Room for improvement: A review and evaluation of 24 soil thermal conductivity parameterization schemes commonly used in land-surface, hydrological, and soil-vegetation-atmosphere transfer models

Abstract: Effective thermal conductivity of soils (λeff) is a critical parameter for agriculture, environment science, and engineering. Functions to estimate λeff from readily available soil properties, known as soil thermal conductivity (STC) schemes, are needed by land-surface models (LSMs), hydrological models, and soil-vegetation-atmosphere transfer (SVAT) models to study the land surface energy balance, heat flux, and soil thermal regime under various climates and geographic regions. The selection of a STC scheme can result in large differences in temperature estimates in LSMs, sometimes masking the effects of climate change. Therefore, accurate selection of a STC scheme is critically important to LSM estimates. Although a number of STC schemes have been incorporated in various LSMs, no study has systematically evaluated their performance. Therefore, the objectives of this study were to review and evaluate STC schemes employed by LSMs by comparing (1) predicted and measured STCs and (2) modelled land surface temperature (LST) using the Community Land Model at three selected sites and the corresponding LST data from the moderate resolution imaging spectrometer (MODIS). In total, 24 STC schemes were collated from 38 mainstream LSMs, SVAT, and hydrological models. They were divided into three categories based on model types: one physically-based scheme, eight linear/non-linear regression schemes, and 13 normalized schemes. We also include two schemes that express STC as a function of matric potential (ψ, hereafter referred as λeff (ψ) schemes). The first three types of STC schemes were evaluated with a large compiled dataset consisting of 439 unfrozen and frozen measurements of λeff from 16 soils. The λeff (ψ) schemes were evaluated with simultaneously measured λeff (ψ) from eight soils from two separate or independent studies. Results showed that none of the STC schemes could be used to accurately predict λeff for all soil types. STC scheme performance largely depended on the size (number of samples) and characteristics (e.g., soil types) of the data used for comparison. Some STC schemes work well on certain types of soils, but care should be taken for larger scale applications. LSM simulated LST for 24 STC schemes varied when compared with MODIS LST. In general, the STC schemes performed better in medium- and coarse-textured soils than in fine-textured soils. However, large discrepancies were observed on the estimated LST using different STC schemes for medium and coarse-textured soils. We recommend that LSM modelers be mindful of the inherent bias in STC schemes on the surface temperature estimates and hence overall model predictions. Orchestrated efforts are urgently needed on the part of the soil science, hydrology, and climatology communities to develop a more extensive and systematic λeff database for development and evaluation of improved STC schemes for wider and more accurate applications.