中国农业科学 ›› 2025, Vol. 58 ›› Issue (8): 1564-1578.doi: 10.3864/j.issn.0578-1752.2025.08.008

• 土壤肥料·节水灌溉·农业生态环境 • 上一篇    下一篇

基于RothC模型的东北旱地和稻田土壤有机碳动态变化模拟研究

张昊鑫1(), 于晟玥1, 雷秋良1(), 杜新忠1, 张继宗1, 安妙颖1, 樊秉乾1, 罗加法2, 刘宏斌1   

  1. 1 农业农村部面源污染控制重点实验室/北京昌平土壤质量国家野外科学观测研究站/北方干旱半干旱耕地高效利用全国重点实验室/中国农业科学院农业资源与农业区划研究所, 中国北京 100081
    2 AgResearch Ruakura研究中心,新西兰汉密尔顿 3214
  • 收稿日期:2024-07-25 接受日期:2024-09-19 出版日期:2025-04-16 发布日期:2025-04-21
  • 通信作者:
    雷秋良,E-mail:
  • 联系方式: 张昊鑫,E-mail:zhx13243155075@163.com。
  • 基金资助:
    鄂尔多斯市“揭榜挂帅”项目(JBGS-2021-001); 国家自然科学基金(U20A20114); 黑土地保护与利用科技创新工程专项(XDA28130200); 中央级公益性科研院所基本科研业务费专项(1610132024010)

Simulating Soil Organic Carbon Dynamic Changes in Dryland and Paddy Field of Northeast China Using RothC Model

ZHANG HaoXin1(), YU ShengYue1, LEI QiuLiang1(), DU XinZhong1, ZHANG Jizong1, AN MiaoYing1, FAN BingQian1, LUO JiaFa2, LIU HongBin1   

  1. 1 Key Laboratory of Non-Point Source Pollution Control, Ministry of Agriculture and Rural Affairs/Changping Soil Quality National Observation and Research Station/State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    2 AgResearch Ruakura Research Centre, 10 Bisley Road, Hamilton 3214, New Zealand
  • Received:2024-07-25 Accepted:2024-09-19 Published:2025-04-16 Online:2025-04-21

摘要:

【目的】探究RothC在东北地区旱地和稻田土壤有机碳储量动态变化的适用性及不同模型校准方法对于模型模拟性能的影响。【方法】选取了一个典型的旱地和一个典型的稻田长期试验点,旱地试验来自中国科学院海伦农业生态试验站(2004—2015年),稻田试验来自八五〇农场的试验数据(2010—2017年)。每个试验点选取两个试验处理分别为仅施无机肥(NPK)和施无机肥+秸秆还田(NPKS),用于模型模拟验证与性能评价。针对稻田土壤除采用RothC外,还选取了两个修正版本RothC_p和RothC_0.6用于适用性评价,选用3种不同模型校准方法分别为平衡法(M1)、参数优化法(M2)、传递函数法(M3),分析不同模型校准方法对于模型模拟性能的影响。选用归一化均方根误差(nRMSE)、平均差(MD)、一致性指数(d)作为模型评价指标。【结果】海伦站的有机碳输入量表现出明显的波动趋势,NPK、NPKS处理的年均碳投入量分别为1.71、3.52 t·hm-2,八五〇农场的有机碳投入较为稳定,NPK、NPKS处理年均有机碳输入量分别为1.89、5.90 t·hm-2。海伦站的模拟验证结果显示,采用不同模型校准方法进行模拟时其nRMSE均小于5%,d在0.60—0.74,说明不同模型校准方法下的模型性能均表现为优秀,RothC能够准确的模拟出旱地NPK、NPKS处理的SOC储量变化趋势。当采用M2方法时NPK、NPKS处理的nRMSE最小,分别为3.46%、3.09%。八五〇农场的模拟验证结果显示,RothC和RothC_p的MD范围为-1.47—-13.41,nRMSE范围为2.90%—26.48%,d值均小于0.1,说明这两个模型大幅度高估了SOC储量的增加,RothC和RothC_p不能够模拟出稻田SOC储量的变化趋势。RothC_0.6在NPK处理下,其MD范围为-0.08—0.44,nRMSE的范围为0.24%—0.85%,d值范围为0.31—0.76;而在NPKS处理下,MD范围为-5.71—-6.22,nRMSE范围为11.21%—12.12%,d值范围为0.12—0.13,说明RothC_0.6能够模拟出NPK处理下SOC储量的动态变化,但大幅高估了NPKS处理的SOC储量的变化。【结论】RothC和RothC_0.6分别适用于东北地区旱地和稻田秸秆不还田情况下SOC储量动态变化的研究,能够准确模拟出SOC储量的变化趋势;不同模型校准方法对于模型模拟性能有影响但均在可接受范围,而传递函数法(M3)计算过程简单、节省模型运行时间,且模型模拟性能较佳,因此本研究推荐优先使用传递函数法(M3)用于模型校准。

关键词: RothC模型, 土壤有机碳, 秸秆还田, 模型校准方法, 旱地, 稻田, 东北

Abstract:

【Objective】 This study explored the applicability of the RothC model for simulating soil organic carbon (SOC) dynamics in dryland and paddy fields in Northeast China and evaluated the impact of various calibration methods on simulation performance.【Method】 This study selected one typical dryland and one typical paddy field as long-term experimental sites. The dryland experiment was conducted at the Heilongjiang Agricultural Ecology Experimental Station of the Chinese Academy of Sciences (2004-2015), and the paddy field experiment utilized data from the 850 Farm (2010-2017). At each experimental site, two treatments were selected for model simulation validation and performance evaluation: one with fertilization only, without straw return (NPK), and the other with both fertilization and straw returning (NPKS). For the paddy field soil, in addition to the RothC model, two modified versions, including RothC_p and RothC_0.6, were also selected for suitability evaluation. Three different model calibration methods were employed: the equilibrium method, parameter optimization method, and transfer function method, to analyze the impact of these calibration methods on model simulation performance. Normalized root mean square error (nRMSE), mean difference (MD), and the index of agreement (d) were selected as model evaluation metrics. 【Result】At the Heilongjiang station, organic carbon input exhibited a significant fluctuating trend, with the average annual carbon input under NPK and NPKS treatments being 1.71 and 3.52 t·hm-², respectively. In contrast, organic carbon input at the 850 Farm was relatively stable, with the average annual carbon input for NPK and NPKS treatments being 1.89 and 5.90 t·hm-², respectively. The simulation validation results from the Heilongjiang station showed that, under different model calibration methods, the nRMSE was consistently below 5%, and the index of agreement (d) ranged from 0.60 to 0.74. This indicated that the model performance was excellent across all calibration methods, and RothC was able to accurately simulate the SOC stock changes for both NPK and NPKS treatments in the dryland. When using the M2 method, the nRMSE for NPK and NPKS was the smallest, at 3.46% and 3.09%, respectively. The simulation validation results for the 850 Farm showed that the MD for RothC and RothC_p ranged from -1.47 to -13.41, with nRMSE values between 2.90% and 26.48% and d-values all below 0.1. This indicated that both models significantly overestimated the increase in SOC stocks and were unable to accurately simulate the changes in SOC stocks in the paddy field. For the RothC_0.6 model under the NPK treatment, the MD ranged from -0.08 to 0.44, with nRMSE values between 0.24% and 0.85% and d-values ranging from 0.31 to 0.76. Under the NPKS treatment, the MD ranged from -5.71 to -6.22, with nRMSE values between 11.21% and 12.12% and d-values between 0.12 and 0.13. These results indicated that RothC_0.6 could accurately simulate the dynamic changes in SOC stocks under the NPK treatment but significantly overestimate the changes in SOC stocks under the NPKS treatment.【Conclusion】RothC and RothC_0.6 were suitable for studying the dynamic changes in SOC stocks under dryland and paddy field conditions without straw returning in the Northeast region, respectively, and could accurately simulate the trends in SOC stocks. The impact of different model calibration methods on simulation performance was not significant. However, the transfer function method was simpler to compute, saved model running time, and provided better simulation performance. Therefore, this study recommended prioritizing the use of the transfer function method for model calibration.

Key words: RothC model, SOC, straw return, model calibration methods, dryland, paddy, Northeast China