A preliminary assessment of glacier melt-model parameter sensitivity and transferability in a dry subarctic environment

Abstract

Abstract. Efforts to project the long-term melt of mountain glaciers and ice-caps require that melt models developed and calibrated for well studied locations be transferable over large regions. Here we assess the sensitivity and transferability of parameters within several commonly used melt models for two proximal sites in a dry subarctic environment of northwestern Canada. The models range in complexity from a classical degree-day model to a simplified energybalance model. Parameter sensitivity is first evaluated by tuning the melt models to the output of an energy balance model forced with idealized inputs. This exercise allows us to explore parameter sensitivity both to glacier geometric attributes and surface characteristics, as well as to meteorological conditions. We then investigate the effect of model tuning with different statistics, including a weighted coefficient of determination (wR 2 ), the Nash-Sutcliffe efficiency criterion (E), mean absolute error (MAE) and root mean squared error (RMSE). Finally we examine model parameter transferability between two neighbouring glaciers over two melt seasons using mass balance data collected in the St. Elias Mountains of the southwest Yukon. The temperature-index model parameters appear generally sensitive to glacier aspect, mean surface elevation, albedo, wind speed, mean annual temperature and temperature lapse rate. The simplified energy balance model parameters are sensitive primarily to snow albedo. Model tuning with E, MAE and RMSE produces similar, or in some cases identical, parameter values. In twelve tests of spatial and/or temporal parameter transferability, the results with the lowest RMSE values with respect to ablation stake measurements were achieved twice with a Correspondence to: G. E. Flowers (gflowers@sfu.ca) classical temperature-index (degree-day) model, three times with a temperature-index model in which the melt parameter is a function of potential radiation, and seven times with a simplified energy-balance model. A full energy-balance model produced better results than the other models in nine of twelve cases, though the tuning of this model differs from that of the others.

Comparison of thermal structure and evolution between neighboring subarctic glaciers

“…The HOBO units contain Onset™H08-032-08 sensors shielded by an RS1 radiation shield and are installed on poles at a nominal height of 2 m above the glacier surface. These data are described in detail by MacDougall and , MacDougall et al [2011], .…”

“…Two unnamed valley glaciers on the continental side of the St. Elias Mountains of Yukon, Canada (Figure ), comprise the field site. These two glaciers were originally selected for a study of subregional variability of glacier response to climate [e.g., MacDougall and Flowers , ; MacDougall et al , ] and provide an opportunity to compare internal and external controls on glacier dynamics within a similar environmental setting. The glaciers are ∼10 km apart and of comparable size and experience the same climate conditions.…”

Oscillatory subglacial drainage in the absence of surface melt

“…Meteorological data were recorded by an automatic weather station on the glacier just downstream of the study area (MacDougall et al, 2011). In this paper, we utilize the temperature record from a HMP45C212 TRH air temperature probe as well as surface height soundings recorded by a The coordinate system used is UTM zone 7 north.…”

Glacio-hydrological melt and runoff modelling: a limits of acceptability framework for model selection

“…The challenge here was to reconcile three signatures that characterise glacier melt over different spatial and temporal scales. This is not a straightforward task, particularly when using temperature index models that lump a number of spatially and temporally variable terms from the full 15 energy balance equation into a handful of calibration parameters which may lack robustness in space and time (MacDougall et al, 2011;Matthews et al, 2015;Gabbi et al, 2014). The inclusion of solar and topographic effects in the TIM 2 and TIM 3 melt model structures addressed some of these limitations.…”

“…The importance of characterising temperature lapse rates for glacio-hydrological modelling is well known because it has a strong control on spatial patterns of melt simulations Heynen et al, 2013;MacDougall et al, 2011). In fact while many studies employ a fixed temperature lapse rate, in reality seasonal variations in surface characteristics (e.g.…”