Techniques for monthly rainfall regionalization in southwestern Colombia

• Published in: Anais da Academia Brasileira de Ciências Vol. 94; no. 4; p. e20201000
• Main Authors: CANCHALA, TERESITA, OCAMPO-MARULANDA, CAMILO, ALFONSO-MORALES, WILFREDO, CARVAJAL-ESCOBAR, YESID, CERÓN, WILMAR L., CAICEDO-BRAVO, EDUARDO
• Format: Journal Article
• Language: English
• Published: Academia Brasileira de Ciências 01.01.2022
• Subjects: Hierarchical Clustering Analysis (HCA); MULTIDISCIPLINARY SCIENCES; Non-Linear Component Analysis (NLPCA); Principal Component Analysis (PCA); rainfall; regionalization; Self-Organizing Map (SOM); rainfall; Principal Component Analysis (PCA); Self-Organizing Map (SOM); Hierarchical Clustering Analysis (HCA); Non-Linear Component Analysis (NLPCA); regionalization
• ISSN: 0001-3765; 1678-2690; 1678-2690
• DOI: 10.1590/0001-3765202220201000

Abstract The knowledge of rainfall regimes is a relevant requirement for many activities such as water resources planning, risk management, agriculture activities management, and other hydrologic applications. The present study has consisted of validating four techniques (one linear, one non-linear, and two hybrids) that allow identifying homogenous regions. We take the monthly rainfall in the Southwestern Colombia (Nariño), an area of 33,268 km2 characterized by complex topography and local factors that can influence the rainfall behavior, to test all techniques. The results showed overall the best performance for the approach related to non-linear principal component analysis and self-organizing map. However, in all mainly prevail two regions: the Andean Region and Pacific Region with a bimodal and unimodal regime, respectively. The bimodal one dominates over the Andes mountains range and the unimodal one the coastal zone. The application of non-linear approaches provided a better understanding of the seasonality of rainfall, and the results may be useful for water resource management.