Geology topics

Christoph Mitterer

Publications and source records attributed to Christoph Mitterer.

4 recordsLinked to original sources

USGS2026

Tracking water in snow: Bridging sensor and and model simulations

Forecasting wet slab avalanches remains one of the most demanding challenges in operational avalanche forecasting. A key obstacle is the difficulty of observing and quantifying liquid water content (LWC) and tracking its infiltration into the snowpack. Field measurements are labor-intensive, highly variable in space, and often influenced by user application or sensor design — making them impractical for forecasting operations. In this study, we evaluated LWC measurements obtained from two capacitive sensors, the SLF SnowPro-17 and -25, and compared them with simulations from the snow cover model SNOWPACK. We analyzed five selected days with pronounced melt events during spring 2025 at Bridger Bowl Ski Area, Montana. The SnowPro-17 measured LWC on the sidewall and the SnowPro-25 was inserted into the pit wall. We then analyzed measurements from the two sensors to assess consistency and potential bias. For each of the five days, we also analyzed liquid water infiltration by layer, comparing field measurements with model-simulated LWC. This allowed us to assess how well the model captured both the timing and magnitude of infiltration processes relative to in-situ observations. Our results show that, the absolute LWC values differ by approximately 1% by vol. on average between measurements from the capacitive sensors, but the timing of liquid water infiltration is largely consistent across both measurements and model simulations. Within our sample, the SnowPro-17 consistently reports higher mean LWC values than the SnowPro-25, and exhibits greater variability, indicated by a larger standard deviation. We found good agreement when we compared in-situ sensor measurements to LWC infiltration in snow cover model simulations. This suggests that, despite measurement challenges and sensor variability, SNOWPACK can provide valuable information on the timing of LWC infiltration. For practitioners, this work highlights that reliable forecasting of wet slab avalanches depends on understanding when and how liquid water reaches critical layers. Because direct LWC measurements are rarely feasible in operational settings, using physically based models may offer a practical pathway forward.

Montana↗

USGS2026

Climate-driven shifts in snowpack conditions favorable for wet snow avalanches: Insights from snow cover model simulations

Forecasting wet snow avalanches remains a major challenge due to their temporal and spatial variability. Long-term climate trends are producing more variable and extreme weather, creating conditions outside historical experience, and increasing forecasting uncertainties. To assess how climate change affects the frequency and timing of wet snow avalanches, we examined the long-term conditions that support wet snow formation. Specifically, we ask: Will the onset of wet snow avalanche conditions or season end shift toward the mid century? And will maximum snow height decrease in avalanche starting zones? To address these questions, we analyze long-term snow cover simulations across our study site in Glacier National Park, Montana, located in an intermountain snow climate. We examined trends in simulated snow depth, and timing of liquid water infiltration over the past four decades (1979–2023) using a physically based snow cover model. To force the model with meteorological data, we use the long-term regional hydroclimate reanalysis dataset U.S. Geological Survey CONUS404 with 4 km spatial resolution and 1h temporal resolution. The use of gridded meteorological inputs to drive a one-dimensional point model introduces inherent scale mismatches. To address this limitation, we use multiple grid points within the study area to simulate a more representative, generalized snowpack, and filter the grid points based on elevation to distinguish between upper- and mid-elevation starting zones. Finally, we analyzed shifts in snow depth, melt-out-day, and LWCindex (bulk liquid water content (LWC) normalized by the residual water content). We use snow depth as a proxy for changes in slab thickness, the LWCindexas an indicator of wet snow avalanche conditions, and melt-out date as a measure of shifts in season length. We ran simulations on flat terrain and on north- and south-facing virtual slopes (38°), across mid-elevation (1,600–1,900 m) and upper-elevation (≥1,900 m) sites for the historical dataset as baseline and a Pseudo-Global-Warming (PGW) dataset to identify a warming driven shift. Across all aspects and both elevation bands, PGW forcing produces a consistent reduction in annual maximum snow depth (median shift −0.15 to −0.26 m, p<0.001) over 44 seasons, with the largest reductions on north-facing slopes and a modest but significant strengthening of this reduction over the 44-year simulated record. Melt-out starts 10–14 days earlier under PGW across all aspect–elevation combinations (p<0.001), again most pronounced on north-facing, higher-elevation terrain. The LWCindex threshold-crossing dates likewise shift earlier under PGW, indicating an earlier onset of wet snow avalanche conditions across the domain. Together, these results point to a snowpack that is both thinner and wetter earlier in the season under future warming, with the strongest signal consistently on north-facing slopes at higher elevation. This work lays the groundwork for climate-informed forecasting and mitigation and offers historical context for avalanche operations in a changing climate. Understanding long-term trends can help avalanche professionals interpret current observations, improve wet snow avalanche forecasting, and develop more effective mitigation strategies in a warming climate.

Montana↗

USGS2023

Comparing snowpack meteorological inputs to support regional wet snow avalanche forecasting

Wet snow avalanches are predicted to increase in frequency with climate change and are often difficult to forecast. Improving our understanding of wet snow avalanche timing will help with current forecasting challenges. The onset of wet snow avalanching is closely tied to the temporal progression of liquid water flow through the seasonal snowpack. Measuring the flow of water through the snowpack in-situ is difficult due to the spatial variability of snow depth and structure. However, physical snowpack models can potentially simulate this process. The accuracy of snowpack models is heavily dependent upon the quality of the meteorological input data. A thorough investigation of model output differences using several different meteorological inputs for forecasting water movement and wet snow avalanches has not yet been thoroughly investigated. Here, we evaluate indicators of regional wet snow avalanches produced by the SNOWPACK model using different meteorological input. We compare the accuracy of SNOWPACK modeled outputs driven by two different numerical weather prediction (NWP) forecast models: the High-Resolution Deterministic Prediction System (HRDPS) and the North American Model (NAMnest). We leverage hourly automated weather station data, daily operational avalanche observations along the Going-to-the-Sun Road in Glacier National Park, Montana, United States, and in-situ snow stratigraphy and wetness profile observations to validate the SNOWPACK modeled outputs. This research is directly applicable to avalanche forecasting operations and future avalanche research as wet snow avalanche timing evolves due to climate change.

Montana↗

USGS2023

Temporal evolution of slab and weak layer properties during the transition from dry to wet snowpack conditions

Wet-snow slab avalanches are destructive and may become more prevalent in a warming climate. This type of avalanche remains challenging to forecast because the underlying processes leading to wet-snow slab avalanche release are poorly understood. In this study, we examine the temporal evolution of weak layer and slab liquid water content (LWC), critical cut length, and propagation saw test (PST) results during the season's first critical melt period at our study site in the Madison Mountains of southwest Montana. We used snowpack profiles and in-situ weather station data to initialize and force the 1-D physics-based snow cover model SNOWPACK throughout the winter and spring seasons. We then used a high-resolution numerical weather model to force SNOWPACK simulations to forecast the onset of the transition from dry to wet conditions. From April 10-12, 2023, we conducted 67 PSTs, 1053 LWC measurements, 20 hardness profiles, and a full snow profile each morning and early evening. During the first two days of sampling, we observed a transition from low to high propagation propensity with decreasing cut lengths and increasing LWC. On Day 3, we observed consistently low propagation propensity, even as LWC levels remained elevated and comparable to the preceding period of high propagation propensity. This indicates that there is a point where the relationship we observed through the first two days between increasing LWC, increasing propagation propensity, and decreasing cut length no longer holds. Our results further suggest PST propagation mode may help pinpoint the onset, peak, and decline of wet-snow fracture propagation propensity.

Montana↗