Deriving Drought Vulnerability Index using Geographically Weighted Principal Component Analysis (GWPCA) and K-Means Clustering for Nile Basin

Abstract

Climate impacts are particularly noticeable for the nations that share the Nile basin with an increase in hotter temperatures and fluctuating precipitation which expands natural catastrophes. Provincial work is required to precisely predict floods and dry seasons, thus preparing, and adapting to climatic events to build climate resilience among these Nile basin nations. In this context, an index indicating vulnerability to drought is derived for the Nile basin using Geographically Weighted Principal Component Analysis (GWPCA) and K-means clustering. Several climate indicators images related to atmosphere, land, and ocean are collected to build clusters categorized as high, mild, and low drought risk. Additionally, STL decomposition is conducted for the Palmer Drought Severity Index (PDSI) using time series data from 2010–2020 for the Nile basin to identify exceptional drought events for the past decade. Furthermore, correlations among PDSI and other climate indicators are analyzed using time series.