H21B-0508
Geophysical Surveys Over a Terminal Moraine
Alpine watersheds represent the headwaters of many major rivers in western Canada. Consequently, understanding the hydrological cycle within these watersheds is critical for modeling the effects of climate change on water resources in western Canada and for developing informed water management strategies. Terminal moraines represent a significant hydrological response unit within many alpine watersheds in western Canada. Recent studies suggest that these features may provide sites for water storage. The preliminary results of a geophysical survey of a terminal moraine exhibiting geomorphological characteristics suggesting an ice-core will be presented. It is hypothesized that bedrock topography and the presence of ice creates barriers and channels groudwater flow. The focus of the survey was to delineate the hydrologically significant features within the moraine using electrical resistivity imaging (ERI), seismic refraction, and ground penetrating radar (GPR). Buried ice was easily detected using ERI due to high resistivity of over 1 MOhm-m. However, it was not as extensive as expected. Seismic refraction proved to be most useful in detecting the underlying bedrock. GPR images showed many reflection fragments but were noisy and difficult to interpret. Regions of relatively high electrical conductivity suggest some degree of channelization of groundwater in the vicinity of a tarn.
H21B-0509
Estimating 3D Variation in Active-Layer Thickness Beneath Arctic Streams Using Ground- Penetrating Radar
Our earlier ground-penetrating radar (GPR) investigations beneath arctic streams revealed greater active layer thicknesses beneath stream channels than beneath the adjacent terrestrial tundra. Presented here are results from 3D GPR data sets which were gathered over three sites to measure the active layer thickness variation within local streambed morphology. Three sites were selected based on their geomorphic differences. The first site is a high-energy water flow reach with a cobble to gravel streambed lining and riffle-pool morphology (alluvial stream). The second site is a deeply incised low-energy water flow reach with a beaded morphology and organic streambed lining (peat stream). The last site features a beaded morphology but with alluvial material lining the pool areas. GPR data were acquired using a pulsed radar system with a high-powered 1000V transmitter. The transmitting and receiving 200 MHz antennas were placed at the bottom of a small rubber boat for data acquisition. Profiles were gathered by pulling the boat across the stream from bank-to-bank while triggering at a constant time interval. Lines were collected at ~30cm intervals and continued upstream until a riffle-pool sequence was covered. Precise spatial data were collected using DGPS in conjunction with the GPR data. In addition, temperature data were recorded using thermocouples placed at varying substream depths located within or near the study sites to aid and verify GPR interpretations and numerical heat flow models. Results from the alluvial stream site illustrates greater thaw depths beneath riffle and gravel bar features compared to the neighboring pool areas while the beaded stream sites indicate the opposite, greater thaw depths beneath pools and thinner thaws beneath the connecting runs.
H21B-0510
Comparison of two years of snowmelt infiltration using electrical resistivity and TDR
Infiltration during snowmelt can be highly heterogeneous due to the formation of ice on the ground surface below the snow cover. In cases where snow is contaminated such as e.g. along highways and airports due to de-icing agents and other contaminants, it is important to be able to predict the zone of infiltration since this will determine the potential for retention and degradation in the unsaturated zone. The infiltration processes was monitored using time-lapse electrical resistivity measured in shallow electrodes in a glacial deposit near Oslo airport. TDR measurements were used to calibrate changes in water contents with changes in electrical resistivity values. In the first snowmelting event (April, 2006) there was hardly any ground frost, while in the second event (March, 2007) ground frost caused redistribution of meltwater and focussed infiltration. The observed infiltration pattern is supported by numerical simulation of infiltration in a 2D unsaturated profile.
H21B-0511
New Multi-Day Snow Cover Products From Combination Of Terra And Aqua MODIS Daily Snow Cover Data
This study develops an algorithm and automated scripts to produce multi-day Terra or Aqua, and Terra-Aqua snow cover image composites, with flexible starting and ending dates and a user-defined cloud cover threshold. Taking the northern Xinjiang, China and the 2003-2004 hydrologic year as an example, and using a cloud cover of 10% as a user-defined threshold, the algorithm generated 152 multi-day Terra-Aqua composite images in the test area, an average of 2.4 days (composite period) per image. Compared with the annual mean snow cover ~30% from standard MODIS 8-day Terra or Aqua composite product (only 47 images per year), the new multi-day Terra-Aqua composite product gives a mean snow cover of 18.7%, while both having similar percentage of annual mean cloud cover (~5%) and similar snow classification accuracy (~94%). In addition, some lake ices were misclassified as snow in the standard 8-day Terra or Aqua composite images. This suggests that the standard algorithm for producing 8-day composite products for the study area may have some limitations. Further investigations for other regions are needed. In any case, the new multi-day composite algorithm, however, correctly combine the daily images into composite images. Therefore, those new composite products generated from our algorithm and scripts are a significant contribution to the current MODIS snow cover product series and future NPOESS snow cover products.
H21B-0512
A New Multiple-layer Snow Model for Land Surface Models
Snow is an important component of the land surface energy and water budgets. In the western U.S., mountain snowmelt accounts for 70-90% of the annual streamflow. The ability to prescribe and predict mountain snowpack is critically important to hydrologic and atmospheric predictions. Recent studies have shown that significant underestimations of snow water equivalent (SWE) by land surface models exist for mountainous regions, when compared to observations. Our analysis has shown that uncertainties in the forcings due to scale incompatibility and possibly other factors cannot explain the majority of the large SWE underestimation found between the model and SNOpack TELemetry (SNOTEL) for mountainous regions, especially for moderate and deep snowpacks. Motivated by the significant underestimations of SWE, especially during the snow melt process, we develop a new snow model which includes a more complete description of the snow physics of the heat transfer processes (e.g., heat conduction, heat transfer by vapor diffusion, liquid water retention, extinction of short wave radiation, melting and refreezing, etc.) within a snow pack. A flexible time-varying multi-layer approach is employed for the new snow model. The new layer structure and physical processes of the new snow model allow the snow properties of each of the multiple snow layers to be simulated more accurately, especially for deep snowpacks, where current models do not perform well. This new model is coupled with the Variable Infiltration Capacity (VIC) land surface model and will be tested using 18-year observed data available in the Valdai water-balance research site in Russia for shallow snowpacks, and data from the California SNOTEL sites for deep snowpacks.
H21B-0513
Development of High Spatial and Temporal Measures of Snow Thermal Regimes using Fiber Optic Distributed Temperature Sensing Systems
Recent advances in fiber optic Raman spectra response instruments, known generically as Distributed Temperature Sensing (DTS), can allow high resolution temperature mapping of many environmental processes. DTS uses the scattered light in a standard telecommunications fiber optic cable to infer absolute temperature along the entire length of the fiber. These methods allow for the remote acquisition of temperature at spatial resolutions of ~1 meter over cable lengths of up to 10 km and at temporal frequencies of up to 0.1 Hz. The first applications of this technology to snow monitoring are reported here and show significant promise for high resolution mapping of energy balances and melting phenomenon. Measurements along a 330 m fiber during late-spring snowmelt at Mammoth Mountain, California showed basal snow temperatures of 0°C±0.2°. For those zones where the fiber optic cable traversed bare ground, surface temperatures approached 40°C during midday. Data from Dry Creek experimental watershed in Idaho across a small stream valley showed little variability of temperature on the north-facing, snow-covered slope, but clearly showed melting patterns and the effects of solar heating on south-facing slopes. The durability of the fiber optic was shown to be excellent, as no major damage or breaks during a full winter of burial by several meters of snow. This proof-of-concept experiment indicates that Raman spectra distributed temperature sensing represents a significant advance over traditional, point measurements of temperature.
H21B-0514
Snowcover Variability on a High-Altitude Rangeland in North Park, Colorado
Snowpacks in high-altitude plateaus, such as North Park, Colorado, are subject to dramatic changes in depth and area on large and small scales. The shallow depths of snow cover enhance the effects of albedo-induced melt. Over a winter the snowpack can completely disappear and re-accumulate several times. Low night temperatures can aid in persisting a shallow snowpack late into the spring. Wind contributes greatly to the variability of snow cover in this environment where the terrain is relatively flat and vegetative cover is small. The variability of the ground cover density and profile height provided by shrubs creates numerous, random depressions and isolated voids that can capture blowing snow. Spatial variability in snowpack cover can be observed between areas with different densities of vegetative cover. Differences in albedo between ground cover densities become most apparent during the initial accumulation and during the snowmelt phase. These differences are also associated with variability in snow present: early in the snow season snow depths can be greater around shrubs while late in the season depths can be lesser than the surrounding open areas. Snow depths, especially early in the season, can be misleading due to void space under more dense braches that have become prone due to the overlying snow mass. To assess net accumulation and snowmelt patterns, albedo, snow depth, and snow density were measured within and between rangeland shrubs. The predominant shrub type is big mountain sage (Artemisia tridentata), but some cinquefoil (Potentilla fruticosa) are also present.