Bayesian Approach to Estimate Proglacial Lake Volume (BE-GLAV)

Abstract

We present a new model called Bayesian Estimated Glacial Lake Volume (BE-GLAV) to estimate the volume of proglacial lakes. Presuming the lake cross-section as trapezoidal, BE-GLAV uses a Bayesian calibration approach to adjust the cross-sectional geometry to match modeled and observed lake surface widths. We validated our model using bathymetric measurements from lakes spread across High Mountain Asia (specifically, the Himalaya and Tien-Shan), with aerial extents ranging from 0.01 to 5.5 km2. The modeled lake volumes agreed with the measured lake volume with a root-mean-square absolute uncertainty of ∼14%. With minimum and maximum errors of ∼0.3% and ∼61.2%, BE-GLAV performed well compared to 10 other models in a model inter-comparison experiment. Using the measured set of volumes, our model can constrain both the root mean square (RMS) error and the maximum percentage error in modeled lake volume, unlike other models, some of which can compute just the RMS uncertainty.