Implementation of remote sensing algorithms to estimate TOC, Chl-a, and TDS in a tropical water body; Sanalona reservoir, Sinaloa, Mexico.

The present study implements a methodology to estimate water quality values using statistical tools and remote sensing techniques in a tropical water body Sanalona. Linear regression models developed by Box-Cox transformations and processed data from LANDSAT-8 imagery (bands) were used to estimate TOC, TDS, and Chl-a of the Sanalona reservoir from 2013 to 2020 at five sampling sites measured every 6 months. A band discriminant analysis was carried out to statistically fit and optimize the proposed algorithms. Coefficients of determination beyond 0.9 were obtained for these water quality parameters (r2TOC = 0.90, r2TDS = 0.95, and r2Chl-a = 0.96). A comparison between the estimated and observed water quality was carried out using different data for validation. The validation of the models showed favorable results with R2TOC = 0.8525, R2TDS = 0.8172, and R2Chl-a = 0.9256. The present study implemented, validated, and compared the results obtained by using an ordered and standardized methodology proposed for the estimation of TOC, TDS, and Chl-a values based on water quality parameters measured in the field and using satellite images.

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 association 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 algorithms were validated using historical information collected from 2012 to 2019 and four temperature variation intervals from 3.2 to 5.4 °C (worst forecast) to 0.9-2.3 °C (best forecast) were used for each trajectory using 0.1 °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.