Reference List

Resources / Saturday, December 2nd, 2017

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Gharari, Shervan; Gupta, Hoshin V.; Clark, Martyn P.; Hrachowitz, Markus; Fenicia, Fabrizio; Matgen, Patrick; and Savenije, Hubert H.G. (2021). “Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning from data.” Water Resources Research,

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Goodwell, A. and Kumar, Praveen (2015). “Information Theoretic Measures to Infer Feedback Dynamics in Coupled Logistic Networks.” Entropy 17.11: 7468-7492.

Goodwell, A. E., Jiang, P., Ruddell, B. L., & Kumar, P. (2020). Debates—Does information theory provide a new paradigm for Earth science? Causality, interaction, and feedback. Water Resources Research, 56, e2019WR024940.

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Gupta, H.V., Ehsani, M. R., Roy, T., Sans-Fuentes, M. A., Ehret, U., & Behrangi, A. (2021). Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples. arXiv preprint arXiv:2102.12675. Full Gupta reference.

Gupta, H.V. and Nearing, G.S. (2014). Debates—The future of hydrological sciences: A (common) path forward? Using models and data to learn: A systems theoretic perspective on the future of hydrological science, Water Resour. Res., 50, 5351–5359. Full Gupta reference.

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Jiang, P. and Kumar, P. (2019) “Using Information Flow for Whole System Understanding from Component Dynamics.” Water Resources Research.

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Kumar, P., & Gupta, H. V. (2020). Debates—Does Information Theory provide a new paradigm for Earth Science?. Water Resources Research, 56, e2019WR026398.

Kumar, P. and Ruddell, B.L. (2010). Information Driven Ecohydrologic Self-Organization. Entropy 2010, 12, 2085–2096.

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Moges, E., et al. (2022), HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool, Frontiers in Earth Science, No.30, September 2022.

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Nearing, G. S., Gupta, H. V. and Crow, W. T. (2013). Information loss in approximately Bayesian estimation techniques: A comparison of generative and discriminative approaches to estimating agricultural productivity. Journal of Hydrology 507, 163-173. Full Nearing reference.

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Nearing, G. S., Ruddell, B. L., Bennett, A. R., Prieto, C., & Gupta, H. V. (2020). Does information theory provide a new paradigm for earth science? Hypothesis testing. Water Resources Research, 56, e2019WR024918.

Nearing, G. S., Ruddell, B. L., Clark, M. P., Nijssen, B., & Peters-Lidard, C. (2018). Benchmarking and Process Diagnostics of Land Models. Journal of Hydrometeorology, (2018).

Nearing, G. S., Ruddell, B., Clark, M. P. and Nissan, B. (2017) “Process-Level Diagnostics of Terrestrial Hydrology Models.” American Meteorological Society 31st conference on Hydrology, Seattle, WA USA.

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Pechlivanidis, I.G., Gupta, H., and Bosshard, T. (2018). “An Information Theory Approach to identifying a representative subset of hydro-climatic simulations for impact modeling studies.” Water Resources Research, doi:10.1029/2017WR022035.

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Pechlivanidis, I. G., Jackson, B., McMillan, H. K., & Gupta, H. V. (2012). Using an informational entropy-based metric as a diagnostic of flow duration to drive model parameter identification. Global NEST Journal, 14(3), 325–334.

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