FlexMod · A Flexible, Data-driven Model Framework to Predict Soil Responses to Land-use and Climate Change
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2020-05-01 → 2022-04-30
- EU contribution
- €184,708
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
A Flexible, Data-driven Model Framework to Predict Soil Responses to Land-use and Climate Change
Soil organic matter is the largest land carbon (C) pool, vulnerable to land-use change and climate change. Given initiatives to increase land C storage such as ‘4 per mil’, the Bonn Challenge, and UN REDD, there is now a critical need for robust soil C stock change predictions to evaluate the effectiveness of soil management decisions, in the presence of climate change affecting decomposition processes. Yet, soil C models have large disparities and uncertainties in their projections due to an inadequate model structure to relate to existing measurements. In this project, the first objective is to develop a soil carbon modeling framework that incorporates measurements and their uncertainties from long-term warming and management experiments. The aim is to use standard statistical and Bayesian methods to calibrate the Millennial model at the site scale as well as using soil C pools that are changing decadally in response to management. The second objective is to incorporate the calibrated Millennial model into the Earth System global land surface model (ORCHIDEE) developed and used at LSCE, the host institution, for building an ensemble of future projections on decadal scales. The main delivered products are a state-of-the-art soil C cycling model representing measurable pools which is publicly available and constrained by data at global scales, as well as advances in understanding about the capacity of soils to store C and their sensitivity to climate and land management.
Data: CORDIS, © European Union
Project 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.
Original text from CORDIS.
Participants
- UNIVERSITE DE VERSAILLES SAINT-QUENTIN EN YVELINES · VERSAILLESCoordinatorFrance
Links
Data: CORDIS, © European Union