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:

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.

Thornwell et al. 2026. Reinforcement Learning Approach to Parameter Estimation in Ecohydrological Model.