In this study, historical data from the London International Airport station has been used, along with 11 different Atmosphere Ocean Global Climate Models (AOGCMs), which are used to predict future climate variables. These models produced a total of 27 different data sets of annual maximum precipitation over a period of 117 years, for storm durations of 1, 2, 6, 12 and 24 hours.

The current Environment Canada recommended distribution is the Gumbel (EV1) distribution, and the current United States distribution is the Log-Pearson type 3 (LP3). This report investigates a third distribution, the Generalized Extreme Value (GEV) distribution, in the context of the Upper Thames River Watershed.

The historical data set and the data sets derived from AOGCMs were used with the GEV, LP3 and EV1 distributions, and the goodness of fit tests were performed to select which was most appropriate distribution. L-Moment Ratio diagrams were also constructed to help establish the most suitable distribution. All results showed that GEV was very appropriate to the Upper Thames River Watershed data, and it was often the favored distribution.

This report shows the need for more studies to be carried out on the GEV distribution, to ensure we are using the most appropriate methods for predicting these extreme precipitation events.

Several of the model sectors are built from the basic structure of the previous version of ANEMI. However, they are integrated in a novel way, the water sectors in particular. The integration of optimization within the simulation framework of the ANEMI model is timely, as recognition of the importance of energy based economic activities in determining long-term Earth-system behaviour grows. Experimentation with different policy scenarios demonstrated their consequences on future behaviour of the society-biosphere-climate-economy-energy system through feedback based interactions. The use of ANEMI model improves both, scientific understanding and socio-economic policy development strategy.

This report describes the model structure in details and illustrates its use through the analysis of three policy scenarios.

This report presents a set of examples that illustrate (a) probabilistic and (b) fuzzy set approaches for solving various water resources management problems. The main goal of this report is to demonstrate how information provided to water resources decision makers can be improved by using the tools that incorporate risk and uncertainty. The uncertainty associated with water resources decision making problems is quantified using probabilistic and fuzzy set approaches. A set of selected examples are presented to illustrate the application of probabilistic and fuzzy simulation, optimization, and multi-objective analysis to water resources design, planning and operations. Selected examples include dike design, sewer pipe design, optimal operations of a single purpose reservoir, and planning of a multi-purpose reservoir system. Demonstrated probabilistic and fuzzy tools can be easily adapted to many other water resources decision making problems.

From a hazards perspective, vulnerability assessments provide insights into responses necessary to prevent loss of life, damages, or in worst cases disasters. From a climate change perspective, capturing the differential elements of vulnerability is a prerequisite for developing adaptation policies that will promote equitable and sustainable development.

Risk is defined in this study as the intersection of a hazard (flooding) with vulnerability. The risk measure enables conclusions and recommendations to be made regarding the reliability of the infrastructure network within the city to adapt to the changing climate conditions.

The study results are meant to identify and prioritize areas of high risk or interest within the city which are recommended for further investigation. These recommendations are meant to aid in policy development as it relates to municipal infrastructure and the future.

This study presents a methodology for updating the rainfall IDF curves for the City of London incorporating various uncertainties associated with the assessment of climate change impacts on a local scale. Overall, two objectives have been achieved: first, an extensive investigation of the possible realizations of future climate from 29 scenarios developed from Atmosphere-Ocean Global Climate Models (AOGCM) and scenarios are performed using a downscaling based disaggregation approach. Annual maximum series of rainfall are fitted to Gumbel distribution to develop IDF curves for 1, 2, 6, 12 and 24 hour durations for 2, 5, 10, 25, 50 and 100 years of return periods. Next, the associated uncertainties are estimated using nonparametric kernel estimation approach and the resultant IDF curve is developed based on a probabilistic approach.

The results indicate that rainfall patterns in the City of London will most certainly change in future due to climate change. The use of the multi-model approach, rather than a single scenario is encouraged. Inherent uncertainties associated with different AOGCMs are quantified by a kernel based plug-in estimation approach. The resultant scenario indicates approximately 20- 40% changes in different duration rainfalls for all return periods. Use of a probability based intensity-duration-frequency curve is encouraged in order to apply the updated IDF information with higher level of confidence.

Combined, climate and hydrologic modeling, were used to generate input flow data for hydraulic modelling. Standard computer software HEC-RAS is used for hydraulic computation of water elevation. The existing HEC-RAS models of the Upper Thames River basin are not georeferenced and therefore they cannot be used for hydraulic modeling under climate change. Consequently, it was necessary to develop new HEC-RAS models for the rivers and creeks of London that were considered in this project.

Geometric input data for new HEC-RAS models were created using HEC-GeoRAS software, which is an extension of ArcGIS computer package for spatial analysis. In the preprocessing phase the HEC-GeoRAS is used to create a digital terrain model from the contour lines shape file provided by the city of London. In the next step the following geometric data layers were generated: river center line, bank lines, flowpaths, cross sections, and bridges. Required attributes were assigned to each of the layers. In the last step of the pre-processing stage the input file for the HEC-RAS hydraulic analysis was prepared. The hydraulic analysis starts with the geometric data import, followed with the preparation of the hydraulic structures data and flow data. A very detailed quality control was performed on the cross sections data generated during the pre-processing phase. The roughness coefficient values were determined using the existing HEC-RAS models and aerial photography of the basin. Data on bridges, taken from the existing models and drawings were integrated with the rest of the data.

Two climate scenarios (historic and wet) developed by climate and hydrologic modeling (Eum and Simonovic, 2009) were used and water surface elevation profiles were calculated for 100- and 250- year return periods. The computation results were used to assemble the HEC-RAS GIS export file for floodplain mapping. The Arc Map software package was used to create water surface GIS layer. Overlaying this layer with the terrain provided for calculation of floodplain boundaries and inundation depths. The floodplain maps generated using this process are used in vulnerability assessments of London’s public infrastructure to climate change currently in progress.

The results of water surface profile computations are presented in tabular form for the 250- year flood under historic and wet climate scenarios. The final floodplain maps along Main Thames for both scenarios show minor deviation of the floodplain boundaries when compared with the existing floodplain lines. However, the water depth difference is up to 50 cm. The area upstream from the culvert on Pottersburg Creek (close to the intersection of Trafalgar St. and Clarke St.) is identified as critical due to the high extent of flooding. The flooding at this location is caused by insufficient culvert opening that creates a backwater effect. Areas of special concern are identified where the floodplain mapping results are not sufficiently accurate due to inaccuracies in the contour lines. The main recommendation based on the work presented in this report is that new georeferenced cross sections should be surveyed in order to increase the accuracy of the floodplain mapping process. The hydraulic analyses should be repeated with more accurate input data and the resulting floodplain maps should be revised accordingly.

The science policy communication process has been established through the set of interviews and workshops. Interviews were used (a) to identify the issues of importance to be incorporated in the model development and (b) to formalize a set of policy scenarios that will provide input for policy making. Workshops were used to communicate science to policy developers and discuss the issues of importance for policy development. The research was fundamentally based on a multi-disciplinary approach that assisted in bridging the research domain to the policy domain. Ultimately, the feedback from the interviews and workshops was embedded in the development of the model and its scenarios, and made it possible to transform policy questions into model scenarios. In other words, by linking science and policy domains, the research team was able to produce a science-based and policy-relevant tool.

Limitations to the work mainly reflect the current stage of research and model development. As the strategic research continues on the integrated system dynamics model of the social-economic-climatic system, these limitations are likely to be overcome. The other key limitation is in the selection of the government partners. While the current group of partners has provided valuable insight, further research will aim to expand the group of partners across different departments. This will not only reflect a broader range of interests, but will also more accurately represent a systems view of government. Furthermore, a broader range of disciplinary biases will be consulted, including government policymakers who work more intimately with science and policy research.

Assessment methodology requires identification of climate loading on the municipal infrastructure. Climate and hydrologic modeling methodology and results are presented in this report as the basis for the impact assessment work. A weather generator model combined with principle component analysis (WGPCA) and HEC-HMS hydrologic model are used in this study. The WG-PCA model is used to generate two different climate scenarios named: (a) historic scenario and (b) wet scenario, representing the lower and the upper bound of potential climate change, respectively. Generated meteorological data (precipitation and temperature) is used with the hydrologic model (HEC-HMS) and transformed into flow at multiple locations within the study region. Lastly, the flow frequency analysis is conducted to provide input into a hydraulic model that is used in mapping the floodplains for two climate scenarios considered (Sredojevic and Simonovic, 2009).

Using 43-years of historical data from 1964 to 2006 at 15 stations in the Upper Thames River basin, the WG-PCA generates a feasible future scenario of precipitation and temperature for 200 years – the historic climate scenario. The historic data is used to represent the business-as-usual condition that assumes there is no change in the social-economic-climatic system in the future. This scenario simulates climate change that may occur as a consequence of the already existing conditions and is considered in this study as the lower bound of climate change impact on the region under consideration. The second climate scenario employs CCSRNIES global climate model (GCM) with B21 emission scenario for time slice of 2040-2069 together with the historical data to generate a feasible future scenario we named in this work as a wet climate scenario – upper bound of climate change impact on the region under consideration. The results demonstrate the WG-PCA regenerates well the 25th, 50th, 75th percentile statistical values of precipitation and temperature for the historic scenario. Use of data perturbation process within the weather generator model generates data out of the range of values within the observed data. For the wet scenario, the WG-PCA generates the future that reflects the monthly climate shift of GCM model used (CCSRNIES B21) in the study.

The generated annual precipitation extreme values for 200 years are processed to extract the largest annual flood event for the entire basin and corresponding annual peak flow is used in flood frequency analysis. An assumption introduced in this work is that the largest floods are generated from extreme precipitation events. Several probability distributions including Gumbel, LP3, and GEV are utilized in this study. The flood frequency analysis results obtained using Gumbel distribution for the historic and the wet climate scenarios are compared with the current data used by the Upper Thames River Conservation Authority (UTRCA) for flood plain management. The difference exists between the current data and the data generated for two scenarios, as expected, and the direction of change varies with the location in the basin.

During the work on this study, the major deficiency in observed flow data is noticed across the basin – specially for the locations within the City of London. Therefore, continuous monitoring system at the various sites in the basin is needed to provide the accurate hydrologic information that should enhance the results of modeling work. If and when the new observed data is collected, the hydrologic modeling analysis can enhanced and consequently flood flow frequency analysis can be verified.

The objective of this report is to assess the change in IDF curves for use by the City of London under changing climatic conditions. This assessment is completed using (a) only data collected at the London Airport (b) for the period 1961 - 2002. This is all the information that is available from the Environment Canada (EC).

An original methodology is developed in this study to update the rainfall intensity duration frequency (IDF) curves under changing climatic conditions. A non-parametric K-Nearest Neighbour weather generator algorithm operating on a daily time step is used to synthetically create long time series of weather data. The weather generator algorithm is developed to employ data collected by the Environment Canada for use in IDF analysis, including eight for-the-day-maximums of 5, 10, 15, 30 minutes, 1, 2, 6 and 12 hour, along with daily rainfall time series. The weather generator uses (a) a sophisticated shuffling mechanisms to produce synthetic data similar to the observed record; and (b) a perturbation mechanism that pushes the simulated data outside of their historic bounds, thereby generating sequences of extreme rainfall that are likely, but not yet been observed.

Two climate scenarios are used in the analysis: (i) historic climate change scenario (that reshuffles and perturbs the observed data), and (ii) wet scenario (that modifies the observed record according to Global Circulation Model simulation outputs and then uses this data as the weather generator input). Results of the study include tabular and graphical presentation of updated IDF curves for the London Airport. Results are generated for return periods of 2, 5, 10, 25, 50, 100 and 250 years.

The study presents the results of three simulations that differ in the historic input data. The first simulation analysis is based on the original London Airport data set for the period 1961 – 2001 obtained from the EC (eight for-the-day-maximums of 5, 10, 15, 30 minutes, 1, 2, 6 and 12 hour, and daily rainfall time series). Due to limitations of the original data set in correctly representing daily rainfall, the second simulation analysis is based on the combination of the original for-the-day-maximums for the period 1961 – 2002 (eight for-the-day-maximums of 5, 10, 15, 30 minutes, 1, 2, 6 and 12 hour) with hourly data collected at London Airport. Since the hourly data set also had some deficiencies, the third simulation analysis is performed that used the same combination of input data as the second analysis with modifications added to the last three years of observations. It is recommended that the modified data set be used for drawing conclusions of the study.

The simulation results indicate that rainfall magnitude will increase under climate change for all durations and return periods. The outputs of the study indicate that: (i) the rainfall magnitude will be different in the future, (ii) the wet climate scenario reveals significant increase in rainfall intensity for a range of durations and return periods, and (iii) the increase in rainfall intensity and magnitude may have major implications on ways in which current (and future) municipal water management infrastructure is designed, operated, and maintained. Our recommendation is that the current IDF curves should be revised to reflect the potential impact of climate change.

Results of comparison between the updated IDF curves for modified data set indicate small difference between the historic and wet climate change scenarios. This difference ranges between 0.1% and 12.2% with average value of approximately 4.5%. Therefore the recommendation is to proceed with potential revisions of the standards using the historic climate change scenario.

Comparison between the updated IDF curves for modified data set (historic climate change scenario) and the EC IDF curves shows a difference that ranges between 10.7 % and 34.9% with average value of approximately 21%. Based on this comparison our recommendation to the City of London is to proceed with change of IDF curves in the range of 20%. Detailed economic analyses should be performed to justify the necessary investment that this change will require.

In this study, statistical downscaling using a modified K-NN weather generator with perturbation and principle component analysis (WG-PCA) is employed to investigate the potential impacts of climatic change in the Upper Thames River basin. A total of 22 stations around the basin are used as inputs, each with 27 years of observed historical data. Monthly change factors are applied to the observed data from six AOGCMs, each with two to three emission scenarios. The resulting datasets are used as inputs to the WG-PCA algorithm to produce 324 years of synthetic data for two time periods, the 2020s (2011-2040) and the 2080s (2071-2100). The performance of the weather generator is evaluated by comparing a synthetic historical dataset to the observed data.

The WG-PCA algorithm is able to satisfactorily reproduce the observed monthly total precipitation values. While there is a slight overestimation in the mean of some months and an underestimation in others, the values of the observed means are well within the inter-quartile range (25th and 75th percentile) of the simulated data, thus performance is considered very good. The outliers in the historical simulated values indicate the added variability by the WG-PCA weather generator. Total monthly wet-day box plots are made, and results show that there are underestimations of the mean observed data in some months. The statistical hypothesis tests show that the difference between the mean and variance of the observed and simulated precipitation are similar. Frequency distribution curves of wet-spell lengths for winter and summer months also show a very close agreement between the observed and simulated values. Overall, the performance of the WG-PCA weather generator in reproducing historical values is very good.

The AOGCM outputs are compared using box plots of total monthly precipitation values. The results show different predictions of future precipitation, however most models predict an increase in total monthly precipitation for winter for both the 2020s and the 2080s. Summer values are less conclusive as some models predict an increase in total precipitation while other predict a decrease. The general trends of wet-spell intensities show an increase in wet spell intensity for longer time spells for winter in both time periods. For summer wet-spells, both time periods predict that shorter spells will increase in intensity as long ones decrease. The results for AOGCM simulation are quite variable, thus it is important to include several models and emission scenarios in climate change impact assessments.

Five interconnected components constitute the full energy sector: demand, resources, economics, production, and emissions. The energy demand component calculates changes over time in heatenergy and electric-energy demand as a result of economic activity, price-induced efficiency measures, and technological change. Energy resources models changes in the amounts of three non-renewable energy resources -- coal, oil, and natural gas -- as a result of depletion and new discoveries. Energy economics, the largest of the energy sector components, models investment into the maximum production capacities for primary energy and electricity, based on market forces or the prescriptions of policy makers. Energy production represents the supply portion of the energy sector by producing primary (heat) and secondary (electrical) energy to meet energy demands; six electricity production technologies are included, and other options can be added relatively easily. Finally, energy emissions calculates the carbon emissions resulting from the combustion of fossil fuels to meet energy demands, and includes important non-energy processes such as cement production and natural gas flaring.

The body of the report is organized into seven chapters and four appendices. Chapter one serves as an introduction to the document and describes the basic principles and structure of the energy sector. Chapters two through four begin with a brief literature review and description of relevant real-world data, explain the model structure and its development, and end with a summary of preliminary model results. Specifically, chapter two describes the energy supply components of the model (resource extraction and electricity investment and production), chapter three describes the energy demand component, and chapter four describes carbon emissions modelling. Chapter five provides background information on modelling technological change. Chapter six explains the manner in which the energy sector was calibrated to a 1960 start-date and its integration into the larger multi-sectoral model of Davies and Simonovic (2008). Chapter seven describes the integrated model's capabilities and use, limitations, and areas for improvement. The four appendices provide a full listing of all energy sector equations and cross-reference each to the relevant section of the report body (Appendix A), and describe and explain alternative approaches toward the modelling of electricity production capacity (Appendix B), fuel prices (Appendix C), and energy demand (Appendix D).

In the first part of this research, we use a fuzzy set theory based methodology for consideration of different sources of uncertainty in the stage and discharge measurements and their aggregation into a combined uncertainty. The uncertainty in individual measurements of stage and discharge is represented using triangular fuzzy numbers and their spread is determined according to the ISO – 748 guidelines. The extension principle based fuzzy arithmetic is used for the aggregation of various uncertainties into overall stage discharge measurement uncertainty.

In the second part of the research we use fuzzy nonlinear regression for the analysis of the uncertainty in the single valued stage – discharge relationship. The methodology is based upon fuzzy extension principle. All input and output variables as well as the coefficients of the stage - discharge relationship are considered as fuzzy numbers. Two different criteria; the minimum spread and the least absolute deviation are used for the evaluation of output fuzziness. The results of the fuzzy regression analysis lead to a definition of lower and upper uncertainty bounds of the stage – discharge relationship and representation of discharge value as a fuzzy number.

The third part of this research considers uncertainties in a looped rating curve with an application of the Jones formula. The Jones formula is based on approximate form of unsteady flow equation, which leads to an additional uncertainty. In order to take into account of the uncertainties due to the use of approximate formula and measurement of discharge values, the parameters of the Jones formula are considered fuzzy numbers. This leads to a fuzzified form of Jones formula. Its spread is determined by a multi-objective genetic algorithm. We used a criterion to minimize the spread of the fuzzified Jones formula so that the measurements points are bounded by the lower and upper bound curves.

The study therefore considers individual sources of uncertainty from measurements to the single valued and looped rating curves. The study also shows that the fuzzy set theory provides an appropriate methodology for the analysis of the uncertainties in a nonprobabilistic framework.

This study presents an integrated reservoir management system for the Upper Thames River basin that includes: (1) a Weather Generator (WG) model; (2) a hydrologic model; and (3) a differential evolutionary optimization model. It is used to develop the alternative optimal operating rule curves for three reservoirs in the basin that will take into consideration the impact of climate change. Alternative curves developed using the proposed methodology represent one of the possible climate change adaptation strategies for the use of existing storage in the basin.

Three different weather scenarios are employed to verify the integrated reservoir management system; (1) Case 1: scenarios set | generated with the original WG model of Sharif and Burn (2006) with one variable (precipitation); (2) Case 2: scenario set ||: generated with original WG model with three variables named WG3; (3) scenario set |||: generated with the modified WG that is combined with Principal Component Analysis using three variables WG-PCA3. The results of this study indicate that the rule curves developed using B11(dry) climate scenario show the best result for the scenarios set | because there is no significant flood events in the case 1 and for the scenario set || and the scenario set ||| generated by WG3 and WG-PCA3, the B11 (PCA) rule curves provide the best result for B11, B11(PCA), and historic(PCA) scenarios and the B21 rule curves represent the best results for B21 and B21(PCA) scenarios. Another notable result is that the flood operations would be required until April if the B21(wet) scenario occurs in the future. In addition, the WG-PCA3 provides more wet weather conditions than the original WG model.

The resulting model is implemented in a system dynamics modelling interface called Vensim DSS (Ventana Systems, 2003), which emphasizes the roles of nonlinearity and feedback in determining system behaviour. Both the diagrammatic and mathematical bases of Vensim are described in detail, as are the adjustable components of the model. Several sample experiments are conducted to illustrate the use of Vensim and the analytical tools it provides. The appendices list the model code – as mathematical equations – that forms the basis of the numerical simulations executed with the model, as well as the contents of a CD-ROM version of the model available from the authors, and previous reports in the series.