Published:

Thornwell 2022. Learning From Machines: Insights in Forest Transpiration Using Machine Learning Methods. https://pdxscholar.library.pdx.edu/open_access_etds/6199/

Pre-print:

Thornwell et al. 2026. Assessing global drivers of forest transpiration using clustered machine learning models. https://arxiv.org/abs/2605.22755

Jiang et al. 2025. Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems. https://arxiv.org/abs/2509.01972

Jiang et al. 2025. Structure-Function Coherent Coarsening for Cross-Resolution Ecohydrological Modeling. https://www.researchgate.net/publication/397895734_Structure-Function_Coherent_Coarsening_for_Cross-Resolution_Ecohydrological_Modeling

Under development:

Thornwell et al. 2026. Ecosystem Representation in Large-Scale Watershed Modeling VELMA Model Setup across 24 Puget Sound Watersheds.

Thornwell et al. 2026. Initializing Realistic Chemistry Pools in Regional Ecosystem Model.