Spatial soil information is fundamental for environmental modelling and land use management. Spatial representation (maps) of separate soil attributes (both laterally and vertically) and of soil-landscape processes are needed at a scale appropriate for environmental management. The challenge is to develop explicit, quantitative, and spatially realistic models of the soil-landscape continuum to be used as input in environmental models, such as hydrological, climate or vegetation productivity (crop models) while addressing the uncertainty in the soil layers and its impact in the environmental modelling. This contemporary research would greatly benefit from synergies between pedometrics and spectroscopy/remote sensing scientists. There is the need to create models linking soil properties with ancillary environmental variables, such as proximal and remote sensing data. Modern advances in soil sensing, geospatial technologies, and spatial statistics are enabling exciting opportunities to efficiently create soil maps that are more consistent, detailed, and accurate than previous maps while providing information about the related uncertainty. The pillars of this paradigm are: a) the link between spectroscopy and wet soil laboratory analysis, seeking for the best strategy to evolve soil quality analysis; b) the link between proximal and remote sensing, with soil analysis; c) the link between proximal/remote sensing and pedometrics for extrapolating relationships established at point support to the spatial and temporal extent covered by proximal/remote sensing. Examples of implementation and use of digital soil maps in different disciplines such as agricultural (e.g. crops, food production) and environmental (e.g. element cycles, water, climate) modelling are welcomed. All presentations related to the tools of digital soil mapping, the philosophy and strategies of digital soil mapping at different scales and for different purposes are welcome.

In complex systems, such as terrestrial ecosystems uncertain information (whether in observation, measurement, interpretation or models) is the norm, and this impinges on most knowledge that earth scientists generate. It is important to quantify and account for uncertainty in our models and predictions otherwise results can be misleading. This is particularly important when predictions are to be used in a decision-making process where the end user needs to be able to properly evaluate the risk involved.

Quantitative estimation of uncertainty is a difficult challenge, that continually calls for the development of more refined tools. Many diverse methods have been developed, such as non-linear kriging in spatial prediction, stochastic simulation modelling and other error propagation approaches and even methods including the use of expert elicitation, but many challenges still remain. A second and often overlooked challenge with uncertainty is how to communicate it effectively to the end users such as scientists, engineers, policy makers, regulators and the general public.

In this session, we will examine the state of the art of both uncertainty quantification and communication in earth systems sciences. We shall give attention to three components of the problem: 1) new methods and applications of uncertainty quantification, 2) how to use such information for risk assessment, and 3) how to communicate it to the end-user. Dealing with uncertainty across all these three layers is a truly multidisciplinary task, requiring input from diverse disciplines (such as earth science, statistics, economics and psychology) to ensure that it is successful. The main aim of this session is to connect the three components of the problem, offering multiple perspectives on related methodologies, connecting scientists from different fields dealing with uncertainty and favouring the development of multidisciplinary approaches.

Soils provide many essential functions which are indispensable for terrestrial ecosystems and the health of human societies. Beyond the production of biomass these functions are nutrient cycling, filter and buffer for water, climate regulation and habitat for an overwhelming biodiversity.
In view of an increasing pressure on agricultural soils and the need for sustainable soil management all these functions need to be taken into account, especially in organic farming fields. They emerge from complex interactions between physical, chemical and biological processes in soil. This need to be understood and disentangled to predict soil quality and the impact of agricultural soil management on soil functions by the use of indicators and simulation models.
Various international project consortiums are working on related research questions, such as the Soil Security Programme (SSP), BonaRes or LANDMARK. With this session, we aim to bring together the expertise of those and similar projects to combine the gained knowledge and identify still open research gaps for future work.
We seek contributions which (i) enhance our current process understanding of how soil management practices impact one or more soil functions, (ii) show how to quantify soil functions based on suitable proxies or indicators, (iii) present modelling approaches for simulating one or more soil functions, and (iv) demonstrate how soil functions resist and recover from perturbations. Advanced information technologies in modern decision support systems integrated along with large and complex databases, models, tools, and techniques, to improve the decision-making process in soil quality management are also welcome.

This session has been promoted by:
Sustainable Agro-ecosystems (AGRISOST, https://www.agrisost.org/en/)
International Soil Modeling Consortium (ISMC, https://soil-modeling.org/)

Soil organic matter (SOM) is an ecosystem property that emerges from a suite of complex biological, geochemical, and physical interactions across scales. As the largest pool of actively-cycling terrestrial carbon, understanding how SOM persistence and vulnerability will respond to global change is critical. However, Earth System Models (ESMs) are often unable to capture emergent SOM patterns and feedbacks at across smaller spatial and temporal scales. Identifying, prioritizing, and scaling key driving mechanisms from detailed process models to advance ESMs is crucial, and better empirical constraints on SOM pools and fluxes are urgently needed to advance understanding and provide model benchmarks. Interdisciplinary research and observation networks collecting long-term, geographically-distributed data can help elucidate key mechanisms, and international efforts that synthesize and harmonize these data are needed to inform data-model comparisons.

We invite theoretical and empirical contributions that investigate controls on SOM across scales, from detailed process understanding to emergent landscape-scale dynamics in natural and managed ecosystems. We seek modelling studies that work across scales, data analyses that leverage multi-site networks and/or long-term experiments, or collaborations between empiricists and modelers within and across networks. Studies that use novel tools across scales, from microbial -omics to remote sensing, are also welcome.

This session has been promoted by:
• Sustainable Agro-ecosystems (AGRISOST, https://www.agrisost.org/en/)
• International Soil Modeling Consortium (ISMC, https://soil-modeling.org/)

Geostatistics is commonly applied in the Water, Earth and Environmental sciences to quantify spatial variation, produce interpolated maps with quantified uncertainty and optimize spatial sampling designs. Extensions to the space-time domain are also a topic of current interest. Due to technological advances and abundance of new data sources from remote and proximal sensing and a multitude of environmental sensor networks, big data analysis and data fusion techniques have become a major topic of research. Furthermore, methodological advances, such as hierarchical Bayesian modeling and machine learning, have enriched the modelling approaches typically used in geostatistics.

Earth-science data have spatial and temporal features that contain important information about the underlying processes. The development and application of innovative space-time geostatistical methods helps to better understand and quantify the relationship between the magnitude and the probability of occurrence of these events.

This session aims to provide a platform for geostatisticians, soil scientists, hydrologists, earth and environmental scientists to present and discuss innovative geostatistical methods to study and solve major problems in the Water, Earth and Environmental sciences. In addition to methodological innovations, we also encourage contributions on real-world applications of state-of-the-art geostatistical methods.

Given the broad scope of this session, the topics of interest include the following non-exclusive list of subjects:
1. Advanced parametric and non-parametric spatial estimation and prediction techniques
2. Big spatial data: analysis and visualization
3. Optimisation of spatial sampling frameworks and space-time monitoring designs
4. Algorithms and applications on Earth Observation Systems
5. Data Fusion, mining and information analysis
6. Integration of geostatistics with optimization and machine learning approaches
7. Application of covariance functions and copulas in the identification of spatio-temporal relationships
8. Geostatistical characterization of uncertainties and error propagation
9. Bayesian geostatistical analysis and hierarchical modelling
10. Functional data analysis approaches to geostatistics
11. Geostatistical analysis of spatial compositional data
12. Multiple point geostatistics
13. Upscaling and downscaling techniques
14. Ontological framework for characterizing environmental processes

Modeling soil and vadose zone processes is vital for estimating physical states, parameters and fluxes from the bedrock to the atmosphere. While the media soil, air and water physically affect biogeochemical processes, transport of nutrients and pollutants, and infiltration-runoff generation, the implications on ecosystem functions and services and terrestrial storage capacities are vital to the understanding of global, land use and climate change. Advanced measurement techniques, increased availability of high-frequency models and data, and the need for terrestrial system understanding challenge vadoze zone modeling concepts, budging model parameterizations from static to near dynamic. This session aims to bring together scientists advancing the current status in modelling soil processes from the pore to the catchment and continental scale. We welcome contributions with a specific focus on soil hydrological processes but also those that address the role of soil structure on land surface processes, soil biogeochemical processes and their interactions with hydrology, transport of pollutants, and soil vegetation atmosphere modelling.

The terrestrial vegetation carbon balance is controlled not just by photosynthesis, but by respiration, carbon allocation, turnover (comprising litterfall, background mortality and disturbances) and wider vegetation dynamics. Observed, and likely future, changes in vegetation structure and functioning are the result of interactions of these processes with atmospheric carbon dioxide concentration, climate and human activities. The quantification and assessment of such changes has proven extremely challenging because of a lack of observations at large scales and over the long time periods required to evaluate trends.

Thus, our current understanding of the environmental controls on vegetation dynamics and properties, and, in turn, their impact on carbon stocks in biomass and soils, is limited. The behaviour of vegetation models regarding many of the processes mentioned above remains under-constrained at scales from landscape to global. This gives rise to high uncertainty as to whether the terrestrial vegetation will continue to act as a carbon sink under future environmental changes, or whether increases in autotrophic respiration or carbon turnover might counteract this negative feedback to climate change. For instance, accelerated background tree mortality or more frequent and more severe disturbance events (e.g. drought, fire, insect outbreaks) might turn vegetation into carbon sources. Likewise, understanding how these shifts in dynamics will influence forest composition is crucial for long-term carbon cycle projections.

Uncertainties and/or data gaps in large-scale empirical products of vegetation dynamics, carbon fluxes and stocks may be overcome by extensive collections of field data and new satellite retrievals of forest biomass and other vegetation properties. Such novel datasets may be used to evaluate, develop and parametrize global vegetation models and hence to constrain present and future simulations of vegetation dynamics. Where no observations exist, exploratory modelling can investigate realistic responses and identify necessary measurements. We welcome contributions that make use of observational approaches, vegetation models, or model-data integration techniques to advance understanding of the effects of environmental change on vegetation dynamics, tree mortality and carbon stocks and fluxes at local, regional or global scales and/or at long time scales.

Environmental systems often span spatial and temporal scales covering different orders of magnitude. The session is oriented in collecting studies relevant to understand multiscale aspects of these systems and in proposing adequate multi-platform and inter-disciplinary surveillance networks monitoring tools systems. It is especially aimed to emphasize the interaction between environmental processes occurring at different scales. In particular, a special attention is devoted to the studies focused on the development of new techniques and integrated instrumentation for multiscale monitoring high natural risk areas, such as: volcanic, seismic, energy exploitation, slope instability, floods, coastal instability, climate changes and other environmental context.
We expect contributions derived from several disciplines, such as applied geophysics, geology, seismology, geodesy, geochemistry, remote and proximal sensing, volcanology, geotechnical, soil science, marine geology, oceanography, climatology and meteorology. In this context, the contributions in analytical and numerical modeling of geological and environmental processes are also expected.
Finally, we stress that the inter-disciplinary studies that highlight the multiscale properties of natural processes analyzed and monitored by using several methodologies are welcome.

A key goal within geomorphic research is understanding the processes linking topographic form, erosion rates, and sediment production, transport and deposition. Numerical modelling, by allowing the creation of controlled analogues of natural systems, provides exciting opportunities to explore landscape evolution and generate testable predictions.

In this session, we invite contributions that use numerical modelling to investigate landscape evolution in a broad sense, and over a range of spatial and temporal scales. We welcome studies using models to constrain one or more of: erosion rates and processes, sediment production, transport and deposition, and sediment residence times. We also particularly wish to highlight studies that combine numerical modelling with direct Earth surface process monitoring techniques, such as topographic, field, stratigraphic, or geochronological data. Contributions using numerical models to unravel the interaction between environmental variables such as precipitation and lithology are further encouraged. There is no geographical restriction: studies may be focused on mountain environments or sedimentary basins, or they may establish links between the two. Studies beyond planet Earth are welcome too.

The interactions between geo-environmental and anthropic processes are increasing due to the ever-growing population and its related side effects (e.g., urban sprawl, land degradation, natural resource and energy consumption, etc.). Natural hazards, land degradation and environmental pollution are three of the possible “interactions” between geosphere and anthroposphere. In this context, spatial and spatiotemporal data are of crucial importance for the identification, analysis and modelling of the processes of interest in Earth and Soil Sciences. The information content of such geo-environmental data requires advanced mathematical, statistical and geomorphometric methodologies in order to be fully exploited.

The session aims to explore the challenges and potentialities of quantitative spatial data analysis and modelling in the context of Earth and Soil Sciences, with a special focus on geo-environmental challenges. Studies implementing intuitive and applied mathematical/numerical approaches and highlighting their key potentialities and limitations are particularly sought after. A special attention is paid to spatial uncertainty evaluation and its possible reduction, and to alternative techniques of representation of spatial data (e.g., visualization, sonification, haptic devices, etc.).

In the session, two main topics will be covered (although the session is not limited to them!):
1) Analysis of sparse (fragmentary) spatial data for mapping purposes with evaluation of spatial uncertainty: geostatistics, machine learning, statistical learning, etc.
2) Analysis and representation of exhaustive spatial data at different scales and resolutions: geomorphometry, image analysis, machine learning, pattern recognition, etc.