Snowpack Depth Research Articles

Influence of climate on seasonal patterns of stem increment of balsam fir in a boreal forest of Québec, Canada

It is important to develop a better understanding of the climatic factors controlling the growth of boreal forests. Dendrometer measurements were used to characterize inter-annual variation in seasonal patterns of stem diameter increment of balsam fir trees (n=3) over seven growing seasons (2004–2010) in a boreal forest of Québec, Canada. For the period studied, cumulative seasonal growth ranged from 1.1mm to 2.9mm. Cumulative seasonal growth was a function of the timing of tree growth initiation and cessation along with the maximum growth rate observed throughout the growing season. The start and finish of diameter growth showed variations of 21 and 53 days, respectively, and duration of the growing season ranged from 38 to 107 days while maximum growth rates observed throughout a season ranged from 36.0μmd−1 to 57.6μmd−1. Interestingly, similar cumulative annual growth can be achieved through very different seasonal growth patterns, depending on the inter-annual variation of the three factors mentioned above. Air temperature and photosynthetically active radiation appeared to regulate the initiation of tree growth with high photosynthetically active radiation and cool spring conditions delaying the start of growth. The maximum growth rate within a given season was related to snowpack depth and the timing of snowmelt. Deeper snowpack that melt earlier in spring was associated to higher maximum growth rates during the following season. Apart from exceptional climatic conditions that led to a very early growth cessation in 2006, the timing of growth cessation cannot be explained by climatic variables, suggesting that this phenological event is internally controlled by a physiological mechanism. Overall, the results indicate that the onset of growth as well as the maximum growth rate were regulated by climatic triggers. Consequently, changes in climate seasonality may have considerable effects on both seasonal pattern of growth and tree growth itself.

Frog population viability under present and future climate conditions: a Bayesian state‐space approach

1. World-wide extinctions of amphibians are at the forefront of the biodiversity crisis, with climate change figuring prominently as a potential driver of continued amphibian decline. As in other taxa, changes in both the mean and variability of climate conditions may affect amphibian populations in complex, unpredictable ways. In western North America, climate models predict a reduced duration and extent of mountain snowpack and increased variability in precipitation, which may have consequences for amphibians inhabiting montane ecosystems. 2. We used Bayesian capture-recapture methods to estimate survival and transition probabilities in a high-elevation population of the Columbia spotted frog (Rana luteiventris) over 10 years and related these rates to interannual variation in peak snowpack. Then, we forecasted frog population growth and viability under a range of scenarios with varying levels of change in mean and variance in snowpack. 3. Over a range of future scenarios, changes in mean snowpack had a greater effect on viability than changes in the variance of snowpack, with forecasts largely predicting an increase in population viability. Population models based on snowpack during our study period predicted a declining population. 4. Although mean conditions were more important for viability than variance, for a given mean snowpack depth, increases in variability could change a population from increasing to decreasing. Therefore, the influence of changing climate variability on populations should be accounted for in predictive models. The Bayesian modelling framework allows for the explicit characterization of uncertainty in parameter estimates and ecological forecasts, and thus provides a natural approach for examining relative contributions of mean and variability in climatic variables to population dynamics. 5. Longevity and heterogeneous habitat may contribute to the potential for this amphibian species to be resilient to increased climatic variation, and shorter-lived species inhabiting homogeneous ecosystems may be more susceptible to increased variability in climate conditions.

Impact of a reduced winter snowpack on litter arthropod abundance and diversity in a northern hardwood forest ecosystem

Projected changes in climate for the northeastern USA over the next 100 years include a reduction in the depth and duration of the winter snowpack, which could affect soil temperatures and frost regimes. We conducted a snow-removal experiment in a northern hardwood forest at the Hubbard Brook Experimental Forest in central New Hampshire over 2 years to induce soil freezing and evaluate its effect on the abundance, richness, and diversity of soil arthropods during the growing season. Snow removal at the beginning of winter increased the depth and duration of soil frost, decreased soil temperatures, and led to a reduced abundance of some arthropod taxa, including Araneae (reduced by 57%; P=0.0001), Pseudoscorpionida (75%; P<0.0001), Hymenoptera (57%; P=0.0033), Collembola (24%; P=0.0019), adult Coleoptera (23%; P=0.0057), and larval Diptera (33%; P<0.0001) and an increase in other taxa, including Hemiptera (increased by 7%; P=0.032). Taxa that did not respond significantly to snow removal included Chilopoda (P=0.55), Acari (P=0.66), Diplopoda (P=0.66), adult Diptera (P=0.54), and larval Coleoptera (P=0.39). A delayed snowpack over two winters also resulted in decreased arthropod richness by 30% (P<0.0001) and Simpson's index of diversity by 22% (P=0.0002) during the two subsequent growing seasons. Results of this study demonstrate that predicted changes in the winter snowpack and depth and duration of soil frost may reduce the abundance and alter the community composition of arthropods living in the forest floor of northern hardwood forests, which could have implications for the structure and function of northern forest ecosystems.

Winter CO2 fluxes in a sub-alpine grassland in relation to snow cover, radiation and temperature

Carbon dioxide (CO2) emissions were measured over a period of 3 years at the sub-alpine Swiss CARBOMONT site Rigi Seebodenalp. Here we show, that winter respiration contributes larger than expected to the annual CO2 budget at this high altitude, rich in belowground organic carbon grassland (7–15% C by mass). Furthermore the contribution of winter emissions to the annual CO2 budget is highly dependent on the definition of “winter” itself. Cumulative winter respiration determined over a 6 month period from 15th of October until 15th of April contributed 23.3 ± 2.4 and 6.0 ± 0.3% to the annual respiration during the years under observation, respectively. The insulation effect of snow and a lowering of the freezing point caused by high concentrations of soil organic solutes prevented the soil from freezing. These conditions favored higher soil temperatures resulting in relatively high respiratory losses. The duration of snow cover and micrometeorological conditions determining the photosynthetic activity of the vegetation during snow-free periods influenced the size and the variability of the winter CO2 fluxes. Seasonal values are strongly influenced by the days at the end and the beginning of the defined winter period, caused by large variations in length of periods with air temperatures below freezing. Losses of CO2 from the snow-covered soil were highest in winter 2003/2004. These high losses were partially explained by higher temperatures in the topsoil, caused by higher air temperatures just before snowfall. Thus, losses are not a consequence of higher soil temperatures registered during the summer heat wave 2003. However, water stress in summer 2003 might have caused an increment in dead organic matter in the soil providing additional substrate for microbial respiration in the following winter. Although considerable day-to-day fluctuations in snow effluxes were recorded, no conclusive and generally valid relationship could be found between CO2 losses from the snow pack and snow depth, rate of snow melt, wind speed or air pressure. This suggests that time lags and hysteresis effects may be more important for understanding winter respiration than concurrent environmental conditions in most ecosystems of comparable type.

Accumulation and melt dynamics of snowpack from a multiresolution regional climate model in the central Sierra Nevada, California

[1] The depth and timing of snowpack in the Sierra Nevada Mountains are of fundamental importance to California water resource availability, and recent studies indicate a shift toward earlier snowmelt consistent with projected impacts of anthropogenic climate change. In order for future studies to assess snowpack variability on seasonal to centennial time scales, physically based models of snowpack evolution at high spatial resolution must be improved. Here we evaluate modeled snowpack accuracy for the central Sierra Nevada in the Weather Research and Forecasting regional climate model coupled to the Noah land surface model. A simulation with nested domains at 27, 9, and 3 km grid spacings is presented for November 2001 to July 2002. Model outputs are compared with daily snowpack observations at 41 locations, air temperature at 31 locations, and precipitation at 10 locations. Comparison of snowpack at different resolutions suggests that 27 km simulations substantially underestimate snowpack, while 9 and 3 km simulations are closer to observations. Regional snowpack accumulation is accurately simulated at these high resolutions, but model snowmelt occurs an average of 22–25 days early. Some error can be traced to differences in elevation and observation scale between point-based measurements and model grid cells, but these factors cannot explain the persistent bias toward early snowmelt. A high correlation between snowmelt and error in modeled surface air temperature is found, with melt coinciding systematically with excessively cold air temperatures. One possible source of bias is an imbalance in turbulent heat fluxes, erroneously warming the snowpack while cooling the surface atmosphere.

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.

Representing Grass– and Shrub–Snow–Atmosphere Interactions in Climate System Models

Abstract A vegetation-protruding-above-snow parameterization for earth system models was developed to improve energy budget calculations of interactions among vegetation, snow, and the atmosphere in nonforested areas. These areas include shrublands, grasslands, and croplands, which represent 68% of the seasonally snow-covered Northern Hemisphere land surface (excluding Greenland). Snow depth observations throughout nonforested areas suggest that mid- to late-winter snowpack depths are often comparable or lower than the vegetation heights. As a consequence, vegetation protruding above the snow cover has an important impact on snow-season surface energy budgets. The protruding vegetation parameterization uses disparate energy balances for snow-covered and protruding vegetation fractions of each model grid cell, and fractionally weights these fluxes to define grid-average quantities. SnowModel, a spatially distributed snow-evolution modeling system, was used to test and assess the parameterization. Simulations were conducted during the winters of 2005/06 and 2006/07 for conditions of 1) no protruding vegetation (the control) and 2) with protruding vegetation. The spatial domain covered Colorado, Wyoming, and portions of the surrounding states; 81% of this area is nonforested. The surface net radiation, energy, and moisture fluxes displayed considerable differences when protruding vegetation was included. For shrubs, the net radiation, sensible, and latent fluxes changed by an average of 12.7, 6.9, and −22.7 W m−2, respectively. For grass and crops, these fluxes changed by an average of 6.9, −0.8, and −7.9 W m−2, respectively. Daily averaged flux changes were as much as 5 times these seasonal averages. As such, the new parameterization represents a major change in surface flux calculations over more simplistic and less physically realistic approaches.

Influence of dust and black carbon on the snow albedo in the NASA Goddard Earth Observing System version 5 land surface model

Present-day land surface models rarely account for the influence of both black carbon and dust in the snow on the snow albedo. Snow impurities increase the absorption of incoming shortwave radiation (particularly in the visible bands), whereby they have major consequences for the evolution of snowmelt and life cycles of snowpack. A new parameterization of these snow impurities was included in the catchment-based land surface model used in the National Aeronautics and Space Administration Goddard Earth Observing System version 5. Validation tests against in situ observed data were performed for the winter of 2003.2004 in Sapporo, Japan, for both the new snow albedo parameterization (which explicitly accounts for snow impurities) and the preexisting baseline albedo parameterization (which does not). Validation tests reveal that daily variations of snow depth and snow surface albedo are more realistically simulated with the new parameterization. Reasonable perturbations in the assigned snow impurity concentrations, as inferred from the observational data, produce significant changes in snowpack depth and radiative flux interactions. These findings illustrate the importance of parameterizing the influence of snow impurities on the snow surface albedo for proper simulation of the life cycle of snow cover.

Assessment of airborne LIDAR for snowpack depth modeling

The Institut Geologic de Catalunya (IGC) and the Institut Cartografic de Catalunya (ICC) have begun a joint project to model snowpack depth distribution in the Nuria valley (a 38 km 2 basin located in the Eastern Pyrenees) in order to evaluate water reserves in mountain watersheds . The evaluation was based on a remote sensing airborne LIDAR survey and validated with field-work calculations. Previous studies have applied geostatistical techniques to extrapolate sparse point data obtained from costly field-work campaigns. Despite being a recently developed technique, LIDAR has become a useful method in snow sciences as it produces dense point data and covers wide areas. The new methodology presented here combines LIDAR data with field-work, the use of geographical information systems (GIS) and the stepwise regression tree (SRT), as an extrapolation technique. These methods have allowed us to map snowpack depth distribution in high spatial resolution. Extrapolation was necessary because raw LIDAR data was only obtained from part of the study area in order to minimise costs. Promising results show high correlation between LIDAR data and field data, validating the use of airborne laser altimetry to estimate snow depth. Moreover, differences of total snow volume calculated from modeled snowpack distribution and total volume from LIDAR data differ by only 1 %.

Combined tree-ring width and δ13C to reconstruct snowpack depth: a pilot study in the Gongga Mountain, west China

Tree-ring width (TRW) and stable carbon isotope (δ13C) in tree-ring cellulose of subalpine fir (Abies fabri) were used to develop high-resolution climate proxy data to indicate snow-depth variations in the Gongga Mountain, west China. Tree radial growth- and δ13C-climate response analyses demonstrated that the TRW and δ13C at the timberline (3,400 m.a.s.l.) are mainly influenced by temperature and precipitation of previous growth seasons and current summer (June to August) under cold and humid conditions. Considering the analogous control factors on both tree growth and carbon isotope discrimination (Δ13C) and snow accumulation, the negative and significant relationships between tree-ring parameters (TRW and Δ13C) and mean monthly snowpack depth were found. Herein, by combining two tree-ring parameters, a primary snow-depth reconstruction (previous October to current May) over the reliable period A.D. 1880–2004 was estimated. The reconstruction explains 58.0% of the variance in the instrumental record, and in particular captures the longer-term fluctuations successfully. Except the period with extreme higher snowpack depth around 1990, the snowpack depth seems to fluctuate in a normal way. The reconstruction agrees with the nearby snowpack depth record in Kangding and the mean observed snowpack-depth variations of the stations on the Tibetan Plateau, particularly at long-term scales. The snowpack depth in low-frequency fluctuations, during the past century, agrees quite well with the Eastern India precipitation covering the period of previous October–current May. Our results suggest that combing tree-ring width and δ13C in certain subalpine tree species growing on the Tibetan Plateau may be an effective way for reconstructing regional snowpack variations.

Nonlinear responses of wolverine populations to declining winter snowpack

AbstractUnderstanding the population‐level impacts of climate change is critical for effectively managing ecosystems. Predators are important components of many systems because they provide top−down control of community structure. Ecological theory suggests that these species could be particularly susceptible to climate change because they generally occur at low densities and have resource‐limited populations. Yet, our understanding of climate‐change impacts on predators is hindered by the difficulty in assessing complex, nonlinear dynamics over the large spatial scales necessary to depict a species’ general response to abiotic forcing. Here we use fur‐return data to characterize population dynamics of a snow‐adapted carnivore, the wolverine, across most of its North American range. Using novel modeling techniques, we simultaneously measured the impact of winter snowpack on wolverine dynamics across critical thresholds in snowpack depth and two domains of population growth. Winter snowpack declined from 1970 to 2004 in nearly the entire region studied, concordant with increases in Northern Hemisphere temperature anomalies. Fur returns have declined in many areas; our models show that snowpack has strong, nonlinear effects on wolverine population dynamics. Importantly, wolverine harvests dropped the fastest in areas where snowpack declined most rapidly and also where snowpack had the greatest effect on population dynamics. Moreover, declining snow cover appears to drive trends in wolverine population synchrony, with important implications for overall persistence. These results illustrate the vulnerability and complex responses of predator populations to climate change. We also suggest that declining snowpack may be an important and hitherto little‐analyzed mechanism through which climate change alters high‐latitude ecosystems.

Spring and summertime diurnal surface ozone fluxes over the polar snow at Summit, Greenland

Continuous surface‐layer ozone flux measurements over the polar, year‐round snowpack at Summit, Greenland, resulted in deposition velocities (vd) that were smaller than most previous assumptions and model inputs. Substantial seasonal differences were seen in the ozone vd behavior. Spring, daytime ozone vd values showed low variability and were consistently ≤0.01 cm s−1. During summer, ozone fluxes displayed distinct diurnal cycles, and evidence for regular occurrences of bi‐directional behavior. Summer, daytime vd ranged between ∼0.01 to 0.07 cm s−1. Maximum summertime downward fluxes (ozone deposition) coincided with the hours of maximum solar radiation, i.e., noon–afternoon. During summer nighttime hours upward ozone fluxes were observed. These upward fluxes were interpreted as ozone production in a shallow layer near and above the snow surface with resulting upward ozone fluxes out of the shallow surface layer. Comparisons with published observations from temperate, midlatitude sites suggest different controls and behavior of ozone fluxes, and that ozone fluxes over snow depend on a myriad of parameters, including solar irradiance, snow chemical and physical properties, snowpack depth, and the type of substrate underneath the snow.

Organic Contaminant Release from Melting Snow. 2. Influence of Snow Pack and Melt Characteristics

Large reservoirs of organic contaminants in seasonal snowpack can be released in short pulses during spring snowmelt, potentially impacting the receiving ecosystems. Laboratory experiments using artificial snow spiked with organic target substances were conducted to investigate the behavior of six organic contaminants with widely variable distribution properties in melting snow. Whereas the influence of a chemical's equilibrium phase partitioning on the elution behavior is explored in a companion paper, we discuss here the impact of snow properties and melt features, including the snowpack depth, the temperature at the interface between soil and snow, the meltwater content the internal ice surface area, and the existence of distinct snow layers. Water-soluble organic substances are released in high concentrations at the beginning of a melt period when a deep and aged snowpack undergoes intense melting. Warm ground can cause notable melting at the snow bottom leading to a delayed and dampened concentration peak. Hydraulic barriers in layered snow packs cause preferential meltwater flow which also mitigates the early contaminant flush. Hydrophobic organic pollutants that are associated with particles accumulate near the snow surface and are released at the end of melting. Dirt cones at the surface of a dense snowpack enhance this enrichment. The findings of this laboratory study will aid in the understanding of the behavior of organic pollutants during the melting of more complex, natural snow covers.

Climate informed flood frequency analysis and prediction in Montana using hierarchical Bayesian modeling

It is widely acknowledged that climate variability modifies the frequency spectrum of extreme hydrologic events. Traditional hydrological frequency analysis methods do not account for year to year shifts in flood risk distributions that arise due to changes in exogenous factors that affect the causal structure of flood risk. We use Hierarchical Bayesian Analysis to evaluate several factors that influence the frequency of extreme floods for a basin in Montana. Sea surface temperatures, predicted GCM precipitation, climate indices and snow pack depth are considered as potential predictors of flood risk. The parameters of the flood risk prediction model are estimated using a Markov Chain Monte Carlo algorithm. The predictors are compared in terms of the resulting posterior distributions of the parameters that are used to estimate flood frequency distributions. The analysis shows an approach for exploiting the link between climate scale indicators and annual maximum flood, providing impetus for developing seasonal forecasting of flood risk applications and dynamic flood risk management strategies

The effect of soil freezing on N cycling: comparison of two headwater subcatchments with different vegetation and snowpack conditions in the northern Hokkaido Island of Japan

Climate change models predict that the snowpacks of temperate forests will develop later and be shallower resulting in a higher propensity for soil freezing. In the northern most island of Japan, Hokkaido, snowpack depth decreases from west to east. This snowpack depth gradient provided a unique opportunity to test the effects of variable snowpack and soil freezing on N biogeochemistry. The Shibecha Northern Catchment in Shibecha Experimental Forest, eastern Hokkaido had deciduous trees and a mean annual snowpack of 0.7 m while the M3 catchment in Uryu Experimental Forest, western Hokkaido had mixed deciduous and coniferous tree species and a mean annual snowpack of 2.0 m. We conducted a field study (October 2004–April 2005) to determine if differences in Shibecha and Uryu soil extractable N, N mineralization, and nitrification were controlled by the variability in soil freezing during winter or tree species composition that affected the quality of the forest floor. The mixed deciduous and coniferous trees forming the Uryu forest floor had a higher C:N ratio (25.0 vs. 22.4 at Shibecha), higher lignin:N ratio (15 vs. 8.8), and higher lignin concentrations (0.28 vs. 0.18 g lignin g−1). These differences in forest floor quality contributed to higher net N mineralization and nitrification in Shibecha compared to Uryu. In Shibecha, soil remained frozen for the entire study. For Uryu, except for an early period with cold temperatures and no snow, the soil generally remained unfrozen. As a result of the early winter cold period and soil freezing, extractable soil NH 4 + did not change but NO 3 − increased. Reciprocal 0–5 cm mineral soil transplants made between Shibecha and Uryu and incubated during winter at 0, 5, and 30 cm suggested that soil freezing resulted in greater net N mineralization yet lower nitrification regardless of the soil origin. The effect of soil freezing should be considered when evaluating differences in N dynamics between temperate ecosystems having a propensity for soil freezing.

Mixed‐conifer understory response to climate change, nitrogen, and fire

AbstractCalifornia's Sierra Nevada mountains are predicted to experience greater variation in annual precipitation according to climate change models, while nitrogen deposition from pollution continues to increase. These changes may significantly affect understory communities and fuels in forests where managers are attempting to restore historic conditions after a century of altered fire regimes. The objective of this research was to experimentally test the effects of increasing and decreasing snowpack depth, increasing nitrogen, and applying prescribed fire to mixed‐conifer forest understories at two sites in the central and southern Sierra Nevada. Understory response to treatments significantly differed between sites with herb biomass increasing in shrub‐dominated communities when snowpack was reduced. Fire was a more important factor in post‐treatment species richness and cover than either snowpack addition or reduction. Nitrogen additions unexpectedly increased herbaceous species richness. These varied findings indicate that modeling future climatic influences on biodiversity may be more difficult than additive prediction based on increasing the ecosystem's two limiting growth resources. Increasing snowpack and nitrogen resulted in increased shrub biomass production at both sites and increased herb production at the southern site. This additional understory biomass has the potential to increase fuel connectivity in patchy Sierran mixed‐conifer forests, increasing fire severity and size.

Variations in snow surface properties at the snowpack-depth, the slope and the basin scale

AbstractVariations of snow surface and snowpack properties affect avalanche formation. In up to four north-facing slopes above the tree line near Davos, Switzerland, snow surface properties were characterized. Penetration resistance was measured with a snow micro-penetrometer. The sampling scheme was designed to allow a multi-scale approach covering the snowpack depth scale (0.5–5 m), the slope scale (5–100 m) and the basin scale (100–1000 m). Observations and measurements were compared to the data of a nearby automatic weather station (AWS). The AWS data were also used to model snow-cover stratigraphy and its evolution with the numerical snow-cover model SNOWPACK. Comparing the four slopes showed that surface properties observed manually were similar among the three slopes that were sheltered, and often different from the slope that was wind-exposed. However, the penetration resistance of the surface layer was in most cases significantly different among slopes, although most values were &lt;0.1 N, indicating very low hardness. These seemingly contradictory results follow from the different measurement support of the two methods. It is presently unclear which amount of variation at a given scale is relevant for avalanche formation. The geostatistical analysis and an analysis aimed at identifying the causes of variability were not conclusive. No patterns emerged that would allow conclusions regarding the effect on avalanche formation. Finding the causes of variability seems to require high-resolution terrain and weather models that are presently not readily available.

A comparison of measurement methods: terrestrial laser scanning, tachymetry and snow probing for the determination of the spatial snow-depth distribution on slopes

AbstractDetermination of the spatial snow-depth distribution is important in potential avalanche-starting zones, both for avalanche prediction and for the dimensioning of permanent protection measures. Knowledge of the spatial distribution of snow is needed in order to validate snow depths computed from snowpack and snowdrift models. The inaccessibility of alpine terrain and the acute danger of avalanches complicate snow-depth measurements (e.g. when probes are used), so the possibility of measuring the snowpack using terrestrial laser scanning (TLS) was tested. The results obtained were compared to those of tachymetry and manual snow probing. Laser measurements were taken using the long-range laser profile measuring system Riegl LPM-i800HA. The wavelength used by the laser was 0.9 μm (near-infrared). The accuracy was typically within 30 mm. The highest point resolution was 30 mm when measured from a distance of 100 m. Tachymetry measurements were carried out using Leica TCRP1201 systems. Snowpack depths measured by the tachymeter were also used. The datasets captured by tachymetry were used as reference models to compare the three different methods (TLS, tachymetry and snow probing). This is the first time that the accuracy of TLS systems in snowy and alpine weather conditions has been quantified. The relative accuracy between the three measurement methods is bounded by a maximum offset of ±8 cm. Between TLS and the tachymeter the standard deviation is 1σ = 2 cm, and between manual probing and TLS it is up to 1σ = 10 cm, for maximum distances for the TLS and tachymeter of 300 m.

The Effects of Layers in Dry Snow on Its Passive Microwave Emissions Using Dense Media Radiative Transfer Theory Based on the Quasicrystalline Approximation (QCA/DMRT)

A model for the microwave emissions of multilayer dry snowpacks, based on dense media radiative transfer (DMRT) theory with the quasicrystalline approximation (QCA), provides more accurate results when compared to emissions determined by a homogeneous snowpack and other scattering models. The DMRT model accounts for adhesive aggregate effects, which leads to dense media Mie scattering by using a sticky particle model. With the multilayer model, we examined both the frequency and polarization dependence of brightness temperatures (Tb's) from representative snowpacks and compared them to results from a single-layer model and found that the multilayer model predicts higher polarization differences, twice as much, and weaker frequency dependence. We also studied the temporal evolution of Tb from multilayer snowpacks. The difference between Tb's at 18.7 and 36.5 GHz can be 5 K lower than the single-layer model prediction in this paper. By using the snowpack observations from the Cold Land Processes Field Experiment as input for both multi- and single-layer models, it shows that the multilayer Tb's are in better agreement with the data than the single-layer model. With one set of physical parameters, the multilayer QCA/DMRT model matched all four channels of Tb observations simultaneously, whereas the single-layer model could only reproduce vertically polarized Tb's. Also, the polarization difference and frequency dependence were accurately matched by the multilayer model using the same set of physical parameters. Hence, algorithms for the retrieval of snowpack depth or water equivalent should be based on multilayer scattering models to achieve greater accuracy.

Ob' River flood inundations from satellite observations: A relationship with winter snow parameters and river runoff

Using a multisatellite method, including passive microwave land surface emissivities, along with active microwave, visible and near infrared observations developed to estimate global inundated area, we examine the spatial and temporal variations of the 1993–2000 monthly inundation extents over a Boreal environment, the Ob River basin. Over the entire watershed, the mean extent of inundation during the snow‐free months is 2.6 × 105 km2, consistent with previous independent static or satellites‐derived estimates. The maximum in yearly inundation, showing propagation from south to north between April and June, exhibits strong seasonal and inter‐annual variations. The consistency of the inundation estimates is then analyzed at local or basin‐wide scale using different in situ or satellite‐derived snow and runoff parameters. The results show a strong relationship between the inundation extent and the snowmelt date, the snowpack depth at three in situ stations located in the southern part of the basin. Over the northern part, results show that flooding is more closely linked to the amount of water coming downstream from the southern part of the basin. A close systematic relationship is also found between the inundation extent and the local in situ runoff at six locations as well as with the altimeter‐derived discharge measured at the Ob estuary. This case study evaluation shows the potential of these data sets to provide consistent information about the seasonal and inter‐annual variations of inundation over a major Boreal river basin. These results also suggest new potential to improve the description of the snow‐inundation‐runoff relationship that are fundamental for climate and hydrological models.