Unravelling the sources of uncertainty in glacier runoff projections in the Patagonian Andes (40–56° S)

Abstract

Abstract. Glaciers are retreating globally and are projected to continue to lose mass in the coming decades, directly affecting downstream ecosystems through changes in glacier runoff. Estimating the future evolution of glacier runoff involves several sources of data uncertainty, which to date have not been comprehensively assessed on a regional scale. In this study, we used the Open Global Glacier Model (OGGM) to estimate the evolution of each glacier (with area > 1 km2) in the Patagonian Andes (40–56° S). As sources of uncertainty, we used different glacier inventories (n = 2), ice thickness datasets (n = 2), historical climate datasets (n = 4), general circulation models (GCMs; n = 10), emission scenarios (Shared Socioeconomic Pathways, SSPs; n = 4) and bias correction methods (BCMs; n = 3) to generate 1920 possible scenarios over the period of 1980–2099. In each scenario, glacier runoff and melt time series were characterised by 10 glacio-hydrological signatures (i.e. metrics). We used the permutation feature importance of random forest regression models to assess the relative importance of each source of uncertainty on the signatures of each catchment. Considering all scenarios, 34 % ± 13 % (mean ± 1 standard deviation) of the glacier area has already peaked in terms of glacier melt (the year 2020), and 68 % ± 21 % of the glacier area will lose more than 50 % of its volume this century. Considering the glacier melt signatures, the future sources of uncertainty (GCMs, SSPs and BCMs) were the main source in only 17 % ± 21 % of the total glacier area. In contrast, the reference climate was the main source in 69 % ± 22 % of the glacier area, highlighting the impact of calibration choices on baseline conditions, model parameters and the initial starting geometry for future projections. The results provide a basis for prioritising future efforts (e.g. the improvement of reference climate characterisation) to reduce glacio-hydrological modelling gaps in poorly instrumented regions such as the Patagonian Andes.