Quantifying the Climatic Signature of Deforestation: A Spatio-Statistical Analysis of Forest Loss, Surface Temperature, and Surface Energy Balance in Zimbabwe

Quantifying the Climatic Signature of Deforestation: A Spatio-Statistical Analysis of Forest Loss, Surface Temperature, and Surface Energy Balance in Zimbabwe | 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 Quantifying the Climatic Signature of Deforestation: A Spatio-Statistical Analysis of Forest Loss, Surface Temperature, and Surface Energy Balance in Zimbabwe Perkins Watambwa, Terrence Mushore, Hillary Mugiyo, Rutendo Sibanda, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8759612/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 Zimbabwe’s tropical dry forests have experienced persistent deforestation with measurable climatic consequences. We assemble a harmonised national dataset that integrates Landsat forest-cover change, ERA5 near surface meteorology, and MODIS Terra NDVI for 2001–2024, and develop a spatio statistical pipeline that aligns gridded series to districts and provinces, constructs baseline anomalies, and estimates deforestation sensitivities using monthly two way fixed effects and annual mixed effects. We derive a biophysical exposure proxy from vegetation and surface energy components, and we propagate this choice transparently through all figures and tables. The monthly panel identifies a robust thermal signature, with positive temperature anomalies per unit exposure, and a coherent re partitioning of surface energy from latent to sensible heat consistent with suppressed evapotranspiration and enhanced turbulent heating. Annual mixed effects confirm a strong energy balance signal and show a positive association with precipitation anomalies in the national aggregate, while spatial diagnostics reject spatial randomness and reveal pronounced clustering for hydrological sensitivity at the province scale. We translate district level sensitivities into scenario maps for user specified exposure changes, enabling direct policy interpretation at administrative scales. The contribution is methodological and applied: a reproducible end to end pipeline that couples hierarchical and panel estimators with spatial diagnostics and scenario translation, and empirical evidence that clarifies where and how the climatic footprint of deforestation manifests across Zimbabwe’s heterogeneous regions. Deforestation Surface Energy Balance Thermal Signature Spatial Statistics Pannel Mixed Effects Models 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-8759612","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599624382,"identity":"85660ed2-e7d4-45b4-92dd-9b80b219e20c","order_by":0,"name":"Perkins 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