Using ground-penetrating radar, topography and classification of vegetation to model the sediment and active layer thickness in a periglacial lake catchment, Western Greenland, link to shapefiles

The geometries of a catchment constitute the basis for distributed physically based numerical modeling of different geoscientific disciplines. In this paper results from ground-penetrating radar (GPR) measurements, in terms of a 3D model of total sediment thickness and active layer thickness in a periglacial catchment in western Greenland, is presented. Using the topography, thickness and distribution of sediments is calculated. Vegetation classification and GPR measurements are used to scale active layer thickness from local measurements to catchment scale models. Annual maximum active layer thickness varies from 0.3 m in wetlands to 2.0 m in barren areas and areas of exposed bedrock. Maximum sediment thickness is estimated to be 12.3 m in the major valleys of the catchment. A method to correlate surface vegetation with active layer thickness is also presented. By using relatively simple methods, such as probing and vegetation classification, it is possible to upscale local point measurements to catchment scale models, in areas where the upper subsurface is relatively homogenous. The resulting spatial model of active layer thickness can be used in combination with the sediment model as a geometrical input to further studies of subsurface mass-transport and hydrological flow paths in the periglacial catchment through numerical modelling.

(Table T1) Mineralogy of ODP Leg 199 and DSDP Hole 16-162 sediments - comparison of methods

During Ocean Drilling Program Leg 199 in the equatorial Pacific, visible and near-infrared spectroscopy (VNIS) was used to measure the reflectance spectra (350-2500 nm) of 1343 sediment samples. Reflectance spectra were also measured for a suite of 60 samples of known mineralogy, thereby providing a local ground-truth calibration of spectral features to percentages of calcite, opal, smectite, and illite. The associated algorithm was used to calculate mineral percentages from the 1343 spectra. Using multiple regression and VNIS mineralogy, multisensor track physical properties and light spectroscopy data were then converted into continuous high-resolution mineralogy logs.