Application of multivariate methods and geoestatistics to model the relationship between CO2 emissions and physicochemical variables in the Hidrosogamoso reservoir, Colombia

Abstract: Aim This article deals with the estimation of a model for CO2 emissions in the Hidrosogamoso reservoir based on the organic matter level and water quality. This is in order to determine the impact of the creation of a tropical reservoir on the generation of greenhouse gases (GHG), and to establish the water quality and emissions dynamics. We hypothesize that the spatial variability of emissions is determined by water quality and carbon cycling in water. Methods Multivariate techniques were applied to determine the relationships between CO2 and certain physicochemical variables measured in the reservoir between February and May 2015, taking samples in 10 stations and measuring 14 variables (water quality parameters and CO2). Factor, cluster, discriminant and regression analysis, as well as the geostatistical technique kriging, were used. Results We observed that all variables except dissolved organic carbon have strong linear relationships. Nitrate, total-P, total solids and total suspended solids are related due to the presence of nutrients in the water; chlorophyll a and biodegradable dissolved organic carbon due to organic carbon; and alkalinity and dissolved solids due to dissolved minerals. The sampling stations can be classified into two homogeneous groups. The first consists of the stations peripheral to the reservoir and the second of stations inside the reservoir. This difference is due mainly to the behavior of chlorophyll a and biodegradable dissolved organic carbon, and these two variables are also the best predictors for CO2, with a maximum adjustment of 70%. Conclusions Our main conclusion is that the production of CO2 is due to decomposition of flooded organic carbon, depends on the soils flooded and the tributary water quality, and that the production of this gas will, based on the literature, continue for 5 to 10 years depending on the nature of the forest flooded.