Monte Carlo Experiments For Uncertainty Investigation of Glacier Melt Discharge Predictions Through Surface Energy Balance Analysis

The spatial representativeness of point records is a concern in glacier discharge predictions. A Monte Carlo-based global sensitivity approach is used to investigate the predictive uncertainty in the net radiation (Rn) as the major component driving glacier melt in the Bolivian Andes. The Rn is inferred through the Surface and Energy Balance Algorithm, calibrated with point dry-season records monitored on a glaciers ablation area. High uncertainties are expected in the vicinity of the monitoring station (surface albedo (a) between 0.81 and 0.79, specific melt discharge (SMD) between 72 and 88 L s-1 km-2); smaller uncertainties are expected on the glacier boundaries (q between 0.10 and 0.08, SMD between 128 and 143 L s-1 km-2). Thus, with the incoming long wave radiation (RL) as the most sensitive model parameter, the spatial variability in a determines the spatial variability in the SMD predictive uncertainties.