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Understanding and predicting large scale w … Understanding and predicting large scale watershed-ecosystem dynamics requires datasets that empower research at both the local and continental scale. Yet, creating, maintaining and delivering diverse harmonized datasets to researchers and decision-makers is costly and a relatively rare endeavor. In our lab, we have been working on two different projects meant to make it easier for anyone to better understand and predict the hydrobiogeochemical behavior of watersheds, big and small. In Macrosheds, we have harmonized all of the small watershed-ecosystem datasets in the LTER, CZO, USFS, and other programs where there is, at a minimum, data on streamflow and concentration of at least one dissolved constituent (e.g. Nitrate). This dataset provides a critical complement to datasets from larger watersheds like CAMELS and CAMELS-Chem, enabling more focused interrogation of watershed behavior at the scale of small streams. Second, we are actively rebuilding and improving on AquaSat - a dataset built to empower broader use of remote sensing for water quality. This data is focused on large rivers and lakes, visible to LandSat satellites (typically wider than 60 meters). Through both of these projects, we have learned critical lessons about what data end-users actually need, how to make their lives easier, the limits of data portals, and the community required to maintain open source software.required to maintain open source software. +
matt.ross@colostate.edu +
CSDMS 2024: Coastlines, Critical Zones and Cascading Hazards: Modeling Dynamic Interfaces from Deep Time to Human Time +
Matthew +
Colorado State University +
Ross +
Invited oral presentation +
Colorado +
Tidy and... tedious: empowering large-scale watershed science through inclusive, welcoming data harmonization +
5OwtklNmoHE +
United States +
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21:02:59, 14 December 2023 +
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22:33:24, 11 June 2025 +
Terrestrial Working Group +, Education and Knowledge Transfer (EKT) Working Group +, Cyberinformatics and Numerics Working Group +, Hydrology Focus Research Group +, Critical Zone Focus Research Group +, Ecosystem Dynamics Focus Research Group + and Artificial Intelligence & Machine Learning Initiative +
