This study examines a large dataset collected over various seasons of the year in Kandla Creek, Gulf of Katchchh, India, to identify and assess the contributions of the sources affecting the water quality. Principal components analysis was applied to simplify and understand the complex relationships among water quality parameters. Five principal components were found responsible for the data structure and 76% of the total variance of the data set. Absolute principal component scores receptor model provided apportionment of various sources contributing to the water quality. Our study reveal that the port activities contributed 80% of the observed turbidity, 70% of suspended solids and 68% of petroleum hydrocarbons; agricultural runoff contributed approximately 69% of the observed phosphate, 57% of the nitrate, and 63% of the nitrite; and industrial discharges contributed approximately 92% of the observed ammonia