Projected Changes in the Dynamics of Flood Hazard in the Grand River Basin, Canada.

In this study future flooding frequencies have been estimated for the Grand River catchment located in south-western Ontario, Canada. Historical and future climatic projections made by fifteen Coupled Model Inter-comparison Project-3 climate models are bias-corrected and downscaled before they are used to obtain mid- and end of 21st century streamflow projections. By comparing the future projected and historically observed precipitation and temperature records it is found that the mean and extreme temperature events will intensify in future across the catchment. The increase is more drastic in the case of extreme events than the mean events. The sign of change in future precipitation is uncertain. Further flow extremes are expected to increase in magnitude and frequency in future across the catchment. The confidence in the projection is more for low return period (<10 years) extreme events than higher return period (10-100 years) events. It can be expected that increases in temperature will play a dominant role in increasing the magnitude of low return period flooding events while precipitation seems to play an important role in shaping the high return period events.

Dynamic Resilience to Climate Change Caused Natural Disasters in Coastal Megacities Quantification Framework.

Objectives: The framework is designed to provide (i) for better understanding of factors contributing to urban resilience; and (ii) for comparison of climate change adaptation options. Methodology: Disasters occur at the intersection of hazards and vulnerabilities. As the climate changes, so do the patterns of climate hazards. Coastal megacities are faced with many challenges including (i) increased exposure to natural hazards such as hurricanes, typhoons, storm surges, sea-level rise and riverine flooding; (ii) pressures of increasing urbanization and population growth; and (iii) increased complexity of interacting subsystems. An original method for quantification of resilience is provided through spatial system dynamics simulation. The quantitative resilience framework combines economic, social, organizational, health and physical impacts of climate change caused natural disasters on coastal megacities. The developed measure defines resilience as a function of time and location in space. The framework is being implemented through the system dynamics model in an integrated computational environment. Conclusion: Data collection for the Coastal Megacity Resilience Simulator (CMRS) model input and discussions with local decision makers are actively being pursued concurrent with the model development for the primary case study coastal city of Vancouver, British Columbia, Canada. Future work includes developing policy driven adaptation scenarios, resilience model simulations, transfer of the resilience model to local community and capacity building.

Assessment of Uncertainty in Flood Flows under Climate Change Impacts in the Upper Thames River Basin, Canada.

The assessment of climate change impacts on frequency of floods is important for management of flood disasters. It is recognized that methods for the assessment are subject to various sources of uncertainty (choice of climate model and emission scenario, course spatial and temporal scales, etc.). This study investigates the climate change related uncertainty in the frequency of flood flows for the Upper Thames River basin (Ontario, Canada) using a wide range of climate models. Climate model outputs are downscaled using the change factor approach for 30-year time slices centered on years 2020, 2050 and 2080. To estimate natural variability, a stochastic weather generator is used to produce synthetic time series for each horizon and for each climate scenario. A number of realizations out of historical range are also produced for the 1979-2005 baselines using the weather generator. A continuous daily hydrologic model was then used to generate daily flow series for the baseline and for the future time horizons. A peak-over-threshold (POT) with Generalized Pareto Distribution is used to produce flood frequency curves for the four time horizons. The uncertainty involved with the POT modelling is also considered. The results indicate that use of unbounded GPD model should be employed for flood frequency analysis. A large uncertainty exists in all the projected future design floods. Probabilistic assessment of the uncertainty is carried out and it provides the estimation of flood magnitude-return period relationship with high level of confidence.

Quantifying Uncertainties in the Modelled Estimates of Extreme Precipitation Events at Upper Thames River Basin.

Assessment of climate change impact on hydrology at watershed scale incorporates downscaling of global scale climatic variables into local scale hydrologic variables and evaluation of future hydrologic extremes. The climatological inputs obtained from several global climate models suffer the limitations due to incomplete knowledge arising from the inherent physical, chemical processes and the parameterization of the model structure. Downscaled output from a single AOGCM with a single emission scenario represents only one of all possible future climate realizations; averaging outputs from multiple AOGCMs might underestimate the extent of future changes in the intensity and frequency of climatological variables. These available methods, thus cannot be representative of the full extent of climate change. Present research, therefore addresses two major questions: (i) should climate research adopt equal weights from AOGCM outputs to generate future climate?; and (ii) what is the probability of the future extreme events to be more severe? This paper explores the methods available for quantifying uncertainties from the AOGCM outputs and provides an extensive investigation of the nonparametric kernel estimator based on choice of bandwidths for investigating the severity of extreme precipitation events over the next century. The Sheather-Jones plug-in kernel estimate appears to be a major improvement over the parametric methods with known distribution. Results indicate increased probabilities for higher intensities and frequencies of events. The applied methodology is flexible and can be adapted to any uncertainty estimation studies with unknown densities. The presented research is expected to broaden our existing knowledge on the nature of the extreme precipitation events and the propagation and quantification of uncertainties arising from the global climate models and emission scenarios.