Effects of Temperature Forcing Provenance and Extrapolation on the Performance of an Empirical Glacier-Melt Model

Temperature-index models are popular tools for glacier melt-modeling due to their minimal data requirements and generally favorable performance. We examine the effects of temperature forcing provenance and extrapolation on the performance of one such model applied to a small glacier in the Saint Elias Mountains of northwestern Canada. The model is forced with air temperatures recorded (a) on two glaciers, (b) at two nearby ice-free locations, and (c) by two low-elevation valley stations. We extrapolate these temperatures using constant lapse rates and assess model performance by comparing measured and modeled cumulative summer ablation at a network of stakes over five melt seasons. When the model is calibrated individually for each temperature forcing and lapse rate, the variation in model performance is modest relative to inter-annual variations associated with melt-season conditions and calibration data quality. Despite 100% in some cases). While model parameters calibrated in this way suffer from error compensation and exhibit equifinality, the lapse rates associated with minimum model error exhibit inter-annual variation that can be related to prevailing meteorological conditions. When the model is instead calibrated at the point scale without employing a lapse rate, and the resulting parameters are paired with an arbitrary temperature forcing, lapse rates associated with minimum model error vary widely between forcing types and years. Low-elevation stations distal from the study site sometimes outperform the calibration station, but the prescribed lapse rate becomes critical in this case. With either calibration method, lapse rates that minimize model error for the valley stations are generally steeper than the measured environmental lapse rates.