Assessing Climate-Based Tourism Suitability in Nigeria (1991–2024) Using the Tourism Climate Index and TerraClimate Dataset

Authors

Ugo Uwadiako Enebeli, Rejoice Kaka Hassan, Yakubu Joel Cherima, Fiyidi Mikailu, Yonwul Jacqueline Dakyen, Kebiru Umoru, Zubairul Islam, Ebelechukwu Lawrence Enebeli

Abstract

Tourism and outdoor recreation in Nigeria are tightly coupled to meteorological conditions that vary sharply across latitude, seasons, and terrain. We develop a nation-scale, monthly assessment of climate suitability for tourism for 1991–2024 by deriving intermediate meteorological metrics and composite indices from TerraClimate (~4.6-km) and assembling the Tourism Climate Index (TCI) via Temperature–Humidity Index (THI)–based daytime (CID) and 24-h (CIA) comfort components. Inputs (tmmx, tmmn, pr, srad, vap, vs) were scaled and stacked in Google Earth Engine; astronomical radiation terms (Ra, daylength) and sunshine hours (Ångström–Prescott) were computed in R (terra). Outputs comprise harmonized rasters and area-weighted summaries for all 36 states and the Federal Capital Territory using OSGOF ADM1 boundaries. Results reveal a coherent, process-based geography. The semi-arid north achieves the highest mean TCI—e.g., Yobe 84.55, Sokoto 83.90, Jigawa 82.57, Gombe 82.41, Adamawa 82.19, Kebbi 82.35, Kano 82.12—driven by more frequent sunshine (S) and lower precipitation penalties (R), with CID/CIA near the comfort pivot. Humid coastal states score lower primarily due to persistent cloud/rain and high vapor pressure that elevates THI (e.g., Rivers 70.60, Bayelsa 70.92, Akwa Ibom 71.75, Delta 72.32, Abia 73.03, Lagos 74.61). State-level standard deviations are modest (~1.1–3.1 TCI units), but wide p10–p90 gaps along the coast indicate strong monsoonal seasonality; northern states show tighter intra-annual ranges. Orographic and ecotonal states (Plateau, Taraba) exhibit the broadest dispersion, reflecting elevation-controlled thermal and cloud variability. Methodologically, the pipeline is transparent and reproducible, linking remote-sensing–compatible inputs to policy-facing summaries. Practical implications include calendar-aware event scheduling, destination portfolio shifts by season, and integration of TCI calendars with health/safety protocols for heat and heavy rainfall. Limitations—THI as a proxy for effective temperature, generic Ångström–Prescott coefficients, ~4-km grid resolution, and ADM1 aggregation motivate sensitivity tests (e.g., PET/UTCI substitution, local sunshine calibration) and finer operational products. This work provides climate-comfort baseline to support tourism planning and climate-risk management.

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