Geostatistical treatment of data for the determination of glacier geometry/tratamiento geoestadístico de datos para la determinación de la geometría glaciar
- Autores: Darlington Mensah
- Directores de la Tesis: Jaime Otero García (dir. tes.)
- Lectura: En la Universidad Politécnica de Madrid ( España ) en 2021
- Idioma: español
- Materias:
- Enlaces
- Tesis en acceso abierto en: Archivo Digital UPM
- Resumen
The last century (1902–2015) has seen an average sea level rise of 0.16 mm/year, and recent decades have seen it accelerate, most likely due to human-caused climate change. A primary contributor to this rise is the loss of mass of glaciers and ice sheets due to increased melting, which produces ice and water discharges into the ocean. Studying the glacier geometry could help to comprehend the factors affecting the loss of mass of glaciers. The glacier geometry can be determined by the correct representation of topographic magnitudes (e.g. surface and bed elevations) in digital elevation models (DEM). This requires the use of sufficient and accurate data sets and a technique to estimate topographic magnitudes in places where data are not available. For the latter, the most used technique is geospatial interpolation.
The above reasons support the main objective of this research, which consists of estimating the geometry of the glaciers (surface, thickness, bed, and boundary) using geostatistical tools to treat the data. This requires using sufficiently dense and precise field data and its correct processing before its usage. As part of the data preparation, we developed a software tool for converting geographic coordinates to Universal Transverse Mercator (UTM) and vice versa. Similarly, the positions of the Ground-Penetrating Radar (GPR) data obtained using the old Global Positioning System (GPS) receiver equipment before the disappearance of Selective Availability, which suffered from large systematic errors, are corrected. It should be noted that the field data used in this thesis are obtained from the Hurd glacier, located on Livingston Island, South Shetland Island, Antarctica, as a result of fieldwork developed in different campaigns over two decades by the research group to which the author and director of this thesis belong.
From a methodological point of view, this thesis studies the effect of different kriging interpolation parameters on the prediction results. A method is also developed to reconstruct the temporal evolution of the surface of a glacier (restitution method for surfaces and ice thicknesses) between two known surface topographies using data of seasonal mass balance during the evolutionary period. Finally, two methods are compared to estimate the topography of the glacier bed from data obtained by GPR, with a novel semi-automatic technique for estimating the boundary of the glacier.
To elaborate, we compare in this thesis three theoretical semivariogram models (stable, spherical, and exponential) using ice thickness data obtained from a synthetic glacier (synthetically generated ice thickness DEM) generated from field data measured in Hurd glacier. As a first result, we observe that the best fits of the semivariogram are produced with the stable model. Additionally, we study the behaviour of these semivariogram models by interpolating the ice thickness DEM using ordinary kriging and compared the resulting ice thickness DEMs with the original synthetic glacier. From this, we observe that the stable model shows the best results in interpolation. Furthermore, due to the high computational time involved when interpolating large data sets using kriging, we make a performance comparison between different techniques of working with such datasets. We make the comparison between octant search (select neighbours per spatial octant from preceding and following time stamps separately), minimum distance search (select neighbours based on minimum distance to kriging location), and decimation search (select one neighbour-datum in each multiple of 10 m) criteria when working with 13676 data points. Each search criteria are applied to the original data point, reducing the number of data points to ~1310. From this, we noted that both the octant search and minimum distance search criteria produce artefacts in the interpolated map, while the interpolated map resulting from the decimation search criteria is smooth and free of artefacts. The conclusions derived from the previous studies (study of the effect of kriging parameters on interpolation results) are applied to implement a new restitution method of glaciers surfaces and ice thickness. This method is presented with four different implementations (models), depending on whether or not the accumulation of snow is memorized at each time step, and if the seasonal surface mass balance (SMB) is taken into account using profiles, balance sheet or SMB maps. The correct functionality of the four models is subsequently validated by comparing a set of surface measurements carried out in 2007 and the corresponding elevations restituted using each model. Although the change in elevation between 2001 and 2007 exceeded 10 m, over 80% of the points estimated using any of the four models had errors below 1 m, which only occurs in 33% of the points estimated by a linear interpolator. As a result of the conclusions derived from the study of the selection of kriging parameters and the novel method of restitution of surfaces, we compare two methods to estimate glacier bed topographies from data obtained using GPR. Compared to the traditional method of subtracting its ice thickness DEM from the glacier surface, the basis of the method proposed here transforms, one by one, points with ice thickness data into points on the glacier bed and later combines them with the available topographic data of the surrounding terrain. Thus, the entire set of data: glacier bed and surrounding topography are interpolated, obtaining a joint estimate of the bed topography and its surroundings, ensuring smooth continuity between them. This proposed method allows working with ice thickness data from different dates, which is extremely useful as it enables successive measurement campaigns to complement the deficiencies detected in previous measurements. Furthermore, this method presents a novel technique (semi-automatic) to estimate the glacial boundary on those dates when the surface topography of the glacier's surface is known.
Finally, using the proposed geostatistical tools and methods, we have been able to successfully estimate the geometry of the Hurd glacier at any time within the period between the topographic measurements of its surface obtained in 2001 and 2013. This has been achieved by obtaining both the evolution of the surface topography and its boundary at any time during that period and its bed topography. The evolution of the glacier thickness map is generated from 2001 to 2013 using both results.
