Snow distribution in a steep mid-latitude alpine catchment

Snow distribution patterns are still poorly understood in steep alpine catchments because of mass redistribution from wind and avalanching. Snow models rarely operate with sufficient resolution, physics or input data to resolve this issue explicitly, and existing sub-grid parameterisations are rarely tested in this type of terrain. To address this issue daily snow cover observations, obtained from a ground-based camera, are combined with a snow melt model to estimate the spatial distribution of snow water equivalent (SWE) in a mountainous alpine catchment. Results show the importance of slope in controlling the spatial distribution of SWE and snow duration. This distribution is linked to the physical process of gravitational transport, where there is removal of snow from steep slopes and preferential deposition on moderate-angle slopes. From a modelling perspective, if sub-grid snow variability is parameterised using a log-normal probability distribution (as is common in hydrological and land-use models) then ignoring steep/shallow slope differences leads to an overestimation of melt at the beginning of the melt season, and a premature end to the snow melt season. When modelling SWE in complex terrain, care should be taken to consider reduced SWE on steep slopes.     

Characteristics of snow cover in the Hindukush,Karakoram and Himalaya region using Landsat satellite data

Estimation of snow cover characteristics (snow grain size, snow contamination, snow depth and liquid water content) from satellite data are important components for many hydrological models used for the water resource management. This research aimed to use Landsat ETM+ (Enhanced Thematic Mapper Plus) data to estimate the snow pack characteristics in northern Pakistan. The Normalized Difference Snow Index (NDSI), Snow Contamination Index (SCI) and Snow Grain Size Index (SGI) are applied to estimate the snow cover characteristics in northern Pakistan for the first time. Qualitative maps are generated to show the snow cover distribution, snow contamination concentration and snow grain size distribution over snow cover area. Our results show that NDSI, SCI and SGI can be effectively used to identify snow area, contaminated snow and ageing snow. Furthermore, the results of the current study indicate that in the HKH region 99.8% of the snow is least contaminated whereas 94.50% of the area has fine snow grain size. As no such attempt in the past has been made in northern Pakistan, it is envisaged that the results of this study will be helpful in planning remote sensing data for the water resource management and in characterizing the snow cover for the climate change applications in the region. Copyright © 2011 John Wiley & Sons, Ltd.     

Snow cover variability and snowmelt in a high‐altitude ungauged catchment

Snow variability is an integrated indicator of climate change, and it has important impacts on runoff regimes and water availability in high‐altitude catchments. Remote sensing techniques can make it possible to quantitatively detect the snow cover changes and associated hydrological effects in those poorly gauged regions. In this study, the spatial–temporal variations of snow cover and snow melting time in the Tuotuo River basin, which is the headwater of the Yangtze River, were evaluated based on satellite information from the Moderate Resolution Imaging Spectroradiometer snow cover product, and the snow melting equivalent and its contribution to the total runoff and baseflow were estimated by using degree–day model. The results showed that the snow cover percentage and the tendency of snow cover variability increased with rising altitude. From 2000 to 2012, warmer and wetter climate change resulted in an increase of the snow cover area. Since the 1960s, the start time for snow melt has become earlier by 0.9–3 days/10a and the end time of snow melt has become later by 0.6–2.3 days/10a. Under the control of snow cover and snow melting time, the equivalent of snow melting runoff in the Tuotuo River basin has been fluctuating. The average contributions of snowmelt to baseflow and total runoff were 19.6% and 6.8%, respectively. Findings from this study will serve as a reference for future research in areas where observational data are deficient and for planning of future water management strategies for the source region of the Yangtze River. Copyright © 2015 John Wiley & Sons, Ltd.     

Ice processes on medium‐sized north‐temperate lakes

Lake ice supports a range of socio‐economic and cultural activities including transportation and winter recreational actives. The influence of weather patterns on ice‐cover dynamics of temperate lakes requires further understanding for determining how changes in ice composition will impact ice safety and the range of ecosystem services provided by seasonal ice cover. An investigation of lake ice formation and decay for three lakes in Central Ontario, Canada, took place over the course of two winters, 2015–2016 and 2016–2017, through the use of outdoor digital cameras, a Shallow Water Ice Profiler (upward‐looking sonar), and weekly field measurements. Temperature fluctuations across 0°C promoted substantial early season white ice growth, with lesser amounts of black ice forming later in the season. Ice thickening processes observed were mainly through meltwater, or midwinter rain, refreezing on the ice surface. Snow redistribution was limited, with frequent melt events limiting the duration of fresh snow on the ice, leading to a fairly uniform distribution of white ice across the lakes in 2015–2016 (standard deviations week to week ranging from 3 to 5 cm), but with slightly more variability in 2016–2017 when more snow accumulated over the season (5 to 11 cm). White ice dominated the end‐of‐season ice composition for both seasons representing more than 70% of the total ice thickness, which is a stark contrast to Arctic lake ice that is composed mainly of black ice. This research has provided the first detailed lake ice processes and conditions from medium‐sized north‐temperate lakes and provided important information on temperate region lake ice characteristics that will enhance the understanding of the response of temperate lake ice to climate and provide insight on potential changes to more northern ice regimes under continued climate warming.     

Snow distribution from SSM/I and its relationships to the hydroclimatology of the Mackenzie River Basin,Canada

The spatial and temporal distribution of snow cover extent (SCE) and snow water equivalent (SWE) play vital roles in the hydrology of northern watersheds. We apply remotely sensed Special Sensor Microwave Imager (SSM/I) data from 1988 to 2007 to explore the relationships between snow distribution and the hydroclimatology of the Mackenzie River Basin (MRB) of Canada and its major sub-basins. The Environment Canada (EC) algorithm is adopted to retrieve the SWE from SSM/I data. Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow cover extent products (MOD10A2) are used to estimate the different thresholds of retrieved SWE from SSM/I to classify the land cover as snow or no snow for various sub-basins in the MRB. The sub-basins have varying topography and hence different thresholds that range from 10 mm to 30 mm SWE. The accuracy of snow cover mapping based on the combination of several thresholds for the different sub-basins reaches ≈ 90%. The northern basins are found to have stronger linear relationships between the date on which snow cover fraction (SCF) reaches 50% or when SWE reaches 50% and mean air temperatures, than the southern basins. Correlation coefficients between SCF, SWE, and hydroclimatological variables show the new SCF products from SSM/I perform better than SWE from SSM/I to analyze the relationships with the regional hydroclimatology. Statistical models relating SCF and SWE to runoff indicate that the SCF and SWE from EC algorithms are able to predict the discharge in the early snow ablation seasons in northern watersheds.     

A comparison of MODIS and NOHRSC snow‐cover products for simulating streamflow using the Snowmelt Runoff Model

Remote sensing is an important source of snow‐cover extent for input into the Snowmelt Runoff Model (SRM) and other snowmelt models. Since February 2000, daily global snow‐cover maps have been produced from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS). The usefulness of this snow‐cover product for streamflow prediction is assessed by comparing SRM simulated streamflow using the MODIS snow‐cover product with streamflow simulated using snow maps from the National Operational Hydrologic Remote Sensing Center (NOHRSC). Simulations were conducted for two tributary watersheds of the Upper Rio Grande basin during the 2001 snowmelt season using representative SRM parameter values. Snow depletion curves developed from MODIS and NOHRSC snow maps were generally comparable in both watersheds: satisfactory streamflow simulations were obtained using both snow‐cover products in larger watershed (volume difference: MODIS, 2·6%; NOHRSC, 14·0%) and less satisfactory streamflow simulations in smaller watershed (volume difference: MODIS, −33·1%; NOHRSC, −18·6%). The snow water equivalent (SWE) on 1 April in the third zone of each basin was computed using the modified depletion curve produced by the SRM and was compared with in situ SWE measured at Snowpack Telemetry sites located in the third zone of each basin. The SRM‐calculated SWEs using both snow products agree with the measured SWEs in both watersheds. Based on these results, the MODIS snow‐cover product appears to be of sufficient quality for streamflow prediction using the SRM in the snowmelt‐dominated basins. Copyright © 2005 John Wiley & Sons, Ltd.     

Evaluation of snow products over the Tibetan Plateau

Four satellite‐based snow products are evaluated over the Tibetan Plateau for the 2007–2010 snow seasons. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover daily L3 Global 500‐m grid products (MOD10A1 and MYD10A1), the National Oceanic and Atmospheric Administration Interactive Multisensor Snow and Ice Mapping System (IMS) daily Northern Hemisphere snow cover product and the Advanced Microwave Scanning Radiometer – Earth Observing System Daily Snow Water Equivalent were validated against Thematic Mapper (TM) snow cover maps of Landsat‐5 and meteorological station snow depth observations. The overall accuracy of MOD10A1, MYD10A1 and IMS is higher than 91% against stations observations and than 79% against Landsat TM images. In general, the daily MODIS snow cover products show better performance than the multisensor IMS product. However, the IMS snow cover product is suitable for larger scale (~4km) analysis and applications, with the advantage over MODIS to allow for mitigation for cloud cover. The accuracy of the three products decreases with decreasing snow depth. Overestimation errors are most common over forested regions; the IMS and Advanced Microwave Scanning Radiometer – Earth Observing System Snow Water Equivalent products also show poorer performance that the MODIS products over grassland. By identifying weaknesses in the satellite products, this study provides a focus for the improvement of snow products over the Tibetan plateau. The quantitative evaluation of the products proposed here can also be used to assess their relative weight in data assimilation, against other data sources, such as modelling and in situ measurement networks. Copyright © 2015 John Wiley & Sons, Ltd.     

Projected 21st century climate change on snow conditions over Shasta Dam watershed by means of dynamical downscaling

Snow is an important component of the Earth's climate system and is particularly vulnerable to global warming. It has been suggested that warmer temperatures may cause significant declines in snow water content and snow cover duration. In this study, snowfall and snowmelt were projected by means of a regional climate model that was coupled to a physically based snow model over Shasta Dam watershed to assess changes in snow water content and snow cover duration during the 21st century. This physically based snow model requires both physical data and future climate projections. These physical data include topography, soils, vegetation, and land use/land cover, which were collected from associated organizations. The future climate projections were dynamically downscaled by means of the regional climate model under 4 emission scenarios simulated by 2 general circulation models (fifth‐generation of the ECHAM general circulation model and the third‐generation atmospheric general circulation model). The downscaled future projections were bias corrected before projecting snowfall and snowmelt processes over Shasta Dam watershed during 2010–2099. This study's results agree with those of previous studies that projected snow water equivalent is decreasing by 50–80% whereas the fraction of precipitation falling as snowfall is decreasing by 15% to 20%. The obtained projection results show that future snow water content will change in both time and space. Furthermore, the results confirm that physical data such as topography, land cover, and atmospheric–hydrologic data are instrumental in the studies on the impact of climate change on the water resources of a region.     

博斯腾湖流域积雪变化及影响因素

积雪是西北干旱地区河流的主要补给源,是绿洲的生命线.积雪的时空变化是全球变化的区域响应敏感因子之一,同时也是影响西北干旱地区地表水资源变化的主要因子之一.本研究利用MODIS雪盖产品、地表温度、SSM/I雪深、DEM等数据,通过GIS空间分析及地统计分析功能,系统分析了博斯腾湖流域雪盖、雪深的时空变化规律及其与影响因素之间的关系.研究表明,研究区雪深和雪盖多年月平均值从8月份到1月份达到最大值,到7月份降到最低值.但月最大雪深却出现在3月份.雪盖、雪深与地温相关系数分别达到-0.878、-0.853,与分布高程均值相关系数分别达到-0.626和-0.791.雪深最大值受海拔影响有明显的陡坎效应.从12月到8月份随着时间的推移雪的深度在降低,陡坎向高海拔方向移动.9-11月份雪深在加深,陡坎向低海拔方向移动.同一高程段雪深的变幅反应坡向对雪深的影响,变幅越宽坡向影响越大.并且变幅也有先从低海拔到高海拔移动,然后再回到低海拔的特点.本研究对了解该研究区积雪特性的研究有很大作用,可为在该地区开展融雪径流模拟等研究提供重要的参考信息.     

Evaluating the effect of snow and ice melt in an Alpine headwater catchment and further downstream in the River Rhine

Abstract

The runoff regime of glacierized headwater catchments in the Alps is essentially characterized by snow and ice melt. High Alpine drainage basins influence distant downstream catchments of the Rhine River basin. In particular, during the summer months, low-flow conditions are probable with strongly reduced snow and ice melt under climate change conditions. This study attempts to quantify present and future contributions from snow and ice melt to summer runoff at different spatial scales. For the small Silvretta catchment (103 km²) in the Swiss Alps, with a glacierization of 7%, the HBV model and the glacio-hydrological model GERM are applied for calculating future runoff based on different regional climate scenarios. We evaluate the importance of snow and ice melt in the runoff regime. Comparison of the models indicates that the HBV model strongly overestimates the future contribution of glacier melt to runoff, as glaciers are considered as static components. Furthermore, we provide estimates of the current meltwater contribution of glaciers for several catchments downstream on the River Rhine during the month of August. Snow and ice melt processes have a significant direct impact on summer runoff, not only for high mountain catchments, but also for large transboundary basins. A future shift in the hydrological regime and the disappearance of glaciers might favour low-flow conditions during summer along the Rhine.

Citation Junghans, N., Cullmann, J. & Huss, M. (2011) Evaluating the effect of snow and ice melt in an Alpine headwater catchment and further downstream in the River Rhine. Hydrol. Sci. J. 56(6), 981–993.     

Dynamics of snow ablation in a small Alpine catchment observed by repeated terrestrial laser scans

The spatio‐temporal distribution of snow in a catchment during ablation reflects changes in the total amount of snow water equivalent and is thus a key parameter for the estimation of melt water run‐off. This study explores possible rules behind the spatial variability of snow depth during the ablation season in a small Alpine catchment with complex topography. The snow depth observations are based on more than 160 000 terrestrial laser scanner data points with a spatial resolution of 1 m, which were obtained from 11 scanning campaigns of two consecutive ablation seasons. The analysis suggests that for estimating cumulative snow melt dynamics from the catchment investigated, assessing the initial snow distribution prior to the melt season is more important than addressing spatial differences in the melt behaviour. Snow volume and snow‐covered area could be predicted well using a conceptual melt model assuming spatially uniform melt rates. However, accurate results were only obtained if the model was initialized with a pre‐melt snow distribution that reflected measured mean and standard deviation. Using stratified melt rates on the other hand did not improve the model results. At least for sites with similar meteorological and topographical conditions, the model approach presented here comprises an efficient way to estimate snow depletion dynamics, especially if persistent snow accumulation pattern between years facilitate the characterization of the initial snow distribution prior to the melt. Copyright © 2011 John Wiley & Sons, Ltd.     

Evaluating the potential of a blended passive microwave‐interactive multi‐sensor product for improved mapping of snow cover and estimations of snow water equivalent

The retrieval of Snow Water Equivalent (SWE) from remote sensing satellites continues to be a very challenging problem. In this paper, we evaluate the accuracy of a new SWE product derived from the blending of a passive microwave SWE product based on the Advanced Microwave Sounding Unit (AMSU) with a multi‐sensor snow cover extent product based on the Interactive Multi‐sensor Snow and Ice Mapping System (IMS). The microwave measurements have the ability to penetrate the snow pack, and thus, the retrieval of SWE is best accomplished using the AMSU. On the other hand, the IMS maps snow cover more reliably due to the use of multiple satellite and ground observations. The evolution of global snow cover from the blended, the AMSU and the IMS products was examined during the 2006 snow season. Despite the overall good inter‐product agreement, it was shown that the retrievals of snow cover extent in the blended product are improved when using IMS, with implications for improved microwave retrievals of SWE. In a separate investigation, the skill of the microwave SWE product was also examined for its ability to correctly estimate SWE globally and regionally. Qualitative evaluation of global SWE retrievals suggested dependence on land surface temperature: the lower the temperature, the higher the SWE retrieved. This temperature bias was attributed in part to temperature effects on those snow properties that impact microwave response. Therefore, algorithm modifications are needed with more dynamical adjustments to account for changing snow cover. Quantitative evaluation over Slovakia in central Europe, for a limited period in 2006, showed reasonably good performance for SWE less than 100 mm. Sensitivity to deeper snow decreased significantly. Copyright © 2007 John Wiley & Sons, Ltd.     

Developing machine learning-based snow depletion curves and analysing their sensitivity over complex mountainous areas

A snow depletion curve (SDC), the relationship between snow mass (e.g., snow depth [SD]) and fractional snow cover area (SCF), is essential to parameterize the effect of snowpack within a physically based snow model. Existing SDCs are constructed using traditional statistic methods may not be applicable in complex mountainous areas. In this study, we developed an information fusion framework to define the relationship between SCF and SD as well as 12 auxiliary factors by using a traditional statistical method and four prevailing machine learning (ML) algorithms, which have comprehensively considered the variable conditions that cause spatiotemporal heterogeneity of snow cover. We also performed a single-dimensional sensitivity analysis to investigate the physical rationality of the newly developed SDCs. The Northern Xinjiang, Northwest China, is selected as the study area, and the data from 46 meteorological stations covering five snow seasons from 2010 to 2015 are used. The results illustrated that ML techniques can be used to establish high-accuracy and robust SDCs for complex mountainous areas. Compared with SDCs constructed by traditional statistical, the performance of the four ML-based SDCs is significantly improved, the RMSE values can be reduced by 50%, R² above 0.75, and an average relative variance close to 0. ML-based SDCs predicted SCF values showed a range of sensitivities to different input variables (e.g., Land surface temperature, aspect, longwave radiation and land cover type), in addition to SD, that were physically representative of effects that snow cover is sensitive to. Moreover, the complexity of SDCs can be reduced by removing insensitive input variables.     

Accuracy assessment of the MODIS snow products

A suite of Moderate‐Resolution Imaging Spectroradiometer (MODIS) snow products at various spatial and temporal resolutions from the Terra satellite has been available since February 2000. Standard products include daily and 8‐day composite 500 m resolution swath and tile products (which include fractional snow cover (FSC) and snow albedo), and 0·05° resolution products on a climate‐modelling grid (CMG) (which also include FSC). These snow products (from Collection 4 (C4) reprocessing) are mature and most have been validated to varying degrees and are available to order through the National Snow and Ice Data Center. The overall absolute accuracy of the well‐studied 500 m resolution swath (MOD10_L2) and daily tile (MOD10A1) products is ～93%, but varies by land‐cover type and snow condition. The most frequent errors are due to snow/cloud discrimination problems, however, improvements in the MODIS cloud mask, an input product, have occurred in ‘Collection 5’ reprocessing. Detection of very thin snow (<1 cm thick) can also be problematic. Validation of MOD10_L2 and MOD10A1 applies to all higher‐level products because all the higher‐level products are all created from these products. The composited products may have larger errors due, in part, to errors propagated from daily products. Recently, new products have been developed. A fractional snow cover algorithm for the 500 m resolution products was developed, and is part of the C5 daily swath and tile products; a monthly CMG snow product at 0·05° resolution and a daily 0·25° resolution CMG snow product are also now available. Similar, but not identical products are also produced from the MODIS on the Aqua satellite, launched in May 2002, but the accuracy of those products has not yet been assessed in detail. Published in 2007 by John Wiley & Sons, Ltd.     

SNOW COVER CHARACTERIZATION USING MULTIBAND FMCW RADARS

The use of radars to characterize the physical properties of a snow cover offers an attractive alternative to manual snow pit measurements. Radar techniques are non-invasive and have the potential to cover large areas of a snow-covered terrain. A promising radar technique for snow cover studies is the frequency modulated continuous wave (FMCW) radar. The use of a multiband radar approach for snow cover studies was investigated in order to fully exploit the capabilities of FMCW radars. FMCW radars operating at and near the C-, X- and K_a-bands were used to obtain radar profiles over a wide range of snow cover conditions. These frequency-dependent radar signatures were used to identify important snow cover features such as ice and depth hoar layers. Snow grain size information was also obtained from the frequency-dependent scattering losses that were observed in the snow cover. Several case studies of FMCW radar profiles are presented in order to demonstrate the advantages of a multiband radar approach for monitoring the spatial and temporal variability of snow cover properties and/or processes over an extended area.     

Low‐cost monitoring of snow height and thermal properties with inexpensive temperature sensors

Small, self‐recording temperature sensors were installed at several heights along a metal rod at five locations in a case study catchment. For each sensor, the presence or absence of snow cover was determined on the basis of its insulating effect and the resulting reduction of the diurnal temperature oscillations. Sensor coverage was then converted into a time series of snow height for each location. Additionally, cold content was calculated. Snow height and cold content provide valuable information for spring flood prediction. Good agreement of estimated snow heights with reference measurements was achieved and increased discharge in the study catchment coincided with low cold content of the snow cover. The results of the proposed distributed assessment of snow cover and snow state show great potential for (i) flood warning, (ii) assimilation of snow state data and (iii) modelling snowmelt process. Copyright © 2010 John Wiley & Sons, Ltd.     

An analysis of snow cover patterns in a small alpine catchment

Snow cover patterns in a 9.4 km² basin in the Austrian Alps are examined during spring and summer 1989. Digital mono-plotting from oblique aerophotographs is used for mapping. on the basis of a square grid with 25 m spacing, snow cover as mapped during nine surveys is analysed as a function of elevation and slope. During winter conditions the snow cover is found to be much better related to these terrain features than during the late ablation period.     

Investigating snowpack across scale in the northern Great Lakes–St. Lawrence forest region of Central Ontario,Canada

This study investigates scaling issues by evaluating snow processes and quantifying bias in snowpack properties across scale in a northern Great Lakes–St. Lawrence forest. Snow depth and density were measured along transects stratified by land cover over the 2015/2016 and 2016/2017 winters. Daily snow depth was measured using a time‐lapse (TL) camera at each transect. Semivariogram analysis of the transect data was conducted, and no autocorrelation was found, indicating little spatial structure along the transects. Pairwise differences in snow depth and snow water equivalent (SWE) between land covers were calculated and compared across scales. Differences in snowpack between forested sites at the TL points corresponded to differences in canopy cover, but this relationship was not evident at the transect scale, indicating a difference in observed process across scale. TL and transect estimates had substantial bias, but consistency in error was observed, which indicates that scaling coefficients may be derived to improve point scale estimates. TL and transect measurements were upscaled to estimate grid scale means. Upscaled estimates were compared and found to be consistent, indicating that appropriately stratified point scale measurements can be used to approximate a grid scale mean when transect data are not available. These findings are important in remote regions such as the study area, where frequent transect data may be difficult to obtain. TL, transect, and upscaled means were compared with modelled depth and SWE. Model comparisons with TL and transect data indicated that bias was dependent on land cover, measurement scale, and seasonality. Modelled means compared well with upscaled estimates, but model SWE was underestimated during spring melt. These findings highlight the importance of understanding the spatial representativeness of in situ measurements and the processes those measurements represent when validating gridded snow products or assimilating data into models.     

Evaluation of the MODIS snow cover fraction product

Eleven years of daily 500 m gridded Terra Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD10A1) snow cover fraction (SCF) data are evaluated in terms of snow presence detection in Colorado and Washington states. The SCF detection validation study is performed using in‐situ measurements and expressed in terms of snow and land detection and misclassification frequencies. A major aspect addressed in this study involves the shifting of pixel values in time due to sensor viewing angles and gridding artifacts of MODIS sensor products. To account for this error, 500 m gridded pixels are grouped and aggregated to different‐sized areas to incorporate neighboring pixel information. With pixel aggregation, both the probability of detection (POD) and the false alarm ratios increase for almost all cases. Of the false negative (FN) and false positive values (referred to as the total error when combined), FN estimates dominate most of the total error and are greatly reduced with aggregation. The greatest POD increases and total error reductions occur with going from a single 500 m pixel to 3×3‐pixel averaged areas. Since the MODIS SCF algorithm was developed under ideal conditions, SCF detection is also evaluated for varying conditions of vegetation, elevation, cloud cover and air temperature. Finally, using a direct insertion data assimilation approach, pixel averaged MODIS SCF observations are shown to improve modeled snowpack conditions over the single pixel observations due to the smoothing of more error‐prone observations and more accurately snow‐classified pixels. Copyright © 2012 John Wiley & Sons, Ltd.     

Snow cover and snowmelt of an extensive High Arctic wetland: spatial and temporal seasonal patterns

Abstract

This study examined the end-of-winter snow storage, its distribution and the spatial and temporal melt patterns of a large, low gradient wetland at Polar Bear Pass, Bathurst Island, Nunavut, Canada. The project utilized a combination of field observations and a physically-based snowmelt model. Topography and wind were the major controls on snow distribution in the region, and snow was routinely scoured from the hilltop regions and deposited into hillslopes and valleys. Timing and duration of snowmelt at Polar Bear Pass were similar in 2008 and 2009. The snowmelt was initiated by an increase in air temperature and net radiation receipt. Inter-annual variability in spatial snowmelt patterns was evident at Polar Bear Pass and was attributed to a non-uniform snow cover distribution and local microclimate conditions. In situ field studies and modelling remain important in High Arctic regions for assessing wetland water budgets and runoff, in addition to model parameterization and validation of satellite imagery.

Editor Z.W. Kundzewicz

Citation Assini, J. and Young, K.L., 2012. Snow cover and snowmelt of an extensive High Arctic wetland: spatial and temporal seasonal patterns. Hydrological Sciences Journal, 57 (4), 738–755.