Interactions of Environmental Variables and Water Use Efficiency in the Matopiba Region via Multivariate Analysis

Dimas de Barros Santiago, Humberto Alves Barbosa, Washington Luiz Félix Correia Filho and José Francisco de Oliveira-Júnior
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Dimas de Barros Santiago: Postgraduate Program in Meteorology, Academic Unit of Atmospheric Sciences (UACA), Federal University of Campina Grande (UFCG), Campina Grande 58429-140, PB, Brazil
Humberto Alves Barbosa: Laboratory of Satellite Image Analysis and Processing (LAPIS), Institute of Atmospheric Sciences, Campus A. C. Simões, Federal University of Alagoas, Maceió 57072-900, AL, Brazil
Washington Luiz Félix Correia Filho: Institute of Mathematics, Statistics, and Physics (IMEF), Federal University of Rio Grande (FURG), Rio Grande 96203-900, RS, Brazil
José Francisco de Oliveira-Júnior: Institute of Mathematics, Statistics, and Physics (IMEF), Federal University of Rio Grande (FURG), Rio Grande 96203-900, RS, Brazil

Sustainability, 2022, vol. 14, issue 14, 1-13

Abstract: This study aimed to evaluate the interaction of environmental variables and Water Use Efficiency (WUE) via multivariate analysis to understand the importance of each variable in the carbon–water balance in MATOPIBA. Principal Component Analysis (PCA) was applied to reduce spatial dimensionality and to identify patterns by using the following data: (i) LST (MOD11A2) and WUE (ratio between GPP-MOD17A2 and ET-MOD16A2), based on MODIS orbital products; (ii) Rainfall based on CHIRPS precipitation product; (iii) slope, roughness, and elevation from the GMTED and SRTM version 4.1 products; and (iv) geographic data, Latitude, and Longitude. All calculations were performed in R version 3.6.3 and Quantum GIS (QGIS) version 3.4.6. Eight variables were initially used. After applying the PCA, only four were suitable: Elevation, LST, Rainfall, and WUE, with values greater than 0.7. A positive correlation (≥0.78) between the variables (Elevation, LST, and Rainfall) and vegetation was identified. According to the KMO test, a series-considered medium was obtained (0.7 < KMO < 0.8), and it was explained by one PC (PC1). PC1 was explained by four variables (Elevation, LST, Rainfall, and WUE), among which WUE (0.8 < KMO < 0.9) was responsible for detailing 65.77% of the total explained variance. Positive scores were found in the states of Maranhão and Tocantins and negative scores in Piauí and Bahia. The positive scores show areas with greater Rainfall, GPP, and ET availability, while the negative scores show areas with greater water demand and LST. It was concluded that variations in variables such as Rainfall, LST, GPP, and ET can influence the local behavior of the carbon–water cycle of the vegetation, impacting the WUE in MATOPIBA.

Keywords: MATOPIBA; water use efficiency; principal component analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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