Global mapping of surface 500-m anthropogenic heat flux supported by multi-source data

Abstract

Anthropogenic heat emission affects the surface energy budget, energy exchange, and urban heat islands. Accurate quantification of anthropogenic heat flux (AHF) is significant for understanding the urban climate and improving human settlements. However, accurate global AHF dataset with high spatial heterogeneity (e.g., hundreds of meters) is still limited in practical application. In this study, we integrated multi-source data (global energy consumption statistics, nighttime light data, LandScan population density data, and regional AHF data) to model the global surface 500 m × 500 m AHF in 2016. The evaluation results indicate that the R2 between the global AHF integrated with regional fine data (GAHF) and the national energy consumption is 0.77, which is higher than the R2 (0.70) between the initial modeled AHF (GAHForg) and the national energy consumption. Besides, the mean R2 of GAHF and comparison dataset (0.84) is slightly higher than that of GAHForg (0.83) on the regional scale. On the grid scale, the correlation coefficient of GAHF and comparison dataset is improved compared with that of GAHForg. The absolute difference between GAHForg and China's surface AHF (CAHF) is 9.36 W·m−2, and that of between GAHF and CAHF is 0.97 W·m−2. These results demonstrate the feasibility of the global AHF estimation scheme, and the reliability of the global AHF estimates. The results can serve the global environment and climate change research.