Spatiotemporal Dynamics of Urban Resilience: An Integrated Assessment across Four Case Studies (1996–2024)

Spatiotemporal Dynamics of Urban Resilience: An Integrated Assessment across Four Case Studies (1996–2024) | 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 Article Spatiotemporal Dynamics of Urban Resilience: An Integrated Assessment across Four Case Studies (1996–2024) Francis Deng Clement, Zhou Shutian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9209478/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 Urban hydrological resilience—the capacity of urban regions to efficiently manage water resources—confronts substantial challenges due to the forces of escalating urbanization and climate change. In many regions, the expansion of impervious surfaces, alterations to drainage systems, and increased frequency of heavy rainfall events exacerbate runoff and alter soil moisture behavior. These impacts are distributed unequally: cities in the Global South face the greatest exposure yet possess the least institutional capacity and data infrastructure to respond. Notwithstanding the increasing focus on urban flood risk and climate adaptation, a deficiency persists in the availability of systematic, transferable frameworks for forecasting and managing soil moisture (SM) variability across diverse climatic and urbanization scenarios. This study aims to fill a significant gap by creating a comprehensive evaluation approach that combines various data sources to connect observed soil moisture dynamics with urban morphology and hydroclimatic factors. The approach is illustrated through four case studies conducted between 1996 and 2024. The framework supports practical decision-making by incorporating resilience measures and establishing a tiered structure focused on planning, which can be integrated into municipal GIS operations. The aim is to quantify urban resilience patterns across four contrasting case studies: the Greater Bay Area (GBA), a subtropical megalopolis; the United Arab Emirates, characterized by arid extreme events; southern Madagascar, which is experiencing drought vulnerability; and the Andean páramos, serving as a natural reference system. We investigate three key questions: (1) How does urban form quantitatively influence hydrological resilience? (2) Can multi-source sensing accurately predict spatially explicit resilience metrics? (3) What empirical thresholds can inform actionable planning frameworks? Our results uncover a "precipitation paradox": Urbanization increases local precipitation (+ 12% in the GBA) while simultaneously inhibiting infiltration, leading to a 400% increase in peak flow. The conversion of agricultural land accounts for 70% of impervious surface expansion in the GBA, reducing soil water-holding capacity by 35% and causing the Robustness Index (RI) to decline from 2.8 to 1.4 over the period from 1996 to 2024. Multi-source fusion, which includes Street View and remote sensing, achieved a mean cross-validated R² of 0.58 for soil moisture prediction. This performance validates the measurement capacity of our framework. An extreme rainfall event in the UAE in April 2024, which recorded 254 mm in 24 hours, is very likely to have become more frequent under current climate conditions, with recent attribution studies estimating a central probability ratio of about 30, but with extensive confidence intervals. This shift illustrates the attribution capabilities of our framework without relying on a single deterministic return‑period value. We propose an empirically grounded three-tier planning framework (Conservation RI > 2.5, Mitigation 1.5 < RI < 2.5, and Engineering RI < 1.5), validated across diverse climate-development contexts. Flood frequency data indicate a pattern of 0.3, 1.9, and 5.8 events per decade for the respective tiers. Additionally, the compact urban design pattern (CI ≈ 1.61) results in less disruption of soil moisture levels than the extensive development pattern (CI > 1.75). Furthermore, the system demonstrates chaotic behavior at CI = 1.75. This framework uses satellite data to monitor large areas while enabling detailed observations at smaller scales. This dual capability enables scientists to develop a solid scientific basis for urban resilience, which in turn assists planners in creating quantifiable assessment methods that can be applied across a range of global contexts. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards Urban Resilience Soil Moisture Dynamics Climate Adaptation Urban Planning Multi-source Information Fusion Extreme Rainfall Events Applied Geography 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. 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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-9209478","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":630829430,"identity":"e680fa56-4573-45a0-9514-a61bea6b6a92","order_by":0,"name":"Francis Deng Clement","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"Deng","lastName":"Clement","suffix":""},{"id":630829431,"identity":"7ceae481-63fd-4c2a-9ced-3d265920b0ec","order_by":1,"name":"Zhou Shutian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCSBOgDE+QMQMCGlhbIBpYZwBUU2EFph1zDzEaJGf3WP+4EHNHSCj+Zi07Y4/iQ3szdskGIAiuIDBnTOGDQnHnjEwzjmWJp17xiCxgedYmQQDUASnFokcoBa2wwzMEjlm0rltQC1AhgRjw2HcDpsB0vLvMAMbSIslSIv8G/xaGG4AtSS2HWbgAWlhBNvCg1+LwY20whmJfYcZJCTSki1724yN23jSii0SjuFzWPKGjz++HQYxDt742SYn289+eOONDzV4HAYF9Q0wFhuISCCoYRSMglEwCkYBPgAAB7RQGTKZtvQAAAAASUVORK5CYII=","orcid":"","institution":"Nantong University","correspondingAuthor":true,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Shutian","suffix":""}],"badges":[],"createdAt":"2026-03-24 09:08:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9209478/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9209478/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109257842,"identity":"304d4086-7ab1-4dca-9e4d-507d924d1474","added_by":"auto","created_at":"2026-05-14 10:26:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10831374,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptAnonymised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9209478/v1_covered_0134061b-2824-429f-ab5c-13f3d867fa21.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal Dynamics of Urban Resilience: An Integrated Assessment across Four Case Studies (1996–2024)","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Urban Resilience, Soil Moisture Dynamics, Climate Adaptation, Urban Planning, Multi-source Information Fusion, Extreme Rainfall Events, Applied Geography","lastPublishedDoi":"10.21203/rs.3.rs-9209478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9209478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban hydrological resilience\u0026mdash;the capacity of urban regions to efficiently manage water resources\u0026mdash;confronts substantial challenges due to the forces of escalating urbanization and climate change. In many regions, the expansion of impervious surfaces, alterations to drainage systems, and increased frequency of heavy rainfall events exacerbate runoff and alter soil moisture behavior. These impacts are distributed unequally: cities in the Global South face the greatest exposure yet possess the least institutional capacity and data infrastructure to respond. Notwithstanding the increasing focus on urban flood risk and climate adaptation, a deficiency persists in the availability of systematic, transferable frameworks for forecasting and managing soil moisture (SM) variability across diverse climatic and urbanization scenarios.\u003c/p\u003e \u003cp\u003eThis study aims to fill a significant gap by creating a comprehensive evaluation approach that combines various data sources to connect observed soil moisture dynamics with urban morphology and hydroclimatic factors. The approach is illustrated through four case studies conducted between 1996 and 2024. The framework supports practical decision-making by incorporating resilience measures and establishing a tiered structure focused on planning, which can be integrated into municipal GIS operations.\u003c/p\u003e \u003cp\u003eThe aim is to quantify urban resilience patterns across four contrasting case studies: the Greater Bay Area (GBA), a subtropical megalopolis; the United Arab Emirates, characterized by arid extreme events; southern Madagascar, which is experiencing drought vulnerability; and the Andean p\u0026aacute;ramos, serving as a natural reference system. We investigate three key questions: (1) How does urban form quantitatively influence hydrological resilience? (2) Can multi-source sensing accurately predict spatially explicit resilience metrics? (3) What empirical thresholds can inform actionable planning frameworks? Our results uncover a \"precipitation paradox\": Urbanization increases local precipitation (+\u0026thinsp;12% in the GBA) while simultaneously inhibiting infiltration, leading to a 400% increase in peak flow. The conversion of agricultural land accounts for 70% of impervious surface expansion in the GBA, reducing soil water-holding capacity by 35% and causing the Robustness Index (RI) to decline from 2.8 to 1.4 over the period from 1996 to 2024. Multi-source fusion, which includes Street View and remote sensing, achieved a mean cross-validated R\u0026sup2; of 0.58 for soil moisture prediction.\u003c/p\u003e \u003cp\u003eThis performance validates the measurement capacity of our framework. An extreme rainfall event in the UAE in April 2024, which recorded 254 mm in 24 hours, is very likely to have become more frequent under current climate conditions, with recent attribution studies estimating a central probability ratio of about 30, but with extensive confidence intervals. This shift illustrates the attribution capabilities of our framework without relying on a single deterministic return‑period value. We propose an empirically grounded three-tier planning framework (Conservation RI\u0026thinsp;\u0026gt;\u0026thinsp;2.5, Mitigation 1.5\u0026thinsp;\u0026lt;\u0026thinsp;RI\u0026thinsp;\u0026lt;\u0026thinsp;2.5, and Engineering RI\u0026thinsp;\u0026lt;\u0026thinsp;1.5), validated across diverse climate-development contexts. Flood frequency data indicate a pattern of 0.3, 1.9, and 5.8 events per decade for the respective tiers. Additionally, the compact urban design pattern (CI\u0026thinsp;\u0026asymp;\u0026thinsp;1.61) results in less disruption of soil moisture levels than the extensive development pattern (CI\u0026thinsp;\u0026gt;\u0026thinsp;1.75). Furthermore, the system demonstrates chaotic behavior at CI\u0026thinsp;=\u0026thinsp;1.75. This framework uses satellite data to monitor large areas while enabling detailed observations at smaller scales. 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