Quantitative effects of soil organic matter on thermal conductivity modeling

Abstract

Soil thermal conductivity (λ) is a crucial parameter for energy transfer between the land and the atmosphere. An increasing number of studies have noted that changes in soil organic matter content (ϕ_som) considerably influence λ. However, only a few studies have focused on quantifying the effects of soil organic matter (SOM) on λ modeling. In this study, the performances of 15 λ models that consider the effects of SOM were evaluated based on a compiled dataset consisting of 1569 measurements from 208 soils (0 %≤ϕ_som≤40 %, mass content). Two random forest-based explainable artificial intelligence models, Shapley additive explanations (SHAP) and permutation importance (PI), were also applied to assess the SOM effects. The results showed that the model developed from normalizing Kersten function had the best prediction accuracy because of its realistic function types and parameter settings. The models of Johansen (1975), Balland and Arp (2005), Su et al. (2016), and Yan et al. (2019) performed the best, with NSE ≥ 0.71 and RMSE ≤ 0.3 W m^-1 K^-1. Moreover, PI and SHAP demonstrate that the effect of SOM on λ is nonlinear, and the influence of SOM should be considered when ϕ_som >2.5 %. There is a strong interaction between SOM and bulk density when ϕ_som>2.5 %, significantly influencing λ.