Simulation of Water Temperature in a Small Pond Using Parametric Statistical Models: Implications of Climate Warming
Publication: Journal of Environmental Engineering
Volume 142, Issue 3
Abstract
Changes in temperature and precipitation patterns due to global warming are likely to affect the quantity and quality of water in different water bodies. Water temperature modeling techniques are usually employed to study the effects of global climate change on stream and river ecosystems. This study aims to identify a suitable air–water temperature relationship for a small aquatic pond in a semiarid region of India and examine the effects of increased water temperature on the small pond’s attributes. The performance of two parametric statistical models—simple linear regression (SLR) and four-parameter nonlinear logistic regression (NLR) models—was evaluated. The developed models were field tested for mean, minimum, and maximum air–water temperatures on daily, weekly, and monthly timescales. The model parameters were estimated from the measured air–water temperature time-series data using the least-squares optimization method. Model performance was evaluated using three statistical indicators—the index of agreement (), Nash–Sutcliffe modeling efficiency (), and root mean square error (RMSE). The performances of the SLR and NLR models were found to be comparable for all three data series and timescales. However, the NLR model was found to perform relatively better compared to the SLR model for all three timescales. Results also revealed better correlations between the measured and simulated water temperatures on weekly and monthly timescales compared to the daily timescale. Application of the SLR model for projecting changes in attributes of a small aquatic pond in a semiarid region of India under changing climate scenarios revealed a 1.3 to 3.7°C increase in pond water temperature with increases in air temperature from 1.5 to 4.3°C by the end of 2080. This increase in water temperature will cause the water evaporation rate to increase by 8.3–30.3% and the hydroperiod and saturated dissolved oxygen to decrease by 3–26 days and 2.2–6.5%, respectively.
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Journal of Environmental Engineering
Volume 142 • Issue 3 • March 2016
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© 2015 American Society of Civil Engineers.
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Received: Apr 26, 2015
Accepted: Sep 9, 2015
Published online: Nov 30, 2015
Published in print: Mar 1, 2016
Discussion open until: Apr 30, 2016
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