This study investigated how underlying biophysical attributes affect the characterization of the Surface Urban Heat Island (SUHI) phenomenon using (and comparing) two statistical techniques: global regression and geographically weighted regression (GWR). Land surface temperature (LST) was calculated from Landsat 8 imagery for 20 July 2015 for the metropolitan areas of Austin and San Antonio, Texas. We sought to examine SUHI by relating LST to Lidar-derived terrain factors, land cover composition, and landscape pattern metrics developed using the National Land Cover Database (NLCD) 2011. The results indicate that (1) land cover composition is closely related to the SUHI effect for both metropolitan areas, as indicated by the global regression coefficients of building fraction and NDVI, with values of 0.29 and −0.74 for Austin, and 0.19 and −0.38 for San Antonio, respectively. The terrain morphology was also an indicator of the SUHI phenomenon, implied by the elevation (0.20 for Austin and 0.09 for San Antonio) and northness (0.20 for Austin and 0.09 for San Antonio); (2) the SUHI phenomenon of Austin on 20 July 2015 was affected by the spatial pattern of the land use and land cover (LULC), which was not detected for San Antonio; and (3) with a local determination coefficient higher than 0.8, GWR had higher explanatory power of the underlying factors compared to global regression. By accommodating spatial non-stationarity and allowing the model parameters to vary in space, GWR illustrated the spatial heterogeneity of the relationships between different land surface properties and the LST. The GWR analysis of SUHI phenomenon can provide unique information for site-specific land planning and policy implementation for SUHI mitigation.

Urban heat islands (UHIs) are a key topic in urban climate studies. However, systematic criteria for UHI comparisons were lacking prior to 2012, when the concept of Local Climate Zones (LCZs) was introduced. By relating remotely sensed land surface temperature (LST) to LCZs, we explored the applicability of LCZs in surface urban heat island (SUHI) investigations and compared the LST variation within and among LCZs for the Austin, San Antonio, and Dallas-Fort Worth metropolitan areas in Texas, United States. Landsat 8 images from one summer and one winter day in 2015 were used to obtain LSTs to measure and model SUHIs. LCZs that were characterized by different land covers had the greatest LST variations, and LCZs that were further characterized by various urban morphological properties (including building density and height of roughness) also showed significant LST differences. Moreover, LCZ 9 (sparsely built), LCZ 10 (heavy industry), LCZ D (low plants), LCZ E (bare rock or paved), and LCZ F (bare soil or sand) tended to contribute to contradictory heating/cooling effects in different metropolitan areas, primarily due to the spatial arrangement and geographic locations of LCZs. The close association between LCZs and LST demonstrates that LCZs are valuable for comparative analysis of the SUHI phenomenon between different cities and can be helpful for examination of the evolution of SUHIs over time. Our findings further suggest that understanding the spatial distribution of LCZs can benefit the development of mitigation strategies for SUHIs.

Above and belowground compartments in ecosystems are closely coupled on daily to annual timescales. In mature forests, this interlinkage and how it is impacted by drought is still poorly understood. Here, we pulse‐labelled 100‐year‐old trees with ¹³CO₂ within a 15‐year‐long irrigation experiment in a naturally dry pine forest to quantify how drought regime affects the transfer and use of assimilates from trees to the rhizosphere and associated microbial communities. It took 4 days until new ¹³C‐labelled assimilates were allocated to the rhizosphere. One year later, the ¹³C signal of the 3‐h long pulse labelling was still detectable in stem and soil respiration, which provides evidence that parts of the assimilates are stored in trees before they are used for metabolic processes in the rhizosphere. Irrigation removing the natural water stress reduced the mean C residence time from canopy uptake until soil respiration from 89 to 40 days. Moreover, irrigation increased the amount of assimilates transferred to and respired in the soil within the first 10 days by 370%. A small precipitation event rewetting surface soils altered this pattern rapidly and reduced the effect size to +35%. Microbial biomass incorporated 46 ± 5% and 31 ± 7% of the C used in the rhizosphere in the dry control and irrigation treatment respectively. Mapping the spatial distribution of soil‐respired ¹³CO₂ around the 10 pulse‐labelled trees showed that tree rhizospheres extended laterally 2.8 times beyond tree canopies, implying that there is a strong overlap of the rhizosphere among adjacent trees. Irrigation increased the rhizosphere area by 60%, which gives evidence of a long‐term acclimation of trees and their rhizosphere to the drought regime. The moisture‐sensitive transfer and use of C in the rhizosphere has consequences for C allocation within trees, soil microbial communities and soil carbon storage.