The use of principal component analysis for the construction of the Water Poverty Index

ABSTRACT In relation to water resources, indexes can be created to express the multiple dimensions involved with it to aid the planning and management of basins. In this regard, the Water Poverty Index is globally used, but one of its criticisms includes the subjectivity associated with how the sub-indexes are weighted. Therefore, in this study, we applied principal component analysis (PCA) to determine the sub-indexes’ weight: resource, access, capacity, use, and environment of the Seridó river basin. This new index with PCA presents an average range with broader values compared to methodologies without, allowing clear identification of the disparities among the cities and the possibility to better prioritize investments concerning water poverty reduction. Our results show that this approach makes it possible to qualitatively identify geographical locations that have greater water poverty compared to others. Additionally, with this approach, it can be determined whether water poverty is caused due to natural characteristics or deficits in water infrastructure investment, providing insight into social fragilities as well. Overall, the presented hierarchical tool in this study has a high value to improve the planning of water resource uses.