Evaluating the performance of percolation‐based effective medium approximation and sigmoidal model for modeling thermal conductivity of tropical soils

Abstract Soil thermal conductivity ( λ ) is essential for understanding coupled matter and energy flux in porous systems. However, its prediction has remained unsettled due to its intricate relationship with soil properties such as soil moisture content ( θ ) and texture. This study investigates λ–θ relationship in 15 tropical soils from Ghana. The results show that soil λ – θ relationship is strongly nonlinear and depends on texture with fine‐textured soil exhibiting a flat tail in λ the dry range. Four distinct domains, namely, hydration, pendular, funicular, and capillary, ranging from dry to wet condition, were identified. Two modeling approaches, percolation‐based effective medium approximation (P‐EMA) and an empirical sigmoidal model (SM), were evaluated on the λ ( θ ) dataset. An explicit λ ( θ ) P‐EMA variant outperformed a θ ( λ ) variant, achieving performance on par with the SM. However, the explicit λ ( θ ) P‐EMA performs well in the hydration regime, whereas the SM performs well in the capillary regime. The explicit λ ( θ ) P‐EMA and SM had difficulty fitting the nonlinear behaviour at intermediate θ beyond the pendular domain, possibly due to the structural complexity, which is also linked to phase distribution. This study advances our knowledge on λ – θ relationships in Ghanaian soils.

Authors

Institutions

Publication Details

Journal
Soil Science Society of America Journal
Published
2025-09-01
DOI
https://doi.org/10.1002/saj2.70118
Primary Topic
Soil and Unsaturated Flow
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluating the performance of percolation‐based effective medium approximation and sigmoidal model for modeling thermal conductivity of tropical soils

Samuel K. Kumahor, B. Nyarko‐Ackom
Soil Science Society of America Journal
Soil and Unsaturated Flow
article

Evaluating the performance of percolation‐based effective medium approximation and sigmoidal model for modeling thermal conductivity of tropical soils

Samuel K. Kumahor, B. Nyarko‐Ackom
article en

Abstract

Abstract Soil thermal conductivity ( λ ) is essential for understanding coupled matter and energy flux in porous systems. However, its prediction has remained unsettled due to its intricate relationship with soil properties such as soil moisture content ( θ ) and texture. This study investigates λ–θ relationship in 15 tropical soils from Ghana. The results show that soil λ – θ relationship is strongly nonlinear and depends on texture with fine‐textured soil exhibiting a flat tail in λ the dry range. Four distinct domains, namely, hydration, pendular, funicular, and capillary, ranging from dry to wet condition, were identified. Two modeling approaches, percolation‐based effective medium approximation (P‐EMA) and an empirical sigmoidal model (SM), were evaluated on the λ ( θ ) dataset. An explicit λ ( θ ) P‐EMA variant outperformed a θ ( λ ) variant, achieving performance on par with the SM. However, the explicit λ ( θ ) P‐EMA performs well in the hydration regime, whereas the SM performs well in the capillary regime. The explicit λ ( θ ) P‐EMA and SM had difficulty fitting the nonlinear behaviour at intermediate θ beyond the pendular domain, possibly due to the structural complexity, which is also linked to phase distribution. This study advances our knowledge on λ – θ relationships in Ghanaian soils.

Soil Science Society of America JournalVol. 89(5)
University of Ghana (GH)
Openalex Percentile: Top 75%
Soil and Unsaturated Flow
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.