Accurate assessment of soil organic carbon (SOC) stocks is essential for understanding the global carbon cycle. However, substantial uncertainties remain in mountainous regions such as Tibet, where the commonly used fixed-depth (FD) approach overlooks spatial variability in soil thickness (ST). This omission leads to systematic overestimation in shallow soils (due to the accounting of carbon in non-existent soil layers) and underestimation in deep, incompletely sampled profiles. To address these dual biases, this study introduces an integrated framework that, for the first time, couples the equivalent soil mass (ESM) method-which corrects for bulk density-related biases-with spatial modeling constrained by ST. The methodology involved: (1) deriving pointscale ESM-based SOC stocks for standard depth intervals (0-30, 30-50, and 50-100 cm) using equal-area quadratic smoothing splines; (2) generating spatially explicit, wall-to-wall predictions at 1-km resolution through quantile regression forests, employing an inverse probability of censoring weighting QRF (IPCW-QRF) for ST and a standard QRF for SOC; and (3) performing pixel-level integration of SOC stocks constrained by the predicted ST to avoid carbon assignment to non-soil layers. The ST model achieved a validation R2 of 0.57, revealing a distinct spatial pattern of deeper soils in the north and south and shallower soils in the central Plateau, with a mean depth of 90.40 +/- 35.50 cm. The SOC model achieved a validation R2 of 0.56 for the topsoil (0-30 cm), with accuracy decreasing in deeper layers due to sparser observations and reduced influence of surface-based covariates. After ST-constrained integration, the total SOC stock within the reliable prediction area (area of applicability, AOA) was 4.72 Pg C (90 prediction interval: 0.99-19.43 Pg C), while the total stock across Tibet ranged from 2.58 to 47.50 Pg C for the 0-100 cm layer. These findings demonstrate that explicitly incorporating ST and ESM effectively eliminates the systematic biases inherent in the FD approach, offering a robust and scalable framework for accurate carbon accounting in complex, high-relief terrains.