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A Flexible, Data-driven Model Framework to Predict Soil Responses to Land-use and Climate Change

Objective

Soil organic matter is the largest land carbon (C) pool, vulnerable to land-use change and climate change. Soil C models are used to assess current organic C stocks and make predictions under future conditions. These models are typically developed to make predictions over centennial timescales. Given the ‘4 per mil’ initiative, there is now a critical need for annual-to-decadal soil C stock predictions to evaluate land management decisions and hold participants accountable to stated goals. The project proposes a new soil model framework to make predictions at annual-to-decadal timescales by developing a Bayesian forecasting model from a deterministic soil carbon model with the capacity to ingest multiple data types, propagate uncertainty from data and parameters into predictions, and update predictions when new data become available. The main focus is probabilistic prediction of soil C changes under land use and climate change for the next two decades. Specifically, the project plans build a forecasting model version of the Millennial model, recently developed by the researcher with University colleagues. The Millennial model is an evolution of the commonly used soil C model Century – also incorporated in the global land surface model (ORCHIDEE) of the host institution (LSCE) - but in contrast to Century, Millennial includes soil pools that correspond directly to measurements. First, we will develop the Bayesian calibration of modeled temperature response against warming experiment data, using the Millennial model to integrate measurements from the multi-national, collaborative whole-soil warming experiments FORHOT and BBSFA. Then, we will develop the Bayesian calibration of the modeled land management response against field data with different amounts and quality of added litter. We will then incorporate this new model into ORCHIDEE to predict soil C storage for near term land-based mitigation objectives of the Paris Climate Agreement.

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Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

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Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) H2020-MSCA-IF-2018

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Coordinator

UNIVERSITE DE VERSAILLES SAINT-QUENTIN EN YVELINES
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 184 707,84
Address
AVENUE DE PARIS 55
78035 VERSAILLES
France

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Region
Ile-de-France Ile-de-France Yvelines
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 184 707,84
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