Other integrated approaches involve the use of statistical and spatial analyses, as well as hydrologic modeling, to examine the effects of land use on water quality ( Tong et al. Tools, such as hydrological models, that are coupled with geographic information systems (GIS) and remote sensing provide powerful techniques for conducting these kinds of studies ( Zoppou 2001 Conway and Lathrop 2005 Wang et al. Resource and regulatory authorities use yield estimates to help prioritize efforts with regard to land use management and best practices. Load estimates are essential for the establishment and monitoring of total maximum daily loads (TMDLs) as mandated by the Clean Water Act (CWA). Nutrient loads that are transported by a stream during a given period of time are particularly important when considering the quantity of nutrients that enter a lake or reservoir. Estimates of nutrient concentrations, loads and yields are useful for analyzing a water body and help to identify source areas and to develop mitigation strategies. They are useful in developing models that describe complex natural processes and complicated systems through sets of equations that explain the problems and solve them. Watershed models are fundamental to water resources assessment, development and management. The data-driven model can be an operating tool that can be periodically used to inspect the watershed water quality parameters, especially if TMDL and WQS are established for the watershed. The physical model can be a planning tool whenever significant physical change takes place in the watershed. These arguments suggest the complementary use of both physical and data-driven models. Data-driven models require fewer inputs and can be deployed anywhere in the watershed, while physical models require extensive data inputs and can only be applied to the specific watershed outlets selected in the simulation. RMSE for regression models showed an increase in prediction performance of up to 10.7 %. Comparing the performance of the two modeling approaches, the data-driven models show better performance. For this case study, both data-driven and physical models were considered to simulate total nitrates. While physically based models require the description of system inputs, physical laws and boundary and initial conditions, a data-driven model simply extracts knowledge from a large amount of data with only a limited number of assumptions about the physical behaviour of the system. Data-driven modeling has gained a lot of attention in recent decades in both hydrology and water resources research. However, it is always useful to have modeling alternatives to validate the simulation results of a physically based model with a data-driven one. The 5 y water quality simulation resulted in finding total nitrates loadings at both point and nonpoint sources. Appropriate consideration was given to the effective impervious area (EIA). We developed a water quality model for the highly urbanized Chicago River watershed based on hydrologic simulation using BASINS/HSPF.