SSS10.1

The importance of carbon fluxes between soils and atmosphere and their storage capacities have made soils a key component of nature-based solutions to mitigate climate change. Consequently, the need to forecast soil carbon dynamics at the decadal time scales is becoming a key research avenue for soil scientists and biogeochemists. One of the most important tools to fill this objective is the use of models, which have been developed and implemented since the mid- 20th century. Presuming that integrating more mechanisms would improve these models, some models of increased complexity were recently developed. Indeed, since roughly two decades, several approaches have been proposed to better represent the effect of some key mechanisms in particular related to soil biology and soil physics. For instance, few models are now able to describe explicitly and reliably the importance of soil microorganisms on the soil carbon dynamics from plot to global scale. Here, we will discuss what is the rationale behind model complexity increase, what are the limitations associated and discuss the status of evaluation for model prediction. In particular, we will show that despite model of increased complexity may provide accurate predictions in some conditions, those complex models also came with their own implicit assumptions and limitations that must be well understood before using complex models to forecast soil carbon dynamics and feed policy decisions. We also consider that models of lower complexity, which have been generally developed earlier, have also their own assets and have often been better evaluated. As a consequence, models of lower complexity may be considered as more robust and more adapted to forecast soil carbon dynamic and the improvement of their parameterization should also be considered as a valuable alternative. We will also present why multi model approach is important to reduce uncertainties and explain why using model ensemble, when implemented a diversity of carefully evaluated models, is a key method to forecast soil carbon dynamics. Finally, we will show that the fabulous growth of model complexity and the societal needs associated to soil is an incredible opportunity for soil scientists to increase the understanding of soil carbon cycle, in particular in the context of global changes, and to improve our future predictions on soil carbon. 

How to cite: Guenet, B., Le Noé, J., Bruni, E., Abiven, S., Barré, P., and Cécillon, L.: Do we necessarily need to increase model complexity to forecast soil carbon dynamics?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5394, https://doi.org/10.5194/egusphere-egu22-5394, 2022.

Over the past years, many mechanistic soil organic carbon models have been developed. While these models offer a clear improvement in process representation compared to classic SOC models (e.g. of microbial mineralization and mineral protection of OC), the increase in model complexity, and thus the number of uncertain model parameters, is generally not supported by sufficient available data. Thus, model parameters can generally not be sufficiently constrained through a calibration with typically used data, like soil carbon contents, because multiple, similarly good solutions are possible (referred to as the equifinality problem).

A common approach to reduce equifinality in SOC models is to use ¹⁴C isotopes of SOC as an additional constraint during model calibration. While this approach has shown to improve parameter identifiability (i.e., the identification of unique parameters that lead to optimal results), data on the Δ¹⁴C of SOC is often not available due to high measurement costs. Therefore, we explored the potential of constraining a SOC model with the more widely available ratio of stable carbon isotopes of SOC (δ¹³C). While simulations of Δ¹⁴C generally better constrain the turnover time of slowly cycling SOC pools, simulations of δ¹³C allow to constrain additional processes, such as mixing of OC from aboveground and belowground sources and recycling of SOC through microbes. To do so, we developed a novel mechanistic, microbially driven model that simulates depth profiles of SOC (SOILcarb). In addition to total OC, the model simulates the δ¹³C and Δ¹⁴C value of SOC, by incorporating multiple processes affecting these isotopic ratios in soils.

Our results show that SOILcarb was able to accurately simulate depth profiles of total SOC, δ¹³C and Δ¹⁴C of a forest soil. To optimally explore the parameter space, we used a differential evolution calibration algorithm and extracted all parameter sets that led to a reliable simulation of depth profiles given a fixed error margin. The results showed that simulations calibrated only on total OC data did not results in a good fit of simulated depth profiles of either δ¹³C or Δ¹⁴C. In contrast, simulations using either δ¹³C or Δ¹⁴C as an additional model constraint led to accurate simulations of depth profiles of total OC, δ¹³C and Δ¹⁴C by reducing the range in, and absolute value of, parameter values related to mainly vertical transport and protection rates of OC. Notably, parameters related to microbial OC uptake rates and microbial turnover were not better constrained by either isotopic ratio. Our results show that additional constraints on parameter values, in addition to total SOC, are necessary to increase confidence in model parameters of mechanistic SOC models, while more work is needed to better constrain microbial processes in these models.

How to cite: Van de Broek, M., Govers, G., Schrumpf, M., and Six, J.: Evaluation of dual carbon isotope constraints (δ13C and Δ14C) on the parameterisation of a mechanistic, depth explicit soil organic carbon model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7417, https://doi.org/10.5194/egusphere-egu22-7417, 2022.

Exudation of organic carbon triggers complex spatial and temporal patterns of biophysical and biochemical processes in the root-influenced soil (rhizosphere). We use process-based modeling as a tool to gain insights into microbial interactions and carbon cycling in the rhizosphere. Here, we present a trait-based rhizosphere model that accounts for two different functional microbial groups (copiotrophs, oligotrophs) that differ according to life-history strategies, microbial physiology (e.g., dormancy) and carbon turnover (small and large polymers). The model is calibrated and validated against experimental data from the literature. We apply a parameter search algorithm that identifies plausible parameter spaces by conditioning model outputs to parameter and process constraints that reflect current ecological knowledge. We show the general concept of the model, first simulations after model conditioning, and a concept for coupling the rhizosphere model with the structural-functional plant model CPlantBox to cover the whole-plant scale.

How to cite: Sircan, A., Giraud, M., Lobet, G., Schnepf, A., Streck, T., and Pagel, H.: Trait-based modeling of microbial carbon turnover in the rhizosphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13346, https://doi.org/10.5194/egusphere-egu22-13346, 2022.

To know the quantity of fine root biomass is crucial to understanding ecosystem structure and function. Soil characteristics and fertility are mainly determined by fine root dynamics, the turnover of this material is a major contribution of organic matter to soil structure and mineral nutrient cycles, and also the main source of carbon storage. Many studies of forests are mainly estimates of above-ground biomass. Here we report on differential patterns of fine root biomass allocation in Peruvian forests. Peru is divided into three large sub-regions, Amazon, Andean, and coast. These different ecosystems have also around 84 of the 103 ecosystems types and 28 of the 32 climates on the planet. A field study was conducted installing one-hectare permanent plots in each sub-region, this research was part of an intensive monitoring effort of the carbon cycle and functional traits in primary and secondary forests. To evaluate fine root biomass, 1 m² cross-section pits were set up in the plots where soil samples were collected every 10 cm up to 1 m depth and every 25 cm up to 2 m depth. Roots were retrieved and sorted into different diameter categories ≤0.5, 0.5 - 2.5, 2.5 - 5, and> 5 cm. Samples were rinsed with tap water over a 500-um sieve to loosen the soil and facilitate root sorting and to ensure that the sieve was fine enough to retain the finer roots. The roots after sorting were weighed fresh and then dried at 80°C for 48 hours, weighed to the nearest 0.0001 g, and stored in plastic vials for chemical analysis.

Total root biomass measurements displayed strong regional differences. Coastal dry forest at 370 to 422 m in elevation, where a one ha plot can support 651-681 stems (> 10 cm dbh) had root biomass values ranging from 7960 kg ha^-1 to 8130 kg/ha^-1. Andean forest plots at 1780 m.a.s.l., with 586 stems contained 17020 kg ha^-1 and Amazonian forest at 415 m.a.s.l., with 689 stems, had 33410 kg ha^-1 fine root biomass. Our results support the hypothesis that large root biomass in tropical forests is related to ecosystem type, climatic variables (temperature and moisture), and nutrients. The low bulk density and fine root biomass in tropical forests are inversely related to temperature and moisture. Fine-root turnover decreased with soil depth, which can also have important implications for the soil carbon stock and C cycling.

How to cite: Salinas, N., Cosio, E., Tito, R., Roman-Cuesta, R. M., Nina, A., Boza, T., Cruz, R., and Pedraza, M.: Fine-root biomass and soil properties across Peruvian forests, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10791, https://doi.org/10.5194/egusphere-egu22-10791, 2022.

Snowbed ecosystems in temperate mountains are threatened by surrounding chionophilous grasslands, triggered by the longer growing season and higher temperatures induced by climate change. Such vegetation shift would imply future changes in edaphic biogeochemical processes that could modify CO₂ emissions. To assess the role of snowbeds in terms of carbon storage, we sampled three soil profiles along the snowmelt gradient under different vegetation types (grassland and snowbed) and species (the dwarf shrub Salix herbacea and the dominant grass at plot level) in three non-calcareous Pyrenean localities. We measured soil organic carbon (SOC), pH, bulk density and texture. The soils studied were characterized by low pH and high total soil organic carbon (TOC), which is more abundant at early snowmelting profiles and at the topsoil. Soils on shales had the highest TOC mean values and the lowest variability (10.7%±0.11 at the topsoil), although no significant differences were observed between plots under different vegetation types. SOC stock was between 18-46 MgC·ha^{− 1} at the first 5 cm of early snowmelting topsoil with Salix herbacea, whereas at the late snowmelting topsoil with grasses it was between 17-37 MgC·ha^{− 1}. Our results show that TOC is mainly explained by the situation along the snowmelt gradient, soil depth, and parent material, and suggest that changes in snow cover duration will translate into higher TOC accumulation in current snowbed patches.

How to cite: Roca, N., Illa, E., Pla, R., Guinot, M., and Ninot, J. M.: Topsoil SOC stocks in Pyrenean snowbeds, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13269, https://doi.org/10.5194/egusphere-egu22-13269, 2022.

It is crucial to understand what influences the dynamics of soil aggregates, because soil organic matter (SOM) stabilized inside aggregates is the fraction of SOM that is most susceptible to anthropogenic activity. Yet, there is a lack of numerical process models that include the dynamics of aggregate formation and breakdown and to date, no model represents the important connection between microbial carbon use efficiency (CUE) and aggregate formation. Here, we introduce a model of microbially mediated aggregate formation, which includes litter-stoichiometry and -quality dependent CUE and simulates soil aggregate formation facilitated by the microbial excretion of binding substances. The model is evaluated against measured data of microbial biomass, SOM content and intra-aggregate SOM from a long term bare-fallow experiment in a tropical sandy soil, which was subject to plant litter addition of different qualities. The benefit of simulating aggregates in a model of SOM dynamics is assessed by comparing it against a version that does not, both being separately calibrated to the same dataset. Our results show that the developed model can effectively represent the microbial growth response that follows litter addition and the formation as well as the delayed breakdown of soil aggregates, after the microbial growth peaked. As shown by a higher modelling efficiency and a lower Akaike information criterion, the model version that includes aggregate formation outperforms the one that does not in the simulation of total organic carbon, total N and for the decomposition of litter. Additionally, it can represent the temporal dynamics of C stored in the silt and clay fraction. Yet, while the model could capture the temporal dynamic of aggregates as a result of litter quality, the amount of C in aggregates in the control treatment without litter addition was underestimated. Our results suggest that aggregate formation is an important process that could be included into SOM models to improve the simulation of both aggregated and non-aggregate pools. However, the underestimation of aggregate C in the control could be a hint, that abiotic aggregate formation may be a relevant factor, especially in low input systems, and may also need to be included.

How to cite: Laub, M., Blagodatsky, S., Van de Broek, M., Schlichenmaier, S., Six, J., Kunlanit, B., Vityakon, P., and Cadisch, G.: A numerical model for microbially mediated soil aggregate formation considering the effect of crop residue quality, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5224, https://doi.org/10.5194/egusphere-egu22-5224, 2022.

The increasing demand for food and bio-energy gives need to optimize soil productivity, while securing other soil functions such as nutrient cycling and buffer capacity, carbon storage, biological activity, and water filter and storage. Mechanistic simulation models are an essential tool to fully understand and predict the complex interactions between physical, biological and chemical processes of soil with those functions, as well as the feedbacks between these functions.

We present a systemic soil model to simulate the impact of different management options and changing climate on the named soil functions by integrating them within a simplified system. The model operates on a 1d soil profile and integrates different processes including dynamic water distribution, soil organic matter turnover, crop growth, nitrogen cycling, microbial activity, and root growth.

We present the main features of our model by simulating a long-term field experiment and comparing the simulation results with measured data on yield, carbon, nitrogen and microbial biomass. Additional simulations on soil tillage show the relevance of soil structure for the main soil functions. This is possible due to the dynamic water submodel, which allows for a non rigid pore structure.

To evaluate the simulation results, we propose a combination of our mechanistic modelling with an indicator-based approach for a dynamic soil function evaluation.

How to cite: Weller, U., König, S., Betancur-Corredor, B., Lang, B., Ließ, M., Mayer, S., Stößel, B., Vogel, H.-J., Wiesmeier, M., and Wollschläger, U.: Systemic modelling of soil functions under the impact of agricultural management, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5635, https://doi.org/10.5194/egusphere-egu22-5635, 2022.

Soil compaction is a form of soil degradation that adversely impacts soil mechanical and hydraulic properties, potentially affecting productivity and is often linked to increases in nitrous oxide emissions. However, we lack a quantitative understanding of the extent and environmental impacts of soil compaction. This is partially due to gaps in our knowledge of how compaction impacts soil physical properties spatially and temporally, how it impacts soil's hydrological functioning and how these impacts ultimately modify nitrification and denitrification in the soil. We propose to address this challenge by embedding a new? model of soil structure dynamics into an agro-ecosystem modelling framework to explicitly account for soil compaction impacts on soil functions such as soil moisture dynamics, plant growth and GHG emissions. We tested this model to assess the effect of soil compaction by animal treading in livestock-grazing systems. We considered random movement of cattle in a confined area that is discretized in square cells with given soil bulk density and saturated hydraulic conductivity. Changes in these properties in response to animal treading are then inferred using a soil rheology model based on Bingham's law. We modeled five two-month long grazing seasons in consecutive years using 18 years of weather data from the North Wyke experimental platform in Devon, United Kingdom. Our model predicts an increase of bulk density of up to 20% and a decrease in hydraulic conductivity of up to 95% due to animal treading. Such compaction-induced changes in soil pore space and related hydraulic functions led to a relative increase in N₂O emissions from the compacted areas of up to 200% and a related decrease in yield of up to 15%, which is in agreement with ranges reported in the literature. By providing a mechanistic framework that calculates the impacts of soil management on soil properties and functions, our work advances the ability to test management strategies that might help to ameliorate the environmental impact of animal treading in grasslands.

How to cite: Romero-Ruiz, A., Coleman, K., Segura, C., Cardenas, L., Milne, A., and Whitmore, A.: Modelling soil structure dynamics and nitrous oxide emissions in compacted soils by animal treading, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8490, https://doi.org/10.5194/egusphere-egu22-8490, 2022.

Biological Soil Crusts (biocrusts) are communities of algae, lichens, mosses, cyanobacteria, and other nonvascular organisms living in the soil surfaces. Biocrusts are a key factor in the protection of arid and semiarid ecosystems and, therefore, playing a major role against desertification. Biocrusts are also of profound importance in sand dune areas, as they are recognized as the first colonizers after environmental disturbances and can help preventing sediment remobilization. Moreover, biocrusts have shown to be of importance in soil protection against erosion, but also nutrient cycling in the Pampa biome in Brazil. Here, natural geomorphological processes and soil misuse led to the expansion of sediment remobilization areas, generating a severe problem - the difficulty of fixing field vegetation and crops. The present study investigates the behavior and interrelationships of biocrusts specifically in areas that suffer sandization in the Brazilian Pampa biome and verifies their relationship with soils and soil organisms. We analyzed biocrusts in three consecutive stages. Starting with a taxonomic exploration of the dominant component of cyanobacteria, proceeding to its characterization and finally determining its importance. We investigated two study sites in São Francisco de Assis and samples were collected in May 2016 and October 2019. The sites are characterized by sandy soils that suffer gullying, one without human intervention and the other one with artificially stabilized ravines. The analysis of biological material was carried out with microscopy, and it could be determined that the composition consists of 13 taxa of cyanobacteria and one filamentous species, Stigonema sp., could be specifically highlighted. These black to dark green spotty communities on the soil surface played an important role in particle aggregation, which can be granulated and show macroscopic forms. When analyzing the location of the biocrusts within the topography, it was observed that they occur in more humid places, occupying the same positions in all relief compartments. Biocrusts mostly develop on the top of the gullies or on upper and more stabilized slopes, especially when facing southern orientation. Taking into account the biocrust cover, we can identify different morphologies such as smooth, rolling and pinnacle blocks, which showed us different combinations according to the degree of evolution related to the micromorphology of the relief. We found that the presence of these biocrusts as an element of nutrient source and balance generator can lead to a reduction of soil erosion and was thus of outmost importance for the restoration of this biome.

How to cite: webber, C. L., Werner, V. R., Seitz, S., Scholten, T., and Bremer, U. F.: Biological soil crusts as a mechanism to protect areas under sandization processes in southern Brazil, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3722, https://doi.org/10.5194/egusphere-egu22-3722, 2022.

The sensitivity of a landscape to changes is expressed by the likelihood that a given change in the controls of a system will produce a sensible, recognizable and persistent response in the properties of the system (Brunsden & Thornes, 1979).
The aim of this study is to investigate the sensitivity of an alpine soil landscape for human-induced perturbation in terms of land use changes that might affect soil properties and consequently affect also the stability of the soil landscape. The latter is of particular relevance since in alpine landscapes, land use effects tend to be intensified by extreme climatic and topographic conditions (Gordon, 2001). The study area is located in the southern Alps in the Canton Ticino (Switzerland) which is characterized by a centennial history of land use changes. The landscape can be subdivided into the following main land use-topography units: (i) forest areas in “natural” conditions on south and north facing slopes (reference state), (ii) pasture, (iii) meadow, (iv) cultivated agricultural terraces, and (v) abandoned terraces with forest regrowth in the last 40 years. Land use effects were verified by comparing the different land use-topography units with the reference state of “natural” forested slope. 
The following physicochemical key soil properties and soil water dynamics were measured that are known to be vulnerable to land use changes: saturated hydraulic conductivity, soil organic carbon (TOC), soil bulk density, hydrophobicity and aggregate stability as well as surface runoff generation and related soil erosion. Especially the latter can be considered the main contributor to soil degradation in alpine landscapes, reflecting the sensitivity of the soil landscape to land use changes. 
Statistical methods were used to detect changes, differences and correlations of key soil properties and soil water dynamics in the different land use-topography units in comparison to the natural conditions.
The results of the analysis show that land use and land use changes have a significant impact on soil properties and soil water dynamics. Most of the investigated soil properties show statistically significant differences compared to the reference condition. Land use-induced vegetation changes have a considerable effect on soil’s TOC which is directly related to hydrophobicity and inversely related to bulk density. Soil water dynamics are also land use-specific showing higher values of surface runoff in forests and abandoned terraces where soil hydrophobicity plays a significant role. Even though, land use and related vegetation change had significant effects on investigated soil properties, no effect on soil erosion. Hence, due to the particular characteristics in the lithology and soils of the study area, the soil landscape in the Onsernone valley seems to be resistant at least under the present-day climate variability.

Reference
Brunsden, D., Thornes, J. B. (1979) – Landscape sensitivity and change. Transactions Institute of British Geographers, 4(4), 403–484.
Gordon, J. E., Brazier, V., Thompson, D. B. A., & Horsfield, D. (2001) – Geo-ecology and the conservation management of sensitive upland landscapes in Scotland. Catena, 42(2–4), 323–332

How to cite: Bettoni, M., Vogel, S., and Maerker, M.: Sensitivity of a soil landscape to land use changes in a southern Alpine valley (Ticino, Switzerland), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7645, https://doi.org/10.5194/egusphere-egu22-7645, 2022.

Vegetation and erosion interact with each other through a variety of processes, contributing to the formation and evolution of landscapes. The present work focuses on the humid badlands of Draix-Bleone observatory, in the French Alps. In this observatory, long-term records of hydrology and sediment fluxes are available for several catchments of varying size and vegetation cover. We aim to characterize and quantify the interactions between vegetation and erosion in these badlands.

One the one hand, we previously found that vegetation, where it is able to maintain, strongly limits badland erosion. On the other hand, vegetation recolonization has been observed over the last decades and we hypothesize that this growth is controlled by topographic and erosive mechanisms.

We use aerial images for several dates in the Laval catchment of size 0.86 km2. We classify each image to map vegetation cover and compare the extent of vegetation cover from one date to the other. We then extract the newly vegetated areas and search for environmental factors that can explain why these areas have been colonized rather than others. We combine factors such as slope and drainage area that are related to erosive processes, to biological factors that relate to the dispersion and colonization capacity of previously existing vegetation.

Preliminary findings indicate that vegetation has mainly recolonized areas that are in the vicinity of existing vegetation patches and with low to intermediate slopes. No effect of aspect is found. This suggests that recolonization is limited by erosive processes, but not by water availability.

How to cite: Le Bouteiller, C. and Klotz, S.: Erosion controls vegetation recolonization in Draix-Bleone badlands, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13249, https://doi.org/10.5194/egusphere-egu22-13249, 2022.

In arid environments, vegetation tends to self-organize into patches separated by bare soil. This is necessitated by the lack of water for sustaining a continuous vegetation cover and facilitated by the attraction of water from barren interpatch areas by the vegetation. This process is a positive feedback which introduces spatially heterogeneity into otherwise homogeneous environments, characterised by regular patterns. These patterns are typically considered to be periodic and distinguished on hand of their wavelength. Such patterns have so far been studied with numerical models which generate periodic patterns in homogeneous environments. However, environments are rarely homogeneous, as topography and soil-hydraulic properties vary in space. This raises the questions to which degree heterogeneity of vegetation is self-organized or imposed by the environment, and how environmental heterogeneity interacts with the self-organization process. In contrast to the persisting conceptual model of periodic patterns, natural vegetation exhibit a high degree of irregularity. Several studies have linked this irregularity to heterogeneity in the environment, but a comprehensive theory for analysing the irregularity has not yet been established. Furthermore remains the extend of irregularity unexplored on a global scale. To fill this gap, we, demonstrate empirically the global prevalence of irregularity in vegetation patterns and find that natural vegetation patterns are stochastic, rather than periodic. We then propose a stochastic framework to conceptually describe and measure the regularity, based on the spectral density of the patterns. In addition to the dominant wavelength, measuring the spatial scale, it reveals a novel parameter, measuring the regularity. The parameter is determined by the correlation structure and discriminates gradually between the limit cases of periodicity and white noise. Applied to natural and computer-generated patterns, we find that the former are highly irregular, while the latter are close to periodic. We reproduce the stochasticity of patterns with numerical models by introducing spatial heterogeneity of the model coefficients. We provide a fresh look at the nature of vegetations patterns and present a comprehensive theory for a more holistic understanding of self-organized systems.

How to cite: Kästner, K., Caviedes Voullieme, D., Frechen, N., and Hinz, C.: Theory and empirical evidence for the irregularity of self-organized vegetation patterns, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11905, https://doi.org/10.5194/egusphere-egu22-11905, 2022.