Severe Tropical Cyclone Harold was a very powerful tropical cyclone which caused widespread destruction in the Solomon Islands, Vanuatu, Fiji, and Tonga in April 2020. It was first noted as a developing tropical low within a trough of low-pressure on April 1, when it was located to the east of Papua New Guinea. Over the next day, the system moved south-eastwards over the Solomon Sea, before it was classified as a tropical cyclone and named Harold by the Australian Bureau of Meteorology.
TC Harold Made landfall in Kandavau, Fiji on the 08/04 00:00 UTC, however the predicted cyclone path was available by the 3rd of April. Utilising geospatial overlay techniques, it is possible estimate the potential exposure for different villages and communities before the landfall and take necessary measured for prepared and evacuation.
By going through this exercise, we are going to perform Population Exposure Analysis using some geoprocessing functionalities of ARCGIS,
|File Name||Data Type||Source|
|fji_polbnda_adm1_district.shp||Polygon Shapefile (Vector)||HDX|
|fji_polbnda_adm2_province.shp||Polygon Shapefile (Vector)||HDX|
|fji_polbnda_adm3_tikina.shp||Polygon Shapefile (Vector)||HDX|
|HAROLD20_Cyclone_Path_JRC.shp||Polygon Shapefile (Vector)||GDACS|
As soon as the datasets are loaded, you will notice the geodata is visualised in extreme left and right corners, and that zooming into feature extent or whole extent does not help investigate Fiji closely. This unique challenge is due to Fiji’s geographic location on both sides of the ante meridian line (180 Degree longitude line). To resolve this issue, we will need to change the coordinate system of the Data frame into a local coordinate system known as Fiji Map Grid 1986. Notice that you might not have this problem if your coordinate system is already set up as Fiji Map Grid 1986.
After this you shall be able see the whole of Fiji in the map display without being divided by the 180 line.
What is the purpose of setting the transformation in the data frame properties?
From a visual inspection, can you please list five Tikina that might be severely affected by the cyclone? What are the criteria that you have applied to come up with the list?
“Intersect” is a common tool used in GIS for overlay analysis. This tool extracts the overlapping portion of an input and an intersect feature. The following graphic demonstrates the operation of the intersect tool.
At this step of the exercise we are going to intersect “Tikina Boundary” and “Windspeed zones” to identify which part of Tikina will be exposed to what level of wind hazard.
You will notice that this layer contains records with Tikina, Province and Division level.
To calculate the estimated affected population by administrative unit, we need to intersect both the “HAROLD20_Cyclone_Path_JRC.shp” polygon layer and the “fji_polbnda_adm3_tikina.shp” administrative unit layer.
The output file “adm3_wind_zones” will be automatically added to the layers window.
Can you identify how many Tikina is inside 120 km/hr wind speed zones?
In this step we are going to utilize the zonal statistics tool to calculate the exposed population inside each council and province. The tool is available on the Spatial Analysis toolbox. It works by taking a raster input value and a feature or raster as zone data and aggregates the values for any zones.
There are many sources for population data available. However, due to its accuracy, accessibility, and availability we have chosen a Worldpop dataset for this exercise.
Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (2019 Revision of World Population Prospects). The mapping approach is Random Forest-based dasymetric redistribution.
A dialog box will pop up asking you to calculate pyramid for faster display of raster.
To obtain the estimation of population living within different wind speed zones areas by administrative units we are going to use Zonal Statistics Tools available in Arc Toolbox:
If you inspect the new “pop_winds_adm3.dbf”, you will see it has only FID_, COUNT, AREA, SUM but no name of admin. This can be solved by joining “pop_winds_adm3.dbf” with “adm3_wind_zones” using a common key FID_ and FID.
What is the total estimate of affected population by provinces (Give only three most affected provinces)? Also provide the total affected population.
The joined you have performed earlier is temporary. To save the join into a new file:
To summarize the output of “adm3_wind_zones” we need to copy attribute data to excel and use a pivot table to organize the results.
As a result, you will see a table as follows:
The table shows the total population by Tikina likely to be exposed to each wind speed throughout the cyclone path.
Change the text format to number and remove decimals for better data representation. And save it as “Population Exposure” in the following folder X:\UNOSAT_ADV_FJI\Training_Material\M4\Practical\Data_Output\
If you are planning evacuation of safety precautions which Tikinas will be your priority?
Congratulations on completing a population exposure analysis using forecasted cyclone path. It is important to remember the forecasted cyclone paths are updated frequently. And it is always a good idea revise these exposure statistics as soon as new forecasts is available and shows a lot of change.
Using the same principle of superposition or overlaying you shall now to need create an exposure analysis report which may include following features:
The data can be found in the following folder: X:\UNOSAT_ADV_FJI\Training_Material\M4\Practical\Data_Input\Vector
While performing the analysis keep an emergency management end user in mind.