Open AccessHydrological Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science
Author(s) -
Acharya Bharat Sharma,
Ahmmed Bulbul,
Chen Yunxiang,
Davison Jason H.,
Haygood Lauren,
Hensley Robert T.,
Kumar Rakesh,
Lerback Jory,
Liu Haojie,
Mehan Sushant,
Mehana Mohamed,
Patil Sopan D.,
Persaud Bhaleka D.,
Sullivan Pamela L.,
URycki Dawn
Publication year - 2022
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2022ea002320
Subject(s) - decipher , interoperability , computer science , cyberinfrastructure , data sharing , icon , data science , field (mathematics) , world wide web , medicine , genetics , alternative medicine , mathematics , pathology , pure mathematics , biology , programming language
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 (Findable, Accessible, Interoperable, and Reusable: ( 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, provide more accurate and faster alternatives to better understand different hydrological processes. The Integrated, Coordinated, Open, Networked (ICON) 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.