Estimation of iceberg density in the Grand Banks of Newfoundland

• Main Author: Kelly, Robert, 1969-
• Other Authors: Chouinard, Luc E. (advisor)
• Format: Thesis
• Language: English
• Published: McGill University 1996
• Subjects: Physical Geography; Engineering; Civil; Newfoundland
• Online Access: http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=23746

Icebergs offshore Newfoundland represent hazards to both ships and constructed facilities, such as off-shore oil production facilities. Collision with icebergs represent hazards for both surface and sub-surface facilities. In the latter case, hazards are associated with seabed scouring by the iceberg keel. In both cases, hazard analysis requires estimation of the flux of icebergs and their size distribution. Estimates of the flux of icebergs can be achieved by obtaining separate estimates of iceberg densities and of drift patterns of iceberg velocities. The objective of this thesis is to develop and apply estimation procedures for the density of icebergs using presently available data sets. The most comprehensive of these data sets is compiled by the International Ice Patrol (IIP), starting in 1960. The IIP database comprises data from several sources and for icebergs of varying sizes. In addition, the spatial coverage of surveys does not appear to be uniform throughout the year. Several non-parametric density estimation procedures are investigated. The objective is to eliminate any apparent high densities in the estimates due to the non-uniform coverage of the region during surveys and retain statistically significant features in the spatial variation of densities. Several kernel estimators are examined: (1) a uniform square kernel, (2) a uniform circular kernel, (3) a Normal kernel, and (4) an adaptive kernel. Uniform kernels have the advantage of computational efficiency, however, they do not account for spatial variations in the densities and produce over-smoothing in regions of peak iceberg densities and under-smoothing in regions of low iceberg densities. The adaptive kernel is computationally more demanding, but appears to fulfill all the desired requirements for preserving significant features and eliminating erratic estimates.