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Inter- and Transdisciplinary Sessions
SSS – Soil System Sciences
Programme group chairs:
Jose Alfonso Gomez,
David C. Finger
SSS11 – Metrics, Informatics, Statistics and Models in Soils
Soil biogeochemical data-modeling integration
Soil biogeochemical data-modeling integration focuses on:
- soil hydrology and its links with soil respiration and biogeochemistry
- biogeochemical processes studied in feedbacks with soil structure and by high-resolution imaging
- biogeochemical models development and up-scaling issues
Water is a critical driver for soil biogeochemical processes. Hydrologic connections within the soil pore network facilitate flow and transport that enable microbial processing of soil organic materials, and other redox-associated biogeochemical processes. As extreme events such as droughts and storms increase in frequency, a focused understanding of the coupling between water, microorganisms, and biogeochemistry is needed to improve both empirical understanding and simulation models of C cycling processes at all scales. Dormant microorganisms may revive, or functional shifts in microbial activities may occur, that can be related to changing hydrologic states. Studies that couple hydrology to soil structure, microbial C cycling and biogeochemistry are welcome, as are those that emphasize ‘omics-based diagnostics or metrics for monitoring and predicting soil microbial community activities and biodiversity in response to hydrologic changes.
Digital Soil mapping for Soil Sustainability and Security
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. 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 production of high-quality soil maps is a key issue because it enables stakeholders (e.g. farmers, planners, other scientists) to understand the variation of soils at the landscape, field, and sub-field scales. The products of digital soil mapping should be integrated within other environmental models for assessing and mapping soil functions and addressing soil security issues and support sustainable management.
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 also welcome.
Modelling of soil functions in agricultural systems
The importance of soil quality and its functions such as nutrient cycling, carbon sequestration, water quality and biodiversity for a sustainable agriculture is more and more recognized. As a limited resource, soil is permanently under pressure and new management strategies for optimizing yields are developed continuously. It often remains unclear how such management strategies influence the various soil functions and their interactions.
Computational models can help to understand and predict effects of a changing environment on soil functions and their relationship by describing soil processes and organism dynamics. However, combining different interrelated functions and processes of a complex system such as soil remain rather challenging.
With this session, we want to address several open questions for tackling this challenge, including (but not limited to): How to quantify soil functions for parametrizing such models? What is the specific relationship between different soil functions? How much details are needed to adequately describe the system, while keeping models simple enough for understanding their dynamics? How important is the incorporation of space? What can we gain from such models to optimize field experiments? How should such models be designed to provide implications for management strategies?
We invite contributions on theoretical and mechanistic simulation models incorporating one or more soil functions relevant for agricultural systems; as well as experimental or field studies which may help to improve modelling approaches.
We especially aim to stimulate a discussion with experts from various fields of soil science including biology, physics, and chemistry.
Remote Sensing and Coupled Data Assimilation for Earth System Models and their Compartments
Data assimilation is becoming more important as a method to make predictions of Earth system states. Increasingly, coupled models for different compartments of the Earth system are used. This allows for making advantage of varieties of observations, in particular remotely sensed data, in different compartments. This session focuses on weakly and strongly coupled assimilation of in situ and remotely sensed measurement data across compartments of the Earth system. Examples are data assimilation for the atmosphere-ocean system, data assimilation for the atmosphere-land system and data assimilation for the land surface-subsurface system. Optimally exploiting observations in a compartment of the terrestrial system to update also states in other compartments of the terrestrial system still has strong methodological challenges. It is not yet clear that fully coupled approaches, where data are directly used to update states in other compartments, outperform weakly coupled approaches, where states in other compartments are only updated indirectly, through the action of the model equations. Coupled data assimilation allows to determine the value of different measurement types, and the additional value of measurements to update states across compartments. Another aspect of scientific interest for weakly or fully coupled data assimilation is the software engineering related to coupling a data assimilation framework to a physical model, in order to build a computationally efficient and flexible framework.
We welcome contributions on the development and applications of coupled data assimilation systems involving models for different compartments of the Earth system like atmosphere and/or ocean and/or sea ice and/or vegetation and/or soil and/or groundwater and/or surface water bodies. Contributions could for example focus on data value with implications for monitoring network design, parameter or bias estimation or software engineering aspects. In addition, case studies which include a precise evaluation of the data assimilation performance are of high interest for the session.