Pyry Kettunen

The accelerating complexity of global water challenges—driven by hydrologic intensification, a growing and urbanizing population, and proliferation of observational data—demands a new generation of water‑domain researchers who are both computationally fluent and capable of critically integrating artificial intelligence (AI) into scientific workflows. Yet, most geoscience doctoral programs provide limited training in open, reproducible computational methods, and generic AI tools often underperform in specialized environmental domains while lacking transparent attribution of sources. To address these gaps, the Digital Waters Flagship initiative designed and implemented an innovative doctoral‑level course that integrates open‑science software training with student‑driven co‑development of a domain‑adapted large‑language model (LLM) for hydrologic research assistance.The course employs a flipped‑classroom model within the Digital Waters Virtual Research Environment (VRE), where students learn standardized, reproducible workflows using a repository structure composed of six core elements spanning data access, processing, modeling, visualization, and computational environments. Exceptional student repositories are publicly disseminated as open digital water use cases. In parallel, doctoral researchers participate in the co‑design of a hydrology‑focused research chatbot, DIWA ReChat, which is trained on authentic student‑generated workflow components and equipped with automatic knowledge‑source attribution to ensure transparency and proper crediting of contributions.Course outcomes are evaluated through (1) pre‑/post‑assessment of computational competency, (2) evidence of improved reproducibility enabled by shared VRE infrastructure, and (3) empirical improvements in domain‑adapted LLM performance based on both conventional accuracy metrics and student‑designed AI efficacy criteria. Together, the course and chatbot development process demonstrate a scalable model for integrating open‑science education with responsible, domain‑aware AI tool creation. This work highlights a pathway for cultivating computationally capable researchers who can both leverage and critically evaluate AI in support of robust, transparent hydrologic science.

The recent emergence of urban air mobility (UAM) is transforming urban landscape, with studies predicting its increasing role in the coming decades. This emergence of UAM calls for developing decision-support tools for planning of launch and landing sites (LLS) in constrained and complex urban environments. Such decision-support tools must be useful and usable in the context where planning processes related to this emerging technology are also emerging themselves. In practice, this means carefully designing the functionality of the tool itself according to the current and future user needs, as well as clarifying the roles and responsibilities of different actors in the planning process. This research presents the development of a geospatial decision-support tool for identifying the potential urban UAM LLS for drones with a diameter of up to 3 meters. In addition, this study presents the preliminary evaluation of this decision-support tool with a set of potential users, including urban planners from the cities of Helsinki (Finland), Stockholm (Sweden) and Hamburg (Germany).