Sublimation from thin snow cover at the edge of the Eurasian cryosphere in Mongolia

Sublimation from thin snow cover at the edge of the Eurasian cryosphere in Mongolia was calculated using the aerodynamic profile method and verified by eddy covariance observations using multiple‐level meteorological data from three sites representing a variety of geographic and vegetative conditions in Mongolia. Data were collected in the winter and analysed from three sites. Intense sublimation events, defined by daily sublimation levels of more than 0·4 mm, were predominant in their effect on the temporal variability of sublimation. The dominant meteorological elements affecting sublimation were wind speed and air temperature, with the latter affecting sublimation indirectly through the vapour deficit. Seasonal and interannual variations in sublimation were investigated using long‐interval estimations for 19 years at a mountainous‐area meteorological station and for 24 years at a flat‐plain meteorological station. The general seasonal pattern indicated higher rates of sublimation in both the beginning and ending of the snow‐covered period, when the wind speed and vapour deficit were higher. Annual sublimation averaged 11·7 mm at the flat‐plain meteorological station, or 20·3% of the annual snowfall, and 15·7 mm at the site in the mountains, or 21·6% of snowfall. The sum of snow sublimation and snowmelt evaporation represented 17 to 20% of annual evapotranspiration in a couple observation years. Copyright © 2008 John Wiley & Sons, Ltd.     

On the winter evolution of snow thermophysical properties over land‐fast first‐year sea ice

The geophysical, thermodynamic and dielectric properties of snow are important state variables that are known to be sensitive to Arctic climate variability and change. Given recent observations of changes in the Arctic physical system (Arctic Climate Impact Assessment, 2004), it is important to focus on the processes that give rise to variability in the horizontal, vertical and temporal dimensions of the life‐history of snow on sea ice. The objectives in this study are to present these ‘state’ variables and to investigate the processes that govern variability in the vertical, horizontal and temporal dimension by using a case study over land‐fast first‐year sea ice for the period December 2003 to June 2004. Results from two sampling areas (thin and thick snowpacks) show that differences in snowpack thickness can substantially change the vertical and temporal evolution of snow properties. During the late fall and early winter (cooling period) we measured no significant changes in the physical properties, except for thin snow‐cover salinity, which decreased throughout the period. Fall‐snow desalination was only observed under thin snowpacks with a rate of ?0·12 ppt day^?1. Significant changes occurred in the late winter and early spring (warming period), especially for snow grain size. Snow grain kinetic growth of 0·25–0·48 mm·day^?1 was measured coincidently with increasing salinity and wetness for both thin and thick snowpacks. Copyright © 2006 John Wiley & Sons, Ltd.     

Observations of late winter Canadian tundra snow cover properties

Tundra snow cover is important to monitor as it influences local, regional, and global‐scale surface water balance, energy fluxes, as well as ecosystem and permafrost dynamics. Observations are already showing a decrease in spring snow cover duration at high latitudes, but the impact of changing winter season temperature and precipitation on variables such as snow water equivalent (SWE) is less clear. A multi‐year project was initiated in 2004 with the objective to quantify tundra snow cover properties over multiple years at a scale appropriate for comparison with satellite passive microwave remote sensing data and regional climate and hydrological models. Data collected over seven late winter field campaigns (2004 to 2010) show the patterns of snow depth and SWE are strongly influenced by terrain characteristics. Despite the spatial heterogeneity of snow cover, several inter‐annual consistencies were identified. A regional average density of 0.293 g/cm³ was derived and shown to have little difference with individual site densities when deriving SWE from snow depth measurements. The inter‐annual patterns of SWE show that despite variability in meteorological forcing, there were many consistent ratios between the SWE on flat tundra and the SWE on lakes, plateaus, and slopes. A summary of representative inter‐annual snow stratigraphy from different terrain categories is also presented. © 2013 Her Majesty the Queen in Right of Canada. Hydrological Processes. © 2013 John Wiley & Sons, Ltd.     

Past and projected future changes in snowpack and soil frost at the Hubbard Brook Experimental Forest,New Hampshire,USA

Long‐term data from the Hubbard Brook Experimental Forest in New Hampshire show that air temperature has increased by about 1 °C over the last half century. The warmer climate has caused significant declines in snow depth, snow water equivalent and snow cover duration. Paradoxically, it has been suggested that warmer air temperatures may result in colder soils and more soil frost, as warming leads to a reduction in snow cover insulating soils during winter. Hubbard Brook has one of the longest records of direct field measurements of soil frost in the United States. Historical records show no long‐term trends in maximum annual frost depth, which is possibly confounded by high interannual variability and infrequency of major soil frost events. As a complement to field measurements, soil frost can be modelled reliably using knowledge of the physics of energy and water transfer. We simulated soil freezing and thawing to the year 2100 using a soil energy and water balance model driven by statistically downscaled climate change projections from three atmosphere‐ocean general circulation models under two emission scenarios. Results indicated no major changes in maximum annual frost depth and only a slight increase in number of freeze–thaw events. The most important change suggested by the model is a decline in the number of days with soil frost, stemming from a concurrent decline in the number of snow‐covered days. This shortening of the frost‐covered period has important implications for forest ecosystem processes such as tree phenology and growth, hydrological flowpaths during winter, and biogeochemical processes in soil. Published in 2010 by John Wiley & Sons, Ltd.     

Investigation of the snow‐cover dynamics in the Upper Euphrates Basin of Turkey using remotely sensed snow‐cover products and hydrometeorological data

The Euphrates and Tigris rivers serve as the most important water resources in the Middle East. Precipitation in this region falls mostly in the form of snow over the higher elevations of the Euphrates Basin and remains on the ground for nearly half of the year. This snow‐covered area (SCA) is a key element of the hydrological cycle, and monitoring the SCA is crucial for making accurate forecasts of snowmelt discharge, especially for energy production, flood control, irrigation, and reservoir‐operation optimization in the Upper Euphrates (Karasu) Basin. Remote sensing allows the detection of the spatio‐temporal patterns of snow cover across large areas in inaccessible terrain, such as the eastern part of Turkey, which is highly mountainous. In this study, a seasonal evaluation of the snow cover from 2000 to 2009 was performed using 8‐day snow‐cover products (MOD10C2) and the daily snow‐water equivalent (SWE) product. The values of SWE products were obtained using an assimilation process based on the Helsinki University of Technology model using equal area Special Sensor Microwave Imager (SSM/I) Earth‐gridded advanced microwave scanning radiometer—EOS daily brightness‐temperature values. In the Karasu Basin, the SCA percentage for the winter period is 80–90%. The relationship between the SCA and the runoff during the spring period is analysed for the period from 2004 to 2009. An inverse linear relationship between the normalized SCA and the normalized runoff values was obtained (r = 0·74). On the basis of the monthly mean temperature, total precipitation and snow depth observed at meteorological stations in the basin, the decrease in the peak discharges, and early occurrences of the peak discharges in 2008 and 2009 are due to the increase in the mean temperature and the decrease in the precipitation in April. Copyright © 2011 John Wiley & Sons, Ltd.     

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.     

Improvement of distributed snowmelt energy balance modeling with MODIS‐based NDSI‐derived fractional snow‐covered area data

Describing the spatial variability of heterogeneous snowpacks at a watershed or mountain‐front scale is important for improvements in large‐scale snowmelt modelling. Snowmelt depletion curves, which relate fractional decreases in snow‐covered area (SCA) against normalized decreases in snow water equivalent (SWE), are a common approach to scale‐up snowmelt models. Unfortunately, the kinds of ground‐based observations that are used to develop depletion curves are expensive to gather and impractical for large areas. We describe an approach incorporating remotely sensed fractional SCA (FSCA) data with coinciding daily snowmelt SWE outputs during ablation to quantify the shape of a depletion curve. We joined melt estimates from the Utah Energy Balance Snow Accumulation and Melt Model (UEB) with FSCA data calculated from a normalized difference snow index snow algorithm using NASA's moderate resolution imaging spectroradiometer (MODIS) visible (0·545–0·565 µm) and shortwave infrared (1·628–1·652 µm) reflectance data. We tested the approach at three 500 m² study sites, one in central Idaho and the other two on the North Slope in the Alaskan arctic. The UEB‐MODIS‐derived depletion curves were evaluated against depletion curves derived from ground‐based snow surveys. Comparisons showed strong agreement between the independent estimates. Copyright © 2010 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.     

Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

A 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.     

Evaluation of snow models in terrestrial biosphere models using ground observation and satellite data: impact on terrestrial ecosystem processes

Snow is important for water management, and an important component of the terrestrial biosphere and climate system. In this study, the snow models included in the Biome‐BGC and Terrestrial Observation and Prediction System (TOPS) terrestrial biosphere models are compared against ground and satellite observations over the Columbia River Basin in the US and Canada and the impacts of differences in snow models on simulated terrestrial ecosystem processes are analysed. First, a point‐based comparison of ground observations against model and satellite estimates of snow dynamics are conducted. Next, model and satellite snow estimates for the entire Columbia River Basin are compared. Then, using two different TOPS simulations, the default TOPS model (TOPS with TOPS snow model) and the TOPS model with the Biome‐BGC snow model, the impacts of snow model selection on runoff and gross primary production (GPP) are investigated. TOPS snow model predictions were consistent with ground and satellite estimates of seasonal and interannual variations in snow cover, snow water equivalent, and snow season length; however, in the Biome‐BGC snow model, the snow pack melted too early, leading to extensive underpredictions of snow season length and snow covered area. These biases led to earlier simulated peak runoff and reductions in summer GPP, underscoring the need for accurate snow models within terrestrial ecosystem models. Copyright © 2007 John Wiley & Sons, Ltd.     

Snow density variations: consequences for ground‐penetrating radar

Reliable hydrological forecasts of snowmelt runoff are of major importance for many areas. Ground‐penetrating radar (GPR) measurements are used to assess snowpack water equivalent for planning of hydropower production in northern Sweden. The travel time of the radar pulse through the snow cover is recorded and converted to snow water equivalent (SWE) using a constant snowpack mean density from the drainage basin studied. In this paper we improve the method to estimate SWE by introducing a depth‐dependent snowpack density. We used 6 years measurements of peak snow depth and snowpack mean density at 11 locations in the Swedish mountains. The original method systematically overestimates the SWE at shallow depths (+25% for 0·5 m) and underestimates the SWE at large depths (?35% for 2·0 m). A large improvement was obtained by introducing a depth–density relation based on average conditions for several years, whereas refining this by using separate relations for individual years yielded a smaller improvement. The SWE estimates were substantially improved for thick snow covers, reducing the average error from 162 ± 23 mm to 53 ± 10 mm for depth range 1·2–2·0 m. Consequently, the introduction of a depth‐dependent snow density yields substantial improvements of the accuracy in SWE values calculated from GPR data. Copyright © 2005 John Wiley & Sons, Ltd.     

Estimating the distribution of snow water equivalent and snow extent beneath cloud cover in the Salt–Verde River basin,Arizona

The temporal and spatial continuity of spatially distributed estimates of snow‐covered area (SCA) are limited by the availability of cloud‐free satellite imagery; this also affects spatial estimates of snow water equivalent (SWE), as SCA can be used to define the extent of snow telemetry (SNOTEL) point SWE interpolation. In order to extend the continuity of these estimates in time and space to areas beneath the cloud cover, gridded temperature data were used to define the spatial domain of SWE interpolation in the Salt–Verde watershed of Arizona. Gridded positive accumulated degree‐days (ADD) and binary SCA (derived from the Advanced Very High Resolution Radiometer (AVHRR)) were used to define a threshold ADD to define the area of snow cover. The optimized threshold ADD increased during snow accumulation periods, reaching a peak at maximum snow extent. The threshold then decreased dramatically during the first time period after peak snow extent owing to the low amount of energy required to melt the thin snow cover at lower elevations. The area having snow cover at this later time was then used to define the area for which SWE interpolation was done. The area simulated to have snow was compared with observed SCA from AVHRR to assess the simulated snow map accuracy. During periods without precipitation, the average commission and omission errors of the optimal technique were 7% and 11% respectively, with a map accuracy of 82%. Average map accuracy decreased to 75% during storm periods, with commission and omission errors equal to 11% and 12% respectively. The analysis shows that temperature data can be used to help estimate the snow extent beneath clouds and therefore improve the spatial and temporal continuity of SCA and SWE products. Copyright © 2004 John Wiley & Sons, Ltd.     

Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high‐elevation catchment

In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km²). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments.     

First‐year sea ice spring melt transitions in the Canadian Arctic Archipelago from time‐series synthetic aperture radar data, 1992–2002

This paper synthesizes 10‐years' worth of interannual time‐series space‐borne ERS‐1 and RADARSAT‐1 synthetic aperture radar (SAR) data collected coincident with daily measurement of snow‐covered, land‐fast first‐year sea ice (FYI) geophysical and surface radiation data collected from the Seasonal Sea Ice Monitoring and Modeling Site, Collaborative‐Interdisciplinary Cryospheric Experiment and 1998 North Water Polynya study over the period 1992 to 2002. The objectives are to investigate the seasonal co‐relationship of the SAR time‐series dataset with selected surface mass (bulk snow thickness) and climate state variables (surface temperature and albedo) measured in situ for the purpose of measuring the interannual variability of sea ice spring melt transitions and validating a time‐series SAR methodology for sea ice surface mass and climate state parameter estimation. We begin with a review of the salient processes required for our interpretation of time‐series microwave backscatter from land‐fast FYI. Our results suggest that time‐series SAR data can reliably measure the timing and duration of surface albedo transitions at daily to weekly time‐scales and at a spatial scales that are on the order of hundreds of metres. Snow thickness on FYI immediately prior to melt onset explains a statistically significant portion of the variability in timing of SAR‐detected melt onset to pond onset for SAR time‐series that are made up of more than 25 images. Our results also show that the funicular regime of snowmelt, resolved in time‐series SAR data at a temporal resolution of approximately 2·5 images per week, is not detectable for snow covers less than 25 cm in thickness. Copyright © 2007 John Wiley & Sons, Ltd.     

Influence of canopy density on snow distribution in a temperate mountain range

We analyse spatial variability and different evolution patterns of snowpack in a mixed beech–fir stand in the central Pyrenees. Snow depth and density were surveyed weekly along six transects of contrasting forest cover during a complete accumulation and melting season; we also surveyed a sector unaffected by canopy cover. Forest density was measured using the sky view factor (SVF) obtained from digital hemispherical photographs. During periods of snow accumulation and melting, noticeable differences in snow depth and density were found between the open site and those areas covered by forest canopy. Principal component analysis provided valuable information in explaining these observations. The results indicate a high variability in snow accumulation within forest areas related to differences in canopy density. Maximum snow water equivalent (SWE) was reduced by more than 50% beneath dense canopies compared with clearings, and this difference increased during the melting period. We also found significant temporal variations: when melting began in sectors with low SVF, most of the snow had already thawed in areas with high SVF. However, specific conditions occasionally produced a different response of SWE to forest cover, with lower melting rates observed beneath dense canopies. The high values of correlation coefficients for SWE and SVF (r > 0·9) indicate the reliability of predicting the spatial distribution of SWE in forests when only a moderate number of observations are available. Digital hemispherical photographs provide an appropriate tool for this type of analysis, especially for zenith angles in the range 35–55 . Copyright © 2007 John Wiley & Sons, Ltd.     

Effects of frozen soil and snow cover on cold‐season soil water dynamics in Tokachi,Japan

Despite the potential impact of winter soil water movements in cold regions, relatively few field studies have investigated cold‐season hydrological processes that occur before spring‐onset of snowmelt infiltration. The contribution of soil water fluxes in winter to the annual water balance was evaluated over 5 years of field observations at an agricultural field in Tokachi, Hokkaido, Japan. In two of the winters, soil frost reached a maximum depth of 0·2 m (‘frozen’ winters), whereas soil frost was mostly absent during the remaining three winters (‘unfrozen’ winters). Significant infiltration of winter snowmelt water, to a depth exceeding 1·0 m, occurred during both frozen and unfrozen winters. Such infiltration ranged between 126 and 255 mm, representing 28–51% of total annual soil water fluxes. During frozen winters, a substantial quantity of water (ca 40 mm) was drawn from deeper layers into the 0–0·2 m topsoil layer when this froze. Under such conditions, the progression and regression of the freezing front, regulated by the thickness of snow cover, controlled the quantity of soil water flux below the frozen layer. During unfrozen winters, 13–62 mm of water infiltrated to a depth of 0·2 m, before the spring snowmelt. These results indicate the importance of correctly evaluating winter soil water movement in cold regions. Copyright © 2010 John Wiley & Sons, Ltd.     

Snow-distribution and melt modelling for glaciers in Zackenberg river drainage basin,north-eastern Greenland

A physically based snow-evolution modelling system (SnowModel) that includes four sub-models: MicroMet, EnBal, SnowPack, and SnowTran-3D, was used to simulate eight full-year evolutions of snow accumulation, distribution, sublimation, and surface melt from glaciers in the Zackenberg river drainage basin, in north-east Greenland. Meteorological observations from two meteorological stations were used as model inputs, and spatial snow depth observations, snow melt depletion curves from photographic time lapse, and a satellite image were used for model testing of snow and melt simulations, which differ from previous SnowModel tests methods used on Greenland glaciers. Modelled test-period-average end-of-winter snow water equivalent (SWE) depth for the depletion area differs by a maximum of 14 mm w.eq., or ∼6%, more than the observed, and modelled test-period-average snow cover extent differs by a maximum of 5%, or 0·8 km², less than the observed. Furthermore, comparison with a satellite image indicated a 7% discrepancy between observed and modelled snow cover extent for the entire drainage basin. About 18% (31 mm w.eq.) of the solid precipitation was returned to the atmosphere by sublimation. Modelled mean annual snow melt and glacier ice melt for the glaciers in the Zackenberg river drainage basin from 1997 through 2005 (September–August) averaged 207 mm w.eq. year⁻¹ and 1198 mm w.eq. year⁻¹, respectively, yielding a total averaging 1405 mm w.eq. year⁻¹. Total modelled mean annual surface melt varied from 960 mm w.eq. year⁻¹ to 1989 mm w.eq. year⁻¹. The surface-melt period started between mid-May and the beginning of June and lasted until mid-September. Annual calculated runoff averaged 1487 mm w.eq. year⁻¹ (∼150 × 10⁶ m³) (1997–2005) with variations from 1031 mm w.eq. year⁻¹ to 2051 mm w.eq. year⁻¹. The model simulated a total glacier recession averaging − 1347 mm w.eq. year⁻¹ (∼136 × 10⁶ m³) (1997–2005), which was almost equal to previous basin average hydrological water balance storage studies − 244 mm w.eq. year⁻¹ (∼125 × 10⁶ m³) (1997–2003). Copyright © 2007 John Wiley & Sons, Ltd.     

Application of a distributed blowing snow model to the Arctic

Transportation, sublimation and accumulation of snow dominate snow cover development in the Arctic and produce episodic high evaporative fluxes. Unfortunately, blowing snow processes are not presently incorporated in any hydrological or meteorological models. To demonstrate the application of simple algorithms that represent blowing snow processes, monthly snow accumulation, relocation and sublimation fluxes were calculated and applied in a spatially distributed manner to a 68-km² catchment in the low Arctic of north-western Canada. The model uses a Landsat-derived vegetation classification and a digital elevation model to segregate the basin into snow ‘sources’ and ‘sinks’. The model then relocates snow from sources to sinks and calculates in-transit sublimation loss. The resulting annual snow accumulation in specific landscape types was compared with the result of intensive surveys of snow depth and density. On an annual basis, 28% of annual snowfall sublimated from tundra surfaces whilst 18% was transported to sink areas. Annual blowing snow transport to sink areas amounted to an additional 16% of annual snowfall to shrub–tundra and an additional 182% to drifts. For the catchment, 19·5% of annual snowfall sublimated from blowing snow, 5·8% was transported into the catchment and 86·5% accumulated on the ground. The model overestimated snow accumulation in the catchment by 6%. The application demonstrates that winter precipitation alone is insufficient to calculate snow accumulation and that blowing snow processes and landscape patterns govern the spatial distribution and total accumulation of snow water equivalent over the winter. These processes can be modelled by relatively simple algorithms, and, when distributed by landscape type over the catchment, produce reasonable estimates of snow accumulation and loss in wind-swept regions. © 1997 John Wiley & Sons, Ltd.     

Some aspects of the hydroclimatology of the Quesnel River Basin,British Columbia,Canada

In conjunction with available climate data, surface runoff is investigated at 12 gauges in the Quesnel watershed of British Columbia to develop its long‐term (1926–2004) hydroclimatology. At Quesnel itself, annual mean values of air temperature, precipitation and runoff are 4·6 °C, 517 and 648 mm, respectively. Climate data reveal increases in precipitation, no significant trend in mean annual air temperature, but an increasing trend in mean minimum temperatures that is greatest in winter. There is some evidence of decreases in winter snow depth. On the water year scale (October–September), a strong positive correlation is found between discharge and precipitation (r = 0·70, p < 0·01) and a weak negative correlation is found between precipitation and temperature (r = ? 0·36, p < 0·01). Long‐term trends using the Mann‐Kendall test indicate increasing annual discharge amounts that vary from 8 to 14% (12% for the Quesnel River, p = 0·03), and also a tendency toward an earlier spring freshet. River runoff increases at a rate of 1·26 mm yr^?1 m^?1 of elevation from west to east along the strong elevation gradient in the basin. Discharge, temperature and precipitation are correlated with the large‐scale climate indices of the Pacific Decadal Oscillation (PDO) and El‐Niño Southern Oscillation (ENSO). Copyright © 2009 John Wiley & Sons, Ltd.