Abstract
Accuracy analysis of a process-based model is important for evaluating the reliability of model estimates of crop growth. Uncertainties in projections of crop growth may derive from different sources in modelling. The parameter-induced uncertainty is one of the important aspects. Here we calibrated the parameters for rice, wheat and maize combined with observed data of aboveground biomass (AGB) and leaf area index (LAI) at 16 Chinese Ecosystem Research Network (CERN) sites under different rotation systems and subsequently validated the model at these sites using the data independent of calibration. The results showed that the simulated AGB and LAI exhibited good agreement with the observations. The model performance for rice and maize was better than that for wheat. The statistical analysis of model performance showed that the RMSE (root mean square error), RMD (relative mean deviation) and EF (model efficiency) were 32.52%, − 0.95% and 0.87 of the means, respectively. The three components of the modelling uncertainty, bias of mean (UM), bias of slope (UR) and random residue (UE) accounted 0.1%, 0.9% and 99% of the total errors, respectively. The main contributor to the error was the random disturbances, indicating that the parameters calibration in this study had reached relatively reasonable conditions on the whole. Although the model displayed an overall good prediction in crops AGBs and LAI, there were still notable bias at some sites due to non-random errors (UM and UR). This indicated that there were still uncertainties in the modelling procedure, e.g. the model mechanism or parameterization. The uncertainty of the simulated results may greatly restrict the application of a model. To effectively and reasonably apply a model, it is necessary to evaluate and analyse the main sources of uncertainty in the simulated results. The parameter-induced uncertainty analysis in this study showed that, at the site scale, the range of uncertainty brought by the changes in three parameters (SLA, PL and α) to the modelling results (95% CI) of Agro-C covered more than 90% of the observations and brought approximately 21% uncertainty to the simulated AGBs of the three major crops.
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Funding
This work was supported by the National Natural Science Foundation of China (Grant No. 41605088), National Key R&D Program of China (Grant No.2017YFE0104600) and the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (EarthLab).
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Zhang, Q., Zhang, W., Li, T. et al. Accuracy and uncertainty analysis of staple food crop modelling by the process-based Agro-C model. Int J Biometeorol 65, 587–599 (2021). https://doi.org/10.1007/s00484-020-02053-1
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DOI: https://doi.org/10.1007/s00484-020-02053-1