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

Projektinformationen

FlexMod

ID Finanzhilfevereinbarung: 834169

DOI 10.3030/834169

Das Projekt endete am 31 Dezember 2021

EK-Unterschriftsdatum 10 Mai 2019

Startdatum 1 Mai 2020

Enddatum 30 April 2022

Finanziert unter

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Gesamtkosten € 184 707,84

EU-Beitrag € 184 707,84

Koordiniert durch

UNIVERSITE DE VERSAILLES SAINT-QUENTIN-EN-YVELINES.
France

Ziel

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.

Wissenschaftliches Gebiet

• Naturwissenschaften › Geowissenschaften und verwandte Umweltwissenschaften › Atmosphärenwissenschaften › Klimatologie › Klimaänderungen

Schlüsselbegriffe

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

Programm/Programme

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

Thema/Themen

• MSCA-IF-2018 - Individual Fellowships

Aufforderung zur Vorschlagseinreichung

H2020-MSCA-IF-2018

Finanzierungsplan

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

Koordinator

UNIVERSITE DE VERSAILLES SAINT-QUENTIN-EN-YVELINES.

• Netto-EU-Beitrag: € 184 707,84
• Adresse: Avenue de paris 55 ; 78035 Versailles ; Frankreich
• Region: Ile-de-France › Ile-de-France › Yvelines
• Aktivitätstyp: Higher or Secondary Education Establishments
• Weitere Finanzmittel: € 0,00