The effect of sea-level rise on Great Britain

Original Map of Great Britain

A rise in sea level of 10 meters

A rise in sea level of 50 meters

A rise in sea level of 100 meters

The graphics shown show a picture of Great Britain and the effect of sea level rising 10 meters, 50 meters, and 100 meters. The black mass represents the ocean and the lighter parts represent the land mass. You can see the white parts diminishing as sea level rises. 

I got the data from Christian Hill's online book in Chapter 7 on matplotlib.

The graphics was created from an .npy file which had arrays of the altitudes of 10 km x 10 km hectad squares of Great Britain. 

I was told to use imshow to plot the map given in the .npy file and plot the three different maps where sea level rises 10m, 50m and 100m. I also had to figure out the percentage of land area remaining, relative to its present value. 

.imshow

I used .imshow to plot the map after I opened the npy file with numpy's load function. 

The code looks like this:

plt.imshow(a, interpolation='nearest', cmap='gray')

plt.show()

where plt represents matplotlib.pyplot. Interpolation= 'nearest' was supposed to make the map more clear, but I don't think it did anything. cmap='gray' might be for the color scale.

Manipulating the arrays

To find the altitudes of the land mass when there is a sea level rise of 10m, I subtracted every element in my array by 10. This was done by just calling the given array a and subtracting 10. Since some values became negative, I had to replace all negative values by 0. That was done by the following code: 

z[z<0] = 0

where z is an array. The above code is basically saying, for all negative values in the array z, let them equal 0.

Deducing percentage of land remaining

The next step was the deduce the percentage of land mass remaining after a rise in sea level of 10m, 50m, or 100m.

Since the numpy arrays gives the average altitudes per 100km^2 of area of Great Britain, we know that we can find the total area of the map by counting the total number of elements in the arrays. There are 8118 hectad squares whose area is 811800 km^2. That counts the area of the oceans too. To just find the land mass area, we need to subtract the hectads that have an average altitude of 0. 

To count how many hectads had altitude 0, I used a for loop like so:

counter = 0

for element in a:

    for element in element:

        if element == 0:

            counter += 1

print(counter)

where a is an array.

In the original map, there were 5261 hectads with altitude 0. I subtracted the number of hectads with altitude 0 from the original number of hectads (8118) to get the number of hectads of land mass. I got that the land mass of the original map is 285700 km^2. 

I used a similar for loop to find the number of hectads of land mass with altitude greater than 0 for the latter 3 maps. Then, I compared the number with the original map's land mass. 

When sea level rises to 10m, the percentage of land remaining compared to the original land is 83.86%.

When sea level rises to 50m, the percentage of land remaining compared to the original land is 64.02%.

When sea level rises to 100m, the percentage of land remaining compared to the original land is 45.15%. 

Does it look like that in the maps?

Final Thoughts

I am very happy to know how to load some type of image through python. Although I did not create the code for the map, I have a bit of knowledge of how pictures can be created through arrays! That is very cool. I always wondered how pictures of maps were created through code, and now I know, kind of.

Climate change is also something I am very interested about. I hope to do my final project at Metis related to climate change. 

On a side note, I will be attending the Spring cohort's career day today to see them present their final projects. I am excited about that. 

Github code - Sea Level