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Abstract The hydrologic community has experienced a surge in interest in machine learning in recent years. This interest is primarily driven by rapidly growing hydrologic data repositories, as well as success of machine learning in various academic and commercial applications, now possible due to increasing accessibility to enabling hardware and software. This overview is intended for readers new to the field of machine learning. It provides a non‐technical introduction, placed within a historical context, to commonly used machine learning algorithms and deep learning architectures. Applications in hydrologic sciences are summarized next, with a focus on recent studies. They include the detection of patterns and events such as land use change, approximation of hydrologic variables and processes such as rainfall‐runoff modeling, and mining relationships among variables for identifying controlling factors. The use of machine learning is also discussed in the context of integrated with process‐based modeling for parameterization, surrogate modeling, and bias correction. Finally, the article highlights challenges of extrapolating robustness, physical interpretability, and small sample size in hydrologic applications. This article is categorized under:Science of Water 

Much of modern science takes place in a computational environment, and, increasingly, that environment is programmed using R, Python, or Julia. Furthermore, most scientific data now live on the cloud, so the first step in many workflows is to query a cloud database and load the response into a computational environment for further analysis. Thus, tools that facilitate programmatic data retrieval represent a critical component in reproducible scientific workflows. Earth science is no different in this regard. To fulfill that basic need, we developed R, Python, and Julia packages providing programmatic access to the U.S. Geological Survey’s National Water Information System database and the multi-agency Water Quality Portal. Together, these packages create a common interface for retrieving hydrologic data in the Jupyter ecosystem, which is widely used in water research, operations, and teaching. Source code, documentation, and tutorials for the packages are available on GitHub. Users can go there to learn, raise issues, or contribute improvements within a single platform, which helps foster better engagement and collaboration between data providers and their users. 

Hydroinformatics and water data science topics are increasingly common in university graduate settings through dedicated courses and programs as well as incorporation into traditional water science courses. The technical tools and techniques emphasized by hydroinformatics and water data science involve distinctive instructional styles, which may be facilitated by online formats and materials. In the broader hydrologic sciences, there has been a simultaneous push for instructors to develop, share, and reuse content and instructional modules, particularly as the COVID-19 pandemic necessitated a wide scale pivot to online instruction. The experiences of hydroinformatics and water data science instructors in the effectiveness of content formats, instructional tools and techniques, and key topics can inform educational practice not only for those subjects, but for water science generally. This paper reports the results of surveys and interviews with hydroinformatics and water data science instructors. We address the effectiveness of instructional tools, impacts of the pandemic on education, important hydroinformatics topics, and challenges and gaps in hydroinformatics education. Guided by lessons learned from the surveys and interviews and a review of existing online learning platforms, we developed four educational modules designed to address shared topics of interest and to demonstrate the effectiveness of available tools to help overcome identified challenges. The modules are community resources that can be incorporated into courses and modified to address specific class and institutional needs or different geographic locations. Our experience with module implementation can inform development of online educational resources, which will advance and enhance instruction for hydroinformatics and broader hydrologic sciences for which students increasingly need informatics experience and technical skills.