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

Información del proyecto

FlexMod

Identificador del acuerdo de subvención: 834169

DOI

10.3030/834169

Proyecto finalizado el 31 Diciembre 2021

Fecha de la firma de la CE 10 Mayo 2019

Fecha de inicio 1 Mayo 2020

Fecha de finalización 30 Abril 2022

Financiado con arreglo a

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coste total

€ 184 707,84

Aportación de la UE

€ 184 707,84

Coordinado por

UNIVERSITE DE VERSAILLES SAINT-QUENTIN EN YVELINES
France

Objetivo

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.

Ámbito científico (EuroSciVoc)

• ingeniería y tecnología › ingeniería ambiental › gestión de recursos naturales › ordenación del territorio
• ciencias naturales › ciencias de la tierra y ciencias ambientales conexas › ciencias de la atmósfera › climatología › cambios climáticos

Palabras clave

• carbon cycling
• soil warming
• land use change
• soil decomposition
• predictive modeling
• Bayesian prediction

Programa(s)

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions PROGRAMA PRINCIPAL
  
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
  

Tema(s)

• MSCA-IF-2018 - Individual Fellowships
  

Régimen de financiación

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

Convocatoria de propuestas

H2020-MSCA-IF-2018

Coordinador

UNIVERSITE DE VERSAILLES SAINT-QUENTIN EN YVELINES

• Aportación neta de la UEn: € 184 707,84
• Dirección: AVENUE DE PARIS 55 ; 78035 VERSAILLES ; Francia
• Región: Ile-de-France › Ile-de-France › Yvelines
• Tipo de actividad: Higher or Secondary Education Establishments
• Coste total: € 184 707,84