Landscape factors can improve the predictive ability of fuel moisture models for assessing wildfire risk

Landscape factors can improve the predictive ability of fuel moisture models for assessing wildfire risk | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Landscape factors can improve the predictive ability of fuel moisture models for assessing wildfire risk Katy Ivison, Laura Graham, Kerryn Little, Alice Orpin, Nicholas Kettridge This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4143610/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Apr, 2026 Read the published version in Fire Ecology → Version 1 posted 4 You are reading this latest preprint version Abstract Background: Fuel moisture content (FMC) is a critical component of wildfire risk. The spatiotemporal patterns of FMC of many key temperate fuels (e.g. heather, gorse, bracken, moor grass, litter, organic soil layer) are largely unknown and unravelling the drivers of FMC is challenging. Current models designed to predict FMC, principally of dead fuels, generally consider only weather variables. However, landscape factors affect water retention and availability of moisture within soils, and are likely to influence FMC of live and organic ground fuels. We investigate the potential effects of landscape factors on FMC. We carried out a large-scale fuel sampling campaign from 2021–2023 of eighteen different fuel types across 43 sites through five different climate regions of the UK. We implemented boosted regression trees to determine the influence of fifteen variables on FMC, which included weather, temporal, and landscape data. Results: We found that landscape influences FMC, particularly for the organic ground fuels, live bracken and live moor grass. Dead fuels were generally influenced least by landscape factors. The predictive ability of our models was good (mean correlation between predicted and observed FMC of > 0.5) in twelve of eighteen fuels and for most fuels, including landscape factors increased the variance in FMC explained. Predictive ability was greater in models including landscape factors (compared with models just containing weather and temporal variables) for live fuels, litter and the organic layer. For dead fuels, moss and twigs, including landscape factors did not increase the models’ predictive ability. Conclusions: We have shown that landscape factors are important to consider when developing fuel moisture models, but that this is mainly important for live fuels and organic ground fuels. We therefore recommend tailoring models to each fuel type to yield the best performance. fuel moisture wildfire landscape peat live fuel dead fuel Calluna Full Text Supplementary Files FileS1Sampledates.xlsx Landscapesupplementary.docx Cite Share Download PDF Status: Published Journal Publication published 21 Apr, 2026 Read the published version in Fire Ecology → Version 1 posted Reviewers agreed at journal 31 Mar, 2024 Reviewers invited by journal 30 Mar, 2024 Editor assigned by journal 26 Mar, 2024 First submitted to journal 21 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4143610","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285753322,"identity":"08eeae44-3266-490a-96fc-b64bfaa6dea5","order_by":0,"name":"Katy 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Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"fuel moisture, wildfire, landscape ,peat, live fuel, dead fuel, Calluna","lastPublishedDoi":"10.21203/rs.3.rs-4143610/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4143610/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Fuel moisture content (FMC) is a critical component of wildfire risk. The spatiotemporal patterns of FMC of many key temperate fuels (e.g. heather, gorse, bracken, moor grass, litter, organic soil layer) are largely unknown and unravelling the drivers of FMC is challenging. Current models designed to predict FMC, principally of dead fuels, generally consider only weather variables. However, landscape factors affect water retention and availability of moisture within soils, and are likely to influence FMC of live and organic ground fuels. We investigate the potential effects of landscape factors on FMC. We carried out a large-scale fuel sampling campaign from 2021–2023 of eighteen different fuel types across 43 sites through five different climate regions of the UK. We implemented boosted regression trees to determine the influence of fifteen variables on FMC, which included weather, temporal, and landscape data.\u003c/p\u003e\n\u003cp\u003eResults: We found that landscape influences FMC, particularly for the organic ground fuels, live bracken and live moor grass. Dead fuels were generally influenced least by landscape factors. The predictive ability of our models was good (mean correlation between predicted and observed FMC of \u0026gt; 0.5) in twelve of eighteen fuels and for most fuels, including landscape factors increased the variance in FMC explained. Predictive ability was greater in models including landscape factors (compared with models just containing weather and temporal variables) for live fuels, litter and the organic layer. For dead fuels, moss and twigs, including landscape factors did not increase the models’ predictive ability.\u003c/p\u003e\n\u003cp\u003eConclusions: We have shown that landscape factors are important to consider when developing fuel moisture models, but that this is mainly important for live fuels and organic ground fuels. We therefore recommend tailoring models to each fuel type to yield the best performance.\u003c/p\u003e","manuscriptTitle":"Landscape factors can improve the predictive ability of fuel moisture models for assessing wildfire risk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-03 06:36:48","doi":"10.21203/rs.3.rs-4143610/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-03-31T12:51:34+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-31T02:16:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-26T09:42:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Fire Ecology","date":"2024-03-21T08:49:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"fire-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"feco","sideBox":"Learn more about [Fire Ecology](https://www.springer.com/journal/42408)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/feco/default.aspx","title":"Fire Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8c2ded63-ae79-4d4a-8954-6183cc780f58","owner":[],"postedDate":"April 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:07:21+00:00","versionOfRecord":{"articleIdentity":"rs-4143610","link":"https://doi.org/10.1186/s42408-026-00478-4","journal":{"identity":"fire-ecology","isVorOnly":false,"title":"Fire Ecology"},"publishedOn":"2026-04-21 16:00:06","publishedOnDateReadable":"April 21st, 2026"},"versionCreatedAt":"2024-04-03 06:36:48","video":"","vorDoi":"10.1186/s42408-026-00478-4","vorDoiUrl":"https://doi.org/10.1186/s42408-026-00478-4","workflowStages":[]},"version":"v1","identity":"rs-4143610","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4143610","identity":"rs-4143610","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}