Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

FlexMod

Financiado

Cerrado

A Flexible Data driven Model Framework to Predict Soil Responses to Land use an...

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. Soil C models are used to assess current organic C stocks and make predictions under future conditions. These models are ty... ver más

30/04/2022

UNIVERSITE DE VERS...

185K€

Presupuesto del proyecto: 185K€

Líder del proyecto

UNIVERSITE DE VERSAILLES SAINTQUENTINENYVELIN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-04-30

MSCA-IF-2018: Individual Fellowships

I+D

Cerrada hace 6 años

0% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

UNIVERSITE DE VERSAILLES SAI...

184.71K€ | Lider

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Proyectos interesantes

D3DC Studying the 3D dynamic evolution of soil organic carbon dri... 209K€ Cerrado

PHISHES PHysically-Based Integrated Soil HEalth Simulation Platform 6M€ Cerrado

SOBIGLOBIC Matching soil biodiversity with global biogeochemical cycles 342K€ Cerrado

CARBO-EXTREME The terrestrial Carbon cycle under Climate Variability and E... 5M€ Cerrado

MOSS Models of Soil Hydraulic Properties with Adjustable Soil Str... Cerrado

PACE A Precipitationshed Approach to Changing Extremes 188K€ Cerrado

Duración del proyecto: 35 meses Fecha Inicio: 2019-05-10
Fecha Fin: 2022-04-30

Líder del proyecto

UNIVERSITE DE VERSAILLES SAINTQUENTINENYVELIN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 185K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores