How can the author get the following conclusion from the QQ plot?

Question

In this paper: https://www.tandfonline.com/doi/pdf/10.1080/02664763.2021.1940109, the authors have two actual datasets (e.g., 59 observations showing continuous annual flood data) and the authors want to fit it with a Gumbel distribution.

The authors say that

"it is important to first have an idea of the underlying distribution of the two data sets. To get an idea of the underlying distribution of the two data sets, first construct a Q-Q plot by plotting the observed versus expected magnitudes, see Figure 1. "

Question: why can the author conclude that

It is clear from Figure 1 that the data points fall approximately on the straight line; therefore, it can be concluded that the extreme value distribution is an appropriate distribution for both flood data sets.

Answer 1

Those curves look close to the diagonal. The small apparent discrepancies in a tail do not look substantial to me.

As an illustration not using Gumbel data, here is an example of a normal distribution Q-Q plot using points drawn from R's rnorm function. You can see one extreme negative sample value but no even moderately extreme positive sample values in this particular sample; such things can easily happen with samples. Using different seeds would have produced different patterns but many of them would have similar sorts of small apparent discrepancies visible.

set.seed(2022)
X <- rnorm(50)
qqnorm(X, pch=3, col="blue")
abline(0,1, col="red")