Steven R. FASSNACHT's Abstracts

Journal Publications

Refereed Conference Proceedings

Saavedra, F., S.K. Kampf, S.R. Fassnacht, and J. Sibold, accepted. A snow climatology of the Andes Mountains from MODIS snow cover data. International Journal of Climatology.
This study develops a method for characterizing snow climatology in the Andes Mountains using the 8-day maximum binary snow cover product from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The objectives are to: (1) identify regions with similar snow patterns, and (2) identify snow persistence zones within these regions. Within a study area between 8-39oS, snow regions are defined using the (1) minimum elevation of snow cover, (2) rate of change of snow persistence with elevation, and (3) timing of the minimum elevation snow cover. In tropical latitudes (8-23oS), snow cover is constrained to high elevations (>5,000 m), and these areas have steep changes in snow persistence with elevation. Minimal differences in the elevation of snow on the east and west sides of the range suggest that temperature is a primary control on snow presence. Snow cover has minimal seasonal variability in the tropics between 8-14oS, but it peaks in austral fall (March) after the wet season from 14-23oS. In mid-latitudes (south of 23oS) snowline decreases in elevation with increasing latitude, and snow persistence changes with elevation are more gradual than in tropical regions. Snow cover peaks in the austral winter throughout the mid-latitudes. Differences in elevations of snow accumulation between the east and west sides of the Andes are greatest between 28-37oS, where high mountain peaks produce a strong orographic effect and precipitation shadow. Within the snow regions, four snow zones are defined based on the average fraction of the year that snow persists: (1) little or no snow, (2) intermittent, (3) seasonal, and (4) permanent snow zones. Tropical latitudes have snow cover only on the highest peaks. Areas of seasonal and permanent snow zones are greatest between latitudes 28-37oS as a result of higher precipitation than mountains further North and higher elevations than mountains further South.
Keywords: seasonal snow, climatology, snow zones, MODIS snow cover, Andes Mountains, snow regions

Sexstone, G.A., D. Clow, D. Stannard, and S.R. Fassnacht, accepted. Comparison of methods for quantifying surface sublimation over seasonally snow-covered terrain. Hydrological Processes (accepted March 2016).
Snow sublimation can be an important component of the snow-cover mass balance, and there is considerable interest in quantifying the role of this process within the water and energy balance of snow-covered regions. In recent years, robust eddy covariance instrumentation has been used to quantify snow sublimation over snow-covered surfaces in complex mountainous terrain. However, eddy covariance can be challenging for monitoring turbulent fluxes in snow-covered environments because of intensive data, power, and fetch requirements, and alternative methods of estimating snow sublimation are often relied upon. To evaluate the relative merits of methods for quantifying surface sublimation, fluxes calculated by the eddy covariance, Bowen ratio-energy balance, bulk aerodynamic flux, and aerodynamic profile methods and their associated uncertainty were compared at two forested openings in the Colorado Rocky Mountains. Biases between methods are evaluated over a range of environmental conditions and limitations of each method are discussed. Mean surface sublimation rates from both sites ranged from 0.33 to 0.36 mm day-1, 0.14 to 0.37 mm day-1, 0.10 to 0.17 mm day-1, 0.03 to 0.10 mm day-1 for the eddy covariance, Bowen ratio-energy balance, bulk aerodynamic flux, and aerodynamic profile methods, respectively. The eddy covariance and/or bulk aerodynamic flux methods are concluded to be superior for estimating surface sublimation in snow-covered forested openings. The surface sublimation rates quantified in this study are generally smaller in magnitude compared to previously published studies in this region and help to refine sublimation estimates for forested openings in the Colorado Rocky Mountains.

Fassnacht, S.R., G.A. Sexstone, A. Kashipazha, J.I. López-Moreno, M.F. Jasinski, S.K. Kampf, and B.C. Von Thaden, 2016. Deriving Snow-cover Depletion Curves for Different Spatial Scales from Remote Sensing and Snow Telemetry Data. Hydrological Processes, 30(11), 1708-1717, [doi:10.1002/hyp.10730].
During the melting of a snowpack, snow water equivalent (SWE) can be correlated to snow-covered area (SCA) once snow free areas appear, which is when SCA begins to decrease below 100%. This amount of SWE is called the threshold SWE. Daily SWE data from snow telemetry (SNOTEL) stations were related to SCA derived from Moderate Resolution Imaging Spectro-radiometer (MODIS) images to produce snow-cover depletion curves. The snow depletion curves were created for an 80,000 km2 domain across southern Wyoming and northern Colorado encompassing 54 SNOTEL stations. Eight yearly snow depletion curves were compared and it is shown that the slope of each is a function of the amount of snow received.Snow-cover depletion curves were also derived for all the individual stations, for which the threshold SWE could be estimated from peak SWE and the topography around each station. A station's peak SWE was much more important than the main topographic variables that included location, elevation, slope, adn modeled clear sky solar radiation. The threshold SWE was mostly illustrated inter-annual consistency.
Keywords: snow depletion curves, snowmelt, SCA, SWE, SNOTEL, MODIS

Fassnacht, S.R., M.L. Cherry, N.B.H. Venable, and F. Saavedra, in press. Snow and albedo climate change impacts across the United States Northern Great Plains. The Cryosphere, 10, 329-339, [doi:10.5194/tc-10-1-2016].
In areas with a seasonal snowpack, a warmer climate could cause less snowfall, a shallower snowpack, and a change in the timing of snowmelt, all which could reduce the winter albedo and yield an increase in net short-wave radiation. Trends in temperature, precipitation (total and as snow), days with precipitation and snow, and winter albedo were investigated over the 60-year period from 1951 to 2010 for 20 meteorological stations across the Northern Great Plains. This is an area where snow accumulation is shallow but persistent for most of the winter (November to March). The most consistent trends were minimum temperature and days with precipitation, both of which increased at a majority of the stations. Among the stations included, a decrease in the modelled winter albedo was more prevalent than an increase. There was substantial spatial variability in the climate trends. For most variables, the period of record used influenced the magnitude and sign of the significant trends.

Toro, M., A. Quesada, A. Camacho, M. Oliva, A. Alcamí, D. Antoniades, M. Bañón, S.R. Fassnacht, E. Fernández-Valiente, L. Galan, S. Giralt, I. Granados, A. Justel, E.J. Liu, A. López-Bueno, A. Martínez-Cortizas, S. Pla-Rabes, A. Rastrojo, E. Rico, C. Rochera, B. Van de Vijver, D. Velazquez, J.A. Villaescusa, and W.F. Vincent, 2015. CEDEX research activities in Antarctica. Aquatic ecosystems in Byers Peninsula (Livingston Island, maritime Antarctica). Ingeniería Civil, 179, 175-191.
Since 2001 CEDEX has taken part in many Antarctic joint research projects with different institutions from Spain and other countries, developing scientific activities in the International Camp of Byers Peninsula (Livingston Island, South Shetland Islands, Antarctica). This place was designed as an Antarctic Specially Protected Area (No. 126) because the importance and value of its terrestrial and aquatic habitats. It is one of the largest ice-free areas of maritime Antarctica, with the highest diversity of environments and geological, hydrological and biological processes in the whole region, all of them in a pristine state. Byers Peninsula is considered the most significant limnological area in the Antarctic Peninsula region because it hosts a high number of lakes, ponds and streams, with an exceptional fauna and flora diversity, including the most singular, representative or endemic Antarctic species. Furthermore, the lakes sedimentary record is one of the widest and complete archives in Antarctic Peninsula region for the palaeoecological and climatic study of the Holocene. Because Byers Peninsula is an Antarctic biodiversity "hotspot", and it is located in one of the areas in the Earth where global warming is being more significant, it must be considered as a suitable international reference site for limnetic, terrestrial and coastal studies, and long term monitoring programmes.

Webb, R.W., S.R. Fassnacht, and M.N. Gooseff, 2015. Wetting and drying variability of the shallow subsurface beneath a snowpack in California's Southern Sierra Nevada. Vadose Zone Journal, 14(8), 10 pages [doi:10.2136/vzj2014.12.0182]
In recent years, regions such as the western United States have been projected to have drastically changing snow and soil moisture conditions in mountainous regions. The importance of sub-surface flow during snowmelt for groundwater recharge and streamflow has long been known. This study was conducted to investigate the variability of soil wetting and drying dynamics within a network of soil moisture sensors beneath a seasonally persistent snowpack in California's southern Sierra Nevada. Data were analyzed to quantify the persistence of soil moisture, the rate of change in soil moisture, and vertical gradients of soil moisture during the 2009, 2010, and 2011 water years at varying elevations (1750-2000 m), aspects (north, south, and flat), and canopy conditions (open, drip edge, and under canopy). Results from this study demonstrate snowmelt water moving through a wetted shallow region of the soil in most locations while other locations display drier conditions in the top layers of soil with higher rates of soil moisture change deeper within the soil. This occurs at various depths within the top meter of soil, displaying high variability across the catchment and under different canopy conditions. Over 1,400 pulse events above a rate of change in water content of 0.06 cm/cm per day were observed with many above 0.10 cm/cm per day. Average numbers of pulse events per year ranged from 1 to 18 with a median of 4.3 for all sensors was observed. The lower elevation sensors averaged 6.0 wetting events per year reaching at least 60 cm depth and the upper sensors averaged 4.7 with standard deviations among elevation and aspect clusters ranging from 2.5 to 3.0 for upper elevation sites and 2.9 to 3.2 for lower elevation sites. This demonstrates the high variability of the wetting and drying dynamics of the top meter of soil at the sub-hillslope scale. These results have implications for groundwater recharge, plant production, and streamflow response in a changing climate.

Fassnacht, S.R., and R.M. Records, 2015. Large Snowmelt versus Rainfall Events in the Mountains. Journal of Geophysical Research - Atmospheres, 120, 2375-2381 [doi:10.1002/2014JD022753].
While snow is the dominant precipitation type in mountain regions, estimates of rainfall are used for design, even though snowmelt provides most of the runoff. Daily data were used to estimate the 10- and 100-year 24-hour snowmelt, precipitation, and rainfall events at 90 Snow Telemetry (SNOTEL) stations across the Southern Rocky Mountains. Three probability distributions were compared and the Pearson Type III distribution yielded the most conservative estimates. Precipitation was on average 33% and 28% more than rainfall for the 10- and 100-year events. Snowfall exceeded rainfall at most of the stations and was on average 53% and 38% more for the 10- and 100-year events. On average, snowmelt was 15% and 8.9% more than precipitation. Where snow accumulation is substantial, it is recommended that snowmelt be considered in conjunction with rainfall and precipitation frequencies to develop flood frequencies.
Keywords: snowmelt, Southern Rocky Mountains, frequency analysis, probability distributions

López-Moreno, J.I., J. Revuelto, S.R. Fassnacht, C. Azorín-Molina, S.M. Vicente-Serrano, E. Morán-Tejeda, and G.A. Sexstone, 2015. Snowpack variability across various spatio-temporal resolutions. Hydrological Processes, 29(6), 1213-1224, [doi:10.1002/hyp.10245].
High-resolution snow depth (SD) maps (1x1m) obtained from terrestrial laser scanner measurements in a small catchment (0.55km2) in the Pyrenees were used to assess small-scale variability of the snowpack at the catchment and sub-grid scales. The coefficients of variation are compared for various plot resolutions (5x5, 25x25, 49x49, and 99x99m) and eight different days in two snow seasons (2011-2012 and 2012-2013). We also studied the relation between snow variability at the small scale and SD, topographic variables, small-scale variability in topographic variables. The results showed that there was marked variability in SD, and it increased with increasing scales. Days of seasonal maximum snow accumulation showed the least small-scale variability, but this increased sharply with the onset of melting. The coefficient of variation (CV) in snowpack depth showed statistically significant consistency amongst the various spatial resolutions studied, although it declined progressively with increasing difference between the grid sizes being compared. SD best explained the spatial distribution of sub-grid variability. Topographic variables including slope, wind sheltering, sub-grid variability in elevation, and potential incoming solar radiation were also significantly correlated with the CV of the snowpack, with the greatest correlation occurring at the 99x99m resolution. At this resolution, stepwise multiple regression models explained more than 70% of the variance, whereas at the 25x25m resolution they explained slightly more than 50%. The results highlight the importance of considering small-scale variability of the SD for comprehensively representing the distribution of snowpack from available punctual information, and the potential for using SD and other predictors to design optimized surveys for acquiring distributed SD data.
Keywords: snow variability, sub-grid resolution, terrestrial laser scanner, Pyrenees

Records, R.M., M. Arabi, S.R. Fassnacht, W.G. Duffy, M. Ahmadi, and K.C. Hegewisch, 2014. Climate change and wetland loss impacts on a Western river's water quality. Hydrology and Earth System Sciences, 18, 4509-4527.
An understanding of potential stream water quality conditions under future climate is critical for the sustainability of ecosystems and the protection of human health. Changes in wetland water balance under projected climate could alter wetland extent or cause wetland loss (e.g., via increased evapotranspiration and lower growing season flows leading to reduced riparian wetland inundation) or altered land use patterns. This study assessed the potential climate induced changes to in-stream sediment and nutrient loads in the snowmelt-dominated Sprague River, Oregon, western US. Additionally, potential water quality impacts of combined changes in wetland water balance and wetland area under future climatic conditions were evaluated. The study utilized the Soil and Water Assessment Tool (SWAT) forced with statistical downscaling of general circulation model (GCM) data from the Coupled Model Intercomparison Project 5 (CMIP5) using the Multivariate Adaptive Constructed Analogs (MACA) method. Our findings suggest that, in the Sprague River, (1) mid-21st century nutrient and sediment loads could increase significantly during the high-flow season under warmer, wetter climate projections or could change only nominally in a warmer and somewhat drier future; (2) although water quality conditions under some future climate scenarios and no wetland loss may be similar to the past, the combined impact of climate change and wetland losses on nutrient loads could be large; (3) increases in stream total phosphorus (TP) concentration with wetland loss under future climate scenarios would be greatest at high-magnitude, low-probability flows; and (4) loss of riparian wetlands in both headwaters and lowlands could increase outlet TP loads to a similar degree, but this could be due to distinctly different mechanisms in different parts of the watershed.

Fassnacht, S.R., D.C. Deitemeyer, and N.B.H. Venable, 2014. Capitalizing on the daily time step of snow telemetry data to model the snowmelt components of the hydrograph for small watersheds. Hydrological Processes, 28(16), 4654-4668 [doi:10.1002/hyp.10260].
Daily time step data from National Resources Conservation Service snow telemetry (SNOTEL) stations were used to estimate components of the snowmelt-dominated hydrograph for three small watersheds in Colorado and Wyoming. Thirty-three years of SNOTEL data was paired with streamflow data to estimate the annual run-off volume (Q100) and peak streamflow (Qpeak), as well as the timing of 20, 50, and 80% (tQ20, tQ50, and tQ80) of the annual streamflow. Multivariate regression models were derived from snow water equivalent (SWE) data for one basin (Joe Wright Creek), and the model components were then retained and applied to the other two watersheds. The basin-specific coefficients developed for the Joe Wright model were also applied to the other two basins to evaluate direct transferability.
Peak SWE, date of peak SWE, and number of consecutive days with snow on the ground up to the date of peak SWE had the strongest correlation to streamflow and formed the basis of the models. The optimal model configuration performed well for the tQ20, tQ50, and Q100, with poorer performance for Qpeak and tQ80. Applying the Joe Wright Creek coefficients directly to the other watersheds yielded reasonable results.
Keywords: SNOTEL, streamflow, hydrograph timing, regression modelling, statistical modelling

Sexstone, G.A., and S.R. Fassnacht, 2014. What drives basin scale spatial variability of snowpack properties in the Front Range of Northern Colorado? The Cryosphere, 8, 329-344 [doi:10.5194/tc-8-329-2014].
This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow density and snow water equivalent (SWE) variability at the basin scale (100s to 1000s km2). Historic snow course snowpack density observations were analyzed within a multiple linear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were completed on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE near peak snow accumulation. Bivariate relations and multiple linear regression models were developed to understand the relation of snow density and SWE with terrain variables (derived using a geographic information system (GIS)). Snow density variability was best explained by day of year, snow depth, UTM Easting, and elevation. Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance, and model validation suggests the model is transferable to independent data within the bounds of the original data set. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many time-consuming measurements of SWE are often not feasible. A comparison with a previously developed snow density model shows that calibrating a snow density model to a specific basin can provide improvement of SWE estimation at this scale, and should be considered for future basin scale analyses. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and/or UTM Northing) were the most important SWE model variables, suggesting that orographic precipitation and storm track patterns are likely driving basin scale SWE variability. Terrain curvature was also shown to be an important variable, but to a lesser extent at the scale of interest.

Meromy, L., N.P. Molotch, T.E. Link, S.R. Fassnacht, and R. Rice, 2013. Subgrid variability of snow water equivalent at operational snow stations in the western United States. Hydrological Processes, 27(17), 2383-2400 [doi, 10.1002/hyp.9355].
The spatial distribution of snow water equivalent (SWE) is a key variable in many regional-scale land surface models. Currently, the assimilation of point-scale snow sensor data into these models is commonly performed without consideration of the spatial representativeness of the point data with respect to the model grid-scale SWE. To improve the understanding of the relationship between point-scale snow measurements and surrounding areas, we characterized the spatial distribution of snow depth and SWE within 1-, 4- and 16-km2 grids surrounding 15 snow stations (snowpack telemetry and California snow sensors) in California, Colorado, Wyoming, Idaho and Oregon during the 2008 and 2009 snow seasons. More than 30 000 field observations of snowpack properties were used with binary regression tree models to relate SWE at the sensor site to the surrounding area SWE to evaluate the sensor representativeness of larger-scale conditions. Unlike previous research, we did not find consistent high biases in snow sensor depth values as biases over all sites ranged from 74% overestimates to 77% underestimates. Of the 53 assessments, 27 surveys indicated snow station biases of less than 10% of the surrounding mean observed snow depth. Depth biases were largely dictated by the physiographic relationship between the snow sensor locations and the mean characteristics of the surrounding grid, in particular, elevation, solar radiation index and vegetation density. These scaling relationships may improve snow sensor data assimilation; an example application is illustrated for the National Operational Hydrologic Remote Sensing Center National Snow Analysis SWE product. The snow sensor bias information indicated that the assimilation of point data into the National Operational Hydrologic Remote Sensing Center model was often unnecessary and reduced model accuracy.
Keywords: snow, modelling, snow water equivalent, water resources, SNOTEL

Richer, E.E., S.K. Kampf, S.R. Fassnacht, and C.C. Moore, 2013. Spatiotemporal index for analyzing controls on snow climatology: application in the Colorado Front Range. Physical Geography, 34, 85-107, [doi, 10.1080/02723646.2013.787578].
Mountain snowpacks are important water supplies that are susceptible to climate change, yet snow measurements are sparse relative to snowpack heterogeneity. We used remote sensing to derive a spatiotemporal index of snow climatology that reveals patterns in snow accumulation, persistence, and ablation. Then we examined how this index relates to climate, terrain, and vegetation. Analyses were based on Moderate Resolution Imaging Spectroradiometer eight-day snow cover from 2000 to 2010 for a mountain watershed in the Colorado Front Range, USA. The Snow Cover Index (SCI) was calculated as the fraction of years that were snow covered for each pixel. The proportion of SCI variability explained by independent variables was evaluated using regression analysis. Independent variables included elevation, northing, easting, slope, aspect, northness, solar radiation, precipitation, temperature, and vegetation cover. Elevation was the dominant control on SCI patterns, due to its influence on both temperature and precipitation. Grouping SCI values by elevation, we identified three distinct snow zones in the basin: persistent, transitional, and intermittent. The transitional snow zone represents an area that is sensitive to losing winter snowpack. The SCI can be applied to other basins or regions to identify dominant controls on snow cover patterns and areas sensitive to snow loss.
Keywords: snow cover, mountain climatology, snow zones, snowpack transition zone, Colorado Front Range

López-Moreno, J.I., S.R. Fassnacht, J.T. Heath, K. Musselman, J. Revuelto, J. Latron, E. Morán, and T. Jonas, 2013. Spatial variability of snow density over complex alpine terrain: implications for estimating snow water equivalent. Advances in Water Resources, 55, 40-52 [doi, 10.1016/j.advwatres.2012.08.010].
This study analyzes spatial variability of snow depth and density from measurements made in February and April of 2010 and 2011 in three 1-2 km2 areas within a valley of the central Spanish Pyrenees. Snow density was correlated with snow depth and different terrain characteristics. Regression models were used to predict the spatial variability of snow density, and to assess how the error in computed densities might influence estimates of snow water equivalent (SWE).
The variability in snow depth was much greater than that of snow density. The average snow density was much greater in April than in February. The correlations between snow depth and density were generally statistically significant but typically not very high, and their magnitudes and signs were highly variable among sites and surveys. The correlation with other topographic variables showed the same variability in magnitude and sign, and consequently the resulting regression models were very inconsistent, and in general explained little of the variance. Antecedent climatic and snow conditions prior to each survey help highlight the main causes of the contrasting relation shown between snow depth, density and terrain. As a consequence of the moderate spatial variability of snow density relative to snow depth, the absolute error in the SWE estimated from computed densities using the regression models was generally less than 15%. The error was similar to that obtained by relating snow density measurements directly to adjacent snow depths.
Keywords: Snow depth and density, Snow water equivalent (SWE), Spatial variability, Pyrenees

Fassnacht, S.R., J.I. López-Moreno, M. Toro, and D.M. Hultstrand, 2013. Mapping Snow Cover and Snow Depth across the Lake Limnopolar Watershed on Byers Peninsula (Livingston Island) in Maritime Antarctica. Antarctic Science, 25(2), 157-166 [doi:10.1017/S0954102012001216].
Few parts of Antarctica are not permanently covered in ice. The retreat of the ice sheet from Byers Peninsula on western Livingston Island, Maritime Antarctica, has provided a new area of seasonal snow cover. Snow surveys were conducted in late November 2008 at the time of peak accumulation across the 1 km2 Lake Limnopolar watershed. Topographic variables were derived from a digital elevation model to determine the variables controlling the presence or absence of snow and the distribution of snow depth. Classification with binary regression trees showed that wind related variables dominated the presence and depth of snow. The product of the sine of aspect and the sine of slope was the first variable in both regression trees. Density profiles were also measured and illustrated a relatively homogeneous snowpack over space at peak snow accumulation.
Keywords: binary regression trees, density, South Shetland Islands, topographic drivers

Fassnacht, S.R., K.A. Dressler, D.M. Hultstrand, R.C. Bales, and G. Patterson, 2012. Temporal Inconsistencies in Coarse-scale Snow Water Equivalent Patterns: Colorado River Basin Snow Telemetry-Topography Regressions. Pirineos, 167, 167-186 [doi:10.3989/Pirineos.2011.166008].
The relation between snow water equivalent (SWE) and 28 variables (27 topographically-based topographic variables and canopy density) for the Colorado River Basin, USA was explored through a multi-variate regression. These variables include location, slope and aspect at different scales, derived variables to indicate the distance to sources of moisture and proximity to and characteristics of obstacles between these moisture sourcesand areas of snow accumulation, and canopy density. A weekly time step of snow telemetry (SNOTEL) SWE data from 1990 through 1999 was used. The most important variables were elevation and regional scale (81 km2) slope. Since the seasonal and inter-annual variability is high, a regression relationship should be formulated for each time step. The inter-annual variation in the relation between SWE and topographic variables partially corresponded with the amount of snow accumulated over the season and the El Nino Southern Oscillation cycle.
Keywords: Colorado River, SNOTEL, snow water equivalent, surface topography

Venable, N.B.H., S.R. Fassnacht, G. Adyabadam, Tumenjargal S., M. Fernández-Giménez, and B. Batbuyan, 2012. Does the Length of Station Record Influence the Warming Trend That is Perceived by Mongolian Herders near the Khangai Mountains? Pirineos, 167, 71-88 [doi:10.3989/Pirineos.2012.167004].
Temperatures changes can be difficult to infer from changes in vegetation patterns or other ecological changes, yet warming can be inferred through changes in the habits of people who live in close connection with their natural environment. Herders near the Khangai Mountains of central Mongolia have perceived a warming trend in recent years. Since it is difficult to determine the exact time period over which perceived warming has occurred, we examined the statistical difference in changes based on the length of data and the specific period of record used in the analysis. We used temperature data from five meteorological stations for up to 50 years (1961-2010). We examined varying lengths of record from 15 to 50 years with varying start periods (1961 through 1986), based on the length of record. We found that the most statistically significant changes occurred for the longest time periods and for the annual average minimum temperatures. We also found that one very cold winter, in particular 2009-2010 decreased the warming trend and for shorter periods of record reduced the statistical significance.
Keywords: Climate change, warming, Mongolia, statistical significance, herder perceptions

López-Moreno, J.I., S.R. Fassnacht, S. Beguería, and J.B.P. Latron, 2011. Variability of snow depth at the plot scale: implications for mean depth estimation and sampling strategies. The Cryosphere, 5, 617-629, [doi:10.5194/tc-5-617-2011].
Snow depth variability over small distances can affect the representativeness of depth samples taken at the local scale, which are often used to assess the spatial distribution of snow at regional and basin scales. To assess spatial variability at the plot scale, intensive snow depth sampling was conducted during January and April 2009 in 15 plots in the Rio Ésera Valley, central Spanish Pyrenees Mountains. Each plot (10x10 m; 100m 2) was subdivided into a grid of 1m2 squares; sampling at the corners of each square yielded a set of 121 data points that provided an accurate measure of snow depth in the plot (considered as ground truth). The spatial variability of snow depth was then assessed using sampling locations randomly selected within each plot. The plots were highly variable, with coefficients of variation up to 0.25. This indicates that to improve the representativeness of snow depth sampling in a given plot the snow depth measurements should be increased in number and averaged when spatial heterogeneity is substantial.
Snow depth distributions were simulated at the same plot scale under varying levels of standard deviation and spatial autocorrelation, to enable the effect of each factor on snowpack representativeness to be established. The results showed that the snow depth estimation error increased markedly as the standard deviation increased. The results indicated that in general at least five snow depth measurements should be taken in each plot to ensure that the estimation error is <10 %; this applied even under highly heterogeneous conditions. In terms of the spatial configuration of the measurements, the sampling strategy did not impact on the snow depth estimate under lack of spatial autocorrelation. However, with a high spatial autocorrelation a smaller error was obtained when the distance between measurements was greater.

Fassnacht, S.R., M. Toro Velasco, P.J. Meiman, and Z.C. Whitt, 2010. A Local Aeolian Influence in the Surface Roughness of Melting Snow, Byers Peninsula, Antarctica. Hydrological Processes, 24(14), 2007-2013 [doi, 10.1002/hyp.7661].
The surface of the snowpack is the bottom boundary layer for air movement, and its roughness influences aerodynamics. The presence of aeolian deposits on a snowpack decreases its albedo and is shown to decrease the roughness of the surface. During snowmelt in the Lake Limnopolar basin on Byers Peninsula of Livingston Island of the South Shetland Islands, Antarctica, wind moved coarse soil grains (1-4 mm particles) from a bare, dry and snow-free area to an adjacent snowpack. This addition of large soil particles rapidly changed the snowpack surface characteristics. Within several days, the sun-cups, initially present on the melting snow surface, had been smoothed out in areas where soil was deposited on the snow surface. The differences in the snowpack surface were assessed using digital imagery of a roughness board inserted into the snow, both parallel and perpendicular to the dominant wind direction. The random roughness was twice as variable for the clean snow compared to the snow with soil; it was 27% more and 26% less perpendicular versus parallel to the wind for the clean snow and snow with soil, respectively. Variogram analysis showed that the clean snow had up to four different scales of roughness over the 55 x 55 cm area of analysis, with fractal dimensions varying from 1.33 to 1.83. The snow with soil did not vary substantially from 0.1 to 55 cm with fractal dimensions of 1.65 in parallel and perpendicular to the wind.
Keywords: wind transport, surface roughness, snowmelt, Antarctica

López-Moreno, J.I., B. Alvera, J. Latron, and S.R. Fassnacht, 2010. Instalación y Uso de un Colchón de Nieve para la Monitorización del Manto de Nieve, Cuenca Experimental de Izas (Pirineo Central). Cuadernos de Investigación Geográfica (Journal of Geographical Research), 36(1), 73-85.
Snowpack density is one of the most difficult hydroclimatic variables to be monitored in mountain environments. Snow pillows are are commonly used tool for this purpose. Despite they have been widely used in North America, Scandinavia, and several mountain areas, they have not been installed yet in any Spanish mountain system. In 2005, a snow pillow was installed in Izas Experimental basin (Central Pyrenees). Since then, it has provided information on the evoluation of density and snow water equivalent (SWE) for a three years period. This paper reviews the history of the use of snow pillows, specifically its utilization and considerations to have into account when they are installed. Moreover, results obtained during three monitored years are presented. Several uncewrtainty sources are detected, and the usefulness for understanding the hydrological behaviour of high mountain environments is highlighted.
Keywords: snow, density, snow water water (SWE), snow pillow, Central Pyrenees

Fassnacht, S.R., 2010. Temporal changes in small scale snowpack surface roughness length for sublimation estimates in hydrological modeling. Cuadernos de Investigación Geográfica (Journal of Geographical Research), 36(1), 43-57.
Snowpack aerodynamic surface roughness length (zo) is a critical variable in estimating heat transfers to and from a snow surface and thus sublimation rates. This variable has been shown to be site specific. To illustrate a temporal variation in zo, laboratory experiments were performed using a small evaporation pan sitting on a load cell with a constant wind flow over the snow surface. Comparing multi-layer meteorological data above the pan to sublimation measured from mass change showed a decrease in the snowpack surface roughness length as the snow metamorphosed. The sensitivity of snowpack zo changes over time in modeling of sublimation was examined using hourly meteorological data for the winter of 2000-2001 at Syracuse, New York and Leadville Colorado for several scenarios, including increasing or decreasing zo after a snowfall event, considering directionality of zo as a function of the wind direction, and a ratio of latent heat to momentum roughness lengths. The base case used a constant zo of 0.01 metres. The modeled differences were a function of the values of zo, which varied with the frequency of occurrence of fresh snow and the distribution of wind from various directions. The temporal and spatial variability in surface roughness is crucial in computing the energy and mass balance of a snowpack.
Keywords: roughness length, snow, sublimation

Fassnacht, S.R., C.M. Heun, J.I. López-Moreno, and J.B.P. Latron, 2010. Variability of Snow Density Measurements in the Rio Esera Valley, Pyrenees Mountains, Spain. Cuadernos de Investigación Geográfica (Journal of Geographical Research), 36(1), 59-72.
An accurate assessment of snow depth and snow density is essential to determine that amount of water stored in the snowpack, i.e., snow water equivalent (SWE). The measurement of snow density is much more difficult and time consuming than snow depth. The variability in snow density is evaluated for a 5.4-km stretch of the Rio Esera headwaters in the Spanish Pyrenees Mountains. The traditional snow tube method is compared to the more labour intensive but accurate snow pit method. The former method measures snow depth and extracts a snow core that is weighed. The latter method uses a wedge cutter to extract a 1-L snow sample to estimate density at 10-cm intervals through the snowpack. The variability in snowpack density is not systematic and can only be explained at lower elevation when the snowpack is known to be melting, as identify by an isothermal snowpack at zero degrees Celsius. This occurred during a mid-January survey. A late-April survey showed that these lower elevation sites were still more dense.
Keywords: snow density, snow water equivalent, Ésera Valley, Pyrenees Mountains

Fassnacht, S.R., and J.E. Derry, 2010. Defining similar regions of snow in the Colorado River Basin using self-organizing maps. Water Resources Research, 46, W04507 [doi:10.1029/2009WR007835].
We used self-organizing maps (SOMs) to define regions of homogeneity in the Colorado River Basin using snow telemetry (SNOTEL) snow water equivalent (SWE) data. SOMs are a specific application of artificial neural networks. Daily data for 216 stations using 15 years (1991-2005) of data from 1 October through 30 June were used. To identify areas of similar snow accumulation, persistence, and ablation patterns, data were transformed by dividing by the 15 year average peak SWE. Three experiments were performed to determine how the regions of homogeneous snowpack characteristics changed. The number of groups was increased from 4 to 6 to 9 to 16. By increasing this resolution, more subtle variations were defined. The temporal resolution of the data was decreased from daily to weekly to monthly to yearly. The accumulation and ablation of the snowpack over time represents a plot called a niveograph, which was summarized for yearly data by three variables. These were peak SWE, length of season, and date of peak SWE. Very similar results were produced using daily, weekly, and monthly time steps. However, using peak SWE produced only 50% of the same groupings, while using the other annual summary variables, even together, produced less than 25% of the same groupings. Using 18 physiographic variables to represent the SNOTEL stations yielded groups that were similar to those from using peak SWE but more evenly distributed in space. Using Ward's method of cluster analysis could only be performed with the individual annual summary variables. It produced groupings similar to the comparable SOM application but slightly less representative of the daily data groupings.
Keywords: snow, SNOTEL

Fassnacht, S.R., M.W. Williams, and M.V. Corrao, 2009. Changes in the surface roughness of snow from millimetre to metre scales. Ecological Complexity, 6(3), 221-229 [doi:10.1016/j.ecocom.2009.05.003].
The roughness of snow influences the movement of air across the snow surface and resulting transfers of energy. Here we focus on the roughness of the snowpack surface to determine its range of variability for different snow conditions (e.g., time since last snowfall), across spatial scales that ranged from 0.01 cm (card) to more than 1000 cm (transect) or more than 5-orders of magnitude, and due to the deposition of aeolian constituents. Digital photogrammetry of snow surfaces was used to compute two roughness metrics at two mountain sites in north-central Colorado. These metrics are the random roughness (RR) that disregards the spatial structure and the fractal dimension (D) computed from variogram analysis. At the crystal scale, D is between 1.67 (card) and 1.60 (board), which increases to 1.77 between 0.1 and 1.0 cm. At longer scales, D is 1.53 (board) to 1.56 (transect). There was no significant change in surface roughness during the accumulation season, with RR values at about 0.002. During the melt season the surface roughness doubled, with the RR values increasing from about 0.002 to 0.004. Snow was more rough parallel to the wind when dunes were present, and roughness varied spatially. The average RR value computed for the white snow surface of 0.014 is substantially greater than the value computed for the red dust surface of 0.0032. Due to undulations of smaller amplitude and as a result of the dust itself, the red dust surface is more random(D is 2.62 versus 2.23 for the white snow). Our results show that there is consistency in roughness over different scales, yet large scale processes (e.g., wind and radiation activity) influence the magnitude of roughness metrics much more than small scale processes (e.g., crystal form and metamorphism).
Keywords: Snow, Surface roughness, Roughness index, Fractal dimension, Aeolian dust

Fassnacht, S.R., J.D. Stednick, J.S. Deems, and M.V. Corrao, 2009. Metrics for assessing snow surface roughness from digital imagery. Water Resources Research, 45, W00D31, [doi:10.1029/2008WR006986].
Digital image profiles of snowpack surfaces were acquired concurrently with 1-cm resolution manual measurements. The manual measurements confirmed that unaltered digital images accurately represented a two-dimensional roughness profile of the snowpack surface. Roughness indices, such as random roughness, that have been used to represent soil surfaces were computed, and their utility for quantifying snowpack surface roughness is illustrated. Variogram analysis was used to determine the fractal dimension and scale break. Surface characteristics were a function of the scale, with a rough snow surface and graupel yielding similar results. A relatively smooth snow surface showed no crystal-scale features and had a fractal dimension approaching that of a random surface.

Deems, J.S., S.R. Fassnacht, and K.J. Elder. Interannual consistency in fractal snow depth patterns at two Colorado mountain sites. Journal of Hydrometeorology, 9(5), 977-988.
Fractal dimensions derived from log-log variograms are useful for characterizing spatial structure and scaling behavior in snow depth distributions. This study examines the temporal consistency of snow depth scaling features at two sites using snow depth distributions derived from lidar datasets collected in 2003 and 2005. The temporal snow accumulation patterns in these two years were substantially different, but both years represent nearly average 1 April accumulation depths for these sites, with consistent statistical distributions. Two distinct fractal regions are observed in each log-log variogram, separated by a scale break, which indicates a length scale at which a substantial change in the driving processes exists. The lag distance of the scale break is 15 m at the Walton Creek site and 40 m at the Alpine site. The datasets show consistent fractal dimensions and scale break distances between the two years, suggesting that the scaling features observed in spatial snow depth distributions are largely determined by physiography and vegetation characteristics and are relatively insensitive to annual variations in snowfall. Directional variograms also show consistent patterns between years, with smaller fractal dimensions aligned with the dominant wind direction at each site.

Ryan, W.A., N.J. Doesken, and S.R. Fassnacht, 2008. Preliminary results of ultrasonic snow depth sensor testing for National Weather Service (NWS) snow measurements in the US. Hydrological Processses, 22(15), 2748-2757 [doi:10.1002/hyp.7065].
During the 2006-2007 winter season, 17 sites across the US including Alaska tested an automated snow measurement system. This article aims to describe successes and failures of this system and provide insight into data collected this season. The system was designed in collaboration with both Environment Canada and Snow Sensor Study participants during the summer of 2006. This system included three Campbell Scientific SR-50 sensors oriented 120 o from one another and a temperature probe centred in the plot. Data collection efforts were successful with minimal amounts of data missing because of system or sensor failures. The system integrated automated retrieval of data from dataloggers, as well as automated file transfer protocol (FTP) to the study website for data archival and graphical display.
Overall, the sensors and installation worked well with only a few problems noted. The sensors compared well with both manual observations taken adjacent to each sensor as well as traditional total snow depth (TSD) on ground measurements. The comparison to depths, taken adjacent to the sensors, allows for investigation of frost heave and indicates periods where the sensors were not functioning properly. The comparison to TSD on ground reveal problems with siting at some locations that are recommended to be remedied by re-installation or re-location of those sites prior to the 2007-2008 snow season. These results are preliminary and research will be ongoing for signal processing, snowfall algorithm development and optimal installation in preparation for the 2007-2008 snow season. This research has potential to return important snow observations to national weather service(NWS) observing networks that were discontinued when automation began as well as provide continuous snowpack monitoring to data users.

Ryan, W.A., N.J. Doesken, and S.R. Fassnacht, 2008. Evaluation of ultrasonic snow depth sensors for U.S. snow measurements. Journal of Atmospheric and Oceanic Technology, 25(5), 667-684.
Ultrasonic snow depth sensors are examined as a low cost, automated method to perform traditional snow measurements. In collaboration with the National Weather Service, nine sites across the United States were equipped with two manufacturers of ultrasonic depth sensors: the Campbell Scientific SR-50 and the Judd Communications sensor. Following standard observing protocol, manual measurements of 6-h snowfall and total snow depth on ground were also gathered. Results show that the sensors report the depth of snow directly beneath on average within +/-1 cm of manual observations. However, the sensors tended to underestimate the traditional total depth of snow-on-ground measurement by approximately 2 cm. This is mainly attributed to spatial variability of the snow cover caused by factors such as wind scour and wind drift.
After assessing how well the sensors represented the depth of snow on the ground, two algorithms were created to estimate the traditional measurement of 6-h snowfall from the continuous snow depth reported by the sensors. A 5-min snowfall algorithm (5MSA) and a 60-min snowfall algorithm (60MSA) were created. These simple algorithms essentially sum changes in snow depth using 5- and 60-min intervals of change and sum positive changes over the traditional 6-h observation periods after compaction routines are applied. The algorithm results were compared to manual observations of snowfall. The results indicated that the 5MSA worked best with the Campbell Scientific sensor. The Campbell sensor appears to estimate snowfall more accurately than the Judd sensor due to the difference in sensor resolution. The Judd sensor results did improve with the 60-min snowfall algorithm. This technology does appear to have potential for collecting useful and timely information on snow accumulation, but determination of snowfall to the current requirement of 0.1 in. (0.25 cm) is a difficult task.

Bales, R.C., K.A. Dressler, B. Imam, S.R. Fassnacht, and D. Lampkin, 2008. Fractional snow cover in the Colorado and Rio Grande basins, 1995-2002. Water Resources Research, 44, W01425 [doi:10.1029/2006WR005377].
A cloud-masked fractional snow-covered area (SCA) product gridded at 1 km was developed from the advanced very high resolution radiometer for the Colorado River and upper Rio Grande basins for 1995-2002. Cloud cover limited SCA retrievals on any given 1-km2 pixel to on average once per week. There were sufficient cloud-free scenes to map SCA over at least part of the basins up to 21 days per month, with 3 months having only two scenes sufficiently cloud free to process. In the upper Colorado and upper Grande, SCA peaked in February-March. Maxima were 1-2 months earlier in the lower Colorado. Averaged over a month, as much as 32% of the upper Colorado and 5.5% of the lower Colorado were snow covered. Snow cover persisted longest at higher elevations for both wet and dry years. Interannual variability in snow cover persistence reflected wet-dry year differences. Compared with an operational (binary) SCA product produced by the National Operational Hydrologic Remote Sensing Center, the current products classify a lower fraction of pixels as having detectable snow and being cloud covered (5.5% for SCA and 6% for cloud), with greatest differences in January and June in complex, forested terrain. This satellite-derived subpixel determination of snow cover provides the potential for enhanced hydrologic forecast abilities in areas of complex, snow-dominated terrain. As an example, we merged the SCA product with interpolated ground-based snow water equivalent (SWE) to develop a SWE time series. This interpolated, masked SWE peaked in April, after SCA peaked and after some of the lower elevation snow cover had melted.

Fassnacht, S.R., 2007. Data time step to estimate snowpack accumulation at select United States meteorological stations. Hydrological Processes, 21(12), 1608-1615.
When estimating the water balance for a cold region watershed, that is one that receive a substantial portion of its annual precipitation as snow, accumulation and other winter hydrological processes must be considered. For many of theses watersheds, all but the most fundamental meteorological data (temperature and precipitation), are either not measured or not measured at a reasonable time step. Of particular importance are wind data, as wind influences underestimates of precipitation due to wind undercatch and losses of snow from the snowpack, specifically, snowpack sublimation, and the occurrence and magnitude of blowing snow. Estimating snow accumulation to yield snowmelt amounts requires summing of gauged precipitation and gauge undercatch, and subtracting minus snowpack sublimation and blowing snow transport. The first two components are computed on a daily time step, while the latter two are computed on an hourly time step. From five National Weather Service meteorological stations (Pullman WA, Rawlins WY, Leadville CO, Rhinelander WI, Syracuse NY), the variations in computed snowpack mass losses minus undercatch using data at different time intervals show that at most sites it is difficult to use monthly time steps for computations derived using hourly or daily data. At the relative dry and cold Leadville, Colorado site the computations were transferable between time steps.
Keywords: solid precipitation, meteorological data, undercatch, sublimation, blowing snow

Dressler, K.A., S.R. Fassnacht, and R.C. Bales, 2006. A comparison of snow telemetry (SNOTEL) and snowcourse measurements in the Colorado River Basin. Journal of Hydrometeorology, 7(4), 705-712.
Temporal and spatial differences in snow-water equivalent (SWE) at 240 snow telemetry (SNOTEL) and at 500 snow course sites and a subset of 93 collocated sites were evaluated by examining the correlation of site values over the snow season, interpolating point measurements to basin volumes using hypsometry and a maximum snow extent mask, and variogram analysis. The lowest correlation at a point (r = 0.79) and largest interpolated volume differences (as much as 150 mm of SWE over the Gunnison basin) occurred during wet years (e.g., 1993). Interpolation SWE values based on SNOTEL versus snow course sites were not consistently higher or lower relative to each other. Interpolation rmse was comparable for both datasets, increasing later in the snow season. Snow courses correlate over larger distances and have less short-scale variability than SNOTEL sites, making them more regionally representative. Using both datasets in hydrologic models will provide a range of predicted streamflow, which is potentially useful for water resources management.

Fassnacht, S.R., 2006. Upper versus Lower Colorado River sub-basin streamflow: characteristics, runoff estimation and model simulation. Hydrological Processes, 20, 2187-2205 [doi:10.1002/hyp.6202].
Streamflow in the upper Colorado River in the western USA is always snowmelt dominated, whereas the lower river's perennial streamflows are snowmelt dominated only 50% of the time. The magnitude and timing of peak flows is important for water resources management. In the upper basin the annual maximum daily discharge usually occurs in May or June, and in the lower basin this peak is observed to occur in any month except May or June. The timing of one-half of the specific runoff is used as a second measure of the variability in timing and magnitude of streamflows. For the upper basin, nine watersheds are used to illustrate streamflow trends, with the Yampa River used as a sample sub-basin. For the lower basin, five watersheds are used, of which Salt River is used as sample sub-basin. The differences in monthly flow variation over 20-year time periods (1920-1939, 1940-1959, 1960-1979, and 1980-1999) are substantial for the Salt River but not for the Yampa River.
Three model types were used to estimate streamflow characteristics. An autocorrelation model was used to generate winter specific runoffs, which were more reasonable for the Yampa River than the Salt River. A regression between snow water equivalent (SWE) and winter specific runoff showed a good correlation for the two sub-basins. A weaker relationship exists between SWE and non-winter flows for the sample lower basin watershed. Streamflow was simulated relatively well using the Precipitation Runoff Modeling System hydrological model.
Keywords: peak flow, specific runoff, Colorado River, snow course, statistical modelling, hydrological modelling

Fassnacht, S.R., Z.-L. Yang, K.R. Snelgrove, E.D. Soulis, and N. Kouwen, 2006. Effects of averaging and separating soil moisture and temperature in the presence of snow cover in a SVAT and hydrological model. Journal of Hydrometeorology, 7(2), 298-304.
The energy and water balances at the earth's surface are dramatically influenced by the presence of snow cover. Therefore, soil temperature and moisture for snow-covered and snow-free areas can be very different. In computing these soil state variables, many land surface schemes in climate models do not explicitly distinguish between snow-covered and snow-free areas. Even if they do, some schemes average these state variables to calculate grid-mean energy fluxes and these averaged state variables are then used at the beginning of the next time step. This latter approach introduces a numerical error in that heat is redistributed from snow-free areas to snow-covered areas, resulting in a more rapid snowmelt. This study focuses on the latter approach and examines the sensitivity of soil moisture and streamflow to the treatment of the soil state variables in the presence of snow cover by using WATCLASS, a land surface scheme linked with a hydrologic model. The model was tested for the 1993 snowmelt period on the Upper Grand River in Southern Ontario, Canada. The results show that a more realistic simulation of streamflow can be obtained by keeping track of the soil states in snow-covered and snow-free areas.

Deems, J.S., S.R. Fassnacht, and K.J. Elder, 2006. Fractal distribution of snow depth from LiDAR data. Journal of Hydrometeorology, 7(2), 285-297.
Snowpack properties vary dramatically over a wide range of spatial scales, from crystal microstructure to regional snow climates. The driving forces of wind, energy balance, and precipitation interact with topography and vegetation to dominate snow depth variability at horizontal scales from 1 to 1000 m. This study uses land surface elevation, vegetation surface elevation, and snow depth data measured using airborne lidar at three sites in north-central Colorado. Fractal dimensions are estimated from the slope of a logtransformed variogram and demonstrate scale-invariant, fractal behavior in the elevation, vegetation, and snow depth datasets. Snow depth and vegetation topography each show two distinct fractal distributions over different scale ranges (multifractal behavior), with short-range fractal dimensions near 2.5 and longrange fractal dimensions around 2.9 at all locations. These fractal ranges are separated by a scale break at 15-40 m, depending on the site, which indicates a process change at that scale. Terrain has a fractal distribution over nearly the entire range of scales available in the data. Directional differences in the fractal dimensions for each parameter are also present at multiple scales, and are related to the wind direction frequency distributions at each site. The results indicate that different sampling resolutions may yield different results and allow rescaling in specific scale ranges. Resolutions of 10 m and finer are consistently self-similar, as are resolutions greater than 30 m, though the coarser resolutions show nearly random distributions.

Fassnacht, S.R., and J.S. Deems, 2006. Measurement sampling and scaling for deep montane snow depth data. Hydrological Processes 20, 829-838 [doi:10.1002/hyp.6119].
The resolution of snow depth measurements was scaled from a nominal horizontal resolution of approximately 1.5 m to 3, 5, 10, 20, and 30 m using averaging (AVG) and resampling with a uniform random stratified sampling (RSS) scheme. The raw snow depth values were computed from airborne light detection and ranging data by differencing summer elevation measurements from winter snow surface elevations. Three montane study sites from the NASA Cold Lands Processes Experiment, each covering an 1100 m by 1100 m area, were used. To examine scaling, log-log semi-variograms with 50 log-width bins were created for both of the different subsetting methods, i.e. RSS and AVG. From the raw data, a scale break, going from a structured to a nearly spatially random system, was observed in each of the log-log variograms. For each site, the scale break was still detectable at slightly greater than the resampling resolution for the RSS scheme, but at approximately twice the subsetting resolution for the AVG scheme. The resolution required to identify the scale break was still from 5 to 10 m, depending upon the location and sampling method.
Keywords: snow depth, sampling, scaling, variograms, LiDAR

Dressler, K.A., G.H. Leavesley, R.C. Bales, and S.R. Fassnacht, 2006. Evaluation of gridded snow water equivalent and satellite snow cover products for mountain basins in a hydrologic model. Hydrological Processes, 20, 673-688 [doi:10.1002/hyp.6130].
The USGS precipitation-runoff modelling system (PRMS) hydrologic model was used to evaluate experimental, gridded, 1 km2 snow-covered area (SCA) and snow water equivalent (SWE) products for two headwater basins within the Rio Grande (i.e. upper Rio Grande River basin) and Salt River (i.e. Black River basin) drainages in the southwestern USA. The SCA product was the fraction of each 1 km2 pixel covered by snow and was derived from NOAA advanced very high-resolution radiometer imagery. The SWE product was developed by multiplying the SCA product by SWE estimates interpolated from National Resources Conservation Service snow telemetry point measurements for a 6 year period (1995-2000). Measured SCA and SWE estimates were consistently lower than values estimated from temperature and precipitation within PRMS. The greatest differences occurred in the relatively complex terrain of the Rio Grande basin, as opposed to the relatively homogeneous terrain of the Black River basin, where differences were small. Differences between modelled and measured snow were different for the accumulation period versus the ablation period and had an elevational trend. Assimilating the measured snowfields into a version of PRMS calibrated to achieve water balance without assimilation led to reduced performance in estimating streamflow for the Rio Grande and increased performance in estimating streamflow for the Black River basin. Correcting the measured SCA and SWE for canopy effects improved simulations by adding snow mostly in the mid-to-high elevations, where satellite estimates of SCA are lower than model estimates.
Keywords: assimilation, snow water equivalent, snow-covered area, hydrologic modelling, PRMS

Fassnacht, S.R., 2004. Estimating alter-shielded gauge snowfall undercatch, snowpack sublimation, and blowing snow transport at six sites in the coterminous United States. Hydrological Processes, 18(18), 3481-3492 (doi:10.1002/hyp.5806).
Computing the monthly and winter water balance for cold regions can be difficult due to data scarcity. Historically, the spatial and temporal resolution, and the number of variables measured have been limited. Currently these data are once again becoming limited. To estimate the net snowpack accumulation, measured precipitation must be adjusted to consider precipitation underestimation due to gauge undercatch, the snow lost to sublimation, and blowing snow transport. Using existing formulations, hourly meteorological data were used to estimate snowpack sublimation and blowing snow transport losses for three winters at six National Weather Service (NWS) Automated Surface Observation Stations across the coterminous United States. Wind-induced undercatch was estimated from daily data for the colocated NWS Alter-shielded gauges. For the average wind speed sites (the average wind speed was from 2.4 to 4.3 m/s), 70% of the snow that fell was caught, while at the low wind site (1.3 m/s), 90% was caught and only 46% was caught at the high wind site (5.6 m/s). Average snowpack sublimation ranged from 7 mm per month at either low wind or low precipitation sites to over 20 mm per month at average wind sites with either average precipitation and low humidity or high precipitation and moderate humidity. Blowing snow transport was only important at higher wind sites (>4 m/s). A distinct relationship was not obvious for average monthly meteorology for undercatch versus snowpack sublimation plus blowing snow losses. Seasonally, they are approximately equal for more snowy and wet environments.
Keywords: snowfall undercatch, snow sublimation, blowing snow transport, water balance, meteorological stations

Molotch, N.P., S.R. Fassnacht, R.C. Bales, and S.R. Helfrich, 2004. Estimating the distribution of snow water equivalent and snow extent beneath cloud cover in the Salt-Verde River basin, Arizona. Hydrological Processes, 18(9), 1595-1611, [doi:10.1002/hyp.1408].
The temporal and spatial continuity of spatially distributed estimates of snow water equivalent (SWE) and snow-covered area (SCA) is limited by the availability of cloud-free satellite imagery, as SCA is required 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. An accuracy optimization function of gridded positive accumulated degree-days (ADD) and binary SCA (derived from the Advanced Very High Resolution Radiometer (AVHRR)) was used to define a threshold temperature to define the area capable of having snow cover. The optimized threshold temperature increased during snow accumulation periods, reaching a peak at maximum snow extent. The thresholds then decrease during the first time period after peak snow extent due to the low amount of energy required to melt the "intermittent" snow cover at lower elevations. The area defined as being capable of having snow cover was then used to define the extent of the SWE interpolation. The simulated snow capable area was compared to observed SCA from AVHRR to assess the simulated snow map accuracy. During periods without precipitation, the average commission and omission errors were 12.2% and 8.0% respectively. Commission and omission errors increased to 12.9% and 18.7% during periods of precipitation. The analysis shows that temperature data can be useful in defining the snow extent beneath clouds and therefore improve the spatial and temporal continuity of SCA and SWE products.
Keywords: snow water equivalent, snow cover, time series, temperature, hydrological data

Fassnacht, S.R., F. Yusuf, and N. Kouwen, 2004. Paralysing January 1999 snowstorms produced minimal streamflow for Southern Ontario. Canadian Water Resources Journal, 29(1), 1-12.
On January 2, 1999, the city of Toronto and surrounding regions of southern Ontario, Canada were brought to a standstill by a large storm event that resulted in near-record snowfalls. Several smaller winter storms followed and by January 15 approximately 100 cm of snow had fallen across the area, creating a flooding concern for water resources managers. A statistical analysis of the 1999 snowpack depth and snow water equivalent (SWE) for the Grand River Basin showed that the snow depths were the largest on record with two-week snow depth increases at several sites having return periods from 50 to 200 years. However, the amount of water in the snowpack was small, with the return period for SWE being between two and 21 years. Above freezing temperatures occurred in mid-January partially melting the pack and producing some streamflow. No flooding occurred, and the spring peak streamflows were amongst the lowest on record.

Fassnacht, S.R., K.A. Dressler, and R.C. Bales, 2003. Snow water equivalent interpolation for the Colorado River Basin from snow telemetry (SNOTEL) data. Water Resources Research, 39(8), 1208 (doi:10.1029/2002WR001512).
Inverse weighted distance and regression non-exact techniques were evaluated for interpolating methods snow water equivalent (SWE) across the entire Colorado River Basin of the western United States. A 1-km spacing was used for the gridding of snow telemetry (SNOTEL) measurements, for the years 1993, 1998, and 1999, which on average represented a higher than average, average and lower than average snow years. Due to the terrain effects, the regression techniques (hypsometric elevation and multi-variate physiographic parameter) were found to be superior to the weighted distance approaches (inverse distance weighting squared, and optimal power inverse distance weighting). A regression detrended inverse weighted distance method was developed for the hypsometric and multi-variate techniques, in order to preserve the point SNOTEL data. Based on root mean square error analysis and estimates of SWE volumes in different elevation zones for the entire basin and for sub-basins, the elevation detrended method with a point-by point regression was found to be the most appropriate technique. Various search radii and anisotropies of the search ellipse were tested with the hypsometric method, producing only small difference in the root mean square error and SWE volumes.
Keywords: snow water equivalent, SNOTEL, spatial interpolation, Colorado River

Fassnacht, S.R., and E.D. Soulis, 2002. Implications during transitional periods of improvements to the snow processes in the Land Surface Scheme - Hydrological Model WATCLASS. Atmosphere-Ocean, 40(4), 389-403.
The representation of snow processes is crucial in both hydrological models and land surface schemes. The importance of detailed physical representation for four snow processes into the WATCLASS hydrological-land surface scheme model is examined. The snow processes are the occurrence of mixed precipitation, the density of fresh snow, the maximum snowpack density and canopy snowfall interception. It is shown that the inclusion of the non-static processes does not significantly improve the simulated streamflow. The changes in the simulation of state variables, in particular, the snowpack depth, snow water water equivalent, soil temperature and soil moisture content are small, but may become important during transitional periods, such as the initial accumulation and depletion of snow-covered area during snowmelt. This substantially alters the surface heat fluxes during these periods.

Fassnacht, S.R., N. Kouwen, and E.D. Soulis, 2001. Surface temperature adjustments to improve weather radar representation of multi-temporal winter precipitation accumulations. Journal of Hydrology, 253(1-4), 148-168.
Hydrologists and water resources managers who work in areas that receive a significant portion of the annual precipitation in the form of snowfall rely on good approximates of snow accumulation in order to assess snowpack volumes for snowmelt streamflow estimation. Weather radar rainfall estimation has been used for hydrological modelling and radar has been used for the estimation of snowfall from individual events, yet radar has rarely been used to measure snowfall accumulation over time periods longer. Snowfall estimates for weekly, monthly, and seasonal accumulation periods have been compared to measured Nipher-shielded Belfort precipitation gauge quantities. A local scaling issue that caused overestimates is discussed. To enhance the accumulation estimates, the conventional scan radar images were adjusted using the near surface air temperatures. The adjustment for mixed precipitation improved the accumulation estimates, while the subsequent particle shape adjustment for snow crystal shape did not further enhance the radar estimates.
Keywords: snowfall, snow accumulation, weather radar, mixed precipitation, snow particles

Fassnacht, S.R., 2000. Flow modelling to establish a suspended sediment sampling schedule in two Canadian Deltas. Hydrology and Earth System Sciences, 4(3), 425-438.
The approximate travel times for suspended sediment transport through two multi-channel network is estimated using flow modelling. The focus is on the movement of high sediment concentrations that travel rapidly downstream. Since suspended sediment transport through river confluences and bifurcation movement is poorly understood, it is assumed that the sediment moves at approximately the average channel velocity during periods of high sediment load movement. Calibration of the flow model is discussed, with an emphasis on the incorporation of cross-section data, that are not referenced to a datum, using a continuous water surface profile. Various flow regimes are examined for the Mackenzie and the Slave River Deltas in the Northwest Territories, Canada, and a significant variation in travel times is illustrated. One set of continuous daily sediment measurements throughout the Mackenzie Delta are used to demonstrate that the travel time estimates are reasonable.
Keywords: suspended sediment, multi-channel river systems, flow modelling, sediment transport

Fassnacht, S.R., and F.M. Conly, 2000. The persistence of a scour hole on the East Channel of the Mackenzie Delta, NWT. Canadian Journal of Civil Engineering, 27(4), 798-804.
Anomalies in the bathymetry of river channels are of great practical concern for designing sub-bed pipeline crossings. Of particular interest is the long-term stability of deep holes. Bathymetric evidence indicates that one unusually deep hole in the East Channel of the Mackenzie River, referred to as a scour hole, has existed as early as 1956. Detailed hydraulic and morphologic data were first collected in 1985, and again in 1992 in order to assess the spatial and temporal stability of the feature. Even with a record flood on the Mackenzie River in 1988, the hole, with a maximum depth approaching 30 metres, was vertically stable over the seven year period. However, lateral erosion and sedimentation have resulted in a shift in the horizontal position of the scour hole, with a maximum horizontal erosion of approximately 2 metres per year. The average rates of lateral outward movement were observed to be 0.8 metres per year.
Keywords: Mackenzie Delta, rivers, fluvial sediment, channel stability, scour, scour hole

Fassnacht, S.R., E.D. Soulis, and N. Kouwen, 1999. Algorithm application to improve weather radar snowfall estimates for winter hydrologic modelling. Hydrological Processes, 13(18), 3017-3039.
Algorithms were applied to weather radar data to improve the precipitation estimation for winter hydrologic modelling. The radar data were adjusted to consider the occurrence of mixed precipitation at above freezing air temperatures, the shape of snow particles, and a site specific scaling phenomena. Radar data, uncorrected and corrected gridded gauge precipitation data were used as input to the linked WATFLOOD/CLASS hydrologic model for simulation of streamflow. WATFLOOD performed the horizontal water routing and CLASS performed the vertical energy and water budgetting. Modelling of the Grand River watershed that is within the coverage of the Atmospheric Environment Service C-band radar in King City, Ontario, Canada for the five winters from 1993 to 1997 illustrated that on average the adjusted radar images produced +/- 15% of the observed runoff volumes whereas the corrected gauge precipitation yielded 35% less runoff than observed. Substantial seasonal variation was observed. Radar provided more realistic winter precipitation quantities for streamflow modelling than the corrected gauge data. Application of the algorithms improved upon the raw radar estimates.
Keywords: hydrologic modelling, weather radar, precipitation gauges, winter hydrology, snow

Fassnacht, S.R., J. Innes, N. Kouwen, and E.D. Soulis, 1999. The specific surface area of fresh dendritic snow crystals. Hydrological Processes, 13(18), 2945-2962.
The surface area to mass ratio or specific surface area (SSA) is an often neglected characteristic of the snowpack that varies substantially with time, and with the shape of the individual snow crystal for fresh snow. The SSA for the dendritic shape of snow crystals was computed using a series of images presented in Bentley and Humphries (1931). The specific images were dendritic crystals (P1d, P1e, P1f) and crystals that take a partial dendritic form and have ends or extensions (P2a, P2b, P2d, P2e, P2f, P2g) according to the Magono and Lee (1966) snow crystal classification. Image analysis using known geometric relationships between length and width and particle size distributions examined the spatial properties of 50 sample snow crystals. Probability distribution functions were derived for SSA and these compared well with measured and other computed estimates of fresh snow SSA. For the non-rimed condition, the average SSA was 0.182 m2/g with a range from 0.09 to 0.33 m2/g. The presence of rime is discussed. Depending on the shape of the rime particles and the degree of surface coverage, the SSA can be doubled (20% coverage for needle or plate rime). Fractal analysis was performed to determine various geometric relationships that characterize the dendritic form of snow crystal.
Keywords: snow crystals, specific surface area, dendrites, Bentley images, fractal analysis

Fassnacht, S.R., 1997. A multi-channel suspended sediment transport model for the Mackenzie Delta, NWT. Journal of Hydrology, 197(1-4), 128-145.
To model the suspended sediment transport through the Mackenzie River Delta, Northwest Territories, Canada, a one-dimensional multi-channel suspended sediment model (FOSH-MC) has been developed. The model links an established network flow model that has been successfully applied to the Mackenzie Delta with an existing suspended sediment model. Sediment travel times along channels, that are useful to establish suspended sediment sampling schedules, are estimated as a model output product. The sediment output also includes total loads at each network node and reach suspended sediment concentrations. The model can route both cohesive and non-cohesive suspended sediment.
This research is a first attempt to dynamically model sediment transport through the Mackenzie Delta. All previous efforts have examined long-term fluxes. The FOSH-MC model has the potential to trace the pathways of contaminants through the Mackenzie Delta. The model also has the potential to be applied to other multi-channel networks that primarily carry suspended sediment.
Keywords: sediment, sediment transport, suspended sediment, multi-channel network, travel time, modelling, Mackenzie Delta

Refereed Conference Proceedings

Fassnacht, S.R., and M.Hultstrand, 2015. Snowpack Variability and Trends at Long-term Stations in Northern Colorado, USA. Proceedings of the International Association of Hydrological Sciences (Hydrologic Non-Stationarity and Extrapolating Models to Predict the Future), [doi:10.5194/piahs-92-1-2015].
The individual measurements from snowcourse stations were digitized for six stations across northern Colorado that had up to 79 years of record (1936 to 2014). These manual measurements are collected at the first of the month from February through May, with additional measurements in January and June. This dataset was used to evaluate the variability in snow depth and snow water equivalent (SWE) across a snowcourse, as well as trends in snowpack patterns across the entire period of record and over two halves of the record (up to 1975 and from 1976).
Snowpack variability is correlated to depth and SWE. The snow depth variability is shown to be highly correlated with average April snow depth and day of year. Depth and SWE were found to be significantly decreasing over the entire period of record at two stations, while at another station the significant trends were an increase over the first half of the record and a decrease over the second half. Variability tended to decrease with time, when significant.

Fassnacht, S.R., N.B.H. Venable, J. Khishigbayar, and M.L. Cherry, 2013. The Probability of Precipitation as Snow Derived from Daily Air Temperature for High Elevation Areas of Colorado, United States. Cold and Mountain Region Hydrological Systems Under Climate Change: Towards Improved Projections (Proceedings of symposium H02, IAHS-IAPSO-IASPEI Assembly, Gothenburg, Sweden, July 2013) IAHS, 360, 65-70.
Precipitation phase affects the energy balance of the Earth's surface. Snow formation depends upon atmospheric conditions and is driven mainly by temperature. Dewpoint and air temperature thresholds at or near freezing temperatures have been used to determine precipitation phase in some climates, but may not adequately represent the same phase of precipitation in snowy and semi-arid regions, nor are relative humidity data available at many stations. The objective of this study is to describe relations between average air temperature and probability of snow for nine high elevation (>2000 m) meteorological stations across central Colorado, USA. Fifty years of data were analysed, generating snow probabilities from ratios of the number of days with snow and days with precipitation. These were compared to the average daily temperature during precipitation using 0.2oC intervals. Best-fit linear relations reveal higher probabilities of snowfall in the study areas at temperatures several degrees warmer than previously published curves.
Keywords: snowfall, precipitation, semi-arid climate

Fassnacht, S.R., and G.G. Patterson, 2013. Niveograph Interpolation to Estimate Peak Accumulation at Two Mountain Sites. Cold and Mountain Region Hydrological Systems Under Climate Change: Towards Improved Projections (Proceedings of symposium H02, IAHS-IAPSO-IASPEI Assembly, Gothenburg, Sweden, July 2013) IAHS, 360, 59-64.
The typical assumption that 1 April SWE represents the peak annual SWE can be improved by fitting a modelled time series plot of SWE derived from daily SWE measurements, to the monthly data typically available from snow courses. For each year, first of the month SWE values were used to adjust the average daily time series to produce estimates of peak SWE. The average annual from (a) the entire time series and (b) specific years averaged for high, medium and low snow accumulation were implemented. For a station in Northern Colorado (Joe Wright, average annual peak SWE of 681 mm) and a station in eastern Arizona (Hannagan Meadow, average annual peak SWE of 334 mm), this method produced good estimates of peak SWE. These estimates were improved when the amount of snow on 1 April or 1 March was considered for Joe Wright and Hannagan Meadow, respectively.
Keywords: snow, peak SWE, niveographs, SNOTEL

Fassnacht, S.R., T. Sukh, M. Fernández-Giménez, B. Batbuyan, N.B.H. Venable, M. Laituri and G. Adyabadam, 2011. Local understanding of hydro-climatic changes in Mongolia. Cold Region Hydrology in a Changing Climate (Proceedings of symposium H02 held during IUGG2011 in Melbourne, Australia, July 2011), IAHS, 346, 120-129.
Air temperatures in semi-arid regions have increased more over the past few decades than those in many other parts of the world. Mongolia has an arid/semi-arid climate where large portions of the population are herders whose livelihood depends upon limited water resources. This paper combines local knowledge and understanding of recent changes in water availability in streams, springs, and wells with an analysis of climatic and hydrological change from meteorological station data to illustrate the degree of change among Mongolian water resources. We find that herders' perceptions of hydro-climatic change are very similar to the results of the station-based analysis. Additionally, since station data are spatially limited, local knowledge can emphasize smaller-scale variability in changes to climate and hydrology. For this paper, we focus on a site in the Khangai Mountains and another in the Gobi desert-steppe, both in Central Mongolia.

Fassnacht, S.R., E.D. Soulis, and N. Kouwen, 2003. Radar precipitation for winter hydrological modelling. Information from Weather Radar and Hydrological Modelling (Proceedings IUGG 2003 Symposium HS02, Sapporo Japan, July 2003), IAHS, 281, 35-42.
Interpolated precipitation gauge measurements and weather radar snowfall estimates were used as input to a physically based hydrological model (WATCLASS). The gauge measurements were corrected for wind undercatch according to WMO standards. Post-processing of the radar data was undertaken to consider underestimation due to the use of winter radar coefficients for liquid precipitation. Streamflow was simulated for the Upper Grand River Basin in central southwestern Ontario, Canada for the five winters from 1993 - 1997. For each year, except 1995, the radar data provided precipitation estimates that were better, in terms of simulated runoff volumes, than those provided by the gauge data. Along with runoff volumes, peak streamflows were more closely estimated from radar precipitation than gauge precipitation. Gauge estimates consistently yielded lower than observed peak streamflows.
Keywords: snow hydrologic modelling, weather radar, precipitation gauges, winter hydrology

Fassnacht, S.R., K.R. Snelgrove, and E.D. Soulis, 2001. Daytime incoming longwave radiation approximation for physical hydrological modelling. Soil-Vegetation-Atmosphere Transfer Schemes and Large-Scale Hydrological Models (Proceedings Sixth IAHS Scientific Assembly Symposium S5, Maastricht, July 2001), IAHS, 270, 279-286.
Since incoming long-wave radiation is not routinely measured in Canada, when it is required as a meteorological parameter, such as input to a physically-based hydrological model, the data must be derived. These data have been successfully computed as a function of near surface air temperature and cloud cover. However, cloud cover data are also not routinely measured. A method is described to compute the cloud cover fraction, for use to estimate the long-wave radiation, from a comparison of measured to theoretical short-wave radiation at three sites in central southwestern Ontario. The daytime cloud cover fraction is on average slightly more than 0.50. The impact of different long-wave radiation estimates from varying cloud cover fraction assumptions is illustrated in terms of simulated streamflow resulting from snowmelt.
Keywords: incoming long-wave radiation, short-wave radiation, snowmelt modelling, cloud cover

Fassnacht, S.R., E.D. Soulis, and N. Kouwen, 2001. Enhancing weather radar winter precipitation accumulation estimates. Remote Sensing in Hydrology 2000 (Proceedings IAHS Remote Sensing and Hydrology Conference 2000, Santa Fe, April 2000), IAHS, 267, 46-49.
Snowfall estimates for weekly, monthly, and seasonal accumulation periods have been compared to measured Nipher-shielded Belfort precipitation gauge quantities. A local scaling issue that caused overestimates is discussed. To enhance the accumulation estimates, the conventional scan radar images were adjusted using the near surface air temperatures. The adjustment for mixed precipitation improved the accumulation estimates, while the subsequent particle shape adjustment for snow crystal shape did not further enhance the radar estimates.
Keywords: snowfall, mixed precipitation, rainfall, precipitation measurement, radar

Fassnacht, S.R., E.D. Soulis, and N. Kouwen, 1999. Shape characteristics of freshly fallen snowflakes and their short-term changes. Interactions between the Cryosphere, Climate and Greenhouse Gases (Proceedings IUGG 99 Symposium HS2, Birmingham, July 1999), IAHS, 256, 111-122.
The shape of newly formed snowflakes is an important qualitative parameter as input to the development of a snowpack, and for potential atmospheric scavenging, while changes in the shape of freshly fallen crystals influence the metamorphosis and transport of snow and contaminants. This paper uses known spatial properties to simulate the needle-shaped crystal structures that can occur in the range of -4 to -6 degrees C. Probability distribution functions (pdfs) are derived for the surface area to mass ratio, ranging from 0.10 to 0.30 m2.g-1. Observations of freshly fallen flakes are compared to predicted pdfs. The modification in the snow crystal shape, immediately after accumulation, is estimated from laboratory experiments that measured sublimation rates directly from the pack. For fresh flakes, there is a rapid decrease in the effective surface area up to 50% over only 2-3 days.

updated: 2016-01-22