Identifying critical land-uses or source areas is important to prioritize resources for cost-effective stormwater management. This study investigated the use of information on ionic composition as a fingerprint to identify the source land-use of stormwater runoff. We used 12 ionic species in stormwater runoff monitored for a total of 20 storm events at five sites with different land-use compositions during the 2012-2014 wet seasons. A stepwise forward discriminant function analysis (DFA) with the jack-knifed cross validation approach was used to select ionic species that better discriminate the land-use of its source. Of the 12 ionic species, 9 species (K+, Mg2+, Na+, NH4+, Br-, Cl-, F-, NO2-, and SO42-) were selected for better performance of the DFA. The DFA successfully differentiated stormwater samples from urban, rural, and construction sites using concentrations of the ionic species (70%, 95%, and 91% of correct classification, respectively). Over 80% of the new data cases were correctly classified by the trained DFA model. When applied to data cases from a mixed land-use catchment and downstream, the DFA model showed the greater impact of urban areas and rural areas respectively in the earlier and later parts of a storm event.