Forecasting water quality using seasonal ARIMA model by integrating in-situ measurements and remote sensing techniques in Krishnagiri reservoir, India

Abstract

The Krishnagiri reservoir is the main source of irrigation in Tamil Nadu, India. It has been reported to be hypereutrophic for over a decade with sediment and nutrient load sources responsible for the degradation of water quality. Remotely sensed satellite imagery analysis plays a significant role in assessing the water quality for developing a management strategy for reservoirs. The present study is an attempt to demonstrate the improvement in the chlorophyll-a (chl-a) estimation in the Krishnagiri reservoir by integrating remote sensing and in-situ measurements. Multiple regression equations were developed with the reflectance of Green, Red, NIR and SWIR1 bands of the Operational Land Imager (OLI) sensor of Landsat 8 satellite yielded the coefficient of determination for chlorophyll-a (chl-a) as 0.812, total dissolved solids (TDS) as 0.945 and electrical conductivity (EC) as 0.960 respectively. The developed regression model was further utilised to forecast chl-a and EC of the reservoir through the seasonal auto regressive integrated moving average (SARIMA) model. It is found that chl-a prediction showed that the reservoir continued to be hypereutrophic and EC significantly changed from a class C3 (high salinity) to class C4 (very high salinity). These results are alarming and an immediate reduction of the external load from the catchment through effective watershed management programs should be implemented. HIGHLIGHTS Empirical equations for optically active water quality parameters (chl-a, TDS, EC) are developed using a remote sensing technique.; Regression analysis results were improved by including Red Band of LANDSAT 8 OLI imageries.; Chl-a and EC of Krishnagiri reservoir have been forecasted using ARIMA model.;

Keywords

• eutrophication
• multiple regression
• remote sensing
• seasonal auto-regressive integrated moving average model