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Ecological niche modeling is an important tool to evaluate the spatial distribution of terrestrial species, however, its applicability has been little explored in the aquatic environment. Microcystis sp., a species of cyanobacteria, is widely recognized for its ability to produce a group of toxins known as microcystins, which can cause death of animals as fish, birds and mammals depending on the amount of toxin absorbed. Like any taxonomic group, cyanobacteria has environmental thresholds, therefore, a suitable ecological niche will define their distribution. This study was conducted in Aguamilpa Hydroelectric Reservoir, an artificial ecosystem that started operations in 1994. In this system we evaluated the potential distribution of Microcystis sp., by generating a prediction model based on the concept of ecological niche MAXENT, using a Digital Elevation Model in cells of 100 m x 100 m (1 ha) spatial resolution and monitoring eleven physicochemical and biological variables and nutrients in water. The distribution maps were developed using ArcMap 9.2®. The results indicated that Microcystis sp., is distributed mainly in the upper tributary basin (Huaynamota basin) during the dry season. There was less chance to find cyanobacteria in the entire system during the cold dry season, while during the warm dry season cyanobacteria was recognized at the confluence of two rivers. During the rainfall season there were no reports of cyanobacteria presence. This species is often associated with arising trophic processes of anthropogenic origin; therefore, attention is required in specific areas that have been identified in this work to improve Aguamilpa’s watershed management and restoration. It was also recognized the importance of phosphorus and nitrogen interaction, which determines the distribution of Microcystis sp., in the Aguamilpa Reservoir. The results of this study demonstrated that ecological niche modeling was a suitable tool to assess the spatial distribution of microalgae in freshwater environments.

The use of water quality models is determined to a great extent by their ability to accurately reproduce observed data series and by their predictive capability without the need to adjust the calibrated parameters. However, the observed abiotic variables involved in a system are measured with a level of uncertainty. The sensitivity analysis concepts and generalized methodology of uncertainty analysis were used via a computer tool called UNCSIM. As a first step, a parametric optimization model of the Aguamilpa reservoir water quality was accomplished. The aforementioned analysis identified that the wind sheltering coefficient (WSC), Chezy bottom friction solution (FRICC), and coefficient of bottom heat exchange (CBHE) were the parameters of the CE-QUAL-W2 model that significantly influenced the behaviour of temperature and dissolved oxygen concentration in the reservoir. Afterwards, the uncertainty of the water quality model was evaluated through the modification of the hydrological and climatological information, which had a major influence on the simulation of the system. This analysis showed the possible changes in hydrodynamic and water quality characteristics of the reservoir, including an increase in the thermocline due to a possible air temperature increase and a rainfall decrease in the region. The innovative coupling routine of the different modules for the sensitivity and uncertainty analysis developed in this research establishes the basis for the future development of a modelling platform to conduct water quality simulations of the Aguamilpa reservoir in real time through continuous meteorological and water discharge information.