Abstract:The use of Unmanned Aerial Vehicles (UAV) for photogrammetric surveying has recently gained enormous popularity. Images taken from UAVs are used for generating Digital Surface Models (DSMs) and orthorectified images. In the glaciological context, these can serve for quantifying ice volume change or glacier motion. This study focuses on the accuracy of UAV-derived DSMs. In particular, we analyze the influence of the number and disposition of Ground Control Points (GCPs) needed for georeferencing the derived products. A total of 1321 different DSMs were generated from eight surveys distributed on three glaciers in the Swiss Alps during winter, summer and autumn. The vertical and horizontal accuracy was assessed by cross-validation with thousands of validation points measured with a Global Positioning System. Our results show that the accuracy increases asymptotically with increasing number of GCPs until a certain density of GCPs is reached. We call this the optimal GCP density. The results indicate that DSMs built with this optimal GCP density have a vertical (horizontal) accuracy ranging between 0.10 and 0.25 m (0.03 and 0.09 m) across all datasets. In addition, the impact of the GCP distribution on the DSM accuracy was investigated. The local accuracy of a DSM decreases when increasing the distance to the closest GCP, typically at a rate of 0.09 m per 100-m distance. The impact of the glacier's surface texture (ice or snow) was also addressed. The results show that besides cases with a surface covered by fresh snow, the surface texture does not significantly influence the DSM accuracy.

Runoff predictions are affected by several uncertainties. Among the most important ones is the uncertainty in meteorological forcing. We investigated the skill propagation of meteorological to runoff forecasts in an idealized experiment using synthetic data. Meteorological forecasts with different skill were produced with a weather generator and fed into two different hydrological models. The experiments were repeated for two glacierized catchments of different sizes and morphological characteristics, and for scenarios of different glacier coverage. The results show that for catchments with high glacierization (>50%), the runoff forecast skill is more dependent on the skill of the temperature forecasts than the one for precipitation. This is because snow and ice melt are strongly controlled by temperature. The influence of the temperature forecast skill diminishes with decreasing glacierization, while the opposite is true for precipitation. Precipitation starts to dominate the runoff skill when the catchment's glacierization drops below 30%, or when the total contribution of ice and snow melt is less than about 60%. The skill difference between meteorological forecasts and runoff predictions proved to be independent from the lead time, and all results were similar for both the considered hydrological models. Our results indicate that long-range meteorological forecasts, which are typically more skillful in predicting temperature than precipitation, hold particular promise for applications in snow-and glacier-dominated catchments.

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