Case study for Chicago River watershed: Physical modeling vs. data driven modeling for an urban watershed

Naila Mahdi, Texas A&M University at Qatar, Doha, Qatar


A water quality model based on hydrologic simulation was developed for a highly urbanized watershed. BASINS/HSPF model was applied to the watershed with appropriate consideration given to the effective impervious area (EIA). The results from the five-year water quality simulation resulted in finding of nutrients loadings of both point and non-point sources. However, it is always useful to have modeling alternatives and to validate the simulation results of physically based models with data driven ones. Data driven modeling has gained a lot of attention in the last decades in both hydrology and water resources research. While physical based models require the description of the systems input, physical laws and boundary and initial conditions, a data driven model simply extracts knowledge from large amount of data with only limited number of assumptions about the physical behavior of the system. For this study, both data driven and physical models were developed. Comparing the performance of the two model approaches suggests that data driven models show better performance. RMSE for regression models showed up to 10.7 % increase in prediction performance. The data driven models require fewer inputs and can be deployed anywhere in the watershed while the physical model require extensive data inputs and can only be applied in the specific watershed outlets selected in the simulation. These arguments make it logical to suggest the use of both physical and data driven models complementarily. The physical model can be used whenever significant physical change takes place in the watershed as a planning tool while the data driven model can be used as an operating tool that can be used periodically to inspect the watershed water quality parameters, especially if TMDL and WQS are established for the watershed. Keywords: Urban Watershed, Water Quality, Physical Watershed Model, Data Driven Modeling.

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