Debates—Does Information Theory Provide a New Paradigm for Earth Science? Causality, Interaction, and Feedback

Goodwell, Allison E.; Jiang, Peishi; Ruddell, Benjamin L.; Kumar, Praveen

doi:10.1029/2019wr024940

Cited by 50 publications

(47 citation statements)

References 65 publications

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“…Entropy was originally developed in communications, to describe the randomness in a message that defines a required channel size to transmit and receive messages (Shannon, 1948). However, it has since been recognized as a robust method to capture potentially nonlinear and multivariate relationships within many types of systems, since it is based on the structure of the pdf s rather than linear correlation measures (Goodwell et al, 2020; Kumar & Gupta, 2020).…”

Section: Information Measures To Capture Time Dependenciesmentioning

confidence: 99%

Information Flows: Characterizing Precipitation‐Streamflow Dependencies in the Colorado Headwaters With an Information Theory Approach

Franzen

Farahani

Goodwell

2020

Water Resources Research

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Watersheds aggregate precipitation signals of many intensities and from many locations into a single observable streamflow at an outlet point. This dependency between precipitation and streamflow varies seasonally and can shift over time due to changes in land cover, climate, human water uses, or changes in properties of precipitation events themselves. We apply information theory-based measures to capture temporal linkages, or information transfers, from daily precipitation occurrence at different locations in a basin to streamflow at an outlet. We detect critical magnitudes of precipitation and lag times associated with the strongest precipitation-streamflow relationships, and further partition information transfers to determine relative contributions from the knowledge of wet versus dry past states. Based on an analysis of daily U.S. Geological Survey (USGS) streamflow and Climate Prediction Center (CPC) gridded gauge-based precipitation data sets in the Colorado Headwaters Basin, this dependency is strongest in fall, the longest dominant lag times occur in spring, and the strengths of dependencies have increased in spring and summer over the past 65 years. These features relate to both seasonal and spatial characteristics of precipitation and the landscape. A partitioning of information components shows that in this basin, the particular knowledge of a lagged, or past, wet state tends to be more informative to flow than a lagged dry state, even though dry days are more frequent. This study introduces several signatures of precipitation-streamflow relationships that can also more broadly characterize strengths, thresholds, and timescales associated with various interconnected processes.

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Section: Information Measures To Capture Time Dependenciesmentioning

confidence: 99%

Information Flows: Characterizing Precipitation‐Streamflow Dependencies in the Colorado Headwaters With an Information Theory Approach

Franzen

Farahani

Goodwell

2020

Water Resources Research

Self Cite

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“…The flourishing development of complexity science 1 ; 2 has shed light on research questions and applications in many interdisciplinary fields, for instance, climate change 3 ; 4 ; 5 , epidemiology 6 ; 7 and ecosystem sciences at multiple scales 8 ; 9 ; 10 . In this burgeoning science, complex network models play a central role in the quantitative analysis, synthesis and design (including predictions) of ecosystems and their visual representation.…”

Section: Introductionmentioning

confidence: 99%

Inferring Ecosystem Networks as Information Flows

Convertino

2021

Preprint

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The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed Optimal Information Flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and α-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective α-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities.

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“…Smith [4], use mutual information (MI) to calculate nonlinearity and looks at MI as a measure of total dependence between random variables. Goodwell et al [5] discusses the advantages and disadvantages of applying information theory to the links of different variables in earth sciences, trying to find a measure of these links. Multiple more or less sophisticated methods of detecting Granger-type causality between different factors for different fields [6][7][8], have been developed.…”

Section: Introductionmentioning

confidence: 99%

Quantification of the Direct Solar Impact on Some Components of the Hydroclimatic System

Mares¹,

Mares²,

Dobrica³

et al. 2021

Preprint

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This study addresses the causal links between external factors and the main hydro-climatic variables. There is a gap in the literature on the description of a complete chain in addressing the structures of direct causal links of solar activity on the terrestrial variables. This is why, the present study uses the extensive facilities of the application of information theory in view of recent advances in different fields. Also, by other methods (e.g. neural networks) first are tested the existence non-linear links of solar-terrestrial influences on hydro-climate system. The results are promising related to the solar impact on terrestrial phenomena which is discriminant in space-time domain. The implications prove robust for determining the causal measure of climate variables under direct solar impact which makes it easier to consider solar activity in climate models, by appropriate parametrizations. This study found that hydro-climatic variables are sensitive to solar impact only for certain frequencies (periods) and these have a coherence with the Solar-Flux only for some lags of the Solar-Flux (in advance).

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Debates—Does Information Theory Provide a New Paradigm for Earth Science? Causality, Interaction, and Feedback

Cited by 50 publications

References 65 publications

Information Flows: Characterizing Precipitation‐Streamflow Dependencies in the Colorado Headwaters With an Information Theory Approach

Information Flows: Characterizing Precipitation‐Streamflow Dependencies in the Colorado Headwaters With an Information Theory Approach

Inferring Ecosystem Networks as Information Flows

Quantification of the Direct Solar Impact on Some Components of the Hydroclimatic System

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