Forecasting water quality using seasonal ARIMA model by integrating <i>in-situ</i> 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 degradation water quality. Remotely sensed satellite imagery analysis plays significant role assessing quality developing management strategy reservoirs. present study an attempt demonstrate improvement chlorophyll-a (chl-a) estimation by integrating remote sensing in-situ measurements. Multiple regression equations were developed reflectance Green, Red, NIR SWIR1 bands Operational Land Imager (OLI) sensor Landsat 8 yielded coefficient determination as 0.812, total dissolved solids (TDS) 0.945 electrical conductivity (EC) 0.960 respectively. model was further utilised forecast chl-a EC through seasonal auto regressive integrated moving average (SARIMA) model. found that prediction showed continued significantly changed from class C3 (high salinity) C4 (very high salinity). These results are alarming immediate reduction external catchment effective watershed programs should implemented.