Caterina Valeo

Caterina Valeo
Mechanical Engineering

PhD (McMaster)

Office: EOW 543

Research Areas

  • Disturbance Modelling in Forested Regions
  • Sustainable Urban Development of Water Resources
  • Climate Change Impacts and Analysis using Artificial Neural Networks
  • Pollutant Dispersion Modelling in Rivers and Nearshore Regions

Research Description

Pine beetle infestations and forest fires are large-scale disturbances that impact watershed hydrology. Our research develops and uses complex hydrological models to incorporate the Pine beetle life cycle and forest infestation scenarios to predict resulting water yields in managed watersheds. These models are also used to determine the amount of duff (organic layer) that is consumed during a forest fire in order to predict forest regeneration patterns. Our models provide highly distributed spatial predictions of consumed duff given hydrological conditions and canopy characteristics (provided by remote sensing). This research effort plays a necessary role in forest management.

Low impact development (LID) in urban regions is now recognized as the only option in sustainable urban design. LID tools such as bioretention cells, bioswales and permeable pavement types are studied to determine the best designs to capture stormwater, treat stormwater pollutants in situ and/or limit release to receiving bodies. This research aims to develop the best possible designs for LIDs given local conditions and recommend operation and maintenance procedures in Canadian climates.

Research into climate change impacts on urban hydrology is enhanced through the use of artificial neural networks and fuzzy logic based methods. These methods are used to obtain quantile estimates for rainfall/flood frequency analysis essential in engineering design of water infrastructure by avoiding the problem of distribution determination while increasing the accuracy of the estimated quantiles.

Accurate estimation of pollutant dispersion in rivers and nearshore areas is achieved by synergizing two different approaches for water quality prediction. The complex integration of a hydrological, a hydraulic and a water quality model (WASP) predict macrophyte and periphyton development in rivers receiving Wastewater Treatment Plant discharge. Macrophyte and periphyton competition models are developed to improve the resulting impacts on dissolved oxygen levels affecting fish habitat. The second approach uses Artificial Neural Networks to estimate the impacts of bathymetry, and other hydraulic variables on macrophyte and periphyton relationships. These results are integrated with WASP model predictions to enhance the modelling process.