Jiang, Peishi, and Praveen Kumar. “Information transfer from causal history in complex system dynamics.” Physical Review E 99.1 (2019): 12306. https://link.aps.org/doi/10.1103/PhysRevE.99.012306

Bennett, Andrew, et al. “Quantifying process connectivity with transfer entropy in hydrologic models.” Water Resources Research (2019). https://doi.org/10.1029/2018WR024555

Ruddell, Benjamin L., Darren T. Drewry, and Grey S. Nearing. “Information Theory for Model Diagnostics: Structural Error is Indicated by Trade‐Off Between Functional and Predictive Performance.” Water Resources Research. https://doi.org/10.1029/2018WR023692

Gerken, T., B.L. Ruddell, R. Yu, P.C. Stoy, and D.T. Drewry (2019), Robust observations of land-to-atmosphere feedbacks using the information flows of FLUXNET, NPJ Climate and Atmospheric Science, 2:37, https://www.nature.com/articles/s41612-019-0094-4.

Session HS3.5, “Information Theory in the Geosciences”, will be held at the European Geophysical Union spring 2020 meeting. Submissions are welcomed! Conveners: Cristina Prieto, with Grey Nearing, Rui A. P. Perdigão, Benjamin Ruddell, and Steven Weijs.

The SITES 2019 course will use a Google Drive link. Instructors should use a google account email address to join the Google Group “SITES_2019_Instructors@googlegroups.com” for edit access. Students should use a google account email address to join the Google Group “SITES_2019_Students@googlegroups.com” for read-only access. This link should take you directly to the 2019 Course Content […]

Yu, R., Ruddell, B. L., Kang, M., Kim, J., & Childers, D. (2019). Anticipating global terrestrial ecosystem state change using FLUXNET. Global change biology. https://doi.org/10.1111/gcb.14602 Ecosystems can be characterized as complex systems that traverse a variety of functional and structural states in response to changing bioclimatic forcings. A central challenge of global change biology is the […]

Summer School on Information Theory in the Earth Sciences (3rd-8th June 2019) For more information and to register Information theory provides a powerful conceptual framework for learning, model building, and prediction in the Earth sciences. It extends probability theory in certain important ways that make it particularly applicable to questions related to value and uncertainty […]

Postdoctoral Scholar in Watershed Hydrology/Hydroinformatics The Environmental Systems Dynamics Laboratory (http://esdlberkeley.com) at UC Berkeley seeks a postdoctoral scholar with expertise in data science and hydrology. The position is funded jointly through the Gordon and Betty Moore Foundation Data-Driven Discovery Program and a U.S. Geological Survey Powell Center for Synthesis grant. The position will be focused […]

AGU Session Title: H053. Better Informed than Uncertain: Applications of Information Theory in the Earth Sciences Information Theory (IT) quantifies the information content, or uncertainty and changes in uncertainty, within any form of structured content, including data relevant to earth systems. IT has a broad and growing applicability within the geosciences in the contexts of […]