Including parameter uncertainty in an intercomparison of physically-based snow models

Snow models that solve coupled energy and mass balances require model parameters to be set, just like their conceptual counterparts. Despite the physical basis of these models, appropriate choices of the parameter values entail a rather high degree of uncertainty as some of them are not directly measurable, observations are lacking, or values are not adaptable from literature. In this study, we test whether it is possible to reach the same performance with energy balance snow models of varying complexity by means of parameter optimization. We utilize a multi-physics snow model which enables the exploration of a multitude of model structures and model complexities with respect to their performance against point-scale observations of snow water equivalent and snowpack runoff observations, and catchment-scale observations of snow cover fraction and spring water balance. We find that parameter uncertainty can compensate structural model deficiencies to a large degree, so that model structures cannot be reliably differentiated within a calibration period. Even with deliberately biased forcing data, comparable calibration performances can be achieved. Our results also show that parameter values need to be chosen very carefully, as no model structure guarantees acceptable simulation results with random (but still physically meaningful) parameters

The importance of snowmelt spatiotemporal variability for isotope-based hydrograph separation in a high-elevation catchment

Seasonal snow cover is an important temporary water storage in high-elevation regions. Especially in remote areas, the available data are often insufficient to accurately quantify snowmelt contributions to streamflow. The limited knowledge about the spatiotemporal variability of the snowmelt isotopic composition, as well as pronounced spatial variation in snowmelt rates, leads to high uncertainties in applying the isotope-based hydrograph separation method. The stable isotopic signatures of snowmelt water samples collected during two spring 2014 snowmelt events at a north- and a south-facing slope were volume weighted with snowmelt rates derived from a distributed physicsbased snow model in order to transfer the measured plotscale isotopic composition of snowmelt to the catchment scale. The observed δ$^{18}$O values and modeled snowmelt rates showed distinct inter- and intra-event variations, as well as marked differences between north- and south-facing slopes. Accounting for these differences, two-component isotopic hydrograph separation revealed snowmelt contributions to streamflow of 35±3 and 75±14% for the early and peak melt season, respectively. These values differed from those determined by formerly used weighting methods (e.g., using observed plot-scale melt rates) or considering either the north- or south-facing slope by up to 5 and 15 %, respectively

Performance of Complex Snow Cover Descriptions in a Distributed Hydrological Model System and Simulation of Future Snow Cover and Discharge Characteristics: A Case Study for the High Alpine Terrain of the Berchtesgaden Alps

The water balance in high Alpine regions in its full complexity is so far insufficiently understood. Large altitudinal gradients, a strong variability of meteorological variables in time and space, complex hydrogeological settings, and heterogeneous snow cover dynamics result in high uncertainties in the quantification of the water flux and storage terms. In this study, the deterministic model system WaSiM-ETH was complemented with physically based formulations to describe high Alpine specific snow processes. To enhance the reproduction of snow deposition and ablation processes, the new system calculates the energy balance of the snow cover considering the terrain-dependent radiation fluxes, as well as lateral snow transport processes induced by wind and gravitation. Test site for the study is the Berchtesgaden National Park (Bavarian Alps, Germany) which is characterized by extreme topography and climate conditions. The performance of the enhanced model system is analyzed and validated via measurements of the snow water equivalent and snow depths, satellite-based remote sensing data, and runoff gauge data. The model has proven to work stable in reproducing seasonal snow cover development over a wide range of elevations. It was able to reproduce the general spatial patterns and most of the fine scale details of snow coverage. With increasing snow model complexity, model efficiency (Nash-Sutcliffe coefficient) for simulated runoff increases from 0.57 to 0.68 in a high Alpine headwater catchment and from 0.62 to 0.64 in total. To assess possible impacts of a changing climate on the regional hydrology, the optimized model system was forced with dynamically downscaled climate simulations. Model results are compared between the control period 1971 - 2000 and the scenario period 2021 - 2050. The projected future precipitation characteristics are the main driver for the consequent changes in the regional hydrology. Mean snow cover duration decreases significantly (-19 d/year), whereas the absolute changes in seasonal snowmelt and runoff amounts are projected to remain relatively small