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Hydrological Perspectives on Integrated, Coordinated, Open, Net- worked (ICON) Science
  • +12
  • Sushant Mehan,
  • Bharat Acharya,
  • Bulbul Ahmmed,
  • Robert Hensley,
  • Dawn URycki,
  • Sopan Patil,
  • Haojie Liu,
  • Mohamed Mehana,
  • Yunxiang Chen,
  • Bhaleka Persaud,
  • Jason Davison,
  • Jory Lerback,
  • Lauren Haygood,
  • Pamela Sullivan,
  • Rakesh Kumar
Sushant Mehan
Postdoctoral Scholar at The Ohio State University, Columbus, OH, U.S.A., Postdoctoral Scholar at The Ohio State University, Columbus, OH, U.S.A., Postdoctoral Scholar at The Ohio State University, Columbus, OH, U.S.A.

Corresponding Author:[email protected]

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Bharat Acharya
Department of Mines, State of Oklahoma, Oklahoma City, OK 73106, USA, Department of Mines, State of Oklahoma, Oklahoma City, OK 73106, USA, Department of Mines, State of Oklahoma, Oklahoma City, OK 73106, USA
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Bulbul Ahmmed
Los Almos National Laboratory, WA, USA, Los Almos National Laboratory, WA, USA, Los Almos National Laboratory
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Robert Hensley
National Ecological Observatory Network, National Ecological Observatory Network, National Ecological Observatory Network
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Dawn URycki
Department of Biological and Ecological Engineering, Oregon State University, Department of Biological and Ecological Engineering, Oregon State University, Department of Biological and Ecological Engineering, Oregon State University
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Sopan Patil
Bangor University, Bangor University, Bangor University
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Haojie Liu
Faculty of Agricultural and Environmental Sciences, University of Rostock, Justus-von-Liebig-Weg 6, 18059, Rostock, Germany, Faculty of Agricultural and Environmental Sciences, University of Rostock, Justus-von-Liebig-Weg 6, 18059, Rostock, Germany, Faculty of Agricultural and Environmental Sciences, University of Rostock, Justus-von-Liebig-Weg 6, 18059, Rostock, Germany
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Mohamed Mehana
Los Alamos National Laboratory, Los Alamos National Laboratory, Los Alamos National Laboratory
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Yunxiang Chen
Pacific Northwest National Laboratory, Pacific Northwest National Laboratory, Pacific Northwest National Laboratory
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Bhaleka Persaud
Global Water Futures Program, University of Waterloo, 200 University Ave W, Waterloo, Ontario, Canada, N2L 3G1, Global Water Futures Program, University of Waterloo, 200 University Ave W, Waterloo, Ontario, Canada, N2L 3G1, Global Water Futures Program, University of Waterloo, 200 University Ave W, Waterloo, Ontario, Canada, N2L 3G1
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Jason Davison
Catholic University of America, Catholic University of America, Catholic University of America
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Jory Lerback
University of California, Los Angeles, Department of Earth, Planetary, and Space Sciences, 595 Charles Young Drive East, Los Angeles, CA 90095, University of California, Los Angeles, Department of Earth, Planetary, and Space Sciences, 595 Charles Young Drive East, Los Angeles, CA 90095, University of California, Los Angeles, Department of Earth, Planetary, and Space Sciences, 595 Charles Young Drive East, Los Angeles, CA 90095
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Lauren Haygood
The University of Tulsa & Oklahoma State University, The University of Tulsa & Oklahoma State University, The University of Tulsa & Oklahoma State University
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Pamela Sullivan
Oregon State University, Oregon State University, Oregon State University
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Rakesh Kumar
Nalanda University, Nalanda University, Nalanda University
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Abstract

This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them. Each commentary focuses on a different perspective as follows: (i) field, experimental, remote sensing, and real-time data research and application (Section 1); (ii) Inclusive, equitable, and accessible science: Involvement, challenges, and support of early career, marginalized racial groups, women, LGBTQ+, and/or disabled researchers (Section 2); and (iii) an ICON perspective on machine learning for multiscale hydrological modeling (Section 3). Hydrologists depend on data monitoring, analyses, and simulations from these diverse scientific disciplines to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (in-situ: lab, plots, and field experiments) and secondary sources (ex-situ: remote sensing, UAVs, hydrologic models) that are typically openly available and discoverable. Hydrology-oriented organizations have pushed our 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. With increasing amounts of data, it has become difficult to decipher various complex hydrologic processes. However, machine learning, a branch of artificial intelligence, provides accurate and faster alternatives to understand different biogeochemical and hydrological processes better. Diversity, equity, and inclusivity are essential in terms of outreach and integration of peoples with historically marginalized identities into this professional discipline and respecting and supporting the local environmental knowledge of water users.