The revision has addressed many of the issues raised in the first review. There has been a significant improvement in the content, flow, and structure, which has increased the readability of the manuscript. In addition, authors have incorporated two significant changes in the methodology section: (1) the selection of prior, and (2) the initialization of SOC pools. Reviews of this newly added section are provided in the subsequent paragraphs. Another area of concern is in the newly updated result section 3.2, which reported the posterior estimates from the inverse modeling using SIR algorithms. Here, the marginal distribution for the posterior is similar to the marginal distribution from the prior, indicating little or no influence of the dataset on the posterior suggesting nothing is learned from the data assimilation exercise. This is contrary to the title’s claim of “robust DayCent model calibration …”. For these reasons, I recommend a major revision before recommendation for publication. More details are provided below:
SOC pool initialization: One of the major changes involved replacing the long historical simulation with measured SOC, which was adjusted backward in time and initialized at the start of the Experiment. With the update, the posterior parameter can be defined as p(θ|D,M,MAOM) and now conditions on data (D), model (M) and measured mineral associated organic matter (MAOM). Therefore, any future simulation leveraging this study and aiming to understanding the regional or national GHG balance within SSA, as represented by the four-experiment station, now also requires measured or estimated value of MAOM. Therefore, the simulation approach, which requires MAOM measurement, may limit broader use of the model in SSA region. Furthermore, the initialization of SOC pools in the process-based ecosystem model may introduce significant bias in the model’s estimates of SOC stock changes (Fallon and Smith, 2000, Zhou et. al., 2023). Both methods: (1) long historical simulations to equilibrium and (2) initializing model pools with measurement—have been extensively studied and used in literature. In my personal viewpoint, both methods are equally valid given adequate testing and reasoning. Both methods have strengths and weaknesses. The strength led to higher accuracy, while the weaknesses can introduce significant bias and contributing toward higher uncertainty.
Prior distribution: Another significant change in the updated manuscript is the introducing of a new prior distribution during the SIR step. As a result, the revised manuscript uses two sets of prior distribution: a uniform prior for the global sensitivity analysis, and a Gaussian prior for the SIR step. This is uncommon and generally not accepted in Bayesian inferences, as the prior is considered our initial beliefs about an uncertain parameter before observing any data. Therefore, introducing two beliefs for the same set of parameters in the model is unusual. The authors selected coefficients of variations ranging between 5% and 30% for DayCent parameters based on the level of range provided in its manual. However, they did not provide details on how these selection for coefficient of variation satisfy one of the requirements for SIR or similar method, which is that the prior range should cover the entire range of the posterior (Galman, 2014). Also, more detail should be provided on the choice for the coefficient of variation to convincing demonstrate that the empirical data suggest that the prior range is adequate enough for the theoretical understanding of these parameters.
Posterior distribution: The manuscript calibrated 13 model parameters after conducting a parameter screening using the GSA. In section 3.2 of the results, the posterior was presented in Figure 2. Throughout the text, a single parameter estimate was provided, but it was not specified which statistics (e.g., mean, mode, median, etc.) was presented. The posterior should be summarized with sufficient statistics, such as the mean, standard deviation, and 95% credible intervals. If the single parameter estimates the mean, mode, or median, there is a significant disagreement between the text and the figure for parameters clteff(1,2,&4) and pmco2(1&2). The reported posterior estimates of 19.1 and 0.82 fall well outside the curve region with higher density. I believe this could be a miscalculation or misinterpretation, and should be thoroughly investigated.
Some minor comments and corrections:
Line 20-23: The author claim that: “The model performance and the match between the cross-evaluation posterior credibility intervals for different sites indicated the robustness of the model parameterization and the reliability of the DayCent model for spatial upscaling of simulation.” However, the manuscript did not perform a large-scale simulation, and the claim for “spatial upscaling” should be removed or justified.
Line 23: provide quantitative values (i.e., EF for daily N2O) instead of just mentioning negative value.
Line 70: The terms “validated” and “evaluated” were used interchangeably throughout the manuscript. For instance, in line 9, “cross-evaluation” is used but in line 164, “cross-validation” is used.
Line 102: If the mitigation of CO2 emissions is due to C sequestration, it would be advisable to use the term “C sequestration” instead of “mineralization of SOC”
Line 134: Should this
“Tithonia diversifolia (TD) green manure and Calliandra calothyrsus (CC) prunings, low quality stover of Zea mays (MS) and sawdust from Grevillea robusta trees (SD), locally available farmyard manure (FYM) and a control treatment”
be written as following.
“Tithonia diversifolia (TD) green manure, Calliandra calothyrsus (CC) prunings, low quality stover of Zea mays (MS), sawdust from Grevillea robusta trees (SD), locally available farmyard manure (FYM) and a control treatment”
In Table 1. values for model parameters and coefficient of variation seems truncated given the table description (i.e., parameter values and coefficient of variations were missing)
Equation before line 185, if SOC stock estimates are for 0-30 cm as IPCC-recommended, it should be:
〖SOC〗_30 (kg 〖ha〗^(-1) )= (1- β^30)/(1-β^15 )*〖SOC〗_15
Provide the value used for beta^15 and beta^30 used in the equation. The equation number is also missing.
Line-261, it is a little confusing and not clear what the author wants to convey. Specifically, data availability and which model parameters and value used for initialization.
Line-266: It was not clear whether the author’s discussion about aboveground biomass (AGB), yield (Y), and harvest index (HI) is based on the measured data or modeled values. In DayCent Y = HI*AGB (for grain crops). However, the parameter HIMAX (maximum harvest index) is adjusted due to stress to (HI <= HIMAX).
Line-395: Please clarify what multiply/divided by 3 and 10 means. Maybe it is self-explanatory when full view of Table-1 is available.
Equaton-3: The Likelihood function provided in Equation-3 is applicable to only one type of measurement, such as Yield or SOC. Please provide details on how multiple likelihoods—for SOC, Yield, and Harvest Index were combined, if at all, for the final Bayesian calibration. If they were not combined, please provide an explanation.
Line 571: The posterior credibility intervals in analog to confidence intervals in frequentist statistics and posterior prediction interval analog to prediction intervals. The coverage probability (i.e., 95% of observed) within the 95% Posterior prediction interval is only valid comparison but not with posterior credibility intervals (note that posterior credibility interval < posterior prediction interval).
Prove all the missing equations used in the analysis, (one such example is the equation for aggregated model output for the GSA) in the supplementary section.
Reference:
Falloon, P. D., & Smith, P. (2000). Modelling refractory soil organic matter. Biology and Fertility of Soils, 30(5–6), 388–398. https://doi.org/10.1007/s003740050019
Zhou, W., Guan, K., Peng, B., Margenot, A., Lee, D., Tang, J., Jin, Z., Grant, R., DeLucia, E., Qin, Z., Wander, M. M., & Wang, S. (2023). How does uncertainty of soil organic carbon stock affect the calculation of carbon budgets and soil carbon credits for croplands in the U.S. Midwest? Geoderma, 429, 116254. https://doi.org/10.1016/j.geoderma.2022.116254
Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B., 2014.Bayesiandata analysis, Third edit. ed, BDA3. Boca Raton : CRC Press, Boca Raton. |