Abstract
Predictability of river water temperature is a big argument for many environmental applications, hydrology and ecology research. River water temperature mainly depends on several parameters of water bodies such as streamflow, Dissolved Oxygen (DO), oxidation reduction potential, pollutant temperature, thermal effluent discharges, ground water interactions and its surrounding atmosphere. Commonly, the river water temperature always has been correlated with air temperature as a substitute due to the ease of applicability for rivers with some limitations over detailed meteorological and thermal data. In addition to air temperature the most influencing component of water body is streamflow which is having strong response towards the water temperature. An evaluation of integrated river water temperature and streamflow fluctuations is proposed to evidence biological activity, chemical specimen, oxygen solubility, self-purification capacity of a river system and variation of flows due to hydro-climatic changes. Prediction of river water temperature at various locations over a river basin is vital for the water quality management. The present study aims to estimate the river water temperature (RWT) under air temperature changes and local to regional anthropogenic activities of streamflow over Krishna river basin, India. A standardized multi-linear regression model is developed for predicting river water temperature over various discharge locations of the Krishna river basin. The performance of the model was tested mainly for 8 stations using historical daily data of river temperature, air temperature and river discharges from 1991 to 2005. The attainment of a model is carried out for these stations with air temperature, stream flows are predictors whereas water temperature as predictability component through training, testing. In this study the correlation coefficients between air minimum, maximum and average temperature with river water temperature are determined for selecting the possible air temperature variable to be considered in the analysis. The correlation coefficients are obtained with air minimum temperature (r = 0.541-0.772), air maximum temperature (r= 0.267-0.613) and air average temperature (r = 0.463-0.715) for all stations and it is observed that air minimum has the highest correlation coefficient value and it could be suitable for modeling. The performance of the multi-linear regression model for training and testing was obtained in terms of Root Mean Square Error (RMSE = 0.934 to 4.556), Correlation of determination (R = 0.984 and 0.986).
