Spatiotemporal Changes of Surface Water and Its Drivers Using Geographically Weighted Regression: A Case Study of Gazipur City Corporation, Bangladesh

Spatiotemporal Changes of Surface Water and Its Drivers Using Geographically Weighted Regression: A Case Study of Gazipur City Corporation, Bangladesh | 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 Spatiotemporal Changes of Surface Water and Its Drivers Using Geographically Weighted Regression: A Case Study of Gazipur City Corporation, Bangladesh S. M. Nazmul Haque, A S M Shanawaz Uddin, Wakil Ahmed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6998568/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 Understanding the changes of surface water (SW) in rapidly urbanizing regions is crucial for sustainable urban planning and environmental management. This study investigates the spatiotemporal variation of surface water in Gazipur City Corporation (GCC), Bangladesh, and explores the influence of land use/land cover (LULC) change and topography on SW distribution. Landsat-based surface reflectance imagery from 2004 and 2023—pre- and post-establishment of GCC—was used to assess seasonal variations and long-term LULC changes. Classification of LULC types (impervious, vegetation, bare, and waterbody) was performed using the Random Forest (RF) algorithm in Google Earth Engine (GEE), supported by spectral indices and ground control points (GCPs). Seasonal changes in surface water were assessed using the optimum threshold value of the Modified Normalized Difference Water Index (MNDWI) obtained by Receiver Operating Characteristic (ROC) curve analysis, while the rate of change in LULC was calculated for 500 spatial sub-regions. To explore spatially varying relationships between surface water loss and potential drivers, a Geographically Weighted Regression (GWR) model was applied using Python. Results show a significant reduction in both permanent and seasonal surface water over the study period, primarily in areas undergoing rapid urbanization. GWR results revealed strong negative relationships between surface water change and impervious, bare, and vegetation change rates, while elevation was found to be statistically insignificant (p = 0.88). The study emphasizes the importance of localized modeling in understanding hydrological responses to urban growth and provides valuable insights for flood risk management and sustainable land planning in urban Bangladesh. Surface Water Remote Sensing GEE Urbanization Local Regression 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-6998568","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480335333,"identity":"afe6d015-f29d-4f70-a9e8-56a5708db2c9","order_by":0,"name":"S. M. 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This study investigates the spatiotemporal variation of surface water in Gazipur City Corporation (GCC), Bangladesh, and explores the influence of land use/land cover (LULC) change and topography on SW distribution. Landsat-based surface reflectance imagery from 2004 and 2023—pre- and post-establishment of GCC—was used to assess seasonal variations and long-term LULC changes. Classification of LULC types (impervious, vegetation, bare, and waterbody) was performed using the Random Forest (RF) algorithm in Google Earth Engine (GEE), supported by spectral indices and ground control points (GCPs). Seasonal changes in surface water were assessed using the optimum threshold value of the Modified Normalized Difference Water Index (MNDWI) obtained by Receiver Operating Characteristic (ROC) curve analysis, while the rate of change in LULC was calculated for 500 spatial sub-regions. To explore spatially varying relationships between surface water loss and potential drivers, a Geographically Weighted Regression (GWR) model was applied using Python. Results show a significant reduction in both permanent and seasonal surface water over the study period, primarily in areas undergoing rapid urbanization. GWR results revealed strong negative relationships between surface water change and impervious, bare, and vegetation change rates, while elevation was found to be statistically insignificant (p = 0.88). 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