Abstract
Supraglacial debris is significant in many regions and complicates modeling of glacier melt, which is required for predicting glacier change and its influences on hydrology and sea-level rise. Temperature-index models are a popular alternative to energy-balance models when forcing data are limited, but their transferability among glaciers and inherent uncertainty have not been documented in application to debris-covered glaciers. Here, melt factors were compiled directly from published studies or computed from reported melt and MERRA-2 air temperature for 27 debris-covered glaciers around the world. Linear mixed-effects models were fit to predict melt factors from debris thickness and variables including debris lithology and MERRA-2 radiative exchange. The models were tested by leave-one-site-out cross-validation based on predicted melt rates. The best model included debris thickness (fixed effect) and glacier and year (random effects). Predictions were more accurate using MERRA-2 than on-site air temperature data, and pooling MERRA-2-derived and reported melt factors improved cross-validation accuracy more than including additional predictors such as shortwave or longwave radiation. At one glacier where monthly ablation was measured over 4 years, seasonal variation of melt factors suggested that heat storage significantly affected the relation between melt and energy exchange at the debris surface.
Highlights
Glacier melt contributes significantly to streamflow and is important for hydro-electric power generation and irrigation in many places around the world (IPCC, 2014)
The objectives of this study were to (1) quantify the accuracy of classical Temperature-index models (TIM) and extended TIM for debris-covered glaciers, (2) test whether air temperature data provenance affects prediction accuracy, (3) derive a generalizable relation for sub-debris melt factors that can be transferred with a known margin of error, and (4) evaluate the usefulness of MERRA-2 data for modeling sub-debris glacier melt
Accuracy of modeled melt factors applied to new glaciers without calibration The best-performing models generated predictions that were within ± 25% error only around 50% of the time, while the root-mean-square error (RMSE) and root-mean-square relative error (RMSRE) of the highest performing model, 8.7 mm w.e. d−1 and
Summary
Glacier melt contributes significantly to streamflow and is important for hydro-electric power generation and irrigation in many places around the world (IPCC, 2014). Glacier melt is contributing to the global sea-level response to climate change (Radić and Hock, 2014). To address the effects of ongoing and future glacier changes on water resources, glacier-related hazards and sea-level rise, managers and policy-makers depend on the output from melt models driven by climate data. The physical processes controlling glacier surface melt are well understood and energybalance models are accurate at a range of scales (Hock, 2005). TIM are adequate for a wide range of applications and can even exceed the performance of energy-balance models when forcing data are limited (Gabbi and others, 2014; Réveillet and others, 2018). The physical processes explicitly represented in energy-balance models are reduced to simplified, site-specific empirical parametrizations in TIM, which may limit their transferability
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