Abstract. New (a)biotic conditions resulting from climate change are expected to change disturbance dynamics, such as windthrow, forest fires, droughts, and insect outbreaks, and their interactions. These unprecedented natural disturbance dynamics might alter the capability of forest ecosystems to buffer atmospheric CO2 increases, potentially leading forests to transform from sinks into sources of CO2. This study aims to enhance the ORCHIDEE land surface model to study the impacts of climate change on the dynamics of the bark beetle, Ips typographus, and subsequent effects on forest functioning. The Ips typographus outbreak model is inspired by previous work from Temperli et al.\u00a0(2013) for the LandClim landscape model. The new implementation of this model in ORCHIDEE r8627 accounts for key differences between ORCHIDEE and LandClim: (1)\u00a0the coarser spatial resolution of ORCHIDEE; (2)\u00a0the higher temporal resolution of ORCHIDEE; and (3)\u00a0the pre-existing process representation of windthrow, drought, and forest structure in ORCHIDEE. Simulation experiments demonstrated the capability of ORCHIDEE to simulate a variety of post-disturbance forest dynamics observed in empirical studies. Through an array of simulation experiments across various climatic conditions and windthrow intensities, the model was tested for its sensitivity to climate, initial disturbance, and selected parameter values. The results of these tests indicated that with a single set of parameters, ORCHIDEE outputs spanned the range of observed dynamics. Additional tests highlighted the substantial impact of incorporating Ips typographus outbreaks on carbon dynamics. Notably, the study revealed that modeling abrupt mortality events as opposed to a continuous mortality framework provides new insights into the short-term carbon sequestration potential of forests under disturbance regimes by showing that the continuous mortality framework tends to overestimate the carbon sink capacity of forests in the 20- to 50-year range in ecosystems under high disturbance pressure compared to scenarios with abrupt mortality events. This model enhancement underscores the critical need to include disturbance dynamics in land surface models to refine predictions of forest carbon dynamics in a changing climate.
", "keywords": ["cycle du carbone", "[SDE] Environmental Sciences", "http://aims.fao.org/aos/agrovoc/c_24242", "P40 - M\u00e9t\u00e9orologie et climatologie", "mod\u00e8le de simulation", "Ips typographus", "http://aims.fao.org/aos/agrovoc/c_16411", "http://aims.fao.org/aos/agrovoc/c_2391", "http://aims.fao.org/aos/agrovoc/c_1666", "K70 - D\u00e9g\u00e2ts caus\u00e9s aux for\u00eats et leur protection", "http://aims.fao.org/aos/agrovoc/c_6111", "http://aims.fao.org/aos/agrovoc/c_4549f84e", "perturbation de l'\u00e9cosyst\u00e8me", "surveillance \u00e9pid\u00e9miologique", "mod\u00e9lisation", "s\u00e9cheresse", "changement climatique", "QE1-996.5", "http://aims.fao.org/aos/agrovoc/c_230ab86c", "U10 - Informatique", " math\u00e9matiques et statistiques", "Geology", "H10 - Ravageurs des plantes", "http://aims.fao.org/aos/agrovoc/c_331583", "s\u00e9questration du carbone", "dynamique des populations", "[SDE]Environmental Sciences", "http://aims.fao.org/aos/agrovoc/c_30153", "http://aims.fao.org/aos/agrovoc/c_17299"]}, "links": [{"href": "https://doi.org/20.500.11850/706699"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11850/706699", "name": "item", "description": "20.500.11850/706699", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11850/706699"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-11T00:00:00Z"}}, {"id": "11250/3082084", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:24:59Z", "type": "Journal Article", "created": "2023-05-08", "title": "Soil organic carbon models need independent time-series validation for reliable prediction", "description": "AbstractNumerical models are crucial to understand and/or predict past and future soil organic carbon dynamics. For those models aiming at prediction, validation is a critical step to gain confidence in projections. With a comprehensive review of ~250 models, we assess how models are validated depending on their objectives and features, discuss how validation of predictive models can be improved. We find a critical lack of independent validation using observed time series. Conducting such validations should be a priority to improve the model reliability. Approximately 60% of the models we analysed are not designed for predictions, but rather for conceptual understanding of soil processes. These models provide important insights by identifying key processes and alternative formalisms that can be relevant for predictive models. We argue that combining independent validation based on observed time series and improved information flow between predictive and conceptual models will increase reliability in predictions.