Abstract. Water use efficiency (WUE), a key ecohydrological indicator linking carbon assimilation and vegetation water loss, is critical for understanding ecosystem responses under changing hydro-climatic conditions. Process-based land surface models (LSMs) are widely used to represent carbon-water interactions; however, their ability to simulate ecosystem-scale WUE across contrasting climates remains limited. This study evaluates the performance of the Interactions between Soil-Biosphere-Atmosphere model with A-gs photosynthesis scheme (ISBA-A-gs) implemented within the SURFEX land surface modelling platform in simulating gross primary productivity (GPP), evapotranspiration (ET), and WUE (GPP/ET) for maize grown under temperate (France, FR-Lam) and tropical semi-arid (India, Ind-IITH) climates. The model was driven by site-specific meteorological and vegetation variables across six growing seasons under sprinkler irrigation at FR-Lam, and two seasons (monsoon and winter) under alternate furrow irrigation (AFI) at Ind-IITH. Model calibration revealed that FR-Lam is characterized by relatively higher cuticular conductance and pronounced atmospheric control on stomatal behaviour, whereas at Ind-IITH, AFI-induced adjustments in mesophyll conductance and soil moisture stress thresholds. At FR-Lam, ISBA-A-gs simulated the seasonal mean cumulative GPP, ET, and WUE of 1039 ± 20 gC m-2, 610 ± 31 kg H2O m-2, and 1.70 ± 0.10 gC kg-1 H2O, respectively, as compared to measured values of 1026 ± 30 gC m-2, 562 ± 42 kg H2O m-2, and 1.82 ± 0.11 gC kg-1 H2O correspondingly. At Ind-IITH, the model simulated the seasonal mean cumulative GPP, ET, and WUE of 766 ± 15 gC m-2, 567 ± 30 kg H2O m-2, and 1.35 ± 0.11 gC kg-1 H2O, respectively, as compared to measured values of 793 ± 11 gC m-2, 522 ± 20 kg H2O m-2, and 1.51 ± 0.12 gC kg-1 H2O correspondingly. Further, the diagnostic analysis using the GPP·VPD0.5-ET relationship revealed that ISBA-A-gs realistically captures the coupling between carbon assimilation and transpiration-driven water loss. Overall, ISBA-A-gs demonstrates strong capability in simulating carbon and water fluxes of maize, particularly in representing WUE dynamics under contrasting climate regimes.

Partitioning evapotranspiration (ET) into soil evaporation (E) and plant transpiration (T) remains challenging but is essential for understanding the dynamics of crop water use, especially in water-limited systems where plants are sensitive to soil water content dynamics. In this study, in order to evaluate available ET partitioning methods, various independent direct and indirect methods for quantifying E and T, including microlysimeters, isotopy, and sap flow were collected in a rainfed sunflower field. Data analysis was supplemented by modeling using the land surface model SiSPAT (Simple Soil Plant Atmospheric Transfer) for two-time windows, an intensive observation period in the summer and the entire growing season. Partitioning methods based on high frequency sampling of CO2 - H2O concentrations (FVS, MREA, CEC) showed consistent results during periods of dense canopy cover but tended to overestimate transpiration during bare soil and early growth stages. Water-use efficiency-based approaches (uWUE16, WUE21) and stomatal conductance models (CEP, Priego18) were used to link gross primary productivity (GPP) to transpiration. These methods captured physiological controls on transpiration at the seasonal scale but differed in magnitude and sensitivity to key input parameters. SiSPAT simulations represented both dry and wet periods, reflecting variations in soil moisture and atmospheric demand. During the intensive observation period, SiSPAT transpiration agreed well with sap flow measurements, while uWUE16 and CEP produced T/ET values similar to SiSPAT for both periods. Although only SiSPAT explicitly used fractional vegetation cover (Fc) as an input, T/ET estimates from all methods aligned well with the 1:1 line in T/ET vs Fc space. Deviations above or below this line were consistently explained by dry soil surface or dry root-zone conditions, respectively. Overall, these results demonstrate that combining independent observations with complementary partitioning frameworks provides a robust and physically consistent interpretation of ET partitioning across contrasting soil moisture and canopy development conditions.

Accurate estimation of Gross Primary Productivity (GPP) for European winter wheat is critical for assessing regional food security and understanding land-atmosphere carbon exchange. Light Use Efficiency (LUE) models are widely applied in natural ecosystems, but their performance in dynamic agricultural landscapes, particularly for key crops like winter wheat, remains underexplored. To bridge this gap, we developed IB-WSE-LUE (INRAE-BORDEAUX-water stress enhanced-light use efficiency), a novel GPP model specifically tailored for winter wheat. This model leverages high-resolution Sentinel-2 satellite data and comprehensively integrates key environmental stress factors, enabling GPP simulation at an unprecedented 10-meter spatial resolution. We compared IB-WSE-LUE against thirteen established GPP models, using both tower-based meteorological data and the ERA5 reanalysis dataset (the latter ensuring broader applicability across large scales without reliance on extensive in-situ measurements). Validation demonstrated IB-WSE-LUE's superior performance, achieving average R2 improvements of 11.9% (with tower data) and 8.8% (with ERA5 dataset) in daily GPP simulations for European winter wheat. Furthermore, IB-WSE-LUE more accurately captured spatial, seasonal, and interannual GPP variations and significantly reduced the common underestimation at high GPP levels observed in other models. Its robust performance extended to drought and high-temperature conditions, demonstrating that water stress exerts a stronger influence on winter wheat GPP than temperature stress, a feature accurately captured by our model. This study provides a robust, high-resolution, and spatially transferable framework for accurately monitoring and predicting winter wheat GPP across large agricultural regions, offering key insights for food security assessments and improved agricultural land management in a changing climate.

Management practices that increase the surface albedo of cultivated land could mitigate climate change, with similar effectiveness to practices that reduce greenhouse gas emissions or favor natural CO 2 sequestration. Yet, the efficiency of such practices is barely quantified. In this study, we quantified the impacts of seven different management practices on the surface albedo of winter wheat fields (nitrogen fertilizer, herbicide, fungicide, sowing, harvest, tillage, and crop residues) by analyzing observed daily albedo dynamics from eight European flux-tower sites with interpretable machine learning. We found that management practices have significant influences on surface albedo dynamics compared with climate and soil conditions. The nitrogen fertilizer application has the largest effect among the seven practices as it increases surface albedo by 0.015 ± 0.004 during the first two months after application, corresponding to a radiative forcing of −4.39 ± 1.22 W m −2 . Herbicide induces a modest albedo decrease of 0.005 ± 0.002 over 150 d after application by killing weeds in the fallow period only, resulting in a magnitude of radiative forcing of 1.33 ± 1.06 W m −2 which is higher than radiative forcing of other practices in the same period. The substantial temporal evolution of the albedo impacts of management practices increases uncertainties in the estimated albedo-mediated climate impacts of management practices. Although these albedo effects are smaller than published estimates of the greenhouse gas-mediated biogeochemical practices, they are nevertheless significant and should thus be accounted for in climate impact assessments.

In Europe, the heterogeneous features of crop systems with majority of small to medium sized agricultural holdings, and diversity of crop rotations, require high-resolution information to estimate cropland Net Ecosystem Exchange (NEE) and its two main components of Gross Ecosystem Exchange (GEE) and the Ecosystem Respiration (RECO). In this context, this paper presents an assimilation of high-resolution Sentinel-2 indices with eddy covariance measurements at selected European cropland flux sites in a new modified version of Vegetation Photosynthesis Respiration Model (VPRM). VRPM is a data-driven model simulating CO2 fluxes previously applied using satellite-derived vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study proposes a modification of the VPRM by including an explicit soil moisture stress function to the GEE and changing the equation of RECO. It also compares the model results driven by S2 indices instead of MODIS. The parameters of the VPRM model are calibrated using eddy-covariance data. All possible parameters optimization scenarios include the use of the initial version vs. the proposed modified VPRM, S2, or MODIS vegetation indices, and finally the choice of calibrating a single set of parameters against observations from all crop types, a set of parameters per crop type, or one set of parameters per site. Then, we focus the analysis on the improvement of the model with distinct parameters for different crop types vs. parameters optimized without distinction of crop types. Our main findings are: (1) the superiority of S2 vegetation indices over MODIS for cropland CO2 fluxes simulations, leading to a root mean squared error (RMSE) for NEE of less than 3.5 μmolm-2s-1 with S2 compared to 5 μmolm-2s-1 with MODIS (2) better performances of the modified VPRM version leading to a significant improvement of RECO, and (3) better performances when the parameters are optimized per crop-type instead of for all crop types lumped together, with lower RMSE and Akaike information criterion (AIC), despite a larger number of parameters. Associated with the availability of crop-type land cover maps, the use of S2 data and crop-type modified VPRM parameterization presented in this study, provide a step forward for upscaling cropland carbon fluxes at European scale.

Recent studies highlighted that the modelling of surface fluxes of momentum, sensible and latent heat is the second most important issue in the weather and climate numerical models. Eddy-covariance measurements are used to implement and validate those models, but one systematic error is identified and makes the comparison between models and observations tricky : the Energy Balance Non-Closure (EBNC). A one-year field experiment is performed in Toulouse, south-west of France, to document the variability of the land-atmosphere exchanges around the Météopole permanent site. Three indicators that aim to quantify the surface and fluxes heterogeneity are defined and applied to several domain sizes (footprint scale, 1-km-scale and 10-km-scale analysis). They are cross-analysed with the EBNC to investigate the impact of the surface heterogeneity on the non-closure. The results showed that while no systematic nor strong relationship was found, the footprint-scale analysis and the sensible heat flux studies at a 1-km and 10-km-scale seem to have a tendency to match with the variations of the EBNC. By extending the studied domain from 1 km to 10 km, we incorporate a greater heterogeneity in latent heat flux than in sensible heat flux. The impact of the organisation of the surface was studied through an indicator based on the length scale of the surface patches. The results did not suggest fundamentally different outcomes but they did highlight the differences between the 1-km and the 10-km scales, and between the sensible and latent heat flux analysis.

The meterological phenomena draw their energy from the Earth’s surface and dissipate most of their energy close to the surface. The land surface, through its topography, soil moisture, temperature or vegetation activity, impacts the atmosphere from daily to seasonal time scale. The Working Group on Numerical Experimentation survey on systematic errors established that the outstanding errors in the modelling of surface fluxes of momentum and sensible and latent heat is the second most important issue. Therefore to reduce these biases, an accurate assessment of the Land-Atmosphere (L-A) exchanges, and their correct representation, are essential for weather and climate forecasts. The evaluation of L-A exchanges in numerical weather and climate prediction models in the context of heterogeneous surfaces is the main objective of the Models and Observations for Surface-Atmosphere Interactions (MOSAI) project. The main objective of our study corresponds to the first scientific objective of MOSAI : to investigate and determine the uncertainty and representativeness of L-A exchanges measured over heterogeneous landscapes by reference towers. To achieve these objectives, dedicated field experiments are needed to document the variability of the L-A exchanges within a grid mesh around the ACTRIS sites. A set of a one year-field campaign per site are currently performed at Météopole (Toulouse, 2021), at SIRTA (Palaiseau, 2022) and at P2OA (Lannemezan, 2023). Two long-term objectives are defined with the aim of completing the measured surface flux at the ACTRIS sites. The first objective concerns the horizontal representativeness of the local measured surface flux in the heterogeneous landscape at the scale of the ESM or NWP grid. The second one is here to quantify the surface flux uncertainties (random and systematic errors) and provide information on the SEB non-closure at each site. Results concerning the second objective will be presented using the Météopole campaign during which six sites with different surface types were implemented to estimate the Surface Energy Balance (SEB). The arrangements of the different surface patchesis studied to quantify the heterogeneity of our landscape (patches size distribution,...). With that, several indicators that aim to quantify the surface flux heterogeneities are investigated and applied to several domain sizes (from footprint to larger domains). These indicators are cross-analysed with the SEB non-closure to investigate a possible impact of surface heterogeneity and secondary circulations on SEB non-closure.

Soil water content (SWC) sensors are widely used for scientific studies or for the management of agricultural practices. The most common sensing techniques provide an estimate of volumetric soil water content based on sensing of dielectric permittivity. These techniques include frequency domain reflectometry (FDR), time domain reflectometry (TDR), capacitance and even remote-sensing techniques such as ground-penetrating radar (GPR) and microwave-based techniques. Here, we will focus on frequency domain reflectometry (FDR) sensors and more specifically on the questioning of their factory calibration, which does not take into account soil-specific features and therefore possibly leads to inconsistent SWC estimates. We conducted the present study in the southwest of France on two plots that are part of the ICOS ERIC network (Integrated Carbon Observation System, European Research and Infrastructure Consortium), FR-Lam and FR-Aur. We propose a simple protocol for soil-specific calibration, particularly suitable for clayey soil, to improve the accuracy of SWC determination when using commercial FDR sensors. We compared the sensing accuracy after soil-specific calibration versus factory calibration. Our results stress the necessity of performing a thorough soil-specific calibration for very clayey soils. Hence, locally, we found that factory calibration results in a strong overestimation of the actual soil water content. Indeed, we report relative errors as large as +115 % with a factory-calibrated sensor based on the real part of dielectric permittivity and up to + 245 % with a factory-calibrated sensor based on the modulus of dielectric permittivity.

Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002-2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983-2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

Crop phenology data offer crucial information for crop yield estimation, agricultural management, and assessment of agroecosystems. Such information becomes more important in the context of increasing year-to-year climatic variability. The dataset provides in-situ crop phenology data (first leaves emergence/phenophase, BBCH10-BBCH13, date, and Harvest date) of major European crops (wheat, corn, sunflower, rapeseed) from seventeen field study sites in Bulgaria and two in France. Additional information such as the area of each site, coordinates, the sowing date, method and equipment used for phenophase data estimation, and photos of the France sites are also provided. The georeferenced ground-truth dataset provides a solid base for better understanding and therefore improves the prediction capability of crop growth using remote sensing.

Crop phenology data offer crucial information for crop yield estimation, agricultural management, and assessment of agroecosystems. Such information becomes more important in the context of increasing year-to-year climatic variability. The dataset provides in-situ crop phenology data (first leaves emergence and harvest date) of major European crops (wheat, corn, sunflower, rapeseed) from seventeen field study sites in Bulgaria and two in France. Additional information such as the sowing date, area of each site, coordinates, method and equipment used for phenophase data estimation, and photos of the France sites are also provided. The georeferenced ground-truth dataset provides a solid base for a better understanding of crop growth and can be used to validate the retrieval of phenological stages from remote sensing data.

Nitrous oxide (N2O) emissions were measured and compared on 2 typical crop rotations of a grain farm and a dairy farm with feed cropping, over 5 years (from 2012 to 2016) in southwestern France. The annual N2O emissions of the 5 typical rotational crops of the region (summer crops: irrigated maize and sunflower; winter crops: winter wheat, rapeseed and barley) varied from 0.95 +/- 0.88 to 7.96 +/- 1.73 kgN ha-1, with the highest values observed on the dairy farm plot and for summer crops. N2O emissions were analysed on a daily, monthly, seasonal and annual basis, and correlated with their main direct or indirect drivers, i. e. water and nitrogen (mineral or organic) supply amount, rotational crops, vegetation covering and tillage. We observed a marked seasonal pattern of N2O emission peaks. On average, more than 50% of N2O emissions occurred during spring for summer crops, and more than 40% occurred in winter for winter crops. We have identified agricultural practices that increase N2O emissions. In particular, our results show that when the soil is left bare or with limited crop development, spring mineralization of organic N residues (from previous crop or winter cover crop) results in N losses, partly as emissions of N2O, which are detrimental to agronomic performance (low NUE).We also conducted an agronomic assessment of annual N2O emissions versus nitrogen surplus and nitrogen use efficiency (NUE), which lead us to discuss agricultural practices that may mitigate N2O emissions while optimizing agronomic and economic performance of crops. Indeed, we point out that N surplus and N fate may be controlled through the right timing of sowing, cover crop, irrigation and fertilization.

The Global Energy and Water cycle Exchanges and World Climate Research Program have pointed out the importance of the land-atmosphere (L-A) coupling for weather and climate models. The Working Group on Numerical Experimentation survey on systematic errors established that the outstanding errors in the modelling of surface fluxes of momentum and sensible and latent heat is the second most important issue. Earth System Models (ESM) and Numerical Weather Prediction (NWP) systems often have large biases in their representation of surface-atmosphere fluxes when compared to observations. The detailed quantification and reduction of these biases are still on-going efforts in many modelling centres. The Models and Observations for Surface-Atmosphere Interactions (MOSAI) project aims at contributing to this effort. The first step to achieve this objective is to conduct a fair and correct evaluation of the L-A interactions simulated by ESM and NWP models. This is based on (1) reliable references against which the simulated L-A exchanges can be evaluated, and (2) relevant comparison methods able to point out the ESM and NWP system weaknesses. These points define the two first scientific objectives of MOSAI project. The first scientific objective is to investigate and determine the uncertainty and representativeness of L-A exchanges measured over heterogeneous landscapes. Three one-year campaigns are planned to document this heterogeneity on three of the ACTRIS instrumented sites in France. The objective is to make these permanent fluxes measurements well documented in terms of uncertainty, surface energy imbalance and surface heterogeneity representativeness at the scale of the model grid-mesh. The second scientific objective is to propose and test two methods to evaluate the L-A exchanges in ESM using long-term measurements. The first approach is based on sensitivity studies performed with 3D models or with their corresponding single-column version, either forced by data from the MOSAI one-year campaigns or coupled with their LSM, and for which an atmospheric forcing will be derived from operational analyses. The second approach relies on Artificial Intelligence methods (Neural Network or Random Forest) to test the dependency of the surface fluxes to several meteorological variables, at the same time for observation and models. These two methods will allow identifying specific weaknesses of each model at different spatial and time scales. The second step of the project concerns the improvement of the L-A exchanges simulated by ESM and NWP systems. The coupling between land surface models (LSM) and atmospheric models is based on several simplifications which are different when considering Large-Eddy Simulation (LES), weather or climate models. The third scientific objective of MOSAI project addresses some of these underlying simplifications in the coupling between LSM and atmospheric models, and their impacts on the simulated L-A exchanges. After determining the importance of a realistic heterogeneous landscape versus percentages of unified landscape to correctly simulate the surface flux in ESM and NWP, differential treatment of the boundary-layer parameterizations will be developed, so that the atmosphere model can describe as many sub-columns as the number of land-surface patches to explicitly represent the L-A coupling.

A precise estimation of the surface energy budget is a challenge to better understand, model and forecast both weather and climate, and their dependence on external constraints. However, in the micrometeorology community, it is already well known that the energy balance closure is an issue over many surfaces (Foken, 2008). Previous studies often focused on turbulent fluxes, effects of advection or time averaging for the flux calculation. This study is dedicated to evaluating the accuracy of the ground heat flux, thanks to a field experiment carried out on the ICOS site of Lamasquère (FR-Lam). Both the soil heat flux and storage are often difficult to precisely measure because thermal conductivity and heat capacity are strongly dependent on the very local soil texture (% clay, % sand, % organic matter) and water content. The experimental campaign is conducted over a wheat field from autumn 2021 to summer 2022, and heat pulse sensors are installed in various pits to measure thermal parameters (conductivity and heat capacity) at different depths within the ploughing crust. This device currently complements the operational measurements of temperature and soil water content profiles (sensors at 5, 10, 30, 50 and 100 cm), meteorological measurements and turbulent fluxes from an Eddy Covariance set-up. In a first step, we will present the results of the sensor measurements for the soil thermal parameters, their local dynamics in relation to the soil parameters, the vegetation development and the weather. Then, using these measurements and the temperature profiles in the soil, we will evaluate the accuracy of the ground heat flux in order to estimate its impact on the closure of the energy balance of vegetated surfaces. These results will be also compared to measurements from self-calibrated heat flux plate and the heat storage in the soil layer above the plate.

Agriculture represents 14% of global anthropogenic greenhous gases (GHG) emissions, 46% of this amount being due to N2O emissions from soils (UNEP, 2012). N2O is a powerful GHG (IPCC, 2013) and its emissions from agricultural soils are related to physical-chemical parameters which depend on climate (temperature, rain…), soil properties (Robertson et al., 1989) and farming practices (irrigation, tillage, fertilization…) (Tellez-Rio et al., 2015). The IPCC Tier 1 emission factor remains widely used to estimate annual N2O budgets from agricultural soils by taking into account the annual amount of N input only. However, not taking into account the environmental controlling factors may introduce high uncertainty in N2O budget estimation. Our study aims at highlighting the key drivers of N2O emissions from two agricultural sites in the South West of France and at proposing an improved, simple and accessible methodology to estimate N2O budget at crop plot and seasonal scale. For this purpose, we benefited from a unique long time series of daily N2O fluxes (from 2011 to 2016) measured with 6 closed automated chambers on two ICOS sites with contrasted agricultural management (FR-Lam and FR-Aur). N2O annual budget vary from 1.04 to 7.96 kgN ha-1 yr-1 for winter wheat and maize crop, respectively. The effects of fertilization, rain and irrigation, plant development, spring mineralization and deep tillage on N2O emissions were investigated. Significant correlations between rain combined with fertilization and plant development, deep tillage or spring mineralisation was found with R² of 0.91, 0.99 and 0.85, respectively. We took advantage of these results to develop an empirical model, including N input quantity, residual N, leaf area index and water input in order to estimate seasonal and annual N2O budget. At the seasonal scale, the model output matched well with the observed budget, with a R² and a RMSE of 0.87 and 0.33 kgN ha-1 at FR-Lam and of 0.92 and 0.12 kgN ha-1 at FR-Aur, respectively. It also gave good statistical scores at the crop year scale with a R² of 0.96 and a low RMSE of 0.43 kgN ha-1 when binding data from both sites. Using the IPCC Tiers 1 methodology gave lower and more scattered results with a R² of 0.46 and a RMSE of 1.46 kgN ha-1. For sites where N2O fluxes are not monitored, that new methodology may be an alternative and a more precise methodology than the IPCC Tiers 1 approach. It has also the advantage to require only few and accessible input variables. REFERENCES IPCC, 2013. Climate Change 2013: The Physical Science Basis. Cambridge University Press, Cambridge. Robertson et al., 1989. Aerobic denitrification in various heterotrophic nitrifiers. Antonie van Leeuwenhock., 56, 289-299. Tellez-Rio et al., 2015. N2O and CH4 Emissions from a Fallow–wheat Rotation with Low N Input in Conservation and Conventional Tillage under a Mediterranean Agroecosystem. Sci. Total Environ., 508, 85–94. UNEP, 2012. Growing greenhouse gas emissions due to meat production.

The primary objective of this study is to evaluate the representation of the energy budget for irrigated maize crops in soil–vegetation–atmosphere transfer (SVAT) models. To this end, a comparison between the original version of the interactions between the soil–biosphere–atmosphere (ISBA) model based on a single-surface energy balance and the new ISBA-multi-energy balance (ISBA-MEB) option was carried out. The second objective is to analyze the intra- and inter-seasonal variability of the crop water budget by implementing ISBA and ISBA-MEB over six irrigated maize seasons between 2008 and 2019 in Lamasquère, southwest France. Seasonal dynamics of the convective fluxes were properly reproduced by both models with R2 ranging between 0.66 and 0.80 (RMSE less than 59 W m−2) for the sensible heat flux and between 0.77 and 0.88 (RMSE less than 59 W m−2) for the latent heat flux. Statistical metrics also showed that over the six crop seasons, for the turbulent fluxes, ISBA-MEB was consistently in better agreement with the in situ measurements with RMSE 8–30% lower than ISBA, particularly when the canopy was heterogeneous. The ability of both models to partition the evapotranspiration (ET) term between soil evaporation and plant transpiration was also acceptable as transpiration predictions compared very well with the available sap flow measurements during the summer of 2015; (ISBA-MEB had slightly better statistics than ISBA with R2 of 0.91 and a RMSE value of 0.07 mm h−1). Finally, the results from the analysis of the inter-annual variability of the crop water budget can be summarized as follows: (1) The partitioning of the ET revealed a strong year-to-year variability with transpiration ranging between 40% and 67% of total ET, while soil evaporation was dominant in 2008 and 2010 due to the late and poor canopy development; (2) drainage losses are close to null because of an impervious layer at 60 cm depth; and (3) this very specific condition limited the inter-annual variability of irrigation scheduling as crops can always extract water that is stored in the root zone.

In the micrometeorology community, it is well known that the turbulent fluxes measured with eddy covariance (EC) systems do not usually equal the available energy. Hence, qualitative knowledge of the impact of different vegetation types, and climatic variables on this 'nonclosure' is essential. This study analyzed a unique database of EC flux measurements covering 8 growing seasons of 3 crops (maize, wheat, and rapeseed) cultivated over two close agricultural sites (FR-Lam and FR-Aur) in southwestern France. For data analysis, some dry and wet cropping seasons of the same crop type were selected; then, their phenological stages were identified to investigate their effect on the energy balance closure (EBC), and flux partitioning. The results showed that the systematic effect of each site on the EBC was stronger than the influence of crop type and stage, as EBC was generally higher at FR-Aur (82%) than at FR-Lam (67%), even for the same crop type. The assessed effect of rainfall, and phenological stages on energy partitioning revealed that during the wet seasons, over 42% of the net radiation (Rn) was accounted for by the latent heat flux (LE), which was 9% higher than the recorded LE in the dry year during the active vegetation period. Similarly, the ground heat flux (G) was observed to be very sensitive to vegetation; G accounted for 30% of Rn when vegetation was low, whereas at the peak of vegetation, it fell below 16% due to canopy shading. Closure was also assessed under various atmospheric stability conditions and wind sectors, and it was observed to be higher under unstable conditions, and in prevailing wind directions. Analysis of the sensible heat advection (AH) revealed that AH accounts for more than half of the imbalance at both sites.