The rapid increase in Water Temperature Rivers (WTR) observed globally in recent decades and projections for the coming decades under climate change scenarios make water temperature prediction essential to assess chan...The rapid increase in Water Temperature Rivers (WTR) observed globally in recent decades and projections for the coming decades under climate change scenarios make water temperature prediction essential to assess changes in aquatic biota. Statistical models for stream temperature prediction have been widely used because they are computationally simple, involve few parameters, and because of their relatively good accuracy. However, these models have not been evaluated in Peruvian Andean rivers. This work evaluates the main water temperature statistical models from the literature and fits them with data recorded in the Ichu River experimental watershed, Huancavelica-Peru. Three well-known models were reviewed: the Stefan & Preud’homme linear regression model and the Mohseni & Stefan 3- and 4-parameter logistic regression models. Ichu river water temperatures were simulated using the SWAT (Soil and Water Assessment Tool) hydrometeorological model, which defaults to the Stefan & Preud’homme model. Modifications and adjustment of coefficients of the evaluated models were configured in the SWAT code using the “Latin Hypercube Sampling” technique. The evaluated models showed poor performance in predicting the water temperature in the Ichu River with NSE (Nash-Sutcliffe Efficiency) values ranging from -2.6 to 0.49, while the modified models showed NSE values of 0.72 in all three cases. Findings suggest that the statistical models shown in the literature should be validated for Andean rivers.展开更多
文摘The rapid increase in Water Temperature Rivers (WTR) observed globally in recent decades and projections for the coming decades under climate change scenarios make water temperature prediction essential to assess changes in aquatic biota. Statistical models for stream temperature prediction have been widely used because they are computationally simple, involve few parameters, and because of their relatively good accuracy. However, these models have not been evaluated in Peruvian Andean rivers. This work evaluates the main water temperature statistical models from the literature and fits them with data recorded in the Ichu River experimental watershed, Huancavelica-Peru. Three well-known models were reviewed: the Stefan & Preud’homme linear regression model and the Mohseni & Stefan 3- and 4-parameter logistic regression models. Ichu river water temperatures were simulated using the SWAT (Soil and Water Assessment Tool) hydrometeorological model, which defaults to the Stefan & Preud’homme model. Modifications and adjustment of coefficients of the evaluated models were configured in the SWAT code using the “Latin Hypercube Sampling” technique. The evaluated models showed poor performance in predicting the water temperature in the Ichu River with NSE (Nash-Sutcliffe Efficiency) values ranging from -2.6 to 0.49, while the modified models showed NSE values of 0.72 in all three cases. Findings suggest that the statistical models shown in the literature should be validated for Andean rivers.
