FLOW DURATION CURVE ESTIMATION AT UNGAUGED BASIN USING REGIONALIZATION APPROACHES

FLOW DURATION CURVE ESTIMATION AT UNGAUGED BASIN USING REGIONALIZATION APPROACHES | 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 FLOW DURATION CURVE ESTIMATION AT UNGAUGED BASIN USING REGIONALIZATION APPROACHES Hafiz Waseem Sajjad, Muhammad Waseem, Abu Bakar Arshed, Ali Haider Abbasi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4185382/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 Long-term hydrological information is essential in many parts of the world because of low density and inadequate spatial distribution of hydrometric networks specifically in Pakistan. The goal of this study to delineate the homogeneous region and to assess the streamflow in ungauged basin using regionalization approaches. Current study introduced a Simple Tyler Skill Score (STSS) technique to ensemble the output of regionalization approaches i.e., Artificial Neural Network, (ANN), Inverse Distance Weightage (IDW) and Stepwise Regression (SWR) for better predication of hydrological information. STSS method is mainly based on the weight derived from coefficient of determination (R 2 ) and hydrological variables. The overall performance evaluation was performed by using coefficient of determination (R 2 ), correlation coefficient (r), root mean square error (RMSE) and percent bias (PBIAS), which revealed that STSS provided more robust estimation of flow duration curve as compared to other methods. Moreover, ANN performance was comparatively better than the SWR and IDW method. The result emphasizes that in upper Indus basin, the characteristics of the watershed matter more than the physical distance between gauged and ungauged areas. This study can provide the direction for the hydrological estimation independent of hydrological modeling in data scarce regions where hydrological conditions are less addressed. Artificial Neural Network Flow Duration curve (FDC) Inverse Distance Weightage (IDW) Simple Tyler Skill Score (STSS) Stepwise Regression (SWR) Ungauged catchment Full Text Additional Declarations No competing interests reported. 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-4185382","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286919890,"identity":"51766a95-3d39-4525-8878-900be7f4c7b6","order_by":0,"name":"Hafiz Waseem Sajjad","email":"","orcid":"","institution":"University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hafiz","middleName":"Waseem","lastName":"Sajjad","suffix":""},{"id":286919891,"identity":"3c38fcc0-3893-4b76-82d7-6a29dbfad4c2","order_by":1,"name":"Muhammad Waseem","email":"","orcid":"","institution":"University of Engineering and 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The goal of this study to delineate the homogeneous region and to assess the streamflow in ungauged basin using regionalization approaches. Current study introduced a Simple Tyler Skill Score (STSS) technique to ensemble the output of regionalization approaches i.e., Artificial Neural Network, (ANN), Inverse Distance Weightage (IDW) and Stepwise Regression (SWR) for better predication of hydrological information. STSS method is mainly based on the weight derived from coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) and hydrological variables. The overall performance evaluation was performed by using coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), correlation coefficient (r), root mean square error (RMSE) and percent bias (PBIAS), which revealed that STSS provided more robust estimation of flow duration curve as compared to other methods. Moreover, ANN performance was comparatively better than the SWR and IDW method. The result emphasizes that in upper Indus basin, the characteristics of the watershed matter more than the physical distance between gauged and ungauged areas. 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