Multi-Objective Optimization of Reservoir Rule Curves Incorporating Environmental Flow Requirements

Multi-Objective Optimization of Reservoir Rule Curves Incorporating Environmental Flow Requirements | 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 Multi-Objective Optimization of Reservoir Rule Curves Incorporating Environmental Flow Requirements SUKANYA J NAIR, Elanchezhiyan Duraisekaran, Sarmistha Singh, Subhasis Mitra, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9290154/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Maintaining ecological flows in regulated rivers remains a major challenge for reservoir operations. In irrigation-dominated systems, releases from conservation reservoirs often deplete reservoir storage by the end of the irrigation season, thereby constraining the provision of environmental flows. In addition, reservoir rule curves are typically derived from historical data that do not explicitly incorporate ecological flow requirements. This study presents a multi-objective optimization framework for modifying existing reservoir rule curves to incorporate environmental flows. The rule curves of a conservation reservoir are optimized to minimize irrigation and environmental flow deficits simultaneously. The rule curve was optimized using the Multi-Algorithm Genetically Adaptive Method (AMALGAM), and the implications of selecting various trade-off solutions were discussed. The proposed approach is applied to the Malampuzha Reservoir in Kerala, India, demonstrating its potential to improve compliance with ecological flow requirements while maintaining irrigation performance. The results showed that the trade-off solution selected after the multi-objective optimization method using the Knee Point Method (KPM) and the Achievement Scalarization Function (ASF) yielded distinct optimal solutions. The reliability achieved for environmental flow was 0.90 for KPM and 0.85 for ASF, higher than the 0.5 achieved under the existing operation. In addition, the average irrigation deficit decreased from 4.97 Mm³ to 4.72–4.75 Mm³, and the average e-flow deficit decreased from 0.73 Mm³ to 0.13–0.15 Mm³.Over the 20-year simulation period, the total spill is reduced to 699.27 Mm³ (ASF) and 721.59 Mm³ (KPM), compared with 755.82 Mm³ with the existing rule curves. Thus, the optimized rule curves for the conservation reservoir minimize both irrigation and the environmental flow deficits. Moreover, the reduction in spill and environmental flow deficits is achieved without compromising the irrigation releases. The developed framework can be used to update existing rule curves for conservation reservoirs that do not currently incorporate environmental flows. Optimization Rule curve AMALGAM Environmental flows Pareto-optimal Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 28 Apr, 2026 Editor invited by journal 28 Apr, 2026 Editor assigned by journal 06 Apr, 2026 First submitted to journal 01 Apr, 2026 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-9290154","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631074470,"identity":"87e48dfe-eff4-43eb-946e-6781c88c27f0","order_by":0,"name":"SUKANYA J 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In irrigation-dominated systems, releases from conservation reservoirs often deplete reservoir storage by the end of the irrigation season, thereby constraining the provision of environmental flows. In addition, reservoir rule curves are typically derived from historical data that do not explicitly incorporate ecological flow requirements. This study presents a multi-objective optimization framework for modifying existing reservoir rule curves to incorporate environmental flows. The rule curves of a conservation reservoir are optimized to minimize irrigation and environmental flow deficits simultaneously. The rule curve was optimized using the Multi-Algorithm Genetically Adaptive Method (AMALGAM), and the implications of selecting various trade-off solutions were discussed. The proposed approach is applied to the Malampuzha Reservoir in Kerala, India, demonstrating its potential to improve compliance with ecological flow requirements while maintaining irrigation performance. The results showed that the trade-off solution selected after the multi-objective optimization method using the Knee Point Method (KPM) and the Achievement Scalarization Function (ASF) yielded distinct optimal solutions. The reliability achieved for environmental flow was 0.90 for KPM and 0.85 for ASF, higher than the 0.5 achieved under the existing operation. In addition, the average irrigation deficit decreased from 4.97 Mm\u0026sup3; to 4.72\u0026ndash;4.75 Mm\u0026sup3;, and the average e-flow deficit decreased from 0.73 Mm\u0026sup3; to 0.13\u0026ndash;0.15 Mm\u0026sup3;.Over the 20-year simulation period, the total spill is reduced to 699.27 Mm\u0026sup3; (ASF) and 721.59 Mm\u0026sup3; (KPM), compared with 755.82 Mm\u0026sup3; with the existing rule curves. Thus, the optimized rule curves for the conservation reservoir minimize both irrigation and the environmental flow deficits. Moreover, the reduction in spill and environmental flow deficits is achieved without compromising the irrigation releases. The developed framework can be used to update existing rule curves for conservation reservoirs that do not currently incorporate environmental flows.\u003c/p\u003e","manuscriptTitle":"Multi-Objective Optimization of Reservoir Rule Curves Incorporating Environmental Flow Requirements","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 22:39:26","doi":"10.21203/rs.3.rs-9290154/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-28T12:46:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Water Resources Management","date":"2026-04-28T05:36:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T10:39:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Water Resources Management","date":"2026-04-02T02:45:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"029838d5-238b-4d09-9b6d-425dfd3e7a2a","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T22:39:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 22:39:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9290154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9290154","identity":"rs-9290154","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}