Understanding ecosystem gross primary productivity, evapotranspiration, and water use efficiency of maize using ISBA-A-gs land surface model over temperate and tropical semi-arid climates
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.
The manuscript titled “Understanding ecosystem gross primary productivity, evapotranspiration, and water use efficiency of maize using ISBA-A-gs land surface model over temperate and tropical semi-arid climates” evaluates the performance of the ISBA-A-gs model in simulating key ecohydrological fluxes (, , and ) across two distinct climate regimes (temperate France and tropical semi-arid India). The authors successfully calibrated site-specific parameters to capture carbon-water dynamics under different irrigation practices. While the application of a process-based model to cross-climatic regimes is interesting and relevant, the manuscript is excessively lengthy and dense, which occasionally makes it difficult to understand the core findings. Streamlining the text and restructuring the presentation of results will significantly improve readability and impact.
The Introduction provides an excellent, comprehensive background on , maize agro-ecosystems, and the history of the ISBA model framework. However, it is excessively long, making it difficult to understand the overarching research gap until lines 196–201. I recommend streamlining the introduction to establish a more direct line of sight to the study's novel contributions. Furthermore, the alignment between the stated objectives and the results should be tightened. Specifically, it is not immediately clear how objectives (iii) and (iv) are systematically isolated and resolved in the text. Please restructure the Results and Discussion sections to explicitly map back to these objectives.
The Results section reads more like an extended narrative and contains text that belongs elsewhere. For example: Section 3.1 (Site Meteorology and Vegetation): This section describes the environmental forcing data rather than new experimental or model findings. To tighten the manuscript, this text should be significantly condensed and integrated into Section 2.1 (Experimental sites description) within the Materials and Methods section.
Section 3.3 (Model Calibration): This section is redundant as it essentially reiterates the exact numerical values already clearly presented in Table 5 and Table 6. The text should be condensed to focus strictly on the biophysical interpretation of why these parameters shifted between the sites and irrigation treatments, rather than repeating the data tables.
Line 444: The description of the optimization algorithm requires more technical clarity. The authors state that a "one-at-a-time" sensitivity analysis was performed followed by 50 simulations per parameter. It is unclear if the final parameter selection was achieved via automated iterative optimization or manual tuning. Please explicitly state the exact mathematical or algorithmic approach used to isolate the final "optimal values" to ensure reproducibility.
Figures 6 and 8: In the scatter plots comparing daily observations against simulations, the authors have plotted a best-fit regression line. While a 1:1 line allows the reader to visually assess under- or over-estimation (bias) immediately. I highly recommend modifying these figures to display a 1:1 line.
Line 199: Please explain what is meant by "climate-specific parameterization".
Minor correction: Please correct the narrative citation style. When the authors' names are part of the sentence structure, only the year should be in parentheses (e.g., change "developed by (Noilhan and Planton, 1989)" to "developed by Noilhan and Planton (1989)"). This occurs in several places in the text (e.g., Calvet et al., 1998; Boone et al., 2017).