A Flexible, Data-driven Model Framework to Predict Soil Responses to Land-use and Climate Change
Sector: Education • Location: France
Source: EU Funding & Tenders Portal
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 predic
Project Information FAQ
Project Information
Want to explore the full details? View the full report
Participants
Sponsoring Agency | Obfuscated Data |
Company | Obfuscated Data |
Status
Original status | ended |
Taiyo status | Obfuscated Data |
Taiyo last update | 00-00-0000 |
Available timestamps | 00-00-0000 |
Available timestamp type | Obfuscated Data |
Contact
Contact name | Obfuscated Data |
Phone | 0000000000 |
ObfuscatedData@email.com | |
Address | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Description
Description | 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 sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
Region | Obfuscated |
Country | Obfuscated |
State | Obfuscated Data |
County | Obfuscated |
Location | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
More Details
Project Type | Obfuscated Data |
Article Published Date | Obfuscated Data |
