Snow Depth Mapping Research Articles

Mapping snow depth in open alpine terrain from stereo satellite imagery

Abstract. To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70 m resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km2) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4 m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a −0.48 m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is −0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m Pléiades dDEM to a 2 m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km2). The UAV-derived snow depth map exhibits the same patterns as the Pléiades-derived snow map, with a median of −0.11 m and a SD of 0.62 m when compared to the snow-probe measurements. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available.

Linking snowfall and snow accumulation to generate spatial maps of SWE and snow depth

AbstractIt is critically important but challenging to estimate the amount of snow on the ground over large areas due to its strong spatial variability. Point snow data are used to generate or improve (i.e., blend with) gridded estimates of snow water equivalent (SWE) by using various forms of interpolation; however, the interpolation methodologies often overlook the physical mechanisms for the snow being there in the first place. Using data from the Snow Telemetry and Cooperative Observer networks in the western United States, we show that four methods for the spatial interpolation of peak of winter snow water equivalent (SWE) and snow depth based on distance and elevation can result in large errors. These errors are reduced substantially by our new method, i.e., the spatial interpolation of these quantities normalized by accumulated snowfall from the current or previous water years. Our method results in significant improvement in SWE estimates over interpolation techniques that do not consider snowfall, regardless of the number of stations used for the interpolation. Furthermore, it can be used along with gridded precipitation and temperature data to produce daily maps of SWE over the western United States that are comparable to existing estimates (which are based on the assimilation of much more data). Our results also show that not honoring the constraint between SWE and snowfall when blending in situ data with gridded data can lead to the development and propagation of unrealistic errors.

Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations

Abstract. Detailed information on the spatiotemporal snow depth distribution is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Today, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network like the one in Switzerland, with more than one measurement station per 10 km2 on average, is not able to capture the large spatial variability of snow depth present in alpine terrain.Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, in most countries such data acquisition is costly if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UASs), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Compared to manual measurements, such systems are relatively cost-effective and can be applied very flexibly to cover terrain not accessible from the ground. In this study, we map snow depth at two different locations: (a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and (b) an exposed location on a peak (2500 m a.s.l.) in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) of less than 0.07 to 0.15 m on meadows and rocks and a RMSE of less than 0.30 m on sections covered by bushes or tall grass, compared to manual probe measurements. This new measurement technology opens the door for efficient, flexible, repeatable and cost-effective snow depth monitoring over areas of several hectares for various applications, if the national and regional regulations permit the application of UASs.

Operational algorithm for generation of snow depth maps from discrete data in Indian Western Himalaya

In this paper, an algorithm is proposed for generation of snow depth maps. The efficacy of the algorithm has been established through a case study in lower and middle Himalayas, India. The algorithm is a modified version of the spatial interpolation method proposed earlier in Swiss Alps. The method uses discrete point data supplemented with remotely sensed derived information data to create snow depth maps at spatial resolution of 0.5km. In situ snow depth observations from 14 locations, automatic weather station (AWS) recorded snow depth from 9 locations, moderate-resolution imaging spectroradiometer (MODIS) images and shuttle radar topographic mission (SRTM) DEM form the database. The algorithm is based on the dependency of snow depth on elevation above mean sea level, which is later adjusted through the in situ snow depth observations to represent the local and regional characteristics of the snow distribution. The algorithm has been validated for different days of the winter season 2012–2013 using leave-one-out station cross-validation method. The mean absolute error (MAE) and root mean square error (RMSE) in estimation of snow depth have been observed as ~34cm and ~42cm respectively during the season. The snow depth maps generated from the proposed algorithm may be useful in assessment of snow avalanche hazards as well as in various hydrological and glaciological studies in the inaccessible cryospheric region of the Western Himalaya.

Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation

Abstract. We investigate snow depth distribution at peak accumulation over a small Alpine area ( ∼ 0.3 km2) using photogrammetry-based surveys with a fixed-wing unmanned aerial system (UAS). These devices are growing in popularity as inexpensive alternatives to existing techniques within the field of remote sensing, but the assessment of their performance in Alpine areas to map snow depth distribution is still an open issue. Moreover, several existing attempts to map snow depth using UASs have used multi-rotor systems, since they guarantee higher stability than fixed-wing systems. We designed two field campaigns: during the first survey, performed at the beginning of the accumulation season, the digital elevation model of the ground was obtained. A second survey, at peak accumulation, enabled us to estimate the snow depth distribution as a difference with respect to the previous aerial survey. Moreover, the spatial integration of UAS snow depth measurements enabled us to estimate the snow volume accumulated over the area. On the same day, we collected 12 probe measurements of snow depth at random positions within the case study to perform a preliminary evaluation of UAS-based snow depth. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to −0.073 m and a RMSE equal to 0.14 m. We have also explored how some basic snow depth statistics (e.g., mean, standard deviation, minima and maxima) change with sampling resolution (from 5 cm up to ∼ 100 m): for this case study, snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1 m. This provides a possible indication of sampling resolution in similar conditions.

Mapping starting zone snow depth with a ground-based lidar to assist avalanche control and forecasting

The distribution of snow depth in avalanche starting zones exerts a strong influence on avalanche potential and character. Extreme depth changes over short distances are common, especially in wind-affected, above-treeline environments. Snow depth also affects the ease of avalanche triggering. Experience shows that avalanche reduction efforts are often more successful when targeting shallow trigger point areas near deeper slabs with explosives or ski cutting. Our paper explores the use of high-resolution (cm scale) snow depth and snow depth change maps from terrestrial laser scanning (TLS) data to quantify loading patterns for use in both pre-control planning and in post-control assessment.We present results from a pilot study in three study areas at the Arapahoe Basin ski area in Colorado, USA. A snow-free reference data set was collected in a summer TLS survey. Mapping multiple times during the snow season allowed us to produce time series maps of snow depth and snow depth change at high resolution to explore depth and slab thickness variations due to wind redistribution. We conducted surveys before and after loading events and control work, allowing the exploration of loading patterns, slab thickness, shot and ski cut locations, bed surfaces, entrainment, and avalanche characteristics. We also evaluate the state of TLS for use in operational avalanche control settings.

Mapping snow depth from manned aircraft on landscape scales at centimeter resolution using structure-from-motion photogrammetry

Abstract. Airborne photogrammetry is undergoing a renaissance: lower-cost equipment, more powerful software, and simplified methods have significantly lowered the barriers to entry and now allow repeat mapping of cryospheric dynamics at spatial resolutions and temporal frequencies that were previously too expensive to consider. Here we apply these advancements to the measurement of snow depth from manned aircraft. Our main airborne hardware consists of a consumer-grade digital camera directly coupled to a dual-frequency GPS; no inertial motion unit (IMU) or on-board computer is required, such that system hardware and software costs less than USD 30 000, exclusive of aircraft. The photogrammetric processing is done using a commercially available implementation of the structure from motion (SfM) algorithm. The system is simple enough that it can be operated by the pilot without additional assistance and the technique creates directly georeferenced maps without ground control, further reducing overall costs. To map snow depth, we made digital elevation models (DEMs) during snow-free and snow-covered conditions, then subtracted these to create difference DEMs (dDEMs). We assessed the accuracy (real-world geolocation) and precision (repeatability) of our DEMs through comparisons to ground control points and to time series of our own DEMs. We validated these assessments through comparisons to DEMs made by airborne lidar and by a similar photogrammetric system. We empirically determined that our DEMs have a geolocation accuracy of ±30 cm and a repeatability of ±8 cm (both 95 % confidence). We then validated our dDEMs against more than 6000 hand-probed snow depth measurements at 3 separate test areas in Alaska covering a wide-variety of terrain and snow types. These areas ranged from 5 to 40 km2 and had ground sample distances of 6 to 20 cm. We found that depths produced from the dDEMs matched probe depths with a 10 cm standard deviation, and were statistically identical at 95 % confidence. Due to the precision of this technique, other real changes on the ground such as frost heave, vegetative compaction by snow, and even footprints become sources of error in the measurement of thin snow packs (< 20 cm). The ability to directly measure such small changes over entire landscapes eliminates the need to extrapolate limited field measurements. The fact that this mapping can be done at substantially lower costs than current methods may transform the way we approach studying change in the cryosphere.

Snow Depth Retrieval with UAS Using Photogrammetric Techniques

Alpine areas pose challenges for many existing remote sensing methods for snow depth retrieval, thus leading to uncertainty in water forecasting and budgeting. Herein, we present the results of a field campaign conducted in Tasmania, Australia in 2013 from which estimates of snow depth were derived using a low-cost photogrammetric approach on-board a micro unmanned aircraft system (UAS). Using commercial off-the-shelf (COTS) sensors mounted on a multi-rotor UAS and photogrammetric image processing techniques, the results demonstrate that snow depth can be accurately retrieved by differencing two surface models corresponding to the snow-free and snow-covered scenes, respectively. In addition to accurate snow depth retrieval, we show that high-resolution (50 cm) spatially continuous snow depth maps can be created using this methodology. Two types of photogrammetric bundle adjustment (BA) routines are implemented in this study to determine the optimal estimates of sensor position and orientation, in addition to 3D scene information; conventional BA (which relies on measured ground control points) and direct BA (which does not require ground control points). Error sources that affect the accuracy of the BA and subsequent snow depth reconstruction are discussed. The results indicate the UAS is capable of providing high-resolution and high-accuracy (<10 cm) estimates of snow depth over a small alpine area (~0.7 ha) with significant snow accumulation (depths greater than one meter) at a fraction of the cost of full-size aerial survey approaches. The RMSE of estimated snow depths using the conventional BA approach is 9.6 cm, whereas the direct BA is characterized by larger error, with an RMSE of 18.4 cm. If a simple affine transformation is applied to the point cloud derived from the direct BA, the overall RMSE is reduced to 8.8 cm RMSE.

Snow depth mapping in high-alpine catchments using digital photogrammetry

Abstract. Information on snow depth and its spatial distribution is crucial for numerous applications in snow and avalanche research as well as in hydrology and ecology. Today, snow depth distributions are usually estimated using point measurements performed by automated weather stations and observers in the field combined with interpolation algorithms. However, these methodologies are not able to capture the high spatial variability of the snow depth distribution present in alpine terrain. Continuous and accurate snow depth mapping has been successfully performed using laser scanning but this method can only cover limited areas and is expensive. We use the airborne ADS80 optoelectronic scanner, acquiring stereo imagery with 0.25 m spatial resolution to derive digital surface models (DSMs) of winter and summer terrains in the neighborhood of Davos, Switzerland. The DSMs are generated using photogrammetric image correlation techniques based on the multispectral nadir and backward-looking sensor data. In order to assess the accuracy of the photogrammetric products, we compare these products with the following independent data sets acquired simultaneously: (a) manually measured snow depth plots; (b) differential Global Navigation Satellite System (dGNSS) points; (c) terrestrial laser scanning (TLS); and (d) ground-penetrating radar (GPR) data sets. We demonstrate that the method presented can be used to map snow depth at 2 m resolution with a vertical depth accuracy of ±30 cm (root mean square error) in the complex topography of the Alps. The snow depth maps presented have an average accuracy that is better than 15 % compared to the average snow depth of 2.2 m over the entire test site.

Quantifying the effects of vegetation structure on snow accumulation and ablation in mixed‐conifer forests

AbstractSnowmelt from forested, mountainous environments in the western United States is a critical regional water resource for streamflow and ecological productivity. These landscapes are undergoing rapid changes from the combined effects of forest fires, insect infestation and climate change. Numerous observational studies demonstrate that trees control snowpack accumulation and ablation over scales of tens of metres. Representing forest heterogeneity in models is important for understanding how changes in climate and vegetation cover affect the snowpack; yet, many snow models simplify a forest into two categories: canopy covered and non‐canopy covered. We combine existing parameterizations of mass and energy fluxes within a new three‐dimensional framework informed by Airborne Laser Swath Mapping (ALSM)‐derived canopy maps and evaluated with ALSM‐derived snow depth maps to explicitly simulate snow cover in relation to heterogeneous canopy. Model results capture much of the observed snow variability depicted in the 1‐m ALSM‐derived snow depth maps. Observations and modelled results identify open areas <15 m from tree canopies as having more snow and more snow variability than areas >15 m from tree canopies, and modelled results predict that open areas <15 m from tree canopies have 30–40% more net snow water input than areas that are underneath tree canopies and 10–25% more net snow water input than areas that are >15 m from tree canopies. Furthermore, 1‐m simulations give higher estimates for net snow water input than coarser resolution simulations, mainly in areas with fewer trees. These results suggest the importance of explicitly representing canopy edges in snow models. Copyright © 2014 John Wiley & Sons, Ltd.

Snowpack variability across various spatio‐temporal resolutions

AbstractHigh‐resolution snow depth (SD) maps (1 × 1 m) obtained from terrestrial laser scanner measurements in a small catchment (0.55 km2) in the Pyrenees were used to assess small‐scale variability of the snowpack at the catchment and sub‐grid scales. The coefficients of variation are compared for various plot resolutions (5 × 5, 25 × 25, 49 × 49, and 99 × 99 m) and eight different days in two snow seasons (2011–2012 and 2012–2013). We also studied the relation between snow variability at the small scale and SD, topographic variables, small‐scale variability in topographic variables. The results showed that there was marked variability in SD, and it increased with increasing scales. Days of seasonal maximum snow accumulation showed the least small‐scale variability, but this increased sharply with the onset of melting. The coefficient of variation (CV) in snowpack depth showed statistically significant consistency amongst the various spatial resolutions studied, although it declined progressively with increasing difference between the grid sizes being compared. SD best explained the spatial distribution of sub‐grid variability. Topographic variables including slope, wind sheltering, sub‐grid variability in elevation, and potential incoming solar radiation were also significantly correlated with the CV of the snowpack, with the greatest correlation occurring at the 99 × 99 m resolution. At this resolution, stepwise multiple regression models explained more than 70% of the variance, whereas at the 25 × 25 m resolution they explained slightly more than 50%. The results highlight the importance of considering small‐scale variability of the SD for comprehensively representing the distribution of snowpack from available punctual information, and the potential for using SD and other predictors to design optimized surveys for acquiring distributed SD data. Copyright © 2014 John Wiley & Sons, Ltd.

Mapping the annual evolution of snow depth in a small catchment in the Pyrenees using the long-range terrestrial laser scanning

This paper presents the methodology used to develop snow depth distribution maps for a small catchment in the Central Spanish Pyrenees covering 55 ha in a 1:10,000 scale. The Main Map was obtained using LiDAR (light detection and ranging) technology from a long-range Terrestrial Laser Scanner (TLS) in six field surveys undertaken during the 2012 winter–spring period. This technique enabled the acquisition of information at a very high resolution concerning the spatial variability of snow cover, providing snow depth information for remote areas where data acquisition is complex and hazardous. We describe the methodological steps and the quality assessment applied in developing the maps. Comparison with manual measurements confirmed the reliability of the snow depth maps, including areas located at large distances from the scanner (800 m). This method provides a promising tool for future investigations of snow dynamics in mountainous environments.

Mapping snow depth across the West Midlands using social media‐generated data

WeatherVolume 68, Issue 3 p. 82-82 Research Article Mapping snow depth across the West Midlands using social media-generated data Catherine L. Muller, Corresponding Author Catherine L. Muller Birmingham Urban Climate Laboratory (BUCL), School of Geography, Earth and Environmental Sciences, University of Birmingham Correspondence to: Catherine L. Muller c.l.muller@bham.ac.ukSearch for more papers by this author Catherine L. Muller, Corresponding Author Catherine L. Muller Birmingham Urban Climate Laboratory (BUCL), School of Geography, Earth and Environmental Sciences, University of Birmingham Correspondence to: Catherine L. Muller c.l.muller@bham.ac.ukSearch for more papers by this author First published: 26 February 2013 https://doi.org/10.1002/wea.2103Citations: 12Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume68, Issue3March 2013Pages 82-82 RelatedInformation

Lidar measurement of snow depth: a review

AbstractLaser altimetry (lidar) is a remote-sensing technology that holds tremendous promise for mapping snow depth in snow hydrology and avalanche applications. Recently lidar has seen a dramatic widening of applications in the natural sciences, resulting in technological improvements and an increase in the availability of both airborne and ground-based sensors. Modern sensors allow mapping of vegetation heights and snow or ground surface elevations below forest canopies. Typical vertical accuracies for airborne datasets are decimeter-scale with order 1 m point spacings. Ground-based systems typically provide millimeter-scale range accuracy and sub-meter point spacing over 1 m to several kilometers. Many system parameters, such as scan angle, pulse rate and shot geometry relative to terrain gradients, require specification to achieve specific point coverage densities in forested and/or complex terrain. Additionally, snow has a significant volumetric scattering component, requiring different considerations for error estimation than for other Earth surface materials. We use published estimates of light penetration depth by wavelength to estimate radiative transfer error contributions. This paper presents a review of lidar mapping procedures and error sources, potential errors unique to snow surface remote sensing in the near-infrared and visible wavelengths, and recommendations for projects using lidar for snow-depth mapping.

An algorithm for generating soil moisture and snow depth maps from microwave spaceborne radiometers: HydroAlgo

Abstract. A systematic and timely monitoring of land surface parameters that affect the hydrological cycle at local and global scales is of primary importance in obtaining a better understanding of geophysical processes and in managing environmental resources as well as natural disasters. Soil moisture and snow water equivalent are two quantities that play a major role in these applications. In this paper an algorithm for hydrological purposes (called hereinafter HydroAlgo), which is able to generate maps of snow depth (SD) and soil moisture content (SMC) from AMSR-E data, has been developed and implemented within the framework of the JAXA ADEOS-II/AMSR-E and GCOM/AMSR-2 programs, as well as of a project of the Italian Space Agency that is devoted to civil protection from floods and landslides. As auxiliary output, the algorithm also generates maps of vegetation biomass (VB). An initial phase of pre-processing includes the improvement of spatial resolution, as well as masking for urban areas, water bodies, and dense vegetation. The algorithm was then split into two branches, the first of which focused on the retrieval of SMC and the second, on SD. Both parameters were retrieved using Artificial Neural Network (ANN) methods. The algorithm was calibrated using a wide set of experimental data collected on three sites: Mongolia and Australia (for SMC), and Siberia (for SD), integrated with model simulations. These results were then validated by comparing the algorithm outputs with experimental data collected on two additional sites: a part of a watershed in Northern Italy, and a large portion of Scandinavia. An additional test of the algorithm was also performed on a large scale, and included sites characterized by differing climatic and meteorological conditions.

Terra MODIS 영상과 지상 적설심 자료를 이용한 적설분포도 구축기법 연구

본 연구는 2001년 1월, 2004년 3월, 2005년 12월 그리고 2010년 1월의 4개 폭설사상을 대상으로 3가지 적설심분포도를 구축하고 그 결과를 비교하고자 하였다. 첫째는 우리나라 76개의 지상기상관측소의 최심적설자료를 대상으로 IDW (Inverse Distance Weighting) 기법을 적용하여 구축한 경우(Snow Depth Map; SDM), 둘째는 SDM를 Terra MODIS (MODerate resolution Imaging Spectroradiometer) 영상에 의한 적설분포지역(Snow Cover Area; MODIS SCA)으로 추출한 경우(SDM+MODIS SCA; SDM_M), 셋째는 둘째 경우에 DEM (Digital Elevation Model)을 이용하여 고도에 따른 적설심 감률을 고려한 경우(SDM_M+DEM; SDM_MD)이다. 4개년도의 적설분포도를 작성한 결과, Terra MODIS 영상에 의한 적설분포면적은 남한면적(<TEX>$99,575.3km^2$</TEX>)의 62.9%, 44.1%, 52.0%, 69.0% 였다. 3가지 경우에 대한 평균 적설심을 비교한 결과, SDM보다 SDM_M이 각각 0.9cm, 1.9cm, 0.8cm, 1.5cm 작게, SDM_M보다 고도를 고려한 SDM_MD는 1.3cm, 0.9cm, 0.4cm, 1.2cm 크게 나타났다. This study is to make snowfall distribution map for the 4 heavy snowfall events of January 2001, March of 2004, December of 2005 and January of 2010, and compare the results for three cases of construction methods. The cases are to generate the map by applying IDW(Inverse Distance Weighting) interpolation to 76 ground measured snowfall point data (Snow Depth Map; SDM), mask out the SDM with the MODIS snow cover area (MODIS SCA) of Terra MODIS (MODerate resolution Imaging Spectroradiometer) (SDM+MODIS SCA; SDM_M), and consider the snowdepth lapse rate of snowfall by elevation (Digital Elevation Model; DEM) to the second case (SDM_M+DEM; SDM_MD). By applying the MODIS SCA, the SCA of 4 events was 62.9%, 44.1%, 52.0%, and 69.0% for the area of South Korea. For the average snow depth, the SDM_M decreased 0.9cm, 1.9cm, 0.8cm, and 1.5cm compared to SDM and the SDM_MD increased 1.3cm, 0.9cm, 0.4cm, and 1.2cm respectively.

Can snow depth be used to predict the distribution of the high Arctic aphid Acyrthosiphon svalbardicum (Hemiptera: Aphididae) on Spitsbergen?

BackgroundThe Svalbard endemic aphid Acyrthosiphon svalbardicum (Heikinheimo, 1968) is host specific to Dryas octopetala L. ssp octopetala (Rosaceae). It has been hypothesized that the aphid is present on those areas with a thin winter snow cover and which therefore clear of snow earlier in the season. This early snow clearance results in a longer growing period and allows the aphid to experience at least the minimum number of degree days required to complete its life cycle. However, this hypothesis lacked a detailed field validation. We aimed to test the relationship between the aphid distribution and time of snow clearance at landscape scale, mapping snow depth at peak of snow accumulation for the two succeeding years 2009 and 2010 and examining site occupancy and plant phenology the following summers. Additionally, the distribution range mapped by Strathdee & Bale (1995) was revisited to address possible changes in range along the coast of the fjord.ResultsA linear relation between snow depth and timing of snow melt was found but with strong inter-annual and landscape variation. Both snow depth and plant phenology were found to affect patch occupancy. In August, the aphid, at the three life stages scored (viviparae, oviparae/males and eggs), was present most frequently in those D. octopetala patches with the most advanced plant phenology and which showed shallower snow depths in spring. However, many patches predicted to contain aphids were empty. The aphid distribution range has expanded 4.7 km towards the fjord mouth from 1995.ConclusionsSnow depth alone, and hence date of snow clearance, cannot precisely define species distribution at landscape scale, as this cannot explain why are they unoccupied patches under shallow snow depths with advanced plant phenology. We nonetheless present a model Arctic system that could form the basis for long term monitoring for climate- driven species shifts.

Mapping snow depth return levels: smooth spatial modeling versus station interpolation

Abstract. For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth) computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965–1966 to 2007–2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.

A map analysis of patterned‐ground along a North American Arctic Transect

Arctic patterned‐ground features have been described individually, but never examined as parts of integrated landscape/ecosystems that vary along the Arctic climate gradient. Here we examine the complex interrelationships between patterned ground, climate, vegetation and soil along a north‐south transect through all five bioclimate subzones of the North American Arctic. We mapped the vegetation, biomass, end‐of‐summer thaw depths, and snow cover on twenty 10 × 10‐m grids. The vegetation maps illustrate the transition of vegetation types and patterns from north to south. Biomass maps showed lower biomass in the centers of patterned‐ground features than in areas between features, and increasing biomass from north to south. Thaw‐depth maps showed deeper thaw in the centers of features than between features, and shallow thaw on the north and south ends of the transect. Snow depth maps showed less snow on patterned‐ground features subject to differential frost heave compared to areas between features which did not heave, and a north‐south gradient of increasing snow depth. The maps also documented the change from small nonsorted polygons to larger nonsorted circles from north to south, and increasing pattern size with moisture. Principal components analysis revealed underlying relationships between patterned‐ground landscapes and measured vegetation and environmental variables. Climate in combination with the vegetation was the most important factor affecting patterned ground on zonal sites, but soil moisture, texture and chemistry were also important.

Continuous mapping of distributed snow depth for mobility models using shaped solutions

Abstract High-resolution ground vehicle mobility models are used by the U.S. Army to investigate new vehicle concepts, conduct force-on-force simulations, and–in the future–support terrain analysis for battle planning. Use of these models in winter conditions requires a method to realistically represent the distribution of snow depth and density over a landscape. The objective of this work was to develop a method to use a recently identified pinched-cone-shaped solution domain to map snow properties. The method was developed with stringent computational and data storage efficiency requirements in mind and is demonstrated by mapping snow depth on the Ethan Allen Firing Range in northern Vermont, USA. The pinched-cone-shaped plots in a cylindrical coordinate system r = F ( θ , z ) where the no-slope snow depth is taken as the vertex (0,0), the radii ( r ) represent the snow depth relative to the no-slope case, and r is a function of azimuth ( θ ) and terrain slope ( z ). The pinched-cone equation uses a circular function with one or two coefficients that describe the slope-azimuth-dependent expansion of the pinched cone as snow depth differentiation increases with increased terrain slope. Equations that relate the expansion coefficients and no-slope snow depth to elevation then permit continuous mapping of snow depth with respect to terrain slope, azimuth, and elevation. The mapping is applied within five forest categories (open, sparse, deciduous, mixed, and conifer). The influence of snow depth and terrain slope on vehicle speed predictions is illustrated.