An Integrated Risk Assessment Framework for Compound Flooding in Canadian Urban Environments
The simultaneous or subsequent occurrence different flood drivers including heavy rainfall, river overflow, storm tides, among others threaten Canadian communities and infrastructure especially in coastal environments. Analysis of drivers of flooding in isolation without proper characterization of their interrelationships can lead to a significant underestimation of flood risk. This can severely undermine resilience measures and lead to the misallocation of investment in flood protection. In this project, we will develop an integrated statistical and physically-based modelling framework to simulate and predict compound flood risks under climate change in Canadian coastal zones to develop effective mitigation plans. The proposed approach will quantifythe dependencies between multiple flood hazards and identify/characterize compound events using a novel multivariate statistical approach. We will set up and calibrate a land surface modelcoupled to a hydrodynamic model to characterize the multivariate behaviour of flooding. The resulting framework will assess the impacts of compound flooding and characterize the contribution of each driver to the impacted areas under future scenarios considering the effects of more intense hydroclimatic events and sea-level rise in a changing climate. In collaboration with the Institute for Catastrophic Loss Reduction (ICLR), we will disseminate the results from the proposed project directly to insurance industry involved in the management of urban flood risks. This project will provide river forecast centres, conservation authorities, insurance industries, municipalities, among other stakeholders with data, rigorous methodology, and personnel to analyze and predict compound flooding and will significantly contribute to improved resilience of Canadian cities and communities.
Assessment of Prototype Scour Data
Scour is the removal of riverbed sediment which can be induced due to the presence of channel contraction, hydraulic structures, natural fluctuations in discharge, or changes in sediment supply in a fluvial environment. Scour and erosion have been identified as the primary cause of the majority of bridge failures in North America. Several investigators have concluded that about 50% of all bridge collapses occur due to scourͲrelated complications. The prevalence of scourͲinduced bridge collapses is indicative of the criticality of scour estimation in the interest of public safety as well as mitigation of infrastructure costs, as this type of collapse often requires significant investment for design and construction of replacement bridges, fault analysis and potential rehabilitation. The available bridge design codes are mostly extracted from estimates of scour at the laboratory scale (experiments in reducedͲscale physical models), acquired under highly controlled conditions. Some sources of model inaccuracy include scale effects in physical hydraulic modelling; a lack of understanding of the flow physics of the phenomenon; and the limitations of current computational methods used to model sediment transport. A way to improve the prediction ability of current scour methodologies could be using observed scour values in real rivers to verify/correct such methods, as this proposed research intends to do. The aim of the present project is to study prototype scour data at various field site as well as to use computational tools to assess the efficacy of scour estimation methods for investigating the possible improvements to existing approaches.
Automatic Identification of Phytoplankton using Deep Learning
Under the impact of global climate changes and human activities, harmful algae blooms (HABs) have become a growing concern due to negative impacts on water related industries, such as safe water supply for fish farmers in aquaculture. The current method of algae identification requires water samples to be sent to a human expert to identify and count all the organisms. Typically this process takes about 1 week, as the water sample must be preserved and then shipped to a lab for analysis. Once at the lab, a human expert must manually look through a microscope, which is both time-consuming and prone to human error. Therefore reliable and cost effective methods of quantifying the type and concentration of algae cells has become critical for ensuring successful water management. Blue Lion Labs, in partnership with the University of Waterloo, is building an innovative system to automatically classify multiple types of algae in-situ and in real-time by using a custom imaging system and deep learning. This will be accomplished using two main steps. First, the technical team will work with an in-house algae expert (a phycologist) to build up a labelled database of images. Second, the research team will design and build a novel neural network architecture to segment and classify the images generated by the imaging system. The result of this proposed research will dramatically reduce the analysis time of a water sample from weeks to hours, and therefore will enable fish farmers to better manage harmful algae blooms.
Real-time flood forecasts using parallel cloud computing and intelligent algorithms
Development of flood forecast early warning systems is critical as storm and spring melt events intensify due to climate change. Predicting these events with precision and adequate warning time presents a significant technological and computational challenge for managers of water resource systems.
Effective flood forecasting and warning systems combine several complex modelling tools. Climate forecast, hydrologic and hydraulic models are used together to forecast flows, water levels and flood inundation extents. Such models are computationally expensive, and each has associated uncertainties that must be quantified.
This project addresses these issues by coupling models with Machine Learning algorithms and high-performance computing infrastructures. The approach will enable the existing flood forecasting platform (known as ISWMS) to better i) quantify uncertainties associated with flood forecasts and ii) minimize run-time of the computationally expensive models embedded within the forecasting system.