Geography and Environmental Management

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Examining the Ecosystem Evolution of Nikanotee Fen Watershed: An Ecohydrological Perspective
(University of Waterloo, 2024-07-10) Popovic, Natasa
Recognition of the environmental ramifications of long-term natural resource development in the Athabasca Oil Sands Region (AOSR) has prompted the implementation of sustainable land use practices. This includes obligatory regulations that require impacted landscapes to be returned to their pre-disturbance functionality. Ecosystem function (e.g., carbon sequestration) is driven by soil-plant-atmosphere exchanges of energy, carbon and water resources. Given the magnitude of disturbance during surface mining (i.e., removal of vegetation and the subsurface) the reestablishment of ecosystem function requires extensive reclamation. This involves the complete reconstruction of surface and subsurface ecosystem components including the establishment of a hydrological regime. Thus, reclamation ultimately creates ‘new’ landscapes, often beginning with a bare ground phase followed by planting campaigns, and the eventual development of widespread plant communities. An understanding of ecohydrological processes throughout the different post-construction evolutionary phases is necessary to evaluate i) ecosystem function and trajectory and ii) reclamation design and success. As the natural, undisturbed landscape of the AOSR consists of hydrologically connected upland forests and fen peatlands, two pilot-scale watersheds incorporating these landscapes have been constructed to examine the viability and design of multi-landscape reclamation endeavours in the region. This thesis captures the evolving ecohydrological regime during the first seven years (2013 - 2019) post-construction at one of the novel, constructed watersheds in the region, Nikanotee Fen Watershed (NFW). Ecosystem function and evolution of both the fen and upland were quantified based on key ecohydrological indicators (net ecosystem exchange (NEE), evapotranspiration (ET) and water-use efficiency (WUE)) using a multi-scale (ecosystem and plant community), multi-method (remote sensing, eddy covariance, instantaneous chamber measurements) approach. Over the course of the study period, both landscapes exhibited significant biophysical evolution, from bare ground to fully vegetated ecosystems, resulting in physical (e.g., altered albedo, surface roughness and producing plant-mediated shading) and functional (e.g., transpiration and carbon sequestration) transformations. Initially, during bare ground conditions, surface-atmosphere exchanges were driven by abiotic factors (atmospheric and edaphic conditions) and controlled by soil water availability. In the fen, due to near-surface water table, high soil moisture content, surface ponding and the low albedo of wet, bare peat, most available energy was partitioned to latent heat and a high degree of decoupling between the surface-atmosphere was observed. During this time, surface evaporation rates were consistently high and comparable to open-water values in the region. Due to the lack of plant community, the fen was a source of CO2 and WUE was low and driven by high evaporative losses. In the upland, the exposed dry sand-loam cover soil resulted in higher albedo and equal partitioning between latent and sensible heat. The drier edaphic conditions resulted in limited evaporation, with only small increases in response to precipitation events. Moreover, these conditions resulted in net carbon losses and similar to the fen, limited WUE. Once plant community became established, edaphic controls decreased, and surface-atmosphere exchanges were driven by plant-mediated responses to atmospheric conditions. In the fen, as water availability remained high, latent heat continued to be the dominant energy flux, but a larger proportion of available energy was partitioned to sensible heat, particularly during drier periods. Widespread plant coverage and the establishment of a thick litter layer supressed surface evaporative losses and increased transpiration, resulting in lower ET rates compared to bare ground conditions and greater surface-atmosphere coupling. Coinciding with plant growth, the fen quickly evolved from a CO2 source to a sink by year three post-construction. Moreover, once fully vegetated, WUE remained relatively stable despite seasonal hydrometeorological variability. Here, stable WUE trends reflect well-developed rooting architecture of the plant community and a well-connected groundwater network between the two landscape units resulting in hydrological self-regulation sufficient to maintain adequate plant function during periods of water stress. In the upland, the growth and development of treed species resulted in a marked increase in latent heat flux, ET rates, CO2 uptake and WUE, with seasonal trends mirroring plant phenology. However, at the conclusion of this study the upland was still functioning as a minor CO2 source, but is expected to become a sink in the near future as trees continue to mature. Overall, an examination of ecohydrological processes and feedbacks during early development suggests the constructed system is evolving towards a functional, self-sustaining ecosystem that’s able to withstand periodic environmental stress. Furthermore, rates and seasonal trends of key ecohydrological indicators (ET, NEE, WUE) at the constructed watershed were comparable to those observed at surrounding natural and post-disturbance boreal landscapes providing further support of ecosystem function. Results from this study provide insight to early ecosystem function and trajectories and can be applied to future designs and planting prescriptions to improve long-term reclamation success.
Evaluation of Wind Flows and Turbulent Fluxes in Complex Terrain of Canadian Rockies
(University of Waterloo, 2024-05-28) Rohanizadegan, Mina
In mountains, the role of diurnal wind (i.e. valley, slope winds) due to differential heating, radiation and topography in controlling fluxes of heat and water vapour is not well understood. Since data in high mountain areas are limited, high resolution models can help resolve near-surface processes and their diurnal changes to use as an input to hydrological models for more accurate predictions of evapotranspirartion and future water resources. Improvements over recent years in the resolution of Numerical weather prediction (NWP) models and large-eddy-simulation (LES) have had made great progress on resolving the atmospheric boundary layer (ABL) and boundary layer processes over mountainous terrain. In this work, the Weather Research and Forecasting (WRF) model is used to simulate flow in LES mode over the complex terrain of the Fortress Mountain and Marmot Creek research basins (MCRB and FMRB, respectively), Kananaskis Valley,Canadian Rockies, Alberta in mid- and late summer. The days selected in this study allow for development of thermally-induced wind circulation and ABL processes. However, the use of terrain-following coordinates in most numerical weather prediction models results in errors that propagate through the domain and can result in numerical instability. To avoid this issue when simulating flow over steep terrain a local smoothing approach was used, where smoothing is applied only where slope exceeds some predetermined threshold. The results are compared with global smoothing, which uniformly filters terrain, and is already implemented in WRF. Local smoothing with the cumulus parametrization activated only for the parent domain provides better predictions for surface wind direction, improved predictions for net radiation, and better RMSE for humidity, and was used for the rest of the analysis on turbulence kinetic energy (TKE) and near- surface processes. The model shows that valley flows are impacted by wind gusts and topographic wind originated from higher elevations blowing into the valley. In this study, up-valley flows were stronger in the wide but deeper Kananaskis Valley in MCRB, as compared to the narrower and shallower valley in FMRB. In addition, cold-air pools seem to linger longer in the deeper and wider valley at MCRB, but air temperature was lower in the early morning at the shallower but narrower valley at FMRB. The removal of the cold air pool due to temperature rise happened earlier in the valley in FMRB than in the valley of MCRB due to an elevated inversion layer of the deeper valley. Boundary layer processes and turbulence in complex terrain are influenced by thermally-induced flows, as well as dynamical or non-local winds. Data from three high-frequency eddy covariance systems at a northwest-facing slope location, and at two ridgetops at the south and north valley side walls of the Fortress Valley were combined with LES to investigate the influence of diurnal mountain flows on TKE. Simulated cross sections showed up-valley flow was inclined toward the northern valley wall at the southeast side of the valley, and the interactions between the up-valley flow and the cross-ridge flows contribute to TKE in the valley. It was found that there is a strong correlation between TKE and wind speed at ridgetops, while TKE in the valley correlated strongly with the wind speed at the northern ridgetop. Furthermore, TKE budget analysis showed that horizontal shear could be an important source of TKE production at the northwest-facing slope station in the Fortress Valley. The variability observed in TKE budget components across different locations within this high mountain basin indicates the significance of both horizontal and vertical exchange processes in the mechanisms governing TKE production. The final portion of this study evaluated model predictions of sensible and latent heat fluxes versus observations at three eddy-covariance locations in the Fortress Valley. The differences between model predictions and observations illustrates the crucial role of soil moisture, along with net radiation, in controlling the heat and evaporative fluxes in mountainous terrain. The observations over July and August were further used to quantify the variability of the sensible and latent fluxes with soil moisture content and net radiation, as influenced by elevation and vegetation. Observations showed that despite variations in vegetation type and elevation, the latent heat flux exhibited a weak correlation with soil moisture at each site but displayed a strong correlation with net radiation at all sites for both wet and dry days. But when all study sites were compared together for mid- versus late summer sunny days, it was noted that the local topography and soil moisture, radiation, and local flows can all have important impacts on turbulent fluxes. The findings also indicate that longer term data with a wider range of soil moisture, and topographical features (i.e slopes, aspect) will be beneficial for more in depth future studies on exchange processes in mountainous terrains.
Watershed Classification in the Great Lakes Basin: Implications for Water Quality and Agricultural Management Practices
(University of Waterloo, 2024-05-28) Hassan, Amina
In recent years, the Great Lakes have faced a resurgence of cyanobacterial harmful algae blooms (cHAB), primarily attributed to non-point sources, notably agricultural activities. While significant efforts have been directed toward implementing conservation practices to mitigate nutrient losses, existing literature often examines the efficacy of best management practices (BMPs) and spatiotemporal drivers of nutrient loss separately, neglecting their interconnectedness. Recent studies suggest that conservation practices' effectiveness may vary spatially, necessitating targeted interventions to avoid trade-offs. This study aims to delineate distinct ecoregions based on known spatiotemporal drivers of nutrient loss and analyze their implications for water quality across different land use-land cover (LULC) types. Using Google Earth Engine (GEE), two Cascade K-means clustering analyses were conducted separately on climate and geophysical variables, resulting in three distinct ecoregions for each domain. These findings were integrated with data from the Provincial Water Quality Monitoring Network (PWQMN) and HYDAT stations to assess patterns in water quality degradation and nutrient loss mechanisms across ecoregions. Additionally, statistically downscaled climate change datasets from Environment and Climate Change Canada (ECCC) were utilized to determine shifts in climate conditions across established climate ecoregions. Furthermore, climatic ecoregions displayed a latitude-dependent pattern in water quality degradation. Under projected climate changes, the coolest regions are anticipated to resemble current conditions in the warmest regions, leading to a northward shift in agricultural suitability. These findings underscore the necessity of adopting a context-dependent approach to agricultural management practices, especially in light of projected climate shifts. A one-size-fits-all approach to BMP recommendations and implementation falls short, highlighting the importance of tailored strategies to address the unique challenges posed by each ecoregion.
The water and energy balance of Lake Miwasin: a pilot-scale oil sands pit lake
(University of Waterloo, 2024-05-21) Zabel, Austin
Energy companies in the Athabasca Oil Sands Region in Alberta, Canada are studying the viability of incorporating pit lakes into reclamation closure designs to both sequester tailings and re-integrate the mining lease into the broader natural landscape. Lake Miwasin is a pilot-scale oil sands pit lake encompassed by a constructed catchment where the volume of the water cap is not actively managed. This research quantified the water and energy balances of Lake Miwasin during the open water season for two consecutive years. As the constructed catchment lacks both natural waterbodies and connectivity to a legacy groundwater system, freshwater additions to the lake during the summer season were governed by rainfall. Above average rainfall during the first year triggered surface water inflow events that diluted the over-winter water volume by ~ 25%. The second year had below average rainfall resulting in minimal surface water inflow and a 30 cm drop in lake stage. The lake became thermally stratified during the open water season absorbing high amounts of energy in the spring and releasing this energy in late summer/fall. Despite being constructed at a pilot-scale, the timing and magnitude of the maximum heat content were comparable to small natural waterbodies. The small fetch and surrounding landscape features led to a sheltering effect reducing wind action at the surface contributing to lower correlations between climatic variables and the surface energy fluxes compared to larger neighboring lakes. This research indicates variable climatic conditions, lake size, and surrounding landscape features will influence the water balance and energetics of future oil sands pit lakes. Consideration of the presented results and continued research is required to guide the implementation of these contemporary landscape features throughout the Athabasca Oil Sands Region.
Enhancing Open Government Data Quality: A Quantitative Evaluation Assessment for Cross- Jurisdictional Open Data Programs in Waterloo Region
(University of Waterloo, 2024-05-06) Li, Xuxuan
This study builds on the previous research for identifying the current issues and gaps existing for the cross-jurisdictional data quality of the open data programs in the Waterloo region, not only as the governments in the Waterloo region have a unique two-tier municipalities structure, but also how the four municipalities the City of Waterloo, the City of Kitchener, the City of Cambridge and the Region of Waterloo shares one same data portal. The goals of this study are to understand what data quality metrics are important for the quality of open data, and how an evaluation tool can be created to effectively measure the data quality for the open data in the Region of Waterloo. A quantitative approach was used for measuring individual metrics of the data quality dimensions such as completeness, timeliness, metadata, and usability. The results show there are still a lot of improvements that can be made by the lower-tier municipalities on quality assurance, regular maintenance, and updates of data policies. The results also indicated that upper-tier municipalities like the regional government of Waterloo can take the leading role in improving the overall data quality of open data programs by creating open metadata and data standards. Additionally, the results also note the insufficient of both current and previous research and provide suggestions for future studies in similar settings.
Advancements in Road Lane Mapping: Comparative Analysis of Deep Learning-based Semantic Segmentation Methods Using Aerial Imagery
(University of Waterloo, 2024-05-01) Liu, Xuanchen (Willow)
The rapid advancement of autonomous vehicles (AVs) underscores the necessity for high-definition (HD) maps, with road lane information being crucial for their navigation. The widespread use of Earth observation data, including aerial imagery, provides invaluable resources for constructing these maps. However, to fully exploit the potential of aerial imagery for HD road map creation, it is essential to leverage the capabilities of artificial intelligence (AI) and deep learning technologies. Conversely, the domain of remote sensing has not yet fully explored the development of specialized models for road lane extraction, an area where the field of computer vision has made significant progress with the introduction of advanced semantic segmentation models. This research undertakes a comprehensive comparative analysis of twelve deep learning-based semantic segmentation models, specifically to measure their skill in road lane marking extraction, with a special emphasis on a novel dataset characterized by partially labeled instances. This investigation aims to examine the models' performance when applied to scenarios with minimal labeled data, examining their efficiency, accuracy, and ability to adapt under conditions of limited annotation and transfer learning. The outcome of this study highlights the distinct advantage of Transformer-based models over their Convolutional Neural Network (CNN) counterparts in the context of extracting road lanes from aerial imagery. Remarkably, within the state-of-the-art models, such as Segmenting Transformers (SegFormer), Shifted Window (Swin) Transformer, and Twins Scaled Vision Transformer (Twins-SVT) exhibit superior performance. The empirical results on the Waterloo Urban Scene dataset mark substantial progress, with mean Intersection over Union (IoU) scores ranging from 33.56% to 76.11%, precision from 64.33% to 77.44%, recall from 66.0% to 98.96%, and F1 scores from 44.34% to 85.35%. These findings underscore the benefits of model pretraining and the distinctive attributes of the dataset in strengthening the effectiveness of models for HD road map development, announcing new possibilities in the advancement of autonomous vehicle navigation systems.
An Evaluation of the Impact of Seasonal Land Cover Change on Evapotranspiration Estimates at the Catchment Scale in the Upper Gundar River Basin, Tamil Nadu, India
(University of Waterloo, 2024-04-24) Senthilkumaran, Akash
Changes in the water cycle influence the energy balance of the Earth. The water cycle is represented using the water balance equation, in which Evapotranspiration (ET) is a vital parameter. One of the main drivers of the change in ET within a specific area is the change in land cover. This study focuses on estimating ET across the Upper Gundar River Basin located in the state of Tamil Nadu, India. Notable features of this landscape include agriculture throughout the year supported using an extensive network of tanks and borewells, and the presence of Prosopis juliflora, a widely prevalent invasive species known to consume groundwater and moisture. Due to the lack of spatial variability in point ET measurements, ET models using remote sensing imagery as the main forcing data have been widely used to assess the spatial variability and temporal variability based on the principle of surface energy balance. These models are collectively referred to as Surface Energy Balance (SEB) models. The model used in our study is the Surface Energy Balance Algorithm for Land (SEBAL) model to estimate ET for two periods of the year, indicating mid-summer and the end of the northeast monsoon for the years 2006, 2014 and 2021. Since land cover changes drive ET, land cover classification and seasonal change detection are also performed for the same time periods. Imagery from Landsat satellites is used, and one image is chosen to represent the specific season. The major land cover classes chosen in our study are water, pre-growth, agriculture, Prosopis juliflora (prosopis), barren land, and exposed soil. Along with the Landsat imagery, to run SEBAL, Aster DEM is used along with in-situ weather data and GLDAS data. Over 90% levels of overall accuracy were achieved for all year-season combinations for the land cover classification. Using SEBAL, Actual Evapotranspiration (ETa) for all the classes is calculated except the water classes. Due to the lack of in-situ measurements, an intermodal comparison was performed with the EEFlux product available at the same resolution derived using the METRIC algorithm using land cover classes as units of comparison. The comparisons are carried out using correlation coefficient (r), root mean squared error (RMSE), and mean values. Highest mean values were observed for either the agriculture or prosopis class, and the lowest mean value was exhibited by the exposed soil class on all occasions. Within all summers, considering all the years, the average correlation coefficient and RMSE were 0.8, 1.2 mm/day, and for monsoon, the averages were 0.5 and 0.85 mm/day, indicating increased proximity during the monsoon season between SEBAL and EEFlux. Similarly, the range of mean values between classes in summer is 2.12 mm/day, 1.36 mm/day in the monsoon. In terms of the energy fluxes used to determine ETa, a decrease in monsoon is observed for soil heat flux (G), instantaneous net radiant energy (Rn_inst), and net radiation in a day (Rn_24). For sensible heat flux (H), classes with vegetation tend to have lower values in comparison to the classes without vegetation. Finally, average water outflux is calculated encompassing all classes by multiplying the area of a class with mean ETa, and the values observed in summer and monsoon alternatively for the years 2006, 2014, and 2021 in m3/day are 5,142,212, 3,534,906, 2,954,897, 4,046,322, 5,369,191, 4,512,596.
Assessment of Drivers of Algal Biomass in North American Great Lakes via Satellite Remote Sensing
(University of Waterloo, 2024-04-02) Dallosch, Michael
Lakes are regarded as sentinels of change, where shifts in environmental conditions significantly affect lake phenology. A significant consequence of the change is the perceived increase in the frequency, magnitude, and severity of algal blooms in lakes globally. Algal blooms/increased productivity in lakes pose significant ecological, economic and health risks, impacting fisheries, tourism, and freshwater access. The impacts of external nutrient loading from anthropogenic sources are well documented; however, blooms have been observed to occur in even remote lakes. Climate change is a hypothesized driver of these recent algal bloom trends, such as increasing global air temperatures, water temperatures, lake ice loss, precipitation intensity, and drought. Past research on the impact of climatic drivers on algal biomass dynamics has often been limited to lab, mesocosm, or short termed observations, due to limited in situ data. New remote sensing data products make use of historic multispectral satellite image archives to provide greater spatial and temporal coverage of algal biomass concentrations, allowing for longer time series observational studies to be conducted over large areas. Using data provided by the European Space Agency (ESA) Climate Change Initiative (CCI) Lakes project (product version 2.0.0), daily chlorophyll-a (chl-a; proxy of algal biomass), Lake Surface Water Temperature (LSWT) and Lake Ice Cover (LIC) from 2002 to 2020 were derived from five North American Great Lakes: Great Bear Lake (GBL), Great Slave Lake (GSL), Lake Athabasca (LA), Lake Winnipeg (LW), and Lake Erie (LE). Additional atmospheric and lake physical variables were provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5-Land data as part of the ERA5 climate reanalysis product including: 2-m air temperature (T2m), Total Precipitation (PPT), Surface Net Solar Radiation (SNSR), Surface Runoff (SR) and Subsurface Runoff (SSR), Wind Speed (WS) and Lake Mix-Layer Depth (LMLD). Such data products allow for comprehensive time series analysis on the interaction effects of atmospheric and lake physical parameters on algal biomass dynamics. Winter temperatures exhibit the highest rate of change relative to other seasons, where LIC loss is important for Northern hemisphere lakes; however, its effect on algal biomass dynamics is relatively unknown. To investigate how LIC duration alters algal biomass in North American Great Lakes, annual and seasonal algal biomass, LSWT and LIC parameters were calculated for the five study lakes using ESA CCI Lakes data. Algal biomasses (β = 0.01 – 0.75 μg L-1 yr-1) and LSWT (β = 0.03 – 0.14 K yr-1) were found to increase, with a general decrease in LIC (β = -0.88 – -1.08 Days yr-1) from 2002 to 2020. Vector autoregressions (VARs) showed that in Northern Lakes (NL; GBL, GSL and LA), LSWT and LIC parameters provide greater explanatory power for annual/seasonal chl-a concentrations (median adj. r2 = 0.75) compared to Southern Lakes (median adj. r2 = 0.46). Additionally, LIC parameters were found to provide higher explanatory power for NLs during the spring season compared to LSWT. However, higher explanatory power does not indicate predictive capacity, where machine learning methods may provide stronger predictive models. To determine if LIC may act as a predictor of algal biomass parameters, multiple linear regression (MLR) and artificial neural networks (ANN) were constructed using per-pixel observations of annual/seasonal algal biomass, LSWT, and LIC parameters. Irrespective of season, LSWT only models returned lower prediction error (median NRMSE = 0.82) compared to LIC only models (median NRMSE = 0.93). However, models consisting of both LIC and LSWT returned the lowest predictive error (median NRMSE = 0.75). While LIC did not act as a strong predictor of algal biomass, a random forest (RF) classifier was used to determine whether LIC could classify the presence of lake-specific anomalies in chl-a concentrations. The RF model found that LIC parameters (ice on/off) had the highest mean accuracy decrease on average for NLs during the spring season. LIC timings are changing, where it was found to have greater importance on springtime abnormal algal biomass growth in NLs. While LIC was important at this time compared to LSWT, the impact of other important atmospheric and lake physical variables on algal biomass dynamics are not well understood, particularly at a smaller temporal scale (i.e., daily). To assess the potential interaction effects between algal biomass, atmospheric, and lake physical parameters, a network analysis was conducted using a High Order Dynamic Gaussian Bayesian Network (HO-DGBN) for the original time series, the stationary, non-stationary, and residual signals at varying temporal ranges (Δ: daily, three days, weekly, biweekly, and monthly averages). It was found that LSWT, T2m and SNSR were the most important parameters on average, where LSWT exhibited the highest importance on the daily scale compared to the monthly. Additionally, LMLD returned increased importance at longer temporal frequencies, while SSR returned increased importance at shorter temporal frequencies. Temperature interactions were mixed, typically returning both positive and negative interactions, while SNSR typically exhibited a positive interaction with chl-a, while LMLD exhibited a frequent negative interaction. PPT and WS were found to be the least important parameters in all study lakes. This thesis provides some of the first analytical uses of the ESA CCI Lakes product; a product that undergoes regular updates (every two years or so) as new satellite and in situ data become available, and algorithms for the retrieval of chl-a, LSWT and LIC are being improved. As such, improvements are expected in future releases of the product, limiting the accuracy of some findings in the thesis. Of the data presented, there is evidence that LIC is a significant contributor to spring algal biomass dynamics for NLs; however, Southern Lakes (SL; LW and LE) exhibit more complex interactions, likely due to anthropogenic impacts. This thesis identifies the complexity of LSWT interactions with algal biomass and identifies LMLD as a predominantly negative effect in the development of algal biomass. Algal biomasses are increasing, where increases in LSWT yield higher algal biomass peaks (at varying times throughout the year) within the study lakes. Future climate scenarios may provide conditions favorable for algal biomass growth, where Northern landscapes are at the greatest risk.
Methane Cycling in Northern Peatlands Following Wildfire
(University of Waterloo, 2024-03-26) Shingler, Abigail
Peatlands are an important component of the global carbon (C) cycle, they operate as long-term global sinks of atmospheric carbon dioxide (CO2) and sources of methane (CH4). However, they are becoming increasingly vulnerable to disturbances such as wildfire. Understanding the impact of wildfire on greenhouse gas dynamics is important as the frequency and severity of these fires continues to increase. Loss of labile substrate and methanogenic community is often attributed as the driver behind CO2 and CH4 emission reductions from peatland soils post-wildfire. Soil incubations were conducted using samples from both burned and unburned peatlands immediately (Alberta) and 2-years (Ontario) post fire to measure and compare CH4 production potential and oxidation. In-situ CH4 and CO2 flux measurements were conducted at the Alberta site immediately after fire. Environmental variables such as water table depth, soil temperature and moisture were collected at each site. Soil samples from the Ontario site were also analyzed for phenolic compounds, pH, and electric conductivity. In both the recently burned and 2-year post fire incubations, lower CH4 prodution was observed at the burned sites. In-situ field fluxes determined that both ecosystem respiration (ER) and net ecosystem exchange (NEE) was lower and CH4 flux indicated net CH4 uptake at the burned site compared to the natural site, immediately post-fire. Overall, this study enhances our understanding of the impacts of wildfire on greenhouse gas dynamics and carbon storage in peatland ecosystems both immediately and 2-years post-burn. This understanding is important for the establishment of peatland carbon budgets in response to climate change, contributing to the development of accurate and reliable global carbon budgets and climate modelling that can account for the increasing vulnerability of boreal peatlands to fire.
First Nation land and well-being: Exploring the relationship of First Nation land management systems with community well-being, informality within land management, and the development of an agent-based First Nation land-use voting model for experiments on policy adoption at Curve Lake First Nation
(University of Waterloo, 2024-03-15) Fligg, Robert
Land management is a pressing issue for reconciling and reconnecting First Nations with their land. Many First Nations have taken more control and responsibility over the management of their land that is key to their well-being. Currently, two legislative pathways (e.g., the First Nation Land Management (FNLM) regime and frameworks of self-government) provide more control by First Nations over their land outside of the Indian Act, however, there are gaps in societies’ understanding about the relationship of First Nation land management (in the broader sense) and their well-being. The overarching goal of the dissertation, seeks to improve societies’ understanding about the relationship between First Nation land management (broadly defined, including land management systems, property rights systems, land-use policies and planning) and First Nation well-being. Chapter 2 contributes by asking the question: “does the land-management regime of a First Nation correlate with differing levels of community well-being among First Nations as measured using the community well-being (CWB) index?”. It also investigates if there have been temporal effects by asking the question: “do First Nation communities experience different CWB trajectories when under a particular land management regime when they transition from one land management regime to another?” First Nation communities that have more control over managing their land have on average higher CWB scores, however, a community under any land management regime (e.g., under the Indian Act, or sub-set of the Indian Act ‘Reserve Land and Environment Management Program’ (RLEMP), Framework Agreement on First Nation Land Management Act (formerly First Nations Land Management Act (FNLM)) or a framework of self-government) could achieve a high CWB score (e.g., above the non-Indigenous average) depending on key economic factors (e.g., location of a community to an economic area). Regardless of CWB scores a land management regime is crucial to First Nation cultural well-being that may include pathways or mechanisms to develop formal community objectives and policies on land-use practices (e.g., on land relationships and stewardship). Building on Chapter 2, Chapter 3 looks deeper into First Nation land management, land-use practices, policy and planning, and property rights through collaboration with Curve Lake First Nation. Chapter 3 investigates by First Nation member-type (i.e., land holder vs non-land holder, and ‘on’ vs ‘off-reserve’ members) land management knowledge, and the impact member type has on land management and land-use practices. To achieve the objective of Chapter 3, a social survey was created in collaboration with Curve Lake First Nation to investigate formal and informal land-use practices and policy in land management, and whether there was a gap between members “wants and needs” regarding what should happen according to (formal) policy or process and what actually happens on the ground (informal). Although results from Chapter 3 found a correlation of land holder/non-land holder disconnect with uncertainty regarding policy on land use-practices that suggested a need for formal land-use policy and planning, the results also suggested CP holders and non-CP holders agreed that all parcels should be managed and used according to community values. Chapter 4 takes a step toward filling the gap in societies’ understanding by utilizing the knowledge and data from Chapters 2 and 3 in the development of a First Nation land-use voting model to investigate how formal land use policy on individual support for land policy and community land objectives could be conceptualized as a collective well-being. Chapter 4 investigated the objective by asking research questions on “how different member-levels of propensity for land information knowledge, ambition, stewardship, and how they collaborate affect formal land-use plan and potential land-policy adoption”, and secondly “how relationships and changes in members’ knowledge and attitudes affect support of formal land-use policy and its potential adoption?” Responses to the Curve Lake First Nation social survey was further coded for member responses on land related questions about their community, and outside community, on systems of land management, property rights, land-use policy and planning, and opinions on well-being that could be used to empirically inform an agent-based model called the ‘First Nation Land-Use Voting Model’. Model results suggest with greater support for community specific objectives for a balance in socio-economic and cultural well-being, there is greater support for the adoption of formal land-use policy and planning.
Semantic Modelling of an Indoor Parking Garage Using Hand-held GeoSLAM LiDAR Point Clouds
(University of Waterloo, 2024-01-26) Hu, Jingyi (Kristie)
The development of high-definition (HD) digital twin models for underground parking lots presents significant challenges due to the absence of signals from satellite navigation systems, fluctuating lighting conditions, and obstruction-rich environments. These complexities hinder applications that rely on accurate spatial awareness, such as emergency rescue, navigation assistance, and autonomous parking. This thesis presents an elaborate methodology for generating an HD digital model of an indoor parking lot. A LiDAR-based Simultaneous Localization and Mapping (SLAM) system was used for point cloud acquisition and colorization. The methodology encompasses the application of leading-edge algorithms, including line feature extraction, semantic segmentation, and surface reconstruction. The effectiveness of the proposed methodology is underscored by parallel comparisons of ground truth with visual output (e.g., line segmentation, and reconstructed models). Notably, segmentation via DCTNet achieves high-performance metrics in the average class IoU of the model (90.74%) and average F1 score (98.65%). Overall, these demonstrate the efficiency of the proposed methodology in developing a detailed indoor parking garage model using advanced LiDAR-based SLAM technology, addressing challenges in GPS and lighting, and providing crucial insights for future advancements in 3D indoor modelling through comprehensive accuracy assessments and semantic enhancements.
Snow Mapping from Passive Microwave Brightness Temperature and MODIS Snow Product with Machine learning Approaches
(University of Waterloo, 2024-01-25) Du, Jiayi
Snow cover is one of the cryosphere's most critical components, representing a vital geophysical variable for climate and hydrology. Monitoring snow cover in Arctic regions has gained increasing significance, particularly considering recent climate warming. Given the complex spatiotemporal variability, inconvenience of transportation, and the remote locations of many snow-covered areas, remote sensing emerges as an ideal technique for data collection to monitor snow cover across various spatiotemporal scales. In contrast to optical remote sensing, passive microwave (PMW) and active microwave (AMW) satellite sensors remain unaffected by clouds and solar illumination, making them widely employed in snow detection. PMW observations have lower spatial resolution and high temporal resolution than AMW, which are suitable for large-scale snow mapping. Integrating optical data and PMW data can significantly enhance the quality of snow cover information. Various machine learning (ML) methods have been pivotal in environmental remote-sensing research in recent years. With the surge in Earth observation big data and the rapid advancements in ML techniques, an array of innovative methods has emerged to facilitate environmental monitoring on a global scale. Thus, a snow-monitoring method has been proposed based on multi-source remote sensing data and ML. The brightness temperature (Tb) data derived from the Advanced Microwave Scanning Radiometer E/2 (AMSR-E/2) Level 3 product and Moderate Resolution Imaging Spectroradiometer (MODIS) snow product serves as the reference for snow cover area (SCA). This study predominantly selects Oct, Dec, Feb, and Apr from 2012 to 2022 as the study periods. The research uses three ML methods, Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM), for snow cover detection based on PMW and MODIS data in the Arctic. The overall accuracy (The ratio of correctly classified as snow plus correctly classified as non-snow points to the total number of points) of ML models in snow detection surpasses 80%, yet it exhibits regional and seasonal variations. Notably, distinctions in the distribution of MODIS snow and PMW snow become evident in two types of areas: regions where MODIS estimates exceed PMW and those where PMW estimates surpass MODIS. ML-based estimation significantly enhances the accuracy of snow monitoring in the latter category, reducing misclassifications and augmenting the precision of snow cover assessment. When comparing the ML-derived SCA, PMW-derived SCA, and MODIS-derived SCA with the snow depth dataset-derived SCA, the ML method exhibited the highest consistency
Exploring Radarsat-2 SAR Cross-Polarization Ratio Capability for Tundra Snow Depth Estimation Using Numerical and Deep Learning Approach
(University of Waterloo, 2024-01-25) Cao, Yuanhao
Snow is essential to the Earth system, significantly influencing the global climate, freshwater availability, and economic activities. A significant snow cover decline has been reported in vast northern hemisphere areas. The decrease in snow cover will affect more than one-sixth of the world's population who rely on the snow as a freshwater supplement. Snow mass, often expressed as snow water equivalent (SWE), signifies the quantity of water held in the form of snow on the Earth's surface, and it plays a crucial role in the functioning of water, energy, and geochemical processes. Since the seasonal snow has a high spatial variability at regional and local scales, surface observations cannot provide sufficient SWE information. The quality of global SWE estimates needs to be improved. In recent years, C-band spaceborne SAR has shown a high potential for global monitoring of SWE. This thesis aims to explore the current spaceborne C-band SAR signal sensitivity to Arctic tundra snow and define a suitable approach for estimating deep snow depth through the tundra environment. The noticeable variation of C-band backscatter with the snow depth generally demonstrated the C-band sensitivity to dry snow for the deep snow. These areas are dominated by tall vegetation areas such as tall shrubs and riparian shrubs. The cross-polarization ratio method also shows a higher correlation with snow depth than cross-pol and co-pol backscatter, which is generally consistent with earlier research. Also, numerical, and deep learning (DL) approaches were tested based against drone-based snow depth reference data. The result shows that the numerical approach may only be valid in a place with over 2.5 m snowpack. Compared to the numerical approach, the DL approach shows a better evaluation, resulting in a correlation coefficient of R 0.79 between the estimated and target snow depth with a root mean square error (RMSE) of 19 cm. The DL approach can be more suitable for estimating snow depth from C-band SAR observation under the Arctic tundra snow environment.
Beyond Ruins: The Role of Cultural Capital in Post-Disaster Tourism Revival in Kathmandu Valley
(University of Waterloo, 2024-01-25) Harper, Jacqueline
This study offers an in-depth analysis of the nexus between cultural capital, tourism, and disaster recovery, with a particular focus on the aftermath of the 2015 Gorkha earthquake in the Kathmandu Valley, Nepal. Utilizing Yin's (2003) case study methodology, the research illuminates the multifaceted role of cultural capital—embracing its embodied, objectified, and institutionalized facets—in driving post-disaster recovery processes within the tourism sector. Results underscore the intricate challenges and benefits faced by tourism post-earthquake, emphasizing the critical interplay between local cultural heritage and economic vitality. The study identifies the indispensable contributions of institutional cultural capital in spearheading reconstruction efforts and fostering community collaboration. Recommendations highlight the importance of international partnerships, diversified tourism strategies, and community engagement for bolstering post-disaster resilience. While the research enriches the current discourse on cultural capital and disaster recovery in Kathmandu Valley, it advocates for expanded investigations encompassing diverse hazards, tourism modalities, and capital forms to guide sustainable post-disaster development strategies. This master’s thesis is written in the manuscript style and contains the following sections: Chapter 1 – Introduction; Chapter 2 – Literature Review; Chapter 3 – Research Design and Methodology; Chapter 4: Manuscript titled Beyond Ruins: The Role of Cultural Capital in Post-Disaster Tourism Revival in Kathmandu Valley; Chapter 5 – Conclusions and Future Research Needs; References; and Appendices.
Exploring the biopsychosocial landscape of chronic illness: A case study of systemic lupus erythematosus (SLE) from epigenetics to education
(University of Waterloo, 2024-01-16) Shantz, Emily
Systemic lupus erythematosus (SLE), or lupus, is a chronic autoimmune condition and global public health issue. SLE is uniquely characterized as gendered, racialized, episodic, invisible and idiosyncratic. SLE primarily impacts women, and most severely, women of colour. Cardiovascular disease (CVD) is a main driver of morbidity and mortality among SLE populations. Recent literature has begun to characterize both SLE and CVD as “biopsychosocial” and concomitant with place. However, the complex biological-social interplay influencing SLE disease trajectories, and morbidity and mortality from CVD in SLE, is not well understood. This thesis explores the biopsychosocial landscape of SLE with three main objectives: 1) to assess theoretical and methodological support for social epigenetics studies of SLE; 2) to investigate existing literature around social factors influencing the development of CVD in SLE; and 3) to engage knowledge users in the co-production of educational tools about the risks of CVD in SLE. Drawing on health geographical approaches, ecosocial and biopsychosocial theories, and feminist perspectives, a multimethods research design was employed involving narrative review, scoping review, focus groups, and interviews. This transdisciplinary process was supported by an embedded integrated knowledge translation (iKT) approach that included knowledge users as equal partners. This research positions social epigenetics as a novel and transdisciplinary line of inquiry to understand the development and trajectories of chronic diseases. While some theoretical and methodological support exists - with respect to ecosocial and lifecourse theories, and epigenome-wide association studies and exposomic approaches, respectively - expansion in both of these areas is needed with particular attention to intersectionality. Building on this theoretical foundation, and using SLE as a case study, the scoping review revealed several social factors demonstrated to be central to CVD in SLE populations: socioeconomic status, race, mental health, and gender. These results, and complementary information about CVD specific to SLE, were mobilized through the co-development of a lay language patient education resource. Through a focus group with key informants and interviews with patients, knowledge users advised on tailoring content, format, accessibility and inclusivity for the SLE community, with the ultimate goal of improving patient knowledge about CVD. This body of work makes theoretical contributions to the practical application of social epigenetics studies, integrating intersectional perspectives, and bridging basic and social science conceptualizations of health and ill-health. Methodologically, these studies contribute to the study of iKT frameworks and patient engagement in the context of chronic illness. This research collectively adds to our substantive understanding of SLE through a biopsychosocial lens, and the risk landscape of CVD in place. With respect to healthcare policy and practice, the findings herein may provide future targets for CVD risk assessment and prevention in the SLE context, inform educational and social interventions to support SLE treatment, and contribute to the development of a future patient-led research agenda for SLE in Canada.
Lest the Taps Run Dry: Urban Infrastructure, Water Demands and Drought
(University of Waterloo, 2024-01-05) Finley, Sara
Hydrologically-driven urban water shortage situations (urban droughts) are becoming increasingly widespread under the combined forces of urbanization and global climate change. Canadian cities are not exempted from these worries: though most parts of the country receive abundant rainfall on an annual basis, summer droughts driven by sub-annual periods of low relative precipitation or snowmelt anomalies are commonplace in different parts of the country. In cities where climate-sensitive water use is widespread, summer drought conditions can be accompanied by upward swings in municipal water demands in response to hot, dry weather; this combination of reduced supply and surging demand can increase cities’ vulnerability to urban drought on a range of timescales. The research presented in this thesis seeks to evaluate and quantify the role of water demand dynamics in driving urban drought conditions in Canada. It employs a combination of literature and case study review, conceptual exploration, and quantitative analysis of water demand data collected from 15 Canadian cities to assess the degree to which water demand fluctuations can contribute to urban water shortage threats across the country. The research begins with a conceptual review of urban drought and the endogenous drivers that influence its impacts, finding that the experience of drought in the urban context is uniquely dependent on the response actions of water managers and water users who provide the driving force behind short-term changes in demand intended to mitigate drought impacts within the urban system. Next, the analysis shifts to an evaluation of the role of summer water use bylaws imposed in Canadian cities in mitigating short-term increases in urban water demand during the summer months, revealing that these restrictions have little overall impact on seasonal water demand patterns, though the most stringent formats did show some demand dampening effects during short-term periods of exceptionally hot, dry summer weather. The research program concludes with an in-depth analysis of long-term climate and water demand datasets to detect shifts in urban water demand during summer periods of meteorological drought in Canadian cities. This analysis revealed that summer drought periods are indeed strongly correlated with excess summer water demands, though maximum summer temperatures were more influential than drought condition in most cases. Results from the research presented in this thesis suggest that water demands in Canadian cities tend to surge during summer periods of hot and drought-like conditions, thus aggravating the strained supply:demand relationship that drives urban water shortage threats. While findings confirm that the actions of water managers and water users are highly influential in mitigating urban drought impacts, quantitative data analysis finds no indication that the types of seasonal water restrictions commonly imposed in Canadian cities are effective in reducing climate-driven surges in water demand.
Assessment of remotely piloted aircraft data classification of wetland vegetation communities and changes in their pattern with elevation
(University of Waterloo, 2023-11-01) Giura, Brandon
Wetlands host a myriad of services both to the environment and society. While the provision of those services are partly dependent on the terrestrial plant communities that both comprise and are adjacent to a wetland, little has been done to map these communities at high spatial resolutions or measure their composition and configuration. Remotely piloted aircraft (RPA) have been used to capture very-high resolution imagery that can aid our understanding of wetland characteristics and function without harming the local environment. However, a gap remains in our understanding of the composition and configuration of vegetation communities in high-elevation wetlands. As a step toward improving our understanding, an RPA was used to collect multi-spectral data (red edge, green, blue, near infrared) and analyzed to determine the ability to identify and map the composition and configuration of wetland vegetation communities at high elevations in Alberta, Canada. These wetlands have been excluded from the Canadian Wetland Classification System mapped areas. In addition to assessing the ability for automated classification of vegetation communities in an object-based image analysis, the relative contribution each spectral band, their combination in different indices (e.g., NDVI), and a generated digital surface model had on classification accuracy was quantified. Results from a random tree classifier obtained an overall accuracy of 91.43%, total producer’s accuracy of 86.9% and total user’s accuracy of 92.7%. Of 17 inputs (12 image layers and 5 objects features) included in the classifier, the Digital Surface Model had the greatest overall importance (average of 15.9%). While comparison between the classifier and testing samples derived through manual segmentation selection yielded high accuracies, comparison of the automated classification against ground survey plots were substantially less accurate (total overall accuracy 54.5%). Analysis of the composition and configuration of the classified RPA data identified five non-correlated landscape metrics that showed statistically significant differences when wetlands were compared across a gradient in elevation. These results demonstrate that elevation can affect the pattern of wetland vegetation and that further research should be done to determine if and how these pattern changes may affect specific wetland functions. In addition to situating the aforementioned results among the broader literature, the potential for improved vegetation mapping with remotely piloted aircraft is discussed along with the need for a standard set of reporting variables for RPA data collection to facilitate comparative analyses.
Monitoring Seasonal Snow Density from Satellite Based Passive Microwave Remote Sensing and Automatic Weather Stations
(University of Waterloo, 2023-10-26) Welch, Jeffrey
Seasonal snow plays an important role in Earth’s systems and for hydrological applications one of the most important properties is the quantity of liquid water stored in the snowpack, referred to as snow water equivalent (SWE). SWE is related to the depth and density of a snowpack, so accurate estimates of both those properties are necessary to estimate SWE. However, the current understanding of snow density is limited to sparsely distributed in situ samples, which is especially limiting in an environment with restricted access like the Canadian tundra. Models can be used to estimate snow density in lieu of in situ sampling and there are a variety of such models available. However, it was determined that none of the available snow density models were entirely suitable for an environment like the Canadian tundra, each for their own reasons. A new remote sensing algorithm was proposed to estimate snow density from satellite based passive microwave observations and operational automatic weather station (AWS) networks. In this research, an experiment was designed to evaluate the potential for the remote sensing algorithm to monitor snow density in the Canadian Tundra. AWS data were used parametrize a two-layer snowpack model (representing a depth hoar layer underlying a wind slab) and 3D gradient descent machine learning was used to isolate the volume scattering contributions of each layer density independently. New components were added to the machine learning cost function to incorporate prior knowledge and constrain the model’s behaviour. The model was trained at the AWS site in Eureka, Nunavut and was then applied to AWS sites distributed across the Canadian tundra. Model performance was quite consistent at high arctic sites but began to degrade across the subarctic with increased distance from the training site, suggesting the need for more robust model training and forcing in the future. Estimation skill consistently improved over the course of algorithm runs and snow density estimates were often close to the ±10% uncertainty range of the in situ samples by the end of the season – showing good promise for estimating snow density at peak SWE accumulation, which could be useful for applications where total water storage in the snowpack is of concern.
Radiative Forcing & Feedback through the Lens of Solar Geoengineering
(University of Waterloo, 2023-10-24) Virgin, John
Global, annual mean surface temperature continues to rise in the wake of the Paris Agreement goal of limiting warming to 2.C and pursing efforts to limit warming to less than 1.5 C. Research paradigms have arisen to analyze projections of future warming, as well as understanding the drivers of anthropogenic climate change since the preindustrial era. One such paradigm is the characterization of anthropogenic emissions of greenhouse gases as an external radiative forcing on the climate system, as well as feedbacks from the climate response to forcing that augment the rate in which the Earth system reestablishes energy balance. As surface temperatures rise, solar geoengineering has been proposed as a means to deliberately alter Earth’s energy balance and achieve Paris Agreement goals through reducing the amount of incoming shortwave radiation from reaching the surface. Through the lens of the conventional forcing-feedback framework, solar geoengineering is challenging to frame due to the purposeful introduction of an external forcing in order to suppress surface warming, and therefore feedback. Furthermore, the potential for multiple external forcings via solar geoengineering to produce feedbacks from an energetic perspective, even in the absence of surface warming, is poorly understood. This thesis attempts to adapt the forcing-feedback paradigm to define potential radiative feedbacks on the climate system as a result of solar geoengineering through three studies. First, we perform an analyses of radiative forcing and feedback between two versions of the Canadian Earth System Model (CanESM) to understand what is physically driving differences in surface warming. We find little difference in radiative forcing from increased CO2 between the two model versions. More positive radiative feedbacks produce a larger amount of warming in CanESM5, primarily from a reduction boundary layer clouds across the equatorial Pacific that reduced the Earth’s albedo to a greater extent. This analysis was essential to understand how radiative feedbacks, specifically from clouds, can impact the rate surface warming. Next, we analyzed radiative forcing from both increased CO2 and a reduced solar constant using the Community Earth System Model (CESM). We find that the magnitude of solar forcing required to offset the positive radiative forcing from quadrupling CO2 is sensitive to radiative adjustments from both forcings. Radiative adjustments, which are climate responses from an external forcing in the absence of surface warming that impact Earth’s energy balance, as a result of reductions in cloud fraction had a dampening effect on the reduction of the solar constant. This work informed how solar constant tuning, which we used as a proxy for more realistic representations of solar geoengineering, can produce changes in cloud fraction that impact planetary albedo and therefore the amount solar forcing required to achieve energy balance. Finally, we extend the work of the first two studies by defining and investigating potential geoengineering radiative feedbacks in a transient solar geoengineering experiment using CESM. We reduce the solar constant over time in an idealized geoengineering experiment that maintains near-zero global mean surface warming in the wake of increasing CO2 and find a decreasing trend in optically thick tropical clouds. Reductions in cloud fraction reduced planetary albedo, which further decreased the amount of solar forcing needed to achieve the same net energy reduction at the surface, thus producing a positive radiative feedback loop in absence of global mean surface warming. This work highlights the need to understand potential feedbacks from realizable methods of solar geoengineering such as stratosphere aerosol injections.
The Future of Canada's Ski and Mountain Destinations in an Era of Climate Change
(University of Waterloo, 2023-09-28) Knowles, Natalie Linda Bauer
Climate change represents a grand challenge for society and the far-reaching risks for the global tourism sector is no exception. As one of the largest sectors globally, tourism is not only highly impacted by the biophysical impacts of climate change but is also a major source of greenhouse gas emissions contributing to anthropogenic climate change. Tourism’s entrenchment in global socio-ecological systems mean that how tourism identifies and responds to climate change risks will have extensive implications for sport, recreation, livelihoods, culture, real estate, infrastructure, and community resilience in tourism destinations worldwide. While the international tourism sector has highlighted climate change as the primary threat to tourism sustainability, lack of viable climate change adaptation and mitigation strategies raise fundamental questions about the place of tourism in a warmer and decarbonized future. Considering the urgency and salience of these questions for winter tourism specifically, research on highly climate sensitive ski tourism provides important learnings and potential leadership for other tourism sectors that will inevitably face transformative risks as climate change accelerates. This dissertation therefore investigates the complex physical climate and carbon risk within the Canadian ski and mountain tourism system to explore potential pathways towards sustainability and climate resiliency. Canada's diverse ski tourism industry provides an exemplary case study to identify the range of climate and carbon risks, investigate climate adaptations, and understand other socio-ecological factors contributing to or hindering climate impacts, responsiveness, and resilience. Through three interrelated studies, this dissertation combines qualitative and quantitative methodologies using a tourism geography lens to; (1) apply industry-specific climate risk modeling, (2) conduct empirical analysis on the sustainability of snowmaking as a climate adaptation, and (3) understand diverse and inter-connected stakeholder climate risk and response perspectives. Through this process, the research aims to understand the "wicked" challenge of climate change in complex tourism systems, provide information needed for relevant and dynamic climate response planning, decision-making and action, and enable discussions on sustainability transformations and climate resilient futures for diverse mountain tourism destinations. Findings suggest climate risk and resilience is relative across temporal and spatial scales, with potential cross-regional implications for competitiveness and demand patterns. Empirical assessments of snowmaking as a climate adaptation further demonstrate that the national scale is too coarse to evaluate (mal)adaptation or sustainability, instead showing how regional and destination-scale differences in climate impacts, tourism markets, ecosystems (e.g., water availability), energy sources result in differing assessments of adaptation sustainability. Multi-stakeholder narratives situate modelled and observed climate and carbon risks within complex socioeconomic systems and identify diverse actors, structures, and perspectives influencing destination-scale climate (in)action and potential levers to affect more transformative change towards climate resilient futures. More broadly, this dissertation broaches important sustainable tourism and climate change theories, concepts and paradoxes including: temporal and spatial scale, relative climate risk (impacts and adaptive capacity); private-public sector relations and responsibility; (mal)adaptation; scope 3 emissions and current-future tourism mobility; tourism growth and decarbonization; pluralistic value(s) of tourism and sustainability; top-down vs bottom up decision-making; and climate justice, with the aim of extending the important conversation on [sustainable] tourism’s place in a decarbonized economy and warmer world. In investigating the intersection of these research questions, this dissertation presents novel conceptual frameworks, empirical analysis and participatory methods which could be replicated in other tourism contexts and applied to support ski tourism operators and local mountain communities responding to climate change.

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