The architecture of SLAMM is slightly unconventional in terms of the land cover classification. This is hard coded according to the US National Wetland Inventory
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(NWI) scheme (U.S. Fish and Wildlife Service, 1974), which is not widely used elsewhere. In addition, the forcing scenarios and various aspects of the sub-models are also embedded in the source code rather being read from external files. Consequently, changes in its source code are required in order to apply the model to sites outside North America, and to explore changes resulting from a wider set of regional sea-level forcing scenarios.
To this end, the SLAMM code was modified in order to suit the tidal sedimentary environments and habitats more typical of the UK. A simpler land classification is included as it is defined by the INTERREG funded BRANCH project (BRANCH partnership, 2007). In parallel, the land classification conceptual model within SLAMM was modified to automatically update the elevation range of each habitat according to their position to the tidal frame, as used in the BRANCH project (after Chapman, 1960; Pye and French, 1993; Leggett and Dixon, 1994; Blott and Pye, 2004). Consequently, in contrast to the original model, the modified scheme more readily accommodates specific case studies. In addition, the habitat transition rules were modified to include a smoother habitat conversion due to inundation; the transitional marsh is inundated to upper marsh instead of lower marsh, and the dryland to transitional marsh instead of estuarine beach when it is adjacent to the subtidal. Finally, UK-specific sea-level rise scenarios were incorporated into the modified code. Consequently, the modified code can be used to simulate the impacts of sea-level rise in the UK estuarine systems.
The estuarine systems in the UK are generally much smaller in extent than many of the North American systems previously investigated using SLAMM. Their intertidal habitats are also often fragmented, with many saltmarsh units been only a few meters in width. This necessitates application of the modified SLAMM at a higher spatial resolution. Thus, although the highest resolution used to date in the US is 10m (see case studies of Brand Bay NERR and Southern Jefferson County in Mexico (Warren and Pinnacle Inc, 2011a,b)), a 5 m resolution is used in the present UK applications. This corresponds to the upper resolution limit suggested by the original model authors (Clough et al., 2010).
The modified code was firstly applied to a small case study estuary, on the south coast of England, the Newtown estuary, Isle of Wight. Initial simulations indicated significant changes in the distribution of intertidal flat and saltmarsh under the UKCP09 SE mean
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sea-level rise scenario (UKCP, 2009).These changes were compared with results from a previous modelling effort for the Newtown estuary carried out as part of the BRANCH project (BRANCH partnership, 2007). As already noted above, the approach used within the BRANCH project is based on the progressive drowning of the existing topography lacking any mechanistic modelling of habitat transition. In particular, lateral erosion at the tidal flat – saltmarsh transition is totally ignored, while accretion is only taken into account for areas already colonised by saltmarsh vegetation. In contrast, SLAMM presents a step forward by incorporating a flexible decision tree and qualitative relationships to determine the habitat transition. It also takes into account of more processes related to sea-level rise than just inundation, with most important being vertical accretion within the intertidal marsh and flat environments. Importantly, this accretion is also allowed to vary spatially.
The small size of the Newtown estuary allows a comprehensive sensitivity analysis of the basic processes and parameters included in the model. Firstly, the effect of an error at the DEM was investigated, by examining the typical elevation accuracy of the UK LiDAR data, which is generally quoted as being equal to ±0.15 m (French, 2003; CCO, 2013). The results highlighted out the importance of the quality of the terrain information, especially for such low gradient environments, by affecting the initial condition of the habitat distribution, and therefore their further response to the sea-level rise.
The importance of fine resolution simulations was also highlighted. The recommended cell size range of 5 to 30m (Clough et al., 2010) was extended to include a higher resolution of 1m. The results indicate the significant influence of this parameter to the projected habitat distribution. The lower the resolution used, greater the differences are to the projected habitat distribution relevant to the present condition. However, this parameter affects the run time of the simulation, which is significantly increased for fine resolution simulations. For the small Newtown estuary, the run time of a simulation varied from 1 minute for a low resolution simulation (30 m) to ten minutes and six hours for a fine resolution simulation of 5 and 1 m respectively (using a single 2.4GHz cpu). This limits the application of fine resolution simulations in very large areas, where resolution need to be sacrificed for a shorter simulation time.
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A further factor that could affect the quality of the input data is the interpretation accuracy in the identification of the habitat distribution from remote sensor data. The smallest recommended level of accuracy equal to 85% (Anderson, 1971; Anderson et al., 1971, 1976; Olson 2008) is examined here by randomly misclassifying 15% of each category to the closest in terms of elevation one. This analysis highlights the capability of SLAMM to correct errors in the habitat distribution (Clough et al., 2010) based on their position into the tidal frame. However, this process is simulated solely in terms of inundation, ignoring the equally important process of aggradation (i.e. the seaward expansion of specific intertidal wetland units). Therefore the source code was further modified to incorporate this process, enabling SLAMM to capture a more accurate present habitat distribution. The latter considers SLAMM a valuable tool in decision making strategies for case studies with poorly represented habitat distribution data.
Most fundamentally, the process of aggradation further affects the representation of accretional processes into the future habitat projections. The sensitivity of the projected habitat distribution on this extremely important factor was examined here by applying different accretion values for the upper and lower marsh and the tidal flat. The higher the accretion rate applied in a given habitat, the better it is able to cope with the sea- level rise (Reed, 1990; Doody, 2001; Day et al., 2008). However, with the procedure of aggradation disabled, the model cannot capture the process of upland migration in areas with sufficient sediment supply. This particular modification of the source code is therefore vital to the performance of the model.
Finally, the process of lateral erosion was investigated for the tidal flat and the marsh area by applying different erosion rates for each one of them. Based on evidence from studies in southeast England (Burd, 1992; Cooper et al., 2001; van der Wal and Pye, 2004; Wolters et al., 2005) that marsh edge erosion can occur even in estuaries with a small fetch, the model code was modified to reduce the fetch threshold to 0.5 km. The code was also edited to allow erosion of the ‘dryland’; this allows the model to represent the erosion of inactive flood defences. This modification is mostly relevant for estuaries where embankments have failed but continue to limit the fetch until they eroded away (French et al., 2000).
This sensitivity analysis focuses mainly on the output uncertainty at each time-step, due to individual input parameters. An analysis of the cumulative representation would also
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be interesting, but this is outside the scope of this thesis. A deeper investigation of the uncertainty factor could also explore the inter-dependencies between the different input uncertainties and how this might propagate into model projection errors. That would most logically involve a Monte Carlo simulation possibly involving several hundred simulations to cover all the parameters. One of the challenges that might arise here is the selection of appropriate estuarine state indicators (cf Van Koningsveld et al., 2005) to match model output to management needs.
The various source code modifications reported here allow meaningful use of SLAMM beyond the North American context for which it was designed. The modified code accommodates a simplified classification of intertidal habitats that is better suited to application in the UK, and potentially elsewhere in northwest Europe. It might also be argued that a simpler classification is more commensurate with the ability of this kind of rule-based model to resolve changes in habitat based largely on elevation as a determining factor. The detailed floristic composition of wetland subtypes clearly reflects not only the elevation (and its direct effect on hydroperiod) but also factors such as soil structure and chemistry, drainage and competition that may exhibit a much weaker dependence on elevation (Paul, 1993; Boorman et al., 1998; Callaway, 2001; Silvestri et al., 2005).