Sensitivity of snow models in the semi-arid Andes of Chile: A comparison between SNOWPACK and SnowModel

Melting snow in the Andes is a main water resource for the population of Chile. Snow models are used to increase the knowledge about the amount of snow in the mountains. These physically-based models use the energy balance equation to model sublimation and melt correctly. This study is the first comparison between snow models in the semi-arid Andes and focusses on the sensitivity of the models SNOWPACK and SnowModel to its input parameters and parametrizations. SNOWPACK is especially designed for forecasting avalanches in the Alps and SnowModel is often used to model wind transport of snow. The models are evaluated on a point scale with data from the Automatic Weather Station Tapado (-30◦S, 69◦W, 4306 m.a.s.l.) in the year 2017. Firstly, the forcing data of the models (air temperature, relative humidity, precipitation, air pressure, incoming shortwave and longwave radiation and wind direction and velocity) are complemented and their uncertainties are assessed. The temperature and relative humidity data are interpolated with data from nearby stations according to a linear gradient depending on the altitude during the five months that the sensor was not working. The accumulated precipitation has been corrected with the wind velocity by the empirical, exponential formula by MacDonald and Pomeroy (2007). The uncertainty of the precipitation measurement is assessed by the proposal of two types of distributions between the measured data from the precipitation gauge and the precipitation derived from the snow water equivalent (SWE) measurements. Secondly, the models are calibrated with the focus on the parametrizations of the snow albedo and fresh snow density and on different roughness lengths. The snow albedo is a good measure for snow melt, because a high albedo leads to a higher amount of reflected shortwave radiation and eventually less available energy for melt. The snow density provides the conversion between the SWE and the snow depth and controls the way of compaction of the snowpack. The roughness value influences the sublimation rate of the snowpack. After the models have been calibrated, a Monte Carlo approach has been applied to assess the influence of variations in the forcing data on the output of the models. A normally distributed random number based on the measurement uncertainties has been added to the hourly forcing data. The models are run 1000 times with this variable forcing. Results show that the models are predominantly sensitive to the input of precipitation. The measured precipitation is very uncertain and the actual fallen amount of precipitation at the position of the SWE sensor is estimated to be twice as large as the measured amount at the precipitation gauge. The simulated SWE is the amount of precipitation minus melt, sublimation and eventually wind transport and as the precipitation is very uncertain, so is the output of the models. The evolution of the SWE is simulated similarly for both models if the snowpack is only influenced by sublimation. The simulation of melt is done differently in the models, where SNOWPACK models a more gradual melt, because the refreezing of melt water within in the snowpack is simulated more correctly, whereas the melt simulated by SnowModel is dominantly influenced by air temperatures above 0◦C. Furthermore, the models are significantly sensitive to the forced relative humidity and air temperature. This influences the melt, acceptance of precipitation events (SNOWPACK) and the snow density (SnowModel). The influence of air pressure, incoming longwave and shortwave radiation and wind velocity and direction is inconsiderable. The most appropriate model for understanding snowpack processes and model snow depth evolution in the semi-arid Andes is SNOWPACK, because it models the start of the melting period at the right moment, evaluates the internal evolution of the snowpack and parametrizes the albedo better than SnowModel. This thesis demonstrates the importance of good forcing data, especially considering the precipitation and is the first to assess the sensitivity of two snow models in the semi-arid Andes. The empirical parameters in SNOWPACK and SnowModels are not yet adapted to this region and improvements can be made to these parameters, but the physical processes in SNOWPACK are better applicable to this region than the approaches by SnowModel.