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BACKGROUND:
BACKGROUND:
With the prioritization of new computational and analysis strategies, a growing abundance of data streams from sensor networks and remote sensing products, and open and FAIR principles for data and models alike, the time is right for synthesis and integration activities in the critical zone. Synthesis across critical zone (CZ) networks and watershed sites can generate transferable insights, increase resource efficiency and fill fundamental knowledge gaps in science, engineering, and societal issues associated with ongoing changes in critical zone functioning.
SCOPE:
SCOPE:
This workshop will bring together an international cohort of early career scientists to advance modes of collaboration across CZ networks, providing a foundation to do together what would be impossible to do alone. This will set the stage for how CZ science is done over the coming decades. Participants will learn about synthesizing and harmonizing data, interoperable models, and cross-cutting science questions, including societal challenges involved in studying CZ. Participants will identify questions that can only be addressed by working across observatories, by leveraging existing capabilities and adding new dimensions.
HOST:
HOST:
WORKSHOP CONTACTS:
WORKSHOP CONTACTS:
Explicit goals of this workshop are to:
Engage and enhance an international early career network that will spearhead the next generation of cross-cutting CZ research to address prioritized grand challenges, promote open science approaches, and formulate recommendations to tackle the sharing of data, methods and ideas.
Advance the collective knowledge by training attendees in topics such as: data harmonization, use of automated sensors, machine learning techniques and interoperable models that can be used across networks and sites to address long standing CZ questions.
Using workshop as a platform to help facilitate trans-disciplinary investigations of the critical zone and identify next steps for research coordination. Straightforward examples could entail planning for subsequent collaboration efforts, white papers, or workshops. Longer-term plans could entail building a resource of lessons learned and best practices for new site development, establishing norms for open science and data sharing, and facilitating interactions between modelers, data scientists, and data generators.
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