3–4D soil model as challenge for future soil research: Quantitative soil modeling based on the solid phase

Abstract

A 3-4D soil model represents a logical step forward from one-dimensional soil columns (1D), two-dimensional soil maps (2D), and three-dimensional soil volumes (3D) toward dynamic soil models (4D), with time as the fourth dimension. The challenge is to develop modeling tools that account for the states of soil properties, including the spatial structure of solids and pores, as well as their dynamics, including soil mass and solute transfers in landscapes. Our envisioned 3-4D soil model approach aims at improving the capability to predict fundamental soil functions (e.g., plant growth, storage, matter fluxes) that provide ecosystem services in the socioeconomic context.This study provides a structured overview on current soil models, challenges, open questions, and urgent research needs for developing a 3-4D soil model. A 3-4D soil model should provide an inventory of spatially distributed and temporally variable soil properties. As basis for this, we propose a mass balance model for the solid phase, which needs to be supplemented by a model describing its structure. This should eventually provide adequate 3D parameter sets for the numerical modeling of soil functions (e.g., flow and transport). The target resolution is decameters in the horizontal plane and centimeters to decimeters in the vertical direction to represent characteristic soil properties and soil horizons. The actual state of soils and their properties can be estimated from spatial data that represent the soil forming factors, with the use of machine learning tools. Improved modeling of the dynamics of soil bulk density, biological processes, and the pore structure are required to relate the solid mass balance to matter fluxes.A 3-4D soil model can be built from several types of modeling approaches. We distinguish between (1) process models that simulate mass balances, fluxes and soil structure dynamics, (2) statistical pedometric models using machine learning and geostatistics to estimate the soil inventory within landscapes, and (3) pedotransfer functions to link observable attributes to specific model parameters required to simulate soil functions including water and matter fluxes. This should provide the prerequisites to predict the spatial distribution of soil functions and their changes in response to external forcing. This endeavor can draw upon many already established models and techniques, yet combining them into a newly created 3-4D soil model is a truly an ambitious, but promisingThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Coupling scales in process‐based soil organic carbon modeling including dynamic aggregation

“…Our results underline the capability of our model to investigate the opposing factors, which determine the nonlinear distribution of CO 2 in the soil profile. This emphasizes the need for micro–macro models exchanging information on different scales to investigate and quantify the effects of structural changes, variations in environmental conditions, or differences in the degradation processes in the hot spots of soils on carbon turnover (Baveye et al., 2018; Gerke et al., 2022; Pot et al., 2022b; Vereecken et al., 2016). This explicit spatially and temporally dynamic model contributes to understanding the linkage between soil architecture and functions from the micron to the pedon scale.…”

“…The prediction of the fate of carbon in soils or the impact of changes in the environmental conditions requires a fundamental understanding of the underlying processes at different scales. This necessitates the implementation of novel modeling approaches and, together with advanced experimental techniques down to the nanoscale, allows to challenge coherent hypotheses (Baveye et al, 2018;Gerke et al, 2022;Roose et al, 2016;Totsche et al, 2018;Vereecken et al, 2016). Several recent reviews point out the importance of incorporating process mechanisms at the pore scale in models to better understand carbon turnover in soils (Baveye et al, 2018;Meurer et al, 2020;Pot et al, 2022aPot et al, , 2022bVereecken et al, 2016).…”

“…Since the underlying processes are complex, this leads to nonlocal (in space and time) interrelations, which are poorly represented by constant parameters. In contrast to merely heuristic or lumped parameter models, a modeling approach with predictive capability requires that soil structure dynamics is taken into account in a temporally and spatially explicit way (Gerke et al., 2022; Pot et al., 2022b; Stockmann et al., 2013). Only then the influence of, for example, the physical protection of organic matter (OM) on its turnover can be evaluated in detail.…”

Soil science challenges—An interdisciplinary overview of current and future topics

“…The impact of contrasting soil water contents on nutrient cycling is reviewed in this issue by Bauke et al (2022) who conclude that it needs to be studied from the molecular to the landscape scale. As a future perspective for soil system modeling, Gerke et al (2022) present within this issue a holistic approach to predict spatially distributed soil functions and their changes in response to external forcing. The authors cast the vision of a 3-4D soil model based on the solid phase, with time as the fourth dimension.…”

“…As a future perspective for soil system modeling, Gerke et al. (2022) present within this issue a holistic approach to predict spatially distributed soil functions and their changes in response to external forcing. The authors cast the vision of a 3–4D soil model based on the solid phase, with time as the fourth dimension.…”