Book Description

Significant challenges in water resources management arise because of the ever-increasing pressure on the world's heavily exploited and limited water resources. These stressors include demographic growth, intensification of agriculture, climate variability, and climate change. These challenges to water resources are usually tackled using a top-down approach, which suffers from many limitations including the use of a limited set of climate change scenarios, the lack of methodology to rank these scenarios, and the lack of credibility, particularly on extremes. The bottom-up approach, the recently introduced approach, reverses the process by assessing vulnerabilities of water resources systems to variations in future climates and determining the prospects of such wide range of changes. While it solves some issues of the top-down approach, several issues remain unaddressed. The current project seeks to provide end-users and the research community with an improved version of the bottom-up framework for streamlining climate variability into water resources management decisions. The improvement issues that are tackled are a) the generation of a sufficient number of climate projections that provide better coverage of the risk space; b) a methodology to quantitatively estimate the plausibility of a future desired or undesired outcome and c) the optimization of the size of the projections pool to achieve the desired precision with the minimum time and computing resources. The results will hopefully help to cope with the present-day and future challenges induced mainly by climate. In the first part of the study, the adequacy of stochastically generated climate time series for water resources systems risk and performance assessment is investigated. A number of stochastic weather generators (SWGs) are first used to generate a large number of realizations (i.e. an ensemble of climate outputs) of precipitation and temperature time series. Each realization of the generated climate time series is then used individually as an input to a hydrological model to obtain streamflow time series. The usefulness of weather generators is evaluated by assessing how the statistical properties of simulated precipitation, temperatures, and streamflow deviate from those of observations. This is achieved by plotting a large ensemble of (1) synthetic precipitation and temperature time series in a Climate Statistics Space (CSS), and (2) hydrological indices using simulated streamflow data in a Risk and Performance Indicators Space (RPIS). The performance of the weather generator is assessed using visual inspection and the Mahalanobis distance between statistics derived from observations and simulations. A case study was carried out using five different weather generators: two versions of WeaGETS, two versions of MulGETS and the k-nearest neighbor weather generator (knn). In the second part of the thesis, the impacts of climate change, on the other hand, was evaluated by generating a large number of representative climate projections. Large ensembles of future series are created by perturbing downscaled regional climate models' outputs with a stochastic weather generator, then used as inputs to a hydrological model that was calibrated using observed data. Risk indices calculated with the simulated streamflow data are converted into probability distributions using Kernel Density Estimations. The results are dimensional joint probability distributions of risk-relevant indices that provide estimates of the likelihood of unwanted events under a given watershed configuration and management policy. The proposed approach offers a more complete vision of the impacts of climate change and opens the door to a more objective assessment of adaptation strategies. The third part of the thesis deals with the estimation of the optimal size of SWG realizations needed to calculate risk and performance indices. The number of realizations required to reach is investigated utilizing Relative Root Mean Square Error and Relative Error. While results indicate that a single realization is not enough to adequately represent a given stochastic weather generator, results generally indicate that there is no major benefit of generating more than 100 realizations as they are not notably different from results obtained using 1000 realizations. Adopting a smaller but carefully chosen number of realizations can significantly reduce the computational time and resources and therefore benefit a larger audience particularly where high-performance machines are not easily accessible. The application was done in one pilot watershed, the South Nation Watershed in Eastern Ontario, yet the methodology will be of interest for Canada and beyond. Overall, the results contribute to making the bottom-up more objective and less computationally intensive, hence more attractive to practitioners and researchers.