Affiliation(s)
1. School of Civil & Construction Engineering, Oregon State University, Corvallis, OR 97331, USA
2. Faculty of Civil Engineering, National University of Huancavelica, Huancavelica 09001, Peru
5. Faculty of Civil Engineering, National University of Engineering, Lima 15333, Peru
ABSTRACT
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.
KEYWORDS
Water temperature
modeling, Ichu River, Peruvian Andes River, statistical modeling.
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