The study area covers the capital of the Republic of Djibouti (Lat: 11.33 and long: 43.50), located to the southwest. A province of Africa in which climate change is impacting rural populations. The work of (Ozer and Ayan, 2013) describes it. The temporal evaluation will be limited only to the capital. The station at Djibouti International Airport provides historical data on precipitation and min and max temperature. The spatial framework covers the five regions of the Republic of Djibouti, including Tadjourah, Dikhil (Lat: 11.10 and Long: 42.37), Ali-Sabieh (Lat: 11.15 and Long: 42.70), Arta (Lat: 11.50 and Long: 42.83) and Obock (Lat: 11.97 and Long: 43.28). We analyze a daily historical base of the station of the international airport of Djibouti (Lat: 11.33 and long: 43.50). For precipitation, the daily series is between 1980 and 2017. We also had another series of data from 1950 to 1970. For lack of more than 10 years of information, the latter is neglected. The monthly precipitation ranges from 1961 to 2017. With regard to temperature, the daily data series is studied between 1966 to 2009. We also have the same monthly series from 1961 to 2017.
We observe that several criteria support the idea of three main groups, with a maximum of four to eight groups (each supported by only one criterion). Similarly, the average silhouette score decreases when the number of clusters exceeds three.
When comparing temporal versus spatial regionalization, it appears that clustering based on temporal variability does not adequately reflect reality once applied through spatial interpolation of stations grouped by monthly or annual HCA. Moreover, each grouping should ideally be explained by an atmospheric circulation mechanism.
In contrast, in the spatial case, orography is the only factor that can account for the clustering of precipitation pixels. Finally, the 1 km clustering resolution is closer to the interpolation derived from the 35 weather stations than the CHIRPS dataset at 5 km resolution.
