Bias Correction of CMIP6 Models using Quantile Delta Mapping for Projecting Future IDF Curves: Case Study of the Hyderabad Metropolitan Region

Bias Correction of CMIP6 Models using Quantile Delta Mapping for Projecting Future IDF Curves: Case Study of the Hyderabad Metropolitan Region | 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 Bias Correction of CMIP6 Models using Quantile Delta Mapping for Projecting Future IDF Curves: Case Study of the Hyderabad Metropolitan Region Sudarshan Saravanan, Shiva Ji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9155960/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract To effectively retrofit urban water systems and improve urban climate resilience under threats of increased pluvial flooding induced by climate change, it is critical to scale existing Intensity-Duration-Frequency (IDF) curves to quantify the projected burden on urban stormwater infrastructure. To attain this, one needs to understand how well future climate projections understand the local climate patterns, in an efficient manner. This study utilizes the daily-step Quantile Delta Mapping (QDM) method in the Hyderabad Metropolitan Region (HMR) to analyze the biases of 10 NASA NEX-GDDP CMIP6 Global Climate Models (GCMs) on a 12-year retrospective period (1991–2002) and a 12-year prospective period (2003–2014). The CMIP6 models were evaluated on the basis of three metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Percentual bias (pbias), and the Modified Willmott Index ( \((d_1)\) ). While the QDM bias correction significantly increased the RMSE, it moderately stabilized the MAE and the pbias, while improving the Modified Willmott Index ( \((d_1)\) ) across a majority of the models. The models were then ranked based on their MAE, percentual bias and Modified Willmott Index ( \((d_1)\) ), and were utilized to extract the climate change multipliers to scale the IDF curves for the years 2031, 2036, and 2046. Projections indicate that the Hyderabad Metropolitan Region (HMR) will face increased hydro-climatic volatility in the near-future, with short-duration intense storm events increasing significantly and an increase in the average annual precipitation, indicating a more varied temporal precipitation distribution. These findings underscore the need for well-designed flood mitigation strategies using nature-based solutions, which are critical in balancing water scarcity and high-intensity pluvial flooding, due to their ability to increase natural soil moisture capacity and flexibility in water storage. Intensity-Duration-Frequency Curves Quantile Delta Mapping Urban Stormwater Resilience Global Climate Models Full Text Additional Declarations No competing interests reported. Supplementary Files BaselineIDFCurvesRainfallData.csv ProjectedIDFCurvesSSP5852031v3.csv ProjectedIDFCurvesSSP5852036v3.csv ProjectedIDFCurvesSSP2452036v3.csv ProjectedIDFCurvesSSP2452031v3.csv ProjectedIDFCurvesSSP5852046v3.csv ProjectedIDFCurvesSSP2452046v3.csv Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviewers invited by journal 27 Mar, 2026 Editor assigned by journal 18 Mar, 2026 Submission checks completed at journal 18 Mar, 2026 First submitted to journal 18 Mar, 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. 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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-9155960","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614647958,"identity":"e32864f6-788f-43f2-b9bd-e16c4d43c322","order_by":0,"name":"Sudarshan Saravanan","email":"","orcid":"","institution":"Indian Institute of Technology Hyderabad","correspondingAuthor":false,"prefix":"","firstName":"Sudarshan","middleName":"","lastName":"Saravanan","suffix":""},{"id":614647959,"identity":"826ce369-9986-4853-8b89-da5d552a9ed6","order_by":1,"name":"Shiva 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To attain this, one needs to understand how well future climate projections understand the local climate patterns, in an efficient manner. This study utilizes the daily-step Quantile Delta Mapping (QDM) method in the Hyderabad Metropolitan Region (HMR) to analyze the biases of 10 NASA NEX-GDDP CMIP6 Global Climate Models (GCMs) on a 12-year retrospective period (1991\u0026ndash;2002) and a 12-year prospective period (2003\u0026ndash;2014). The CMIP6 models were evaluated on the basis of three metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Percentual bias (pbias), and the Modified Willmott Index (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((d_1)\\)\u003c/span\u003e\u003c/span\u003e). While the QDM bias correction significantly increased the RMSE, it moderately stabilized the MAE and the pbias, while improving the Modified Willmott Index (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((d_1)\\)\u003c/span\u003e\u003c/span\u003e) across a majority of the models. The models were then ranked based on their MAE, percentual bias and Modified Willmott Index (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((d_1)\\)\u003c/span\u003e\u003c/span\u003e), and were utilized to extract the climate change multipliers to scale the IDF curves for the years 2031, 2036, and 2046. Projections indicate that the Hyderabad Metropolitan Region (HMR) will face increased hydro-climatic volatility in the near-future, with short-duration intense storm events increasing significantly and an increase in the average annual precipitation, indicating a more varied temporal precipitation distribution. 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