GIS5103 - GIS Programming - Peer Review 2

Amadeusz Zajac
GIS Programming
GIS 5103
7/18/2018

"Peer Review of Silva and Taborda (2013)"

The Silva and Taborda (2013) paper discusses the use of BeachMM (Beach Morphodynamic Model) tool, and the advantages which it may provide in creating an easy to use and reliable predictive model for beach morphodynamics. The tool comes provided with ArcGis 10, and can easily be found and accessed in the spatial analyst toolbox. Created using the Python scripting language (due mainly to Pythons strong compatibility with the ArcGis software), the BeachMM tool combines the potential of the already existing wave/morphological numerical models of SWAN and XBeach. Because these particular models require a higher level of skill, and lack the more user friendly capabilities of ArcGis, the BeachMM tool scripts main focus was to simplify and accelerate the necessary process for creating predictive models.
The Silva and Taborda (2013) paper rightfully begins by defining capabilities already provided in both: the SWAN and XBeach applications. The purpose of the SWAN model, is mainly to predict the wave size and behavior based on known physical properties of the study region. First and foremost, the shape of the ocean floor as well as any unique, known geographical features can have a considerable impact on the behavior and formation of waves. SWAN also takes into consideration non-constant metrics such as wind direction and intensity, in order to make its prediction. While this is clearly an important factor, it seems to me that an accurate prediction depends greatly on the availability of accurate, meteorological information at the time of the given weather event.
The XBeach model is described as a tool used to evaluate a change to beach geomorphology, specifically when considering a specific natural event, such as a storm. The tool provides reliable predictions due to changes in beach landscape via dune erosion, overwash and breaching, as has been demonstrated on several different occasions in various European countries.
The Silva and Taborda (2013) article moves on to explain the mechanics behind the BeachMM tool, and explains the significance of the order of operations when drawing from the above mentioned model data. The tool can be divided into four basic stages of operation: The BeachMM Python tool implements four main tasks. The first, being the conversion between different bathymetric raster formats, where the data from SWAN and XBeach are made compatible into a single operation. The second being the creation of model input parameter files, according to both user input and bathymetric beach properties. The latter would save some time and confusion for a Gis user who is not normally familiar with SWAN and XBeach. Third step is saving the output data in an ArcGis friendly format, and finally call external models to run within ArcGis.
The paper specifically states, that the BeachMM tool was shown to be effective in reducing work time, increasing the user-friendliness of the above mentioned tools, and increasing the number of platforms that can run operation (specifically ArcGis). Silva and Taborda (2013) do however make a point to state that the reliability of the results hugely depends on the accuracy and quality of the SWAN and XBeach data already provided. I think that this is an extremely important point, and very important to consider for those who are not familiar with working with these models. It is suggested that in order for the BeachMM tool to be proven to work indefinitely, there is a necessity of constantly testing the hypothesis to truly understand the tools reliability.
While the misuse of the BeachMM tool is said to have been minimized by "including in the user interface only the parameters needed for the operational use, being the calibration parameters included in a concealed file only handled by SWAN or XBeach experienced users" the tool is still implied to have troubleshooting drawbacks, and verification of the results still strongly depends on individuals capable of interpreting SWAN and XBeach data.

(link: https://hostsited2l.uwf.edu/content/enforced/1024185-51499GIS5103201805/SupplementalReadings/PotentialsForPeerReview/Silva_et_al_2013.pdf?_&d2lSessionVal=Vtck5anLgZVBTLIENBNOMIwsc&ou=1024185)