Present-day, concepts and techniques from network theory are frequently used to study structures and dynamics of complex systems in various scientific disciplines. Meanwhile, applications cover different fields of geosciences, from hydrology and geomorphology (e.g., scaling of and sediment transport along river networks, spatial structure and interaction of sediment pathways in cascading systems) over climatology to seismology (earthquake networks, volcanic eruptions) and natural hazard research (disaster spreading, causality networks).

As a particular example, climate knowledge discovery is an emerging community effort to find new tools for the analysis of the vast quantities of climate data being generated by observations and model simulations. Successful applications of concepts and algorithms from complex network theory to climate data sets have already provided first insights into the potentials of such an approach. There is reason to assume that tools employing a combination of high-performance analytics and of algorithms motivated by network science, nonlinear dynamics and statistics, as well as of methods from data mining and machine learning, could provide new insights into different features of the geophysical systems.

This session will collect contributions from researchers working on data analysis, data mining, and conceptual modelling in different geoscientific disciplines and bring them together to discuss and exchange knowledge about the current state of complex network approaches in their specific fields.

Given enough interest by the participants, it is intended to collect selected presentations as well as some review chapters in a special publication.

Solicited Speakers:
Jonathan F. Donges (Potsdam, Germany)
Peter Fox (RPI, Troy, United States)