Evaluating the impact of climate change in Northern Peru by analyzing homogeneous regions based on different climate variables and precipitation trend changes.

Changes in future climate are inevitable, strongly impacting water resources and their adaptation strategies. Northern Peru currently faces water scarcity issues, significantly limiting activities such as agriculture, a key economic and social pillar for the region. In addition, there are pronounced fluctuations in precipitation due to the effects of the El Niño phenomenon (ENSO), along with scarcity of information regarding various climatic variables in terms of quantity and quality, making it challenging to accurately account for the resources available for proper management.

Therefore, this research aims to, through cluster analysis, assess the importance of various variables such as elevation, precipitation, maximum and minimum temperature and stationarity index, for the period 1972-2015, in determining the hydrologically homogeneous regions currently present in the sub-basins of the northern region. Various multivariate cluster methods such as hierarchical clustering, partitioning around medoids (PAM), and K-means have been used for this purpose. To assess the validity of the analysis, Hopkins statistics and visual inspection methods were employed. Additionally, various validation tests, including internal and stability measures, were applied to evaluate the effectiveness of the cluster algorithm results. Alongside this analysis, a trend study of precipitation has been conducted, helping identify regions that may be experiencing changes in their rainfall patterns. This trend study was performed using the non-parametric Mann-Kendall test on the 49 stations located in the region. The same procedure was also applied to climate models with projections of precipitation and maximum and minimum temperatures up to the year of 2100 to understand the future behavior due to climate change.

Comparing hydrologically homogeneous regions and potential trend changes found between the current and future climate change situations would help identify potential areas where the analyzed climatic variables undergo significant changes. This would aid in identifying potential adaptation measures, since these variables are crucial for determining water availability.