> Toolkit
Tools & Models
Different models and research tools help both stakeholders and scientists understand environmental and anthropogenic effects and impacts on forest soils. Some models have been improved by researchers in the HoliSoils consortium by refining and providing new inputs to existing models and analytical tools in soil science.
ORCHIDEE Global Land Surface Model
ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) is the land surface model developed by the Pierre Simon Laplace Institute. ORCHIDEE simulates environmental processes such as water, energy, and carbon flow on land, helping scientists understand how the earth’s ecosystems and climate interact.
ECOSSE: Carbon in organic soils
ECOSSE (Estimation of Carbon in Organic Soils – Sequestration and Emissions) simulates soil carbon and nitrogen dynamics in both mineral and organic soils using meteorological, land use, land management and soil data, and simulates changes in organic carbon and greenhouse gas emissions.
EFISCEN Space: European Forest Information Scenario Model
The core of EFISCEN Space is the geographically explicit modelling of forest development in Europe at forest stand level, based on empirical tree plot data from the National Forest Inventory, under realistic forest management conditions.
The I+ Software
The I+ software is used to perform virtual tree selection exercises in designated forest training or marteloscopes. The tool was developed in the course of the projects Integrate+ and INFORMAR and continues to be under the European Network Integrate.
me4soc – Multi-model Ensemble interface for Soil Organic Carbon predictions
me4soc -Multi-model Ensemble interface for Soil Organic Carbon predictions – is a webtool designed to run a multi-model ensemble over European forest sites. It allows to predict the effect of climate, land-use and land management changes on soil organic carbon stocks and greenhouse gas emissions. By benefitting from the complementarity of structurally different models, it provides the level of uncertainty of the predictions.
