Causal interaction in high frequency turbulence at the biosphere–atmosphere interface: Structural behavior

Hernandez Rodriguez, Leila Constanza; Kumar, Praveen

doi:10.1063/5.0131468

Causal interaction in high frequency turbulence at the biosphere–atmosphere interface: Structural behavior

Journal Article · Mon Aug 14 00:00:00 EDT 2023 · Chaos: An Interdisciplinary Journal of Nonlinear Science

DOI:https://doi.org/10.1063/5.0131468· OSTI ID:2282161

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University of Illinois at Urbana-Champaign, IL (United States); University of Illinois at Urbana - Champaign
University of Illinois at Urbana-Champaign, IL (United States)

High-frequency (e.g., 10 Hz) eddy covariance measurements are typically used to estimate fluxes at the land–atmosphere interface at timescales of 15–60 min. These multivariate data contain information about the interdependency at high frequency between the interacting variables such as wind, humidity, temperature, and CO_2⁠. We use data at 10 Hz from an eddy covariance instrument located at 25 m above agricultural land in the Midwestern US, which offers an opportunity to move beyond the traditional spectral analyses to explore causal dependency among variables. In this study, we quantify the structure of inter-dependencies of interacting variables at high frequency represented by a directed acyclic graph (DAG). We compare DAGs to investigate changes in structural differences in causal interactions. We then apply a distance-based classification and -means clustering approach to identify the evolution of the causal structure represented by a DAG. Our method selects an unbiased number of clusters of similar structures and characterizes the similarities and differences between them. We explore a range of dynamic behavior using data from a clear sky day and during a solar eclipse in 2017. Our results show well-defined clusters of similar causal dependencies as the system evolves. Furthermore, our approach provides a methodological framework to understand how causal dependence in turbulence manifests in high-frequency data when represented through a DAG.

View Accepted Manuscript (DOE)

Research Organization:: University of Illinois at Urbana-Champaign, IL (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0001225

OSTI ID:: 2282161

Journal Information:: Chaos: An Interdisciplinary Journal of Nonlinear Science, Journal Name: Chaos: An Interdisciplinary Journal of Nonlinear Science Journal Issue: 8 Vol. 33; ISSN 1054-1500

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

References (29)

Temporal Information Partitioning Networks (TIPNets): A process network approach to infer ecohydrologic shifts: INFORMATION PARTITIONING NETWORKS Goodwell, Allison E.; Kumar, Praveen Water Resources Research, Vol. 53, Issue 7 https://doi.org/10.1002/2016WR020218	journal	July 2017
Beyond rankings: comparing directed acyclic graphs Malmi, Eric; Tatti, Nikolaj; Gionis, Aristides Data Mining and Knowledge Discovery, Vol. 29, Issue 5 https://doi.org/10.1007/s10618-015-0406-1	journal	March 2015
Data clustering: 50 years beyond K-means Jain, Anil K. Pattern Recognition Letters, Vol. 31, Issue 8 https://doi.org/10.1016/j.patrec.2009.09.011	journal	June 2010
Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000: GLOBAL CROP AREAS AND YIELDS IN 2000 Monfreda, Chad; Ramankutty, Navin; Foley, Jonathan A. Global Biogeochemical Cycles, Vol. 22, Issue 1 https://doi.org/10.1029/2007GB002947	journal	March 2008
Maximum entropy spectral analysis of hydrologic data Padmanabhan, G.; Rao, A. Ramachandra Water Resources Research, Vol. 24, Issue 9 https://doi.org/10.1029/WR024i009p01519	journal	September 1988
Causal feedbacks in climate change van Nes, Egbert H.; Scheffer, Marten; Brovkin, Victor Nature Climate Change, Vol. 5, Issue 5 https://doi.org/10.1038/nclimate2568	journal	March 2015
Identifying causal gateways and mediators in complex spatio-temporal systems Runge, Jakob; Petoukhov, Vladimir; Donges, Jonathan F. Nature Communications, Vol. 6, Issue 1 https://doi.org/10.1038/ncomms9502	journal	October 2015
Between order and chaos Crutchfield, James P. Nature Physics, Vol. 8, Issue 1 https://doi.org/10.1038/nphys2190	journal	December 2011
Dynamic process connectivity explains ecohydrologic responses to rainfall pulses and drought Goodwell, Allison E.; Kumar, Praveen; Fellows, Aaron W. Proceedings of the National Academy of Sciences, Vol. 115, Issue 37 https://doi.org/10.1073/pnas.1800236115	journal	August 2018
Well-Separated Clusters and Optimal Fuzzy Partitions Dunn†, J. C. Journal of Cybernetics, Vol. 4, Issue 1 https://doi.org/10.1080/01969727408546059	journal	January 1974
Estimating mutual information Kraskov, Alexander; Stögbauer, Harald; Grassberger, Peter Physical Review E, Vol. 69, Issue 6 https://doi.org/10.1103/PhysRevE.69.066138	journal	June 2004
Quantifying causal coupling strength: A lag-specific measure for multivariate time series related to transfer entropy Runge, Jakob; Heitzig, Jobst; Marwan, Norbert Physical Review E, Vol. 86, Issue 6 https://doi.org/10.1103/PhysRevE.86.061121	journal	December 2012
Quantifying information transfer and mediation along causal pathways in complex systems Runge, Jakob Physical Review E, Vol. 92, Issue 6 https://doi.org/10.1103/PhysRevE.92.062829	journal	December 2015
Interactions of information transfer along separable causal paths Jiang, Peishi; Kumar, Praveen Physical Review E, Vol. 97, Issue 4 https://doi.org/10.1103/PhysRevE.97.042310	journal	April 2018
Information transfer from causal history in complex system dynamics Jiang, Peishi; Kumar, Praveen Physical Review E, Vol. 99, Issue 1 https://doi.org/10.1103/PhysRevE.99.012306	journal	January 2019
Escaping the Curse of Dimensionality in Estimating Multivariate Transfer Entropy Runge, Jakob; Heitzig, Jobst; Petoukhov, Vladimir Physical Review Letters, Vol. 108, Issue 25 https://doi.org/10.1103/PhysRevLett.108.258701	journal	June 2012
Partial Mutual Information for Coupling Analysis of Multivariate Time Series Frenzel, Stefan; Pompe, Bernd Physical Review Letters, Vol. 99, Issue 20 https://doi.org/10.1103/PhysRevLett.99.204101	journal	November 2007
Complexity in the atmosphere Palmer, A. J.; Fairall, C. W.; Brewer, W. A. IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, Issue 4 https://doi.org/10.1109/36.851786	journal	July 2000
Detecting and quantifying causal associations in large nonlinear time series datasets Runge, Jakob; Nowack, Peer; Kretschmer, Marlene Science Advances, Vol. 5, Issue 11 https://doi.org/10.1126/sciadv.aau4996	journal	November 2019
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator Matsumoto, Makoto; Nishimura, Takuji ACM Transactions on Modeling and Computer Simulation, Vol. 8, Issue 1 https://doi.org/10.1145/272991.272995	journal	January 1998
Quantifying the Strength and Delay of Climatic Interactions: The Ambiguities of Cross Correlation and a Novel Measure Based on Graphical Models Runge, Jakob; Petoukhov, Vladimir; Kurths, Jürgen Journal of Climate, Vol. 27, Issue 2 https://doi.org/10.1175/JCLI-D-13-00159.1	journal	January 2014
A Changing Climatology of Precipitation Persistence across the United States Using Information-Based Measures Goodwell, Allison E.; Kumar, Praveen Journal of Hydrometeorology, Vol. 20, Issue 8 https://doi.org/10.1175/JHM-D-19-0013.1	journal	August 2019
The Intensively Managed Landscape Critical Zone Observatory: A Scientific Testbed for Understanding Critical Zone Processes in Agroecosystems Wilson, Christopher G.; Abban, Benjamin; Keefer, Laura L. Vadose Zone Journal, Vol. 17, Issue 1 https://doi.org/10.2136/vzj2018.04.0088	journal	January 2018
Investigating Causal Relations by Econometric Models and Cross-spectral Methods Granger, C. W. J. Econometrica, Vol. 37, Issue 3 https://doi.org/10.2307/1912791	journal	August 1969
Inside the flux footprint: The role of organized land cover heterogeneity on the dynamics of observed land-atmosphere exchange fluxes Hernandez Rodriguez, Leila C.; Goodwell, Allison E.; Kumar, Praveen Frontiers in Water, Vol. 5 https://doi.org/10.3389/frwa.2023.1033973	journal	March 2023
An Information-Theoretic Approach to Self-Organisation: Emergence of Complex Interdependencies in Coupled Dynamical Systems Rosas, Fernando; Mediano, Pedro; Ugarte, Martín Entropy, Vol. 20, Issue 10 https://doi.org/10.3390/e20100793	journal	October 2018
Bundled Causal History Interaction Jiang, Peishi; Kumar, Praveen Entropy, Vol. 22, Issue 3 https://doi.org/10.3390/e22030360	journal	March 2020
Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach Krich, Christopher; Runge, Jakob; Miralles, Diego G. Biogeosciences, Vol. 17, Issue 4 https://doi.org/10.5194/bg-17-1033-2020	journal	February 2020
DIMK-means “Distance-based Initialization Method for K-means Clustering Algorithm” Aldahdooh, Raed T.; Ashour, Wesam International Journal of Intelligent Systems and Applications, Vol. 5, Issue 2 https://doi.org/10.5815/ijisa.2013.02.05	journal	January 2013

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