Forest structure is shaped by forest management practices, land-use changes, and natural disturbances, including droughts, fires, storms, and insect outbreaks that drive species-specific and size-specific mortality. By modifying the carbon-water-energy exchanges with the atmosphere, it influences a stand's capacity to buffer against or succumb to extreme weather events, which in turn determines the long-term stability of the terrestrial carbon stocks. Given the importance of forest structure for forest land sink, land surface models are moving toward explicit representations of forest structure and management strategies. We present a new procedure to initialize forest diameters over Europe and document its implications for simulations of future forest carbon sinks. The simulated diameters for each grid cell covered by forests are initialized toward the diameter from a forest inventory. To this end, a 300-year semi-analytical spinup was carried out to bring the soil carbon pools into equilibrium. A lookup table with the simulated diameter and plant functional type as its entries was built by clearcutting all forests followed by a 200-year simulation over Europe. For each grid point, the year associated with the simulated diameter that is the closest to the observation is selected, enabling the production of new initial state files over Europe. The new initialization procedure makes the initial state of forest more realistic and therefore significantly modifies the evolution of the forest carbon sink. The method could be further extended to initialize other forest state variables such as height or aboveground biomass.

Forests play a crucial role in carbon sequestration and storage, yet uncertainty remains about carbon stocks and sequestration differences between managed and unmanaged forests, which has resulted in considerable scientific and policy debate. This review synthesizes European evidence comparing managed and unmanaged forests addressing three questions: (1) what are the differences in carbon stock and sequestration for all carbon pools between managed and unmanaged forests, (2) what are trade-offs and synergies with other ecosystem services when managing for carbon, and (3) what are limitations of forest management research in identifying trade-offs and synergies between ecosystem services? This review shows that unmanaged forests are important for long-term carbon storage, containing 34

In semi-arid regions, grasses and shrubs often form spatially heterogeneous patterns interspersed with bare soil as a strategy to optimize resource use and maximise productivity. Accurately representing the matrix of vegetation and bare soil in global land surface models is essential for advancing the understanding of the carbon, water, and dust cycles. This study focuses on grasslands using the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms), which originally assumes a globally fixed grassland density representing a fixed number of individuals per unit of land. This assumption, referred to as the fixed density approach, limits the model's ability to capture grassland responses to environmental changes, resulting in unsustainable productivity and unrealistically frequent mortality events, particularly in resource-limited regions. To address these limitations, we introduced a dynamic density approach that simulates grassland density based on indicators of vegetation growth, such as reserve and labile carbon content in the grass. The simulated grassland density was consistent with field-based estimates from five regional case studies and showed a better representation of bare soil in grasslands than the fixed density approach. The emerging positive correlation between precipitation and simulated grassland density supported the validity of the approach. Compared to the fixed density approach, the dynamic density approach substantially reduced simulated mortality events, raised the aridity threshold for frequent mortality, improved the simulated leaf area index (LAI) both globally and in key semi-arid regions, and maintained realistic grassland productivity in regions where the presence of grassland is confirmed by remotely sensed LAI. This study not only demonstrates that simulating grassland density as a function of carbon availability improves ORCHIDEE's capacity to capture grassland dynamics under environmental variability, but also provides a promising foundation for investigating dust dynamics and subsequent land-atmosphere feedbacks in (semi-)arid regions.

Abstract. Arctic-Boreal terrestrial ecosystems are rapidly changing under amplified high-latitude warming, including widespread expansion of shrubs, with consequences for regional carbon and energy balances. Yet, high-latitude vegetation diversity and vegetation-climate interactions remain under-represented in many global land surface models. In ORCHIDEE, the land surface component of the IPSL Earth system model, high-latitude vegetation is represented primarily as boreal trees or grasslands, omitting explicit shrubs. Here, we implement three high-latitude shrub plant functional types (PFTs) (tall deciduous, low deciduous, and evergreen dwarf shrubs) in ORCHIDEE (revision 9269). Following literature recommendations, this classification combines phenology and stature to capture key functional contrasts while keeping the number of new PFTs limited. The implementation builds on ORCHIDEE's existing woody vegetation scheme by recalibrating a targeted set of parameters controlling allometry, carbon allocation, recruitment, mortality and phenology. Parameter values are constrained using synthesised pan-Arctic observations to obtain regionally representative shrub traits. Shrub spatial distributions are prescribed with updated PFT maps that combine ESA CCI products with Arctic and regional shrub mapping information. The resulting shrub PFTs reproduce observed ranges of shrub size and biomass allocation across the Arctic–Boreal domain. Introducing shrubs reduces simulated total aboveground biomass in the Arctic-Boreal region from 54 to 46.7 P g C (-13.5 %) and mean annual gross primary productivity from 498 to 481 g C m−2 yr−1 (-3.4 %) over the simulated period 1992-2020, with a stronger reduction in the tundra region (4.6 to 3 P g C (-34.8 %); and 334 to 289 g C m−2 yr−1 (-13.5 %)), increasing agreement with benchmarking datasets. A key strength of our implementation is its simplicity, as it builds on ORCHIDEE's existing woody vegetation framework. In addition, the use of synthesised pan-Arctic observations provides regionally representative observational constraints, making the methodological choices transferable beyond ORCHIDEE. Overall, this work provides a data-constrained shrub representation in ORCHIDEE with minimal added process complexity and establishes a foundation for future development of shrub-climate interactions and dynamic shrubification processes.

Vegetation demographic models (VDMs) are advanced tools for simulating forest responses to climate and land-use changes, and are essential for projecting carbon cycling and large-scale forest management strategies. Despite their increasing incorporation into Earth System Models, VDMs differ in their demographic assumptions, with no prior quantitative comparison of their performance. We benchmarked nine VDMs against observational data from boreal, temperate and tropical sites, assessing their accuracy in predicting tree growth, carbon turnover, biomass stocks and size distributions. Models were simulated under consistent climate conditions with postdisturbance recovery monitored for at least 420 yr. Postdisturbance carbon recovery trajectories showed significant variability while remaining within observational ranges. Initial regrowth rates varied substantially (0.03-0.60, 0.18-0.70 and 0.35-1.10 kgCm-2 yr-1 for boreal, temperate and tropical sites, respectively), influenced by each model's initial forest state. Models captured mature forest carbon content but showed compensating effects between overestimated growth and underestimated mortality rates. This first multi-model benchmarking identifies growth and mortality rates as critical calibration targets and highlights the need to refine postdisturbance establishment conditions for model development. We outline specific benchmarking variables needed to improve predictions of forest responses to environmental change.

Soil structure is nearly as important as soil texture in determining the soil hydraulic properties at the core scale. Soil structure was also shown to significantly affect runoff and drainage at ecosystem scale (Fatichi et al., 2020; Bonetti et al., 2021). However, its effect on vadose zone hydrology at 100 km scale — at which climate and land surface models are often run — remains unclear. Seminal works (Fatichi et al., 2020; Bonetti et al., 2021) found a small effect of soil structure at these large scales, but this has been linked to the nature of the subgrid parametrization of precipitation (or of soil hydraulic conductivity) in the employed models. Here, we evaluate the effect of soil structure on vadose zone hydrology in the ORCHIDEE land surface model, which models infiltration using a unique subgrid parametrization of soil hydraulic conductivity (Vereecken et al., 2019). In ORCHIDEE, we find a larger effect of soil structure on the water cycle than reported for OLAM (Fatichi et al., 2020). We link this to the subgrid variability of hydraulic conductivity in ORCHIDEE, which ensures that the structural modifications of soil hydraulic properties are activated at all rainfall rates. Finally, we discuss the perspectives for parametrizing the structural modifications of soil hydraulic properties at large scales using soil moisture observations.Bonetti, S., Wei, Z., & Or, D. (2021). A framework for quantifying hydrologic effects of soil structure across scales. Communications Earth & Environment, 2 (1), 1–10. https://doi.org/10.1038/s43247-021-00180-0Fatichi, S., Or, D., Walko, R., Vereecken, H., Young, M. H., Ghezzehei, T. A., Hengl, T., Kollet, S., Agam, N., & Avissar, R. (2020). Soil structure is an important omission in Earth System Models. Nature Communications, 11 (1), 522. https://doi.org/10.1038/s41467-020-14411-zVereecken, H., Weihermüller, L., Assouline, S., Šimůnek, J., Verhoef, A., Herbst, M., Archer, N., Mohanty, B., Montzka, C., Vanderborght, J., Balsamo, G., Bechtold, M., Boone, A., Chadburn, S., Cuntz, M., Decharme, B., Ducharne, A., Ek, M., Garrigues, S., … Xue, Y. (2019). Infiltration from the Pedon to Global Grid Scales: An Overview and Outlook for Land Surface Modeling. Vadose Zone Journal, 18 (1), 180191. https://doi.org/10.2136/vzj2018.10.019

Forests are an important component in the framework of nature-based solutions for mitigating climate change. However, there are still uncertainties about the biogeophysical effects of forest cover changes affecting heat and water fluxes as captured by Earth System Models (ESMs) simulations and observations. In this study, we investigate the differences in the surface temperature response to idealized, complete deforestation and the temperature sensitivity to percentage change in forest cover in ESMs and observations. In this comparison, the separation between local (at the place of deforestation) and non-local (nearby or distant locations) effects is crucial as observations capture only the former. Here, we propose a modified methodology to separate local and non-local effects in climate models suitable for simulations with linear rate of deforestation. The local sensitivity of a climate variable per unit deforested area is represented by the slope of the linear regression, where tree cover is an explanatory variable. The non-local effect is defined as the difference between the overall change in the respective climate variable and the local effect. Our analysis of eleven ESMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) that participated in the idealized global deforestation experiment deforest-glob, reveals a coherent local temperature response among climate models characterized by warming in the tropics and cooling in the northern higher latitudes. The temperature response however varies in magnitude, space and time with ESMs showing distinctive seasonal and spatial patterns. A closer look at the albedo response to deforestation across northern latitudes shows an overestimation in the ESMs in comparison to observations that translates via an emergent constraint (i.e. resulting from the linear relationships between local albedo and surface temperature within the model ensemble) into an overestimation of the overall simulated cooling effect. The overestimation of the local albedo sensitivity cannot be explained solely by the higher percentage of snow cover in ESMs. In terms of local latent heat flux sensitivity, the ESMs ensemble mean is overestimated for the boreal region, but it is in good agreement with the observational constraint in the temperate forests and the tropics. However, the inter-model spread and the internal model variation in these regions are considerable. ESMs having higher local albedo and latent heat flux sensitivities than the current observational constraints can still exhibit a realistic temperature response due to compensatory effects between the two sensitivities. Non-local effects contribute to consistent cooling throughout the globe, which persists also during the summer when the influence of the overestimated albedo sensitivity over snow is weaker. Having a deeper understanding of how local and non-local biogeophysical effects are represented in ESMs can give us insights into the net climate impact of deforestation and help us improve next generation ESMs.

Uncertainty in the dynamics of the Amazon rainforest poses a critical challenge for accurately modeling the global carbon cycle. Current dynamic global vegetation models (DGVMs), which use one or two plant functional types for tropical rainforests, fail to capture observed biomass and mortality gradients in this region, raising concerns about their ability to predict forest responses to global change drivers. Here we assess the importance of spatially varying parameters to resolve ecosystem spatial heterogeneity in the ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) DGVM. Using satellite observations of tree aboveground biomass (AGB), gross primary productivity (GPP), and biomass mortality rates, we optimized two key parameters: the alpha self-thinning (alpha), which controls tree mortality induced by light competition, and the nitrogen use efficiency of photosynthesis (eta), which regulates GPP. The model incorporating spatially optimized alpha and eta parameters successfully reproduces the spatial variability of AGB (R2 = 0.82), GPP (R2 = 0.79), and biomass mortality rates (R2 = 0.73) when compared to remote sensing observations in intact Amazon rainforests, whereas the model using spatially constant parameters has R2 values lower than 0.04 for all observations. Furthermore, the relationships between the optimized parameters and ecosystem traits, as well as climate variables, were evaluated using random forest regression. We found that wood density emerges as the most important determinant of alpha, which is in line with existing theory, while water deficit conditions significantly impact eta. This study presents an efficient and accurate approach to enhancing the simulation of Amazonian carbon pools and fluxes in DGVMs by assimilating existing observational data, offering valuable insights for future model development and parameterization.

Land surface models (LSMs) typically represent soil moisture control on stomatal conductance through an empirical sensitivity function, without considering plant hydrology. This study proposes integrating water transfer representation within the soil-plant-atmosphere continuum in the ORCHIDEE land surface model. This new configuration includes vegetation hydraulic architecture and a stomatal control based on leaf water potential , along with a mechanistic representation of water absorption by roots via radial diffusion around the roots. An adaptive numerical scheme is implemented to prevent numerical instabilities during hydric stress, reducing hourly instabilities by a factor of 2. The implementation and the standard configuration of ORCHIDEE are calibrated and evaluated at FLUXNET sites with eddy-covariance flux measurements. A detailed assessment is carried out at two well-documented forest sites (FR-Hes and FR-Pue), where both configurations perform similarly regarding the seasonal dynamics of latent heat flux (RMSEs of 16.0 W/ for the potential-based configuration and 15.8 W/ for the standard configuration at FR-Hes). An evaluation of leaf water potential at FR-Pue shows correlations of 0.87 and 0.72 for predawn and midday respectively. A second evaluation across 135 sites from the FLUXNET2015 database highlights similar performances for both configurations. Finally, a global assessment of the differences between the two schemes emphasizes the good performance of the hydraulic architecture model. Overall, the new hydraulic architecture provides a more mechanistic description of stomatal conductance response to soil water stress and paves the way for incorporating physiological processes controlling tree mortality and using in situ observations to calibrate plant responses to water stress.

In semi-arid regions, grasslands naturally display a self-organized pattern that optimizes resource utilization and productivity. Representing this type of vegetation in land surface model constitutes a difficult challenge. To simulate these grasses, the ORCHIDEE land surface model treats grass density as the ratio of the area occupied by individuals to the Plant Functional Type (PFT) area, assuming a fixed grass density of 1 for maximal occupancy. However, the fixed maximal grass density lacks the response of grassland to environmental perturbations. In addition, the low biomass contained in certain pixels results in frequent mortality, indicative of resource limitations at the plant individual level. To address this considerable limitation, we introduced dynamic reduction of grass density based on mortality indicators, hence enhancing individual biomass and alleviating mortality occurrences. The adaptive approach significantly decreased mortality events across most pixels while enhancing leaf area index (LAI) for the majority of them. Our findings suggest that optimizing resource through grass density reduction in response to environmental condition, could not only improve individual biomass to alleviate mortality but also enhance overall grassland production.

The Sahel region is characterized by its semi-arid climate and open-canopy agroforestry systems, which play an important role in global carbon dynamics. Parkland agroforestry has the potential to sequester carbon at an average rate of 0.4 tC ha−1 yr−1, which, if expanded to its maximum potential extent, would correspond to an additional carbon stock of approximately 558 TgC compared to treeless croplands. However, land surface models (LSM) used in global climate modeling struggle to represent carbon dynamics in these ecosystems due to the inadequate representation of deep-roots tapping groundwater during dry periods, key environmental control for many agroforestry systems such as the widespread parklands based on the phreatophytic species Faidherbia albida. This study explores the sensitivity of Faidherbia albida parklands to tree density and water availability (rainfall and soil water content in the capillary fringe of the groundwater table) using a new configuration of the ORCHIDEE LSM. To this aim, the ORCHIDEE LSM was modified to simulate the growth of Faidherbia albida by simulating its inverted phenology based on forced temporal series of soil water content of soil layers between 4 and 5 m and water saturation below 5 m and by adjusting the photosynthesis and carbon allocation parameters for Faidherbia albida and associated crops. The model was evaluated against independent eddy covariance and meteorological data from the Niakhar agroforestry site in Senegal. Simulation outputs were analyzed in terms of leaf area index (LAI), gross primary productivity (GPP), latent heat (LE), sensible heat (H) and net radiation (Rn). The model simulated tree GPP of 4.08 ± 0.21 tC ha−1 yr−1 compared to observed GPP of 5.06 ± 0.49 tC ha−1 yr−1. For croplands, the model produced GPP of 7.97 ± 0.89 tC ha−1 yr−1 compared to observed values of 7.78 ± 1.75 tC ha−1 yr−1. Simulations revealed that tree density positively influenced annual carbon uptake but reduced crop harvest at highest tree densities, indicating a trade-off between carbon sequestration and crop yield. Sensitivity analyses showed that interannual variability in soil water content in the capillary fringe of the groundwater table and rainfall influenced differently crop, tree and ecosystem carbon and energy fluxes. Despite its strengths, the model exhibited limited responsiveness of tree productivity to soil water content variability in the capillary fringe of the groundwater table, highlighting the need for enhanced representation of water uptake by tree roots in the model. These findings emphasize the importance of accurately modeling both surface soil water and groundwater dynamics and phenology to predict the responses of semi-arid agroforestry systems to climate variability. This study enhances our understanding of carbon and energy flux partitioning in complex, water-stressed and groundwater dependent agroforestry systems.

Forest structure is shaped by forest management practices, land-use changes and forest disturbances including droughts, fires, storms and insect outbreak. It plays an important role in climate by modifying the carbon-water-energy exchanges with the atmosphere, and affects the capability of forests to undergo future disturbances in a changing climate. Given the importance of forest structure for the climate, land surface models are moving towards explicit representations of forest structure and management strategies. We present a new procedure to initialize forest diameters over Europe and document its implications for simulations of future forest carbon sinks. The simulated diameters for each grid cell covered by forests are initialized toward the diameter from a forest inventory. To this end, a 300-years semi-analytical spinup was carried out to bring the soil carbon and nitrogen pools into equilibrium until the European forests were clearcut. Then, a 150-years biosphere simulation over Europe was performed to build a look-up-table of simulated diameters. For each grid point, the year associated with the simulated diameter that is the closest to the observation is selected, enabling the production of new initial state files over Europe. The new initialization procedure makes the initial state of forest more realistic and therefore is expected to have significant influence on the evolution of the forest carbon sink. In this work, we will assess the effect of the initialization procedure on the simulated land carbon sink and we will evaluate the representation of the diameters in the ORCHDEE LSM. The method could be further extended to initialize other forest state variables such as height or aboveground biomass.

Net primary production (NPP) is a fundamental measure of biomass production in ecosystems. In terrestrial biomes, NPP lacks standard measuring protocols and is difficult to measure. Thus, despite decades of research efforts, NPP data are limited and heterogenous. Moreover, there continues to be a lack of global NPP databases containing harmonized estimates for all major ecosystem types and which account for both above- and belowground production. We present a global database containing records for both above- and belowground production for forests, grasslands, arid shrublands, northern peatlands and tundra at 456 sites. The records are reported as annual production (g m-2yr-1). The NPP data are complemented with detailed site and methodological information, including a method specific estimate for the measurement uncertainty, as well as ancillary data on climatic conditions, soil fertility and management status. This database provides a basis for comparative studies on local, regional and global scales, and may serve as an important benchmarking dataset for the development of DGVMs.

Intra-canopy microclimate and regional climate are two highly related components of the Earth system. On one side, intra-canopy microclimate influences strongly the ecosystem itself by regulating the vegetation/atmosphere exchanges which further influence plant dynamics, carbon sequestration and soil water dynamics. It also influences the biodiversity below the canopy by offering microhabitats or temperature buffering, and the regional climate directly by regulating the water and energy exchanges with the lowest levels of the atmosphere. On the other side, regional climate has a strong impact on intra-canopy microclimate, especially in the context of climate change, by reducing temperature gradients or by controlling the vegetation phenology. Despite this apparent strong imbrication, intra-canopy microclimates are very poorly represented in Land Surface Models (LSMs) and climate models in general, making it complicated to study their impact on climate change mitigation or the impact of climate change on the forests and their microclimates. Because of their time computing requirements, LSMs usually prefer simple models such as the “Big-Leaf” representation. However, in front of the urgent need to represent complex ecosystems and microclimates in Earth system models, first steps in this direction can be made, especially to improve the energy and water fluxes in the soil-vegetation-atmosphere continuum. This study presents recent developments made in the ORCHIDEE LSM. Two models have been implemented in ORCHIDEE in order to move from the current big leaf approach at the grid-cell scale to a representation of vertical gradients associated to the microclimate. To do so, firstly, a representation of the water flow in the soil-plant-atmosphere continuum through a hydraulic architecture model has been introduced. Secondly, a previous multi-layer energy budget representation, including turbulent vertical exchanges within the canopy, has been updated to make it operational with the current trunk of ORCHIDEE. Those two models enable a better representation of the intra-canopy leaf-atmosphere exchanges at the Plant Functional Type (PFT) level. Lastly, a representation of the sub-grid heterogeneity is also being implemented enabling a global representation of each PFT intra-canopy microclimates. This presentation will mainly focus on the multi-layer energy budget representation and its applications for different ecosystems at larger scale. Firstly, a comparison between ORCHIDEE and the forest model MuSICA (Ogée et al. (2003)) was performed over several forest sites highlighting the potential benefits as well as the difficulties of modelling the vertical gradients of temperature, wind and humidity within the canopy. Secondly, a first large scale representation of intra-canopy microclimate and its impact on ORCHIDEE energy and water budgets will be presented. Finally, opening perspectives induced by those developments in the ORCHIDEE LSM will be drawn.

Anthropogenic climate change is altering precipitation regimes at a global scale. While precipitation changes have been linked to changes in the abundance and diversity of soil and litter invertebrate fauna in forests, general trends have remained elusive due to mixed results from primary studies. We used a meta-analysis based on 430 comparisons from 38 primary studies to address associated knowledge gaps, (i) quantifying impacts of precipitation change on forest soil and litter fauna abundance and diversity, (ii) exploring reasons for variation in impacts and (iii) examining biases affecting the realism and accuracy of experimental studies. Precipitation reductions led to a decrease of 39% in soil and litter fauna abundance, with a 35% increase in abundance under precipitation increases, while diversity impacts were smaller. A statistical model containing an interaction between body size and the magnitude of precipitation change showed that mesofauna (e.g. mites, collembola) responded most to changes in precipitation. Changes in taxonomic richness were related solely to the magnitude of precipitation change. Our results suggest that body size is related to the ability of a taxon to survive under drought conditions, or to benefit from high precipitation. We also found that most experiments manipulated precipitation in a way that aligns better with predicted extreme climatic events than with predicted average annual changes in precipitation and that the experimental plots used in experiments were likely too small to accurately capture changes for mobile taxa. The relationship between body size and response to precipitation found here has far-reaching implications for our ability to predict future responses of soil biodiversity to climate change and will help to produce more realistic mechanistic soil models which aim to simulate the responses of soils to global change.

Climate warming has caused a widespread increase in extreme fire weather, making forest fires longer-lived and larger1-3. The average forest fire size in Canada, the USA and Australia has doubled or even tripled in recent decades4,5. In return, forest fires feed back to climate by modulating land-atmospheric carbon, nitrogen, aerosol, energy and water fluxes6-8. However, the surface climate impacts of increasingly large fires and their implications for land management remain to be established. Here we use satellite observations to show that in temperate and boreal forests in the Northern Hemisphere, fire size persistently amplified decade-long postfire land surface warming in summer per unit burnt area. Both warming and its amplification with fire size were found to diminish with an increasing abundance of broadleaf trees, consistent with their lower fire vulnerability compared with coniferous species9,10. Fire-size-enhanced warming may affect the success and composition of postfire stand regeneration11,12 as well as permafrost degradation13, presenting previously overlooked, additional feedback effects to future climate and fire dynamics. Given the projected increase in fire size in northern forests14,15, climate-smart forestry should aim to mitigate the climate risks of large fires, possibly by increasing the share of broadleaf trees, where appropriate, and avoiding active pyrophytes.

Abstract Common parametrizations of soil hydraulic properties rely on unimodal curves, which cannot accurately represent the properties of many macroporous, aggregated, mixed, or compacted soils. Multimodal hydraulic curves are increasingly used to represent these structured soils in eco‐hydrological models, but the dynamics of the processes that shape soil structure—and the resulting dynamics of soil hydraulic properties—are often neglected. In cases such as compaction recovery, where the structure‐shaping process can be modeled, coupling the evolving pore volumes to soil hydraulic properties in a physically based way remains challenging. Here, we show how modeled or estimated soil structure evolution, when expressed as a time series of porosities in a few pore size classes, can be assimilated into established models of soil hydraulic properties. Our method relies on the division of retention models into smooth segments, whose water contents can be independently adjusted. We apply the approach to examples of modeled soil structure evolution from the published literature: one describing soil structure recovery after compaction and one describing structure formation as a result of organic amendment. In the cases considered, the estimated soil hydraulic conductivity varies more strongly than the modeled porosity which drives it. This shows that transport‐related soil functions can be impacted longer (after compaction) or sooner (after amendment) than suggested by the evolution of structural metrics such as porosity. In general, modeling the evolution of soil hydraulic properties in cases such as these paves the way for holistic, process‐based modeling of land management practices and their impact on soil functioning.