Modeling the effect of climate change scenarios on water quality for tropical reservoirs

Impact of natural phenomena and anthropogenic activities on water quality is closely related with temperature increase and global warming. In this study, the effects of climate change scenarios on water quality forecasts were assessed through correlations, prediction algorithms, and water quality index (WQI) for tropical reservoirs. The expected trends for different water quality parameters were estimated for the 2030-2100 period in asso-ciation with temperature trends to estimate water quality using historical data from a dam in Mexico. The WQI scenarios were obtained using algorithms supported by global models of representative concentration pathways (RCPs) adopted by the Intergovernmental Panel on Climate Change (IPCC). The RPCs were used to estimate water and air temperature values and extrapolate future WQI values for the water reservoir. The proposed al-gorithms were validated using historical information collected from 2012 to 2019 and four temperature variation intervals from 3.2 to 5.4 degrees C (worst forecast) to 0.9-2.3 degrees C (best forecast) were used for each trajectory using 0.1 degrees C increases to obtain the trend for each WQI parameter. Variations in the concentration (+/- 30, +/- 70, and +100) of parameters related to anthropogenic activity (e.g., total suspended solids, fecal coliforms, and chemical oxygen demand) were simulated to obtain water quality scenarios for future health diagnosis of the reservoir. The results projected in the RCP models showed increasing WQI variation for lower temperature values (best forecast WQI = 74; worst forecast WQI = 71). This study offers a novel approach that integrates multiparametric statistical and WQI to help decision making on sustainable water resources management for tropical reservoirs impacted by climate change.

Automated summary

This study assesses the effects of climate change on water quality in tropical reservoirs using statistical analysis and water quality index (WQI). It provides a novel approach for predicting future water quality based on historical data.