Integrating ENSO Variability and CMIP6 Projections to Assess Future Rainfall Erosivity in a Tropical Data-Scarce Watershed in Indonesia

Integrating ENSO Variability and CMIP6 Projections to Assess Future Rainfall Erosivity in a Tropical Data-Scarce Watershed in Indonesia | 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 Integrating ENSO Variability and CMIP6 Projections to Assess Future Rainfall Erosivity in a Tropical Data-Scarce Watershed in Indonesia Moh. Fahry Djuraini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7315091/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rainfall erosivity is a critical driver of soil erosion, particularly in tropical watersheds experiencing intense precipitation and hydroclimatic variability. However, few studies have examined how long-term climate change and short-term climate oscillations jointly influence erosivity patterns, especially in data-scarce regions. This study integrates El Niño–Southern Oscillation (ENSO) variability and CMIP6-based climate projections to assess the spatio-temporal trends of annual rainfall erosivity in the Podi Watershed, Central Sulawesi, Indonesia. Bias correction was applied to CHIRPS and 15 CMIP6 models using Quantile Mapping and Mean Ratio methods. CNRM-CM6-1 was identified as the best-performing model for future projections under SSP2-4.5 and SSP5-8.5 scenarios. Results indicate that rainfall erosivity during ENSO phases displays asymmetric responses: strong El Niño reduces erosivity in downstream areas, while weak La Niña significantly increases erosivity upstream. Trend analysis shows a significant historical increase (Sen’s slope = 7.42 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹), with future erosivity remaining stable under SSP2-4.5 but increasing under SSP5-8.5 (Sen’s slope = 4.55). Spatially, erosivity hotspots shift between downstream and midstream areas depending on emission scenarios and ENSO phases. These findings underscore the urgent need to incorporate both interannual climate variability and long-term projections in erosion risk assessments, particularly in ecologically fragile tropical watersheds. The study offers new insights for adaptive watershed management in regions with limited observational data. Climate Analysis and Modeling Meteorology Climatology Physical Geography rainfall erosivity climate change ENSO Podi Watershed CMIP6 projections Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-7315091","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496949630,"identity":"954fbb4a-4185-4489-bc9d-85ed4e59e373","order_by":0,"name":"Moh. 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However, few studies have examined how long-term climate change and short-term climate oscillations jointly influence erosivity patterns, especially in data-scarce regions. This study integrates El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation (ENSO) variability and CMIP6-based climate projections to assess the spatio-temporal trends of annual rainfall erosivity in the Podi Watershed, Central Sulawesi, Indonesia. Bias correction was applied to CHIRPS and 15 CMIP6 models using Quantile Mapping and Mean Ratio methods. CNRM-CM6-1 was identified as the best-performing model for future projections under SSP2-4.5 and SSP5-8.5 scenarios. Results indicate that rainfall erosivity during ENSO phases displays asymmetric responses: strong El Ni\u0026ntilde;o reduces erosivity in downstream areas, while weak La Ni\u0026ntilde;a significantly increases erosivity upstream. Trend analysis shows a significant historical increase (Sen\u0026rsquo;s slope\u0026thinsp;=\u0026thinsp;7.42 MJ\u0026middot;mm\u0026middot;ha⁻\u0026sup1;\u0026middot;h⁻\u0026sup1;\u0026middot;yr⁻\u0026sup1;), with future erosivity remaining stable under SSP2-4.5 but increasing under SSP5-8.5 (Sen\u0026rsquo;s slope\u0026thinsp;=\u0026thinsp;4.55). Spatially, erosivity hotspots shift between downstream and midstream areas depending on emission scenarios and ENSO phases. These findings underscore the urgent need to incorporate both interannual climate variability and long-term projections in erosion risk assessments, particularly in ecologically fragile tropical watersheds. The study offers new insights for adaptive watershed management in regions with limited observational data.\u003c/p\u003e","manuscriptTitle":"Integrating ENSO Variability and CMIP6 Projections to Assess Future Rainfall Erosivity in a Tropical Data-Scarce Watershed in Indonesia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 10:14:45","doi":"10.21203/rs.3.rs-7315091/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8273992-037a-48e7-a222-27d0facfe415","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52786959,"name":"Climate Analysis and Modeling"},{"id":52786960,"name":"Meteorology"},{"id":52786961,"name":"Climatology"},{"id":52786962,"name":"Physical Geography"}],"tags":[],"updatedAt":"2025-08-08T10:14:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-08 10:14:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7315091","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7315091","identity":"rs-7315091","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}