Limited modelled legacy effects of drought on carbon and water fluxes in a temperate oak forest despite hydraulic impairment
Abstract. The projected increase in the intensity, frequency and duration of summer drought extremes under climate change poses a growing risk to forest ecosystems, even in mesic regions such as the UK. To accurately predict the impact of future summer droughts, it is necessary to account for both their immediate and legacy effects, as well as their influence on ecosystem sensitivity to drought over time. We introduce a stomatal optimisation scheme, Profit-Max, within the Joint UK Land Environment Simulator (JULES) land surface model (LSM) to improve predictions of plant response to drought induced water stress. We further extend the model to represent drought legacy effects through two approaches to capture hydraulic impairment of xylem. We evaluate these developments by simulating an old growth oak forest at the Alice Holt site in southern England during the 2022 UK summer (July to September), when rainfall was substantially below average (77 mm of rainfall compared with a 1999–2018 median of 140 mm) and national temperatures exceeded 40 °C for the first time. During the 2022 drought year, the stomatal optimisation scheme predicted reductions in annual GPP and LE, −26 % and −33 % respectively, relative to an average year (2011–2017). This was larger than the measured reduction in LE of −6 %. The hydraulic legacy schemes simulated percentage loss of conductance of 9 % to 40 % ,with a carry-over effect on LE flux (−1 % to −11 %). We further explored ecosystem responses under more extreme future summers, including zero rainfall in spring (similar to the meteorology conditions of 2025) and a 50 % reduction in rainfall over the remainder of the year. Despite substantial increases in the percentage loss of conductance (10 % to 55 %), the simulated reductions in GPP and LE remained modest in 2023, consistent with observations. The magnitude of these legacy effects, however, was sensitive to the representation of hydraulic impairment and recovery. Overall, our results demonstrate the feasibility of representing drought legacy effects within a LSM and highlight the importance of hydraulic impairment assumptions in shaping projected ecosystem responses to extreme drought. While the simulations suggest limited legacy impacts on carbon uptake and latent heat fluxes at Alice Holt following the 2022 drought, evaluation of the underlying plant water status and hydraulic function will require independent observations of water potential and hydraulic function across the soil-plant-atmosphere continuum, as flux tower data alone may not fully constrain longer-term impacts on growth and resilience.
This study investigates how stomatal scheme and hydraulic legacy effect representation affects model behavior in JULES. The authors implemented a stomatal optimization scheme based on whole plant hydraulics (Profit-Max). Compared with existing Medlyn-style model in JULES, the Profit-Max version can better match flux tower observations at a deciduous forest site. Then, the study implemented and compared two different hydraulic legacy effect schemes: one with gradual accumulation (and recovery) of maximum hydraulic conductance loss following Mackay et al. 2015; the other one assumes instantaneous damage and no repairment. Model simulations for an extreme drought year and other drought scenarios predicted modest hydraulic legacy effects on GPP and ET.
While it is always good to see modeling explorations to better represent drought legacy, I have some major concerns of the robustness and usefulness of the study.
First, the two hydraulic impairment schemes and their parameterization are not well justified. I understand it is difficult to directly benchmark simulated PLC but Fig. A2 show large sensitivity of PLC to the three model parameters and it remains unclear to me why the particular set is chosen for interpretation and future simulations in Fig. 5-9. It also appears to me that PLC does not recover in the second scheme but wouldn't growth of new sapwood increase conductance and reduce apparent PLC? Overall, as the key module of the paper, I expect more details and discussions of impairment models. I have to say that the model description (section 2.4.4) is really hard to read due to numerous variable/parameters/subscripts. Reorganization is needed to clarify the model structure and a variable table would be helpful.
Second, there is inconsistency between Table 1 and Figure 3. Table 1 report CTRL predicts almost doubled GPP compared with Profit-Max while Figure 3 shows CTRL greatly underestimate GPP. In fact, I am wondering whether this is a fair comparison because it seems Profit-Max model is calibrated with the tower observation (L180-182) but CTRL does not. I worry this delivers an incorrect message that Profit-Max has substantial advantage over the Medlyn model.
Minor and editorial comments:
L68: mechanist -> mechanistic
L85: related -> similar
L105: capture -> investigate or explore? There are no observations of legacy effects to capture in this study
L115-118: This is the key question and hypothesis of the study, but I feel it does not read novel/useful to the modeling community (cumulative reduction in conductance -> more legacy effects)
L130: To to -> To
L 194: max(An(psi_l)) is confusing to me. Is it equivalent to An when psi_l is 0 (fully saturated)
L 225-235: The whole section 2.4.3 reads confusing to me. Why extremely dry soil water conditions need special treatment? Wouldn't it be easier to simply set a lower threshold for psi_s?
L316: I don't understand eqn 14 to eqn 15, why k(psi) cancel out when dividing two transpiration terms. In addition, is E_extreme the same as E_historic? If not, what's their difference?
L517-520: This statement highly relies on the parameters of each impairment model, which lacks benchmarking and calibration.
Figure 2. Panel (a) is really confusing because y axis represents both height and PLC. I suggest reorganizing panel (a)