| 04 Mar 2026
A 2020 permafrost distribution map over the Qinghai-Tibet Plateau
Abstract. Permafrost on the Qinghai-Tibet Plateau (QTP) is undergoing rapid degradation, yet most existing distribution maps reflect long-term historical averages, failing to capture the current thermal state required for accurate ecological and hydrological modeling. To address this temporal mismatch, this study presents a 1-km QTP permafrost distribution map for the year 2020. We employed an extended ground surface frost number model (FROSTNUM) driven by satellite-derived freezing/thawing indices. To overcome the lack of concurrent field surveys for parameter calibration, we implemented a space-for-time substitution strategy, utilizing a Random Forest regression to robustly estimate the empirical soil parameter (E) based on environmental covariates. The resulting map reveals that in 2020, permafrost covered approximately 1.038 × 10⁶ km² (39.35 % of the plateau), while seasonally frozen ground (SFG) covered 1.466 × 10⁶ km² (55.57 %). Compared to the 2010 baseline, the permafrost area declined by 4.8×104 km2 (a 1.82 % decrease). Spatially, the degradation of permafrost to SFG extensively occurred in the central QTP (accounting for 7.41 % of the total change), and a significant marginal contraction of SFG to non-frozen ground in the southern margin (accounting for 39.62 % of the total change). Validations against 109 independent borehole records from the 2020 period confirms the map’s reliability, achieving an overall accuracy of 0.84 and a Kappa of 0.58. This 2020 map provides an essential, up-to-date resource for quantifying the recent cryospheric shifts and supporting engineering risk assessments in this climate-sensitive region.
The authors present in this manuscript a new high-resolution permafrost distribution map for the Qinghai–Tibet Plateau for the 2020 period. The main novelty lies in the gridded estimation of the empirical soil parameter E under the extended FROSTNUM framework, using a space-for-time substitution strategy in the absence of concurrent large-scale field surveys. Overall, the results of this study have good practical value and provide a useful reference for this community.
Main points:
(1) The final results lack an explicit uncertainty analysis. At present, the manuscript selects a single optimal scheme from multiple methods and configurations to generate the final map, but does not further quantify the uncertainty of the final results. I recommend that the authors include an assessment of the robustness of both the predicted E parameter and the resulting permafrost distribution.
(2) The methodological implications of the space-for-time substitution strategy are not discussed in sufficient depth. In particular, the manuscript would benefit from a clearer discussion of where this strategy is likely to be most reliable, where it may break down, and how this affects the interpretation of the final map.
Minor points:
(1) P7 L172–175. A brief explanation of why F>0.5 is used as the threshold for permafrost classification would improve the clarity of the method description.
(2) P2 L63–68; P4 L110–115. The manuscript refers to the map as being “for 2020” or an “instantaneous snapshot”, while the forcing data actually cover 2016–2020. A more cautious and consistent expression, such as “for the 2020 period,” is recommended.
(3) P12, L308–315. For the discussion of the MLP failure, it would be better to avoid attributing the issue simply to the “black box” nature of deep learning, and instead refer more specifically to the lack of physical constraints and poor generalization.