Assessing the performance of a distributed radiation-temperature melt model on an Arctic glacier using UAV data

• Main Authors: Bash, Eleanor A., Moorman, Brian J.
• Format: Text
• Language: English
• Published: 2019
• Subjects: Arctic; Fountain Glacier; albedo
• Online Access: https://doi.org/10.5194/tc-2019-81; https://www.the-cryosphere-discuss.net/tc-2019-81/

Enhanced temperature index (ETI) models of glacier surface melt are commonly used in studies of glacier mass balance and runoff. With limited data available most models are validated based on ablation stakes and data from automatic weather stations (AWS). With the technological advances of unmanned aerial vehicles (UAVs) and structure-from-motion (SfM), it is possible to measure glacier surface melt in detail over larger portions of a glacier. In this study, we use melt measured using SfM processing of UAV imagery to assess the performance of an ETI melt model in two-dimensions. Imagery collected over a portion of the ablation zone of Fountain Glacier, NU, on July 21 and 24, 2016 was previously used to determine distributed surface melt. Incoming solar radiation and temperature measured at the AWS, along with albedo derived from UAV imagery, are used as inputs for the model which was used to estimate melt from July 21–24, 2016. Modelled melt agrees with melt measured at the AWS within ±0.010 m. Across the study area the median model error (−0.044 m), calculated as the difference between measured and modelled melt, is within the uncertainty of the measurements. A strong link was found between the model error and glacier surface aspect with higher errors linked to south aspects. The highest errors were also linked to the density of water flow features on the glacier surface. The relation between water flow and model error suggests that energy from surface water flow is contributing significantly to surface melt on Fountain Glacier. Deep surface streams with highly asymmetrical banks are observed on Fountain Glacier, but the processes leading to their formation are missing in the model assessed here. The failure of the model to capture flow-induced melt and to under-estimate melt on south aspects would lead to significant underestimation of surface melt should the model be used to project future change.