Mechanistic Framework of Wood Decomposition in Post-Storm Vegetative Debris Piles using Continuous Quality Theory

Author

Thomas T. Lee, California Polytechnic State University, San Luis ObispoFollow

Available at: https://digitalcommons.calpoly.edu/theses/3242

Date of Award

3-2026

Degree Name

MS in Civil and Environmental Engineering

Department/Program

Civil and Environmental Engineering

College

College of Engineering

Advisor

Derek Manheim

Advisor Department

Civil and Environmental Engineering

Advisor College

College of Engineering

Abstract

Storm events generate significant quantities of vegetative debris composed of wood, branches, twigs, and other organic materials. Efficient post-storm resource recovery and carbon accounting depend on reliable predictions of decomposition within these large, heterogeneous debris piles. While small-scale wood decay is well studied, decomposition dynamics within multi-layer debris piles remain poorly characterized, limiting municipalities' ability to estimate material longevity and resource recovery potential.

To address this gap, this study develops the WOOD_DK (Wood Organic Oxidation and DegraDation Kinetics) model, a preliminary, exploratory one-dimensional multilayer decomposition framework simulating coupled temperature, moisture, oxygen, and carbon dynamics in MATLAB. Microbial decomposition follows Continuous Quality Theory (CQT), with parameters calibrated via DREAM (DiffeRential Evolution Adaptive Metropolis) to experimental wood decay time series. Across site-species pairs, posterior parameter medians suggested moderate microbial sensitivity to declining substrate quality (β ≈ 2.57), rapid quality reduction during assimilation (η₁₁ ≈ 0.26), fast microbial growth (u₀ ≈ 0.50 day⁻¹), moderate initial substrate quality (q₀ ≈ 0.50), and carbon conversion efficiency (e₀ ≈ 0.32) consistent with fungal-dominated wood decay systems.

Model verification using site-specific parameter sets produced strong agreement with calibration datasets (average R² = 0.825). Validation tests revealed limited parameter transferability across geographic locations, even within the same substrate species: Pinus radiata parameters calibrated in South Carolina performed well locally (R² = 0.905) but poorly in Georgia (R² = -0.208) and North Carolina (R² = -11.083), demonstrating that transferability is governed primarily by site-species interactions rather than climatic similarity alone.

A dual-compartment moisture framework was developed to distinguish intra-particle water (within wood cell walls and lumens) from inter-particle water (between wood pieces in void space), with inter-particle drainage governed by a Darcy gravity drainage formulation using literature-based saturated hydraulic conductivity (K_sat = 13.9 m day-1; Subroy et al., 2014). DREAM-calibrated base decay rate parameter (u₀ ≈ 0.50 day⁻¹) was adjusted to better reflect the longer timescales of decay expected for large-scale vegetative debris piles (years to decades) compared to the decay timescales of small wood blocks like those used in the calibration data studies (months). Multi-layer simulations of post-storm vegetative debris piles in Venice and Arcadia, Florida – with pile geometry (height = 2.35 m, area = 88.73 m²) based on field measurements of debris staging sites collected following Hurricane Milton (2024) – revealed a plausible monotonically increasing carbon retention gradient with depth under pile-scale adjusted CQT parameters (u₀ = 0.008 day^-1) – surface layers retained approximately 14.5% of initial carbon while bottom layers retained approximately 26.7% after 27.4 years, with approximately 20% of initial pile carbon mass remaining (based on assumed wood density, pile dimensions, and wood carbon fraction = 0.5). This depth-dependent pattern is primarily driven by oxygen availability with surface layers maintaining near-atmospheric oxygen concentrations while deeper layers experience progressive oxygen limitation. Direct application of block-scale CQT parameters produced near-complete carbon loss across all layers within 27.4 years, highlighting a scale mismatch between block-scale calibration data and pile-scale conditions that represents the dominant source of prediction uncertainty.

Although assumptions of homogeneous substrate, one-dimensional mass and energy transfer, and strictly aerobic decomposition limit predictive generalization, the WOOD_DK model establishes a mechanistic hypothesis-generating framework for exploring debris pile decomposition dynamics. Results suggest that depth-resolved field measurements of temperature, moisture, oxygen, and carbon loss are critical for constraining pile-scale model parameters and validating internal stratification patterns predicted by the model.