Daniele de Rigo

European Commission, Joint Research Centre (JRC)

External consultant

Via E. Fermi 2749

Ispra (VA), I-21027








“  As a computational scientist, I have always been working in the field of integrated natural resources management and modelling (INRMM) – particularly forests, soil, water resources. I use machine learning and advanced statistics in modular, semantically-enhanced modelling integrating uneven arrays of information (semantic array programming). Since several years, I focused on modelling forest tree species distribution and suitability in Europe, also under climate change – and forest resources disturbances (wildfires, forest pests), their effects on soil resources (soil erosion, landslides) and the transdisciplinary multiplicity of factors affecting natural disaster analysis, mitigation and management. I am interested in how to facilitate INRMM synergies, integration and scalability to better support policy-making for environmental sustainability and society resilience, and to help to move scientific research toward stronger robustness to uncertainty, reproducibility and cooperation  ”

Scholarly Papers (1)


Robust Modelling of the Impacts of Climate Change on the Habitat Suitability of Forest Tree Species

de Rigo, D., Caudullo, G., San-Miguel-Ayanz, J, Barredo, J.I., 2017. Robust modelling of the impacts of climate change on the habitat suitability of forest tree species. Publication Office of the European Union, 58 pp. ISBN:978-92-79-66704-6 , doi/10.2760/296501
Number of pages: 58 Posted: 05 May 2017
European Commission, Joint Research Centre (JRC), European Commission Joint Research Center - Bio-Economy Unit, European Commission Joint Research Center - Disaster Risk Management Unit and European Commission Joint Research Center - Bio-Economy Unit
Downloads 564 (47,274)



Abies alba, ANN, climate change, diversity, Europe, forest resources, free software, fuzzy, GDAL, GNU bash, GNU/Linux, GNU Octave, integrated modelling, Mastrave modelling library, Maximum Habitat Suitability, Relative Distance Similarity, robust modelling, Semantic Array Programming, uncertainty