UAV-BASED LIDAR HIGH-RESOLUTION SNOW DEPTH MAPPING IN THE SWISS ALPS: COMPARING FLAT AND STEEP FORESTS

How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.

The increasing impact of climate change and human activities on greenhouse gas emissions highlights the need for effective monitoring, especially from the soil. Our design introduces a low-cost solution for measuring soil gas flux that is adaptable to various environments. Additionally, we propose a novel method for ensuring data quality before deploying these systems in the field.

In this study, we present a dataset for the Dischma catchment in eastern Switzerland, which represents a typical high-alpine watershed in the European Alps. Accurate monitoring and reliable forecasting of snow and water resources in such basins are crucial for a wide range of applications. Our dataset is valuable for improving physics-based snow, land surface, and hydrological models, with potential applications in similar high-alpine catchments.

HydroModPy is an open-source toolbox that makes it easier to study and model groundwater flow at catchment scale. By combining mapping tools with groundwater modeling, it automates the process of building, analyzing and deploying aquifer models. This allows researchers to simulate groundwater flow that sustains stream baseflows, providing insights for the hydrology community. Designed to be accessible and customizable, HydroModPy supports sustainable water management, research, and education.

We use novel wet-snow maps from Sentinel-1 to evaluate simulations of a snow-hydrological model over Switzerland. These data are complementary to available in situ snow depth observations as they capture a broad diversity of topographic conditions. Wet-snow maps allow us to detect a delayed melt onset in the model, which we resolve thanks to an improved parametrization. This paves the way to further evaluation, calibration, and data assimilation using wet-snow maps.