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

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. Plain Language Summary The Integrated, Coordinated, Open, Networked (ICON) perspective in the discipline of hydrological sciences helps integrate remote sensing, numerical modeling, data science, and different digital concepts, including machine learning to understand simple to complex hydrological processes at diverse temporal and spatial scales. Other benefits of incorporating the ICON framework include but are not limited to open, shareable, and easy to interpret, accurate, and timely generated monitored/observed or/and simulated water sciences information. Moreover, participation of the community and stakeholders help establish a network where research, education, and collaboration become easy and accessible. Besides, the ICON framework promotes innovation, equality, diversity, inclusion, and open access research in the discipline of hydrology that involves and supports early career, marginalized racial groups, women, lesbian, gay, bisexual, tra