{"id":51512911,"url":"https://github.com/computorg/published-202407-legrand-wildfires","last_synced_at":"2026-07-08T08:02:26.949Z","repository":{"id":247934012,"uuid":"827265097","full_name":"computorg/published-202407-legrand-wildfires","owner":"computorg","description":"Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change","archived":false,"fork":false,"pushed_at":"2026-07-07T07:23:07.000Z","size":32377,"stargazers_count":0,"open_issues_count":3,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2026-07-07T09:11:09.941Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"http://computo-journal.org/published-202407-legrand-wildfires/","language":"TeX","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc-by-4.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/computorg.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2024-07-11T10:06:34.000Z","updated_at":"2026-07-07T07:23:11.000Z","dependencies_parsed_at":"2024-07-28T01:36:15.726Z","dependency_job_id":"8a345929-4bb9-4f92-b3d2-a177260b9ca3","html_url":"https://github.com/computorg/published-202407-legrand-wildfires","commit_stats":null,"previous_names":["computorg/published-202407-legrand-wildfires"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/computorg/published-202407-legrand-wildfires","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202407-legrand-wildfires","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202407-legrand-wildfires/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202407-legrand-wildfires/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202407-legrand-wildfires/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/computorg","download_url":"https://codeload.github.com/computorg/published-202407-legrand-wildfires/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202407-legrand-wildfires/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35257142,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-08T02:00:06.796Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-07-08T08:02:26.047Z","updated_at":"2026-07-08T08:02:26.934Z","avatar_url":"https://github.com/computorg.png","language":"TeX","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change\nJuliette Legrand, François Pimont, Jean-Luc Dupuy, Thomas Opitz\n2024-07-12\n\n### Citation\n\nJuliette Legrand, François Pimont, Jean-Luc Dupuy and Thomas Opitz (July 2024). Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change. Computo.\n\u003chttps://doi.org/10.57750/4y84-4t68\u003e\n\n### Badges\n\n[![build and\npublish](https://github.com/computorg/published-202407-legrand-wildfires/actions/workflows/build.yml/badge.svg)](https://github.com/computorg/published-202407-legrand-wildfires/actions/workflows/build.yml)\n[![reviews](https://img.shields.io/badge/review-report-blue)](https://github.com/computorg/published-202407-legrand-wildfires/issues?q=is%3Aopen+is%3Aissue+label%3Areview)\n[![SWH](https://archive.softwareheritage.org/badge/origin/https://github.com/computorg/published-202407-legrand-wildfires)](https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/computorg/published-202407-legrand-wildfires)\n[![DOI:10.57750/4y84-4t68](https://img.shields.io/badge/DOI-10.57750%2F4y84--4t68-034E79.svg)](https://doi.org/10.57750/4y84-4t68)\n[![Creative Commons\nLicense](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)\n\n### Authors’ affiliations\n\n- Juliette Legrand (Biostatistics and Spatial Processes, INRAE, Avignon, France)\n- François Pimont (Ecologie des Forêts Méditerranéennes (URFM), INRAE, Avignon, France)\n- [Jean-Luc Dupuy](https://biosp.mathnum.inrae.fr/homepage-thomas-opitz) (Ecologie des Forêts Méditerranéennes (URFM), INRAE, Avignon, France)\n- [Thomas Opitz](https://biosp.mathnum.inrae.fr/homepage-thomas-opitz) (Biostatistics and Spatial Processes, INRAE, Avignon, France)\n\n### Abstract\n\nAppropriate spatiotemporal modelling of wildfire activity is crucial for\nits prediction and risk management. Here, we focus on wildfire risk in\nthe Aquitaine region in the Southwest of France and its projection under\nclimate change. We study whether wildfire risk could further increase\nunder climate change in this specific region, which does not lie in the\nhistorical core area of wildfires in Southeastern France, corresponding\nto the Southwest. For this purpose, we consider a marked spatiotemporal\npoint process, a flexible model for occurrences and magnitudes of such\nenvironmental risks, where the magnitudes are defined as the burnt\nareas. The model is first calibrated using 14 years of past observation\ndata of wildfire occurrences and weather variables, and then applied for\nprojection of climate-change impacts using simulations of numerical\nclimate models until 2100 as new inputs. We work within the framework of\na spatiotemporal Bayesian hierarchical model, and we present the\nworkflow of its implementation for a large dataset at daily resolution\nfor 8km-pixels using the INLA-SPDE approach. The assessment of the\nposterior distributions shows a satisfactory fit of the model for the\nobservation period. We stochastically simulate projections of future\nwildfire activity by combining climate model output with posterior\nsimulations of model parameters. Depending on climate models,\nspline-smoothed projections indicate low to moderate increase of\nwildfire activity under climate change. The increase is weaker than in\nthe historical core area, which we attribute to different weather\nconditions (oceanic versus Mediterranean). Besides providing a relevant\ncase study of environmental risk modelling, this paper is also intended\nto provide a full workflow for implementing the Bayesian estimation of\nmarked log-Gaussian Cox processes using the R-INLA package of the R\nstatistical software.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomputorg%2Fpublished-202407-legrand-wildfires","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcomputorg%2Fpublished-202407-legrand-wildfires","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomputorg%2Fpublished-202407-legrand-wildfires/lists"}