Potential Recognition of Flash Flood Disasters in Small Watersheds in China’s Southwestern Mountainous Areas Considering Source Supply Conditions*

Potential Recognition of Flash Flood Disasters in Small Watersheds in China’s Southwestern Mountainous Areas Considering Source Supply Conditions* | 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 Article Potential Recognition of Flash Flood Disasters in Small Watersheds in China’s Southwestern Mountainous Areas Considering Source Supply Conditions* Haizhi Liu, Yaqiang Wang, Chong Xu, Qiaoyun Song, Hui Xu, Zimu Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8121441/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract In the field of flash flood disaster prevention study, great attention has been paid to the role of heavy rainfall and flooding, yet the coupling of flooding and sediment caused by silt deposition is largely neglected. This study revised the impact factor system that considers the spatial heterogeneity in the loose solid material storage, using Aba Prefecture as the target area. The nonlinear relationship was extracted between the susceptibility of flash floods and environmental factors conducive to disasters such as loose deposit sources. This study employed a sensitivity-ensemble learning coupling algorithm to perform early potential recognition of flash flood risk in small watersheds of the southwestern mountainous areas. Based on digital maps for early potential recognition of flash flood risk, the potential volume of landslide sources in typical high-risk watersheds was estimated through the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (Trigrs) with slope as the basic unit. This study established a data-physics dual-driven method for the potential recognition of flash flood disasters that is adaptable to different spatial scales and explored the impact of landslide sediment yield on flash floods, as well as the macro- and micro-mechanisms of flash flood disaster formation in typical small mountainous watersheds. The results indicate that: (1) The coupling disaster caused by flash floods and sediment in Aba Prefecture exhibits a certain degree of spatial clustering, with some impact factors demonstrating similar sensitivity patterns towards disaster occurrence. (2) High risks of flash flood disasters are identified in parts of eastern, central-southern, and a small portion of the northwestern watersheds in Aba Prefecture. These highly prone areas are primarily concentrated along topographic fault zones, which are relatively close to the high-frequency areas of accumulated loose deposit sources, where a larger probability of flooding-sediment coupling disaster can be found. (3) Compared with the results of the flash flood risk survey and assessment, the Certainty Factor-Adaptive Boosting (CF-AdaBoost) coupling algorithm identifies a coverage of 40.7% for high-susceptibility areas and a coverage of 12.4% for very low-susceptibility areas. Compared to the Flood Frequency Index Approach (FFIA), the coverage of high-susceptibility areas increases by 24.2%, while the coverage of very low-susceptibility areas decreases by 5.1%. (4) Post-processing the results of the slope stability model through the division of slope units estimates that the area of high-risk slopes in the Shouxi River basin during the intense rainfall event on August 20th is 4.99×10 7 m 2 . The key source supply areas are primarily concentrated in the upper and middle reaches of the basin, with an estimated sediment transport capacity of 1.219×10 7 m³ for each flood event. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards flash flood disaster source supply ensemble learning potential recognition landslide sediment yield Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Dec, 2025 Reviews received at journal 30 Nov, 2025 Reviews received at journal 27 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 24 Nov, 2025 Editor assigned by journal 24 Nov, 2025 Submission checks completed at journal 24 Nov, 2025 First submitted to journal 15 Nov, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8121441","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":551860852,"identity":"3b7766a2-3159-4549-96bc-3b005a98ea72","order_by":0,"name":"Haizhi Liu","email":"","orcid":"","institution":"Chinese Academy of Meteorological Sciences, China Meteorological Administration","correspondingAuthor":false,"prefix":"","firstName":"Haizhi","middleName":"","lastName":"Liu","suffix":""},{"id":551860853,"identity":"0a973908-5eb9-49f5-8e3d-519fbef65f61","order_by":1,"name":"Yaqiang Wang","email":"","orcid":"","institution":"Chinese Academy of 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This study revised the impact factor system that considers the spatial heterogeneity in the loose solid material storage, using Aba Prefecture as the target area. The nonlinear relationship was extracted between the susceptibility of flash floods and environmental factors conducive to disasters such as loose deposit sources. This study employed a sensitivity-ensemble learning coupling algorithm to perform early potential recognition of flash flood risk in small watersheds of the southwestern mountainous areas. Based on digital maps for early potential recognition of flash flood risk, the potential volume of landslide sources in typical high-risk watersheds was estimated through the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (Trigrs) with slope as the basic unit. 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These highly prone areas are primarily concentrated along topographic fault zones, which are relatively close to the high-frequency areas of accumulated loose deposit sources, where a larger probability of flooding-sediment coupling disaster can be found. (3) Compared with the results of the flash flood risk survey and assessment, the Certainty Factor-Adaptive Boosting (CF-AdaBoost) coupling algorithm identifies a coverage of 40.7% for high-susceptibility areas and a coverage of 12.4% for very low-susceptibility areas. Compared to the Flood Frequency Index Approach (FFIA), the coverage of high-susceptibility areas increases by 24.2%, while the coverage of very low-susceptibility areas decreases by 5.1%. (4) Post-processing the results of the slope stability model through the division of slope units estimates that the area of high-risk slopes in the Shouxi River basin during the intense rainfall event on August 20th is 4.99\u0026times;10\u003csup\u003e7\u003c/sup\u003em\u003csup\u003e2\u003c/sup\u003e. The key source supply areas are primarily concentrated in the upper and middle reaches of the basin, with an estimated sediment transport capacity of 1.219\u0026times;10\u003csup\u003e7\u003c/sup\u003e m\u0026sup3; for each flood event.\u003c/p\u003e","manuscriptTitle":"Potential Recognition of Flash Flood Disasters in Small Watersheds in China’s Southwestern Mountainous Areas Considering Source Supply Conditions*","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 21:11:38","doi":"10.21203/rs.3.rs-8121441/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-08T01:29:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T01:34:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T15:43:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212447620428820180077479524621435759277","date":"2025-11-26T02:58:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66011295257605324802693360337889683069","date":"2025-11-26T00:55:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317414372391454459644553434161439497565","date":"2025-11-25T05:45:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-25T03:30:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-24T16:13:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-24T16:08:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Natural Hazards","date":"2025-11-15T10:37:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Natural Hazards](https://www.nature.com/npjnathazards/)","snPcode":"44304","submissionUrl":"https://submission.springernature.com/new-submission/44304/3","title":"npj Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3eb4972a-d9f3-47c6-9503-a5fdf098a7de","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58727398,"name":"Earth and environmental sciences/Environmental sciences"},{"id":58727399,"name":"Earth and environmental sciences/Hydrology"},{"id":58727400,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2026-02-10T16:10:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 21:11:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8121441","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8121441","identity":"rs-8121441","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}