Research

Development and application of advanced hydrological models for urban stormwater management, flood prediction, and climate change adaptation. Research includes:

• Computational hydrological modeling: Integration of physics-based and data-driven models (PCSWMM, SWMM, EPA-SWMM) for urban drainage system analysis
• Multi-scale watershed modeling: From plot-scale bioretention cells to city-wide stormwater networks
• Uncertainty analysis: Probabilistic and fuzzy logic approaches to address parameter uncertainty in urban hydrology
• Cold climate hydrology: Specialized models for snowmelt, freeze-thaw cycles, and performance of green infrastructure under Canadian climate conditions

Key applications:

• Low Impact Development (LID) optimization and performance assessment
• Combined sewer overflow prediction and mitigation
• Urban flood forecasting and real-time control systems
• Climate change scenario analysis for infrastructure planning

Design, monitoring, and performance evaluation of green infrastructure for sustainable stormwater management in cold climates:

• Permeable pavement systems: Long-term hydraulic performance, clogging mechanisms, and maintenance protocols
• Bioretention cells & rain gardens: Water quality treatment efficiency, heavy metal removal, and evapotranspiration dynamics
• Rain tree trenches (RTTs): Integration of urban forestry and stormwater management for climate resilience
• Green roofs: Thermal performance, stormwater retention, and co-benefits for urban heat island mitigation

Research focus:

• Field monitoring networks with real-time sensor arrays
• Performance metrics for Canadian climate zones
• Life cycle assessment and cost-benefit analysis
• Design guidelines for cold climate applications

Investigation of pollutant fate and transport in urban water systems, with emphasis on:

• Heavy metal removal: Bioretention media performance for lead, zinc, copper, and cadmium
• Treatment pond hydraulics: Optimization of detention pond configurations for sediment and nutrient removal
• First flush analysis: Characterization of high-concentration stormwater pollution events
• Modeling pollutant transport: Advection-dispersion models for water quality in urban drainage networks

Collaborative projects:

• Water quality monitoring partnerships with City of Victoria and City of Vancouver
• Integration of water quality sensors into LID monitoring systems
• Development of treatment train approaches combining multiple LID technologies

Emerging research in artificial intelligence for water resources engineering:

• Predictive modeling: Machine learning for rainfall-runoff prediction and flood forecasting
• Pattern recognition: AI-based analysis of sensor data from LID installations
• Design optimization: Generative adversarial networks (GANs) for exploring LID design alternatives
• Knowledge discovery: Natural language processing for synthesizing research literature on climate adaptation strategies
• Digital twins: Integration of AI with real-time monitoring for adaptive urban water management

Future directions:

• Large language models for automated report generation and knowledge synthesis
• Computer vision for automated inspection of stormwater infrastructure
• Reinforcement learning for real-time control of urban drainage systems

Development and deployment of advanced sensor networks for urban water systems:

• Custom velocity meters: Design of low-cost, Arduino-based flow measurement devices for shallow urban runoff
• Multi-parameter monitoring: Integration of water level, flow velocity, temperature, and water quality sensors
• Data management pipelines: Automated data collection, quality control, and visualization platforms
• Cold climate adaptations: Winterization techniques for year-round monitoring in Canadian climates

Technical innovations:

• Open-source hardware designs for hydrological monitoring
• Wireless sensor networks for distributed monitoring
• Integration with IoT platforms for real-time data access
• Power management solutions for remote field sites