Developing Inundation Models for sea level rise

Intrinsic to this research is an analysis to enable the examination of geographic patterns in the dataset, which involves models that mimic the real world with the combination of several layers of data. The maps produced in the course of this research (Chapter 5) are the results of models developed within the GIS framework. Models were employed in this research as it helps to automate geoprocessing workflow, share geoprocessing knowledge, and record and document methodology. This research used the ArcGIS 9.3.1 ModelBuilder to develop the models for SLR. The Model‘s anatomy as used in this research consists of the project data, tools, and derived data. The input dataset were the project data, the tools were obtained from the Arctoolbox in ArcGIS, and then a

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process (geoprocessing), delivered the derived dataset. The derived datasets (in this instance it has become the input dataset) were combined with other geoprocessing tools to produce another set of data.

This study is about communicating coastal information concerning sea level rise and therefore it is important to share knowledge in preparation of data for analysis and modelling the workflow. The method applied in this research to communicate information follows the four steps in Table 3.4

Table 3.4 Basic GIS Project Steps Adopted

Steps Tasks

Determine the objectives of the project

 Identify the problem to solve

 Break down the problem into measurable criteria

 Determine data requirements Build the database and

prepare the data for analysis

 Identify and obtain relevant data

 Design and implement the database

 Add spatial and attribute data to the database

 Manage and modify the data

Perform the analysis  Determine methodology and sequence of operations

 Process the data

 Evaluate and interpret the results

 Refine the analysis as needed and generate alternatives

Present the results  Create final products for intended audience Source: ESRI Training Manual, (2005-2008).

The objective is to quantify vulnerability to inundation because of sea level rise on the coast. The measurable criteria involve determining the elements that will be more vulnerable to sea level rise. Spatial datasets that relate to these elements were

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determined. The second step involved the building of the database and preparation of the data for analysis. In this step, the relevant datasets were identified and obtained from various public sources, they were imported into ArcGIS, and geodatabases were created to store them. In addition, the dataset examined within ArcGIS necessitated a balance in the coordinate system as well as building attribute tables for the database. Data management tools such as ―clip‖, ―mosaic‖, etc. were used to modify the data in terms of its spatial extents to prepare them in a form by which they can be used for analysis.

The next step, the analysis, involves the determination of the logic and sequence of operations. It actually requires the determination of the workflow of the project and using the right set of tools for the geoprocessing exercise. The application of the geoprocessing tools with the input dataset enables the processing of the data, which then yields another set of data, which could serve as an input for the next procedure in the workflow.

The Research interpreted the final output, which represents the results. Results were refined and presented in maps in Chapter 5 of this research. Figure 3.2 depicts as an example the model used to determine inundation zones within the elevation dataset employed.

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Figure 3.2: Model depicting inundation zones in an elevation data set

An important task to the analysis of this research is finding the right tools that will be needed all through the analysis stages, and creating and customising the tools in a toolbox. The index and the search tabs within the ArcToolbox were used to find the location of the tools within the ArcToolbox. For the efficiency of the workflow of this research, there is the need to create and customise a toolbox because the tools were meant to be used many times in the course of the analysis. The toolbox created for this analysis is named ‗SLR_Toolbox‘ (see Figure 3.3).

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Figure 3.3: Toolbox for Sea Level Rise Inundation Analysis. Toolbox contains the geoprocessing tools (hammer shape) for SLR analysis and the models to run a process.

Tools needed for the analysis were then transported from the system toolbox of the ArcToolbox into the newly created toolbox. The tools necessary for sea level rise analysis include the ―Build Raster Attribute Table‖, ―Create Raster Dataset‖, ―Clip‖,

―Extract By Attributes‖, ―Extract By Mask‖, ―Intersect‖, ―Mosaic‖, and ―Raster to Polygon‖. The Clip tool, which creates a spatial subset of a raster dataset, was needed to generate the area of interest from the datasets obtained. Most of the datasets are global;

therefore, the clip tool is important in delineating it according to the area of interest. The Build Raster Attribute Table, which is located within the Data Management Tools, adds

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a raster attribute table to a raster dataset or updates an existing one. The Create Raster Dataset also located in the Data Management Tools creates a raster dataset as a file or in a geodatabase. In this research, this tool was used to create a raster dataset for the two elevation datasets obtained. The Mosaic tool, which merges multiple input raster dataset into an existing raster dataset, was then applied to join the two elevation datasets into one seamless raster dataset. The intersect tool located in the Analysis Tool was used in this research to compute a geometric intersection of the input features. The features especially the Study Area feature which was used to find the area of overlap between other features for example Barrier, Mud, Delta, and Strand features. The Raster to polygon tool (Conversion Tool) was employed in the analysis because calculations were more easy made in a vector feature rather than a raster in some datasets.

Extract by Attributes (Spatial Analyst Tool), is one tool, which is critical to this research as it extracts the cells of a raster based on a logical query. It involves the input of a raster dataset, the use of the QueryBuilder to create an SQL expression used to select a subset of raster cells. In this research, SQL expression to determine inundation zones for example determining land area that will be inundated in a 1 metre SLR scenario, the ‗StudyA_Elev1‘ represents the input raster, then a query which shows the value of the input dataset and an expression of ―VALUE‖ <=1 was built. With this expression all cells that are less than or equal to 1 are extracted to form the inundation zone in a 1 metre sea level rise scenario. This same procedure was applied to account for the other scenarios considered in this study. The other important tool is the Extract by Mask (Spatial Analyst Tool). This is used to overlay the inundation zones with the critical elements identified in this study. The manner of the operation of the tool is that it extracts the cells of the inundation zones that correspond to the areas defined by a mask. In this case, the mask refers to the critical elements, which include population,

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GDP, urban area, agricultural area, and wetland area. By this operation inundation zones for the critical elements were determined. With the determination of the tools as well as their functions in this project, the ModelBuilder in ArcGIS was used to generate and as well validate the sea level rise analysis conducted with the use of geoprocessing tools.

The outcome is the production of models by running the tools and processes in the model for sea levels. For example from Figure 3.2, Figure 3.4 was adapted to simplify and to show the derivation of inundation zones in an elevation dataset.

The first few processes, which involve creating a raster dataset and mosaic in Figure 3.2, are eliminated from Figure 3.4. The ―Elevation_stat‖ in Figure 3.4 is the same as the

―Elevation_mosaic‖ in Figure 3.2. ―Elevation_stat‖ is a raster dataset that contains attributes for a large area in Nigeria whereas the ―Study_Area‖ input is a feature dataset, which delineates the area extent of the Study Area for this analysis. The tool ―Extract by Mask‖ was used to extract the cells of the ―Elevation_stat‖ to correspond with the area extent of the ―Study_Area‖ to produce an output feature dataset that was named as

―StudyA_Elev1‖ which later formed the input dataset for the next operation. The next operation is determining inundation zones for the sea level rise scenarios.

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Figure 3.4: Simplified Model depicting inundation zones in an elevation dataset

For this task the tool, ―Extract by Attributes‖ was used and as explained earlier, the tool involves the building of a query expression depending on the scenarios required. The output is inundation maps for each of the scenarios considered in this research.

Furthermore, the need to determine inundation zones for the critical elements necessitated overlay analysis to be performed. This involves the use of a spatial analyst tool ―Extract by Mask‖ (its functions already described in the preceding paragraph). The result of this task is the development of models which can be run at any time to find out the extent of inundation for a given sea level rise scenario for any critical element considered in this research. An example is in Figure 3.5. This model highlights the extent of inundation of the urban land area in Nigeria. The ―ngaurextents.asc‖ which is a

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raster dataset represents the total urban area in Nigeria while ―StudyArea_gdp‖ – a feature dataset, represents the delineated study area for this research. Both serve as input parameters into the model. The geoprocessing tool Extract by Mask was introduced into the model to extract the cells of the ―ngaurextents.asc‖ that correspond to the area defined by ―StudyArea_gdp‖ in an overlay analysis. The result of this process yields an output raster dataset, which was named ―urb_StudyA‖. The next stage involves overlaying inundation zones for each scenario as produced from the elevation dataset with the ―urb_StudyA‖. Once again, the ―Extract by Mask‖ tool was employed to perform the geoprocessing task, which then produced the inundation outputs for the scenarios. The model was then run to validate the processes. This procedure was repeated substituting the right inputs for all the models that were built to display the extent of inundation in the various critical elements for all the sea level rise scenarios considered in this research.

Documentation is essential to this type of project as it acts as reminder of the reasons for choices of tools and methodology. It is also essential in communicating with others, and allowing them to be able to run the models built to access the necessary coastal information as it relates to sea level rise. This will be vital for various stakeholders and coastal managers to appreciate and take advantage of the work done and form a basis for decision-making. Documentation is also a means by which this research has been validated by describing the methods, parameters and tools used in this research.