Engineering Geomorphology: a novel professional profile to face applied challenges in earth surface dynamics in Mid‐Europe

2021 ◽  
Author(s):  
Hans Jörg Laimer
Keyword(s):  
Earth Surface ◽  
Surface Dynamics ◽  
Professional Profile

RECENT ADVANCES IN LANDLAB, A SOFTWARE TOOLKIT FOR MODELING EARTH SURFACE DYNAMICS

2018 ◽  
Author(s):  
Nathan J. Lyons ◽  
◽  
Christina Bandaragoda ◽  
Katherine R. Barnhart ◽  
Nicole M. Gasparini ◽  
...  
Keyword(s):  
Earth Surface ◽  
Surface Dynamics ◽  
Software Toolkit ◽  
Recent Advances

scholarly journals An introduction to learning algorithms and potential applications in geomorphometry and earth surface dynamics

2016 ◽  
Author(s):  
Andrew Valentine ◽  
Lara Kalnins
Keyword(s):  
Learning Algorithm ◽  
Learning Algorithms ◽  
Earth Surface ◽  
Surface Dynamics ◽  
Wide Range ◽  
Computational Implementation ◽  
Potential Applications ◽  
Abstract Learning ◽  
The Relationship

Abstract. "Learning algorithms" are a class of computational tool designed to infer information from a dataset, and then apply that information predictively. They are particularly well-suited to complex pattern recognition, or to situations where a mathematical relationship needs to be modelled, but where the underlying processes are not well-understood, are too expensive to compute, or where signals are over-printed by other effects. If a representative set of examples of the relationship can be constructed, a learning algorithm can assimilate its behaviour, and may then serve as an efficient, approximate computational implementation thereof. A wide range of applications in geomorphometry and earth surface dynamics may be envisaged, ranging from classification of landforms through to prediction of erosion characteristics given input forces. Here, we provide a practical overview of the various approaches that lie within this general framework, review existing uses in geomorphology and related applications, and discuss some of the factors that determine whether a learning algorithm approach is suited to any given problem.

Review of Esposito et al. Earth Surface Dynamics

2017 ◽  
Author(s):  
Anonymous
Keyword(s):  
Earth Surface ◽  
Surface Dynamics

scholarly journals Supplementary material to "Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics"

2016 ◽  
Author(s):  
Daniel E. J. Hobley ◽  
Jordan M. Adams ◽  
Sai Siddhartha Nudurupati ◽  
Eric W. H. Hutton ◽  
Nicole M. Gasparini ◽  
...  
Keyword(s):  
Open Source ◽  
Numerical Models ◽  
Earth Surface ◽  
Two Dimensional ◽  
Surface Dynamics ◽  
Creative Computing ◽  
Supplementary Material

Earth Surface Dynamics

2018 ◽  
Keyword(s):  
Earth Surface ◽  
Surface Dynamics

scholarly journals Introduction to ^|^lsquo;Earth Surface Dynamics in the Cryosphere: Review and Outlook^|^rsquo;

2012 ◽  
Vol 121 (2) ◽  
pp. 181-186
Author(s):  
Norikazu MATSUOKA ◽  
Atsushi IKEDA ◽  
Kotaro FUKUI ◽  
Yohta KUMAKI ◽  
Hitoshi KOIDE
Keyword(s):  
Earth Surface ◽  
Surface Dynamics

scholarly journals Monitoring Earth Surface Dynamics With Optical Imagery

Eos ◽  
2008 ◽  
Vol 89 (1) ◽  
pp. 1-2 ◽  
Author(s):  
Sébastien Leprince ◽  
Etienne Berthier ◽  
François Ayoub ◽  
Christophe Delacourt ◽  
Jean-Philippe Avouac
Keyword(s):  
Earth Surface ◽  
Surface Dynamics ◽  
Optical Imagery

scholarly journals Direct dating of lithic surface artifacts using luminescence

2021 ◽  
Vol 7 (23) ◽  
pp. eabb3424
Author(s):  
Luke Andrew Gliganic ◽  
Michael Christian Meyer ◽  
Jan-Hendrik May ◽  
Mark Steven Aldenderfer ◽  
Peter Tropper
Keyword(s):  
Optically Stimulated Luminescence ◽  
Rock Surface ◽  
Earth Surface ◽  
Surface Dynamics ◽  
Dynamic Nature ◽  
South Central ◽  
Central Tibetan Plateau ◽  
Human Use ◽  
Surface Scatter ◽  
Age Constraints

Archaeological surface assemblages composed of lithic scatters comprise a large proportion of the archaeological record. Dating such surface artifacts has remained inherently difficult owing to the dynamic nature of Earth-surface processes affecting these assemblages and because no satisfactory chronometric dating technique exists that can be directly applied to constrain the timing of artifact manufacture, discard, and thus human use of the landscape. Here, we present a dating approach based on optically stimulated luminescence (OSL)—OSL rock-surface burial dating—and apply it to a lithic surface scatter in Tibet. We generate OSL burial ages (age-depth profiles) for each artifact, outline the methodological complexities, and consider the artifact burial ages in the context of local-scale Earth-surface dynamics. The oldest age cluster between 5.2 and 5.5 thousand years is likely related to quarrying activities at the site and thus represents the oldest chronometric age constraints for human presence on the south-central Tibetan plateau.

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