Snowpack Variables Research Articles

Antecedent Snowpack Cold Content Alters the Hydrologic Response to Extreme Rain-on-Snow Events

Abstract Predicting winter flooding is critical to protecting people and securing water resources in California’s Sierra Nevada. Rain-on-snow (ROS) events are a common cause of widespread flooding and are expected to increase in both frequency and magnitude with anthropogenic climate change in this region. ROS flood severity depends on terrestrial water input (TWI), the sum of rain and snowmelt that reaches the land surface. However, an incomplete understanding of the processes that control the flow and refreezing of liquid water in the snowpack limits flood prediction by operational and research models. We examine how antecedent snowpack conditions alter TWI during 71 ROS events between water years 1981 and 2019. Observations across a 500-m elevation gradient from the Independence Creek catchment were input into SNOWPACK, a one-dimensional, physically based snow model, initiated with the Richards equation and calibrated with collocated snow pillow observations. We compare observed “historical” and “scenario” ROS events, where we hold meteorologic conditions constant but vary snowpack conditions. Snowpack variables include cold content, snow density, liquid water content, and snow water equivalent. Results indicate that historical events with TWI > rain are associated with the largest observed streamflows. A multiple linear regression analysis of scenario events suggests that TWI is sensitive to interactions between snow density and cold content, with denser (>0.30 g cm−3) and colder (<−0.3 MJ of cold content) snowpacks retaining >50 mm of TWI. These results highlight the importance of hydraulic limitations in dense snowpacks and energy limitations in warm snowpacks for retaining liquid water that would otherwise be available as TWI for flooding. Significance Statement The purpose of this study is to understand how the snowpack modulates quantities of water that reach the land surface during rain-on-snow (ROS) events. While the amount of near-term storm rainfall is reasonably predicted by meteorologists, major floods associated with ROS are more difficult to predict and are expected to increase in frequency. Our key findings are that liquid water inputs to the land surface vary with snowpack characteristics, and although many hydrologic models incorporate snowpack cold content and density to some degree, the complexity of ROS events justifies the need for additional observations to improve operational forecasting model results. Our findings suggest additional comparisons between existing forecasting models and those that physically represent the snowpack, as well as field-based observations of cold content and density and liquid water content, would be useful follow-up investigations.

Examining the operational use of avalanche problems with decision trees and model-generated weather and snowpack variables

Abstract. Avalanche problems are used in avalanche forecasting to describe snowpack, weather, and terrain factors that require distinct risk management techniques. Although they have become an effective tool for assessing and communicating avalanche hazard, their definitions leave room for interpretation and inconsistencies. This study uses conditional inference trees to explore the application of avalanche problems over eight winters in Glacier National Park, Canada. The influences of weather and snowpack variables on each avalanche problem type were explored by analysing a continuous set of weather and snowpack variables produced with a numerical weather prediction model and a physical snow cover model. The decision trees suggest forecasters' assessments are based on not only a physical analysis of weather and snowpack conditions but also contextual information about the time of season, the location, and interactions with other avalanche problems. The decision trees showed clearer patterns when new avalanche problems were added to hazard assessments compared to when problems were removed. Despite discrepancies between modelled variables and field observations, the model-generated variables produced intuitive explanations for conditions influencing most avalanche problem types. For example, snowfall in the past 72 h was the most significant variable for storm slab avalanche problems, skier penetration depth was the most significant variable for dry loose avalanche problems, and slab density was the most significant variable for persistent-slab avalanche problems. The explanations for wind slab and cornice avalanche problems were less intuitive, suggesting potential inconsistencies in their application as well as shortcomings of the model-generated data. The decision trees illustrate how forecasters apply avalanche problems and can inform discussions about improved operational practices and the development of data-driven decision aids.

Seasonal Patterns in Spectral Irradiance and Leaf UV-A Absorbance Under Forest Canopies.

Plants commonly respond to UV radiation through the accumulation of flavonoids and related phenolic compounds which potentially ameliorate UV-damage to crucial internal structures. However, the seasonal dynamics of leaf flavonoids corresponding to epidermal UV absorbance is highly variable in nature, and it remains uncertain how environmental factors combine to govern flavonoid accumulation and degradation. We studied leaf UV-A absorbance of species composing the understorey plant community throughout two growing seasons under five adjacent tree canopies in southern Finland. We compared the relationship between leaf flavonol index (Iflav—repeatedly measured with an optical leaf clip Dualex) and measured spectral irradiance, understorey and canopy phenology, air temperature and snowpack variables, whole leaf flavonoid extracts, and leaf age. Strong seasonal patterns and stand-related differences were apparent in Iflav of both understorey plant communities and individual species, including divergent trends in Iflav during spring and autumn. Comparing the heterogeneity of the understorey light environment and its spectral composition in looking for potential drivers of seasonal changes in Iflav, we found that unweighted UV-A irradiance, or the effective UV dose calculated according to the biological spectral weighting function (BSWF) for plant growth (PG action spectrum), in understorey shade had a strong relationship with Iflav. Furthermore, understorey species seemed to adjust Iflav to low background diffuse irradiance rather than infrequent high direct-beam irradiance in sunflecks during summer, since leaves produced during or after canopy closure had low Iflav. In conclusion, we found the level of epidermal flavonoids in forest understorey species to be plastic, adjusting to climatic conditions, and differing according to species' leaf retention strategy and new leaf production, all of which contribute to the seasonal trends in leaf flavonoids found within forest stands.

The Phenology of Wilderness Use: Backcountry Recreation in a Changing Climate

AbstractPhenology studies are a critical tool for identifying the ways that changing climate affects species and ecosystems. Here, a phenological framework was used to assess the sensitivity of human behavior to temperature and hydroclimate variables that are likely to change as temperatures warm under twenty-first-century climate change. The timing of visitation to wilderness areas of the Sierra Nevada was used as a case study. Visitation timing was assessed using a backcountry permit database and data collected from weblogs or blogs. Mean, earliest, and latest visitation dates were regressed against temperature, streamflow, and snowpack variables: seasonally averaged air temperatures, snow water equivalent (SWE) in spring months, center of timing (CT), and total annual flow. Mean visitation was sensitive to CT, total annual flow, April and May SWE, and spring and summer temperatures, with visitors advancing 0.20–0.28 days for each day advance in CT and 3.7 to 5.7 days for each degree Celsius increase in summer temperatures. Visitors appear to be partially sensitive to both hydroclimate and temperature, suggesting that visitation may occur earlier as spring snow decreases, but also that because of this partial sensitivity, visitors may interact with ecosystems in a different phenological stage as the climate warms. Managers of these areas should plan for changing timing of visitation and should also consider ways that visitors interacting with different hydroclimatic and ecosystem conditions may influence management strategies.

Measurement of the physical properties of the snowpack

AbstractThis paper reviews measurement techniques and corresponding devices used to determine the physical properties of the seasonal snowpack from distances close to the ground surface. The review is placed in the context of the need for scientific observations of snowpack variables that provide inputs for predictive hydrological models that help to advance scientific understanding of geophysical processes related to snow in the near‐surface cryosphere. Many of these devices used to measure snow are invasive and require the snowpack to be disrupted, thereby precluding the possibility for multiple measurements to be made at the same sampling location. Moreover, many devices rely on the use of empirical calibration equations that may not be valid at all geographic locations. The spatial density of observations with most snow measurement devices is often inadequate. There is a need for improved automation of snowpack measurement instrumentation with an emphasis on field‐based feedback of measurement validity in lieu of postprocessing of samples or data at a lab or office location. The scientific future of snow measurement instrumentation thereby requires a synthesis between science and engineering principles that takes into consideration geophysics and the physics of device operation.

Mercury photochemistry in snow and implications for Arctic ecosystems

Mercury is a toxic and bioaccumulative environmental contaminant, which may be transported to remote regions around the world, such as the Arctic. Snowmelt is a major source of mercury to many surface water environments, but the amount of mercury in snow varies considerably. This variation is due to the balance of mercury retention and losses from snow, which is largely controlled by photochemical mechanisms controlling speciation. As such, quantifying these photochemical reaction rates and the factors affecting them will allow for the prediction of mercury speciation and movement into receiving water bodies. This will consequently improve our ability to predict exposure of aquatic organisms to mercury. This review highlights knowledge gaps in the quantification of mercury photochemical kinetics and the specific research required to advance the science of mercury photochemistry in snow, while examining the physical and chemical snowpack variables that influence snowpack mercury reactions. At present, our lack of mechanistic and kinetic knowledge of mercury reactions in snow is one of the greatest gaps preventing accurate predictions of mercury fate in regions containing seasonal snowpacks.

The effect of spatial variability on the sensitivity of passive microwave measurements to snow water equivalent

Passive microwave (PM) remote sensing measurements are routinely utilized to estimate snow depth and water equivalent (SWE). Both vegetation and physical snowpack variables including snowpack grain size, snow depth, and stratigraphy influence the observed brightness temperature. The natural heterogeneity of snowpack and vegetation states within the microwave footprint occurs at spatial scales shorter than PM observation scales. In this study, we analyze the relationship between PM brightness temperature measurements and the heterogeneity of snowpack and vegetation. Specifically, we explore the question of whether PM observations are sensitive to changes in snow depth even given sub-pixel variability in snow and vegetation. To examine this question, densely sampled, spatially distributed in situ snow properties from multiple study areas made during the NASA Cold Land Processes Experiment (CLPX) are employed in a forward modeling scheme to study the effect of highly variable snow and vegetation properties on the observed PM measurement.In all test cases, this study finds that there exists sensitivity of microwave brightness temperature (Tb) to total snow depth contained within the measurement footprint, regardless of the heterogeneous nature of snow pack properties. Across three study areas, Tb decreases by 23–35K as depth increased up to the signal saturation depth, which ranged from 70 to 120cm. With regard to vegetation sensitivity, forest fractions (F) as little as 0.2 can modify the PM measurement by up to 10K, and F greater than 0.6 mask virtually all of the microwave signal attributable to snow. Finally, with respect to the measurement scale, our results indicate that the scale at which the PM measurement is made does not affect the sensitivity of the Tb to mean snow depth, to the scale (1km) examined herein.

Examining spring wet slab and glide avalanche occurrence along the Going-to-the-Sun Road corridor, Glacier National Park, Montana, USA

Abstract Wet slab and glide snow avalanches are dangerous and yet can be particularly difficult to predict. Wet slab and glide avalanches are presumably triggered by free water moving through the snowpack and the subsequent interaction with layer or ground interfaces, and typically occur in the spring during warming and subsequent melt periods. In Glacier National Park (GNP), Montana, both types of avalanches can occur in the same year and affect the spring opening operations of the Going-to-the-Sun Road (GTSR). We investigated the timing of wet slab and glide avalanche occurrence along the GTSR from 2003 to 2011 using meteorological and snowpack data from two high-elevation weather stations, one SNOTEL site, and an avalanche database to characterize 55 wet slab and 182 glide avalanches. Daily wet slab and glide avalanche occurrence were combined to represent an avalanche day and were compared to non-avalanche days (no avalanche occurrence) for 60 variables (both direct and derived measurements) using a univariate analysis. A classification tree (CART) was then trained to capture the most important variables for examining specific meteorological and snowpack variables that contribute to these types of wet snow avalanches. The CART was 10-fold cross validated using the data for 2003–2010 seasons and resulted in overall predictive accuracy of 73%. We then used the statistically optimal CART as a predictive model for the spring avalanche season of 2011, which resulted in an overall predictive accuracy of 82% for both avalanche and non-avalanche days, and a predictive accuracy of 91% for avalanche days. The results suggest that the role of air temperature and snowpack settlement appear to be the most important variables in wet slab and glide avalanche occurrence. When applied to the 2011 season, the results of the CART model are encouraging and they enhance our understanding of some of the required meteorological and snowpack conditions for wet slab and glide avalanche occurrence.

The effects of snow-N deposition and snowmelt dynamics on soil-N cycling in marginal terraced grasslands in the French Alps

Atmospheric nitrogen (N) deposition increasingly impacts remote ecosystems. At high altitudes, snow is a key carrier of water and nutrients from the atmosphere to the soil. Medium-sized subalpine grassland terraces are characteristic of agricultural landscapes in the French Alps and influence spatial and temporal snow pack variables. At the Lautaret Pass, we investigated snow and soil characteristics along mesotopographic gradients across the terraces before and during snowmelt. Total N concentrations in the snowpack did not vary spatially and were dominated by organic N forms either brought by dry deposition trapped by the snow, or due to snow-microbial immobilization and turnover. As expected, snowpack depth, total N deposited with snow and snowmelt followed the terrace toposequence; more snow-N accumulated towards the bank over longer periods. However, direct effects of snow-N on soil-N cycling seem unlikely since the amount of nitrogen released into the soil from the snowpack was very small relative to soil-N pools and N mineralization rates. Nevertheless, some snow-N reached the soil at thaw where it underwent biotic and abiotic processes. In situ soil-N mineralization rates did not vary along the terrace toposequence but soil-N cycling was indirectly affected by the snowpack. Indeed, N mineralization responded to the snowmelt dynamic via induced temporal changes in soil characteristics (i.e. moisture and T°) which cascaded down to affect N-related microbial activities and soil pH. Soil-NH4 and DON accumulated towards the bank during snowmelt while soil-NO3 followed a pulse-release pattern. At the end of the snowmelt season, organic substrate limitation might be accountable for the decrease in N mineralization in general, and in NH4 + production in particular. Possibly, during snowmelt, other biotic or abiotic processes (nitrification, denitrification, plant uptake, leaching) were involved in the transformation and transfer of snow and soil-N pools. Finally, subalpine soils at the Lautaret Pass during snowmelt experienced strong biotic and abiotic changes and switched between a source and a sink of N.

Modeling strength and stability in storm snow for slab avalanche forecasting

Abstract Some avalanche forecasting programs use a ratio of weak layer strength and overburden stress, along with other variables and observations, to help assess the stability of the upper snowpack in their forecast area. During winter storms, the strength–stress balance may change rapidly, and access to start-zone elevation study plots for manual measurements becomes limited. The shear strength of twelve non-persistent weak layers comprised of precipitation particles was measured during the winters of 2006 and 2007 in Glacier National Park, Canada. We present an empirical model to estimate the shear strength of such non-persistent weak layers up to 4 days after burial. The model is based on a backwards stepwise regression of several remotely acquired precipitation and manual snowpack variables as possible predictors of measured shear strength. The approximate age of the weak layer, the overburden stress resulting from overlying layers, and the average air temperature are the significant predictors of the weak layer strength. With an estimate of overburden stress from remotely measured precipitation, the model can generate both the shear strength of the weak layer and a stability index, SN 38 , at any time during a storm, without any manual inputs. Modeled strengths and the stability index exhibit similar trends as measured values, and most avalanches in the forecast area occurred when the stability index was declining.

Observed Inconsistencies between Snow Extent and Snow Depth Variability at Regional/Continental Scales

Abstract Snow extent and snow depth are two related characteristics of a snowpack, but they do not need to be mutually consistent. Differences between these two variables at local scales are readily apparent. However, at larger scales, which interact with atmospheric circulation and climate, snow extent is the primary variable considered, owing largely to the scarcity of snow depth data. In this study, three regional-/continental-scale gridded snow depth/snow water equivalent (SWE) datasets, derived from station observations or passive microwave satellite sensors, are utilized to quantitatively evaluate the relationship between snow extent and snow depth/SWE over North America. Various statistical methods are used to ensure the robustness of the results, including correlations, composites, and singular value decomposition analyses. Results indicate that continental-scale snow depth variations are substantial in their own right and that depth and extent anomalies are largely unrelated over broad high-latitude regions north of the snow line. Snow extent and snow depth vary more consistently in the vicinity of the snow line, especially in autumn and spring, during which precipitation and ablation can affect both variables. It is also found that deeper (shallower) winter snow translates into larger (smaller) snow-covered areas in the following spring/summer season, and also a longer (shorter) snow season, but only in specific regions. These results suggest a possible influence of snow depth on spring and summer climate. Overall, the observed lack of mutual consistency between these two snowpack variables at continental/regional scales suggests that snow depth variations may be of sufficiently large magnitude, spatial scope, and temporal duration to interact with regional–hemispheric climate, in a manner unrelated to the more extensively studied snow extent variations.

Snowpack properties associated with fracture initiation and propagation resulting in skier-triggered dry snow slab avalanches

This study investigates snowpack properties associated with skier-triggered dry slab avalanches, with a particular view on snowpack conditions favoring fracture propagation. This was done by analyzing a data set of over 500 snow profiles observed next to skier-triggered slabs (including remotely triggered slab avalanches and whumpfs) and on skier-tested slopes that did not release a slab avalanche. The relation of the snowpack variables with fracture initiation and fracture propagation, both of which are required for skier-triggering, was investigated. Specific snowpack characteristics, including hardness difference and difference in crystal size across the failure layer, associated with skier-triggered dry slab avalanches were identified and the frequency of skier-triggering was determined. In order to assess snowpack variables favouring fracture propagation, variables from failure layers associated with skier-triggered slabs that were not remotely triggered and relatively small were contrasted with snowpack variables from failure layers of remotely triggered slab avalanches, whumpfs and relatively large slab avalanches. The properties of the slab overlying the weak layer, as well as the layer above the weak layer, were found to affect fracture propagation. Stiffer slabs were associated with large avalanches as well as whumpfs and remotely triggered avalanches. Furthermore, a correlation analysis of snowpack variables with the size and width of the investigated slab avalanches further accentuated the importance of these slab properties with regards to fracture propagation.

Runoff from glacierized mountains: A model for annual variation and its forecasting

The only practical means of forecasting spring and summer runoff in remote high mountain basins such as the upper Indus in Pakistan is from preseason snow cover on satellite images. Previous studies have shown that regression coefficients of runoff on snow cover differ between basins in magnitude and sign. A conceptual analysis explains this, identifies the glaciological and snowpack variables that control annual variation in meltwater runoff, and forms the basis of a parametric forecasting model that is successfully applied to contrasting tributaries of the upper Indus, despite very crude parameter values. It incorporates the uncertainty due to possible nonaverage summer weather. Suggestions are made about additional remote sensing and ground reconnaissance investigations that could improve forecasting capability in this and similar regions.

Statistical Forecasting Of Snow Avalanches, San Juan Mountains, Southern Colorado, U.S.A.

Abstract Meteorological and snow pack variables are measured on chronologically ordered sequences of avalanche and avalanche-free days. Discriminant analysis is used to define a subset of variables which produce an optimal separation of the two multivariate group means. Two seasons are identified in each of the years considered corresponding to periods of dry- and wet-snow avalanches, and form the basic stratification of avalanche days in the analysis. Days are stratified further within each season on the basis of magnitude and number or releases. Weather and snow parameters are integrated over variable time steps prior to each avalanche or avalanche-free day. This introduces a recursive element into the forecast method. Preliminary testing of the method points to its potential in real-time snow avalanche forecasting on a regional basis.

Statistical Forecasting Of Snow Avalanches, San Juan Mountains, Southern Colorado, U.S.A.

AbstractMeteorological and snow pack variables are measured on chronologically ordered sequences of avalanche and avalanche-free days. Discriminant analysis is used to define a subset of variables which produce an optimal separation of the two multivariate group means. Two seasons are identified in each of the years considered corresponding to periods of dry- and wet-snow avalanches, and form the basic stratification of avalanche days in the analysis. Days are stratified further within each season on the basis of magnitude and number or releases. Weather and snow parameters are integrated over variable time steps prior to each avalanche or avalanche-free day. This introduces a recursive element into the forecast method. Preliminary testing of the method points to its potential in real-time snow avalanche forecasting on a regional basis.