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

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.