Simulation of Water Temperature in a Small Pond Using Parametric Statistical Models: Implications of Climate Warming

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 (