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. 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 was to interpolate measured temperature and precipitation across the country. These interpolated values were then used to calculate the other hydrological variables using the Waterloo Flood Forecasting Model (WATFLOOD). 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. WATFLOOD was calibrated with the Dynamically Dimensioned Search (DDS) algorithm using the difference between the measured and simulated streamflow as the objective function. After a final calibration of 100 separate DDS runs, distributed time series for the hydrological variables were created. A simple assessment was made for the predictive uncertainty in the simulated streamflow results based on the results of the final calibration. As well, the implications of various climate change scenarios were examined in the context of how they would change the hydrological variables. The major recommendations for future study included: finding other gridded datasets that could be used to verify the ones that were created in this study and examining further the magnitudes of the different kinds of predictive uncertainty (data, model, and parameter). The results of this thesis fit in well with the goals of the study on Predictions in Ungauged Basins. This thesis was organized along the principle of “design the process, not the product”. As such, although a set of final products are presented at the end, the most important part of the thesis was the process that achieved these products. Thus it is not assumed that every technique designed in this thesis will be applicable to every other researcher, but it hoped that most researchers in the field will be able to use at least some parts of the techniques developed here.

Concerns regarding climate change have brought about an increased interest in cold region hydrology, leading to the formation of the IP3 research network. This work is part of the IP3 Network, which has the overall goal to evaluate and demonstrate improved predictions of hydrological and atmospheric fields for cold regions. As such this thesis involves a series of calibration and validation experiments on the MESH hydrological model (used by IP3 for predictions) with two cold region case studies. The first case study is the very well instrumented Reynolds Creek Experimental Watershed in Idaho, USA and the second case study is the Wolf Creek watershed in the Yukon Territory. As the MESH model is still in the development phase, a critical component of model development is a thorough analysis of model setup and performance. One intention of this research is to provide feedback for future development of the MESH hydrological model. The Reynolds Creek site was modeled as part of this thesis work. This site was chosen based on the long term, highly distributed and detailed data set. The second site, Wolf Creek, was used for a simplified case study. Models of both case study sites were calibrated and validated to carefully evaluate model performance. Reynolds Creek was calibrated as a single objective problem as well as multi-objective problem using snow water equivalent data and streamflow data for multiple sites. The hydrological simulations for Wolf Creek were fair; further calibration effort and a more detailed examination of the model setup would have likely produced better results. Calibration and validation of Reynolds Creek produced very good results for streamflow and snow water equivalent at multiple sites though out the watershed. Calibrating streamflow generated a very different optimal parameter set compared to calibrating snow water equivalent or calibrating to both snow water equivalent and streamflow in a multi-objective framework. A weighted average multi-objective approach for simultaneously calibrating to snow water equivalent and streamflow can be effective as it yields a reasonable solution that improves the single objective snow water equivalent results without degrading the single objective streamflow results.

Distributed hydrological models have been used for decades to calculate and predict the movement of water and energy within watersheds. These models have evolved from relatively simple empirical applications into complex spatially distributed and physically-based programs. However, the evolution of distributed hydrological models has not involved the improvement of the numerical methods used to calculate the redistribution of water and energy in the watershed. Because of this, many models still use numerical methods that are potentially inaccurate. In order to simulate the transport of water and energy in a hydrological model, typical numerical methods employ an operator splitting approach. Operator splitting (OS) essentially breaks down the set of coupled ordinary differential equations (ODEs) that define a hydrological model into separate ODEs that can be solved individually. The dominant operator splitting method in surface water models is the ordered series approach. Because the ordered series approach treats parallel hydrological processes as if they happen in series, it is prone to errors that can significantly reduce the accuracy of model results. The impact that operator splitting errors have upon hydrologic model results is, to date, unknown. Using a new distributed hydrological model, Raven, the impact of operator splitting errors is investigated. Understanding these errors will lead to better numerical methods for reducing errors in models and to shed light on the shortcomings of hydrological models with respect to numerical method choice. Alternative numerical methods - the explicit Euler and the implicit iterative Heun methods - are implemented and assessed in their ability to minimize errors and produce more accurate distributed hydrological models.

This thesis develops a methodology for mesoscale model verification and validation that is founded on the rigorous constraint imposed by the need to conserve both water mass and isotopes simultaneously. The isoWATFLOOD model simulates δⁱ⁸O in streamflow, which effectively reduces and constrains errors associated with equifinality in streamflow generation by improving internal parameterizations. The WATFLOOD model is a conceptually-based distributed hydrological model used for simulating streamflow on mesoscale watersheds. Given the model’s intended application to mesoscale hydrology, it remains crucial to ensure conceptualizations are physically representative of the hydrologic cycle and the natural environment. Stable water isotopes because of their natural abundance and systematic fractionation have the ability to preserve information on water cycling across large domains. Several coordinated research projects have recently focused on integrating stable water isotopes into global and regional circulation models, which now provides the opportunity to isotopically force land-surface and hydrological models. Where traditionally streamflows are the primary validation criteria in hydrological modelling, problems arise in remote and ungauged basins, or large watersheds where streamflows may not be well monitored. By streamflow validation alone, no insight is obtained on the internal apportioning and physical representation of sub-processes contributing to streamflow. The primary goal of this research is to develop alternative measures to parameterize mesoscale hydrological models in a physically-based manner, and to validate such models over large domains. This research develops improved model parameterizations that facilitate realistic runoff generation process contributions. The examination of runoff generation processes and the subsequent δⁱ⁸O of these processes are performed for two mesoscale watersheds: Fort Simpson, NWT and the Grand River Basin, ON. The isoWATFLOOD model is shown to reliably predict streamflow and δⁱ⁸O of streamflow, and simulates mesoscale isotopic fractionation associated with evaporation. In doing so, a more physically meaningful, robust modelling tool is developed that is practical for operational use. This research also contributes the first continuous record of δⁱ⁸O in streamflow that enables the visualization of spatial and temporal variability and dominant hydrologic controls within mesoscale watersheds.

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. 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 was to interpolate measured temperature and precipitation across the country. These interpolated values were then used to calculate the other hydrological variables using the Waterloo Flood Forecasting Model (WATFLOOD). 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. WATFLOOD was calibrated with the Dynamically Dimensioned Search (DDS) algorithm using the difference between the measured and simulated streamflow as the objective function. After a final calibration of 100 separate DDS runs, distributed time series for the hydrological variables were created. A simple assessment was made for the predictive uncertainty in the simulated streamflow results based on the results of the final calibration. As well, the implications of various climate change scenarios were examined in the context of how they would change the hydrological variables. The major recommendations for future study included: finding other gridded datasets that could be used to verify the ones that were created in this study and examining further the magnitudes of the different kinds of predictive uncertainty (data, model, and parameter). The results of this thesis fit in well with the goals of the study on Predictions in Ungauged Basins. This thesis was organized along the principle of “design the process, not the product”. As such, although a set of final products are presented at the end, the most important part of the thesis was the process that achieved these products. Thus it is not assumed that every technique designed in this thesis will be applicable to every other researcher, but it hoped that most researchers in the field will be able to use at least some parts of the techniques developed here.

Concerns regarding climate change have brought about an increased interest in cold region hydrology, leading to the formation of the IP3 research network. This work is part of the IP3 Network, which has the overall goal to evaluate and demonstrate improved predictions of hydrological and atmospheric fields for cold regions. As such this thesis involves a series of calibration and validation experiments on the MESH hydrological model (used by IP3 for predictions) with two cold region case studies. The first case study is the very well instrumented Reynolds Creek Experimental Watershed in Idaho, USA and the second case study is the Wolf Creek watershed in the Yukon Territory. As the MESH model is still in the development phase, a critical component of model development is a thorough analysis of model setup and performance. One intention of this research is to provide feedback for future development of the MESH hydrological model. The Reynolds Creek site was modeled as part of this thesis work. This site was chosen based on the long term, highly distributed and detailed data set. The second site, Wolf Creek, was used for a simplified case study. Models of both case study sites were calibrated and validated to carefully evaluate model performance. Reynolds Creek was calibrated as a single objective problem as well as multi-objective problem using snow water equivalent data and streamflow data for multiple sites. The hydrological simulations for Wolf Creek were fair; further calibration effort and a more detailed examination of the model setup would have likely produced better results. Calibration and validation of Reynolds Creek produced very good results for streamflow and snow water equivalent at multiple sites though out the watershed. Calibrating streamflow generated a very different optimal parameter set compared to calibrating snow water equivalent or calibrating to both snow water equivalent and streamflow in a multi-objective framework. A weighted average multi-objective approach for simultaneously calibrating to snow water equivalent and streamflow can be effective as it yields a reasonable solution that improves the single objective snow water equivalent results without degrading the single objective streamflow results.

Distributed hydrological models have been used for decades to calculate and predict the movement of water and energy within watersheds. These models have evolved from relatively simple empirical applications into complex spatially distributed and physically-based programs. However, the evolution of distributed hydrological models has not involved the improvement of the numerical methods used to calculate the redistribution of water and energy in the watershed. Because of this, many models still use numerical methods that are potentially inaccurate. In order to simulate the transport of water and energy in a hydrological model, typical numerical methods employ an operator splitting approach. Operator splitting (OS) essentially breaks down the set of coupled ordinary differential equations (ODEs) that define a hydrological model into separate ODEs that can be solved individually. The dominant operator splitting method in surface water models is the ordered series approach. Because the ordered series approach treats parallel hydrological processes as if they happen in series, it is prone to errors that can significantly reduce the accuracy of model results. The impact that operator splitting errors have upon hydrologic model results is, to date, unknown. Using a new distributed hydrological model, Raven, the impact of operator splitting errors is investigated. Understanding these errors will lead to better numerical methods for reducing errors in models and to shed light on the shortcomings of hydrological models with respect to numerical method choice. Alternative numerical methods - the explicit Euler and the implicit iterative Heun methods - are implemented and assessed in their ability to minimize errors and produce more accurate distributed hydrological models.

This study presents the development of a cell-based routing model. The model developed is a two parameter hydrological routing model that uses a coarse resolution stream network to route runoff from each cell in the watershed to the outlet. The watershed is divided into a number of equal cells, which are approximated as cascade of linear reservoirs or tanks. Water is routed from a cell downstream, depending on the flow direction of the cell, using the cascade of tanks. The routing model consists of two phases, first is the overland flow routing, which is followed by the channel flow routing. In this study, the cell-to-cell stream flow routing model is applied to the Brazos River Basin to demonstrate the impact of the cascade of tanks on the flow over a simple linear reservoir method. This watershed was tested with a uniform runoff depth in absence of observed runoff data. A case study on Waller Creek in Austin, Texas with observed runoff depths and stream flow is used to demonstrate the calibration and validation of model parameters.

Thesis (M. Phil.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 148-160).

The objective of this research was to determine the impact of climate change on the Churchill River basin and perform analysis on uncertainty related to this impact. Three hydrological models were used to determine this impact and were calibrated to approximately equivalent levels of efficiency. These include WATFLOODTM, a semi-physically based, distributed model; HBV-EC, a semidistributed, conceptual model; and HMETS, a lumped, conceptual model. These models achieved Nash-Sutcliffe calibration values ranging from 0.51 to 0.71. Climate change simulations indicated that the average of simulations predict a small increase in flow for the 2050s and a slight decrease for the 2080s. Each hydrological model predicted earlier freshets and a shift in timing of low flow events. Uncertainty analysis indicated that the chief contributor of uncertainty was the selection of GCM followed by hydrological model with less significant sources of uncertainty being parameterization of the hydrological model and selection of emissions scenario.