Performance evaluation of statistical downscaling models for future climate change scenario projection.

Performance evaluation of statistical downscaling models for future climate change scenario projection. | 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 Performance evaluation of statistical downscaling models for future climate change scenario projection. Rituraj shukla, Deepak Khare, Anuj Kumar Dwivedi, Ramesh Pal Rudra, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2296502/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 Statistical downscaling (SD) is preferable to dynamic downscaling to derive local-scale climate change information from large-scale datasets. Many SD models are available these days, but comparison of their performance is still inadequately addressed for choosing a reliable SD model. Thus, it is desirable to compare the performance of SD models to ensure their adaptability in future climate studies. In this study, a statistical downscaling model (SDSM) or (multi-linear regression) and the Least Square-Support Vector Machine (LS-SVM) were used to do downscaling and compare the results with those obtained from General Circulation Model (GCM) for identifying the best SD model for the Indira Sagar Canal Command area located in Madhya Pradesh, India. The GCM, Hadley Centre Coupled Model version 3 (HadCM3), was utilized to extract and downscale precipitation, maximum temperature (T max ), and minimum temperature (T min ) for the past period 1961–2001 and then for 2001–2099 under future climate change scenario A2. Before future projections, both SD models were initially calibrated (1961–1990) and validated (1991–2001) to evaluate their performance for precipitation and temperature variables at all gauge stations, namely Barwani, East Nimar, and West Nimar. Results showed that the precipitation trend was under-predicted owing to large errors in downscaling, while temperature was over-predicted by SD models. Future projection results indicated that an increase in precipitation, Tmax, and Tmin patterns at all stations would occur between 2001–2099. Although statistical measures (R 2 , RMSE, SSE, NSE and MAE) showed a close agreement between observed and predicted climate variables, the overall accuracy of the LS-SVM model was better than the SDSM model. Thus, the present study revealed that LS-SVM could be used as a superior statistical downscaling model over the SDSM in future studies. Statistical downscaling SDSM LS-SVM HadCM3 Indira Sagar Canal Command area Full Text 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. 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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-2296502","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":156961922,"identity":"036c1aa5-c6c2-43ec-9901-6aced83a24b4","order_by":0,"name":"Rituraj shukla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYFACHgbGhgIGOQingCGBSC0GDMYQjgEJWhIbiNZi3n/2mOQMA7v0Dcfbr0l8MLiTx8B++AFeLTI38tIkNxgk5244c6YMqPdZMQNPmgFeLRISPGaSDwyYczfcyEmT5jE4nNggwUBAC/8ZkJb6dAOQlj9gLewf8GthyDEDOuxwgsGN9GPSDGAtPIQclmNsOcPguOHMM2eYLXsMniW28eQUEHKY4c2eimp5vuPtD2/8qLiT2M9+fANeLUgA7J4DDGzEqgcC9gdgLaNgFIyCUTAK0AEAHP1HX8ZdmvMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-4543-8981","institution":"University of Guelph College of Physical and Engineering Science","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rituraj","middleName":"","lastName":"shukla","suffix":""},{"id":156961923,"identity":"d638b9bb-ca39-4eda-98fe-c18d472e138f","order_by":1,"name":"Deepak Khare","email":"","orcid":"","institution":"Indian Institute of Technology Roorkee","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Deepak","middleName":"","lastName":"Khare","suffix":""},{"id":156961924,"identity":"52a73c59-da5b-49e5-b1d5-d937e7f61c15","order_by":2,"name":"Anuj Kumar Dwivedi","email":"","orcid":"https://orcid.org/0000-0002-3071-2442","institution":"Indian Institute of Technology Roorkee","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anuj","middleName":"Kumar","lastName":"Dwivedi","suffix":""},{"id":156961925,"identity":"7dfe4d2f-fffc-4f7e-892b-09393c19249f","order_by":3,"name":"Ramesh Pal Rudra","email":"","orcid":"","institution":"University of Guelph Ontario Agricultural College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ramesh","middleName":"Pal","lastName":"Rudra","suffix":""},{"id":156961926,"identity":"340e266d-8620-4f1c-9f7b-003f55c326c3","order_by":4,"name":"Santosh S. 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Many SD models are available these days, but comparison of their performance is still inadequately addressed for choosing a reliable SD model. Thus, it is desirable to compare the performance of SD models to ensure their adaptability in future climate studies. In this study, a statistical downscaling model (SDSM) or (multi-linear regression) and the Least Square-Support Vector Machine (LS-SVM) were used to do downscaling and compare the results with those obtained from General Circulation Model (GCM) for identifying the best SD model for the Indira Sagar Canal Command area located in Madhya Pradesh, India. The GCM, Hadley Centre Coupled Model version 3 (HadCM3), was utilized to extract and downscale precipitation, maximum temperature (T\u003csub\u003emax\u003c/sub\u003e), and minimum temperature (T\u003csub\u003emin\u003c/sub\u003e) for the past period 1961\u0026ndash;2001 and then for 2001\u0026ndash;2099 under future climate change scenario A2. Before future projections, both SD models were initially calibrated (1961\u0026ndash;1990) and validated (1991\u0026ndash;2001) to evaluate their performance for precipitation and temperature variables at all gauge stations, namely Barwani, East Nimar, and West Nimar. Results showed that the precipitation trend was under-predicted owing to large errors in downscaling, while temperature was over-predicted by SD models. Future projection results indicated that an increase in precipitation, Tmax, and Tmin patterns at all stations would occur between 2001\u0026ndash;2099. Although statistical measures (R\u003csup\u003e2\u003c/sup\u003e, RMSE, SSE, NSE and MAE) showed a close agreement between observed and predicted climate variables, the overall accuracy of the LS-SVM model was better than the SDSM model. Thus, the present study revealed that LS-SVM could be used as a superior statistical downscaling model over the SDSM in future studies.\u003c/p\u003e","manuscriptTitle":"Performance evaluation of statistical downscaling models for future climate change scenario projection.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-07 01:55:25","doi":"10.21203/rs.3.rs-2296502/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":"ca1b3342-9e91-440b-8e94-35c8614c2c3b","owner":[],"postedDate":"December 7th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-01-13T21:27:39+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-07 01:55:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2296502","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2296502","identity":"rs-2296502","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}