Accurate modelling of flood flow hydrographs in ungauged catchments is a challenging task due to large errors in the estimation of its response time using existing empirical equations. The time of concentration (Tc) is a key catchment response time parameter needed for forecasting of the peak discharge rate and the timing of the flood event. At least eight different definitions have been presented in the literature for the time of concentration. In this study, a new definition of “Reference Tc” is presented along with a practical procedure for its estimation using readily available basin catchment characteristic parameters with the aim of standardizing this key parameter for practitioners. Nine different empirical models were calibrated and tested on nine catchments of the Credit River watershed, Ontario, Canada to determine which method would provide the most accurate prediction of the Reference Tc. The NRCS velocity method (1986) proved once again to be the most reliable and an accurate method. This study shows that the main reason for the higher accuracy of the NRCS velocity method predictions compared to the empirical equations is attributed to the inclusion of the Manning's roughness coefficient.

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The accuracy of prediction and ease of use of the three popular flood routing models; simplified dynamic Wave, diffusion wave, and full dynamic wave were evaluated. The models were evaluated along a reach of the Credit River Watershed, in Southern Ontario, Canada. The simplified dynamic wave model showed better accuracy and easier formulation when compared against the diffusion wave and the full dynamic wave models. Indicating that the simplified dynamic wave model can be applied to reaches where the diffusion wave and the full dynamic wave models may not be applicable. The principle novel contributions of the paper are (a) the extension of the flood routing formulations by Keskin and Agiralioglu, (b) the use of a prismatic channel and floodplain with varying top-widths, (c) the validation of the methodology through the application of an event simulation to an actual river reach, and (d) comparison of the modeling results to those obtained using the full dynamic wave model and the diffusion wave models.

This study investigates the capability of sequence-to-sequence machine learning (ML) architectures in an effort to develop streamflow forecasting tools for Canadian watersheds. Such tools are useful to inform local and region-specific water management and flood forecasting related activities. Two powerful deep-learning variants of the Recurrent Neural Network were investigated, namely the standard and attention-based encoder-decoder long short-term memory (LSTM) models. Both models were forced with past hydro-meteorological states and daily meteorological data with a look-back time window of several days. These models were tested for 10 different watersheds from the Ottawa River watershed, located within the Great Lakes Saint-Lawrence region of Canada, an economic powerhouse of the country. The results of training and testing phases suggest that both models are able to simulate overall hydrograph patterns well when compared to observational records. Between the two models, the attention model significantly outperforms the standard model in all watersheds, suggesting the importance and usefulness of the attention mechanism in ML architectures, not well explored for hydrological applications. The mean performance accuracy of the attention model on unseen data, when assessed in terms of mean Nash–Sutcliffe Efficiency and Kling-Gupta Efficiency is, respectively, found to be 0.985 and 0.954 for these watersheds. Streamflow forecasts with lead times of up to 5 days with the attention model demonstrate overall skillful performance with well above the benchmark accuracy of 70%. The results of the study suggest that the encoder–decoder LSTM, with attention mechanism, is a powerful modelling choice for developing streamflow forecasting systems for Canadian watersheds.

Many factors contribute to a communities' vulnerability with respect to flooding, including its population, built environment, and concentration of wealth in a small number of highly vulnerable areas that are susceptible to flooding. This paper presents a planning and risk management tool for assessing the vulnerability of communities to flooding, using a combination of Monte Carlo Simulation techniques and multi-criteria analysis. This process has been applied to the Credit River watershed, in Ontario, Canada, to assess the vulnerability of the 22 flood damage centres within the watershed. These flood damage centres have been previously identified in the Canada-Ontario Flood Damage Reduction Program Study (1985). A vulnerability characterization of the Credit River watershed was undertaken in 2007, this work builds upon the previous study. The indices developed in this study provide a quantitative measure of the vulnerability for each of the 22 flood damage centres, and they are also used to estimate the total expected annual direct and indirect damage costs for each of the flood damage centres. The indices are also a useful tool for stakeholder consultation and communication, and can be used for water resources, landuse and emergency planning within the watershed.