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Hydrological Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science
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  • Acharya Bharat Sharma,
  • Bulbul Ahmmed,
  • Yunxiang Chen,
  • Jason H Davison,
  • Lauren Haygood,
  • Robert Hensley,
  • Rakesh Kumar,
  • Jory Lerbeck,
  • Haojie Liu,
  • Sushant Mehan,
  • Mohamed Mehana,
  • Sopan Patil,
  • Bhaleka Persaud,
  • Pamela L Sullivan,
  • Dawn URycki
Acharya Bharat Sharma
Department of Mines
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Bulbul Ahmmed
Los Alamos National Laboratory
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Yunxiang Chen
Pacific Northwest National Laboratory
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Jason H Davison
Catholic University of America
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Lauren Haygood
Oklahoma State University
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Robert Hensley
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Rakesh Kumar
Nalanda University
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Jory Lerbeck
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Haojie Liu
University of Rostock
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Sushant Mehan
University of Wisconsin-Madison

Corresponding Author:[email protected]

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Mohamed Mehana
Los Alamos National Lab
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Sopan Patil
Bangor University
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Bhaleka Persaud
University of Waterloo
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Pamela L Sullivan
Oregon State University
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Dawn URycki
Oregon State University
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Hydrologic sciences depend on data monitoring, analyses, and simulations of hydrologic processes to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (lab, plot, and field experiments) and secondary sources (remote sensing, UAVs, hydrologic models) that typically follow FAIR Principles (FAIR Principles - GO FAIR (go-fair.org)). Easy availability of FAIR data has become possible because the hydrology-oriented organizations have pushed the community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. However, it has become difficult to decipher various complex hydrologic processes with increasing data. Machine learning, a branch of artificial intelligence, provides more accurate and faster alternatives to better understand different hydrological processes. The Integrated, Coordinated, Open, Networked (ICONs) framework provides a pathway for water users to include and respect diversity, equity, and inclusivity. In addition, ICONs support the integration of peoples with historically marginalized identities into this professional discipline of water sciences. This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them.
Apr 2022Published in Earth and Space Science volume 9 issue 4. 10.1029/2022EA002320