Estimation of surface water temperature in small recharge pond from air temperature

A simple linear and a nonlinear logistic regression model for simulating surface water temperature in a recharge pond from air temperature at different time scales was derived utilizing the air-water temperatures data during 2005–09. The parameters of the both the linear and non linear models were estimated from the five years’ time-series data of air and water temperature using the least squares optimization method. The variations in water temperature at daily, weekly and monthly time scales were predicted with high accuracy by the simple linear airwater regression model. Results revealed that as daily air-water temperature was averaged over weekly and monthly, correlations between the measured and simulated water temperature was improved. The methodologies developed herein for simulating surface water temperature in a small recharge pond from air temperature at different time scales could be applied successfully for gap filling of missing or unrecorded water temperature data and could help in evaporation estimation and analysing the effect of climate change on pond water temperature in semi-arid regions of India.