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1980 Nunes, M. H., Camargo, J. L. C., Vincent, G., Calders, K., Oliveira, R. S., Huete, A., Mendes de Moura, Y., Nelson, B., Smith, M. N., Stark, S. C., and Maeda, E. E.: Forest fragmentation impacts the seasonality of Amazonian evergreen canopies, Nat Commun, 13, 917, https://doi.org/10.1038/s41467-022-28490-7, 2022.

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2050 Poulter, B., Ciais, P., Hodson, E., Lischke, H., Maignan, F., Plummer, S., and Zimmermann, N. E.: Plant functional type mapping for earth system models, Geosci. Model Dev., 4, 993–1010, https://doi.org/10.5194/gmd-4-993-2011, 2011.

2051 Powell, T. L., Galbraith, D. R., Christoffersen, B. O., Harper, A., Imbuzeiro, H. M. A., Rowland, L., Almeida, S., Brando, P. M., da Costa, A. C. L., Costa, M. H., Levine, N. M., Malhi, Y., Saleska, S. R., Sotta, E., Williams, M., Meir, P., and Moorcroft, P. R.: Confronting model predictions of carbon fluxes with measurements of Amazon forests subjected to experimental drought, New Phytologist, 200, 350–365, https://doi.org/10.1111/nph.12390, 2013.

2056 Powell, T. L., Wheeler, J. K., Oliveira, A. A. R. de, Costa, A. C. L. da, Saleska, S. R., Meir, P., and Moorcroft, P. R.: Differences in xylem and leaf hydraulic traits explain differences in drought tolerance among mature Amazon rainforest trees, Global Change Biology, 23, 4280–4293, https://doi.org/10.1111/gcb.13731, 2017.

2059 Prentice, I. C., Bondeau, A., Cramer, W., Harrison, S. P., Hickler, T., Lucht, W., Sitch, S., Smith, B., and Sykes, M. T.: Dynamic Global Vegetation Modeling: Quantifying Terrestrial Ecosystem Responses to Large-Scale Environmental Change, in: Terrestrial ecosystems in a changing world, edited by: Canadell, J. G., Pataki, D. E., and Pitelka, L. F., Springer Berlin Heidelberg, 175–192, 2007.

2063 Prentice, I. C., Liang, X., Medlyn, B. E., and Wang, Y.-P.: Reliable, robust and realistic: the three R’s of next-generation land-surface modelling, Atmos. Chem. Phys., 15, 5987–6005, https://doi.org/10.5194/acp-15-5987-2015, 2015.

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2075 Rau, E.-P., Fischer, F., Joetzjer, É., Maréchaux, I., Sun, I. F., and Chave, J.: Transferability of an individual- and trait-based forest dynamics model: A test case across the tropics, Ecological Modelling, 463, 109801, https://doi.org/10.1016/j.ecolmodel.2021.109801, 2022a.

2078 Rau, E.-P., Gardiner, B. A., Fischer, F. J., Maréchaux, I., Joetzjer, E., Sun, I.-F., and Chave, J.: Wind Speed Controls Forest Structure in a Subtropical Forest Exposed to Cyclones: A Case Study Using an Individual-Based Model, Frontiers in Forests and Global Change, 5, 2022b.
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2081 Restrepo-Coupe, N., da Rocha, H. R., Hutyra, L. R., da Araujo, A. C., Borma, L. S., Christoffersen, B., Cabral, O. M. R., de Camargo, P. B., Cardoso, F. L., da Costa, A. C. L., Fitzjarrald, D. R., Goulden, M. L., Kruijt, B., Maia, J. M. F., Malhi, Y. S., Manzi, A. O., Miller, S. D., Nobre, A. D., von Randow, C., Sá, L. D. A., Sakai, R. K., Tota, J., Wofsy, S. C., Zanchi, F. B., and Saleska, S. R.: What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the Brasil flux network, Agricultural and Forest Meteorology, 182–183, 128–144, https://doi.org/10.1016/j.agrformet.2013.04.031, 2013.

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2378 Wu, J., Serbin, S. P., Xu, X., Albert, L. P., Chen, M., Meng, R., Saleska, S. R., and Rogers, A.: The phenology of leaf quality and its within-canopy variation is essential for accurate modeling of photosynthesis in tropical evergreen forests, Global Change Biology, 23, 4814–4827, https://doi.org/10.1111/gcb.13725, 2017.
<figure><img src="image_91.png" /></figure>

<figure><img src="image_92.png" /></figure>

2381 Wu, J., Serbin, S. P., Ely, K. S., Wolfe, B. T., Dickman, L. T., Grossiord, C., Michaletz, S. T., Collins, A. D., Detto, M., McDowell, N. G., Wright, S. J., and Rogers, A.: The response of stomatal conductance to seasonal drought in tropical forests, Global Change Biology, 26, 823–839, https://doi.org/10.1111/gcb.14820, 2020.

2384 Xu, X., Medvigy, D., Powers, J. S., Becknell, J. M., and Guan, K.: Diversity in plant hydraulic traits explains seasonal and inter-annual variations of vegetation dynamics in seasonally dry tropical forests, New Phytol, 212, 80–95, https://doi.org/10.1111/nph.14009, 2016.

2387 Yang, X., Wu, J., Chen, X., Ciais, P., Maignan, F., Yuan, W., Piao, S., Yang, S., Gong, F., Su, Y., Dai, Y., Liu, L., Zhang, H., Bonal, D., Liu, H., Chen, G., Lu, H., Wu, S., Fan, L., Gentine, P., and Wright, S. J.: A comprehensive framework for seasonal controls of leaf abscission and productivity in evergreen broadleaved tropical and subtropical forests, The Innovation, 2, 100154, https://doi.org/10.1016/j.xinn.2021.100154, 2021.

2391 Yao, Y., Joetzjer, E., Ciais, P., Viovy, N., Cresto Aleina, F., Chave, J., Sack, L., Bartlett, M., Meir, P., Fisher, R., and Luyssaert, S.: Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment, Geoscientific Model Development, 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, 2022.

2395 Yao, Y., Ciais, P., Viovy, N., Joetzjer, E., and Chave, J.: How drought events during the last century have impacted biomass carbon in Amazonian rainforests, Global Change Biology, 29, 747–762, https://doi.org/10.1111/gcb.16504, 2023.

2397 Yao, Y., Ciais, P., Joetzjer, E., Li, W., Zhu, L., Wang, Y., Frankenberg, C., and Viovy, N.: The impacts of elevated CO2 on forest growth, mortality and recovery in the Amazon rainforest, Earth System Dynamics Discussions, 1–23, https://doi.org/10.5194/esd-2024-5, 2024.

2400 Yoda, K., Shinozaki, K., Ogawa, H., Hozumi, K., and Kira, T.: Estimation of the total amount of respiration in woody organs of trees and forest communities., J. Biol. Osaka City Univ, 16, 15–26, 1965.

2402 Yu, W., Albert, G., Rosenbaum, B., Schnabel, F., Bruelheide, H., Connolly, J., Härdtle, W., von Oheimb, G., Trogisch, S., Rüger, N., and Brose, U.: Systematic distributions of interaction strengths across tree interaction networks yield positive diversity–productivity relationships, Ecology Letters, 27, e14338, https://doi.org/10.1111/ele.14338, 2024.

2405 Zaehle, S., Sitch, S., Smith, B., and Hatterman, F.: Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics, Global Biogeochem. Cycles, 19, GB3020, https://doi.org/10.1029/2004GB002395, 2005.

2407 Zellweger, F., Frenne, P. D., Lenoir, J., Rocchini, D., and Coomes, D.: Advances in Microclimate Ecology Arising from Remote Sensing, Trends in Ecology & Evolution, 34, 327–341, https://doi.org/10.1016/j.tree.2018.12.012, 2019.

2409 Zhou, S., Duursma, R. A., Medlyn, B. E., Kelly, J. W. G., and Prentice, I. C.: How should we model plant responses to drought? An analysis of stomatal and non-stomatal responses to water stress, Agric. For. Meteorol., 182, 204–214, https://doi.org/10.1016/j.agrformet.2013.05.009, 2013.

2412 Zhou, S., Medlyn, B., Sabaté, S., Sperlich, D., Prentice, I. C., and others: Short-term water stress impacts on stomatal, mesophyll and biochemical limitations to photosynthesis differ consistently among tree species from contrasting climates, Tree Physiology, 34, 1035–46, 2014.
<figure><img src="image_93.png" /></figure>

<figure><img src="image_94.png" /></figure>
<figure><img src="image_95.png" /></figure>

<figure><img src="image_96.png" /></figure>

2419

## Appendix A

2420

<table><caption>Table A1. List of symbols and variables.</caption><thead><tr><th>Symbols</th><th>Definition</th><th>Units</th><th>Nature</th><th>Equations</th></tr></thead><tbody><tr><td colspan="5"><strong>Physical constants</strong></td></tr><tr><td>M<sub>w</sub></td><td>Molar mass of water vapor</td><td>kg mol<sup>-1</sup></td><td>Constant</td><td>12</td></tr><tr><td>R</td><td>ideal gas constant</td><td>J mol<sup>-1</sup> K<sup>-1</sup></td><td>Constant</td><td>12-13, 28-31, 46-47</td></tr><tr><td>V<sub>w</sub></td><td>Partial molal volume of water</td><td>m<sup>3</sup> mol<sup>-1</sup></td><td>Constant</td><td>13</td></tr><tr><td>κ</td><td>von Karman constant</td><td>unitless</td><td>Constant</td><td>8, 15</td></tr><tr><td>g</td><td>Gravity constant</td><td>m s<sup>-2</sup></td><td>Constant</td><td>37</td></tr><tr><td>ρ</td><td>Density of water</td><td>kg m<sup>-3</sup></td><td>Constant</td><td>37</td></tr><tr><td>M<sub>a</sub></td><td>Molecular mass of air</td><td>kg mol<sup>-1</sup></td><td>Constant</td><td>41, 48</td></tr><tr><td>C<sub>p</sub></td><td>Heat capacity of air</td><td>J kg<sup>-1</sup> K<sup>-1</sup></td><td>Constant</td><td>41, 48</td></tr><tr><td>γ</td><td>Psychrometric constant</td><td>Pa K<sup>-1</sup></td><td>Constant</td><td>41</td></tr><tr><td>D<sub>H</sub></td><td>Molecular diffusivity to heat</td><td>m<sup>2</sup> s<sup>-1</sup></td><td>Constant</td><td>46</td></tr><tr><td>σ</td><td>Stefan-Boltzmann constant</td><td>W m<sup>-2</sup> K<sup>-4</sup></td><td>Constant</td><td>44, 48</td></tr><tr><td colspan="5"><strong>Aboveground environment</strong></td></tr><tr><td>PPFD<sub>top</sub></td><td>Photosynthetic photon flux density at canopy top</td><td>μmol photons m<sup>-2</sup> s<sup>-1</sup></td><td>Updated at half hourly step, given as input</td><td>1</td></tr><tr><td>T<sub>top</sub></td><td>Temperature at canopy top</td><td>°C</td><td>Updated at half hourly step, given as input</td><td>4, 6, 44</td></tr><tr><td>VPD<sub>top</sub></td><td>Vapour pressure deficit at canopy top</td><td>kPa</td><td>Updated at half hourly step, given as input</td><td>5, 7</td></tr><tr><td>u<sub>top</sub></td><td>Wind speed at a reference height above the canopy</td><td>m s<sup>-1</sup></td><td>Updated at half hourly step, given as input</td><td>Sections 2.1 and 2.2</td></tr><tr><td>T<sub>night</sub></td><td>Nighttime temperature</td><td>°C</td><td>Updated daily, given as input</td><td>Section 2.2</td></tr><tr><td>PPFD</td><td>Incident photosynthetic photon flux density</td><td>μmol photons m<sup>-2</sup> s<sup>-1</sup></td><td>Computed every half hour</td><td>1, 25</td></tr><tr><td>T</td><td>Temperature</td><td>°C</td><td>Computed every half hour</td><td>4, 42, 46-48</td></tr><tr><td>VPD</td><td>Vapour pressure deficit</td><td>kPa</td><td>Computed every half hour</td><td>5</td></tr><tr><td>u</td><td>Wind speed</td><td>m s<sup>-1</sup></td><td>Computed every half hour</td><td>8, 9, 15, 47</td></tr><tr><td>c<sub>a</sub></td><td>CO<sub>2</sub> concentration</td><td>μmol mol<sup>-1</sup> (ppm)</td><td>Constant</td><td>Section 2.5</td></tr><tr><td>P<sub>ress</sub></td><td>Atmospheric pressure</td><td>Pa</td><td>Constant</td><td>46-47</td></tr><tr><td>PPFD<sub>abs</sub></td><td>Absorbed photosynthetic photon flux density</td><td>μmol photons m<sup>-2</sup> s<sup>-1</sup></td><td>Computed every half hour</td><td>2</td></tr><tr><td>T<sub>mean</sub></td><td>Temperature, averaged per crown layer</td><td>°C</td><td>Computed every half hour</td><td>6</td></tr><tr><td>VPD<sub>mean</sub></td><td>Vapour pressure deficit, averaged per crown layer</td><td>kPa</td><td>Computed every half hour</td><td>7</td></tr><tr><td>LAI</td><td>Cumulated leaf area per ground area</td><td>m<sup>2</sup> m<sup>-2</sup></td><td>Computed daily for each voxel</td><td>1-3, 11, 43</td></tr><tr><td>dens</td><td>Averaged leaf area density per unit ground area</td><td>m<sup>2</sup> m<sup>-2</sup></td><td>Computed daily, averaged per layer</td><td>3, 6-7</td></tr></tbody></table>
<figure><img src="image_97.png" /></figure>

<table><tbody><tr><td>k</td><td>Effective light extinction coefficient</td><td>unitless</td><td>Fixed, computed from k<sub>geom</sub> and absorptance<sub>leaves</sub></td><td>1</td></tr><tr><td>k<sub>geom</sub></td><td>Light extinction coefficient reflecting the geometric arrangement of leaves</td><td>unitless</td><td>Constant, given as input</td><td>1</td></tr><tr><td>absorptance<sub>leaves</sub></td><td>Fraction of absorbed light within a single leaf</td><td>unitless</td><td>Constant, given as input</td><td>1</td></tr><tr><td>LAI<sub>sat</sub></td><td>LAI threshold for computing microenvironmental variation within the canopy</td><td>m<sup>2</sup> m<sup>-2</sup></td><td>Constant</td><td>4-7</td></tr><tr><td>ΔT</td><td>Parameter of the within-canopy variation in temperature</td><td>°C</td><td>Constant</td><td>4, 6</td></tr><tr><td>C<sub>VPD0</sub></td><td>Parameter of the within-canopy variation in vapour pressure deficit</td><td>unitless</td><td>Constant</td><td>5, 7</td></tr><tr><td>u*</td><td>friction velocity</td><td>m s<sup>-1</sup></td><td>Constant</td><td></td></tr><tr><td>d</td><td>zero-plane displacement height</td><td>m</td><td>Computed from the locally averaged canopy height (H)</td><td>8</td></tr><tr><td>z<sub>0</sub></td><td>aerodynamic roughness</td><td>m</td><td>Computed from the locally averaged canopy height (H)</td><td>8</td></tr><tr><td>H</td><td>Top canopy height</td><td>m</td><td>Computed daily</td><td>8-9</td></tr><tr><td>α</td><td>Parameter of wind speed decrease within the canopy</td><td>unitless</td><td>Constant</td><td>9</td></tr></tbody></table>

Water balance

<table><tbody><tr><td>P</td><td>Precipitation</td><td>mm</td><td>Updated daily, given as input</td><td>10</td></tr><tr><td>I</td><td>Interception</td><td>mm</td><td>Computed daily</td><td>10, 11</td></tr><tr><td>Q</td><td>Run-off</td><td>m<sup>3</sup></td><td>Computed daily</td><td>10</td></tr><tr><td>E</td><td>Evaporation from the soil</td><td>kg m<sup>-2</sup> s<sup>-1</sup></td><td>Computed daily</td><td>10, 12</td></tr><tr><td>T</td><td>Tree transpiration</td><td>m<sup>3</sup></td><td>Computed daily</td><td>10</td></tr><tr><td>L</td><td>Leakage</td><td>m<sup>3</sup></td><td>Computed daily</td><td>10</td></tr><tr><td>K</td><td>Parameter for rainfall interception</td><td>mm</td><td>Constant</td><td>11</td></tr><tr><td>T<sub>s</sub></td><td>Temperature at soil surface</td><td>K</td><td>Computed daily</td><td>12, 13</td></tr><tr><td>e<sub>s</sub></td><td>Vapor pressure of the soil surface</td><td>Pa</td><td>Computed daily</td><td>12</td></tr><tr><td>e<sub>a</sub></td><td>Vapor pressure of air above the soil surface</td><td>Pa</td><td>Computed daily</td><td>12</td></tr><tr><td>e<sub>sat</sub></td><td>saturated vapor pressure</td><td>Pa</td><td>Computed daily</td><td>13</td></tr><tr><td>r<sub>soil</sub></td><td>Soil surface resistance</td><td>s m<sup>-1</sup></td><td>Computed daily</td><td>12, 14</td></tr><tr><td>r<sub>aero</sub></td><td>Aerodynamic resistance to heat transfer</td><td>s m<sup>-1</sup></td><td>Computed daily</td><td>12, 15</td></tr><tr><td>Z</td><td>Reference height for r<sub>aero</sub> computation</td><td>m</td><td>Constant</td><td>15</td></tr><tr><td>Z<sub>m</sub></td><td>Momentum soil roughness</td><td>m</td><td>Constant</td><td>15</td></tr><tr><td>ψ<sub>l</sub></td><td>Soil water potential of layer 1</td><td>MPa</td><td>Computed daily</td><td>21</td></tr><tr><td>K<sub>l</sub></td><td>Soil hydraulic conductivity of layer 1</td><td>kg m<sup>-2</sup> s<sup>-1</sup></td><td>Computed daily</td><td>22</td></tr></tbody></table>
<figure><img src="image_98.png" /></figure>

<figure><img src="image_99.png" /></figure>

<table><tbody><tr><td>ψ<sub>soil,top</sub></td><td>Water potential of the top soil belowground voxel</td><td>MPa</td><td>Computed daily</td><td>13</td></tr><tr><td>θ<sub>top</sub></td><td>Water content of the top soil belowground voxel</td><td>m<sup>3</sup></td><td>Computed daily</td><td>14</td></tr><tr><td>θ<sub>fc,top</sub></td><td>water content at field capacity of the top soil belowground voxel</td><td>m<sup>3</sup></td><td>Computed daily</td><td>14</td></tr><tr><td colspan="5">Species and tree characteristics</td></tr><tr><td>LMA</td><td>Leaf mass per area</td><td>g m<sup>-2</sup></td><td>Species-specific means: constant, provided as input; tree-specific values: randomly attributed at tree birth</td><td>26, 27, 32, 56</td></tr><tr><td>LA</td><td>Leaf area</td><td>cm<sup>2</sup></td><td>Species-specific means: constant, provided as input; tree-specific values: randomly attributed at tree birth</td><td>46-47</td></tr><tr><td>N</td><td>Leaf nitrogen content per dry mass</td><td>mg g<sup>-1</sup></td><td>Species-specific means: constant, provided as input; tree-specific values: randomly attributed at tree birth</td><td>26, 27, 32</td></tr><tr><td>P</td><td>Leaf phosphorous content per dry mass</td><td>mg g<sup>-1</sup></td><td>Species-specific means: constant, provided as input; tree-specific values: randomly attributed at tree birth</td><td>26, 27, 32</td></tr><tr><td>wsg</td><td>Wood specific gravity</td><td>g cm<sup>-3</sup></td><td>Species-specific means: constant, provided as input; tree-specific values: randomly attributed at tree birth</td><td>36, 55, 60-61, 66</td></tr><tr><td>π<sub>tlp</sub></td><td>Leaf water potential at turgor loss point</td><td>MPa</td><td>Species-specific means: constant, provided as input; tree-specific values: randomly attributed at tree birth</td><td>39-40, 58, 68, section 2.7.1</td></tr><tr><td>dbh<sub>thres</sub></td><td>Threshold diameter at breast height, beyond which growth senescence starts</td><td>m</td><td>Species-specific means: constant, provided as input; tree-specific values: randomly attributed at tree birth</td><td>62, 63</td></tr></tbody></table>
<figure><img src="image_100.png" /></figure>

<table><thead><tr><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>dbh<sub>max</sub></td><td>Maximal tree diameter at breast height</td><td>m</td><td>Computed once per tree<br>Species-specific means: constant, provided as input</td><td>Section 2.6.4</td></tr><tr><td>h<sub>lim</sub></td><td>Asymptotic height (parameter of Michaelis-Menten function)</td><td>m</td><td>Species-specific means: constant, provided as input</td><td>16</td></tr><tr><td>h<sub>max</sub></td><td>Maximal tree height</td><td>m</td><td>Species-specific means: constant, provided as input</td><td>67</td></tr><tr><td>a<sub>h</sub></td><td>Parameter of Michaelis-Menten function</td><td>m</td><td>Species-specific means: constant, provided as input</td><td>16</td></tr><tr><td>f<sub>reg,s</sub></td><td>Relative abundance of species s in the external seed rain</td><td>unitless</td><td>Species-specific, provide as input</td><td>64</td></tr><tr><td>w<sub>l</sub></td><td>Leaf width</td><td>m</td><td>Computed for each tree</td><td>46-47</td></tr><tr><td>LL</td><td>Leaf lifespan</td><td>yr</td><td>Computed for each tree</td><td>57</td></tr><tr><td>ε<sub>i,j</sub></td><td>Individual effects for trait or variable i and tree j standard deviation for intraspecific variability in trait or variable i</td><td>See traits</td><td>Randomly attributed at tree birth</td><td>Sections 2.4.1 and 2.4.2</td></tr><tr><td>σ<sub>i</sub></td><td></td><td>See traits</td><td>Constant, provided as input</td><td>Sections 2.4.1 and 2.4.2</td></tr><tr><td>dbh</td><td>Tree diameter at breast height</td><td>m</td><td>Tree variable, updated at each timestep</td><td>16, 17, 19, 60-62</td></tr><tr><td>h</td><td>Tree height</td><td>m</td><td>Tree variable, updated at each timestep</td><td>16, 37, 54-55, 58, 60</td></tr><tr><td>cr</td><td>Tree crown radius</td><td>m</td><td>Tree variable, updated at each timestep</td><td>17</td></tr><tr><td>cd</td><td>Tree crown depth</td><td>m</td><td>Tree variable, updated at each timestep</td><td>18, 54</td></tr><tr><td>a<sub>cr</sub></td><td>Coefficients of crown radius allometry</td><td>unitless</td><td>Species-independent constant, provided as input</td><td>17</td></tr><tr><td>b<sub>cr</sub></td><td>Coefficients of crown radius allometry</td><td>unitless</td><td>Species-independent constant, provided as input</td><td>17</td></tr><tr><td>a<sub>cd</sub></td><td>Coefficients of crown depth allometry</td><td>m</td><td>Species-independent constant, provided as input</td><td>18</td></tr><tr><td>b<sub>cd</sub></td><td>Coefficients of crown depth allometry</td><td>unitless</td><td>Species-independent constant, provided as input</td><td>18</td></tr><tr><td>shape_cr own</td><td>Ratio between the radius of the crown at the top of the tree to the radius at the bottom of the crown being a global parameter</td><td>unitless</td><td>Global parameter, provided as input</td><td>Section 2.4.2</td></tr><tr><td>f<sub>gap</sub></td><td>Fraction of gaps (openings) in tree crowns</td><td>unitless</td><td>Constant, provided as input</td><td>Section 2.4.2</td></tr><tr><td>RD</td><td>Tree root depth</td><td>m</td><td>Tree variable, updated at each timestep</td><td>19</td></tr><tr><td>RB<sub>t</sub></td><td>Total tree fine root biomass</td><td>g</td><td>Tree variable, updated at each timestep</td><td>20</td></tr></tbody></table>
<figure><img src="image_101.png" /></figure>

<table><tbody><tr><td>RB<sub>l</sub></td><td>Tree fine root biomass in layer l</td><td>g</td><td>Tree variable, updated at each timestep</td><td>20</td></tr><tr><td>ψ<sub>root</sub></td><td>Soil water potential in the tree root zone</td><td>MPa</td><td>Tree variable, updated at each timestep</td><td>21, 37</td></tr><tr><td>ψ<sub>R,min</sub></td><td>Root water potential below which there is no soil water uptake</td><td>MPa</td><td>Constant</td><td>21</td></tr><tr><td>G<sub>l</sub></td><td>soil-to-root water conductance in layer l</td><td>mmol H<sub>2</sub>O m<sup>-2</sup> s<sup>-1</sup></td><td>Variable, computed for each tree and layer at each timestep</td><td>21, 22</td></tr><tr><td>L<sub>a,l</sub></td><td>Tree total root length per unit area in layer l</td><td>m m<sup>-2</sup></td><td>Variable, computed for each tree and layer at each timestep</td><td>22</td></tr><tr><td>L<sub>v,l</sub></td><td>Tree total root length per unit soil volume in layer l</td><td>m m<sup>-3</sup></td><td>Variable, computed for each tree and layer at each timestep</td><td>23</td></tr><tr><td>SRL</td><td>Specific root length</td><td>m g<sup>-1</sup></td><td>Constant</td><td>22</td></tr><tr><td>r<sub>s</sub></td><td>Mean fine root radius</td><td>m</td><td>Constant</td><td>22</td></tr><tr><td>r<sub>s</sub></td><td>half of the mean distance between roots</td><td>m</td><td>Variable, computed for each tree and layer at each timestep</td><td>22, 23</td></tr></tbody></table>

Leaf physiology

<table><tbody><tr><td>T<sub>l</sub></td><td>Leaf temperature</td><td>°C</td><td>Computed half-hourly for each crown layer</td><td>24, 25, 28-31, 33, 46</td></tr><tr><td>VPD<sub>s</sub></td><td>Vapor pressure deficit at the leaf surface</td><td>kPa</td><td>Computed half-hourly for each crown layer</td><td>35</td></tr><tr><td>c<sub>s</sub></td><td>CO<sub>2</sub> concentration at the leaf surface</td><td>µmol mol<sup>-1</sup> (ppm or µbar)</td><td>Computed half-hourly for each crown layer</td><td>34</td></tr><tr><td>c<sub>i</sub></td><td>CO<sub>2</sub> concentration at carboxylation sites</td><td>µmol mol<sup>-1</sup> (ppm or µbar)</td><td>Computed half-hourly for each crown layer</td><td>24, 34</td></tr><tr><td>A<sub>n</sub></td><td>Leaf-level net carbon assimilation rate</td><td>µmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>24, 52</td></tr><tr><td>A<sub>v</sub></td><td>Leaf-level net carbon assimilation rate limited by Rubisco activity</td><td>µmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>24</td></tr><tr><td>A<sub>j</sub></td><td>Leaf-level net carbon assimilation rate limited by RuBP regeneration</td><td>µmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>24</td></tr><tr><td>R<sub>p</sub></td><td>Photorespiration rate</td><td>µmol C m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>24</td></tr><tr><td>R<sub>d</sub></td><td>Leaf dark respiration rate</td><td>µmol C m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>33</td></tr><tr><td>R<sub>d-M</sub></td><td>Leaf dark respiration rate on a leaf dry mass basis</td><td>nmol CO<sub>2</sub> g<sup>-1</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>32</td></tr><tr><td>V<sub>cmax</sub></td><td>Maximum rate of carboxylation</td><td>µmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>24, 26, 28</td></tr><tr><td>V<sub>cmax-M</sub></td><td>Maximum rate of carboxylation on a leaf dry mass basis</td><td>µmol CO<sub>2</sub> g<sup>-1</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>26</td></tr><tr><td>J</td><td>Electron transport rate</td><td>µmol e<sup>-</sup> m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>24, 25</td></tr><tr><td>J<sub>max</sub></td><td>maximal electron transport capacity</td><td>µmol e<sup>-</sup> m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>25, 29</td></tr></tbody></table>
<figure><img src="image_102.png" /></figure>

<figure><img src="image_103.png" /></figure>

<table><tr><td>J<sub>max-M</sub></td><td>maximal electron transport capacity on a leaf dry mass basis</td><td>µmol e<sup>-</sup> g<sup>-1</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>27</td></tr><tr><td>Γ*</td><td>CO<sub>2</sub> compensation point in the absence of dark respiration</td><td>µbar</td><td>Computed half-hourly for each crown layer</td><td>24, 30</td></tr><tr><td>K<sub>m</sub></td><td>Apparent kinetic constant of the Rubisco</td><td>µbar</td><td>Computed half-hourly for each crown layer</td><td>24, 31</td></tr><tr><td>θ</td><td>Curvature factor</td><td>unitless</td><td>Constant</td><td>25</td></tr><tr><td>α</td><td>Apparent quantum yield to electron transport</td><td>mol e<sup>-</sup> mol photons<sup>-1</sup></td><td>Constant</td><td>25</td></tr><tr><td>LSQ</td><td>Effective spectral quality of light</td><td>unitless</td><td>Constant</td><td>25</td></tr><tr><td>g<sub>s</sub></td><td>stomatal conductance to CO<sub>2</sub></td><td>mol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>34</td></tr><tr><td>g<sub>sw</sub></td><td>stomatal conductance to water vapor</td><td>mol H<sub>2</sub>O m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>35</td></tr><tr><td>g<sub>0</sub></td><td>minimum leaf conductance for water vapor</td><td>mol H<sub>2</sub>O m<sup>-2</sup> s<sup>-1</sup></td><td>Constant, provided by the user</td><td>35</td></tr><tr><td>g<sub>1</sub></td><td>Parameter of the stomatal conductance model</td><td>kPa<sup>0.5</sup></td><td>Computed daily for each tree</td><td>35, 36</td></tr><tr><td>ψ<sub>pd</sub></td><td>Leaf predawn water potential</td><td>MPa</td><td>Tree variable, computed daily</td><td>24, 25, 28, 29, 36-40</td></tr><tr><td>WSF<sub>ns</sub></td><td>Water stress factor for non-stomatal limitation</td><td>unitless</td><td>Tree variable, computed daily</td><td>28, 29, 40</td></tr><tr><td>WSF<sub>s</sub></td><td>Water stress factor for stomatal limitation</td><td>unitless</td><td>Tree variable, computed daily</td><td>36, 38-39</td></tr><tr><td>a</td><td>Parameter of WSF<sub>ns</sub></td><td>unitless</td><td>Constant</td><td>40</td></tr><tr><td>b</td><td>Parameter of WSF<sub>s</sub></td><td>unitless</td><td>Computed from tree-specific ψ<sub>tlp</sub></td><td>39, 39</td></tr><tr><td>E<sub>l</sub></td><td>Leaf-level transpiration rate</td><td>mol H<sub>2</sub>O m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>41</td></tr><tr><td>λ</td><td>Latent heat of water vapor</td><td>J mol<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>41, 42</td></tr><tr><td>s</td><td>slope of the (locally linearized) relationship between saturated vapor pressure and temperature</td><td>Pa K<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>41</td></tr><tr><td>R<sub>ni</sub></td><td>isothermal net radiation</td><td>J m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>41, 43</td></tr><tr><td>g<sub>H</sub></td><td>total leaf conductance to heat</td><td>mol m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>41, 45</td></tr><tr><td>g<sub>bHf</sub></td><td>boundary layer conductance for free convection</td><td>mol m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>45, 46, 50</td></tr><tr><td>g<sub>bHu</sub></td><td>boundary layer for forced convection</td><td>mol m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>45, 47, 50</td></tr><tr><td>g<sub>r</sub></td><td>radiation conductance</td><td>mol m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>45, 48</td></tr><tr><td>g<sub>w</sub></td><td>total leaf conductance to water vapor</td><td>mol H<sub>2</sub>O m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>41, 49</td></tr><tr><td>g<sub>bw</sub></td><td>boundary layer conductance to water vapor</td><td>mol H<sub>2</sub>O m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>49, 50</td></tr><tr><td>S<sub>abs</sub></td><td>Absorbed solar radiation (PAR and NIR)</td><td>J m<sup>-2</sup> s<sup>-1</sup></td><td>Computed half-hourly for each crown layer</td><td>43</td></tr></table>
<figure><img src="image_104.png" /></figure>

<figure><img src="image_105.png" /></figure>

<table><tr><td>B<sub>n,0</sub></td><td>Net longwave radiation at the top of the canopy</td><td>J m<sup>-2</sup> s<sup>-1</sup></td><td>Computed every half hour</td><td>43, 44</td></tr><tr><td>k<sub>d</sub></td><td>Coefficient of extinction of longwave radiation</td><td>Unitless</td><td>Constant</td><td>43</td></tr><tr><td>ε<sub>l</sub></td><td>Emissivity of the canopy leaves</td><td>Unitless</td><td>Constant</td><td>44, 48</td></tr><tr><td>ε<sub>a</sub></td><td>Emissivity of the atmosphere</td><td>Unitless</td><td>Computed every half hour</td><td>44</td></tr></table>

## Tree carbon allocation and demography

<table><tr><td>GPP<sub>ind</sub></td><td>Tree-level gross primary productivity</td><td>gC</td><td>Computed daily</td><td>51</td></tr><tr><td>NPP<sub>ind</sub></td><td>Tree-level net primary productivity</td><td>gC</td><td>Computed daily</td><td>51</td></tr><tr><td>NPP<sub>leaves</sub></td><td>Tree-level net primary productivity allocated to leaf production</td><td>gC</td><td>Computed daily</td><td>56</td></tr><tr><td>NPP<sub>wood</sub></td><td>Tree-level net primary productivity allocated to woody growth</td><td>gC</td><td>Computed daily</td><td>61</td></tr><tr><td>AGB</td><td>Tree aboveground biomass</td><td>kg</td><td>Computed daily</td><td>59-60</td></tr><tr><td>R<sub>maintenan</sub></td><td>Tree-level maintenance respiration</td><td>gC</td><td>Computed daily</td><td>51</td></tr><tr><td>R<sub>stem</sub></td><td>Stem maintenance respiration</td><td>µmol C s<sup>-1</sup></td><td>Computed daily</td><td>54</td></tr><tr><td>R<sub>growth</sub></td><td>Tree-level growth respiration</td><td>gC</td><td>Computed daily</td><td>51</td></tr><tr><td>LA<sub>t</sub></td><td>Tree-level total leaf area</td><td>m<sup>2</sup></td><td>Updated daily</td><td>55</td></tr><tr><td>LA<sub>opt</sub></td><td>Optimal tree leaf area</td><td>m<sup>2</sup></td><td>Updated daily</td><td>Section 2.6.2</td></tr><tr><td>LA<sub>l</sub></td><td>Leaf area in tree crown layer 1</td><td>m<sup>2</sup></td><td>Updated daily</td><td>51, 52-53</td></tr><tr><td>LA<sub>young</sub></td><td>Tree-level young leaf area</td><td>m<sup>2</sup></td><td>Updated daily</td><td>52-53, 56</td></tr><tr><td>LA<sub>mature</sub></td><td>Tree-level mature leaf area</td><td>m<sup>2</sup></td><td>Updated daily</td><td>52-53, 56</td></tr><tr><td>LA<sub>old</sub></td><td>Tree-level old leaf area</td><td>m<sup>2</sup></td><td>Updated daily</td><td>52-53, 56</td></tr><tr><td>q</td><td>Assimilation rate over mature leaf area</td><td>unitless</td><td>Constant</td><td>52</td></tr><tr><td>q'</td><td>Ratio of young or old leaf respiration rate over mature leaf area</td><td>unitless</td><td>Constant</td><td>53</td></tr><tr><td>τ<sub>young</sub></td><td>Leaf residence time in the young age pool</td><td>yr</td><td>Computed for each tree</td><td>56</td></tr><tr><td>τ<sub>mature</sub></td><td>Leaf residence time in the young age pool</td><td>yr</td><td>Computed for each tree</td><td>56</td></tr><tr><td>τ<sub>old</sub></td><td>Leaf residence time in the young age pool</td><td>yr</td><td>Computed for each tree</td><td>56</td></tr><tr><td>SA</td><td>Tree sapwood area</td><td>m<sup>2</sup></td><td>Updated daily</td><td>54, 55</td></tr><tr><td>λ<sub>1</sub></td><td>Parameter for sapwood area computation</td><td>m<sup>2</sup> cm<sup>-2</sup></td><td>Constant</td><td>55</td></tr><tr><td>λ<sub>2</sub></td><td>Parameter for sapwood area computation</td><td>m cm<sup>-2</sup></td><td>Constant</td><td>55</td></tr><tr><td>δ<sub>1</sub></td><td>Parameter for sapwood area computation</td><td>m<sup>2</sup> cm<sup>-2</sup></td><td>Constant</td><td>55</td></tr><tr><td>δ<sub>2</sub></td><td>Parameter for sapwood area computation</td><td>cm<sup>3</sup> g<sup>-1</sup></td><td>Constant</td><td>55</td></tr></table>
<figure><img src="image_106.png" /></figure>

<table><tr><td>f<sub>canopy</sub></td><td>Fraction of NPP<sub>ind</sub> allocated to tree canopy (including leaves, fruits and twigs)</td><td>unitless</td><td>Constant, given as input</td><td>Sections 2.6.1 and 2.6.2</td></tr><tr><td>f<sub>leaves</sub></td><td>Fraction of NPP<sub>ind</sub> allocated to leaves</td><td>unitless</td><td>Constant</td><td>Section 2.6.2, 56</td></tr><tr><td>f<sub>fruit</sub></td><td>Fraction of NPP<sub>ind</sub> allocated to fruits</td><td>unitless</td><td>Constant</td><td>Section 2.6.1</td></tr><tr><td>f<sub>twigs</sub></td><td>Fraction of NPP<sub>ind</sub> allocated to twigs</td><td>unitless</td><td>Constant</td><td>Section 2.6.1</td></tr><tr><td>f<sub>wood</sub></td><td>Fraction of NPP<sub>ind</sub> allocated to wood</td><td>unitless</td><td>Constant, given as input</td><td>Section 2.6.2</td></tr><tr><td>ψ<sub>T,o</sub></td><td>Water potential threshold for accelerating old leaf shedding</td><td>MPa</td><td>Computed daily for each tree</td><td>58</td></tr><tr><td>a<sub>T,o</sub></td><td>Parameter to compute ψ<sub>T,o</sub> (modulates old leaf drought tolerance)</td><td>unitless</td><td>Constant, given as input</td><td>58</td></tr><tr><td>b<sub>T,o</sub></td><td>Parameter to compute ψ<sub>T,o</sub> (modulates the height dependence of leaf susceptibility to drought)</td><td>MPa</td><td>Constant, given as input</td><td>58</td></tr><tr><td>f<sub>o</sub></td><td>Factor of the acceleration of old leaf shedding</td><td>unitless</td><td>Updated daily for each tree</td><td>Section 2.6.2</td></tr><tr><td>δ<sub>o</sub></td><td>Parameter controlling the pace of old leaf shedding acceleration (Δf<sub>o</sub>)</td><td>unitless</td><td>Constant, given as input</td><td></td></tr><tr><td>NSC<sub>r</sub></td><td>Maximal amount of stored non-structural carbohydrates</td><td>gC</td><td>Updated daily for each tree</td><td>59</td></tr><tr><td>ΔV</td><td>Increment of stem volume</td><td>m<sup>3</sup></td><td>Computed daily for each tree</td><td>61</td></tr><tr><td>Senesc</td><td>Growth senescence factor</td><td>unitless</td><td>Computed daily for each tree</td><td>61-62</td></tr><tr><td>Δdbh</td><td>Trunk diameter growth</td><td>m</td><td>Computed daily for each tree</td><td>Section 2.6.4</td></tr><tr><td>dbh<sub>mature</sub></td><td>Diameter threshold beyond which the tree is fertile</td><td>m</td><td>Computed once for each tree</td><td>63</td></tr><tr><td>σ<sub>disp</sub></td><td>Scale parameter of the Rayleigh distribution for seed dispersal</td><td>m</td><td>Constant</td><td>Section 2.7.1</td></tr><tr><td>n<sub>s</sub></td><td>Number of reproduction opportunities per mature tree</td><td>number of seeds</td><td>Constant</td><td>Section 2.7.1</td></tr><tr><td>N<sub>tot</sub></td><td>Intensity of the external seed rain</td><td>number of seeds per hectare</td><td>Constant, given as input</td><td>64</td></tr><tr><td>n<sub>ext,s</sub></td><td>Species-specific number of dispersal events due to the external seed rain</td><td>number of seeds</td><td>Computed once for each species</td><td>64</td></tr><tr><td>n<sub>ha</sub></td><td>Area of the simulated plot</td><td>ha</td><td>Constant, computed from dimensions given as input</td><td>64</td></tr><tr><td>LAI<sub>max</sub></td><td>LAI threshold beyond which the seedling leaf carbon balance is negative</td><td>m<sup>2</sup> m<sup>-2</sup></td><td>Computed once for each tree</td><td>Section 2.7.1</td></tr></table>
<figure><img src="image_107.png" /></figure>

<figure><img src="image_108.png" /></figure>

<table><tr><td>d</td><td>Tree death rate</td><td>events yr<sup>-1</sup></td><td>Updated daily at tree level</td><td>65-66</td></tr><tr><td>d<sub>b</sub></td><td>Background death rate</td><td>events yr<sup>-1</sup></td><td>Computed once per tree</td><td>65</td></tr><tr><td>m</td><td>reference background mortality rate</td><td>events yr<sup>-1</sup></td><td>Constant, provided as input</td><td>66</td></tr><tr><td>wsg<sub>lim</sub></td><td>Parameter of d<sub>b</sub></td><td>g cm<sup>-3</sup></td><td>Constant</td><td>66</td></tr><tr><td>d<sub>starv</sub></td><td>Death rate due to carbon starvation</td><td>events yr<sup>-1</sup></td><td>Updated daily at tree level</td><td>65</td></tr><tr><td>d<sub>treefall</sub></td><td>Death rate due to treefall</td><td>events yr<sup>-1</sup></td><td>Updated daily at tree level</td><td>65</td></tr><tr><td>θ</td><td>Parameter of treefall probability</td><td>m</td><td>Computed once per tree</td><td>67</td></tr><tr><td>d<sub>drought</sub></td><td>Death rate due to drought</td><td>events yr<sup>-1</sup></td><td>Updated daily at tree level</td><td>65</td></tr><tr><td>v<sub>T</sub></td><td>Variance for treefall probability</td><td>unitless</td><td>Computed once per tree</td><td>67</td></tr><tr><td>hurt</td><td>Parameter of secondary treefall probability</td><td>m</td><td>Updated daily for each tree</td><td>Section 2.7.2</td></tr><tr><td>ψ<sub>lethal</sub></td><td>Water potential threshold for drought-induced mortality</td><td>MPa</td><td>Computed once per tree</td><td>68</td></tr></table>
<figure><img src="image_109.png" /></figure>

<table><tr><td>CLM5</td><td>Multi-layer CLM4.5</td><td>TFS-Hydro</td><td></td></tr><tr><td>(Kennedy et al., 2019)</td><td>(Boman et al., 2014)</td><td>(Christoffersen et al., 2016)</td><td></td></tr><tr><td>Stand-based</td><td>DGVM</td><td>TFS with a new module of plant hydraulics</td><td></td></tr><tr><td>g<sub>s</sub> = g<sub>0</sub> + 1.6 × (1 + <sup>g<sub>1</sub></sup>⁄<sub>√D<sub>s</sub></sub>) × <sup>h<sub>s</sub></sup>⁄<sub>c<sub>s</sub></sub></td><td>g<sub>s</sub> is iteratively computed such that (1) further opening does not yield a sufficient carbon gain per unit water loss (defined by a stomatal efficiency parameter) or (2) further opening causes leaf water potential to decrease below the minimum sustainable leaf water potential that prevents xylem cavitation</td><td>“</td><td>g<sub>s</sub> = g<sub>0,0</sub> × [1 + (<sup>ψ<sub>leaf</sub></sup>⁄<sub>ψ<sub>g<sub>5,50</sub></sub></sub>)<sup>a<sub>g<sub>s</sub></sub></sup>]<sup>-1</sup> where ψ<sub>g<sub>5,50</sub></sub> is the leaf water potential at 50% stomatal closure (assumed a 1:1 relationship between ψ<sub>g<sub>s,50</sub></sub> and the water potential at 20% loss of xylem hydraulic conductivity (P<sub>20,x</sub>), following results from (Brodribb et al., 2003), and a<sub>g<sub>s</sub></sub> is the slope of the curve at ψ<sub>leaf</sub> = ψ<sub>g<sub>s,50</sub></sub>, computed from ψ<sub>g<sub>s,50</sub></sub> using the same relationship that relates a<sub>s</sub> and P<sub>50,x</sub>.</td><td>“</td><td>“</td></tr><tr><td>g<sub>0</sub> = g<sub>0,0</sub> × ∑<sub>j</sub> <sup>ψ<sub>j</sub> - ψ<sub>c</sub></sup>⁄<sub>ψ<sub>o</sub> - ψ<sub>c</sub></sub></td><td>(Ball et al., 1987; Collatz et al., 1991)<sup>y</sup> empirical</td><td>“</td><td>g<sub>0</sub> = g<sub>0,0</sub> × ∑<sub>j</sub> <sup>ψ<sub>j</sub> - ψ<sub>c</sub></sup>⁄<sub>ψ<sub>o</sub> - ψ<sub>c</sub></sub></td><td>“</td><td>“</td></tr><tr><td>g<sub>s</sub> = g<sub>0</sub> + g<sub>1</sub> × A × <sup>h<sub>s</sub></sup>⁄<sub>c<sub>s</sub></sub></td><td>Inspired from the SPA model (Williams et al., 1996)<sup>y</sup> optimization within limitations imposed by water use efficiency, plant water storage, and soil-to-leaf water transport</td><td>“</td><td>where ψ<sub>j</sub> is the soil water potential of soil layer j, and ψ<sub>o</sub> and ψ<sub>c</sub> are the soil water potential at which the stomata are fully open and fully closed respectively, and r<sub>j</sub> is the relative root fraction of soil layer j.</td><td>“</td><td>“</td></tr><tr><td>g<sub>s</sub> = g<sub>0</sub> + 1.6 × (1 + <sup>g<sub>1</sub></sup>⁄<sub>√D<sub>s</sub></sub>) × <sup>h<sub>s</sub></sup>⁄<sub>c<sub>s</sub></sub></td><td>(Medlyn et al., 2011)<sup>y</sup> optimization, under light</td><td>“</td><td>The optimization includes a dependence to ψ<sub>leaf</sub>, where ψ<sub>leaf</sub> is computed at each timestep (30-60 min) with Darcy's law (soil-to-leaf pathway, including capacitance).</td><td>“</td><td>“</td></tr><tr><td>V<sub>c,max</sub></td><td>CML4.5 default: V<sub>c,max</sub> = V<sub>c,max,0</sub> × ∑<sub>j</sub> <sup>ψ<sub>j</sub> - ψ<sub>c</sub></sup>⁄<sub>ψ<sub>o</sub> - ψ<sub>c</sub></sub></td><td>“</td><td>“</td><td>“</td><td>“</td></tr><tr><td>Iterative procedure</td><td>An iterative procedure is used to solve the energy balance (determine the leaf temperature and internal CO<sub>2</sub></td><td></td><td></td><td></td><td></td></tr></table>
<figure><img src="image_110.png" /></figure>

<figure><img src="image_111.png" /></figure>

<table><thead><tr><th>ORCHID EE</th><th>CABLE</th><th>MAESPA</th><th>limitation (RuBP regeneration)</th><th>V<sub>c,max</sub> = V<sub>c,max,0</sub> × 2(ψ<sub>leaf</sub><sup>a</sup>g<sub>s</sub>) where ψ<sub>leaf</sub><sup>a</sup> is the leaf water potential at 50% loss of stomatal conductance and a<sub>g<sub>s</sub></sub> is a shape-fitting parameter. The solution for vegetation water potential is the set of values that matches supply with demand, maintaining water balance across each of the vegetation water potential nodes (ψ<sub>root</sub>, ψ<sub>stem</sub>, ψ<sub>sunlit-leaf</sub>, ψ<sub>shade-leaf</sub>) based on Darcy's law (without capacitance).</th><th>concentration), under which transpiration is computed as E<sub>0</sub> = g<sub>s</sub>g<sub>b</sub> / (g<sub>s</sub> + g<sub>b</sub>) .</th></tr></thead><tbody><tr><td>(Krinner et al., 2005; Naudis et al., 2015)</td><td>(De Kauwe et al., 2015a, b)</td><td>(Duursma et al., 2012)</td><td>g<sub>s</sub> is an empirical coefficient*, associated to the water use efficiency (to the Lagrangian).</td><td>Standard version of CABLE (De Kauwe et al., 2015a): g<sub>1</sub> = g<sub>1,0</sub> × ∑<sub>t</sub> (θ<sub>t</sub> - θ<sub>wp</sub>) / (θ<sub>t</sub> - θ<sub>wp</sub>) where t is the volumetric soil water content and t<sub>t</sub> the fraction of root mass in soil layer j, and θ<sub>wp</sub> the soil wilting point and θ<sub>t</sub> its field capacity.</td><td>Following MAESTRA (Medlyn et al., 2007), an iterative procedure is used to solve the energy balance of the canopy of each tree, under which the Pennman-Monteith equation is used to estimate canopy transpiration.</td></tr><tr><td>DGVM</td><td>DGVM (vegetation is represented using a single layer, two-leaf canopy model separated into sunlit and shaded)</td><td>Individual- and stand-based model</td><td>g<sub>s</sub> = g<sub>0</sub> + 1.6 × (1 + g<sub>1</sub>/√D<sub>s</sub>) × A/c<sub>s</sub> where ψ<sub>leaf</sub> is computed at each time step using Darcy's law (E<sub>L</sub> = k<sub>L</sub> × (ψ<sub>silt</sub> - ψ<sub>leaf</sub>))</td><td>New expression for drought sensitivity of gas exchange: g<sub>1</sub> = g<sub>1,0</sub> × exp (b × ψ<sub>pd</sub>), where b is a fitted (species-specific) parameter representing the sensitivity of g<sub>1</sub> to leaf predawn water potential ψ<sub>pd</sub> and taken from (Zhou et al., 2013, 2014), while g<sub>1,0</sub> values are drawn from (Lin et al., 2015).</td><td>transpiration from the vegetation to the atmosphere is controlled by several resistances operating in series, both above (aerodynamic) and within the canopy (stomatal and leaf boundary layer), and a longwave radiative balance through radiative conductance on net available energy. These resistances in serial, result in a relatively weak coupling between the canopy surface and the atmosphere.</td></tr><tr><td></td><td></td><td></td><td></td><td>In the version of (Krinner et al., 2005): The photosynthetic capacities, V<sub>max</sub> and I<sub>max</sub> are multiplied by a water stress factor that is:</td><td></td></tr><tr><td></td><td></td><td></td><td></td><td>g<sub>s</sub> = mA h<sub>r</sub>/c<sub>a</sub> + b</td><td></td></tr></tbody></table>
<figure><img src="image_112.png" /></figure>

<figure><img src="image_113.png" /></figure>

<table><tr><td>with m and b derived from laboratory measurements.</td><td>1 if f<sub>w</sub> &gt; f<sub>o</sub><br/>1 - (f<sub>w</sub> - f<sub>c</sub>) / (f<sub>o</sub> - f<sub>c</sub>) if f<sub>c</sub> &lt; f<sub>w</sub> &lt; f<sub>o</sub><br/>0 if f<sub>w</sub> &lt; f<sub>c</sub><br/>with f<sub>w</sub> the water fraction available for the plant in the root zone, and f<sub>c</sub> and f<sub>o</sub> the soil water fractions inducing, respectively, closure and maximum opening of stomata.</td><td>In the CAN version of (Naudts et al., 2015):<br/>The model calculates plant water supply according to the implementation of hydraulic architecture by (Hickler et al., 2006), i.e. using Darcy's law and accounting for the hydraulic resistances of fine roots, sapwood and leaves. If the transpiration calculated by the energy budget exceeds the amount of water a plant can transport from the soil to its stomata, transpiration is limited to the plant water supply, and stomatal conductance is then recalculated such that the transpiration equals the amount of water a plant can transport. The energy budget and photosynthesis are then recalculated. This may require up to 10 iterations to converge.</td></tr></table>
<figure><img src="image_114.png" /></figure>

<table><tr><td>LPJ</td><td>The model uses the Farquhar model of photosynthesis as generalized for global modelling purposes by (Collatz et al., 1991). In absence of water stress, canopy conductance is derived from the daytime carbon assimilation rate:<br/>g<sub>c</sub> = g<sub>min</sub> + <sup>1.6 A</sup>&frasl;<sub>c<sub>a</sub>(1 &minus; &lambda;)</sub></td><td>(Collatz et al., 1991)</td><td>Under water stress i.e. when min [1; <sup>E<sub>supply</sub></sup>&frasl;<sub>E<sub>demand</sub></sub>] &lt; 1,<br/>The equations of evapotranspiration rate, assimilation rate and the one related assimilation rate and canopy conductance are solved simultaneously to yield values of canopy conductance consistent with the transpiration rate.</td><td>&lt; 1 Daily evapotranspiration is calculated for each PFT as the minimum of a plant- and soil-limited supply function (E<sub>supply</sub>) and the atmospheric demand (E<sub>demand</sub>). E<sub>supply</sub> is the product of a plant root-weighted soil moisture availability and a maximum transpiration rate; E<sub>demand</sub> is calculated following Monteith's empirical relation between evaporation efficiency and surface conductance, that uses g<sub>pot</sub>, the nonwater-stressed potential canopy conductance calculated by the photosynthesis routine</td></tr></table>

*although fitted empirically to leaf exchange experimental data (Lin et al., 2015), attempts have been made to relate g1 to functional traits and/or climatological variables (wood density, Lin et al., 2015; leaf &delta;<sup>13</sup>C, Franks et al., 2018), based on the premise that water use efficiency should be associated to functional strategies. See also values reported in Domingues et al. (2014).
<figure><img src="image_115.png" /></figure>

2434

<table><caption>Table B2. Examples of observational or experimental studies that explored the relative roles of stomatal and non-stomatal limitations of photosynthesis under drought conditions.</caption><thead><tr><th>Key message for vegetation models</th><th>Reference</th><th>Studied system</th><th>Main results</th></tr></thead><tbody><tr><td rowspan="3">Stomatal-limitation only</td><td>Santos et al., 2018</td><td>57 canopy and understory trees within a central Amazonian forest</td><td>Photosynthesis decreased during the extreme dry season, and this was only related to stomatal closure (decline in stomatal conductance) and not to leaf biochemical changes (sustained chlorophyll concentration and fluorescence, and nutrient concentration).</td></tr><tr><td>Rowland et al., 2015</td><td>Trees in the ThroughFall Exclusion and control plots in Caixuana, Amazonia.</td><td>No differences in V<sub>cmax</sub> and J<sub>max</sub> between the throughfall exclusion plot and the control plot.</td></tr><tr><td>Trueba et al., 2019</td><td>Mature individuals of 10 angiosperms species located on the campus of UCLA and a park in LA</td><td>The stomatal and leaf hydraulic systems (50% lost of gs, K<sub>leaf</sub>) show early functional declines before cell integrity is lost. Substantial damage to the photochemical apparatus (maximum quantum yield of the photosystem) occurs at extreme dehydration, after turgor loss and complete stomatal closure, and seems to be irreversible.</td></tr><tr><td rowspan="4">Both stomatal and non-stomatal limitations</td><td>Zhou et al., 2013</td><td>Meta-analysis of 22 experimental datasets where photosynthesis, stomatal conductance and predawn leaf water potential were measured at increasing water stress, spanning a range of plant functional types</td><td>Photosynthesis was found almost universally to decrease more than could be explained by the reduction in g<sub>1</sub> (parameter of the Medlyn model), implying a decline in apparent carboxylation capacity (V<sub>cmax</sub>).</td></tr><tr><td>Zhou et al., 2014</td><td>Two experiments, one in Australia on Eucalyptus, one in Spain on Quercus, on plants grown in glasshouses under control conditions. The non-stomatal response was partitioned into effects on mesophyll conductance (g<sub>m</sub>), the maximum Rubisco activity (V<sub>cmax</sub>) and the maximum electron transport rate (J<sub>max</sub>).</td><td>They found consistency among the drought responses of g<sub>1</sub>, g<sub>m</sub>, V<sub>cmax</sub> and J<sub>max</sub>, suggesting that drought imposes limitations on Rubisco activity and RuBP regeneration capacity concurrently with declines in stomatal and mesophyll conductance. Within each experiment, the more xeric species showed relatively high g<sub>1</sub> under moist conditions, low drought sensitivity of g<sub>1</sub>, g<sub>m</sub>, V<sub>cmax</sub> and J<sub>max</sub>, and more negative values of the critical pre-dawn water potential at which V<sub>cmax</sub> declines most steeply, compared with the more mesic species. Results showed that the decline in V<sub>cmax</sub> is not explained just by the decline in gm, but by the decline in both gm and V<sub>cmax</sub>.</td></tr><tr><td>Egea et al., 2011</td><td>Outputs from a coupled A-gs model that uses a soil water content-dependent water stress factor were compared to leaf-level values obtained from the literature.</td><td>The sensitivity analyses emphasized the necessity to combine both stomatal and non-stomatal limitations of A in coupled A-gs models to accurately capture the observed functional relationships A vs. gs and A/gs vs. gs in response to drought. Accounting for water stress in coupled A-gs models by imposing either stomatal or biochemical limitations of A, as commonly practiced in most ecosystem models, failed to reproduce the observed functional relationship between key leaf gas exchange attributes.</td></tr><tr><td>Drake et al., 2017</td><td>Plants in pots of four tree species originating from constraining</td><td>as soil water content (θ) was reduced under increasing drought, all species responded by reducing gs, resulting in reduced Ci and Asat. However, Asat was reduced to</td></tr></tbody></table>

2435
<figure><img src="image_116.png" /></figure>

<figure><img src="image_117.png" /></figure>

<table><tr><td></td><td>hydrological environment, placed in the field under rainout shelters. Comparison with coupled stomatal conductance-photosynthesis model.</td><td>a larger degree than would be predicted only by stomatal reduction of Ci, indicating a coincident reduction in photosynthetic capacity with declining θ. The best model include both stomatal and non-stomatal limitations.</td></tr></table>

2436

2437

2438

2439

References of Appendix B

2440

2441

Ball, J. T., Woodrow, I. E., and Berry, J. A.: A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions, in: Progress in photosynthesis research, edited by: Biggins, J., Springer Netherlands, 221–224, https://doi.org/10.1007/978-94-017-0519-6_48, 1987.

2442

2443

2444

Bonan, G. B., Williams, M., Fisher, R. A., and Oleson, K. W.: Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil–plant–atmosphere continuum, Geosci. Model Dev., 7, 2193–2222, https://doi.org/10.5194/gmd-7-2193-2014, 2014.

2445

2446

2447

Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W., Gerten, D., Lotze-Campen, H., Müller, C., Reichstein, M., and Smith, B.: Modelling the role of agriculture for the 20th century global terrestrial carbon balance, Global Change Biology, 13, 679–706, https://doi.org/10.1111/j.1365-2486.2006.01305.x, 2007.

2448

2449

2450

Brodribb, T. J., Holbrook, N. M., Edwards, E. J., and Gutiérrez, M. V.: Relations between stomatal closure, leaf turgor and xylem vulnerability in eight tropical dry forest trees, Plant, Cell & Environment, 26, 443–450, https://doi.org/10.1046/j.1365-3040.2003.00975.x, 2003.

2451

2452

2453

Christoffersen, B. O., Gloor, M., Fauset, S., Fyllas, N. M., Galbraith, D. R., Baker, T. R., Kruijt, B., Rowland, L., Fisher, R. A., Binks, O. J., Sevanto, S., Xu, C., Jansen, S., Choat, B., Mencuccini, M., McDowell, N. G., and Meir, P.: Linking hydraulic traits to tropical forest function in a size-structured and trait-driven model (TFS v.1-Hydro), Geosci. Model Dev., 9, 4227–4255, https://doi.org/10.5194/gmd-9-4227-2016, 2016.

2454

2455

2456

2457

Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.: Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer, Agricultural and Forest Meteorology, 54, 107–136, https://doi.org/10.1016/0168-1923(91)90002-8, 1991.

2458

2459

2460

De Kauwe, M. G., Kala, J., Lin, Y.-S., Pitman, A. J., Medlyn, B. E., Duursma, R. A., Abramowitz, G., Wang, Y.-P., and Miralles, D. G.: A test of an optimal stomatal conductance scheme within the CABLE land surface model, Geoscientific Model Development, 8, 431–452, https://doi.org/10.5194/gmd-8-431-2015, 2015a.

2461

2462

2463

De Kauwe, M. G., Zhou, S.-X., Medlyn, B. E., Pitman, A. J., Wang, Y.-P., Duursma, R. A., and Prentice, I. C.: Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe, Biogeosciences, 12, 7503–7518, https://doi.org/10.5194/bg-12-7503-2015, 2015b.

2464

2465
<figure><img src="image_118.png" /></figure>

2466 Domingues, T. F., Martinelli, L. A., and Ehleringer, J. R.: Seasonal patterns of leaf-level photosynthetic gas exchange in an eastern Amazonian rain forest, Plant Ecology & Diversity, 7, 189–203, https://doi.org/10.1080/17550874.2012.748849, 2467 2468 2014.

2469 Drake, J. E., Power, S. A., Duursma, R. A., Medlyn, B. E., Aspinwall, M. J., Choat, B., Creek, D., Eamus, D., Maier, C., Pfautsch, S., Smith, R. A., Tjoelker, M. G., and Tissue, D. T.: Stomatal and non-stomatal limitations of photosynthesis for four tree species under drought: A comparison of model formulations, Agricultural and Forest Meteorology, 247, 454–466, 2470 2471 2472 https://doi.org/10.1016/j.agrformet.2017.08.026, 2017.

2473 Duursma, R. A., Medlyn, B. E., and others: MAESPA: a model to study interactions between water limitation, environmental drivers and vegetation function at tree and stand levels, with an example application to [CO₂]$\times$times$\$ drought interactions, 2474 2475 2012.

2476 Egea, G., Verhoef, A., and Vidale, P. L.: Towards an improved and more flexible representation of water stress in coupled photosynthesis–stomatal conductance models, Agricultural and Forest Meteorology, 151, 1370–1384, 2477 2478 https://doi.org/10.1016/j.agrformet.2011.05.019, 2011.

2479 Franks, P. J., Bonan, G. B., Berry, J. A., Lombardozzi, D. L., Holbrook, N. M., Herold, N., and Oleson, K. W.: Comparing optimal and empirical stomatal conductance models for application in Earth system models, Global Change Biology, 24, 2480 5708–5723, https://doi.org/10.1111/gcb.14445, 2018.

2481 Fyllas, N. M., Gloor, E., Mercado, L. M., Sitch, S., Quesada, C. A., Domingues, T. F., Galbraith, D. R., Torre-Lezama, A., Vilanova, E., Ramírez-Angulo, H., Higuchi, N., Neill, D. A., Silveira, M., Ferreira, L., Aymard C., G. A., Malhi, Y., Phillips, O. L., and Lloyd, J.: Analysing Amazonian forest productivity using a new individual and trait-based model (TFS v.1), Geosci. Model Dev., 7, 1251–1269, https://doi.org/10.5194/gmd-7-1251-2014, 2014. 2482 2483 2484 2485

2486 Hickler, T., Prentice, I. C., Smith, B., Sykes, M. T., and Zaehle, S.: Implementing plant hydraulic architecture within the LPJ Dynamic Global Vegetation Model, Global Ecology and Biogeography, 15, 567–577, https://doi.org/10.1111/j.1466-2487 2488 2489

2489 Kennedy, D., Swenson, S., Oleson, K. W., Lawrence, D. M., Fisher, R., Costa, A. C. L. da, and Gentine, P.: Implementing Plant Hydraulics in the Community Land Model, Version 5, Journal of Advances in Modeling Earth Systems, 11, 485–513, https://doi.org/10.1029/2018MS001500, 2019. 2490 2491

2492 Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cycles, 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005. 2493 2494

2495 Leuning, R.: A critical appraisal of a combined stomatal-photosynthesis model for C3 plants, Plant, Cell & Environment, 18, 339–355, https://doi.org/10.1111/j.1365-3040.1995.tb00370.x, 1995. 2496

2497 Lin, Y.-S., Medlyn, B. E., Duursma, R. A., Prentice, I. C., Wang, H., Baig, S., Eamus, D., de Dios, V. R., Mitchell, P., Ellsworth, D. S., de Beeck, M. O., Wallin, G., Uddling, J., Tarvainen, L., Linderson, M.-L., Cernusak, L. A., Nippert, J. B., Ocheltree, T. W., Tissue, D. T., Martin-StPaul, N. K., Rogers, A., Warren, J. M., De Angelis, P., Hikosaka, K., Han, 2498 2499
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2505 Longo, M., Knox, R. G., Levine, N. M., Alves, L. F., Bonal, D., Camargo, P. B., Fitzjarrald, D. R., Hayek, M. N., Restrepo-2506 Coupe, N., Saleska, S. R., Silva, R. da, Stark, S. C., Tapajós, R. P., Wiedemann, K. T., Zhang, K., Wofsy, S. C., and 2507 Moorcroft, P. R.: Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts, 2508 New Phytologist, 914–931, https://doi.org/10.1111/nph.15185@10.1111/(ISSN)1469- 2509 8137.DroughtImpactsonTropicalForests, 2018.

2510 Manzoni, S., Vico, G., Katul, G., Fay, P. A., Polley, W., Palmroth, S., and Porporato, A.: Optimizing stomatal conductance 2511 for maximum carbon gain under water stress: a meta-analysis across plant functional types and climates, Functional 2512 Ecology, 25, 456–467, https://doi.org/10.1111/j.1365-2435.2010.01822.x, 2011.

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