A Probabilistic Model to Evaluate the Optimal Density of Stations Measuring Snowfall

Abstract

Daily new snow measurements are very important for avalanche forecasting and tourism. A dense network of manual or automatic stations measuring snowfall is necessary to have spatially reliable data. Snow stations in Switzerland were built at partially subjective locations. A probabilistic model based on the frequency and spatial extent of areas covered by heavy snowfalls was developed to quantify the probability that snowfall events are measured by the stations. Area?probability relations were calculated for different thresholds of daily accumulated snowfall. A probabilistic model, including autocorrelation, was used to calculate the optimal spacing of stations based on simulated triangular grids and to compare the capture probability of different networks and snowfall thresholds. The Swiss operational snow-stations network captured snowfall events with high probability, but the distribution of the stations could be optimized. The spatial variability increased with higher thresholds of daily accumulated snowfall, and the capture probability decreased with increasing thresholds. The method can be used for other areas where the area?probability relation for threshold values of snow or rain can be calculated.