土壤热导率影响因素及模型评估研究

Research on influencing factors and model assessment of soil thermal conductivity

【目的】通过对预测模型的评估,综合考虑各方面因素,使各模型在适用条件范围内扬长避短、发挥优势,简洁、快速、精确地获取土壤热导率的预测值,以实现复杂程度上的定量化研究。【方法】对前人提出的16种土壤热导率模型的优势和劣势及应用条件、影响因素进行分析总结,将其中14种模型的预测数据与从文献中收集的实测数据进行比较,通过线性回归分析与均方根误差分析,实现模型评估。【结果】含水率和石英含量对土壤热导率有很大影响,石英的热导率约为7.9 W·m-1·K-1,是所有土壤矿物中最高的,在湿润状态下的土壤热导率远高于干燥状态下的;常温下,Wiener的模型回归系数为0.133和2.208,模型决定系数为0.393和0.820,与其他模型相比偏差明显;而Geo-Mean模型显示出最低回归系数0.668,最高均方根误差0.598,模型的预测值与实测值偏差显著;Zhang等的模型、Chen的模型和Haigh的模型回归系数分别为0.994、0.919和0.891,均方根误差为0.280、0.315和0.394,表现出相对较高的预测精度;Lu等模型的回归系数为0.850,决定系数为0.976,土壤热导率的预测精度一般,而基于Lu等模型改进的苏李君等模型显示最高回归系数(0.997)和决定系数(0.980),表现出最优的性能。【结论】在需要考虑土壤类型的情况下,推荐使用苏李君等的模型,该模型能够更加详细描述土壤物理基本参数对土壤热导率的影响。

【Objective】To comprehensively consider various factors through evaluating prediction models,make full use of each model’s advantages and disadvantages within the scope of applicable conditions,play its advantages,acquire concise,fast and accurate prediction of soil thermal conductivity and realize quantitative research on its complexity degree.【Method】The advantages,disadvantages,application conditions and influencing factors of the previous 16 soil thermal conductivity models are analyzed and summarized.The predicted data of 14 models are compared with their measured data collected from the literature.The model evaluation is realized through linear regression analysis and root mean square error analysis.【Result】Soil thermal conductivity is greatly affected by moisture content and quartz content.The thermal conductivity of quartz is about 7.9 W·m-1·K-1,which is the highest in all soil minerals.The thermal conductivity of soil in humid state is much higher than that in dry state.Under normal temperature condition,the regression coefficients of Wiener model are 0.133 and 2.208,and the decision coefficients are 0.393 and 0.820,which deviates significantly from other models;Geo-Mean model shows the lowest regression coefficient of 0.668 and the highest root mean square error of 0.598,the prediction values deviated significantly from the measured values;The regression coefficients of the models of Zhang et al,Chen and Haigh are 0.994,0.919,0.891 respectively,and the root mean square errors are 0.280,0.315,0.394 respectively,showing relatively high prediction accuracy.The regression coefficient of the model of Lu et al is 0.850,the determination coefficient is 0.976,the prediction accuracy of soil thermal conductivity is general,while the improved model of Su et al based on model of Lu et al shows the highest regression coefficient of 0.997,the highest determination coefficient of 0.980,showing the best performance.【Conclusion】In the case of soil texture,improved model of Lu et al is recommended.This model can describe the effects of basic parameters of soil physics on soil thermal conductivity in more detail.