derived largely from the elevation of the units, as identified by two coast-normal surveyed transects, inundation frequency and depositional environments ([1]; table 1; figure 2). Figure 3 presents the distribution of vulnerability classes [1] in the False Bay area.


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Table 1 Vulnerability of the geological units in the Northern Spencer Gulf. Also included is an ordinal ratio number for use in statistical analysis. Vulnerability category

Geologic symbol

Very high High Moderate Low Very low Not vulnerable

Qhck8, Qhck9 Qhck7 Qhck6 Qhck, Qhck5 Qhck4, Qhcks Qhckl3/Lmm, Qhckl3/Nsts, Qha, Qhem, Qpah, Qpap, Qpcg, Nsa, Water

Water

Category number 1 2 3 4 5 – –

3. Methods 3.1. Digital elevation modeling A triangulated irregular network (TIN) digital elevation model (DEM) of the False Bay area was created using the high-resolution elevation data in the ArcInfo GIS [21]. This TIN-based DEM was then converted to a raster database of 5 m grid cell resolution. A raster-based DEM of 50 m grid cell resolution was also developed for the Northern Spencer Gulf study area from widely available elevation data of 10 metre contour interval. The thin-plate spline technique within the TOPOGRID module [22,23] of ArcInfo [21] was used to interpolate the DEM. 3.2. Distributed coastal process modeling In this study, coastal vulnerability is considered in the context of both inundation and erosion. From the raster DEMs of the Northern Spencer Gulf and the False Bay area, various physical parameters describing the coastal environment which influence the probability of both inundation and erosion can be modeled. The vulnerability of coasts to inundation due to sea-level rise is primarily and directly related to elevation. The vulnerability of coasts to erosion is a result of a complex interplay of factors including not only elevation but also the exposure, aspect and slope of the coast. The exposure and aspect of the coast are dependent upon the direction of the prevailing winds in the Northern Spencer Gulf which are from the south-west. Coastal areas with long fetches of open ocean in a south-westerly direction were considered to have higher exposure and, therefore, vulnerability to erosion from wave attack from this direction, than more protected coastal areas. Also, those cells facing the south-west (south-westerly aspect) were considered to have highest erosion vulnerability and those facing north-east, the lowest. Finally, those areas with steeper gradients were considered to be more vulnerable to erosion than gently-sloping areas. No one factor was considered to influence vulnerability to erosion more than others and none can really be considered alone (e.g., a steep sloping area only has very high risk of erosion if it is exposed and is facing the south-west). A proxy index of exposure was constructed on a regional scale using the hydrological modeling tools within ArcInfo’s

Figure 2. Coastal vulnerability to sea-level rise the Northern Spencer Gulf according to the geological mapping techniques of Harvey et al. [1].

GRID [21] module and the results used in the analysis of both False Bay and the NSG. The index of exposure was created by calculating the number of grid cells of open ocean to the south-west of each grid cell up to a maximum of 300. However, this was a very rigid measure and did not account for either wave refraction or energy dissipation on traversing coastal areas. Hence, a focal mean function was used to calculate for each cell, the mean exposure index of all cells within a 2.5 km radius. This was considered a suitable ap-

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Figure 3. Coastal vulnerability to sea-level rise the False Bay area according to the geological mapping techniques of Harvey et al. [1].

proximation of both the effects of wave refraction and the effects of wave energy dissipation at the coast. Effectively, the more protected the coast from the prevailing south-westerly wave attack regime, and the further inland, the less vulnerability to erosion now and with sea-level rise. Aspect was derived directly from the False Bay and NSG DEMs resulting in rasters with continuous values from 0 to 360 which represent degrees clockwise from north (aspect = −1 for flat areas). This was converted to a linear index where each cell is given a value between 0 and 180 corresponding to how close its aspect is to south-west using a map algebraic conditional statement in GRID [21]. Effectively, the index is inversely related to vulnerability. Horizontal cells are given the moderate value of 90. Slope was also derived from the False Bay and NSG DEMs using the ArcView GIS [24]. The result was a raster where the value of each cell is its slope in degrees from the horizontal plane (0 is flat). The distributed coastal process modeling above produced two sets of digital spatial data layers describing the physical environmental parameters for False Bay at a scale of 5 m, and the Northern Spencer Gulf at a resolution of 50 m. These parameters affect the vulnerability of coastal areas to processes of inundation and erosion from sea-level rise. 3.3. Coastal vulnerability modeling The vector-based digital spatial database of coastal vulnerability from the Harvey et al. [1] assessment was converted to raster at both 5 and 50 m resolution to match both the False Bay and NSG data. The databases for both False Bay and the NSG which included coastal vulnerability class, elevation, exposure, aspect and slope were then imported

into SPSS 8.0 for statistical analysis. Descriptive statistics of elevation, exposure, aspect and slope were calculated for each vulnerability zone. Spearman’s rank-correlation test and linear regression are both used to assess the relationship between the vulnerability classes (ordinal/ratio data) and the four physical environmental parameters for both False Bay and the NSG. The regression model included only those processes significantly influencing coastal vulnerability and was used to create an independent classification of coastal vulnerability in the Northern Spencer Gulf. The regression model was implemented in a map algebraic function in ArcInfo’s GRID module and the values rounded off to their nearest vulnerability class 1–5, with 1 being the highest vulnerability.

4. Results The spatial distribution of coastal elevation, exposure, aspect and slope hi the False Bay area and throughout the Northern Spencer Gulf region is presented in figures 4 and 5, respectively. Mean values of coastal processes for vulnerability classes derived from the mapped geological units in the False Bay area reveal interesting trends. Most notably, mean elevation of the very high vulnerability class is very low (0.45 m) and increases steadily with decreasing vulnerability. Exposure of the highest vulnerability class is high and decreases with vulnerability apart from the higher value in the very low vulnerability class (figure 6). No discernible relationship can be distinguished between coastal vulnerability and either slope or aspect (figure 6).


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Figure 4. Physical environmental parameters affecting coastal processes and vulnerability to sea-level rise in the False Bay area modeled at 5 metre resolution.

Vulnerability classes display similar relationships on a regional scale. Coastal vulnerability displays a direct inverse linear relationship with elevation. Again, coastal vulnerability tends to decrease with exposure yet neither the aspect nor slope of the coast seem to bear any relationship with vulnerability (figure 7). Spearman’s rank-correlation test and linear regression support these trends. On the local scale at False Bay there is a strong, positive correlation between coastal vulnerability and elevation (table 2) and a moderately strong, negative correlation with exposure. Correlations between coastal

vulnerability and both aspect and slope of the coast are very weak (table 2). Correlations between vulnerability and all coastal processes except exposure are slightly stronger for the False Bay area than they are for the entire region. Exposure exhibits a stronger correlation for the Northern Spencer Gulf region. Parametric, step-wise linear regression reveals that coastal vulnerability can be predicted moderately well using elevation alone. Including exposure into the model increases the predictability slightly but aspect and slope do not enhance the model very much at all. Again, the predictability of the

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Figure 5. Physical environmental parameters affecting coastal processes and vulnerability to sea-level rise in the Northern Spencer Gulf region modeled at 50 metre resolution. Table 2 Spearman’s rank-correlation (rho) values between the risk categories (1 = highest, 5 = lowest) and the four coastal processes in both the False Bay study area and the Northern Spencer Gulf.

Vulnerability (1–5) False Bay Vulnerability (1–5) NSG

Elevation

Exposure

Aspect

Slope

0.724*

−0.424*

0.196*

0.182*

0.56*

−0.601*

0.111*

0.145*

* Represents statistical significance at the 99% level.

Figure 6. Mean values of elevation, exposure, aspect and slope within the 5 coastal vulnerability categories for the False Bay study area.

Table 3 Goodness of fit of the 4 stepwise linear regression models in the False Bay study area and the Northern Spencer Gulf. Model 1 includes elevation only, Model 2 includes elevation and exposure, Model 3 includes elevation, exposure and aspect and Model 4 includes all four parameters. Model R 1 2 3 4

Figure 7. Mean values of elevation, exposure, aspect and slope within the 5 coastal vulnerability categories for the Northern Spencer Gulf region.

regression line is slightly higher for the False Bay area than it is for the entire region (table 3). Regression model 2 (table 3) which includes only elevation and exposure as predictive variables was selected

0.688 0.739 0.741 0.742

False Bay Adjusted R R square square change 0.474 0.546 0.55 0.55

0.474 0.072 0.004 0.000

R

Northern Spencer Gulf Adjusted R R square square change

0.571 0.606 0.612 0.613

0.326 0.367 0.375 0.376

0.326 0.041 0.008 0.001

and used to predict of coastal vulnerability in the Northern Spencer Gulf as the model is not significantly enhanced by inclusion of aspect and slope. The regression model involved multiplying the topographic elevation by 0.291, subtracting the index of exposure multiplied by 0.0093 and adding a constant of 1.1563. This algorithm was implemented using map algebra in GRID [21]. Coastal vulnerability to sea-level rise as predicted by physical environmental parameters is broadly similar to that identified from geological units by Harvey et al. [1] (fig-


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ure 8). The same broad patterns appear on a regional scale. However, there is a marked difference in the spatial detail between the two classifications. The regression model is clearly more generalised and does not incorporate the same detail as the geological mapping technique. It also tends to underestimate vulnerability such that many coastal areas of very high vulnerability as classified using geologic mapping, are classified only as high vulnerability under the process modeling approach. To summarise these results, significant relationships are uncovered between the coastal vulnerability classes derived by the geologic mapping technique of Harvey et al. [1] and physical environmental parameters modeled at local and regional scales. Correlations are found between coastal vulnerability and elevation and exposure. In most cases the relationships are stronger on a local scale with the benefit of high-resolution elevation data. However, the relationship between vulnerability and coastal processes on the regional scale were generally only slightly weaker and stronger in the case of coastal exposure.

5. Discussion 5.1. Regional coastal vulnerability assessment The results confirm that coastal vulnerability is related to environmental parameters which can be modeled using elevation data and GIS-based spatial modeling techniques. The relationship between coastal vulnerability and coastal processes is strongest when modeled on a fine scale using high-resolution elevation data yet is only slightly weaker when modeled over an entire region using coarse-scale elevation data. Hence, it is clear that the same processes are operating at both scales and the more precisely they are modeled, the stronger the relationships. Whilst studies of coastal vulnerability have largely relied upon fine-scale, high-resolution studies of localised areas, this study demonstrates that regional assessments can also be made using low resolution elevation data commonly available for entire coastal regions. Harvey et al. [1] have shown significant disparities between the high resolution and low resolution elevation data in a profile comparison at False Bay. Diagrammatic representation suggests that the low resolution data at 10 metre contour interval is inadequate for the scale of modeling required at the coast with projected sea-level rises of less than 1 metre [1]. However, the distributed coastal process modeling in this study complements the low resolution elevation data using information about other physical environmental parameters which influence the coastal processes of inundation and erosion. When the index of exposure to wave attack is combined with the low resolution elevation data, the two combined provide a synergy of coastal process information and powerful indicator of coastal vulnerability. The results of this study suggest that distributed coastal process modeling displays a high degree of compatibility

Figure 8. Coastal vulnerability of the Northern Spencer Gulf to sea-level rise. Vulnerability is calculated using coefficients derived from the second linear regression model which included only elevation and exposure.

with geologic mapping as techniques of coastal vulnerability assessment. Hence, either technique is applicable to regional coastal vulnerability assessment and the results are comparable. The choice of assessment technique in a region is dependent upon a number of factors including the required level of accuracy, data availability, technology and appropriate expertise. Whilst the geological mapping technique may provide a slightly higher level of spatial accuracy this

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is probably negligible when dealing with a regional perspective. If both coastal geological data and low resolution elevation data is available it would be quicker and easier to use the geologic mapping technique of Harvey et al. [1]. However, coastal geologic mapping suitable for coastal vulnerability assessment exists for very few regions. Distributed coastal process modeling would be more suitable for regions where coastal geologic mapping data is not available, yet low resolution contour data is available. The availability of data in a region is a fundamental consideration in selecting a coastal vulnerability assessment technique. Of course, the choice of technique may also be influenced by the availability of particular expertise, in particular geologists and GIS analysts. 5.2. Coastal processes and vulnerability to sea-level rise Quantitative statistical assessment of elevation, exposure, aspect, and slope with coastal vulnerability, revealed that elevation and exposure display the strongest relationships with coastal vulnerability to sea-level rise. These results conformed with expectations because of the obvious relationships between coastal topographic elevation and inundation, as well as the relationship between exposure to wave attack and erosion regimes. The expectation was that the aspect and slope of the coast may also be related to coastal vulnerability. However, they were found to have little effect. These findings can be explained by the fact that coastal gradients are often very low in the Northern Spencer Gulf and indeed many other similar depositional coastal environments, and a difference of one or two degrees may not make much difference to geological development and vulnerability to sea-level rise. The aspect of coastal environments may have limited effect by the same logic. For example, a grid cell may exhibit a slope of 0.5 degrees to the south-west and its neighbouring cell 0.5 degrees to the north-east. Whilst effectively the vulnerability of the two cells is very similar, the aspect index exhibits polar extremes (0 and 180 respectively) in terms of exposure to the prevailing wave attack as modeled in this study. Slope and aspect of the coast may be omitted from any coastal vulnerability assessments using distributed process modeling thereby increasing the simplicity of the model without losing much predictive power. 5.3. Classifying coastal vulnerability Coastal vulnerability assessment using distributed coastal process modeling involves not only the modeling of environmental parameters but also some way of classifying vulnerability. The linear regression prediction used in this study provides one way of creating such a classification. However, vulnerability classification can be performed in any number of ways and any number of classes can be constructed. In a sense, the specifics of vulnerability classification are not important. Regional vulnerability classifications should not attempt to provide absolute predictions about the impacts of sea-level rise. Rather, they should be relative indices which provide information about the areas within a region likely to

be affected more severely than others. The general principles identified in this study need to be applied in a regional classification of vulnerability. These include most importantly that coastal vulnerability is primarily inversely related to elevation and secondarily, positively related to exposure. 6. Conclusion The effects of sea-level rise includes increases in the coastal processes of inundation and erosion. In this study we used GIS-based techniques to model the distribution of physical environmental parameters that influence inundation and erosion. These parameters, particularly elevation and exposure to wave attack, are strongly related to coastal vulnerability to sea-level rise as defined by the geologic mapping technique of Harvey et al. [1]. The relationship between environmental parameters and coastal vulnerability is only slightly stronger at a fine scale than at a regional scale. Thus, the elevation and exposure of the coast may be used to model coastal vulnerability to sea-level rise on a regional scale using commonly available, low resolution elevation data. Distributed coastal process modeling provides a compatible technique to the geologic mapping technique of Harvey et al. [1] for the assessment of coastal vulnerability to sea-level rise in tide-dominated sedimentary coastal environments. Regional assessment can identify the areas most vulnerable to sea-level rise within the region. This information can be used to set priorities for detailed, localised vulnerability assessments in economically, ecologically and culturally important local areas. Acknowledgements The authors are grateful to Mr. Tim Noyce, PlanningSA and the South Australian Department of Environment and Heritage for the provision of topographic data of the False Bay and Northern Spencer Gulf study areas. References [1] N. Harvey, T. Belperio, R.P. Bourman and B.A. Bryan, Asia Pac. J. Env. Dev. (in press). [2] Intergovernmental Panel on Climate Change, The seven steps to the assessment of the vulnerability of coastal areas to sea level rise, Response Strategies Working Group (1991). [3] G. O’Riain, The development of indices to coastal erosion utilising a GIS, Dissertation, University of Dublin, Trinity College (1996). [4] P. Cowell, T. Zeng, W. Hennecke and B. Thorn, in: Proceedings of the Australian Coastal Management Conference, ed. N. Harvey (The University of Adelaide, South Australia, 1996) pp. 185–193. [5] Intergovernmental Panel on Climate Change Working Group 111, Second assessment report, Summary for policymakers: impacts, adaptation and mitigation options, Washington DC (1996). [6] Intergovernmental Panel on Climate Change, Strategies for adaption to sea level rise, The Netherlands (1990). [7] J.R. Eastman and S. Gold, Geo Info Systems 7 (1997) 38–43. [8] V. Gornitz, T.W. White and R.M. Cushman, in: Proceedings of Coastal Zone ‘91, Long Beach, California (8–12 July 1991) pp. 2354– 2368.

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