Simulating Subsurface Stormflow Across Catchments: Parameter Estimation and Uncertainties

Abstract. Subsurface Stormflow (SSF) is an important runoff-generation process, especially in temperate and mountainous regions. It can play a substantial role in catchment-scale flood generation and nutrient or contaminant transport. Due to process heterogeneity and subsurface occurrence, it is difficult to be quantified in the field. Since hydrological models are often calibrated using only discharge data at the catchment outlet, SSF-relevant parameters are often affected by equifinality, leading to large uncertainties in simulated SSF. In this study, we used the widely used bucket-type HBV model to analyse simulated SSF uncertainty across four catchments. We performed Monte Carlo simulations with 1,000,000 parameter sets and selected the top 5 % as behavioural. We subsequently analysed SSF parameter sensitivity in a regional sensitivity analysis and SSF simulation uncertainty in a GLUE-type approach. We examined whether more information on SSF is hidden in specific discharge conditions using a parameter-estimation approach based on percentiles of the flow-duration curve. In addition, we investigated simulated SSF uncertainty by disentangling simulated SSF volume and occurrence uncertainties. We found that there are high uncertainties in simulated SSF volume and occurrence across all catchments, with mostly low sensitivity of SSF-relevant parameters. We further found that SSF parameter identifiability could be improved by percentile parameter estimation, and more so in snow-insensitive catchments than in snow-dominated catchments. However, SSF simulation uncertainty still remained high. In the SSF uncertainty decomposition we could show that SSF occurrence uncertainty contributes a substantial part to total SSF simulation uncertainty. This study therefore emphasises the need to include SSF data into model calibration to ensure a realistic simulation of SSF and thereby achieve better process representation in lumped hydrological models and thus yield more robust prediction models. Our findings suggest that SSF proxy data indicating SSF occurrence in the catchment might already help to reduce SSF simulation uncertainty.