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

• Published in: Sustainability 2022-07, Vol.14 (14), p.8758
• Main Authors: Santiago, Dimas de Barros, Barbosa, Humberto Alves, Correia Filho, Washington Luiz Félix, Oliveira-Júnior, José Francisco de
• Format: Article
• Language: English
• Subjects: Agriculture; Carbon sequestration; Climate change; Ecosystems; Efficiency; Elevation; Hydrologic cycle; Land use; Multivariate analysis; Principal components analysis; Productivity; Rainfall; Remote sensing; River ecology; Sustainability; Variables; Vegetation; Water balance; Water demand; Water use; Water use efficiency
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su14148758

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.