DescriptionIn Spring 2019, eScience Center Netherlands facilitated a workshop to develop a FAIR – Findable, Accessible, Interoperable, Reusable – multi-application platform that experts and non-experts can use to guide their decision-making. Many hydrologists believe that there are too many models in the field. Each new research generation strives to improve current methods with increasing complexity and developing individual models to fit specific situations - and to what end? If other experts struggle to adapt a model, it’s unreasonable to expect a non-expert to gain meaningful insight to address challenges impacting a community or guide policy. A community-driven platform (eWatercycle) is being developed by an international multi-disciplinary team of hydrologists, research software engineers, tinkerers, science policy advisors, and more. The diverse and inclusive team membership is critical to ensure that the best possible tool is developed to address multi-faceted questions and benefit a wide-reaching community. eWatercycle incorporates many popular models (e.g., SUMMA, PCRGLOB-WB, WFLOW, and HYPE). We have incorporated the massive ERA5 climate reanalysis dataset, as well as global stream gauge data, such that users can analyze a system for any region. Considering the potential complexity from eWatercycle’s inclusion of several model types, the team is developing this model framework in close cooperation with potential end-users. We envision end-users may include a government scientist working to inform policy decisions on water management or city officials developing risk management strategies for extreme weather events. Users of eWatercycle will not be required to learn new programming languages or overcome significant barriers to begin using the framework. As a result, users will be able to use eWatercycle to work towards solving region-specific problems with confidence by considering the outcomes of different hydrological models and access to potential uncertainty in the available data and modeling techniques.
|Period||11 Dec 2019|
|Event title||AGU Fall Meeting 2019|
|Location||San Francisco, United States|