Climate regionalization in Bolivia: A combination of non‐hierarchical and consensus clustering analyses based on precipitation and temperature

Abstract

Climate regionalization is an inseparable part of many climate change and environmental studies. Delineating climatologically homogeneous regions enhances the utility of such studies and reduces the biases due to the uncertainties associated with climate model outputs at individual grid points which both lead to better understanding of the atmospheric mechanisms affecting a region's climate. Throughout time, researchers and statisticians have developed different methods to perform regionalization in which the techniques are highly dependent on the nature and accessibility of the data. This research aims to divide Bolivia into smaller, coherent climate subdivisions. To achieve this goal, we first apply the non‐hierarchical k‐means clustering method to climatologies of monthly accumulated precipitation and monthly average temperature separately using a gridded observation dataset for Bolivia spanning from 1979 to 2010. The clustering is performed on the two variables separately to avoid arbitrary attribute scaling and information redundancy as well as to gain a better understanding of these individual variables across Bolivia. Consensus clustering then finds the categorical intersection of the two independent clusters to create homogeneous climate regions. Results from this study show that Bolivia can be divided into 10 climatically distinguishable subdivisions largely explicable by topography and latitude, which are the key climate control factors in the region.

Determinación de áreas hidroclimáticamente homogéneas. Una propuesta técnica

“…Si bien en la presente investigación no se aplican métodos jerárquicos para la clasificación por clústers, los resultados son bastante acertados con ciertas estructuras climáticas o topográficas que existen en Colombia, las técnicas matemáticas aplicadas son reconocidas por su fiabilidad y buenos resultados, es así como Abadi et al (2019) en su investigación aplican también técnicas de Análisis de Componentes Principales para reducción dimensional y el método de k-means para determinar los grupos que determinaran la regionalización hidroclimática (con diferencia en el método de selección del optimo número de clústers, determinación de regiones hidroclimáticas y técnicas de depuración de la información base).…”

A New Method of Achieving Single Three-Dimensional Building Model Automatically Based on Oblique Photography Data

“…is study adopted the density-based DBSCAN clustering method, which divides regions with sufficiently high density into several clusters. Unlike K-means, this method does not need to know the number of clusters in advance and can identify noise points [37]. Afterward, the Alpha Shapes [38] algorithm is used to construct the contours of each building from the classified point cloud.…”

Redefining homogeneous climate regions in Bangladesh using multivariate clustering approaches

“…Furthermore, the proper knowledge of the climatological properties of different regions would be useful in water resources planning and management for multiple purposes (Goyal et al 2019). The identification of a homogeneous climate region also will be helpful to the researchers to understand the atmospheric mechanisms (Abadi et al 2019). It is also important to know the regional properties to figure out the suitable crops and plants for a particular climate region.…”

“…Sahin and Cigizoglu (2010), Goyal et al 2019, andBasalirwa (1995) respectively in Turkey, India, and Uganda also applied clustering approaches for the same purpose. Furthermore, Abadi et al (2019) applied a multivariate clustering approach to divide Bolivia into identical and coherent climatological sub-regions. The clustering approaches have been applied to identify the climate zones of the Conterminous United States by Fovell and Fovell (1993).…”