A stochastic estimate of sea level contribution from glaciers and ice caps using satellite remote sensing

Third Party Funds Group - Sub project

Overall project details

Overall project: SPP 1889: Regional Sea Level Change and Society (SeaLevel)

Project Details

Project leader:
Prof. Dr. Matthias Braun

Contributing FAU Organisations:
Professur für Geographie (Fernerkundung und GIS)

Funding source: DFG / Schwerpunktprogramm (SPP)
Start date: 01/01/2017
End date: 31/07/2019

Abstract (technical / expert description):

Glacier and ice caps outside the large ice sheets have substantially contributed to sea level rise. As model predictions show, this will continue at least until the mid of the 21st century. Models are calibrated either by in-situ measurements of surface mass balance or by remote sensing observations such as length and area changes as well as volume and mass changes. However, those reference measurements are often not well distributed globally or they cover different areas or temporal periods. Only very few attempts have been made so far on a global scale to generate temporally and methodological consistent volume and mass change estimates from remote sensing or to estimate the volume and mass change rates directly from those. In this project we target a statistical sampling of glacier areas in the world and to derive volume and mass changes estimates based on SAR interferometry. We will use data from the German TanDEM-X mission and the Shuttle Radar Topography Mission (SRTM) to cover an observation period 2000-2012 and subsequently compare TanDEM-X to TanDEM-X elevation models to generate geodetic mass balance for an observation period 2012-14 or later. For the first period, we will sample at least 10% of the glaciers and ice cap area between 56°S and 60°N and for the second period we will provide first test areas. It is intended to continue and spatially expand the analysis in the second SPP phase to all glaciers outside the ice sheets. We will integrate our results with the global Randolph Glacier Inventory and climate data in order to do statistical and regional analysis, but also to interpret the observed changes and to distinguish potentially different underlying drivers and processes.

Last updated on 2018-22-11 at 18:21