Snow water equivalent

The water equivalent of a snowpack, or SWE, is the vertical depth of the water layer which would be obtained by melting the snow cover over a given area (WMO, 1981). SWE is related to the depth and density of the snowpack as shown in Equation (5.1) from Pomeroy and Gray (1995):

where HS is the depth of snow (m) and ps is the density of snow (kg m-3). The conversion from a mass of snow (kg m-2) to a depth of water (mm) is based on the fact that 1 mm of water spread over an area of 1 m2 weighs 1 kg.

SWE information is essential for water resource management (e.g. flood forecasting, reservoir management, irrigation scheduling) and much of the effort spent in collecting SWE information around the world is firmly grounded in the economic and safety benefits of water management. SWE information is also required for validating snow models and GCM snow-cover simulations. Correct simulation of seasonal and annual variation in the mass of snow on the ground is needed to capture important direct (e.g. snow albedo) and indirect (e.g. runoff, soil moisture recharge, surface evaporation, clouds) feedbacks between snow and the climate system. For example, snow-related direct and indirect feedbacks are known to be important factors in the strength of the Indian summer monsoon circulation (Barnett etal., 1989; Bamzai and Shukla, 1999). Monitoring of SWE is also required for climate change detection. Global climate warming is projected to be associated with increased precipitation, and the net effect of these trends requires monitoring both snow-cover extent and SWE. For example Brown (2000) presented evidence of increases in early winter snow depth and SWE over North American mid-latitudes during the twentieth century in response to increasing precipitation, while spring snow cover and SWE were observed to decrease in response to warmer temperatures. Groisman et al. (1994) demonstrated that reductions in spring snow cover contribute to spring warming through an enhanced positive feedback at this time of the year.

The most commonly used approach for determining SWE is the gravimetric method which involves taking a vertical core through the snowpack, and weighing or melting the core to obtain the SWE (Fig. 5.1a, d). A variety of coring and weighing systems have been used around the world with varying lengths and diameters depending on measurement units and local snow conditions (see Sevruk, 1992). One of the earliest national SWE observing networks was established in Finland in 1909 (Kuusisto, 1984). However, systematic observation of SWE was not widespread until the middle of the twentieth century. In the U.S.A. for example, the National Weather Service began regular point measurements of SWE at firstorder stations during the winter of 1952/53 (Schmidlin, 1990). In order to obtain representative values of SWE, measurements are often carried out at regular marked intervals along a permanently marked transect or "snow course." Many factors are involved in the design of a snow course (e.g. purpose, accessibility, terrain) and the reader is referred to Goodison et al. (1981) for a detailed discussion. The length of a snow course and the number of sampling points depends on the desired level of accuracy and the spatial variability of the snow cover, usually represented by the coefficient of variability (COV or CV) which is the ratio of the standard deviation to the mean. There is extensive literature on this subject e.g. Goodison et al. (1981), WMO (1981), Sevruk (1992), and Pomeroy and Gray (1995). The spatial variability of snow depth is typically higher than snow density, which means that more depth measurements are required along a snow course than snow density. For example, in hilly terrain, a snow course is generally 120-270 m long with depth and density measurements at about 30 m intervals. In open environments with a shallow snow cover, a snow course may need to be as long as 1-2 km, with density measurements taken 100-500 m apart and depth measurements at about five equally spaced points between the density locations (Pomeroy and Gray, 1995).

The accuracy of manual SWE measurements from snow samplers is discussed in detail in Sevruk (1992). The main systematic error (due to instrumentation) is related to a tendency for additional snow to be forced up into a tube as it is pushed through a snowpack. Random errors associated with observers and snow conditions include the difficulty in keeping loose granular snow in the corer, drainage of water from very wet snow, ice crusts, and sampling very shallow, patchy snow cover. Snow course measurements are usually carried out on a weekly or bi-weekly basis. However, not all courses have regular measurements throughout the snow-cover season. In many cases, measurements made by operational agencies or utilities for runoff management are confined to the late winter and early spring period as the main interest is in determining the peak SWE prior to melt. This can limit the usefulness of some snow course data for climate-related studies. Other limitations for using SWE surface observations in climate-related studies are: the uneven spatial distribution of data, the relatively short periods of continuous observations, the data quality (see Schmidlin, 1990), and a general lack of data availability. The National Snow and Ice Data Center is attempting to address the last problem through data rescue initiatives such as hydrological snow surveys from the former Soviet Union (Krenke, 1998). The uneven spatial and temporal distribution of snow course observations poses a major challenge for the development of global-scale SWE climatologies for validating climate model simulations. The blending of in situ, satellite- and model-derived information is required to provide consistent spatial SWE information over a range of land cover surfaces and terrain types (Hartman etal, 1995; Carroll etal., 1999, 2001).

Automated surface-based observations of SWE are possible from devices such as snow pillows (Fig. 5.1g), which measure the mass of snow over a small area from displaced fluid or a pressure transducer. Snow pillows are usually octagonal or circular in shape, with an area of ~5-10 m2. Snow pillows are most effective for monitoring relatively deep snowpacks in sheltered environments. Interpretation of data can be complicated by "bridging" (from ice or hard snow layers in the snowpack) or by the draining of wet snow (Pomeroy and Gray, 1995). Snow pillows are ideal for remote locations because they are low maintenance and are usually linked to a land-line or satellite data transmission systems to provide real-time information such as the U.S. Department of Agriculture (USDA) Snow Telemetry (SNOTEL) network over the western United States (Rallison, 1981). An advantage of these automated systems is that they can be interrogated on a daily basis to provide more detailed information during the melt season than regular weekly or two-weekly snow course observations. Daily values of SWE from over 600 snow pillow sites in the western U.S.A. from 1979 are available from the USDA National Water and Climate Center. A comprehensive analysis of these data for the 19801998 period was provided by Serreze et al. (1999).

5.2.4 Snowpack stratigraphy

Detailed information on snowpack stratigraphy (e.g. layer density, hardness, grain size, grain type, chemistry, temperature) is required for a wide range of needs such as monitoring avalanche potential, snow trafficability studies, and atmospheric transport and deposition of pollutants. Most of these observations must be made manually by digging a "snow pit" and observing/measuring snow properties. This is a time- and labour-intensive process and snow pits are usually only dug when needed to support critical activities such as avalanche risk assessment, and for research projects. Snow pit data are required to validate detailed snowpack layer models such as CROCUS (Brun et al., 1992), SNOWPACK (Bartelt and Lehning, 2002; Lehning et al., 2002a, b), or SNTHERM (Jordan, 1991) that simulate layer development and snow grain evolution. The terminology and symbols employed in classifying the layers in a snowpack are given in the International Classification for Seasonal Snow on the Ground (Colbeck et al., 1990). Extensive collections of snow pit data from Greenland and the Antarctic are archived at the U.S. National Snow and Ice Data Center. Procedures and methods for the observing and recording of data from snow pit profiles can be found in McClung and Schaerer (2006).

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Renewable Energy 101

Renewable Energy 101

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.

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