Development of a stormwater model for testing the hydrological effectiveness of green infrastructure implementation scenarios in urban sewersheds

As the world becomes increasingly urban, the need to improve understanding and prediction of urban hydrology becomes increasingly important. This case study conducted in the urban sewersheds in the City of Syracuse, New York, focused on one of the most difficult aspects of urban hydrology, stormwater runoff, which contributes to increased flooding and water quality issues around the world. Efforts are underway in many cities to reduce storm-driven runoff volumes through implementation of green infrastructure (GI), but few tools provide the means to assess the potential reductions of these strategies prior to investment and implementation. Therefore, I modified the EPA Storm Water Management Model (SWMM) to simulate runoff at different spatial scales for the studied sewersheds under current conditions and post GI incorporation. The developed model was calibrated and validated through the newly incorporated Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The simulation results indicated that GLUE could significantly reduce uncertainties associated with model input parameters and predictions. Through model validation, I found that the parameters calibrated for the micro-scale SWMM model, compared to those for the macro-scale model, could provide more accurate flow predictions for other sites with different land cover and configuration characteristics. I developed a GI conceptual model to simulate the hydrological behavior of various GI technologies. Using the calibrated micro-scale model in conjunction with this newly developed GI model, a coupled human-natural stormwater model was developed to provide more realistic estimates of potential urban stormwater mitigation at the sewershed scale under different GI implementation scenarios. The modeled scenarios include anticipated government tree planting scenario, and household GI participation scenario that was derived from the citizen surveys. The scenario modeling results indicated that human preference-driven GI implementation could contribute significantly to urban stormwater reduction. In conclusion, the modeling framework developed in this research can be used by local communities and decision makers to support sustainable stormwater management practices while meeting human preferences and needs.