Snow Depth Measurements Research Articles

Regional features of present winter snow accumulation variability in the North Eurasia from data of observations, reanalysis and satellites

The paper presents data on winter snow accumulation on the Russian territory obtained from different sources: ground measurements of snow depth on meteorological stations, water snow equivalent calculated from data of route surveys, reanalysis, and satellite observations. Three large regions were isolated which are distinguished by characters of the snow depth fluctuations and their long-term tendencies. Comparison of the trends calculated from data of reanalysis and satellite measurements demonstrated essential disagreement of them with data of in situ observations.

The ‘teflon basin’ myth: hydrology and hydrochemistry of a seasonally snow-covered catchment

Background: Snow and ice melt provide sensitive indicators of climate change and serve as the primary source of stream flow in alpine basins.Aims: We synthesise the results of hydrological and hydrochemical studies during the period 1995–2014, building on a long history of earlier work focused on snow and water on Niwot Ridge and the adjacent Green Lakes Valley (GLV), which is part of the Niwot Ridge Long Term Ecological Research site (NWT LTER).Methods: These studies are discussed in the context of how snow, snowmelt and runoff reflect changing local climate. We review recent results of snow, snowmelt, hydrology and hydrochemistry from the plot to the basin scale, utilising new tools such as continuous global positioning system (GPS) measurements of snow depth, along with remotely-sensed measurements of snow-covered area and melt, combined with long-term measurements of snow properties, discharge and solute and isotopic content of water.Results and Conclusions: Surface–groundwater interactions are important components of water quantity and quality in alpine basins. Some or most snowmelt infiltrates underlying soils and bedrock, transporting soil and bedrock products to surface waters. Infiltrating snowmelt, along with increased melt of stored ice, increases the hydrologic connectivity between the terrestrial and aquatic systems. Alpine basins are being impacted by increases in atmospheric nitrogen deposition, which has caused changes in soil microbial processes such as nitrification. Nitrate, dissolved organic carbon and dissolved organic nitrogen are thus flushed from soils and talus to streams. Our long-term results show that alpine catchments, such as Green Lake 4 at NWT LTER+, have the greatest sensitivity and least resilience to climate warming, with any warming leading to increased water yields.

Ecological Responses to 52 Years of Experimental Snow Manipulation in High-Alpine Cushionfield, Old Man Range, South-Central New Zealand

Periodic monitoring over 52 years have revealed temporal changes in the vegetation and floristic patterns associated with what has been acclaimed to be the world’s oldest known experimental snow fence, which is located on an exposed high-alpine cushionfield on the Old Man Range in south-central New Zealand. The induced pattern of intermittent snow-lie has been increased by the fence from the normal ∼140 days to more than 200 days (and up to 140 cm deep), estimated from subsurface soil temperatures, together with periodic observations and measurements of snow depth. Some but not all species associated with natural snowbanks on the range have established in areas of induced snow accumulation. The timing of species establishment was not obviously related to relevant features of the local snowbank species or their distribution on the range, but the abundance of various plant species and their functional traits across zones of snowmelt point to competition and plant productivity being associated with the deepest snow in the lee of the fence. In addition, three of the several measured physical and chemical soil factors (Mg, available PO4 3-, and C:N) have differentiated significantly in relation to the vegetation and snow-lie pattern at year 52, although these seem not to be relevant on the basis of the pattern of the same factors in two nearby natural snowbanks on the range.

Probabilistic Forecasts of Snow Water Equivalent and Runoff in Mountainous Areas*

Abstract Good initial states can improve the skill of hydrological ensemble predictions. In mountainous regions such as Switzerland, snow is an important component of the hydrological system. Including estimates of snow cover in hydrological models is of great significance for the prediction of both flood and streamflow drought events. In this study, gridded snow water equivalent (SWE) maps, derived from daily snow depth measurements, are used within the gridded version of the conceptual hydrological model Precipitation Runoff Evapotranspiration Hydrotope (PREVAH) to replace the model SWE at initialization. The ECMWF Ensemble Prediction System (ENS) reforecast is used as meteorological input for 32-day forecasts of streamflow and SWE. Experiments were performed in several parts of the Alpine Rhine and the Thur River. Predictions where modeled SWE estimates were replaced with SWE maps could successfully enhance the predictability of SWE up to a lead time of 25 days, especially at the beginning and the end of the snow season. Additionally, the prediction of the runoff volume was improved, particularly in catchments where the snow accumulation, and thus the runoff volume, had been greatly overestimated. These improvements in predictions have been made without affecting the ability of the forecast system to discriminate between the different runoff volumes observed. A spatial similarity score was first used in the context of SWE forecast verification. This confirmed the findings of the time series analysis and yielded additional insight on regional patterns of extended range SWE predictability.

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.

The Demand for Winter Sports: Empirical Evidence for the Largest French Ski-Lift Operator

This paper investigates the determinants of long-run winter tourism demand for French ski resorts. The data are based on skier visits to the largest ski-lift operator in the world (Compagnie des Alpes) for the winter seasons 1993/1994 to 2011/2012. Using dynamic panel data models for six ski resorts, the study finds relatively low income and price elasticities in absolute terms, with long-run elasticities of 0.64 and −0.40, respectively. Furthermore, the study finds that snow depth measured at the weather station Col de Porte — located at the medium elevation of 1,325 metres above sea level — is a significant predictor of skier days in the high-elevation ski areas. However, the magnitude of the effect of poor snow years is very small: on average, low-snow winter seasons (such as 1989/1990 or 2006/2007) will lead to a reduction in skier visits of about 2.5%. The results are not sensitive with respect to the measurement and timing of snow depth.

Snow depth on Arctic sea ice derived from radar: In situ comparisons and time series analysis

AbstractThe snow radar being flown on NASA's Operation IceBridge, ongoing aircraft campaigns to the Arctic and the Antarctic are providing unique observations of the depth of snow on the sea ice cover. In this paper, we focus on the radar‐derived snow depth results from the 2009–2012 Arctic campaigns. We develop and evaluate the use of a distinct snow layer tracker to measure snow depth based on a Support Vector Machine (SVM) supervised learning algorithm. The snow radar is designed to detect both the air‐snow and snow‐ice interfaces using ultrawideband frequencies from 2 to 8 GHz. The quality, errors, and repeatability of the snow radar snow depth estimates are examined, based on comparisons with in situ data obtained during two separate sea ice field campaigns, the GreenArc 2009 and the CryoVEx 2011 campaigns off Greenland in the Lincoln Sea. Finally, we analyze 4 years (2009–2012) of three annually repeated sea ice flight lines obtained in early spring, located off Greenland and the Canadian Arctic. We examine the annual variations of snow depth differences between perennial and seasonal ice when available. Overall, the snow layer tracker produced consistent, accurate results for snow depths between 0.10 and ∼0.60 m. This was confirmed with comparisons with the two data sets from the in situ measurement campaigns as well as with the time series analysis, and is consistent with other published results.

A snow cover climatology for the Pyrenees from MODIS snow products

Abstract. The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (w.e.) and 150 mm, respectively, for both MOD10A1 and MYD10A1. κ coefficients are within 0.74 and 0.92 depending on the product and the variable for these thresholds. However, we also find a seasonal trend in the optimal SWE and SD thresholds, reflecting the hysteresis in the relationship between the depth of the snowpack (or SWE) and its extent within a MODIS pixel. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both data sets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decrease over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gap-filling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band and aspect classes. There is snow on the ground at least 50% of the time above 1600 m between December and April. We finally analyze the snow patterns for the atypical winter 2011–2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

Terrestrial laser scanner for spatial snow depth and density measurements in mountain environment

Climate change is the main factor that induces alterations in the hydrological cycle. In this context, mountains represent the first indicators of climate change, because they respond rapidly and intensely to climatic and environmental modifications. Obtaining reliable scenarios on water resources availability is a prerequisite to planning management measures as a matter of fact snowfall and the resulting seasonal snow cover represent an important source of water, including surface and subsurface flows.The applicability of terrestrial laser scanning (TLS) techniques to measure snow depth and snow cover was investigated in an area located in the Mascognaz Valley (Ayas municipality, Regione Autonoma Valle d'Aosta, Italy). First aim of this job is recognize the accumulation areas from melting areas through the generation of high dense digital snow elevation model. In this way is possible better understand the snowmelt process in mountain areas that contributes widely to the groundwater recharge. Secondly, the TLS was also used to analyze a snow cross section in order to define a correlation between the reflectance of laser signal and the density of snowpack. In fact, the snow density measure represents a crucial information for the snow water equivalent (S.W.E.) evaluation. Finally, analyzing the spatial snow-depth distribution is important to individuate the potential avalanche-starting zones.

Extraction of Heavy Snowfall Vulnerable Area for 3 Representative Facilities Using GIS and Remote Sensing Techniques

This study is to analyze the heavy snowfall vulnerable area of snow load design criteria for greenhouse, cattle shed and building using ground measured snow depth data and Terra MODIS snow cover area(SCA). To analyze the heavy snowfall vulnerable area, Terra MODIS satellite images for 12 years(2001–2012) were used to obtain the 2014년 10월 20일 접수 Received on October 20, 2014 / 2014년 12월 4일 수정 Revised on December 4, 2014 / 2014년 12월 31일 심사완료 Accepted on December 31, 2014 * 이 논문은 2010년도 정부(미래창조과학부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(NRF2010-0029194). 1 건국대학교 사회환경시스템공학과 Dept. of Civil & Environmental Systems Engineering, Konkuk University 2 한국수자원공사 K-water 연구원 수자원연구소 Water Resources Research Center, K-water Institute, Korea Water Resources Corporation ※ Corresponding Author E-mail : kimsj@konkuk.ac.kr Extraction of Heavy Snowfall Vulnerable Area for 3 Representative Facilities Using GIS and Remote Sensing Techniques 2 characteristics of snow depth and snow cover areas respectively. By comparing the snow load design criteria for greenhouse(cm), cattle shed(kg/m2), and building structure(kN/m2) with the snow depth distribution results by Terra MODIS satellite images, the facilities located in Jeolla-do, Chungcheong-do, and Gangwon-do areas were more vulnerable to exceed the current design criteria.

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.

Weather SDM: estimating snow density with high precision using snow depth and local climate

Snow density is an important measure in hydrology used to convert snow depth to the snow water equivalent (SWE). A model developed by Sturm, Tara and Liston predicts the snow density by using snow depth, the snow age and a snow class defined by the location. In this work this model is extended to include location and seasonal weather-specific variables. The model is named Weather Snow Density Model (Weather SDM). A Bayesian framework is chosen, and the model is fitted to and tested for 4,040 Norwegian snow depth and densities measurements between 1998 and 2011. The final model improved the snow density predictions for the Norwegian data compared to the model of Sturm by up to 50%. Further, the Weather SDM is extended to utilize local year-specific snow density observations (Weather&ObsDensity SDM). This reduced the prediction error an additional 16%, indicating a significant improvement when utilizing information provided by annual snow density measurements.

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Abstract. Repeated light detection and ranging (lidar) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km2 lidar-derived data set of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically based energy-and-mass-balance snow model with satellite, airborne and automated ground-based observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the conterminous United States. Independent validation data are scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation data set with substantial geographic coverage. Within 12 distinctive 500 × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 lidar acquisitions. This supplied a data set for constraining the uncertainty of upscaled lidar estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled lidar snow depths were then compared to the SNODAS estimates over the entire study area for the dates of the lidar flights. The remotely sensed snow depths provided a more spatially continuous comparison data set and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between lidar observations and SNODAS estimates were most drastic, providing insight into the causal influences of natural processes on model uncertainty.

Constraints on snow accumulation and firn density in Greenland using GPS receivers

AbstractData from three continuously operating GPS sites located in the interior of the Greenland ice sheet are analyzed. Traditionally these kinds of GPS installations (where the GPS antenna is placed on a pole deployed into the firn) are used to estimate the local horizontal speed and direction of the ice sheet. However, these data are also sensitive to the vertical displacement of the pole as it moves through the firn layer. A new method developed to measure snow depth variations with reflected GPS signals is applied to these GPS data from Greenland. This method provides a constraint on the vertical distance between the GPS antenna and the surface snow layer. The vertical positions and snow surface heights are then used to assess output from surface accumulation and firn densification models, showing agreement better than 10% at the sites with the longest records. Comparisons between the GPS reflection method and in situ snow sensors at the Dye-2 site show good agreement, capturing the dramatic changes observed in Greenland during the 2012 summer melt season. The geocentric elevation of the snow surface can be inferred by subtracting the snow surface height estimates from the vertical position measurements. It should be possible to use those surface elevation estimates to help validate elevation results obtained from satellite altimetry.

Verification of Snow-depth Measurement Technique Based on Snow Temperature Profile

Verification of Snow-depth Measurement Technique Based on Snow Temperature Profile

Elevation dependency of mountain snow depth

Abstract. Elevation strongly affects quantity and distribution patterns of precipitation and snow. Positive elevation gradients were identified by many studies, usually based on data from sparse precipitation stations or snow depth measurements. We present a systematic evaluation of the elevation–snow depth relationship. We analyse areal snow depth data obtained by remote sensing for seven mountain sites near to the time of the maximum seasonal snow accumulation. Snow depths were averaged to 100 m elevation bands and then related to their respective elevation level. The assessment was performed at three scales: (i) the complete data sets (10 km scale), (ii) sub-catchments (km scale) and (iii) slope transects (100 m scale). We show that most elevation–snow depth curves at all scales are characterised through a single shape. Mean snow depths increase with elevation up to a certain level where they have a distinct peak followed by a decrease at the highest elevations. We explain this typical shape with a generally positive elevation gradient of snow fall that is modified by the interaction of snow cover and topography. These processes are preferential deposition of precipitation and redistribution of snow by wind, sloughing and avalanching. Furthermore, we show that the elevation level of the peak of mean snow depth correlates with the dominant elevation level of rocks (if present).

Impact of climate and land cover changes on snow cover in a small Pyrenean catchment

The seasonal snow in the Pyrenees Mountains is an essential source of runoff for hydropower production and crop irrigation in Spain and France. The Pyrenees are expected to undergo strong environmental perturbations over the 21st century because of climate change (rising temperatures) and the abandonment of agro-pastoral areas (reforestation). Both changes are happening at similar timescales and are expected to have an impact on snow cover. The effect of climate change on snow in the Pyrenees is well understood , but the effect of land cover changes is much less documented. Here, we analyze the response of snow cover to a combination of climate and land cover change scenarios in a small Pyrenean catchment (Bassies, 14.5 km 2 , elevation range 940–2651 m a.s.l.) using a distributed snowpack evolution model. Climate scenarios were constructed from the output of regional climate model projections, whereas land cover scenarios were generated based on past observed changes and an inductive pattern-based model. The model was validated over a snow season using in situ snow depth measurements and high-resolution snow cover maps derived from SPOT (Satellite Pour l'Observation de la Terre – Earth Observation Satellite) satellite images. Model projections indicate that both climate and land cover changes reduce the mean snow depth. However, the impact on the snow cover duration is moderated in reforested areas by the shading effect of trees on the snow surface radiation balance. Most of the significant changes are expected to occur in the transition zone between 1500 m a.s.l. and 2000 m a.s.l. where (i) the projected increase in air temperatures decreases the snow fraction of the precipitation and (ii) the land cover changes are concentrated. However, the consequences on the runoff are limited because most of the meltwater originates from high-elevation areas of the catchment, which are less affected by climate change and reforestation.

Inverse Modeling of GPS Multipath for Snow Depth Estimation—Part I: Formulation and Simulations

Snowpacks provide reservoirs of freshwater. The amount stored and how fast it is released by melting are vital information for both scientists and water supply managers. GPS multipath reflectometry (GPS-MR) is a new technique that can be used to measure snow depth. Signal-to-noise ratio data collected by GPS instruments exhibit peaks and troughs as coherent direct and reflected signals go in and out of phase. These interference fringes are used to retrieve the unknown land surface characteristics. In this two-part contribution, a forward/inverse approach is offered for GPS-MR of snow depth. Part I starts with the physically based forward model utilized to simulate the coupling of the surface and antenna responses. A statistically rigorous inverse model is presented and employed to retrieve parameter corrections responsible for observation residuals. The unknown snow characteristics are parameterized, the observation/parameter sensitivity is illustrated, the inversion performance is assessed in terms of its precision and its accuracy, and the dependence of model results on the satellite direction is quantified. The latter serves to indicate the sensing footprint of the reflection.

Estimation of regional snowline elevation (RSLE) from MODIS images for seasonally snow covered mountain basins

Summary We present a method for estimation of regional snowline elevation (RSLE) from satellite data for seasonally snow covered mountain basins. The methodology is based on finding an elevation for which the sum of snow covered pixels below and land pixels above the RSLE is minimized for each day. The methodology is tested with MODIS daily snow cover product in the period 2000–2013 in the upper Vah basin (Slovakia). The accuracy is evaluated by daily snow depth measurements at seven climate stations and additional snow course measurements at 16 profiles in the period 2000–2013. The results show that RSLE allows accurate estimation of snowline elevation. For the RSLE estimation, two thresholds need to be considered. The thresholds of maximum cloud coverage and minimum number of snow pixels considerably affect the number of days (images) available for estimation. The sensitivity evaluation indicates that the cloud threshold has less effect on the accuracy than the minimum snow threshold. Setting cloud and minimum snow thresholds to 70% and 5% respectively, results in an average RSLE estimation accuracy of 86% at climate stations. The accuracy in the forest is 92% in the winter months and drops to 70% in April. The main factors that control the accuracy and scatter around the snowline are vegetation cover and shading of terrain. The results show that spatial patterns of misclassification correspond well with forest cover and potential insolation duration in winter. The developed RSLE method is more accurate than the previously used methods of snowline elevation estimation, it decreases the scatter around the snowline and can be potentially applied in an improved cloud reduction in MODIS products as well.

Are flat‐field snow depth measurements representative? A comparison of selected index sites with areal snow depth measurements at the small catchment scale

AbstractSingle or a limited number of point observations, such as from index stations, are commonly assumed to be representative for the snow cover of larger areas in many applications. This study presents a systematic investigation of the relationship between point observations and areal mean snow depths ranging from the scale of tens of metres to entire catchments. We analyse aerial snow depth information from four mountain regions in the European Alps, one in the Spanish Pyrenees and one in the Canadian Rocky Mountains, obtained from airborne laser scanning surveys. This rich data set allowed to compare point values with snow cover statistics, reflecting the real snow depth distribution of the investigation areas. We present two contrasting approaches in order to assess the representativeness of typical flat‐field snow depth measurements. In the first approach, we define potential index stations based on topographic characteristics as commonly applied for snow cover monitoring stations. The point observations of these index stations are then compared with the mean values in their vicinities. We show that most of the index stations strongly overestimate the snow depth of the catchment and of their surrounding area at distances of several hundreds of metres. Results confirm the expectation that the larger the support area, the smaller the difference to the mean of the complete catchment. The second approach was to analyse topographic characteristics of all cells with snow depths that deviated less than 10% from the catchment mean. It appears that these representative cells are rather randomly distributed and cannot be identified a priori. In summary, our results show large potential biases of index stations with respect to snow distribution and therefore also snow water equivalent. Copyright © 2014 John Wiley & Sons, Ltd.