Thesis (D. Mus. Arts)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 86-88).

Master of Science / Department of Architectural Engineering / Kimberly Waggle Kramer / The American Society of Civil Engineers (ASCE) has developed standards for the design of snow loads that occur on buildings and structures. These standards are published in the Minimum Design Loads for Buildings and Other Structures, or ASCE 7, and are based on the findings of case studies and other scientific tests. However, design guidance on the possible reduction of leeward snow drifts at the junction of a roof parapet and a moderately sized roof step is limited and not specifically addressed in the ASCE 7. Therefore, a literature review and parametric study were performed to evaluate possible leeward snow drift reduction that could occur at the junction of parapets and roof steps. Leeward drift reduction was estimated using the Fetch Modification Method, the Direct Reduction Method, and the Simplistic Reduction Method for parapets with heights of 30 in. and 48 in. with upwind snow fetch distances from 100 to 300 ft and ground snow loads from 20 to 50 psf. More drift reduction was seen with the 48 in. parapets than with the 30 in. parapets. The Fetch Modification Method and the Direct Reduction Method gave relatively similar reductions across the range of upwind fetch distances, while the Simplistic Reduction Method gave larger reductions overall. Reductions in height for the Fetch Modification Method were between 0.25 ft and 0.42 ft, while the Direction Reduction Method returned 0.08 to 0.63 ft and the Simplistic Reduction Method returned 1.61 to 3.09 ft. Due to the large magnitude of reduction estimated by the Simplistic Reduction Method, the method was considered unconservative. From the results of the Fetch Modification Method and Direct Reduction Method, it could be suggested that parapets 30 in. or 48 in. tall could only provide a small amount of leeward drift reduction, roughly 7% to 8% of the original leeward drift height. Further research should be done to expand the heights of parapets examined and to incorporate testing and full scale observations to verify the reduction of the leeward drift.

An analysis of snow removal injury data from the National Electronic Injury Surveillance System (NEISS) revealed a persistent gender gap in injuries and deaths during snow clearing activities. In general, men, those who identified as White and those aged 60-79 represented the vast majority of injuries and deaths sustained during automated snow removal. Injuries and deaths from manual snow clearing had greater representation across gender lines, as well as across various age groups and race categories. This indicates that a greater cross-section of society relies on the standard shovel in comparison to the snow blower for snow removal. The most likely injuries sustained during shoveling were to the neck and back, while hand and finger injuries were far more common during the use of a snow blower. Similar percentages of cardiac (30%) and non-cardiac chest injuries (70%) were found for both manual and automated modes of snow removal. While the majority of cardiac chest injuries were in those aged 40-59 for shoveling and 60-79 for snow blowing, the majority of cardiac fatalities were in those aged 60-79 for both methods of snow removal. Daily all-cause mortality and daily deaths from acute heart attacks showed a weak but inverse relationship to daily maximum, minimum and average temperatures. Mortality related to temperatures had significant lag effects for two days. Daily all-cause and heart attack mortality were also significantly related to the depth of the existing snowpack. Snow to liquid ratios indicating differences between heavy, wet snow and dry, powdery snow were not significant. However, the water equivalent of the existing snowpack was significantly related to daily mortality. Comparisons between all age and elderly mortality showed weaker and opposite relationships for the elderly group suggesting the use of protective behaviors such as cold and snow avoidance.

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The quantity of seasonal snow stored in the Lake Pukaki catchment, New Zealand has a significant impact on the country's economy through its influence on hydroelectricity generation, tourism, agriculture and conservation. SnowSim is a snow storage model developed for New Zealand conditions that may be used to quantify the catchment's frozen water resource and the melt water derived from that resource. Through implementation on a geographic information system, SnowSim has been applied and optimised to the Lake Pukaki catchment. The optimal parameters found were: temperature-elevation lapse rate of 0.005 ℃ m⁻¹, snow/rain temperature threshold of 2.5 ℃, and a melt to temperature relationship factor ranging from 1 to 6 mm ℃⁻¹ d⁻¹. The melt to temperature relationship factor is significantly reduced from that previously used for a New Zealand wide application of SnowSim. Use of a daily measured lapse rate was found to provide no improvement to the model, considered to be because of the spatial variability of lapse rates. Inclusion of a radiation component also provided no improvement in the model. This is contrary to the experience found in similar model applications in other regions of the world. The lower relative importance of radiation melt (with regard to total melt) in the region compared to continental l℃ations may explain this result. The use of a new precipitation distribution system did improve model results. Daily precipitation measurements were related to a new annual average precipitation surface prior to interpolating them across the region, without any elevation to precipitation relationship. Model free water results required an offset adjustment to bring them into line with measured lake inflows limiting the application of the model to estimation of seasonal variation, relative magnitudes and event frequencies of snow storage. Over four years of data a model output quality criterion of 0.61 (where a value of 1 is a perfect model) was returned. This increased to 0.76 for monthly values indicating a high quality of output at the seasonal scale. Model parameters and output quality are in line with those found using comparable models for various applications around the world. The variety of outputs available from the model provide a valuable resource for applications in the electricity, tourism, conservation and agriculture industries as well as for climate, glacier, snow and mountain research.

Snow initialization is crucial for weather and seasonal prediction, but the National Centers for Environmental Prediction (NCEP) operational models have been found to produce too little snow water equivalent, partly because they assume a constant and unrealistically low snow density for the snowpack. One possible solution is to use the snow density formulation from the Noah land model used in NCEP operational forecast models. While this solution is better than the constant density assumption, the seasonal evolution of snow density in Noah is still found to be unrealistic, through the evaluation of both the offline Noah model output and the Noah snow density formulation itself. A physically based snow density parameterization is then developed, which performs considerably better than the Noah parameterization based on the measurements from the SNOTEL network over the western United States and Alaska. It also performs better than the snow density schemes used in three other models. This parameterization could be easily implemented in NCEP operational snow initialization. With the consideration of up to 10 snow layers, this parameterization can also be applied to multilayer snowpack initiation or to estimate snow water equivalent from in situ and airborne snow depth measurements.

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