GIS Modeling of Intertidal Wetland Exposure Characteristics discusses the analysis of solar radiation and tidal inundation impacts on coastal ecosystems. This analysis addressed whether solar radiation and atmospheric exposure can be modeled using LIDAR derived DEM data with wetland mapping. The authors stated previous methods had limitations due to data quality. Once the data was modeled, it would provide exposure characteristics of Nova Scotia, Canada. Four methods of analysis were used, two using Python scripts.
Early on in the article, the authors of this article, Crowell, Webster, and O'Driscoll discuss the limitations of analyzing solar radiation and tidal inundation. Poor data samples/quality makes the analysis difficult, and the authors do a fair job at showing how GIS analysis along with Python can simplify and improve the desired analysis which they stated extends the localized findings.
When the authors explained how they performed the tidal inundation model analysis, it was clearly explained that it uses a predictive approach. It used a script that found the high risk areas of flood damage. Since the authors clearly stated the use of cell elevation in the LIDAR DEM raster to find the connectivity between adjacent cells. One limitation is the authors do not explain how this improves upon previous analysis. It would also have been good to know why the authors did not account for preservation of momentum or flow rate. One strong argument the authors made for a benefit of using script was the script allowed for a realistic modeling of the tides.
The authors do a good job showing how one script was able to be used alongside another script. The tidal inundation model was used with the solar exposure model. However, it was not clear what the parameters were for performing this script. The data used was from 2009. The authors did not make it clear if using older data would have an impact on the analysis. The authors could have gone into more detail of how the two scripts worked together or how the analysis was performed together.
The article does a good job explaining how the coastal wetland zone script looped through each tidal model delineation to determine the spatial overlap. The authors do a great job stating how the script looked at the lowest and highest elevation and needed to use annual atmospheric and solar-exposure characteristics.
One strength of the article is the authors explained how the script can be applied to other parameters/characteristics such as other chemicals that can impact the areas. The authors did a great job supporting this claim by giving an example of other contaminants. Another strength of the article was mentioning some limitations of the analysis. By stating irregular tides were not captured in the script, it allows the audience to make note and understand why scripts are not perfect even though being fairly accurate.
Overall, this article does a decent job showing why Python scripting can benefit the environmental analysis of solar exposure and tidal patterns. The authors made a great point of how using models to fill in gaps allows to expand upon these kind of findings. However, the authors could have provided more details of how the scripts worked or explained how the analysis would be performed if done manually. It was not completely clear of the data parameters used However, after reading the article, it is clear that using scripting alongside other analysis methods reduces analysis time and allows for far more complex analysis.
Crowell, N., O’Driscoll, N.J., & Webster, T. (2011). GIS Modelling of Intertidal Wetland Exposure Characteristics. Coastal Education & Research Foundation, 44-51.
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