There is a lack of measured, long-term, reliable, and well-distributed hydrological variables in Canada. These hydrological variables include, but are not limited to: temperature, precipitation, ground runoff, evapotranspiration, soil moisture, and snow water equivalent. Knowledge of these variables is important in many different fields, such as: land-use planning, design of engineering structures, agricultural production, flood forecasting, hydro power generation, and especially for any study of climate change. The objective of this thesis was to establish the best possible distributed estimates of these hydrological variables for Canada over the period of 1961-2000.

The first step in the creation of these datasets was to interpolate the monthly average of temperature and precipitation measured at Environment Canada stations across the country. These interpolated values of temperature and precipitation were then used to calculate the other hydrological variables using the Waterloo Flood Forecasting Model (WATFLOOD). In order to use WATFLOOD, it was necessary to properly segment the watershed into grids, each with its own topographic characteristics such as river elevation, slope, and drainage direction; these and other characteristics are referred to as the drainage database. The Waterloo Mapping technique (WATMAP) was developed to use topographic and land cover databases to automatically and systematically derive the information needed to create the drainage database. This technique takes data from the very fine resolutions of Digital Elevation Models and upscales them to the coarser resolutions typically used by mesoscale hydrological models. The drainage database for Canada was created using WATMAP on a polar stereographic grid with a resolution of 51 km. WATFLOOD was then run using the WATMAP-derived drainage database with the interpolated temperature and precipitation as the input data.

Distributed time series for runoff, evapotranspiration, soil moisture, and snow water equivalent were created for the country. The monthly values for these parameters were examined in different climatic regions of Canada. The areas of highest runoff, snow water equivalent, and soil moisture were generally seen along the west coast, the western mountains, and along the east coast. The Arctic showed much less evapotranspiration and a delayed runoff peak.

The calculation of distributed runoff also allowed for the creation of products that could be used in engineering design or watershed planning, such as simulated streamflow records and flow frequency diagrams for any point in the country regardless of whether they were gauged or ungauged.

Among the major conclusions from the study were that the WATMAP technique performed much better than using a method based on the average elevation of the grid squares and that it was important to consider diagonal flow in the creation of the drainage database.

During the calibration, it was found that when an objective function was used that was based on the annual difference in the simulated and measured total runoff, the most important parameters to optimize were those related to the calculation of evapotranspiration. When using an objective function that took into account the timing of the differences in the measured and simulated flow, more parameters were now needed in the calibration, including those that dealt with snow melt, flow through the soil, and river channel roughness.

The recommendations for future study included: finding other gridded datasets that could be used to verify the ones that were created in this study, examining the datasets for trends to see if they reveal any indications of climate change in different regions of the country, and examining further the magnitudes of the different kinds of predictive uncertainty (data, model, and parameter).

Also recommended was that in the future, despite the reliability of the WATFLOOD model in its ability to predict streamflow, other models should be used that have more a more sophisticated treatment of the energy balance and the sub-surface soil layers. These types of models will make it possible to fully utilize the results from finer resolution regional climate models that are currently being run for different climate-change scenarios. (Abstract shortened by UMI.)