In Andean catchments, said features are foreseen to increase with climate change and population growth. Explicitly in headwaters, these records are also heterogeneous because of hydrological variability, dynamic uses of water and land use changes. About heterogeneity, water quality records usually have this characteristic due to the high number of variables that they are comprised of. Concurrently, water quality information has been reported as being insufficient even in more developed countries. In emerging economies, several documents report a lack of hydrological data. Among these obstacles, the combination of scarcity and heterogeneity of water quality data is perhaps one of the most significant. ĭespite these capabilities, several obstacles limit the application of such water quality assessments and models in headwater catchments of developing countries. Hence, water quality assessments and models have great potential to improve water quality management. Models have also been employed to propose pollution control strategies in multiple catchments. These programs include the European Water Framework, the Australian Bioregional Assessments, and several programs based on the total maximum daily load concept. They have been useful in identifying global trends such as the interactions between riverine respiration and organic matter, and for designing water management programs. In these statistics, analyses of principal components (PCA), clusters (CA), and factors (FA) are perhaps the most common. In these assessments, multivariate statistics are often utilized for analyzing complex datasets, and identifying relevant contaminants and sites with similar pollution levels. For example, assessing diverse pollutants in water allows for decision support, designing restauration programs, and detecting hazards along river basins. Water quality assessments and models are broadly used for water management. The proposed framework can be applied in other headwater catchments where information is limited, and where an improved management of water quality is needed. For this reason, improving water quality in the sub-basins at the highest altitudes is required. In addition, during the third stage of the proposed framework, the simulation of alternative scenarios showed that centralized treatment is not sufficient to make water safe for potabilization and agriculture in the catchment. It increased only 14% when verified with a different dataset. This capacity was measured with an objective function to be minimized based on a normalized root mean square error. In the second stage, the model was calibrated reproducing the concentrations of pathogens, organic matter, and most nutrients, and showed a predictive capacity. The problematic constituents in this catchment include pathogens, nutrients, organic matter, and metals such as the highly toxic Cr and Pb, while water pollution is the highest during the driest months of the year (i.e., January to March). Applied to an Andean catchment in Colombia, the first stage of the framework revealed the catchment’s most significant water quality constituents and the most polluted season. The framework involves multivariate analyses of principal components and clusters and follows a novel modeling protocol mainly designed for mountainous streams in developing countries. To address this issue, the authors propose a framework of three stages that allows for: (i) conducting a comprehensive assessment of water quality (ii) the development of a mountain stream water quality model based on said assessment and (iii) the simulation of scenarios with the model to resolve conflicts between uses and quality of water. In these catchments, having scarce and heterogeneous information hinders the development of water quality assessments and predictive models to support management. Water quality is a major concern globally and in headwater catchments of developing countries it is often poorly managed.
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