Water temperature is a critical variable for water quality control and management. Air temperature is often used to estimate stream water temperature by developing regression models. Two direct regression models between hourly water and air temperature were developed for 8 rivers in Alabama and have reasonable model accuracy. The second method using modified sine and sinusoidal wave functions (MSSWF) was then proposed for estimating hourly water temperatures in rivers. The results show significant improvement by using the MSSWF model instead of direct linear and non-linear (logistic) regression models with time lags (4-5 h). Estimates of daily maximum and minimum water temperatures from sine functions were corrected using linear regressions with deviations of estimates of daily maximum and minimum air temperatures from sine functions, and then sinusoidal wave function model was used to estimate hourly water temperatures. Excellent agreement was found between observed and estimated hourly water temperatures using MSSWF models developed for 8 rivers in Alabama with the average Nash-Sutcliffe efficiency of 0.94 for the MSSWF models developed for individual rivers.
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