River ice breakup is a vital process to the morphology and hydrology of many rivers in Canada, often governing peak flows of the river. These events can occur through multiple mechanisms, with the potential for volatile or early breakup events that can have severe impacts to the river. Ice jam flooding can be a potentially devastating result of river ice breakup while early breakup of ice cover in a mid-winter breakup can be unpredictable and greatly alter the remaining ice season. These events are growing increasingly common as a result of climate change, and as a result there is a need to develop prediction tools for these events to aid in decision making support. Past investigations into developing such tools, especially from a data-driven modelling perspective, are challenged by the availability and complexity of the data related to these rare and dangerous to measure events. Therefore, the goal of this dissertation was to develop and apply methods to address the historical challenges and shortcomings in predicting these events through the use of data-driven modelling techniques. This includes: i) development of a stacking ensemble modelling framework for the prediction of ice jam presence during the spring breakup season of a river, utilising variable selection and rare-event forecasting techniques in combination with a comprehensive selection of machine-learning algorithms; ii) return period and trend analysis of mid-winter breakups in conjunction with comprehensive input analysis techniques to identify the key drivers of these events’ severity and develop a means of classifying the flood risk based on hydroclimatic traits; iii) the development of a two-level modelling system for the prediction of the occurrence and timing of mid-winter breakups on a national scale utilising rare event forecasting techniques and imbalanced learning; and iv) development of a novel hybrid semantic and machine learning modelling system in which an ontology is used in conjunction with network analysis techniques to select variables for machine learning models, which is used on a national case study of the prediction of spring breakup timing in Canada. The results of each study in application to their respective case studies demonstrate the effectiveness of the proposed techniques, which are shown to be easily adaptable to other regions or locations. These techniques can form the backbone of decision-making support for communities on rivers that are affected by the unpredictable and oftentimes volatile nature of river ice breakup. / Thesis / Candidate in Philosophy / River ice breakup is a key event to the hydrology of rivers throughout Canada, playing a major role in their physical and ecological characteristics. The timing and mechanism of these events can, however, be unpredictable and volatile, with the effects of climate change only exacerbating these risks. This dissertation focuses on addressing these potential issues through the application of machine learning and hybrid modeling in the prediction of river ice breakup events. Advanced data driven techniques coupled with novel applications of other analytical methods are used to: i) predict the presence of ice jams through the application of stacking ensemble modelling; ii) predict the severity of mid-winter breakups through application of trend and variable analysis; iii) predict the occurrence and timing of mid-winter breakups using rare-event forecasting techniques; and iv) develop a novel hybrid modelling scheme coupling ontology-based semantic modelling and machine learning to predict spring breakup timing. Detailed case studies for each application are provided demonstrating the effectiveness of the discussed techniques.

The Important Bird Areas (IBAs) network of BirdLife International aims to identify sites that are essential for the long-term conservation of the world’s avifauna. A number of global change events have the potential to negatively affect, either directly or indirectly, most bird species, biodiversity in general and associated ecological processes in these areas identified as IBAs. To assist conservation decisions, I assessed a suite of ten landscape scale anthropogenic pressures to 115 Important Bird Areas (IBAs) in South Africa, both those currently placing pressures on IBAs and those that constitute likely future vulnerability to transformation. These threats are combined with irreplaceability, a frequently used measure of conservation importance, to identify the suite of IBAs which are high priority sites for conservation interventions: those with high irreplaceability and are highly vulnerable to anthropogenic threats. A total of 22 (19%) of the South African IBAs are highly irreplaceable and are highly vulnerable to at least some of the pressures assessed. Afforestation, current and potential future patterns of alien plant invasions affect the largest number of highly irreplaceable IBAs. Only 9% of the area of highly irreplaceable IBAs is formally protected. A total of 81 IBAs (71%) are less than 5% degraded or transformed. This result, together with seven highly irreplaceable IBAs found outside of formally protected areas with lower human densities than expected by chance provides an ideal opportunity for conservation interventions. However, all the pressures assessed vary geographically, with no discernible systematic pattern that might assist conservation managers to design effective regional interventions. Furthermore, I used the newly emerging technique of ensemble forecasting to assess the impact of climate change on endemic birds in relation to the IBAs network. I used 50 endemic species, eight bioclimatic envelope models, four climate change models and two methods of transformation to presence or absence, which essentially creates 2400 projections for the years 2070-2100. The consensual projection shows that climate change impacts are very likely to be severe. The majority of species (62%) lose climatically suitable space and 99% of grid cells show species turnover. Five species lose at least 85% of climatically suitable space. The current locations of the South African Important Bird Areas network is very likely ineffective to conserve endemic birds under climate change along a “business a usual” emissions scenario. Many IBAs show species loss (41%; 47 IBAs) and species turnover (77%; 95 IBAs). However, an irreplaceability analysis identified mountainous regions in South Africa as irreplaceable refugia for endemic species, and some of these regions are existing IBAs. These IBAs should receive renewed conservation attention, as they have the potential to substantially contribute to a flexible conservation network under realistic scenarios of climate change. Considering all the global change threats assessed in this study, the Amersfoort-Bethal-Carolina District and the Grassland Biosphere Reserve (IBA codes: SA018; SA020) are the key IBAs in South Africa for conservation prioritisation. / Dissertation (MSc)--University of Pretoria, 2008. / Zoology and Entomology / unrestricted

Water infrastructures have been implemented to support the vital activities of human society. The infrastructure developments at the same time have interrupted the natural catchment response characteristics, challenging society to implement effective water resources planning and management strategies. The Telemark area in southern Norway has seen a large number of water infrastructure developments, particularly hydropower, over more than a century. Recent developments in decision support tools for flood control and reservoir operation has raised the need to compute inflows from local catchments, most of which are regulated or have no observed data. This has contributed for the motivation of this PhD thesis work, with an aim of improving runoff estimation at ungauged catchments, and the research results are presented in four manuscript scientific papers.  The inverse distance weighting, inverse distance squared weighting, ordinary kriging, universal kriging and kriging with external drift were applied to analyse precipitation variability and estimate daily precipitation in the study area. The geostatistical based univariate and multivariate map-correlation concepts were applied to analyse and physically understand regional hydrological response patterns. The Sobol variance based sensitivity analysis (VBSA) method was used to investigate the HBV hydrological model parameterization significances on the model response variations and evaluate the model’s reliability as a prediction tool. The HBV hydrological model space transferability into ungauged catchments was also studied.  The analyses results showed that the inverse distance weighting variants are the preferred spatial data interpolation methods in areas where relatively dense precipitation station network can be found.  In mountainous areas and in areas where the precipitation station network is relatively sparse, the kriging variants are the preferred methods. The regional hydrological response correlation analyses suggested that geographic proximity alone cannot explain the entire hydrological response correlations in the study area. Besides, when the multivariate map-correlation analysis was applied, two distinct regional hydrological response patterns - the radial and elliptical-types were identified. The presence of these hydrological response patterns influenced the location of the best-correlated reference streamgauges to the ungauged catchments. As a result, the nearest streamgauge was found the best-correlated in areas where the radial-type hydrological response pattern is the dominant. In area where the elliptical-type hydrological response pattern is the dominant, the nearest reference streamgauge was not necessarily the best-correlated. The VBSA verified that varying up to a minimum of four to six influential HBV model parameters can sufficiently simulate the catchments' responses characteristics when emphasis is given to fit the high flows. Varying up to a minimum of six influential model parameters is necessary to sufficiently simulate the catchments’ responses and maintain the model performance when emphasis is given to fit the low flows. However, varying more than nine out of the fifteen HBV model parameters will not make any significant change on the model performance.  The hydrological model space transfer study indicated that estimation of representative runoff at ungauged catchments cannot be guaranteed by transferring model parameter sets from a single donor catchment. On the other hand, applying the ensemble based model space transferring approach and utilizing model parameter sets from multiple donor catchments improved the model performance at the ungauged catchments. The result also suggested that high model performance can be achieved by integrating model parameter sets from two to six donor catchments. Objectively minimizing the HBV model parametric dimensionality and only sampling the sensitive model parameters, maintained the model performance and limited the model prediction uncertainty.