ABSTRACT:

This resource includes the Python scripts used to generate figures for the manuscript entitled " A Soil Structure-Based Modeling Approach to Heterotrophic Respiration." The folder soil-heterogeneity-respiration contains following Jupyter notebooks with their description:

Figure 1.ipynb file contains the code used to draw Figure 1 showing CDF of truncated gamma aggregate size distribution compared to the literature data.
Figure 2.ipynb file contains the code used to draw Figure 2 showing how the model captured the effect of land uses on SOC in different aggregate size classes.
Figure 3.ipynb file contains the code used to draw Figure 3 depicting the relationship of different biophysical factors controlling the soil microbial respiration at a relative soil moisture content.
Figure 4.ipynb file contains the code used to draw Figure 4 depicting moisture-respiration relationship for different heterogeneity scenarios.
Figure 5.ipynb file contains the code used to draw Figure 5 depicting moisture-respiration curves for homogeneous vs heterogeneous scenarios.

To find the updated version of the codes, visit the GitHub link: https://github.com/Achla-Jha/soil-heterogeneity-respiration.git

The abstract of the manuscript:

Soil microbial communities play a pivotal role in controlling soil carbon cycling and its climate feedback. Accurately predicting microbial respiration in soils has been challenged by the intricate resource heterogeneity of soil systems. This makes it difficult to formulate mathematical expressions for carbon fluxes at the soil bulk scale which are fundamental for soil carbon models. Recent advances in characterizing and modeling soil heterogeneity are promising. Yet they have been independent of soil structure characterizations, hence increasing the number of empirical parameters needed to model microbial processes. Soil structure, intended as the aggregate and pore size distributions, is, in fact, a key contributor to soil organization and heterogeneity and is related to the presence of microsites and associated environmental conditions in which microbial communities are active. In this study, we present a theoretical framework that accounts for the effects of microsites heterogeneity on microbial activity by explicitly linking heterogeneity to the distribution of aggregate sizes and their resources. From the soil aggregate size distribution, we derive a mathematical expression for heterotrophic respiration that accounts for soil biogeochemical heterogeneity through measurable biophysical parameters. The expression readily illustrates how various soil heterogeneity scenarios impact respiration rates. In particular, we compare heterogeneous with homogeneous scenarios for the same total carbon substrate and microbial biomass and identify the conditions under which respiration in heterogeneous soils (soils having non-uniform distribution of carbon substrate and microbial biomass carbon across different aggregate size classes) differs from homogeneous soils (soils having uniform distribution of carbon substrate and microbial biomass carbon across different aggregate size classes). The proposed framework may allow a simplified representation of dynamic microbial processes in soil carbon models across different land uses and land covers, key factors affecting soil structure.

ABSTRACT:

This resource includes the Python scripts of the modeling framework, simulation result CSV files, and the figures for the manuscript entitled 'Linking Soil Structure, Hydraulic Properties, and Organic Carbon Dynamics: A Holistic Framework to Study the Impact of Climate Change and Land Management.' results_simulations_constant_porosity.csv file contains the simulation results of the modeling framework at constant porosity. results_simulations_dynamic_porosity.csv file contains the simulation results of the modeling framework at dynamic porosity. model_constant_phi is the python script with the description of the modeling framework at a constant porosity that needs to be imported and run along with the simulation constant porosity Jupyter notebook to obtain the simulation results at constant porosity. model_dynamic_phi is the python script describing the modeling framework at a dynamic porosity that needs to be imported and run along with the simulation dynamic porosity notebook to obtain the simulation results at dynamic porosity.

Figure 3 and Figure 4 Jupyter notebooks have the codes for those plots in the manuscript.

To find the updated version of the codes, visit the GitHub link: https://github.com/Achla-Jha/Soil-Structure.git.

The abstract of the manuscript:

Climate change and unsustainable land management practices have resulted in extensive soil degradation, including alteration of soil structure (i.e., aggregate and pore size distributions), loss of soil organic carbon, and reduction of water and nutrient holding capacities. Although soil structure, hydrologic processes, and biogeochemical fluxes are tightly linked, their interaction is often unaccounted for in current ecohydrological, hydrological and terrestrial biosphere models. For more holistic predictions of soil hydrological and biogeochemical cycles, models need to incorporate soil structure and macroporosity dynamics, whether in a natural or agricultural ecosystem. Here, we present a theoretical framework that couples soil hydrologic processes and soil microbial activity to soil organic carbon dynamics through the dynamics of soil structure. In particular, we link the Millennial model for soil carbon dynamics, which explicitly models the formation and breakdown of soil aggregates, to a recent parameterization of the soil water retention and hydraulic conductivity curves and to soil carbon substrate and O$_2$ diffusivities to soil microsites based on soil macroporosity. To illustrate the significance of incorporating the dynamics of soil structure, we apply the framework to a case study in which soil and vegetation recover over time from agricultural practices. The new framework enables more holistic predictions of the effects of climate change and land management practices on coupled soil hydrological and biogeochemical cycles.