| 21 Nov 2025
Improved model predictions of carbon and water fluxes by including drought legacy effects
Abstract. Besides simultaneous influences, droughts have lasting impacts on vegetation by impairing hydraulic and photosynthetic capacities, known as the drought legacy effects. The ignorance of legacy effects in numerical simulations, such as lagged xylem recovery, may lead to significant model-observation discrepancies. However, the limited temporal resolution of most observational data makes it challenging to capture the physiological dynamics necessary to improve model accuracy. Here, we investigated the recovery of carbon flux (represented by gross primary productivity, GPP) and water flux (represented by evapotranspiration, ET) following a severe drought in 2012, using half-hourly eddy-covariance flux observations and weekly predawn leaf water potential measurements from a temperate forest in the Central US. We implemented both optimality-based and empirical stomatal models within a land surface model, testing three drought recovery scenarios for each: no recovery, full recovery, and partial recovery of xylem hydraulic conductance and photosynthetic capacity. Before and during the drought, all stomatal models performed similarly for GPP and ET. Post-drought, assuming no recovery led to underestimated ET; assuming full recovery led to overestimated GPP; and assuming partial recovery improved both, indicating persistent biochemical limitations after drought. The observed carbon-water decoupling during and after the event further points to non-stomatal constraints on photosynthesis and unequal stress on carbon and water fluxes. Our work highlights the need to account for delayed recovery of xylem hydraulics and photosynthetic capacity when modeling drought legacy effects. Further research to mechanistically represent dynamic recovery processes, particularly their timing and magnitude, is essential for improving the modeling of global carbon and water fluxes.
This manuscript presents a modelling study using the CliMA land surface model investigating the effect of assumptions around tree recovery after drought at one site which experienced a severe drought in 2012. The results show that including a mechanism for partial rather than full recovery leads to a better fit to data in the post-drought period. This is an important topic as models still frequently assume instantaneous responses to environmental conditions and there is no standard framework for representing lagged responses.
In general, the analysis is scientifically sound but the paper itself need some edits to make it more robust and easier to understand.
Major comments
Minor comments
L 135 does the MODIS LAI respond to drought at the site? Does having this as an input partially represent the effects of drought already?
L 150 unclear why, given the research questions, it is necessary to evaluate canopy spectra
L 167 “Changing this T/ET ratio does not impact our results.” Some evidence is needed here
L 167 “The CliMA Land model was employed to reproduce the dynamics of carbon and water fluxes during the drought period.” This statement feels superfluous here.
L 171 “several stomatal optimality models” sever feels very vague here
L 189 table 1 – this table has only 1 line, but above you say you also used an optimal model
Section 2.4 – many more details are needed here, preferably in the form of equations. It is not enough to say that vulnerability curves or the data assimilation method is taken from another paper
L 215 Table 2 – unclear what information this table add as everything is just a tick. The notations for the two options for the Medlyn model also do not match those in Table 1
L 231 what is Kmax? Seems like an important parameter but it is the first time it’s mentioned I think
L 257 is this the same method as the data assimilation for the partial recovery scenario above or something else?
L 335 But the model overestimates the coupling during the wilted period as well, so the issue here is not just the full recovery assumption
L 338 this looks like a repetition of the methods here
L 346 it might help here to show a timeseries of model GPP and ET to understand the evolution of recovery
L 421 it feels odd to mention simulations at other sites in the discussion for the first tiem when this was not mentioned in the methods