Probabilistic Characterization of City Scale Subsurface Thermal Environment Heterogeneity

Accurate characterization of the subsurface thermal environment (STE) is crucial for sustainable geothermal energy and urban heat management, yet it remains challenged by pronounced subsurface heterogeneity at the city scale. Conventional approaches, which primarily rely on deterministic numerical modeling based on sparse observations, fail to capture the heterogeneity and spatial distribution of the subsurface thermal environment (STE). Here, we introduce an integrated modeling framework that synergistically combines geostatistics, physics-based modeling, and Bayesian inference to reconstruct the STE under steady-state thermal conditions and quantify its uncertainty. Applied to Changzhou in the densely populated Yangtze River Delta, our framework probabilistically reveals the distribution of key factors (lithology, upper temperature, basal heat flow, thermal conductivity, Darcy flux, and subsurface temperature). A key finding from the sensitivity analysis identifies the upper temperature boundary as the dominant source of modeling uncertainty. We estimate the shallow geothermal potential of Changzhou to be approximately 2.7 × 10⁴ GWh, sufficient to meet its winter heating demand and highlighting its potential as a sustainable energy source for the Yangtze River Delta. Our scalable and uncertainty-aware framework offers a pathway for the global assessment and management of subsurface thermal resources.