Assessment of empirically-derived parameters and their transferability in mountain glacier modeling and application for regional melt projections  under climate change scenarios
Creators: Kotila, Amanda
Publisher: name: University of Alberta Library
Year: 2023

<p>Mountain glaciers, key sources of freshwater to downstream ecosystems and users, are<br>
responsive and vulnerable to changes in climate. Understanding their current influence, their<br>
potential future changes, and consequences of those changes are all important research goals, so<br>
many modeling approaches have been developed to address these questions. However, modeling<br>
at the regional scale can be difficult since input data from field measurements is limited and there<br>
is high spatiotemporal variability. High uncertainty in model predictions can come from empirical<br>
modeling parameters, often based on limited observations which are then applied to other glaciers<br>
in potentially very different topographic settings. In this study, we aim to assess parameter<br>
uncertainty and transferability by revisiting empirical parameters that are commonly used in<br>
glacier modeling and explore potential future glacier behaviours by utilizing a range of values for<br>
each glacier modeling parameter. This approach allows us to quantify uncertainty bands due to<br>
both projected future climate uncertainty and predicted model uncertainty.<br>
First, rather than using a set of single parameter values we explore a range for the value of<br>
each parameter based on their physically meaningful maximum and minimum values. We set up a<br>
modeling framework by coupling glacier melt, surface mass balance, and spline-based volume-area scaling (called evolution hereafter), denoted as CGME model for Coupled Glacier Mass-balance Evolution model, to predict glacier melt runoff. Within the CGME model, we evaluate<br>
two temperature-index melt modeling approaches: the Classical Temperature Index Model<br>
(CTIM), which uses a degree-day approach, and the Pellicciotti Temperature-Index Model<br>
(PTIM), which incorporates radiative melt factors. Our study area is the Athabasca River Basin in<br>
Alberta, Canada, which contains 258 glaciers.<br>
After calibration and optimization, we find that both of the melt models used in our CGME<br>
model predicted similar ranges of uncertainty (i.e., 95 Percent Prediction Uncertainty, 95PPU) in<br>
melt runoff, but the CTIM-based model reproduced more observed data points within its prediction<br>
uncertainty range (71% of observed data were captured within the predicted 95PPU) whereas the<br>
PTIM-based model reproduced 31% of the observed data.<br>
Second, we applied these optimized parameter ranges at the regional scale for the period<br>
1984-2007. Approximately 63% of the glaciers in the region had a normalized uncertainty value<br>
of greater than 0.5 for melt runoff, indicating that the parameter range transferability is not<br>
appropriate for the majority of glaciers in the region and that small glaciers are especially sensitive<br>
to input parameter variability. The framework developed here assesses the parameter<br>
transferability issue, especially in catchments where small-sized glaciers are dominant contributors<br>
to downstream water-ways that may have a cumulative ecological impact.<br>
Further, we explore the impact of potential future change. The glacier model is forced using<br>
4 CMIP6 GCMs under two shared socioeconomic pathways scenarios (SSP126 and SSP585) for<br>
the 258 glaciers for the period 1980-2100. From the maximum physically meaningful range for<br>
each parameter, 100 sets of model input parameters are sampled using Latin Hypercube Sampling<br>
technique. The 100 sets of sampled parameters are used with the future projected and downscaled<br>
climate data to force 100 simulations using CGME model for each glacier. This allows us to assess<br>
the projections’ ranges of uncertainty (using the 95PPU) stemming from input parameterization.<br>
Glacier changes are assessed based on two categorizations: glacier initial area and glacier<br>
initial elevation. Our results, based on size, show that glaciers are predicted to decrease in volume<br>
75-80%, decrease in area 72-78%, and discharge 70-80% of their potential melt runoff in the first<br>
forty years of the simulation period (1980-2019, the historical period). Monthly predicted flow<br>
regimes not only indicate greatly reduced melt runoff as the century progresses, but also the loss<br>
of late spring and early fall melt runoff. Assessing potential changes by glacier initial elevation<br>
indicated similar trends, though low elevation glaciers are predicted to be especially responsive,<br>
discharging ~95% of their melt runoff during the historical period. Monthly melt runoff reflects<br>
similar trends to those found during size analysis, though low elevation glaciers have the most<br>
extreme response. These assessments show the potential range of glacier changes under various<br>
future climate scenarios and the uncertainty stemming from model parameterizations. This can<br>
assist with freshwater resource management as well as adaptation and mitigation planning and<br>
implementation. </p>