Carbon use efficiency has recently been proposed as a central parameter that promotes soil organic carbon storage based on data assimilation with a global soil organic carbon database and a vertical, microbial explicit soil organic carbon model (Tao et al., 2023). In this research, we present a sensitivity study with a vertical soil organic carbon model, COMISSION v2.0 (Ahrens et al., 2020), that not only models microbial interactions explicitly but also represents organo-mineral interactions with a maximum capacity, Q_max, to form mineral-associated organic carbon (MAOC).

The COMISSION model represents the formation of MAOC from microbial necromass and dissolved organic carbon analogous to Langmuir sorption. Empirical studies have provided Q_max parameterizations derived from quantile or boundary line regressions with clay and silt content. For the sensitivity study, we vary Q_max along the full range of observed Q_max values while simultaneously varying carbon use efficiency (CUE). Our results highlight that CUE and Q_max promote soil organic carbon storage to similar degrees along their respective observed ranges. The remaining parameters of the COMISSION model were kept at their calibrated values from a multi-site calibration with soil organic carbon, mineral-associated organic carbon, and radiocarbon profiles (Ahrens et al., 2020). While Q_max and CUE are of similar importance for promoting soil organic carbon storage, they also interact in promoting SOC storage. Higher Q_max values strengthen the promotion of soil organic carbon storage with higher CUE. This positive interaction results from higher microbial necromass with higher CUE and the subsequent association of microbial necromass on mineral surfaces mediated through Q_max. The sensitivity study revealed that CUE is the dominant driver for microbial biomass levels. Q_max affects microbial biomass only to a small degree through 'competition' between mineral surfaces and microbial biomass for dissolved organic carbon. While the effect of Q_max on microbial biomass is small, the relationship between Q_max and microbial biomass is generally negative. At the lower end of the tested range of carbon use efficiencies (CUE < 0.15), further model experiments reveal that imposing a stronger microbial limitation of depolymerization can lead to a negative relationship between CUE and soil organic carbon storage.

Overall, our results highlight that in soil organic carbon models with microbial interactions and a limited capacity to form organo-mineral associations, both processes can be of similar importance in promoting soil organic carbon storage. The current debate in the observational realm, whether there is indeed an upper limit for mineral-associated organic carbon formation, can spark a similar debate in the modeling realm on how to represent mineral-associated organic carbon formation in models mechanistically.

References

Ahrens B, Guggenberger G, Rethemeyer J et al. (2020) Combination of energy limitation and sorption capacity explains 14C depth gradients. Soil Biology and Biochemistry, 148, 107912.

Tao F, Huang Y, Hungate BA et al. (2023) Microbial carbon use efficiency promotes global soil carbon storage. Nature, 618, 981-985.

Funding acknowledgment: Bernhard Ahrens has received funding through the AI4SoilHealth project. The AI4SoilHealth project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101086179.

How to cite: Ahrens, B. and Chettouh, M. A.: Carbon use efficiency and mineralogical capacity are of similar importance for promoting soil organic carbon stocks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18844, https://doi.org/10.5194/egusphere-egu24-18844, 2024.

Agricultural system models are widely recognized as valuable tools for identifying best management practices and addressing the challenges posed by climate change. In this context, the use of model ensembles has been recently recommended for their enhanced performance and accuracy. However, assessing their effectiveness over a large geographical area, such as national scale is often currently lacking. This study focuses on simulating soil organic carbon (SOC) dynamics using an ensemble of models comprising DSSAT, CropSyst, EPIC, and APSIM models, utilizing data derived from five Long-Term Experiments (LTEs) spread across a north-to-south pedoclimatic range transect in Italy. This region is of particular importance as it represents a significant hotspot for climate change. The LTEs featured a robust array of 63 unique experimental protocols, incorporating variation effect in fertilization rates, cropping rotations, and tillage prescriptions. This resulted in a total of 2184 years of simulated data for each model. The dataset employed included SOC stocks and crop yield and biomass. Models underwent independent calibration, with crop and SOC parameters selected based on expert knowledge. Main crop cultivars, such as maize, soybean, sugarbeet, and wheat, were further categorized and calibrated by maturity classes. A similar approach was used for cover crops. The extensive dataset enabled a nuanced exploration of the models’ performance across varied agro-ecological contexts. The models proved capable of accurately reproducing the varied pedo-climatic conditions of the Italian peninsula, contributing to the advancement of our understanding of SOC dynamics.

How to cite: Longo, M., Piccoli, I., Berti, A., Farneselli, M., Tabaglio, V., Ventrella, D., Trestini, S., and Morari, F.: Exploring soil organic carbon dynamics through a multi-model simulation of multiple long-term experiments , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11219, https://doi.org/10.5194/egusphere-egu24-11219, 2024.

Accurately quantifying carbon dynamics in peatlands is critical to assess their role in regulating global climate. Within hotspots of peatland degradation, such as in Europe and South-east Asia, skilful assessment of the spatial and temporal impacts of climate change and different land management options is required to meet emissions reductions targets and improve regional management planning.

To address this challenge, a random forest-based metamodel was evaluated to assess its utility in simulating various greenhouse gas (CO₂) emission components, including Net Ecosystem Exchange (NEE), Gross Primary Productivity (GPP), and Ecosystem Respiration (ER) across two Scottish peatlands. The metamodel mimicked the complex Wetland-DNDC model at a higher level of abstraction with increased efficiency and lower computational time.

While Wetland-DNDC also simulates NEE, GPP and ER, it typically involves a considerable number of parameters related to soil properties, climate data, vegetation characteristics, biogeochemical processes, hydrology, nutrient cycling, and microbial activity. Many of these parameters (more than 100) are challenging to measure in the field, and literature values are often adopted, which may not necessarily reflect local site conditions. In essence, this multidimensional parameter space introduces high uncertainties in modelling carbon fluxes.

In contrast, random forest-based metamodel preserved the key relationships between NEE and input variables (air and soil temperature, water table, precipitation, vegetation, and soil properties) as described in the Wetland-DNDC model with lower parameter requirements (less than 20) and increased accuracy. Similar unique relationships were established for GPP and ER. The random forest-based metamodel represented the Wetland-DNDC model  within the spectrum of input values and parameters across which it was simulated.

The simulation was conducted in two locations across Scotland with contrasting contemporary carbon dynamics: a near natural blanket bog in Cross Lochs, Forsinard, currently functioning as a resilient net carbon dioxide sink (UK-CLS; Lat. = 58.37, Long. = -3.96; altitude = 207 m) and an eroding oceanic blanket bog located in the Cairngorms, currently net emitting carbon dioxide (UK-BAM; Lat. = 56.92, Long. = -3.15, altitude = 642 m). The simulation was validated against eddy covariance flux measurements under varying climate conditions.

In contrast to Wetland-DNDC (R2 = 0.43), the metamodel provided a much-improved fit to the 1:1 line for NEE (R2 = 0.83). Model accuracy was slightly lower for the former (RMSE = 0.72) compared to its metamodel version (RMSE = 0.699). Similar trends were observed for GPP and ER simulations. At a monthly resolution, Wetland-DNDC-derived NEE, GPP, and ER consistently deviated by more than 20% from the eddy covariance-derived estimates, whereas its metamodel version showed deviations of less than 10%. Currently, work is in progress to incorporate management and drought simulation within a metamodel framework, as well as to upscale carbon fluxes from tower to landscape resolution.

The simulation of carbon fluxes using the metamodel-based approach holds the promise of enhancing emission reporting to Tier 3 standards and offers a hopeful avenue for modelling carbon dynamics in peatlands.

How to cite: Mitra, B., Yeluripati, J., Cash, J., Toca, L., Coyle, M., and Artz, R.: Metamodel simulation of carbon fluxes across an eroding and pristine blanket bog in Scotland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8466, https://doi.org/10.5194/egusphere-egu24-8466, 2024.

The Eddy covariance (EC) is a well-known technique used (among others) to investigate the ecosystem exchange of greenhouse gasses (GHGs) between the biosphere and the atmosphere (Burba et al., 2007), often required in studies on soil-plant-atmosphere interactions and GHG emissions/removals from different soil systems. The long data records from EC measurements often experience data gaps due to various reasons (BaldocchiI, 2003) resulting in  many gap-filling methods being developed over the past decades. This study is introducing the new ’miniRECgap’ (Premrov, 2024) computational tool, which is using so-called ‘classic’, traditional robust and validated modelling approaches for gap-filling the missing EC CO₂ flux measurements,  based on the application of environmental temperature and light response functions (Lloyd and Taylor, 1994; Rabinowitch, 1951) in combination with empirical/semi-empirical parameter-optimisation. ‘miniRECgap’ is a very small R package that operates in a user-friendly way via GUI (Graphical User Interface) supported scripts. It is purposely designed to be simple, operating in only 5 steps. The application of ‘miniRECgap’ will be demonstrated using EC CO₂ flux data from an Irish peatland site Clara Bog. Due to its simplicity, it is thought that the new tool may be beneficial for new R users and that it may allow for easier and less time-consuming testing of the potential suitability of ‘classic’ empirical/semi-empirical gap-filling on different datasets.

Acknowledgements

The authors are grateful to the Irish Environmental Protection Agency (EPA) for funding the CO2PEAT project (2022-CE-1100) under the EPA Research Programme 2021-2030.

References

BaldocchiI, D.D. (2003) Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future.  9, 479-492.

Burba, G., Anderson, D., Amen, J., (2007) Eddy Covariance Method: Overview of General Guidelines and Conventional Workflow, AGU Fall Meeting Abstracts, pp. B33D-1575.

Lloyd, J., Taylor, J.A. (1994) On the temperature dependence of soil respiration. Functional Ecology 8, 315-323.

Premrov, A., (2024) miniRECgap. R package  with GUI suported scripts for gap-filling the of Eddy Covariance CO2 flux data.  Copyright: Trinity College Dublin. URL:  'miniRECgap package will be uploaded on GitHub in near future'.

Rabinowitch, E.I. (1951) Photosynthesis and Related Processes. Interscience Publishers.

How to cite: Premrov, A., Yeluripati, J., and Saunders, M.: Introducing the ’miniRECgap’ package with GUI-supported R-scripts for simple gap-filling of Eddy Covariance CO2 flux data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6475, https://doi.org/10.5194/egusphere-egu24-6475, 2024.

Peatland restoration and rehabilitation action has become more widely acknowledged as a necessary response to mitigating climate change risks and improving global carbon storage. Peatland ecosystems require restoration timespans on the order of decades and thus cannot be dependent upon the shorter-term monitoring often carried out in research projects. Hydrological assessments using geospatial tools provide the basis for planning restoration works as well as analysing associated environmental influences. “Restoration” encompasses applications to pre- and post-restoration scenarios for both bogs and fens, across a range of environmental impact fields. A scoping review was carried out to identify, describe, and categorise current process-based modelling uses in peatlands in order to investigate the applicability and appropriateness of eco- and/or hydrological models for northern peatland restoration. Two literature searches were conducted using the Web of Science entire database in September 2022 and August 2023. Of the final 211 papers included in the review, models and their applications were categorised according to this review’s research interests in 7 distinct categories aggregating the papers’ research themes and model outputs. Key themes emerging from topics covered by papers in the database included: modelling restoration development from a bog growth perspective; the prioritisation of modelling GHG emissions dynamics as a part of policymaking; the importance of spatial connectivity within or alongside process-based models to represent heterogeneous systems; and the emerging prevalence of remote sensing and machine learning techniques to predict restoration progress with little physical site intervention. Based on this assessment, CoupModel, DigiBog, and MPeat2D were calibrated for the case of Abbeyleix Bog, Co. Laois, Ireland (ongoing with results expected before April 2024). The exploration of subsequent simulations to represent varying peatland restoration conditions is discussed from an ecohydrological lens.

How to cite: Silva, M.: Ecohydrological modelling on peatlands: scoping review and application of three process-based models to Irish raised bog restoration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18533, https://doi.org/10.5194/egusphere-egu24-18533, 2024.

Soil erosion constitutes an increasing threat to soil productivity and food security. This work describes the potential of using Artificial Neural Networks (ANN) for upscaling soil loss outputs from medium to low scale. The Revised Universal Soil Loss Equation (RUSLE) model was implemented to calculate soil loss rates in two scales in Crete, Greece. Specifically, the RUSLE model was applied in six (6) watersheds across the island using medium spatial resolution satellite images (5m), namely Planetscope. These results were used to feed an ANN model to upscale the mesoscale outputs (5m) to regional outputs (30m-island level). The ANN system was trained using spatial environmental parameters such as the Normalized Difference Vegetation Index, Digital Elevation Model, and topographical slope angle. This "optimized" soil loss derivative later made it possible to compare it with the corresponding final derivative of Crete (regional spatial scale), which emerged from the straightforward processing of RUSLE model with the more "coarse" and generalized data as estimated from the  Landsat-8 satellite images (30m). The statistics revealed that the detailed and high-quality soil loss data, as derived from the upscaling process, provide more precise and reliable results.

How to cite: Alexakis, D. D. and Polykretis, C.: Using Artificial Neural Networks to upscale soil erosion model results from local to regional scale. An example from Crete, Greece., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14678, https://doi.org/10.5194/egusphere-egu24-14678, 2024.

The Soil Fertility (SoilFer) project, led by the Land and Water Division at FAO, seeks to enhance agricultural practices and resilience globally, starting with five countries (Guatemala, Honduras, Zambia, Kenya, and Ghana). The project collaborates with governments and relevant national partners to establish comprehensive national monitoring and mapping systems for soil management, catering to the diverse needs of agriculture stakeholders. The Soil and Water Management Laboratory at the Joint FAO/IAEA Center serves as a crucial hub for advancing research and technical expertise in soil and water management using nuclear and related techniques. Through its multifaceted approach in collaboration with the Land and Water Division, the laboratory contributes significantly to the SoilFer project, through the development and implementation of technical training programs for and expert advising on the application of Mid-Infrared Spectroscopy (MIRS), Cosmic Ray Neutron Sensor (CRNS), and Gamma Ray Spectroscopy (GRS) to soil monitoring and mapping.

The integration of MIRS, CRNS, and GRS technologies within the SoilFer project forms a robust framework for soil monitoring and mapping, as MIRS has been shown to provide detailed insights into soil composition and carbon content, CRNS offers real-time data on soil moisture dynamics, and GRS contributes to the analysis of radioactive isotopes and elemental composition. Given the integrated nature of landscape processes, the adoption of technological approaches must mirror this complexity. Interconnected ecological, hydrological, and geological processes within landscapes necessitate a holistic and integrated technological framework. This approach ensures that diverse data streams, derived from technologies such as remote sensing, geographic information systems (GIS), and advanced sensor networks, can be harmoniously synthesized. Only through such integration can a comprehensive understanding of landscape dynamics be achieved, facilitating informed decision-making and sustainable management practices across multifaceted environmental systems. The project emphasizes the seamless integration of these advanced technologies with soil monitoring and mapping systems, ensuring a comprehensive and effective approach to soil management practices, while improving national capacity and stakeholder engagement in data-based decision making. 

The key objectives of the SoilFer project encompass the development of robust national soil information systems, the implementation of decision support systems targeting soil health, and the promotion of sustainable soil management practices. By fostering collaboration and knowledge exchange, the project aspires to build technical, increase agricultural resilience and ensure food security in the participating countries.

How to cite: Vlasimsky, M., Dercon, G., Said Ahmed, H., Daraboe, S., Yigini, Y., Tong, Y., Peng, Y., Albinet, F., Heiling, M., and Resch, C.: The Joint FAO/IAEA Center and the Soil Fertility Project: Integrating Nuclear and Related Techniques for Modelling to Support Practical Decision Management Support, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5949, https://doi.org/10.5194/egusphere-egu24-5949, 2024.

The AquaCrop model is a powerful tool for crop monitoring, providing a daily estimation of soil-crop-atmosphere dynamics. The model requires a substantial number of input variables and parameters, highlighting the need for identifying those that significantly influence model outputs. Sensitivity analysis is a vital method for this purpose. A key objective of this study is to examine the performance of the AquaCrop model in simulating wheat yield and irrigation water requirement in drylands under two scenarios: first running the model employing a minimal amount of in situ data, and second using all available in situ data. A second focus is to analyze the sensitivity to all crop and soil related input variables and parameters. To do this, a pilot-scale study was undertaken, focusing on a commercial farm in the Al-Jouf province of Saudi Arabia. The farm comprised 200 center-pivot fields of mainly wheat crops. In situ data was collected to calibrate the model for two consecutive growing seasons (2019-2020 and 2020-2021). Using the variance-based Sobol technique, the sensitivity of the AquaCrop model outputs, particularly wheat yield and irrigation water requirement, to crop and soil related input variables and parameters was examined, as were the influential and non-influential inputs on these outputs. Results showed that the second scenario (all data) outperformed the first (minimal data), demonstrating more accurate wheat yield predictions with rRMSE values of 17% and 21% for the 2019-2020 and 2020-2021 growing seasons, respectively. Regarding irrigation water requirement estimations, the second scenario also exhibited lower rRMSE values of 20% and 19% for the same growing seasons. Results also demonstrated that the sensitivity indices of variables and parameters varied with model outputs and growing seasons. By synthesizing inputs sensitivities under different conditions, the influential input variables and parameters were distinguished. Overall, six variables and parameters held significant influence on the analyzed model outputs based on their total-order sensitivity indices. These included duration from sowing to senescence (senescence), duration from sowing to harvesting (maturity), duration from sowing to yield formation (HIstart), base temperature below which growth does not progress (Tbase), minimum air temperature below which pollination failure begins (Tmin_up), and shape factor describing reduction in biomass production (fshabe_b). It was revealed that most variables and parameters were non-influential, which might allow them to be fixed within their ranges to optimize model calibration. The research represents the performance assessment and sensitivity analysis of the AquaCrop model over a desert farming system and offers guidelines for model calibration by delivering information on influential and non-influential input variables and parameters.

How to cite: Almalki, A. S., El Hajj, M. M., Johansen, K., and McCabe, M. F.: A Comprehensive Assessment of the AquaCrop Model in drylands: Performance Examination and Sensitivity Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6832, https://doi.org/10.5194/egusphere-egu24-6832, 2024.

Integrated crop-livestock (ICL) systems can have a complex of effects on soil properties that can influence greenhouse gas emissions (GHG). The ICL aim to capture atmospheric CO₂ and sequester it in the soil, holding promise for reducing GHG emission intensity from livestock products. Moreover, modeling N₂O emissions can help assess the potential impact of N management on the ICL system to optimize the sustainability of agriculture production. Field data were obtained from an ICL experiment of EMBRAPA-Rice and Beans, located on Capivara farm, Santo Antônio de Goiás/GO, Brazil (16°28´S; 49°17´W; 823 m alt.). The ICL experiment was evaluated for four years (2013-2016) with the following crop rotation sequence: pasture-fallow-maize, fallow-soybean, maize-fallow-maize, and beans-fallow. The N₂O data was obtained from the 2013-14 season, which was measured in a static chamber during maize cultivation. The experiment consisted of 9 treatments (N sources and rates) with 5 replicates. The N₂O was measured in 30 sampling events over almost 100 days. The daily N₂O fluxes from the treatments control (No N), urea (UR), calcium ammonium nitrate (CAN), and ammonium sulfate (AS) at an N rate of 150 kg/ha were used to parametrize the DNDC. Model crop and soil parameters were adjusted to better simulate maize production and N₂O emission according to observed data. DNDC simulated CO₂ emissions, quantified as Net Ecosystem Exchange (NEE), were validated against CO₂ emissions derived from eddy-covariance data, using statistical parameters such as R², RMSE, MAE, and Bias. While data refinement is ongoing, preliminary findings indicate that DNDC shows promise for estimating CO₂ emissions IPS under tropical conditions The DNDC had a satisfactory performance in predicting N₂O emission in the ICL system, resulting in a significant correlation with the observed data (r = 0.63, p < 0.001), MAE of 0.024, and RMSE of 0.036. The average daily N₂O-N emission observed was 0.026 kg ha^-1 day^-1 and simulated was 0.025 kg ha^-1 day^-1. The UR, CAN and AS applications showed a peak of N₂O emission on 31^th day after sowing (2 days after fertilization) corresponding to 0.175, 0.217, and 0.163 kg ha^-1 day^-1, respectively, where the model simulated N₂O peaks of 0.151, 0.123, and 0.173 kg ha^-1 day^-1. The accumulated N₂O emissions were 0.513, 1.148 1.738, and 0.890 kg ha^-1 for control, UR, CAN, and AS respectively, in which the simulated by DNDC were 0. 778, 1.612, 1.391, and 1.755 kg ha^-1. In general, the model had a good fit with daily N₂O emissions, but it tended to overestimate the N₂O emission from UR and AS, and underestimate from CAN. Further model parametrization and calibration may be necessary to better predict N₂O and CO₂ emissions. The DNDC satisfactory simulated the N₂O emissions from different N sources applied to ICL system, which can be used to evaluate the potential emissions and mitigation according to N management in ICL.

How to cite: Matos, P. S., Soares, J. R., Carvalho, M. C. S., Madari, B. E., Alves, B. J. R., Jantalia, C. P., Freitas, A. C. R., Mitraa, B., and Yelupirati, J.: Performance of the DNDC in Estimating CO 2 and N 2 O emissions of Integrated Crop-Livestock Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19814, https://doi.org/10.5194/egusphere-egu24-19814, 2024.

This study employs the PHYGROW simulation model to assess the 40-year dynamics of arid grassland in Jordan, focusing on the Leaf Area Index (LAI) as a pivotal indicator of vegetation health. The observed results reveal a notable decline in LAI over the study period, with the highest recorded value in 2005 (2.27) and a subsequent reduction to 1.68 in 2021. Rigorous statistical analyses, including regression analysis, confirm the significance of this downward trend, prompting further investigation into potential contributing factors such as changes in climate, land use, and soil conditions.

Interannual variability analysis identifies specific years marked by noteworthy LAI fluctuations, providing insights into the dynamic responses of the arid grassland ecosystem. Comparison with concurrent climate data underscores the intricate relationship between LAI trends and environmental variables. The study emphasizes the importance of continuous monitoring and understanding the underlying drivers of vegetation dynamics in arid regions.

The observed decrease in LAI holds implications for the overall health and resilience of the ecosystem, highlighting the need for informed decision-making in sustainable land management practices. These findings contribute significantly to the broader understanding of arid land dynamics, guiding future research and collaborative efforts with experts in related fields. Such collaborations are essential for enhancing the robustness and applicability of the results, ultimately informing conservation and resource management strategies tailored to the unique challenges of arid environments.

How to cite: Alhamad, M. N. and Abdullah, S.: Simulation Modeling of Arid Grassland Dynamics in Jordan: A 40-Year Analysis of Leaf Area Index Trends, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4117, https://doi.org/10.5194/egusphere-egu24-4117, 2024.

Plastic production worldwide has increased from 1.5 million tons in 1950 to 390.7 million tons in 2021.1 Nanoplastics (NPTs) have been considered an emergent contaminant entering the environment without any control since they can be formed by the degradation of large-sized plastic inadequately disposed of and considering that only 9% are effectively recycled.2 Just as the NPTs, nanoparticles (NPs) are considered emergent contaminants, and their application in different industrial products raises concern regarding the NPs entering the environment matrices.3 The soil bioaccessibility is an important parameter when considering the contaminants assessment evaluation with biological soil phase, and the study of soil liquid solution, which is called the soil pore water, can elucidate not only the bioaccessibility but also NPTs and NPs mobility, fate, and stability.4 The NPTs’ and NPs’ concentrations in the range of ng L-1 might be a limitation for their evaluation. However, spICP-MS can provide information on size, number concentration, and mass concentration, even in environmental conditions.5 In this study, a typical Brazilian soil used for plant cultivation (Latosol) was employed, and the soil moisture was controlled according to the field capacity determined in advance. Polystyrene (PS) nanoparticles with gold core and silver NPs (AgNPs), considering their abundance in different goods, were used as model nano-contaminants. The soil pore water was collected in two sampling points through a low-pressure lysimetric method using Rhizon® samplers once a week for 45 days of the experiment. In addition, the soil moisture was controlled by monitoring and adding more water to maintain the soil humidity, considering the three field capacity percentages studied. Results showed a downward trend in the number of particles detected in successive collections over time for both nano-contaminants. However, they also demonstrated different behaviours between them. The NPTs were bioaccessible in the pore water after the first days from the beginning of the experiments, and their concentration decreased constantly. At the same time, the NPs presented an inconstant transport through the soil column, gradually becoming bioaccessible. Finally, the concentration proved to be an important and decisive parameter, bringing essential discussion regarding the nano-contaminant's increasing concentration and behaviour in an environmental matrix, demonstrating the necessity to comprehend their interactions with the soil and between each other.

1 S. Maity, R. Guchhait, M. B. Sarkar and K. Pramanick, Plant. Cell Environ., 2022, 45, 1011–1028.
2 P. Zhou, L. Wang, J. Gao, Y. Jiang, M. Adeel and D. Hou, Soil Use Manag., 2023, 39, 13–42.
3 Q. Abbas, B. Yousaf, Amina, M. U. Ali, M. A. M. Munir, A. El-Naggar, J. Rinklebe and M. Naushad, Environ. Int., 2020, 138, 105646.
4 M. Di Bonito, N. Breward, N. Crout, B. Smith and S. Young, in Environmental Geochemistry, Elsevier, 2008, pp. 213–249.
5 J. Jiménez-Lamana, L. Marigliano, J. Allouche, B. Grassl, J. Szpunar and S. Reynaud, Anal. Chem., 2020, 92, 11664–11672.

How to cite: de Andrade, A., Arruda, M. A. Z., Miguel, S., Reynaud, S., and Jiménez-Lamana, J.: Transport and bioaccessibility of nano-contaminants in Brazilian latosol through pore water evaluation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22341, https://doi.org/10.5194/egusphere-egu24-22341, 2024.

In the context of recycling organic waste products or irrigation by treated wastewaters (re-use), the fate of human and veterinary pharmaceuticals in agricultural soils and consequent ground-water contamination are influenced by many factors, including soil properties controlling sorption and dissipation processes (Verlicchi et al., 2015, Mejías et al., 2021, Rietra et al., 2022). Sorption coefficients are among the most sensitive parameters in models used for risk assessment. However, for different classes of pharmaceuticals, the variations in sorption among different soil types are poorly described and understood (Kodesova et al., 2015). Here we reviewed sorption parameters for different classes of pharmaceuticals and their variation with selected soil properties. We also evaluated the sorption isotherms for three pharmaceuticals, ofloxacin, tetracycline, diclofenac and a bactericide,  riclocarban and ten soils from temperate and tropical regions, and assessed the impact of soil properties on Freundlich equation parameters Kf and n. Batch experiments were set up adapting OECD protocol and using initial concentration ranges from 5 to 1000 μg/L. For strongly sorbed molecules, namely ofloxacin, tetracycline and triclocarban, there were strong technical constraints for the quantification of equilibrium concentrations by LC-MS-MS. We used this knowledge from both literature review and experimental data to build pedotransfer functions that allow predicting sorption parameters for a wide range of soils. Sorption of ionizable pharmaceuticals was, in many cases, highly affected by soil pH and CEC whereas soil organic matter content remained a driving factor of sorption for neutral molecular forms.

References:
Kodesova, R., et al. (2015) Science of the Total Environment 511, 435–443.
Mejías, C. et al. (2021) Trends in Environmental Analytical Chemistry 30, e00125.
Rietra, R.P.P.J., et al. (2024) Heliyon 10 (2024) e23718.
Verlicchi, P. & Zambello, E., (2015) Science of The Total Environment 538, 750–767

How to cite: Benoit, P., Godard, C., Deschamps, M., Bernet, N., Delarue, G., Serre, V., and Haudin, C.-S.: Searching for pedotransfer functions to predict sorption of pharmaceuticals from soil properties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21616, https://doi.org/10.5194/egusphere-egu24-21616, 2024.

The proposal for the European Soil Monitoring Law includes an integrated value of soil water holding capacity to be determined as a proxy for soil quality for whole soil districts. As this is a relatively new but interesting approach, a number of details of the assessment procedure remain open at the current stage of formulation. The aim of this study is to quantify the effects of the choice of different options on the overall result, focusing on the delineation of soil districts in different sizes, the detailed definition of the respective soil property, and the treatment of sealed areas.

High-resolution data for soil hydrological properties for two Austrian provinces are used as a basis, including different approaches to calculate soil water holding capacity. The size of the study area corresponds to the maximum size of a soil district as proposed. Thus, a variation of three size levels is possible, namely the whole area, major river catchments, and agro-geographical sub-units. The term soil water holding capacity is basically defined in the proposed EU Directive, but several options for its determination are possible. We used two different pedotransfer functions to derive soil water holding capacity values and an additional method based on averaging results from randomly located sampling points. Soil sealing is a major threat to hydrological soil functionality, and its assessment over large areas is still not standardized. Here, the European LUISA land use/land cover dataset for 2020 (1 km resolution) and a national dataset with higher resolution are used. Both datasets are optionally overlaid with the Copernicus imperviousness layer involves gradual information about surface imperviousness.

By combining all these factors with each other, different ways were evaluated to determine the target value of soil water holding capacity integrated on a regional scale. Differences in the results and their sensitivity to input variations are quantified to inform policy decisions in the implementation of the European Soil Monitoring Law in the member states.

How to cite: Weninger, T., Schwaighofer, I., Darmann, F., Brunner, T., and Strauss, P.: Soil water holding capacity as descriptor of soil health at district scale – a sensitivity study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12129, https://doi.org/10.5194/egusphere-egu24-12129, 2024.

The Horizon Europe project LOESS ‘Literacy boost through an Operational Educational Ecosystem of Societal actors on Soil health’ officially started in June 2023 involving twenty partner organizations in fifteen countries across Europe, lead by the WILA Bonn Science Shop. The final goal of the project is to raise awareness on the importance of soil and of its functions and to increase soil literacy across Europe. To do so, the first step of the project activity is designed to map and connect multiple actors in Communities of Practice (CoPs) at the national level, and engage them to provide an overview of the current level of soil related knowledge and teaching programmes and materials, in order to identify the gap between this material and the educational needs amongst different levels of the society (from pupils to students to citizens).

The Italian chapter is led by two university research groups with different expertise (civil and environmental engineering at the University of Brescia on one hand and social sciences at the University of Sassari on the other) and one NGO (Controvento) whose mission is children not-formal education. The Italian CoP, led by the University of Brescia, is composed of 62 members from both the higher education and the research community, as well as from the primary and secondary education levels (teachers and pupils), from the productive sectors (farmers and spatial planners), from the politics world (local administrators) and from the civil society (NGOs and associations).

This contribution presents the activities performed so far, viz the stakeholder mapping, the creation of the CoP and its first meetings and the community-based participatory activity which was organized on the World Soil Day 2023.

How to cite: Peli, M., Dada, A., Barisani, F., Ventura, V., Pezzagno, M., Barontini, S., and Grossi, G.: The LOESS project to boost soil health literacy across Europe: The case of Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19820, https://doi.org/10.5194/egusphere-egu24-19820, 2024.

Land degradation processes have experienced a significant increase in recent decades, a trend that is projected to escalate further in the absence of any intervention. The need of adopting practices to contain, mitigate and restore degraded land have been stressed also by the new European Mission 'A Soil Deal for Europe'. To guide and support restoration actions, through a common and effective framework, an efficient monitoring approach and an adaptive ecological restoration process is needed. 

The NewLife4Drylands LIFE project provides a Protocol for design, implementation, and maintenance of restoration activities based on Nature-Based Solutions (NBS) within drylands. The Protocol, developed following the principles and inputs of some international restoration standards (SER, IUCN), is based on the identification and monitoring of degradation processes exploiting remote sensing capabilities, with the aim to integrate data derivation procedures into ecological restoration and maintenance activities. The Protocol is supported by a Decision-Making web-tool guiding trough the degradation processes, NBS along with indices/indicators with the aim to reduce the knowledge effort and helps in prioritizing options. 

The Newlife4drylands experience highlighted the heterogeneity and complexity of degradation processes, as resulted from a selected set of degraded pilot sites within Mediterranean Protected Areas, together with the issue for harmonization and standardization of ecological/physical indicators, especially those derived from satellite observations, when used as proxies of land degradation. The integrated use of both available field data (for short-term monitoring) and satellite data (for medium and long-term monitoring) have been explored to identify indicators for evaluating the effectiveness of planned restoration actions. This approach is geared, towards fostering adaptive and collaborative management of the ecological restoration process. 

Therefore, the Protocol acts as support tool for decision-makers, including public administration of Protected Areas, as well as technicians and planners. The proposed approach aims to raise awareness about the needs of drylands and opportunities provided by NBS. It serves as a guide for the identification of specific/local NBS for the restoration of drylands, beginning with the identification of degradation processes.

How to cite: D'Ambrogi, S., Assennato, F., Labadessa, R., Mazzetti, P., Rastelli, V., Riitano, N., and Tarantino, C.: NewLife4Drylands Protocol for dryland restoration in Protected Areas: an innovative tool to support restoration activities., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6126, https://doi.org/10.5194/egusphere-egu24-6126, 2024.

In the Sahel region, landscape configuration is closely linked to factors such as climate, ecology, soil composition, agronomy, livestock, and biology. Over the past decades, significant changes in these factors have been observed, including shorter rainy seasons, irregular precipitation, a decrease in biomass productivity, and rapid population growth, negatively impacting local agricultural and pastoral systems. In response to this pressure, mitigation strategies have been implemented to contribute to the improvement of local food, nutritional, and economic security. Agroforestry systems, involving a combination of trees, shrubs, crops, and animals in the same plot, represent one of these strategies. Therefore, characterizing these systems in the current context of climate change is crucial for sustainable natural resource management.

In this study, three agroforestry landscapes of the Senegalese Sahel were described, spanning a bioclimatic gradient from the Louga region (Ouarkhokh) in the north to the Fatick region (Niakhar) in the center, and the Tambacounda region (Koussanar) in the south. The data utilized included satellite imagery synthesis (Sentinel-2 and Spot), landscape variables (rainfall, evapotranspiration, biomass, and vegetation), spectral indices (NDVI, NDRE, GNDVI), and field data on land use and woody cover. The methodology consisted of three main approaches: (i) landscape stratification involving Sentinel image segmentation in 2021, selection of relevant landscape variables, and mixed discriminant factor analysis to establish landscape heterogeneity; (ii) land use and land cover mapping through supervised pixel-based classification using a Random Forest (RF) machine learning classifier with 500 trees; (iii) floristic diversity analysis by assessing floristic composition and calculating diversity indices (i.e., Shannon, Pielou, and Simpson indices).

Landscape stratification identified seven classes with distinct landscape characteristics. Classes (1, 2, and 4) in the Ouarkhokh site had lower average biomass, rainfall, and actual evapotranspiration values than classes (3 and 4) in the Niakhar site. Similarly, classes (5, 6, and 7) in the Koussanar area had higher average biomass, rainfall, and actual evapotranspiration values than the first two sites. Land use mapping showed vegetation predominance in the Ouarkhokh site, significance in the Koussanar site, and low presence in the Niakhar area. Other identified units (cultivated areas, built-up areas, water, and bare land) were dominant in the Niakhar area, present in the Koussanar site, and low in the Ouarkhokh area. Likewise, vegetation dominated in classes 1, 5, 6, and 7. Class 1 was exclusively found in Ouarkhokh, while classes 5, 6, and 7 were located in the Koussanar site. The majority of cultivated surfaces were in class 3, exclusively located in the Niakhar area. Species richness was higher in the Niakhar area (60 species, 21 families) and lower in the Koussanar area (56 species, 16 families) and Ouarkhokh area (31 species, 13 families). This landscape distribution of land use, landscape classes, and identified species highlights the influence of anthropogenic, soil-related, and climatic factors specific to each site.

How to cite: sylla, D., Diouf, A. A., and Ndao, B.: Variation of woody plants diversity and land use along a bioclimatic gradient of agroforestry landscapes in Senegalese Sahel, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5133, https://doi.org/10.5194/egusphere-egu24-5133, 2024.

In the framework of the NewLife4Drylands LIFE Preparatory project (LIFE20 PRE/IT/000007, 2021-2024) the estimation of SDG 15.3.1 indicator [1], adopted in the UNCCD’s Good Practice Guidance [2], was applied for evaluating Land Degradation (LD) in different Mediterranean Protected Areas (PA). To effectively support PAs managers, joint effort was made in the evaluation of SDG 15.3.1 indicator at the local scale by using satellite Remote Sensing data in the computation of the three main sub-indicators as trend in Land Cover (LC), Primary Production (PP) and Soil Organic Carbon (SOC) stock. Where feasible, local scale sub-indicators were not sourced from open-access global/European databases due to their lack of accuracy at the site scale [3]. LD was estimated not only for the whole PA but also for specific LC classes of interest, considering additional sub-indicators related to pressures and threats affecting the class. This study focuses on the dryland Alta Murgia (IT9120007) PA, in southern Italy, and the wetland Nestos River Delta (GR1150001) PA, in Greece. For Alta Murgia site, featuring semi-natural dry grassland habitats of community interest that are frequently subjected to fire events during the summer season, the Burn Severity (BS) index was included. BS trends were measured by assessing the difference in pre/post–fire Normalized Burn Ratio (NBR) index from Landsat data during summer. Baseline data from 2004, coinciding with the establishment of a National Park within PA, was compared with 2018 for validating field data availability. Nestos River Delta hosts the largest natural riparian forest in Greece and is frequently subjected to hydrological cycle modifications, involving water scarcity due to both inappropriate river management and climate change, in turn hampering the transport of nutrient-rich sediments and the enrichment of soils being at risk of aridification. Within this framework, Hydroperiod and Soil Salinity indices were considered for LD and specific impacts on aquatic vegetation LC. Baseline data from 2017, after the dry climate conditions of 2016-2017, was compared with 2021 for validating field data availability. Both in Alta Murgia and Nestos, LC mappings were obtained by a data-driven pixel-based approach considering Landsat/Sentinel-2, respectively, multi-seasonal imagery and a multi-class Support Vector Machine (SVM) classifier trained with data from in-field campaigns and historical orthophotos interpretation. Time series of MSAVI from Landsat (which replaced standard NDVI for its soil correction benefits [4]) and PPI from Sentinel-2 by Copernicus services, respectively, were used to track grassland PP trends. Lastly, for SOC stock trends, the open-source Trends.Earth QGIS plugin [5], incorporating customized LC data and global SoilGrids product, was adopted to supplement local data limitations. According to its specification, the SDG 15.3.1 indicator was computed by integrating all the sub-indicators according to the principle “one out, all out” obtaining the 3-classes output mapping (Degradation, Improvement, Stable). The findings can support the monitoring and evaluation of LD, guiding protective measures aligned with the Agenda 2030 for Sustainable Development. They, also, highlight the importance of the integration of local scale data and sub-indicators within the UNCCD methodology.

References

[1] https://unstats.un.org/sdgs/metadata/files/Metadata-15-03-01.pdf

[2]https://www.unccd.int/publications/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded-over-total-land

[3] https://doi.org/10.3390/rs13020277

[4] https://doi.org/10.3390/rs12010083

[5] http://trends.earth/docs/en

How to cite: Tarantino, C., Aquilino, M., Vicario, S., Labadessa, R., Cambria, V. E., Georgiadis, C., Vitale, M., Assennato, F., and Mazzetti, P.: SDG 15.3.1 indicator at local scale for monitoring land degradation in protected areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10906, https://doi.org/10.5194/egusphere-egu24-10906, 2024.