Hydrological Perspectives on Integrated, Coordinated, Open, Networked
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.