| 24 Oct 2025
Accelerated Permafrost Degradation in the Source Area of the Yellow River: Spatiotemporal Dynamics of Freeze-Thaw Indices Revealed by High-Resolution DEM-Corrected ERA5-Land Data (1981–2020)
Abstract. Permafrost degradation in the Source Area of the Yellow River (SAYR) has intensified under climate warming. Yet, the spatiotemporal patterns of freeze-thaw (F-T) dynamics remain poorly understood due to the limited availability of high-resolution data. Here, we integrate ERA5-Land reanalysis with a Digital Elevation Model (DEM) to develop a 1 km-resolution monthly surface temperature dataset (1981–2020), corrected for topographic bias using elevation-dependent temperature lapse rates. Based on this dataset, we calculate F-T indices (freezing/thawing index, thaw duration) and analyze their trends. Results show DEM correction significantly improves temperature accuracy (RMSE = 1.22 °C, ubRMSE = 0.38 °C). Over 40 years, air and surface freezing indices declined by –100.43 and –141.85 °C·d/10a, while thawing indices increased by 83.74 and 98.47 °C·d/10a, respectively. Thaw duration extended by 1.17 days/decade, with stronger trends in low-elevation zones. Freeze-thaw ratios (N-factor) exceeded 1 across all sites, indicating accelerated permafrost degradation. Spatial heterogeneity reveals thaw dominance in southeastern valleys (N > 5) versus residual freezing capacity in northwestern highlands (N < 2), driven by altitude and vegetation insulation. This study provides the first long-term, high-resolution F-T dataset for SAYR, demonstrating that topo-climatic gradients and vegetation feedbacks critically regulate permafrost stability. Our findings advance regional permafrost modeling and inform infrastructure resilience strategies in the context of climate change.
This study investigates the spatiotemporal dynamics of freeze–thaw indices using elevation-corrected ERA5-Land data. The authors report an increasing thawing index and a reduced freeze–thaw ratio. However, my primary concern lies with the perceived lack of innovation in this work, while the methodology also appears to be problematic. Please see my comments below.
Major Comments
1) Definination of Permafrost Science
The terms of “perennial permafrost” and “seasonal permafrost” do not appear to be used within the permafrost communicty. Permafrost itself means “permanent frost” (Dobinski 2011). It would be more appropriate to distinguish between permafrost and seasoannly frozen ground.
2) Innoviation
Changes in freezing and thawing indices have already been extensively documented at both global and regional scales (e.g., Frauenfeld et al., 2004; Zhang et al., 2005; Luo et al., 2014; Peng et al., 2016, 2019; Wu et al., 2018; Dashtseren et al., 2018). Although the authors state that this study presents the "first 1-km resolution monthly surface temperature dataset (1981–2020) for the Yellow River headwater area," the conclusions—such as increases in air/surface freezing indices, thawing ratio, and thaw duration—are consistent with existing literature. Therefore, the novel contribution of this study needs to be more clearly articulated. My concern is what's the real contribution of this study?
3) Observations from China Meteorological Data Network
Authors used the daily (ground) surface temperature from the China Meteorological Data Network (http://data.cma.cn). It is important to note that the ground surface temperature is not consistant (Cui et al., 2020). During the manual observation time period, ground surface temperature represents the snow surface temperature when the surface is covered by snow. Since around 2003, the meteorological stations were gradually transformed from manual observations to automatic observations. In automatic observations, ground surface temperature represents the temperature of the soil surface beneath the snow cover (Du et al., 2020). Since the ground surface temperature from China Meteorological Data Network was used for downscaling evaluation and model traning, this inconsistency poses a significant issue that must be addressed. Since the authors used skin temperature data, they would likely prefer the annually measured snow surface temperature. This implies that only the ground surface temperature from before 2003 should be used. The weak correlation between elevation and freezing index shown in Figure S3a, compared to that of the thawing index, may be attributed to such inhomogeneities in surface temperature measurements. The authors should clarify how this issue was handled.
4) Freeze-thaw ratio
The authors used the freeze-thaw ratio (N) as an indicator of permafrost thermal stability, citing Cheng et al. (2003), and applied a threshold of N = 1 to classify permafrost stability. This approach appears problematic. According to Equation 1, the relationship can be expressed as:
The statement in L162-163 then becomes “When MAAT = 0 ºC, freezing and thawing are approximately balanced, reflecting relatively stable permafrost conditions.” This is contrast to the Cheng’s classification (see Cheng 1984 and Ran et al., 2021). The classification proposed by Cheng used the MAGT, not MAAT. Moreover, according to Gruber (2012), permafrost is generally limited to regions where MAAT ≤ –1.5°C. In reality, , permafrost is unlikely to persist in areas where MAAT = 0°C, except under the most favorable local conditions.
Specific Comments
References:
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