Exploring implications of input parameter uncertainties on GLOF modelling results using the state-of-the-art modelling code, r.avaflow

Abstract. Modelling complex mass flow processes like glacial lake outburst floods (GLOFs) for hazard and risk assessments involves substantial data and computational resources, often leading researchers to use low-resolution, open-access data and parameters based on plausibility rather than direct measurement, which, although effective in back analysis, introduces significant uncertainties in forward modelling. To determine the sensitivity of the model outputs stemming from input parameter uncertainties in the forward modelling, we selected nine parameters relevant to GLOF modelling and performed a total of 78 simulations in the physically-based r.avaflow model. Our results indicate that GLOF modelling outputs are notably sensitive to six parameters, which are, in order of importance: 1) volume of mass movements entering lakes; 2) DEM datasets; 3) the origin of mass movements; 4) mesh size; 5) basal frictional angle; and 6) entrainment coefficient. The volume of mass movement impacting lakes has the greatest impact on GLOF output, with an average coefficient of variation (CV) = 47 %, while the internal friction angle had the least impact (CV=0.4 %). We recommend that future GLOF modelling should carefully consider the output uncertainty stemming from the sensitive input parameters identified here, some of which cannot be constrained before a GLOF and must be considered only statistically.