Steven R. Fassnacht

Fractal Distribution of Snow Depth from Lidar Data

Abstract 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 log-transformed 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 long-range 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 cha...

Interannual Consistency in Fractal Snow Depth Patterns at Two Colorado Mountain Sites

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.

Evaluation of Ultrasonic Snow Depth Sensors for U.S. Snow Measurements

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.

Implications during transitional periods of improvements to the snow processes in the land surface scheme ‐ hydrological model WATCLASS

Abstract The representation of snow processes is crucial in both hydrological models and land surface schemes. The importance of the detailed physical representation of four snow processes in 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 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 areas during snowmelt. This substantially alters the surface heat fluxes during these periods.

Spatiotemporal index for analyzing controls on snow climatology: application in the Colorado Front Range

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.

A Comparison of Snow Telemetry and Snow Course Measurements in the Colorado River Basin

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.

The specific surface area of fresh dendritic snow crystals

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 photographed by W. A. Bentley. 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 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 m 2 /g with a range from 0.09 to 0.33 m 2 /g. The presence of rime is discussed, and 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 shaped rime). Fractal analysis was performed to determine various geometric relationships that characterize the dendritic form of snow crystal.

A multi-channel suspended sediment transport model for the Mackenzie Delta, Northwest Territories

Abstract 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 reaches 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.

Large snowmelt versus rainfall events in the mountains

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 h snowmelt, precipitation, and rainfall events at 90 Snow Telemetry 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.

Climate change and wetland loss impacts on a western river's water quality

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 climateinduced 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.