Motivation
ThornthwaiteChang2019 derives ET from temperature alone, so it cannot represent seasonality when the actual ET cycle is out of phase with temperature — most clearly in cold-region forests, where canopy leaf-out lags spring warming by weeks. Thornthwaite ramps ET up with the spring temperature rise before transpiration actually begins, over-estimating early-spring ET; in snowmelt basins this consumes the spring freshet, and the annual water-balance scaling redistributes the bias into other seasons. (Documented as a warning under evapotranspiration_method in configuration.rst; origin: notes/HANDOFF_thornthwaite_ET_phenology.md. On the Crow Wing a prescribed leaf-out factor fixed the spring residual and made the melt factor identifiable.)
Proposal
An optional ET factor that follows observed vegetation green-up (e.g. an NDVI- or remote-sensing-based phenology signal) rather than temperature, so the seasonal ET shape tracks actual leaf-out instead of the temperature curve. This would remove the phasing bias structurally rather than papering over it with et_scale (an annual scalar that can only trade the bias between seasons).
Open design questions: prescribed monthly phenology shape vs. forced from an NDVI time series; how it composes with the existing water-balance correction and et_scale; basin-mean vs. land-cover-resolved.
Relationship to #22 (land-cover DDF prior) — kept separate deliberately
This and #22 both draw on remote-sensing / land-cover input, so it's tempting to fold them into one "forcing/priors from land cover" effort. We're keeping them as separate issues: the melt-factor prior (#22) and a phenology-driven ET formulation are distinct developments that will likely walk different paths (different signals, different parts of the model, different validation), and yoking them risks one blocking the other. Cross-referencing, not merging.
Status
Future work — flagged from the Crow Wing finding, not scheduled.
🤖 Generated with Claude Code
Motivation
ThornthwaiteChang2019derives ET from temperature alone, so it cannot represent seasonality when the actual ET cycle is out of phase with temperature — most clearly in cold-region forests, where canopy leaf-out lags spring warming by weeks. Thornthwaite ramps ET up with the spring temperature rise before transpiration actually begins, over-estimating early-spring ET; in snowmelt basins this consumes the spring freshet, and the annual water-balance scaling redistributes the bias into other seasons. (Documented as a warning underevapotranspiration_methodinconfiguration.rst; origin:notes/HANDOFF_thornthwaite_ET_phenology.md. On the Crow Wing a prescribed leaf-out factor fixed the spring residual and made the melt factor identifiable.)Proposal
An optional ET factor that follows observed vegetation green-up (e.g. an NDVI- or remote-sensing-based phenology signal) rather than temperature, so the seasonal ET shape tracks actual leaf-out instead of the temperature curve. This would remove the phasing bias structurally rather than papering over it with
et_scale(an annual scalar that can only trade the bias between seasons).Open design questions: prescribed monthly phenology shape vs. forced from an NDVI time series; how it composes with the existing water-balance correction and
et_scale; basin-mean vs. land-cover-resolved.Relationship to #22 (land-cover DDF prior) — kept separate deliberately
This and #22 both draw on remote-sensing / land-cover input, so it's tempting to fold them into one "forcing/priors from land cover" effort. We're keeping them as separate issues: the melt-factor prior (#22) and a phenology-driven ET formulation are distinct developments that will likely walk different paths (different signals, different parts of the model, different validation), and yoking them risks one blocking the other. Cross-referencing, not merging.
Status
Future work — flagged from the Crow Wing finding, not scheduled.
🤖 Generated with Claude Code