Panel XB: Sustainability and Aquatic Systems
The frequency of natural hazards occurring in North America present a major challenge for governments due to the damages they cause to residential and commercial infrastructure. Floods, a major weather-related event caused by spring snowmelt and intense precipitation occur yearly and jeopardize the safety of many Canadians. In hydrology, flood events are assumed to occur independently of each other. In this study, we tested the independent flood event assumption by analyzing the influence of the Pacific Decadal Oscillation (PDO) (an atmosphere-ocean climate oscillation of multi-decadal variability of North Pacific sea surface temperatures) on annual flood maxima series from 250 naturally flowing streamflow gauges across the western North American margin. We found that floods are higher in one phase of the PDO than in the other phase using Spearman’s rank correlation ρ and permutation tests on quantile-quantile (Q-Q) plots. The permutation tests showed that 40% of the stations had significantly different flood regimes depending on the PDO phase. We observed a distinct geographic pattern in which higher peak floods occurred in the negative PDO phase in central Alaska-Yukon and northern California-British Columbia, whereas higher peak floods occurred in the positive PDO phase in coastal southern Alaska-Yukon and southern California. We found similar results using Spearman’s rank correlation ρ. We also found similar results with two-sided permutation t-tests in which annual floods associated strongly with extreme PDO events. Furthermore, flood ratios from flood quantiles provide further evidence that the PDO impacts flood frequencies and magnitudes. Therefore, further research on climate oscillations such as the PDO should be conducted before designing new infrastructure near rivers due to the impact these oscillations have on floods. Our result illustrates the importance of re-evaluating baseline flood processes due to the rapid intensification of the hydrological cycle because of climate change.
Climate change is altering a host of physical and biological processes that influence the viability of animal populations. The four possible ways that populations can respond to these changes are to tolerate the new conditions, disperse to more favorable habitats, adapt through evolutionary processes, or go extinct. However, despite considerable gains in the understanding of environmental tolerances and dispersal dynamics of animal populations under climate change, the possibility of adaptation remains poorly studied, especially in vertebrates. Moreover, different populations of the same species can have remarkably different traits that influence their effects on ecosystems and their ability to adapt to new conditions, but the relative importance of multiple trait to adaptive potential is often unknown. Brook trout populations in the small streams of Cape Race, Newfoundland are an ideal study system to explore the key processes that might underly differences in future climate change adaptation across populations of a vertebrate species of direct conservation concern. Despite occurring in a small area (~50km2) and experiencing similar macroclimate conditions, these populations are genetically distinct, display significant trait differentiation, and occupy streams with divergent habitat characteristics. The small size and close proximity of Cape Race streams has facilitated an annual monitoring program that has measured the abundance and body size of nine populations for the last decade, which I will use to compare the effects of seasonal air temperature and rainfall across populations. In addition, I will conduct a series of pond transplant experiments in order to measure how brook trout impact aquatic ecosystems and infer how these impacts may change in the future as a function of body size and fish abundance. Data from the monitoring program, transplant experiment and previous research in Cape Race can then be used to build and compare population-specific models predicting ecological and evolutionary responses to various climate change scenarios. In combination, this research should provide a holistic picture of how different populations of the same species could respond to climate change, spanning organismal traits to ecosystems.
Human alteration of river networks has greatly affected the hydromorphological and ecological quality of natural rivers; particularly fish habitat quality. Due to these alterations, stream evaluation indices have become increasingly important to quantify the overall status of river systems across the globe. In this study, I measured the hydromorphological and ecological conditions of 12 Canadian streams using two indices: The Morphological Quality Index (MQI) and the Qualitative Habitat Evaluation Index (QHEI). Each river was initially evaluated with the MQI using a semi-automated approach based on remotely sensed data, including 1m LiDAR and 5m DEMs, historical aerial photos (1960-2010), and geological data. Field assessments were also conducted to measure fish habitat components and to validate morphological data from the MQI. As a field-based ecological index, calculating the QHEI is resource intensive and limits the number of rivers that can be evaluated. To minimize this restriction, I quantified the relationship between the MQI and QHEI to test whether the MQI can be used to evaluate both a river’s ecological and hydromorphological status. The aim of this study is thus to (1) verify whether a river’s morphology can indicate the fish habitat quality of a stream and (2) use the MQI to evaluate streams using a semi-automated approach. Preliminary analysis revealed a relationship between the MQI and QHEI evaluation methods when field assessments were conducted, but no relationship was present when using the semi-automated approach. Heavily altered sections of a river (e.g., dams) reduce the relationship between both indices because of the effects of longitudinal discontinuity on fish habitat quality. Nevertheless, the MQI has potential to help municipalities become better informed about the status of their local rivers and find solutions to improve degraded streams. However, further modifications to the MQI metrics may be required to generalize this evaluation method for all rivers across various terrains.
High levels of chlorophyll α (Chl α) in waterbodies can cause excessive growth of algae (eutrophication), subsequently causing oxygen depletion and deterioration of the water quality. Anthropogenic factors such as water pollution coupled with the warming effect of climate change are driving these algae blooms. To ensure continued access to sustainable and reliable water resources, it is important to monitor water bodies and potential pollution sources. Satellites are advantageous when monitoring water quality since they provide high spatial and temporal data compared to traditional ground sampling. This study examines the application of the satellite PlanetScope in the quantification of Chl α and then aims to identify non-point pollution sources with a watershed analysis. An ordinary least square regression analysis was used to develop a model predicting the observed (or measured) Chl α concentration across Lake Erie based on the satellite reflectance. The predictor variable(s) for Chl α were the visible and near-infrared bands. The correlation between Chl α and a green and near-infrared spectral index was the highest (R2=0.58, p < .001), and increased when only water samples with less than 30% of total suspended solids were used (R2=0.83, p < .001). The robustness of the model will be tested by validating the model with sampled data from Saginaw Bay and then from Lake Ontario. The Global Multi-resolution Terrain Elevation Data from 2010 was used as a digital elevation model for the watershed analysis. A land use map of the land around Lake Erie made by the European Space Agency was used along with the watershed analysis to identify non-point pollution sources. Further research with more high-resolution satellite data and more observed Chl α data will be needed to improve the model’s accuracy.
This event is brought to you by the Loyola College for Diversity and Sustainability and the Loyola Sustainability Research Centre with the support of the Office of the Vice-President, Research and Graduate Studies; the Faculty of Arts and Science; the Canada Excellence Research Chair in Smart, Sustainable and Resilient Communities and Cities; the John Molson School of Business; and the Departments of Biology; Communication Studies; Economics; Geography, Planning and Environment; Management; and Political Science at Concordia University.