ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Civil Engineering


Civil Engineering

First Advisor

Dong-Jun Seo


Urban flash flooding is a serious problem in large highly populated areas such as the Dallas–Fort Worth metroplex (DFW). Being able to monitor and predict flash flooding at a high spatiotemporal resolution is critical to mitigating its threats and for cost effective emergency management. In this reserach, a high-resolution flash flood forecast system which operates in real time is developed for DFW using a gridded distributed hydrologic model and high-resolution quantitative precipitation estimates from the DFW Demostration Network of the Collaborative Adaptive Sensing of the Atmosphere (CASA) Program high-resolution X band radars and the National Weather Service (NWS) NEXRAD radar. To mitigate hazards and to reduce negative impacts of flooding, urban municipalities operate storm drain networks of varying capacity and complexity. Whereas the conveyance capacities of storm drain systems are generally much smaller than those of the natural channel systems (Rafieeinasab et al. 2015), storm drain networks may significantly alter the severity of flooding and other impacts depending on the location of flooding and the magnitude of rainfall. For accurate flash flood forecasting and effective stormwater planning and management in urban areas, it is necessary to model not only the natural channel systems but also the large and complex networks of storm drains. Most distributed hydrologic models developed for real-time flood forecasting lack the ability to simulate storm drains explicitly. Most urban hydraulic models can simulate storm drains but are not suitable for real-time forecasting for large areas due to computational cost and modeling complexity. In this work, a modular storm drain model that can be easily coupled with existing gridded distributed hydrologic models for real-time flash flood forecasting and stormwater planning and management for large urban areas is described. The integrated model is applied to a 144.6 km2 area consisting of five urban catchments in the Cities of Arlington and Grand Prairie in Texas, US, and the impact of the storm drain network via a combination of simulation experiments, sensitivity analysis and a limited comparison with observed flow is assessed. It is shown how the integrated model may be used to assess the effectiveness of storm drain network over a large area and how areas of potential concern for flooding may be identified under the existing condition and under increased imperviousness. The results show that storm drain modeling increases peak outlet flow for significant events very slightly only for smaller catchments. The simulation experiments with and without storm drain modeling also show that the storm drains reduce surface flow very significantly for a short duration at almost all grid cells in the study area, and that at many locations the flow remains reduced for the entire duration. Sensitivity analysis indicates that significant uncertainties exist in modeling inlet flow and hence partitioning surface runoff into storm drain and natural channel flows. The sources of uncertainties include incomplete information on stormwater infrastructure and uncertainties associated with inlet size, efficiency, clogging and gutter flow modeling. Whereas uncertainty analysis for stormwater infrastructure would be an extremely expensive proposition for both modeling and computing with 1D storm drain-2D surface flow modeling for a large area, the integrated modeling approach developed in this work makes such analysis well within the realm of possibility. The proposed approach hence offers a practical pathway for integrated modeling of storm drains with gridded distributed hydrologic models for large urban areas.


Real-time flash flood forecasting, Stormwater planning and management, Distributed hydrologic models, Storm drain modeling, Equivalent storm drain network


Civil and Environmental Engineering | Civil Engineering | Engineering


Degree granted by The University of Texas at Arlington