Considering the growing population of the earth and the decreasing water resources, the need for reliable and accurate estimation and prediction of streamflow time series is increasing. Due to the climate change and anthropogenic impacts on hydrologic systems, the estimation and prediction of streamflow time series remains a challenge and it is even more difficult for regions where watersheds are ungauged in terms of streamflow. The research presented in this dissertation, was scoped to develop a reliable and accurate methodology for daily streamflow prediction/estimation in ungauged watersheds. The study area in this research encompasses Ontario natural watersheds with various areas spread in different regions. In this research work nonlinear data-driven methods such as Artificial Neural Networks (ANN) and conventional methods such as Inverse Distance Weighted (IDW) as well as their combination are investigated for different steps in streamflow regionalization. As such, Watershed classification prior to regionalization is investigated as an independent step in regionalization. Nonlinear classification techniques such as Nonlinear Principal Component Analysis (NLPCA) and Self-Organizing Maps (SOMs) are investigated for watershed classification and finally a methodology which combines watershed classification, streamflow regionalization and hydrologic model optimization is presented for reliable streamflow prediction in ungauged basins. The results of this research demonstrated that a multi-model approach which combines the results of proposed individual models based on their performance for the gauged similar and close watersheds to the ungauged ones can be a reliable streamflow regionalization model for all watersheds in Ontario. Physical similarity and spatial proximity of watersheds was found to play an important role in similarity between the streamflow time series, hence, it was incorporated in all individual models. It was also shown that watershed classification can significantly improve the results of streamflow regionalization. Investigated nonlinear watershed classification techniques applicable to ungauged watersheds can capture the nonlinearity in watersheds physical and hydrological attributes and classify watersheds homogeneously. It was also found that the combination of watershed classification techniques, regionalization techniques and hydrologic models can impact the results of streamflow regionalization substantially. Furthermore, to evaluate the uncertainty associated with the predictions in ungauged watersheds, an ensemble modelling framework is proposed to generate ensemble predictions based on the proposed regionalization model. / Dissertation / Doctor of Philosophy (PhD)

Research Doctorate - Doctor of Philosophy (PhD) / The purpose of rainfall-runoff modelling, like all environmental modelling is to generate simulations that accurately mimic those encountered in the system being modelled. Once this is achieved, the model may then be used to study the catchment response under conditions that have not previously been observed, such as the determination of extreme flood levels. The complex behaviour of the processes involved in the generation of streamflow mean that to achieve a usable model, simplifications must be made. This inevitably leads to the introduction of model error into the simulations, as these simplifications cannot reproduce the level of response variation encountered in a natural system. As a consequence, a model that performs well at some times may be inappropriate at other times. The MultiModel approach is an alternative method of rainfall-runoff modelling that uses numerous alternative process descriptions to generate a suite of unique rainfall runoff models. These models are calibrated and applied to allow for simulation responses that incorporate not only parameter variability but model structure variability. It is shown that the application of the MultiModel method to four test catchments produced simulated confidence limits that are much more likely to contain flood peaks that are beyond the range encountered during the calibration process than using a single model. This is due to the wider confidence limits generated as a result of the greater structure variability available to the MultiModel. The wider confidence limits are therefore a better reflection of our true understanding of the system being modelled. The prediction of ungauged basins presents an additional challenge to rainfallrunoff modelling. Most methods involve some form of regionalization of model parameters. These approaches are very limited in that they are restricted by model selection and application range. Two unique methods for the prediction of ungauged basins are presented that overcome these restrictions. The first attempts to condition a rainfall-runoff model using uncertain criteria, normally used as a supplement to more common calibration procedures. These criteria include estimates of flood peaks, baseflow, recession and saturated area. It is shown that combinations of these criteria provide a powerful means of constraining the parameter space and reducing the simulation uncertainty. The second approach to model conditioning for ungauged basins uses an alternative method of regionalization that focuses on the estimation of flow characteristics rather than model parameter values. Strong relationships between flow characteristics (such as runoff coefficients, flow duration curves and coefficient of variation) and catchment conditions (such as area, mean annual rainfall and evaporation) are identified for catchments across Australia. Using the estimated ranges of these flow characteristics as assessment criteria, a rainfall-runoff model is successfully conditioned to adequately reproduce the streamflow response of the four test catchments. In particular it is shown that the use of numerous characteristics in tandem further improves the conditioning for the test catchments.

Research Doctorate - Doctor of Philosophy (PhD) / The purpose of rainfall-runoff modelling, like all environmental modelling is to generate simulations that accurately mimic those encountered in the system being modelled. Once this is achieved, the model may then be used to study the catchment response under conditions that have not previously been observed, such as the determination of extreme flood levels. The complex behaviour of the processes involved in the generation of streamflow mean that to achieve a usable model, simplifications must be made. This inevitably leads to the introduction of model error into the simulations, as these simplifications cannot reproduce the level of response variation encountered in a natural system. As a consequence, a model that performs well at some times may be inappropriate at other times. The MultiModel approach is an alternative method of rainfall-runoff modelling that uses numerous alternative process descriptions to generate a suite of unique rainfall runoff models. These models are calibrated and applied to allow for simulation responses that incorporate not only parameter variability but model structure variability. It is shown that the application of the MultiModel method to four test catchments produced simulated confidence limits that are much more likely to contain flood peaks that are beyond the range encountered during the calibration process than using a single model. This is due to the wider confidence limits generated as a result of the greater structure variability available to the MultiModel. The wider confidence limits are therefore a better reflection of our true understanding of the system being modelled. The prediction of ungauged basins presents an additional challenge to rainfallrunoff modelling. Most methods involve some form of regionalization of model parameters. These approaches are very limited in that they are restricted by model selection and application range. Two unique methods for the prediction of ungauged basins are presented that overcome these restrictions. The first attempts to condition a rainfall-runoff model using uncertain criteria, normally used as a supplement to more common calibration procedures. These criteria include estimates of flood peaks, baseflow, recession and saturated area. It is shown that combinations of these criteria provide a powerful means of constraining the parameter space and reducing the simulation uncertainty. The second approach to model conditioning for ungauged basins uses an alternative method of regionalization that focuses on the estimation of flow characteristics rather than model parameter values. Strong relationships between flow characteristics (such as runoff coefficients, flow duration curves and coefficient of variation) and catchment conditions (such as area, mean annual rainfall and evaporation) are identified for catchments across Australia. Using the estimated ranges of these flow characteristics as assessment criteria, a rainfall-runoff model is successfully conditioned to adequately reproduce the streamflow response of the four test catchments. In particular it is shown that the use of numerous characteristics in tandem further improves the conditioning for the test catchments.

Reliable hydrological model simulations are the result of numerous complex interactions among hydrological inputs, landscape properties, and initial conditions. Determination of the effects of these factors is one of the main challenges in hydrological modelling. This situation becomes even more difficult in cold regions due to the ungauged nature of subarctic and arctic environments.<p> This research work is an attempt to apply a new approach for modelling snowcover ablation and snowmelt runoff in complex subarctic environments with limited data while retaining integrity in the process representations. The modelling strategy is based on the incorporation of both detailed process understanding and inputs along with information gained from observations of basin-wide streamflow phenomenon; essentially a combination of deductive and inductive approaches. The study was conducted in the Wolf Creek Research Basin, Yukon Territory, using three models, a small-scale physically based hydrological model, a land surface scheme, and a land surface hydrological model. The spatial representation was based on previous research studies and observations, and was accomplished by incorporating landscape units, defined according to topography and vegetation, as the spatial model elements.<p> Comparisons between distributed and aggregated modelling approaches showed that simulations incorporating distributed initial snowcover and corrected solar radiation were able to properly simulate snowcover ablation and snowmelt runoff whereas the aggregated modelling approaches were unable to represent the differential snowmelt rates and complex snowmelt runoff dynamics. Similarly, the inclusion of spatially distributed information in a land surface scheme clearly improved simulations of snowcover ablation. Application of the same modelling approach at a larger scale using the same landscape based parameterisation showed satisfactory results in simulating snowcover ablation and snowmelt runoff with minimal calibration. Verification of this approach in an arctic basin illustrated that landscape based parameters are a feasible regionalisation framework for distributed and physically based models. In summary, the proposed modelling philosophy, based on the combination of an inductive and deductive reasoning, is a suitable strategy for reliable predictions of snowcover ablation and snowmelt runoff in cold regions and complex environments.

Reliable hydrological model simulations are the result of numerous complex interactions among hydrological inputs, landscape properties, and initial conditions. Determination of the effects of these factors is one of the main challenges in hydrological modelling. This situation becomes even more difficult in cold regions due to the ungauged nature of subarctic and arctic environments.<p> This research work is an attempt to apply a new approach for modelling snowcover ablation and snowmelt runoff in complex subarctic environments with limited data while retaining integrity in the process representations. The modelling strategy is based on the incorporation of both detailed process understanding and inputs along with information gained from observations of basin-wide streamflow phenomenon; essentially a combination of deductive and inductive approaches. The study was conducted in the Wolf Creek Research Basin, Yukon Territory, using three models, a small-scale physically based hydrological model, a land surface scheme, and a land surface hydrological model. The spatial representation was based on previous research studies and observations, and was accomplished by incorporating landscape units, defined according to topography and vegetation, as the spatial model elements.<p> Comparisons between distributed and aggregated modelling approaches showed that simulations incorporating distributed initial snowcover and corrected solar radiation were able to properly simulate snowcover ablation and snowmelt runoff whereas the aggregated modelling approaches were unable to represent the differential snowmelt rates and complex snowmelt runoff dynamics. Similarly, the inclusion of spatially distributed information in a land surface scheme clearly improved simulations of snowcover ablation. Application of the same modelling approach at a larger scale using the same landscape based parameterisation showed satisfactory results in simulating snowcover ablation and snowmelt runoff with minimal calibration. Verification of this approach in an arctic basin illustrated that landscape based parameters are a feasible regionalisation framework for distributed and physically based models. In summary, the proposed modelling philosophy, based on the combination of an inductive and deductive reasoning, is a suitable strategy for reliable predictions of snowcover ablation and snowmelt runoff in cold regions and complex environments.

In many parts of the world, developed as well as developing, rivers are not gauged for continuous monitoring. Streamflow prediction at such "ungauged" river catchments requires information transfer from gauged catchments that are perceived to be hydrologically similar to them. Achieving good predictability at ungauged catchments requires an in-depth understanding of the physical and climatic controls on hydrologic similarity among catchments. This dissertation attempts to gain a better understanding of these controls through three independent research studies that use data from catchments across the continental United States. In the first study, I explore whether streamflow similarity among nearby catchments is preserved across flow conditions. Catchments located across four river basins in the northeast United States are analyzed to quantify the spatio-temporal variability in streamflows across flow percentiles. Results show that similarity in catchment stream response is dynamic and highly dependent on flow conditions. Specifically, the coefficient of variation is high at low flow percentiles and gradually reduces for higher flow percentiles. This study concludes that high variability at low flows is controlled by the dominance of high evaporative demand, whereas low variability at high flows is controlled by the dominance of precipitation input relative to evapotranspiration. In the second study, I examine whether streamflow similarity among catchments exists across a wide range of climatic and geographic regions. Data from 756 catchments across the United States is used and daily streamflow at each catchment is simulated using distance-based streamflow interpolation from neighboring catchments. With this approach, high predictability at a catchment indicates that catchments in its vicinity have similar streamflows. Results show that high predictability catchments are mainly confined to the Appalachian Mountains, the Rocky Mountains, and Cascade Mountains in the Pacific Northwest. Low predictability catchments are located mostly in the drier regions of US to the west of Mississippi river. Results suggest that streamflow similarity among nearby catchments is more likely in humid runoff-dominated regions than in dry evapotranspiration-dominated regions. In the third study, my goal is to identify what constitutes the essential information that must be transferred from gauged to ungauged catchments in order to achieve good model predictability. A simple daily time-step rainfall-runoff model is developed and implemented over 756 catchments located across the United States. Results show that the rainfall-runoff model simulates well at catchments in humid low-energy environments, most of which are located in the eastern part of the US, the Rocky Mountains, and to the west of Cascade Mountains. Within these regions, transfer of the parameter characterizing hydrograph recession provides reliable streamflow predictions at ungauged catchments, with a loss in prediction efficiency of less than 10% in most catchments. The results presented in this dissertation show that climate exerts a strong control on hydrologic similarity among catchments. The results further suggest that an understanding of the interaction between climate and topography is essential for quantifying the spatial variability in catchment hydrologic behavior at a regional scale.

In most regions of the world, and particularly in developing countries, the possibility and reliability of hydrologic predictions is severely limited, because conventional measurement networks (e.g. rain and stream gauges) are either nonexistent or sparsely located. This study, therefore, investigates various systems methods and newly available data acquisition techniques to evaluate their potential for improving hydrologic predictions in poorly gaged and ungaged watersheds.Part One of this study explores the utility of satellite-remote-sensing-based rainfall estimates for watershed-scale hydrologic modeling at watersheds in the Southeastern U.S. The results indicate that satellite-based rainfall estimates may contain significant bias which varies with watershed size and location. This bias, of course, then propagates into the hydrologic model simulations. However, model performance in large basins can be significantly improved if short-term streamflow observations are available for model calibration.Part Two of this study deals with the fact that hydrologic predictions in poorly gauged/ungauged watersheds rely strongly on a priori estimates of the model parameters derived from observable watershed characteristics. Two different investigations of the reliability of a priori parameter estimates for the distributed HL-DHMS model were conducted. First, a multi-criteria penalty function framework was formulated to assess the degree of agreement between the information content (about model parameters) contained in the precipitation-streamflow observational data set and that given by the a priori parameter estimates. The calibration includes a novel approach to handling spatially distributed parameters and streamflow measurement errors. The results indicated the existence of a significant trade-off between the ability to maintain reasonable model performance while maintaining the parameters close to their a priori values. The analysis indicates those parameters responsible for this discrepancy so that corrective measures can be devised. Second, a diagnostic approach to model performance assessment was developed based on a hierarchical conceptualization of the major functions of any watershed system. "Signature measures" are proposed that effectively extract the information about various watershed functions contained in the streamflow observations. Manual and automated approaches to the diagnostic model evaluation were explored and were found to be valuable in constraining the range of parameter sets while maintaining conceptual consistency of the model.

Maintaining the ecological health of streams is vital for sustainable water resources management. Streamflow is a primary factor influencing the structure and function of ecological communities. A quantitative understanding of how stream biota respond to variation in streamflow is required for stream bioassessment. This dissertation focuses on quantifying relationships between streamflow regime and stream macroinvertebrate richness and composition. The contribution comprises statistical models that predict stream macroinvertebrate class from streamflow regime and predict streamflow regime from watershed attributes, and a tool that helps derive watershed attribute variables used in these models. The dissertation is a collection of three papers. In the first paper 12 variables were used to represent streamflow regime at 543 sites in the western US. Principal component analysis (PCA) and K-means clustering were used to obtain statistically independent factors and streamflow regime classes. We examined the relationship between these characterizations of streamflow and macroinvertebrate richness and composition at 63 of the 543 sites where there was also biological data. This analysis identified specific aspects of the streamflow regime that were useful in predicting macroinvertebrate richness and composition and that have potential application in classification-based bioassessment and management. A regional-scale study such as this requires tools for efficiently delineating watersheds and deriving their attributes. Paper two presents a multiple watershed delineation tool that addresses issues such as a) incorrectly positioned outlets and b) large Digital Elevation Models. This tool has capabilities to delineate stream networks with the threshold that determines drainage density being objectively determined so that the resulting networks adhere to geomorphological stream network laws. It also derives a suite of geomorphological watershed attributes that were used in prediction models in paper three. In paper three, we developed statistical models to predict streamflow regime class from watershed attributes. Four popular statistical methods were used and the uncertainty associated with class predictions for each method was quantified. Paper three also identified the watershed attributes that were most important for discriminating streamflow regime classes.

High rates of soil loss and high sediment loads in rivers necessitate efficient monitoring and quantification methodologies so that effective land management strategies can be designed. Constructing a sediment budget is a useful approach to address these issues. Quantifying a sediment budget using classical field-based techniques, however, is labour intensive and expensive for poorly gauged, large drainage basins. The availability of global environmental datasets in combination with GIS techniques provides an opportunity for studying large basins. Following this approach, a framework is presented for constructing sediment budgets for large, data-sparse drainage basins, which is applied to the mountainous upper Indus River basin in northern Pakistan. The methodological framework consists of five steps: (1) analyzing hydro-climatological data for dividing the drainage basin into characteristic regions, and calculating sediment yields; (2) investigation of major controls on sediment yields; (3) identification and mapping of sediment source areas by spatially distributed modelling of erosional processes; (4) spatially distributed modelling of sediment yields; and (5) carrying out the sediment budget balance calculation at the basin outlet. Further analysis carried out on the Indus data has enabled a better understanding of sediment dynamics in the basin.<p> Analysis of the available hydro-climatological data indicates that the basin can be subdivided into three characteristic regions based on whether runoff production and subsequent sediment generation is controlled by temperature (Region 1, upper, glacierized sub-basins), precipitation caused by the monsoon and western disturbances (Region 3, lower sub-basins), or a combination of the two (Region 2, middle reach sub-basins). It is also demonstrated that contrary to the conventional model, the specific sediment yield increases markedly with drainage area along the Indus River. An investigation of major controls on specific sediment yield in the basin indicates that percent snow/ice cover is a major land cover control for specific sediment yield. Spatially distributed erosion modelling predictions indicate that 87% of the annual gross erosion takes place in the three summer months with greatest erosion potential concentrated in sub-basins with high relief and a substantial proportion of glacierized area. Lower erosion rates can be explained by the arid climate and low relief on the Tibetan Plateau, and by the dense vegetation and lower relief in the lower monsoon sub-region. The model predicts an average annual erosion rate of 3.2 mm/a or 868 Mt/a. Spatially distributed sediment yield predictions made with coupled models of erosion and sediment delivery indicate that the Indus sub-basins generally show an increase of sediment delivery ratio with basin area. The predicted annual basin sediment yield is 244 Mt/a and the overall sediment delivery ratio in the basin is calculated as 0.28. The long-term mean annual sediment budget, based on mass balance, is characterized by a gross erosion of 762.9, 96.7 and 8.4 Mt, and a gross storage of 551.4, 66.1, and 6.5 Mt in the upper, middle, and lower regions of the basin, respectively. The sediment budget indicates that the major sources of eroded sediment are located in the Karakoram, in particular in the Hunza basin. Substantial sediment storage occurs on the relatively flat Tibetan Plateau and the Indus River valley reach between Partab Bridge and Shatial. The presented framework for sediment budget construction requires relatively few data, mostly derived from global datasets. It therefore can be utilized for other ungauged or poorly gauged drainage basins of the world.

High rates of soil loss and high sediment loads in rivers necessitate efficient monitoring and quantification methodologies so that effective land management strategies can be designed. Constructing a sediment budget is a useful approach to address these issues. Quantifying a sediment budget using classical field-based techniques, however, is labour intensive and expensive for poorly gauged, large drainage basins. The availability of global environmental datasets in combination with GIS techniques provides an opportunity for studying large basins. Following this approach, a framework is presented for constructing sediment budgets for large, data-sparse drainage basins, which is applied to the mountainous upper Indus River basin in northern Pakistan. The methodological framework consists of five steps: (1) analyzing hydro-climatological data for dividing the drainage basin into characteristic regions, and calculating sediment yields; (2) investigation of major controls on sediment yields; (3) identification and mapping of sediment source areas by spatially distributed modelling of erosional processes; (4) spatially distributed modelling of sediment yields; and (5) carrying out the sediment budget balance calculation at the basin outlet. Further analysis carried out on the Indus data has enabled a better understanding of sediment dynamics in the basin.<p> Analysis of the available hydro-climatological data indicates that the basin can be subdivided into three characteristic regions based on whether runoff production and subsequent sediment generation is controlled by temperature (Region 1, upper, glacierized sub-basins), precipitation caused by the monsoon and western disturbances (Region 3, lower sub-basins), or a combination of the two (Region 2, middle reach sub-basins). It is also demonstrated that contrary to the conventional model, the specific sediment yield increases markedly with drainage area along the Indus River. An investigation of major controls on specific sediment yield in the basin indicates that percent snow/ice cover is a major land cover control for specific sediment yield. Spatially distributed erosion modelling predictions indicate that 87% of the annual gross erosion takes place in the three summer months with greatest erosion potential concentrated in sub-basins with high relief and a substantial proportion of glacierized area. Lower erosion rates can be explained by the arid climate and low relief on the Tibetan Plateau, and by the dense vegetation and lower relief in the lower monsoon sub-region. The model predicts an average annual erosion rate of 3.2 mm/a or 868 Mt/a. Spatially distributed sediment yield predictions made with coupled models of erosion and sediment delivery indicate that the Indus sub-basins generally show an increase of sediment delivery ratio with basin area. The predicted annual basin sediment yield is 244 Mt/a and the overall sediment delivery ratio in the basin is calculated as 0.28. The long-term mean annual sediment budget, based on mass balance, is characterized by a gross erosion of 762.9, 96.7 and 8.4 Mt, and a gross storage of 551.4, 66.1, and 6.5 Mt in the upper, middle, and lower regions of the basin, respectively. The sediment budget indicates that the major sources of eroded sediment are located in the Karakoram, in particular in the Hunza basin. Substantial sediment storage occurs on the relatively flat Tibetan Plateau and the Indus River valley reach between Partab Bridge and Shatial. The presented framework for sediment budget construction requires relatively few data, mostly derived from global datasets. It therefore can be utilized for other ungauged or poorly gauged drainage basins of the world.