LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China.
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China.
Int J Biometeorol. 2021 Apr;65(4):587-599. doi: 10.1007/s00484-020-02053-1. Epub 2021 Jan 8.
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 (U), bias of slope (U) and random residue (U) 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 (U and U). 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.
基于过程的模型精度分析对于评估作物生长模型估计的可靠性非常重要。作物生长预测中的不确定性可能源于模型构建的不同来源。参数诱导不确定性是重要方面之一。本研究结合地上生物量（AGB）和叶面积指数（LAI）的观测数据，对水稻、小麦和玉米在不同轮作系统下的模型参数进行了校准，并利用校准数据以外的站点数据对模型进行了验证。结果表明，模拟的 AGB 和 LAI 与观测值吻合较好。与小麦相比，水稻和玉米模型的性能更好。模型性能的统计分析表明，模型的均方根误差（RMSE）、相对均方偏差（RMD）和模型效率（EF）分别为平均值的 32.52%、-0.95%和 0.87。建模不确定性的三个组成部分，均值偏差（U）、斜率偏差（U）和随机残差（U）分别占总误差的 0.1%、0.9%和 99%。误差的主要来源是随机干扰，这表明本研究的参数校准总体上已经达到了相对合理的条件。尽管模型在作物 AGB 和 LAI 方面总体上具有较好的预测能力，但由于非随机误差（U 和 U）的存在，一些站点的预测仍存在明显的偏差。这表明模型构建过程中仍存在不确定性，例如模型机制或参数化。模拟结果的不确定性可能会极大地限制模型的应用。为了有效地、合理地应用模型，有必要评估和分析模拟结果中主要的不确定性来源。本研究中的参数诱导不确定性分析表明，在站点尺度上，三个参数（SLA、PL 和α）变化引起的建模结果（95%置信区间）的不确定性范围超过了观测值的 90%，并给三种主要作物的模拟 AGB 带来了大约 21%的不确定性。
