College of Resources, Sichuan Agricultural University, Chengdu, 611130, China; Laboratoire de Géologie, UMR 8538, École Normale Supérieure, CNRS, Université PSL, IPSL, 75005, Paris, France.
Laboratoire de Géologie, UMR 8538, École Normale Supérieure, CNRS, Université PSL, IPSL, 75005, Paris, France.
J Environ Manage. 2023 Nov 1;345:118850. doi: 10.1016/j.jenvman.2023.118850. Epub 2023 Aug 21.
Accurate soil organic carbon models are key to understand the mechanisms governing carbon sequestration in soil and to help develop targeted management strategies to carbon budget. The accuracy and reliability of soil organic carbon (SOC) models remains strongly limited by incorrect initialization of the conceptual kinetic pools and lack of stringent model evaluation using time-series datasets. Notably, due to legacy effects of management and land use change, the traditional spin-up approach for initial allocation of SOC among kinetic pools can bring substantial uncertainties in predicting the evolution of SOC stocks. The AMG model can fulfill these conditions as it is a parsimonious yet accurate SOC model using widely-available input data. In this study, we first evaluated the performance of AMGv2 before and after optimizing the potential mineralization rate (k) of SOC stock following a leave-one-site-out cross-validation based on 24 long-term field experiments (LTEs) in the Southwest of China. Then, we used Rock-Eval® thermal analysis results as input variables in the PARTY machine learning model to estimate the initial stable SOC fraction (C/C) for the 14 LTEs where soil samples were available. The results showed that initializing the C/C ratio using PARTY combined with the optimized k further improved the accuracy of model simulations (R = 0.87, RMSE = 0.25, d = 0.90). Combining average measured C/C and k optimization across all 24 LTEs also improved the model predictive capability by 25% compared to using default parameterization, thus suggesting promising avenue for upscaling model applications at the regional level where only a few measurement data on SOC stability can be available. In conclusion, the new version of the AMG model developed in the Tuojiang River Basin context exhibits excellent performance. This result paves the way for further calibration and validation of the AMG model in a wider set of contexts, with the potential to significantly improve confidence in SOC predictions in croplands over regional scales.
准确的土壤有机碳模型是理解土壤碳固存机制并帮助制定针对碳预算的目标管理策略的关键。土壤有机碳 (SOC) 模型的准确性和可靠性仍然受到概念动力学池不正确初始化和缺乏使用时间序列数据集进行严格模型评估的严重限制。值得注意的是，由于管理和土地利用变化的遗留效应，传统的动力学池中 SOC 初始分配的自旋方法会在预测 SOC 储量的演变方面带来大量不确定性。AMG 模型可以满足这些条件，因为它是一种使用广泛可用输入数据的简约而准确的 SOC 模型。在这项研究中，我们首先在基于中国西南部 24 个长期野外实验 (LTE) 的留一站点交叉验证的基础上，优化 SOC 储量的潜在矿化率 (k) 后，评估了 AMGv2 的性能。然后，我们使用 Rock-Eval® 热分析结果作为输入变量，在 PARTY 机器学习模型中估计了 14 个 LTE 中可用土壤样本的初始稳定 SOC 分数 (C/C)。结果表明，使用 PARTY 初始化 C/C 比并结合优化后的 k 进一步提高了模型模拟的准确性 (R=0.87, RMSE=0.25, d=0.90)。与使用默认参数化相比，在所有 24 个 LTE 中平均测量的 C/C 和 k 优化也将模型的预测能力提高了 25%，这表明在只有少数 SOC 稳定性测量数据的情况下，在区域水平上扩展模型应用具有广阔的前景。总之，在沱江流域背景下开发的新版本 AMG 模型表现出色。这一结果为在更广泛的背景下进一步校准和验证 AMG 模型铺平了道路，有可能显著提高在区域尺度上对农田 SOC 预测的信心。
