TY - JOUR

T1 - Hydrological Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

AU - Acharya, Bharat

AU - Ahmmed, Bulbul

AU - Chen, Yunxiang

AU - Davison, Jason

AU - Haygood, Lauren

AU - Hensley, Robert

AU - Kumar, Rakesh

AU - Lerback, Jory

AU - Liu, Haojie

AU - Mehan, Sushant

AU - Mehana, Mohamed

AU - Patil, Sopan

AU - Persaud, Bhaleka

AU - Sullivan, Pamela

AU - URycki, Dawn

PY - 2022/4/12

Y1 - 2022/4/12

N2 - 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.

AB - 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.

KW - ICON principles

KW - community science

KW - diversity

KW - hydrology

KW - machine learning

KW - stakeholders

U2 - 10.1029/2022EA002320

DO - 10.1029/2022EA002320

M3 - Comment/debate

VL - 9

JO - Earth and Space Science

JF - Earth and Space Science

SN - 2333-5084

IS - 4

M1 - e2022EA002320

ER -