Valentina Radic

Anthropogenic climate change is accelerating glacier mass loss, with projections indicating that 70–90% of current glacier volume could disappear by 2100 in many mountain regions, including western Canada. However, these projections carry large uncertainties at the scale of individual glaciers and watersheds, where freshwater impacts are critical. A key uncertainty stems from the widespread use of empirically calibrated temperature-index melt models, which rely solely on temperature and lack process-based understanding and transferability.This thesis investigates the potential of physics-based surface energy balance (SEB) models as alternatives, addressing three key components for regional-scale glacier modeling in western Canada: (1) the effectiveness of dynamical downscaling for climate input to the SEB model, (2) the ability of a glacier evolution model to reproduce observed glacier changes since 1980, and (3) the impact of different downscaling methods on uncertainties in future climate projections derived from a global climate model (GCM).First, SEB input data were evaluated using ERA5 reanalysis and dynamically downscaled Weather Research and Forecasting (WRF) model outputs at multiple glaciers. Both datasets aligned strongly with multi-season meteorological and glaciological measurements, supporting the use of ERA5 for historical melt reconstructions and WRF for downscaling GCM outputs for future projections.Second, an SEB model, enhanced with neural network-based glacier albedo simulations, was integrated into a glacier evolution model driven by ERA5 for 1980–2022. Simulated glacier thinning rates across the region closely matched in-situ observations and remote sensing estimates, revealing that declining summer albedo and increasing longwave radiation and turbulent heat fluxes were primary contributors to mass loss.Finally, a hybrid downscaling approach integrating WRF with statistical downscaling, ranging from simple bias correction to machine learning, was used to downscale climate fields for 2025–2100 and project glacier mass changes. Uncertainty partitioning quantified contributions from downscaling method, training dataset (i.e., whether future projections were included), and emission scenarios.By integrating dynamical downscaling, physics-based SEB models, and neural networks, this thesis advances large-scale glacier mass balance modeling, offering refined insights into glacier-climate interactions and the impacts of climate change on glacier-fed water resources. These findings inform mitigation and adaptation strategies in glacierized regions.

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Turbulent heat fluxes are critical components of glacier surface energy balance models but remain challenging to accurately simulate, particularly during shallow katabatic flow – buoyancy-driven downslope winds prevalent over glaciers in summer. These challenges arise because bulk aerodynamic methods and existing one-dimensional katabatic wind models oversimplify turbulent mixing, and consequently do not accurately simulate turbulent heat fluxes when wind speed maxima occur near the surface. This thesis addresses these limitations by combining multi-year field observations from a glacier in Yukon, Canada, advanced data processing techniques, and numerical modelling to improve the characterization of katabatic flows and turbulent heat fluxes at glacier surfaces.First, multi-height eddy covariance and meteorological data were analyzed to assess bulk aerodynamic methods under different near-surface flow regimes. A novel processing method for single-level eddy covariance data was developed, ensuring the validity of flux calculations during variable flow conditions and low-level wind speed maxima. This method significantly improved agreement between modelled and observed fluxes, particularly in shallow katabatic flow regimes.Second, cospectral analysis of the eddy covariance data revealed a strong correlation between the height of the katabatic jet – confirmed through atmospheric profiling with tethered kite measurements – and peaks in streamwise heat flux cospectra. Turbulent mixing lengths for momentum and heat deviated from standard “law-of-the-wall” scaling, reflecting strong near-surface stability and a sharp thermocline coinciding with the wind speed maximum. These findings demonstrate that the katabatic boundary layer differs substantially from canonical slope flow models.Finally, a two-dimensional katabatic wind model was developed to simulate the suppression of turbulence due to near-surface stability and the localized entrainment of warmer air across the thermocline, which dominates heat transfer processes. Numerical simulations and asymptotic analysis revealed key characteristics of katabatic flow, including velocity and temperature profiles, entrainment rates, and turbulent heat fluxes. By integrating field measurements, data analysis, and modelling, this thesis enhances the process- based understanding of katabatic flow and turbulent heat fluxes, improving surface energy balance models and advancing the accuracy of glacier melt simulations.

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Despite the social, ecological, and cultural importance of glaciers and glacier-fed rivers, a quantification of key glacier controls of streamflow remain elusive and outstanding questions persist. For example: which communities’ water supplies are most vulnerable to the loss of glacier ice? By how much do glaciers modify the streamflow response to heatwaves? First, I use principal component analysis, self-organizing maps, and multivariate linear regression to provide an assessment of community vulnerability to deglaciation in Alberta, Canada, by identifying and predicting signals of glacier runoff in historical streamflow datasets. I combine these models with a new dataset of community water supply sources to find that the most vulnerable locations are the communities of Hinton, Lake Louise, and Rocky Mountain House, as well as the Bighorn Dam, which forms the largest reservoir in the province and provides water for over a million people downstream. Next, I develop an accurate and interpretable convolutional long short-term memory neural network regional hydrological model for streamflow prediction across Alberta and British Columbia, Canada. This deep machine learning model is forced by gridded ERA5 temperature and precipitation data and predicts streamflow at 226 stream gauge stations.Finally, I use this model to systematically investigate the streamflow response to heatwaves. I determine how this streamflow response varies by basin glacier coverage, as well as by heatwave timing, duration, and intensity, under both normal and warmer climate scenarios. I quantify how increasing glacier coverage is associated with both increasing streamflow generation during summer heatwaves, as well as increasing compensation in summer to the loss of snow during spring heatwaves. My results advance understanding on multiple research fronts in glaciology and hydrology: I demonstrate the importance of local-scale water resource data for glacier runoff projections; I emphasize the interpretability of deep machine learning models as a means to apply machine learning to new frontiers in hydrology; and I offer new frameworks and metrics to understand and characterize the hydrological impacts of heatwaves. My findings motivate future inter- and trans-disciplinary research to develop better deep learning hydrological models, and make progress towards answering politically and socially relevant glacio-hydrological research questions.

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The exchange of energy between a glacier surface and its surroundings, known as its surface energy balance (SEB), is a primary control on surface ablation rates. In the modelling of glacier SEB, parameterisation rather than direct measurement is frequently used to estimate one or more of the contributing heat fluxes, with smaller fluxes often deemed negligible. The turbulent fluxes of sensible and latent heat are commonly parameterised using forms of the bulk aerodynamic method. These techniques were developed for flat, uniform surfaces, and substantial uncertainty remains in the validity of their application over sloped, inhomogeneous glacier terrain. A multi-year field campaign was performed on two glaciers in the Purcell Mountains of British Columbia, Canada, where season-long observations of the complete SEB were obtained at multiple locations. The obtained dataset was used to drive an ablation model which showed good agreement with observed rates at seasonal, daily, and sub-daily timescales, effectively closing the energy balance. Through eddy covariance measurements, the turbulent heat fluxes were observed to be important components of SEB at each location, providing 31% of seasonal melt energy, and up to 78% of melt energy on individual days, underlining the need for their accurate estimation. The rain heat flux, often assumed negligible, was a significant contributor to melt energy on daily and sub-daily timescales during heavy rainfall (up to 20% day⁻¹). An evaluation of common turbulent flux parameterisation methods found their performance to be highly sensitive to the choice of roughness length scheme and atmospheric stability function. Observed roughness length values differed from those commonly assumed for glacier surfaces, and varied substantially between locations, highlighting the need for site-specific values. Two techniques were developed for the remote estimation of roughness using digital elevation models, and performed well when compared with in situ observations. The occurrence of shallow, katabatic surface layers with low-level wind maximums was frequently observed over the sloped, glacial test sites. Existing stability parameters and functions used in turbulent flux parameterisation were found to be unreliable in these conditions, as was the commonly employed assumption of constant turbulent flux and friction velocity with height through this layer.

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