The ORCHIDEE-CROP model is a branched version of the process-based land surface model, ORCHIDEE (Krinner et al., 2005), which incorporates a generic crop phenology and harvest module, along with simplified nitrogen fertilization parameterization based on the process-based STICS formalism (Wu et al., 2016). It simulates biophysical and biogeochemical interactions in croplands, as well as plant productivity and harvested yield. A detailed model description is available in Wu et al. (2016). This study employs a previously developed model version calibrated against LAI, flowering and harvesting dates, and crop yield for both winter wheat and maize (https://github.com/yangsssu/ORCHIDEE-CROP.git, last access: 13 February 2026). In the present study, only winter wheat is considered. The derived climate and management information were used to drive the model as forcing data (Sect. 2.1.2 and 2.1.3). Winter wheat is the only vegetation type considered in this analysis. To assess the impacts of winter wheat physiological development and residue coverage on the surface energy balance and water–energy fluxes, we describe below the key processes governing surface energy balance (Sect. 2.2.1), αsurf dynamics (Sect. 2.2.2), hydrology (Sect. 2.2.3), the surface roughness (Sect. 2.2.4), as well as crop growth and development (Sect. 2.2.5).

Foliar yellowing of winter wheat after maturation and crop residues remaining on the field after harvest affect αsurf and turbulent fluxes, but these processes are not currently represented in the model. As such, the absence of these key processes presents a gap in understanding and modeling of the impacts of winter wheat on regional energy and water cycles. We enhanced the ORCHIDEE-CROP model by introducing empirical functions describing temporal dynamics of αsurf, β4 and Z0 as a function of time, fcrop and fres for foliar yellowing and crop residues. In addition, to ensure consistency between simulated and observed crop development periods (from sowing to harvest) at each site, the duration between maturity and harvesting in ORCHIDEE-CROP was calibrated using recorded harvesting dates at each site. In each winter wheat year at each site, simulations were performed iteratively with potential maturity-harvest intervals ranging from 5 to 40 d at 2 d increments. The optimal duration for each case was identified when the simulated harvest date matched the recorded management date. The calibrated durations are summarized in Table S2. It means that the site-specific durations of foliar yellowing before harvesting vary across all sites in the new model, compared to the default 14 d in the old model.

The objective of this model development targets a realistic mean effect size expected among European croplands, rather than capturing in detail the site-specific effects. We focus on improving the representation of albedo properties of winter wheat during the foliar yellowing and residue covering period, while associated impacts on surface energy fluxes are evaluated qualitatively rather than through a full recalibration of the hydrological scheme.

The parameters of αsurf dynamics were calibrated using data from 10 sites with 33 winter wheat site-years. αsurf and LAI observations were applied to identify foliar yellowing and residue covering periods at the available sites (Table S2).

Simulations were performed for 10 sites encompassing 14 farms (Table S2) with the new ORCHIDEE-CROP model version and with the original version of this model (Table 1) for the years for which observations were available. The experiments aimed to evaluate the impacts of the parameterizations of αsurf, β4 and Z0 on water–energy cycles during the foliar yellowing and residue covering periods. In the following, ORC-D refers to the default ORCHIDEE-CROP model configuration prior to any modifications, where “D” denotes the default setup. To assess the respective impacts of the five model modifications, we used five different configurations of the improved model. Configuration ORC-AE includes modifications to αsurf, β4 and Z0 during foliar yellowing and residue covering periods, where “A” refers to surface albedo and “E” to energy-related parameters. Configuration ORC-A includes only the modified αsurf, while ORC-E incorporates adjustments to both β4 and Z0. To further disentangle the individual impacts of β4 and Z0, ORC-Z0 modifies only the Z0, and ORC-β4 refers to only the modification of the β4. The prepared climate forcing data (Sect. 2.1.2), soil texture (sand, silt and clay contents) and crop management information (i.e., sowing date, tillage date, residue covering periods) (Sect. 2.1.3) for each site were used to drive the model. Note that all other management practices prescribed in this model (e.g., nitrogen fertilization) were set as default due to the lack of historical implementation records. There is no irrigation consideration in this analysis.

In addition, we conducted a 10-year simulation (2010–2020) under both current and idealised dry scenarios to quantify the cumulative effects of residue soil cover on water and heat fluxes for the new and old models at six sites if 10-year climate forcing data is available (Table S2). The meteorological forcing data for both scenarios followed the methodology described in Sect. 2.1.2, where the current climate scenario used observed annual climate data from ICOS, and the dry scenario was driven by climate forcing from the driest year (defined as the year with the lowest annual rainfall). The meteorological forcing from the driest year was repeated throughout the simulation period to represent persistent dry conditions. At each site, the sowing and harvesting dates were averaged across the years for which observations were available. The residue covering periods were assumed to persist for 60 d following winter wheat harvest in each year.

We performed sensitivity analyses of the major parameters (i.e. k1, k4, β4, Hc, duration of αsurf increase during residue covering period (Dup)) linking to the αsurf variation and the responses of the energy and water budgets, particularly for the ones without enough constraints from field observations. The sensitivity analysis was conducted by changing the parameters by ±10 %, ±20 % and ±30 % from the initial calibrated values. Their impacts of these parameter changes on Tsurf, H, Esoil and LE were evaluated.

We evaluated the performance of ORCHIDEE-CROP with and without modification against observations of αsurf at site scale. The sites used for evaluation were independent from the ones used for calibration of αsurf.

We computed the coefficient of determination (R2) and RMSE as quality indicators of model performance in the simulation-observation comparison of αsurf during the site calibration and model simulation processes (Wright, 1921; Carbone and Armstrong, 1982): 19R2=1-∑in(yi-fi)2∑in(yi-y¯)220RMSE=∑in(fi-yi)2n

Where n and i are the number of data and the data i in n in dataset; yi and fi are the value of observed data i and the value of fitted data i; y¯ is the mean of the observed data.

With the calibration of the simulation of harvesting date for all sites, we further compared the simulated surface temperature (Tsurf), LE, H from the old and new models against observation during the foliar yellowing and residue covering periods. For each variable, a paired t test was also used to assess the null hypothesis that the mean paired difference between the simulations from the old and new models differed significantly from zero.

During the albedo calibration phase, the estimated parameter values of k1, k2, k3 and k4 (Eqs. 12 and 14) are 0.013 (95 % CI: 0.012–0.013), 1.005 (95 % CI: 0.998–1.017), 0.895 (95 % CI: 0.889–0.902) and 0.0022 (95 % CI: 0.0017–0.0026), respectively. Using these calibrated parameters, the relationship between observed surface albedo (αsurf) and the parameterized αsurf representation used to constrain the model shows a moderate correlation with an R2 of 0.54 during the foliar yellowing period and of 0.62 during the residue covering period for five testing sites. The corresponding RMSE values are 0.04 and 0.03, respectively (Fig. 2). The least-squares regression in these two fitting models captures the overall trend in the data better than models that consider them constant, but falls short of reproducing full variation, as it minimizes average errors rather than explaining outliers.

The refined ORCHIDEE-CROP configuration (ORC-AE), represents the new model version incorporating the modified αsurf together with the refined soil conductance (β4) and surface roughness (Z0) parameterizations, was used to simulate αsurf dynamics at all 33 site years, which were evaluated independently against observations (Fig. S4a and b in the Supplement). This independent evaluation demonstrates substantially stronger agreement, with significantly higher Pearson correlation coefficients compared to the default model configuration (ORC-D).

ORC-AE captured observed αsurf evolution during the foliar yellowing and residue covering periods across all five sites. Using FR-Gri as an example (Fig. 3), ORC-D without considering their effects on αsurf dynamics shows substantial biases in αsurf during the same period. The results of αsurf at all five sites are presented in Fig. S5 in the Supplement. αsurf increases during spring as crops LAI develops due to the enhanced reflected solar radiation of leaves compared to darker soil followed by a stable period before crops reach maturity. After the crop has reached maturity, the yellowing of aboveground crop biomass increases αsurf. This increase is reversed after harvest as dead and senescent plant material (i.e., residues) starts decomposing. Although the refined model can describe αsurf trends in both foliar yellowing and residue covering periods, substantial biases in αsurf remain for other periods which were not addressed in this study. These discrepancies result from inaccuracies of estimating bare soil albedo (Yu et al., 2026), from the growth of weeds which are not resolved in ORCHIDEE-CROP, and from the simplistic representation of vegetation albedo dynamics before maturity is reached.

Compared with the standard version of ORCHIDEE-CROP (ORC-D), statistically significant changes in averaged surface temperature (Tsurf) are detected across sites in ORC-AE and ORC-E (p < 0.05 in t test), resulting from modifications in surface albedo (αsurf), surface roughness (Z0), and soil conductance (β4) (Table S5 in the Supplement). In contrast, modifying albedo alone (ORC-A) does not lead to statistically significant changes in soil evaporation (Esoil) during the foliar yellowing period (p > 0.05). Soil water content (SWC) generally does not exhibit statistically significant changes (p > 0.05) in any of the three model configurations. This indicates that, except for SWC, site-averaged differences in the targeted variables are detectable and exhibit consistent signs and magnitudes across sites.

ORC-AE leads to a statistically significant reduction in Esoil relative to ORC-D during foliar yellowing period, with an averaged decrease of -0.18 ± 0.08 mmd-1 (Fig. 4a). A slightly smaller but consistent reduction in Esoil (-0.17 ± 0.14 mmd-1) occurs during the residue covering period. Changes in β4 and Z0 (ORC-E) exert significant impacts on Esoil that are comparable in magnitude to those simulated by ORC-AE during both periods, whereas changes in αsurf alone (ORC-A) cause relatively minor and statistically insignificant reductions in Esoil for the same periods. The influence of crop residues on Esoil disappears within approximately 40 d after residue coverage (Figs. S6a and S7a in the Supplement).

The reduced Esoil in ORC-AE leads to a modest but not significant increase in SWC over 2 m soil depth during the residue covering period among all sites (13.30 ± 8.02 kgm-2) (Fig. 4c, Table S5). The combined effect is comparable to the sum of the individual contributions from αsurf (ORC-A) and from β4 and Z0 (ORC-E), suggesting an approximately additive effect. Although statistically insignificant, this weak water conservation signal persists toward the end of year, with gradual infiltration into deeper soil layers (Fig. S8 in the Supplement).

Incorporating changes in αsurf, β4 and Z0 in ORC-AE results in a statistically significant reduction in Tsurf during foliar yellowing and residue covering periods with averaged cooling of -1.55 ± 0.93 and -1.90 ± 0.88 °C over all sites, respectively, relative to ORC-D simulation (Fig. 4b). The factorial simulation experiments show that this cooling effect is mainly driven by changes in Esoil and Z0 (ORC-E), whereas increased αsurf alone produces a weaker and statistically insignificant Tsurf reductions. Interaction among αsurf, β4 and Z0 (ORC-AE) lead to a dampened overall effect compared with the individual effects of these factors on Tsurf. The reduction in Tsurf gradually weakened over approximately 60 d as residue cover decreased (Figs. S6d and S7d).

Relative to the ORC-D simulation, statistically significant changes in latent heat fluxes (LE) induced by modifications in surface albedo (αsurf), surface roughness (Z0), and soil conductance (β4) are detected across sites for ORC-AE, ORC-A, and ORC-E configurations (p < 0.05 in t test) (Table S5). Changes in sensible heat fluxes (H) are also statistically significant for ORC-AE and ORC-A, whereas modifications in Z0 and β4 alone (ORC-E) do not produce statistically significant changes in H (p > 0.05). This suggests that the modified model configurations capture systematic variations in surface water and energy exchanges when averaged across sites.

Modified αsurf, β4 and Z0 in ORC-AE leads to statistically significant reductions in LE during the foliar yellowing and residue covering periods, with the changes of -6.84 ± 1.41 and -5.15 ± 4.03 Wm-2, respectively (Fig. 4e). This response is primarily controlled by changes in β4 and Z0, represented by comparable reductions in LE by ORC-E. While αsurf alone (ORC-A) plays a more limited role, the effect remains statistically significant. The corresponding changes in the H are also statistically significant and of similar magnitude to the changes in LE induced by the combined effect of αsurf, β4 and Z0 during the foliar yellowing and residue covering periods, with averaged decreases of -8.10 ± 4.18 and of -7.44 ± 3.98 Wm-2, compared to the ORC-D simulation (Fig. 4f). In both periods, direct radiative effects associated with increased αsurf (ORC-A) contribute most strongly to the reduction in H, whereas changes in the surface–atmosphere energy exchange (ORC-E) leads only to a slight and statistically insignificant increase in H. Consistent with Tsurf response, the influence of crop residues on H weakens over approximately 60 d of residue cover (Figs. S6c and S7c).

The 10-year simulation under current climate shows a minor short-lived increase in SWC at the soil depth of 0.25 m (relative root depth of winter wheat in ORCHIDEE-CROP model) due to the presence of crop residues. Averaged across the simulation period and the six sites, total SWC is 0.24 ± 0.60 kgm-2 (2.81 ± 6.15 %) higher than in the simulation without crop residues (Fig. 5a). No clear carry-over effect of SWC retention from one growing season to the next is observed at any site. This occurs because increased drainage losses compensated for reduced bare soil evaporation (Fig. S9 in the Supplement). Soil temperature (Tsoil) at 0.25 m soil depth penetrable by winter wheat roots decreased by an average of -0.37 ± 0.77 °C (7.56 ± 5.96 %) over the 60 d of residue covering periods across sites, with no consistent temporal evolution during the 60 d residue covering periods over 10 years (Fig. 5b). The largest reduction in Tsoil occurs shortly after harvest, coinciding with the largest increase in αsurf due to residue presence compared to simulation without residue effects.

In the dry scenario, the meteorological forcing corresponds to a single dry year that is repeatedly applied over the 10-year simulation. This idealized experiment is designed to explore the sustained response of the system under persistent dry conditions, rather than to represent future climatic conditions or interannual climate variability.

Under these conditions, the presence of crop residue shows a similar increase in SWC at 0.25 m soil depth under current climate, with an average increase of 0.23 ± 0.53 kgm-2 (3.01 ± 5.97 %) compared to simulations without residues (Fig. 5a).

Although no systematic temporal evolution is observed in near-surface SWC (at the soil depth of 0.25 m), deeper soil layers (50 % soil depth) exhibit a slight gradual increase of soil moisture over the simulation period (not shown) due to the sustained reduced Esoil which increases downward water transport. The decrease in Esoil by crop residues allows more water to remain in the soil, leaving more residual moisture available for the next growing season.

Average Tsoil at 0.25 m soil depth decreases with a statistically significant trend by an average of -0.42 ± 0.80 °C (-8.42 ± 3.58 %) over years (Fig. 5b), consistent with the sustained effects of residue-induced surface cooling.

Sensitivity analyses reveal pronounced nonlinear responses of land surface water and energy processes to variations in model parameters (Fig. S10 in the Supplement). For Tsurf, k1 exhibits the highest sensitivity, as it governs both the rate and maximum magnitude of the 15 d in surface albedo and consequently modulates available surface radiation during the residue covering period (Fig. S10a). A ±30 % perturbation in k1 induces 8.69 % and 30.78 % enhancements in surface cooling, respectively. This reflects a nonlinear response, as k1 controls not only the rate of albedo increase but also the initial value of the subsequent decay phase (Sect. 2.3.2). Combined with the gradual decline in residue fraction over time, changes in k1 modify the cumulative contribution of residue albedo to surface radiation, leading to enhanced cooling in both cases. In contrast, Tsurf is only weakly affected by the hydrological parameter β4 (-4.46 %/+2.98 % change in surface cooling for ± 30 % variations), consistent with the scenario simulations (Fig. 4b). The parameter Dup also exerts only a limited influence on Tsurf (-8.35 %/+2.53 % change in cooling for ± 30 % variations).

For Esoil, β4 is the dominant control (Fig. S10b). A decrease or increase in β4 from the baseline by 30 % results in a 62.48 % reduction or 100.85 % increase in the decline of Esoil, respectively. The parameters k1, k4, and Dup also influence Esoil, with a ±30 % variation leading to -32.40 %/-35.53 %, -17.72 %/+22.35 %, and -29.49 %/-18.57 % changes, respectively.

For H, Dup exerts the strongest control, followed by k1 and β4 (Fig. S10c). Adjusting these parameters by ± 30 % alters H by -10.53 %/+56.34 %, -42.55 %/+36.77 %, and -27.60 %/+27.33 %, respectively. β4 is also the most sensitive parameter controlling LE, reflecting the dependence of LE on Esoil. ±30 % variations in β4 yield +89.78 %/-62.16 % changes in LE (Fig. S10d). In contrast, the strong sensitivity of Esoil to k1 does not propagate to LE, with only -2.66 %/+8.21 % changes observed under equivalent k1 perturbations (Fig. S10c and d).

Foliar yellowing and residue coverage cool the surface across all sites by regulating the surface energy balance and redistributing net radiation to latent and sensible energy fluxes (Fig. 4a). Compared with the initial expectation that would dominate the impact of αsurf, the larger surface temperature (Tsurf) decrease in ORC-E than that in ORC-A reveals that the cooling from the surface–atmosphere water–heat exchange due to modified soil conductance (β4) and surface roughness (Z0) drives the change in Tsurf. Previous work indicated that no-tillage systems with residue coverage at the European scale cooled the surface by approximately 2 °C during hot summer days due to increased αsurf (Davin et al., 2014). Our simulations at site scale further highlight that the local changes in turbulent processes can exert a comparable control over Tsurf, relying on different spatial scales, climate conditions, phenological stages and surface properties (McDermid et al., 2019).

While foliar yellowing and residue coverage induce an albedo-driven redistribution of surface energy budget across all 10 European sites, the interplay between available surface energy and the surface–atmosphere water–heat exchange causes substantial variation in the magnitude and duration of cooling. The increased Z0 during winter wheat growth and residue covering periods enhances surface–atmosphere turbulent heat exchange, potentially promoting more efficient heat dissipation and surface cooling (Winckler et al., 2019; Meier et al., 2022). For example, when isolating the impact of increased Z0 due to foliar yellowing and residue coverage (ORC-Z0), both contribute more to the cooling impact than ORC-β4 across all sites (Fig. S11a in the Supplement). However, the persistence of this surface cooling also depends on the hydrological fluxes between surface and atmosphere. Crop residues potentially warm the surface by decreasing soil evaporation (Esoil) (Hu et al., 2018). This effect can occur through reductions in β4 caused by shielding the soil from direct solar radiation and wind (Fig. S11a), and by reducing near-surface wind speed and enhancing aerodynamic drag via increasing Z0 (Figs. 4a and S11b). Similar to residue coverage, foliar yellowing has a comparable influence on Esoil due to the continuous coverage provided by winter wheat (Fig. 4a). The associated decline in transpiration, driven by reduced stomatal conductance under lower net radiation, may contribute to surface warming (not shown). Residue-induced suppression of Esoil during the residue covering period and radiation-induced decline in transpiration during foliar yellowing, both redirect energy partitioning from latent heat flux (LE) towards sensible heat flux (H), likely offsetting surface cooling (Fig. 4f) (Ramos et al., 2024).

To isolate the drivers of turbulent flux changes during crop yellowing and residue cover periods, we analyzed results from two key model configurations: the surface–atmosphere interaction experiments (ORC-E) and the radiative forcing experiments (ORC-A). Our simulations revealed that the surface–atmosphere interactions (ORC-E) dominate the reduction in LE during both periods (Figs. 4e and S6b), with this effect jointly mediated by changes in β4 and Z0 (Fig. S11d), while αsurf-driven radiative impact is less important. In contrast, the reduced availability of surface energy with a higher albedo during crop yellowing and the residue cover period, together with the subsequently altered turbulent fluxes, contributed to a modest reduction in H during these periods due to their opposing effects (Figs. 4f and S6c). During this process, the enhanced H is primarily mediated by Z0 (Fig. S11c).

The different underlying drivers behind changes in LE and H can be explained by the following: LE represents the energy needed for water vaporization and is therefore constrained by available energy and moisture transport efficiency (Chen et al., 2024). In contrast, H is driven by direct surface–atmosphere heat transfer via conduction and convection, governed by the land-air temperature gradient and turbulent heat exchange (Myhre et al., 2018). For example, we found that during the residue covering period, changes in β4 and Z0 (ORC-E) tend to increase H, while the albedo-induced reduction in net radiation (ORC-A) acts in the opposite direction by limiting the available energy for sensible heating (Fig. 4f). These results highlight that changes in LE and H are not controlled by a single mechanism but emerge from the combined and counteracting effects of radiative forcing and surface–atmosphere coupling. This finding is consistent with earlier findings pointing to a critical role of surface–atmosphere interactions in shaping energy partitioning and near-surface thermal dynamics (Koster et al., 2004; Seneviratne et al., 2010; Dare-Idowu et al., 2021; Denissen et al., 2022; Hsu and Dirmeyer, 2023).

The individual impacts of αsurf (ORC-A) and surface–atmosphere exchange (ORC-E) on the changes in LE and H differ from their combined effect of both (ORC-AE), especially during the residue covering period (Fig. 4). This indicates a strong nonlinear interaction between energy and water dynamics driven by crop residues (Hsu and Dirmeyer, 2021). For example, the decreased LE reduces H during the residue covering period by weakening near-surface turbulence, thereby producing a residual negative impact on H that could not be explained by ORC-A and ORC-E individually (Fig. 4f, Table S5). The complex feedback mechanisms between radiation partitioning and surface–atmosphere coupling require modelling approaches which take into account both interactions (Hsu and Dirmeyer, 2021, 2022).

It should be noted that in both periods, the averaged difference in surface energy fluxes (i.e. LE and H) between the new and old models across all sites are generally modest (Fig. 4e and f) and are of similar magnitudes to the model uncertainty (Table S5), which masks a substantial spatiotemporal variability in the effects of yellowed leaves and covered residues. Temporally, residue-induced albedo enhancement peaks within the first 15 d after harvest and weakens as residues decompose, causing short-lived but locally significant perturbations in water and heat fluxes (Figs. 3, S5 and S6). The residue impact on the water–heat processes persists for approximately one additional month after residue removal through tillage or natural decomposition (Fig. S7). Spatial variations in climate, soil properties, and management practices amplify local differences in residue effects. For example, we found that during the residue covering period, BE-Lon exhibits the largest mean relative decrease in LE in the new model compared to the old model (-27.42 ± 26.20 %), whereas the change at DE-Geb is minimal (5.73 ± 64.90 %). This contrast arises because the higher soil moisture and sandy texture at BE-Lon support greater evaporative fluxes, whereas the limited soil water and clay-rich soil at DE-Geb favor energy dissipation as sensible rather than latent heat (Dumont et al., 2024; Buysse et al., 2023). In addition, long-term simulations show the accumulated residue impact on heat budgets. The 10-year simulation under the dry scenario shows a low but statistically significant cooling trend, with a yearly average of -0.3 °C (Fig. 5). This demonstrates that even modest instantaneous flux changes can produce meaningful long-term surface cooling effects.

With approximately 60 % of global croplands having higher Tsurf than surrounding natural biome types, elevated temperatures, particularly during extreme heat and drought events, pose a major issue for crop yields and food security (Lobell et al., 2012; Hatfield and Prueger, 2015; Lesk et al., 2022; Chen et al., 2024). Our simulations across all sites suggest that residue coverage alleviates heat stress by significantly decreasing maximum surface temperature (Tmax⁡) during approximately 30 d residue covering period and for up to 50 d afterward (Figs. S6e and S7e). This result aligns with previous findings on the cooling effect of crop residues (Davin et al., 2014; Webber et al., 2018; Su et al., 2021). Reduced surface Tmax⁡ after crop harvest mitigates heat stress on soil biota, providing a more favorable growing environment for subsequent crop cycles (Hatfield et al., 2015). Sustained temperatures above 30 °C can stress mesophilic soil microbes, inhibiting decomposition and nutrient cycling (Pietikäinen et al., 2005). Our simulations show that residue cover reduces Tmax⁡ beyond 30 °C on 26 % of days during the residue period and 3.8 % of days afterward, underscoring its role in regulating soil thermal condition. This buffering effect modifies microbial activity and soil organic matter decomposition rates depending on local climatic and environmental conditions, thereby influencing greenhouse gas emissions from soil respiration (Knight et al., 2024).

The increase in H during residue covering period reveals an increased surface-air temperature gradient caused by reduced Tsurf, possibly leading to warming of the lower atmosphere which cannot be quantified by the land surface model only (Figs. 4, S6c, and S7c). This shift in energy partitioning from LE to H, driven by reduced Esoil due to residue coverage, might mitigate surface warming while potentially intensifying regional heat stress. Consequently, it introduces uncertainty regarding the effectiveness of crop residues as a climate mitigation strategy. Notably, the use of fixed air temperature forcing data partially explains the increase in H because the constant air temperature ignores atmospheric feedback (Luyssaert et al., 2014). As a result, the climate mitigation potential of residue management remains uncertain, and its regional impact on energy partitioning cannot be fully quantified. It is essential to consider not only surface cooling but also changes in air temperature, as the latter is more directly linked to climate dynamics. A coupled simulation, feasible with ORCHIDEE-CROP, would be needed for future studies.

Soil water content (SWC) dynamics play a key role in regulating the land–atmosphere interactions, as the increase in surface soil SWC, as due to residue effects on roughness and soil resistance, can decrease H partitioning via promoting Esoil (Myhre et al., 2018). The 10-year simulations further highlight that residue coverage has the potential to increase SWC and thereby mitigate drought stress, particularly under drier conditions with fewer rainfall events (Figs. 5 and S9). In such condition, SWC remains high within the upper 1 m soil for extended periods of up to 3 months. An earlier review suggested that dry soil condition might result in soil compaction and reduced hydraulic conductivity (Singh et al., 2018). This limits infiltration and slow downward water percolation, which might hinder the underlying mechanism responsible for upper-soil SWC retention simulated by the ORCHIDEE-CROP model. Notably, soil texture modulates this effect. For example, rapid water infiltration into deeper layers in sandy soils with high drainage reduces surface SWC availability for cooling and plant uptake (Fatichi et al., 2020; Wankmüller et al., 2024), highlighting how soil hydraulics constrain residue efficacy. The sustained SWC also fosters soil biota activity (e.g., fungi, earthworms), which enhance soil structure through aggregate formation and organic matter decomposition (Li et al., 2009). Improved soil structure increases water-holding capacity, creating a positive feedback loop where residue-induced SWC retention supports biological activity, which in turn amplifies long-term moisture retention. This synergy reveals that residue effects on SWC persist beyond immediate rainfall events, particularly in water-limited systems.

External factors such as tillage timing and precipitation frequency also affect the biophysical impacts of crop residues (Konapala et al., 2020). Early tillage, as observed at the FR-Gri site in 2018 (3 weeks after harvest), retains more SWC in the topsoil and removes the residue cover, thereby temporarily enhancing Esoil and LE after the end of residue covering (Fig. S12 in the Supplement). The strong soil water retention capacity of the silt–loam soil at this site further contributed to this effect (Houot et al., 2000). This process underscores the importance of management practices in regulating the diverse hydrological and thermal effects of residue covering. Targeted strategies are also essential for optimising water use, particularly facing warmer and drier climates (Swain et al., 2025).

Despite improvements to the ORCHIDEE-CROP model in simulating crop albedo changes associated with foliar yellowing and crop residues, as well as their biophysical climate impacts, uncertainties and limitations remain.

αsurf shows the most consistent improvement across sites, reflecting its direct control by the new parameterizations for foliar yellowing and residue covering (Figs. 2 and S4a, b). However, degraded performance is observed at two sites (DE-Geb in 2014 and FR-Aur in 2019; Fig. S5a and c). At these sites, the refined configuration simulates higher αsurf during foliar yellowing and residue covering than those estimated from site radiometer measurements. Surface conditions shown by the site photos suggest that the discrepancy may be related to limitations in the spatial representativeness of point-scale observations, which is further investigated in Sect. S1.2 in the Supplement. Tsurf also improves significantly, especially during the foliar yellowing period (Fig. S4c and d). In contrast, LE and H exhibit moderate improvement during the residue covering period and little to no improvement during the crop growth and foliar yellowing periods (Fig. S4e–g). This performance is expected, as LE and H during the growing season are primarily governed by transpiration processes that are not recalibrated in this study. During the residue covering period, remaining biases in LE and H likely arise from unrepresented processes associated with residue cover, such as water interception and retention by residues and soil hydraulic heterogeneity. Limitation in the representation of land–atmosphere coupling in ORCHIDEE also contributes to persistent uncertainties in simulated turbulent fluxes (Sect. 4.3).

The ORCHIDEE-CROP model exhibits biases in simulating water and energy fluxes, and the performance of the new model in reproducing water–energy fluxes does not improve relative to the previous version when evaluated against site-level measurements at six sites (Fig. S4). Both the new and old models overestimate LE and H during foliar yellowing periods, while underestimating LE and overestimating H during residue covering periods. This overestimation during foliar yellowing periods might come from excessively strong vegetation–atmosphere coupling for crops within ORCHIDEE itself compared with observation (Zhang et al., 2022c). Observations indicate that LE is higher during periods with residue coverage than during periods of bare soil exposure (Fig. S13 in the Supplement). However, it remains unclear to what extent the observed difference in LE is driven by differences in weather conditions rather than by the presence or absence of residues. One possible source of bias during the residue covering period may be the simplification of model parameters. In the present version of ORCHIDEE-CROP, the effects of crop residues on surface–atmosphere coupling are quantified by modulating β4 and Z0. Both variables are represented using uniform assumptions to balance model complexity against the scarcity of data available for parameterization. Specifically, the impact of residues on β4 was represented using an initial reduction factor of 0.5 at the beginning of the residue covering (Sect. 2.3.3). The residue influence on Z0 was prescribed using a fixed residue height (0.5 m), derived from the average of measurements across five winter-wheat sites (Sect. 2.3.4, Table S3). These parameter choices are applied uniformly across all sites and therefore cannot explicitly represent local variation in residue characteristics, soil properties, atmospheric conditions and management practices. As a result, the model is incapable of fully resolving site-specific residue impacts, which potentially contributes to the bias of simulated LE and H at certain sites.

The sensitivity analysis also highlights the uncertainties associated with parameter selection (Fig. S10). The β4 and k1 exert strong control over the spatiotemporal partitioning of available energy between LE and H. The use of fixed parameter values and limited calibration based on only a few sites inevitably contributes to model uncertainty and constrains the representation of local variability.

We manually adjusted the total conductance associated with soil evaporation, transpiration and interception in ORCHIDEE-CROP by scaling it with 1+fsoil during residue cover periods (Chapin et al., 2011). The simulations showed improved agreement with observed Tsurf, LE and H (Fig. S14 in the Supplement), indicating that the net biophysical impact of residue cover enhanced surface–atmosphere water and heat exchanges despite inhibiting soil evaporation, which is not well described in the current model. A test of quantifying the residue impact on Esoil at 15 field experiments distributed globally showed that residue cover exceeding 80 % resulted in an approximately 20 %–30 % reduction in Esoil during the residue covering period (not shown). Therefore, the assumed 50 % initial decline in β4 in the improved model (Eq. 16) might overestimate residue impacts, potentially explaining the lower Esoil and LE simulated across sites in the new model compared with both the initial model version and observations (Fig. S4).

While our modelling shows minor effects of crop residues on SWC, we did not account for effects such as changes in soil texture or organic matter content (Singh et al., 2018), which could improve soil water availability to crops. In addition, we did not account for interactions with other managements such as tillage, which are often co-applied. In summary, the present simulations are particularly well suited for assessing the biophysical impacts of residue cover on the surface energy balance, while the representation of soil–water responses remains an area requiring further model development and the corresponding results should therefore be considered with caution.

For example, specific hydrological processes impacted by crop residues were not accounted for in this study, such as water interception on the surface of residues, and uptake or release of water by residues (Kozak et al., 2007, Swella et al., 2015). Rainfall interception by residues alters both the timing and magnitude of soil–water inputs and enhances evaporation from residue surfaces, thereby modifying surface moisture conditions and heat exchange processes (Mitchell et al., 2012; Thapa et al., 2021). However, contrasting evaporation mechanisms operating in soil and residue layers introduce additional complexity into surface evaporation processes. The omission of these processes likely increases model uncertainty in biophysical simulations under variable rainfall conditions.

The input data used to drive the model introduces several structural uncertainties. First, while site-based meteorological forcing (e.g., air temperature, wind speed, humidity) provides high representativeness, meteorological observations and direct radiation measurements were unavailable at two ClieNFarm sites comprising six farms (UK-Rookery, UK-Winwick, UK-Allerton, BE-AH, BE-BM, and BE-LB; Table S2). For these sites, simulations were instead driven by the hourly 0.25° ERA-interim product and surface albedo was derived from Sentinel-2 observations. This substitution inevitably limits the ability of our model to reproduce site-specific water and energy dynamics. A comparison between satellite-derived albedo at 300 m spatial resolution and albedo calculated from shortwave radiation measurements representing only a few m2 at eight sites reveals systematic differences (Fig. S15 in the Supplement). These differences are likely attributable to spatial scale mismatch, cloud screening effects, and surface heterogeneity, which together dampen albedo variability in the satellite product. While satellite-based albedo enables the inclusion of these six field sites, the substituted values introduce additional uncertainty in the simulated surface energy fluxes. Second, the bare soil albedo dataset used to estimate surface albedo carries uncertainties related to quality-restricted training samples (Yu et al., 2026). For example, the vegetation index thresholding used to identify bare soil may also include mixed surfaces containing crop residues, introducing biases into soil albedo retrievals. Also, the spatial aggregation from 300 m to 0.5° smoothes the field variability of bare soil albedo, reducing the reliability of the model simulation. Third, the incomplete representation of management practices in our model, including fertilizer and pesticide application as well as physical operations such as tillage, limits the model's capacity to reproduce observed variations in crop phenology, soil water content, and surface fluxes across years.

Based on these concerns, evaluating residue effects requires a context-specific approach that accounts for the interactions among climate, soil, and crop characteristics. Process-based land surface models provide an effective framework for such assessments across multiple scales, as they explicitly represent the coupled biophysical and ecological mechanisms controlling surface energy and water exchanges. This approach provides opportunities to guide residue management strategies under diverse environmental settings. Future model developments should incorporate explicit residue modules and site-specific parameterization to better capture spatial heterogeneity in residue impacts on energy and water fluxes. For example, integrating the canopy interception modules developed by crop models to ORCHIDEE-CROP is a good strategy to better represent the residue impact on the hydrological dynamics, such as CropSyst and RZWQM (Kozak et al., 2007). Moreover, an open-sourced global database derived from dedicated field trials monitoring energy exchange is required for parametrizing and evaluating these model developments.