Civil Engineering

Hourly archived rainfall records are separated into individual rainfall events with an Inter-Event Time Definition. Individual storms are characterized by their depth, duration, and peak intensity. Severe events are selected from among the events for a given station. A lower limit, or threshold depth is used to make this selection, and an upper duration limit is established. A small number of events per year are left, which have relatively high depth and average intensity appropriate to small to medium catchment responses. The Generalized Pareto Distributions are fitted to the storm depth data, and a bounded probability distribution is fitted to storm duration. Peak storm intensity is bounded by continuity imposed by storm depth and duration. These physical limits are used to develop an index measure of peak storm intensity, called intensity peak factor, bounded on (0, 1), and fitted to the Beta distribution. The joint probability relationship among storm variables is established, c...

Hourly archived rainfall records are separated into individual rainfall events with an Inter-Event Time Definition. Individual storms are characterized by their depth, duration, and peak intensity. Severe events are selected from among the events for a given station. A lower limit, or threshold depth is used to make this selection, and an upper duration limit is established. A small number of events per year are left, which have relatively high depth and average intensity appropriate to small to medium catchment responses. The Generalized Pareto Distributions are fitted to the storm depth data, and a bounded probability distribution is fitted to storm duration. Peak storm intensity is bounded by continuity imposed by storm depth and duration. These physical limits are used to develop an index measure of peak storm intensity, called intensity peak factor, bounded on (0, 1), and fitted to the Beta distribution. The joint probability relationship among storm variables is established, combining increasing storm depth, increasing intensity peak factor, with decreasing storm duration as being the best description of increasing rainstorm severity. The joint probability of all three variables can be modelled with a bivariate copula of the marginal distributions of duration and intensity peak factor, combined simply with the marginal distribution of storm depth. The parameters of the marginal distributions of storm variables, and the frequency of occurrence of threshold-excess events are used to assess possible shifts in their values as a function of time and temperature, in order to evaluate potential climate change effects for several stations. Example applications of the joint probability of storm variables are provided that illustrate the need to apply the methods developed. I would like to thank Prof. Yiping Guo for his ongoing support over the years. My family has been an inspiration, and without their acceptance of my late return to school, I could never have continued. v Table of Contents Abstract iv Acknowledgements v Chapter 1. Introduction 1. Problem statement 2. Threshold analysis of rainstorm depth and duration statistics at Toronto, Canada 3. A probabilistic description of rain storms incorporating their peak intensity 4. Changes in heavy rain storm characteristics with time and temperature at four locations. 5. Example Applications Chapter 2. Threshold analysis of rainstorm depth and duration statistics at List of Tables 2.1 Rainstorm depth parameters and test statistics. 2.2 Rainstorm duration parameters and test statistics. 2.3 Statistical moments for conventional DDF annual maximum series. 3.1 Correlation of intensity peak factor with storm depth and duration. 3.2 Beta distribution parameters for intensity peak factor. 3.3 Correlation of intensity peak factor with storm depth (I pf − V) for three separate ranges of storm duration.. 3.4 Kendall's τ for intensity peak factor and storm duration (I pf-T), Ali-Mikhail-Haq and Frank copula parameters α. 3.5 Relative Goodness-of-fit, Theoretical models compared to Empirical Plotting Point Distribution. 3.6 Table A1. Summary of statistical analysis results for two stations in Toronto, Canada. 4.1 Station Descriptions, Threshold Analysis of Rainstorm Events March-November, ≤ 24hours 4.2 Threshold statistics 4.3 Difference in values, pre-and post-1980, of statistical parameters. 4.4 J u , average probability of exceedence of u v , pre-and post-1980. 4.5 χ 2 analysis of difference between pre-and post-1980 empirical distributions of storm variables. 4.6 Correlation analysis between V , T , and I pf , pre-1980 and post-1980. 4.7 Correlation analysis between the mean monthly temperature (M M T) when storm events occur and V , T , I pf , pre-and post-1980. 4.8 Average of mean monthly temperature when threshold-excess storm events occurred, pre-and post-1980. 4.9 Summary of major time-span and temperature changes and trends 5.1 Intensity peak factor and return periods of different design storm hyetographs. viii List of Figures 2.1 The plotting point distribution of rainstorm data is compared with the one-parameter exponential distribution. Crosses are used to represent actual measures of storm depth. 2.2 Comparison of the plotting point distribution with Type I and III distributions, storm threshold depth uv = 25 mm. Solid line shows GPD Type I modeled storm depths, dashed line plots Type III. 2.3 Figure 3a (left). Storm duration histogram based upon hourlyrainfall records, using IETD = 6 hours to define individual events. Figure 3b (right). Storm duration histogram, adjusted for start and finish time uncertainty. 2.4 100, 25 and 5-year return period storm-event depth-durations are compared with conventional DDF rainfall accumulation. Box symbol indicates conventional DDF GEV I-modeled rainfall accumulation. Cross symbol is used to show SEA GPD Type I-modelled depth. 2.5 Comparison of event-based and DDF annual maximum rainfall depths, for durations/time intervals of one hour. Actual conventional DDF depths are shown with a box symbol. SEA measures of storm depth are shown with a cross. 2.6 Comparison of event-based annual maximum storm depths for durations of 6 hours or less and DDF annual maximum depths for the 6-hour time interval. 2.7 Comparison of event-based annual maximum storm depths for durations of 12 hours or less, and DDF annual maxima rainfall accumulations for the 12-hour time interval. 3.1 Plot showing actual peak discharge for Mimico Creek (Ontario, Canada) catchment on y-axis, and predicted by regression equation on x-axis. Independent variables for regression equation are runoff depth and peak hour intensity. 3.2 Plotting point or empirical distribution of intensity peak factor (I pf , i pf), based upon Eq. (5), together with Beta distribution in solid line, fitted by method of moments. ix 3.3 Comparisons of plotting position and theoretical joint probability distributions. Plotting position empirical distribution established with Eq. (23) plotted on y-axis. Theoretical distributions based upon Eq. (19) for AMH copula, Eq. (20) for Frank copula, and Eq. (24) for simple joint probability plotted along x-axis. Figure 3(a) for Toronto station, Figure 3(b) for TPIA. Points falling closest to 45-degree line indicate best fit between empirical and theoretical models. 4.1 Storm duration frequency by 3-hour duration increments, pre-and post-1980 4.2 Mean storm depth versus mean monthly temperature 4.3 Mean storm duration and mean intensity peak factor, versus mean monthly temperature 4.4 Storm frequency versus temperature ranges. p-value of difference in proportions shown on reverse scale, significance assessed as p < 0.10 4.5 Mann-Kendall test statistic for Peoria station, on a 20-year moving window basis. 5.1 Maximization of peak discharge for 100-year joint return period. x Ph.D. Thesis-Barry Palynchuk McMaster-Civil Engineering 2. Threshold analysis of rainstorm depth and duration statistics at Toronto, Canada Chapter 2 in this thesis introduces the initial application of threshold analysis to the definition and characterization of extreme rainstorm events. One of the key contributions is the discovery that, for storms with depths exceeding a high threshold, storm depth and duration are not correlated, a rupture with the conventional paradigm of an assumed coupling between the two variables. This chapter has been reformatted from a paper presented by the author and Dr.

Hourly archived rainfall records are separated into individual rainfall events with an Inter-Event Time Definition. Individual storms are characterized by their depth, duration, and peak intensity. Severe events are selected from among the events for a given station. A lower limit, or threshold depth is used to make this selection, and an upper duration limit is established. A small number of events per year are left, which have relatively high depth and average intensity appropriate to small to medium catchment responses. The Generalized Pareto Distributions are fitted to the storm depth data, and a bounded probability distribution is fitted to storm duration. Peak storm intensity is bounded by continuity imposed by storm depth and duration. These physical limits are used to develop an index measure of peak storm intensity, called intensity peak factor, bounded on (0, 1), and fitted to the Beta distribution. The joint probability relationship among storm variables is established, combining increasing storm depth, increasing intensity peak factor, with decreasing storm duration as being the best description of increasing rainstorm severity. The joint probability of all three variables can be modelled with a bivariate copula of the marginal distributions of duration and intensity peak factor, combined simply with the marginal distribution of storm depth. The parameters of the marginal distributions of storm variables, and the frequency of occurrence of threshold-excess events are used to assess possible shifts in their values as a function of time and temperature, in order to evaluate potential climate change effects for several stations. Example applications of the joint probability of storm variables are provided that illustrate the need to apply the methods developed. I would like to thank Prof. Yiping Guo for his ongoing support over the years. My family has been an inspiration, and without their acceptance of my late return to school, I could never have continued. v Table of Contents Abstract iv Acknowledgements v Chapter 1. Introduction 1. Problem statement 2. Threshold analysis of rainstorm depth and duration statistics at Toronto, Canada 3. A probabilistic description of rain storms incorporating their peak intensity 4. Changes in heavy rain storm characteristics with time and temperature at four locations. 5. Example Applications Chapter 2. Threshold analysis of rainstorm depth and duration statistics at List of Tables 2.1 Rainstorm depth parameters and test statistics. 2.2 Rainstorm duration parameters and test statistics. 2.3 Statistical moments for conventional DDF annual maximum series. 3.1 Correlation of intensity peak factor with storm depth and duration. 3.2 Beta distribution parameters for intensity peak factor. 3.3 Correlation of intensity peak factor with storm depth (I pf − V) for three separate ranges of storm duration.. 3.4 Kendall's τ for intensity peak factor and storm duration (I pf-T), Ali-Mikhail-Haq and Frank copula parameters α. 3.5 Relative Goodness-of-fit, Theoretical models compared to Empirical Plotting Point Distribution. 3.6 Table A1. Summary of statistical analysis results for two stations in Toronto, Canada. 4.1 Station Descriptions, Threshold Analysis of Rainstorm Events March-November, ≤ 24hours 4.2 Threshold statistics 4.3 Difference in values, pre-and post-1980, of statistical parameters. 4.4 J u , average probability of exceedence of u v , pre-and post-1980. 4.5 χ 2 analysis of difference between pre-and post-1980 empirical distributions of storm variables. 4.6 Correlation analysis between V , T , and I pf , pre-1980 and post-1980. 4.7 Correlation analysis between the mean monthly temperature (M M T) when storm events occur and V , T , I pf , pre-and post-1980. 4.8 Average of mean monthly temperature when threshold-excess storm events occurred, pre-and post-1980. 4.9 Summary of major time-span and temperature changes and trends 5.1 Intensity peak factor and return periods of different design storm hyetographs. viii List of Figures 2.1 The plotting point distribution of rainstorm data is compared with the one-parameter exponential distribution. Crosses are used to represent actual measures of storm depth. 2.2 Comparison of the plotting point distribution with Type I and III distributions, storm threshold depth uv = 25 mm. Solid line shows GPD Type I modeled storm depths, dashed line plots Type III. 2.3 Figure 3a (left). Storm duration histogram based upon hourlyrainfall records, using IETD = 6 hours to define individual events. Figure 3b (right). Storm duration histogram, adjusted for start and finish time uncertainty. 2.4 100, 25 and 5-year return period storm-event depth-durations are compared with conventional DDF rainfall accumulation. Box symbol indicates conventional DDF GEV I-modeled rainfall accumulation. Cross symbol is used to show SEA GPD Type I-modelled depth. 2.5 Comparison of event-based and DDF annual maximum rainfall depths, for durations/time intervals of one hour. Actual conventional DDF depths are shown with a box symbol. SEA measures of storm depth are shown with a cross. 2.6 Comparison of event-based annual maximum storm depths for durations of 6 hours or less and DDF annual maximum depths for the 6-hour time interval. 2.7 Comparison of event-based annual maximum storm depths for durations of 12 hours or less, and DDF annual maxima rainfall accumulations for the 12-hour time interval. 3.1 Plot showing actual peak discharge for Mimico Creek (Ontario, Canada) catchment on y-axis, and predicted by regression equation on x-axis. Independent variables for regression equation are runoff depth and peak hour intensity. 3.2 Plotting point or empirical distribution of intensity peak factor (I pf , i pf), based upon Eq. (5), together with Beta distribution in solid line, fitted by method of moments. ix 3.3 Comparisons of plotting position and theoretical joint probability distributions. Plotting position empirical distribution established with Eq. (23) plotted on y-axis. Theoretical distributions based upon Eq. (19) for AMH copula, Eq. (20) for Frank copula, and Eq. (24) for simple joint probability plotted along x-axis. Figure 3(a) for Toronto station, Figure 3(b) for TPIA. Points falling closest to 45-degree line indicate best fit between empirical and theoretical models. 4.1 Storm duration frequency by 3-hour duration increments, pre-and post-1980 4.2 Mean storm depth versus mean monthly temperature 4.3 Mean storm duration and mean intensity peak factor, versus mean monthly temperature 4.4 Storm frequency versus temperature ranges. p-value of difference in proportions shown on reverse scale, significance assessed as p < 0.10 4.5 Mann-Kendall test statistic for Peoria station, on a 20-year moving window basis. 5.1 Maximization of peak discharge for 100-year joint return period. x Ph.D. Thesis-Barry Palynchuk McMaster-Civil Engineering 2. Threshold analysis of rainstorm depth and duration statistics at Toronto, Canada Chapter 2 in this thesis introduces the initial application of threshold analysis to the definition and characterization of extreme rainstorm events. One of the key contributions is the discovery that, for storms with depths exceeding a high threshold, storm depth and duration are not correlated, a rupture with the conventional paradigm of an assumed coupling between the two variables. This chapter has been reformatted from a paper presented by the author and Dr.

The Water Quality Analysis Simulation Program (WASP) was employed to create a threedimensional eutrophication model for Hamilton Harbour. Calibration of the model was performed using 1991 field data. Four remedial options, namely improvements to the waste water treatment plants (WWTPs), the combined sewer overflows (CSOs), industrial discharges, and removal of WWTP, CSO, and industrial discharges were examined. The model simulated well the response of the harbor under various loading conditions. Improvements in the harbor's water quality were found to range from minor in the case of CSO improvements to significant in the case of WWTP improvements. Specifically, the chlorophyll a, total phosphorus, and ammonia nitrogen concentrations were reduced by as much as 2% in the case of CSO improvements, between 4 and 14% for the industrial loading improvements, and between 24 and 50% for the WWTP improvements case. INDEX WORDS: Hamilton Harbour, eutrophication, WASP, remedial options.

The design, development, and application of a hydroinformatic system for estimating evapotranspiration is presented. Reference evapotranspiration is calculated via the Penman-Monteith approach as well as the Class A pan evaporation records. Station-and grid-based methods for estimating reference evapotranspiration are developed and coupled to GIS technology. The station-based method provides the user with evapotranspiration estimates at point locations, corresponding to meteorological stations within the study area. The grid-based method is developed in order to provide an improved understanding of the spatial variation in evapotranspiration. A graphical interface provides model execution and functionality for use in the management phase of the analysis. The island of Crete is used as a case study to illustrate the applicability of the system. 

This research investigated the performance of full-scale self-consolidating rubberized concrete (SCRC) and vibrated rubberized concrete (VRC) beams in flexure. The beam mixtures were developed with a maximum possible percentage of crumb rubber (CR) (0 to 50% by volume of sand) while maintaining acceptable fresh properties and minimum strength reduction. The mixture variables included different binder contents, the addition of metakaolin, and the use of air entrainment. The performance of the tested beams was evaluated based on load-deflection response, concrete strain/stiffness, cracking behavior, first crack load, ultimate load, ductility, and toughness. In general, increasing the CR content decreased the mechanical properties, first crack load, stiffness, and self-weight of all SCRC and VRC beams. However, using up to 10% CR enhanced the deformation capacity, ductility, and toughness of tested beams without affecting the flexural capacity. This improvement in the deformation capacity, ductility, and toughness appeared to continue up to 20% CR (but with a slight reduction of the flexural capacity) and then reduced with further increases in the CR content. The results also indicated that although it was possible to produce VRC beams with higher percentages of CR (50% compared to 40% in SCRC), this increased percentage only gave VRC beams an advantage in terms of self-weight reduction, while it had a limited contribution in enhancing the structural performance of the beams.