Climate Warming-Driven Expansion and Retreat of Alpine Scree in the Third Pole over the Past 45 Years

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study proposed a method of mapping alpine scree, investigated variations of alpine scree area, and discussed the possible impact of climate change on alpine scree in the Third Pole. The topic is very interesting and results have potential benefits for understanding changes in novel ecosystems-alpine scree due to climate changes. However, there are several flaws and concerns that need be clarified. My comments are as follows.

1. The critical threshold of slope was assumed as 26.6°, the friction coefficient was set as 0.5, how did authors consider the large spatial heterogeneity of land surface, such as different size of alpine scree.
2. Section 2.3 proposed a simple theoretical framework for the critical conditions of block sliding on a one-dimensional slope, however, actual land surface usually has slope on two dimension, thus, during the moving of alpine scree, it might be subjected to lateral obstruction of terrain.
3. It’s suggested to show results of comparison between the simulated alpine scree and field investigation, which would be better for validation of the method of mapping alpine scree.
4. Were the logistic regression results shown in Table 4 and 5 calculated by using the alpine scree data and variables (i.e., slope, DEM, temperature, precipitation, etc.) dataset during the same period? Because variations of alpine scree would also have impacts on local temperature and precipitation through influencing land surface energy and water budget, if these are the results at the same period, it might be difficult to explain the causal relationship.

Author Response

This study proposed a method of mapping alpine scree, investigated variations of alpine scree area, and discussed the possible impact of climate change on alpine scree in the Third Pole. The topic is very interesting and results have potential benefits for understanding changes in novel ecosystems-alpine scree due to climate changes. However, there are several flaws and concerns that need be clarified. My comments are as follows.

1.The critical threshold of slope was assumed as 26.6°, the friction coefficient was set as 0.5, how did authors consider the large spatial heterogeneity of land surface, such as different size of alpine scree.

[Response 1.1]: We appreciate the reviewer’s insightful comment regarding spatial heterogeneity (e.g., alpine scree size) and its potential influence on model parameters. While friction coefficients are indeed affected by multiple factors—including scree size, moisture, and vegetation—material composition served as the primary determinant for our parameterization. Given that the study area is dominated by loose gravel/scree slopes, we adopted a friction coefficient of 0.5, aligning with typical values for dry, coarse-grained materials (0.4–0.6) widely cited in literature. Although granular size variations contribute to local heterogeneity, regional-scale modeling often prioritizes material-driven friction as a representative value to emphasize first-order physical controls. Future fine-scale studies could explicitly incorporate grain-size distributions to address spatial variability.

 

2.Section 2.3 proposed a simple theoretical framework for the critical conditions of block sliding on a one-dimensional slope, however, actual land surface usually has slope on two dimension, thus, during the moving of alpine scree, it might be subjected to lateral obstruction of terrain.

[Response 1.2]: Our one-dimensional critical slope model (threshold = 26.6°) inherently accounts for the net effect of such obstructions on block mobility. Lateral confinement or obstruction increases the effective resistance to downslope movement, effectively requiring a steeper slope gradient to initiate sliding. Therefore, if blocks are observed moving in areas with lateral obstructions, it implies the local slope gradient overcoming the obstruction exceeds the critical threshold. While the one-dimensional model simplifies the complex three-dimensional motion path, it successfully captures the fundamental initiation condition – a slope steep enough to overcome both basal friction and any additional resistance (like lateral obstruction) – represented by the critical threshold. We acknowledge that modeling the full three-dimensional trajectory would require additional complexity, but the one-dimensional framework remains valid for identifying source areas prone to failure.

 

3.It’s suggested to show results of comparison between the simulated alpine scree and field investigation, which would be better for validation of the method of mapping alpine scree.

[Response 1.3]: The results of comparison between the simulated alpine scree and field investigation is shown in Fig. 4 and Table 1, as “The confusion matrix (table 1) shows that 39 of the 41 alpine scree points were accurate, and all 181 non-scree points were correctly classified. The simulation method achieved an accuracy of 99.10%, precision of 100%, and recall of 95.12%, confirming the feasibility of the alpine scree simulation approach.”

 

4.Were the logistic regression results shown in Table 4 and 5 calculated by using the alpine scree data and variables (i.e., slope, DEM, temperature, precipitation, etc.) dataset during the same period? Because variations of alpine scree would also have impacts on local temperature and precipitation through influencing land surface energy and water budget, if these are the results at the same period, it might be difficult to explain the causal relationship.

[Response 1.4]: Temperature and precipitation change data align temporally with our scree change inventory (capturing current spatial patterns), and the terrain variables (slope, DEM) remain effectively constant across our 1975-2020 study period. 

while micro-scale scree properties can influence local energy budgets, their impact on regional climate patterns is negligible. The climate datasets used (e.g.,1km resolution) represent broad atmospheric forcing, not microscale effects. In addition, scree distributions reflect cumulative climate/geomorphic history rather than driving short-term weather variations. The logistic regression (Table 4 and 5) thus identifies how terrain and established climate regimes correlate with scree occurrence – not transient interactions. Essentially, we are examining how regional climate influences scree distribution, not the reverse.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript explored the changes in the spatial boundary of the alpine scree on the Tibetan Plateau from 1975 to 2020 and their key influencing factors, using land cover data and binary logistic regression. This work is of significance for the in-depth understandings of the dynamics in alpine scree and its response to climate change. It is recommended that author make some minor revisions before publication.

Comments and suggestions are as follows,

1. It is encouraged to further explore spatial variations in the expansion and retreat areas of alpine scree and their key influencing factors.
2. The logistic regression results in Tables 4 and 5 do not show high explanatory power, with the R² value less than 0.4. It is suggested to further consider the influence of regional latent heat.
3. The logistic regression results showed a relative stronger correlation between temperature variations and the area of alpine scree compared to the other influencing factors in Table 4. In lines 344-346, it should be strictly expressed as "……warming primarily contributed to......".
4. The elevation unit is missing in Figure 2.

Author Response

This manuscript explored the changes in the spatial boundary of the alpine scree on the Tibetan Plateau from 1975 to 2020 and their key influencing factors, using land cover data and binary logistic regression. This work is of significance for the in-depth understandings of the dynamics in alpine scree and its response to climate change. It is recommended that author make some minor revisions before publication.

Comments and suggestions are as follows,

1. It is encouraged to further explore spatial variations in the expansion and retreat areas of alpine scree and their key influencing factors.

[Response 2.1]: Many thanks to the Reviewer 2 for the detail comments. The spatial variations in the expansion and retreat areas of alpine scree are now supplemented in Figs. A.3-A.5 and added in the manuscript, as “In terms of spatial distribution, the changes in alpine screes are primarily concentrated in the northern part of the Tibetan Plateau, particularly in Qinghai, where an increase of 1354.08 km² was observed between 1975 and 1995 (Fig. A.3). Overall, screes decreased in Qinghai, Xinjiang, and Tibet from 1975 to 2020, while it increased in Gansu. Compared to arid regions, alpine screes exhibited greater changes in humid areas (Fig. A.4). The significant changes alpine screes are concentrated in the area with an altitude of 4000-6000m (Fig. A.5).”

Climate warming was the primary driver of alpine scree dynamics, contributing to an increase in alpine scree from 1975 to 1995 year, followed by a decrease from 1995 to 2020 year.

 

2. 2. The logistic regression results in Tables 4 and 5 do not show high explanatory power, with the R²value less than 0.4. It is suggested to further consider the influence of regional latent heat.

[Response 2.2]: We acknowledge the relatively low R² values (0.33~0.38) – which isn’t uncommon in large-scale geospatial analyses of complex systems. Two factors likely contribute: first, our substantial sample size (12601~85579 pixels) inherently suppresses R² even with meaningful effects; second, the change in scree accounts for less than 5% of the total area, leading to an imbalance in the predicted targets.

We actually tested the influence of regional latent heat using MOD16A2GF-derived annual sums (8-day composites aggregated via summation). Adding latent heat yielded minimal improvement: R² decreased to 0.11, with a trivial coefficient (-0.001) (Table R1). This suggests latent heat fluxes – while mechanistically relevant – don’t significantly explain regional-scale scree change beyond our existing predictors. We thus prioritized parsimony in the final model but appreciate this thoughtful suggestion for future process-scale investigations.

Table R1 Logistic regression results from 1995 to 2020 year (R²=0.11)

Variable	Coefficient	Standard error	P	Odds ratio	95% CI
slope	-0.040	0.005	0.000	0.961	0.950-0.971
DEM	-0.001	0.000	0.000	0.999	0.999-1.001
Temperature change	-1.724	0.005	0.000	0.178	0.176-0.212
Precipitation change	0.017	0.001	0.000	1.017	1.016-1.019
AI change	0.271	0.012	0.000	1.311	1.309-1.353
latent heat change	-0.001	0.000	0.001	0.999	0.999-1.001
constant	1.684	0.647	0.009	5.387

 

3.The logistic regression results showed a relative stronger correlation between temperature variations and the area of alpine scree compared to the other influencing factors in Table 4. In lines 344-346, it should be strictly expressed as "……warming primarily contributed to......".

[Response 2.3]: This has now been revised in the manuscript, as “In particular, climate warming primarily contributed to the alpine scree dynamics.”

 

4. 4. The elevation unit is missing in Figure 2.

[Response 2.4]: The elevation unit is added now in Figure 2.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

Areas for Possible Improvement

1. The manuscript occasionally conflates "desert", "bare land", and "alpine scree" without always clarifying climatic or geomorphological distinctions.
2. The term "novel ecosystems" in the title is conceptually ambiguous here—it is not defined or framed in the context of ecological theory (Hobbs et al., 2006), which may mislead readers.
3. The threshold slope angle (26.6°) is calculated under static assumptions. However, real-world factors such as freeze-thaw cycles, precipitation, and sediment cohesion are dynamic and spatially heterogeneous. The paper could discuss the sensitivity of this threshold to such variations more deeply.
4. Remote sensing-derived classes (bare rock, bare soil) are sensitive to classification errors. The paper would benefit from discussing uncertainties in input data (e.g., cloud cover, snow misclassification).
5. While climate datasets at 1 km resolution are commendable, temporal averaging or downscaling methods are not thoroughly described. Given the high spatial variability of Tibetan Plateau microclimates, a brief uncertainty assessment is warranted.

6. The discussion could be enriched by referencing studies on alpine or glacial foreland succession from the Alps, Andes, or Rockies, to strengthen the global relevance of the findings.

Comments on the Quality of English Language

The manuscript suffers from repetitive phrasing, awkward syntax, and grammatical inconsistencies (e.g., “in 2020 year” instead of “in 2020”).

Author Response

Areas for Possible Improvement

1. The manuscript occasionally conflates "desert", "bare land", and "alpine scree" without always clarifying climatic or geomorphological distinctions.

[Response 3.1]: Many thanks to the Reviewer 3 for the detail comments. These terms have now been supplemented and explained in section 2.4, as “Bare rock and bare soil are strictly a remote sensing land cover class (unvegetated surface), whereas desert, bare land and alpine scree are ecological designations applied based on context. In ecosystem classification, scree, desert, and bare land all consist of bare rock and bare soil. The key distinction is their proximity to glacier and the critical threshold of slope: bare rock and bare soil in scree are adjacent to glacier with a steep slope (typically >26.6°), while those in desert and bare land are not. Desert is located in arid region, while bare land occurs in more humid area.”

 

1. The term "novel ecosystems" in the title is conceptually ambiguous here—it is not defined or framed in the context of ecological theory (Hobbs et al., 2006), which may mislead readers.

[Response 3.2]: The title has now been changed to “Climate warming-driven expansion and retreat of alpine scree in the Third Pole over the past 45 years”.

 

2. The threshold slope angle (26.6°) is calculated under static assumptions. However, real-world factors such as freeze-thaw cycles, precipitation, and sediment cohesion are dynamic and spatially heterogeneous. The paper could discuss the sensitivity of this threshold to such variations more deeply.

[Response 3.3]: These uncertainties are now supplemented and explained in the discussion section, as “Second, the simulation of alpine scree primarily focuses on surface formation processes under static assumptions (e.g., threshold slope angle). Real-world factors such as freeze-thaw cycles, precipitation, sediment cohesion, wind speed and glacial meltwater are dynamic and spatially heterogeneous. These factors may accelerate the formation of alpine scree, and even push them to flatter slopes. This leads to a decrease in the critical threshold of slope, thereby increasing the uncertainty of the scree simulation.”

 

3. Remote sensing-derived classes (bare rock, bare soil) are sensitive to classification errors. The paper would benefit from discussing uncertainties in input data (e.g., cloud cover, snow misclassification).

[Response 3.4]: This study proposes a method for mapping alpine scree based on existing remote sensing data products. The land use datasets were downloaded from the National Tibetan Plateau Science Data Center (https://data.tpdc.ac.cn/home), and the relevant reference is attached below. During the data processing, object-oriented classification, change detection, and other methods were applied, integrating cloud computing, big data, and machine learning technologies, to obtain data with a spatial resolution of 30 m

References:

Wu, B. Medium resolution land cover data of Qinghai-Tibet Plateau (1980-2020). 2023, doi:10.11888/Terre.tpdc.300593.

 

4. While climate datasets at 1 km resolution are commendable, temporal averaging or downscaling methods are not thoroughly described. Given the high spatial variability of Tibetan Plateau microclimates, a brief uncertainty assessment is warranted.

[Response 3.5]: The climate datasets were downloaded from the National Tibetan Plateau Science Data Center (https://data.tpdc.ac.cn/home). The relevant references are attached below. This dataset is generated in China based on the CRU global 0.5° climate dataset and the WorldClim global high-resolution climate dataset through the Delta spatial downscaling method and has been validated using data from 496 independent meteorological observation stations. The annual average climate data is the average of 12-month datasets.

References:

Peng, S. 1-km monthly mean temperature dataset for china (1901-2023). 2024, doi:10.11888/Meteoro.tpdc.270961.

Peng, S. 1-km monthly precipitation dataset for China (1901-2023). 2024, doi:10.5281/zenodo.3114194.

Peng, S. 1-km annual arid index dataset for China (1901-2023). 2024, doi:10.11888/Atmos.tpdc.300560.

 

5. The discussion could be enriched by referencing studies on alpine or glacial foreland succession from the Alps, Andes, or Rockies, to strengthen the global relevance of the findings.

[Response 3.6]: Thank you for providing useful references. The referencing studies on alpine or glacial foreland succession from the Alps, Andes, or Rockies have now been discussed in the discussion section, as “Research has found that glaciers in high-altitude mountains such as Alps, Andes, or Rockies around the world have melted on a large scale, exposing bedrock and expanding alpine scree[54,55].........Increasing empirical studies have also found that vegetation on high-altitude mountains (i.e., Alps and Tibetan Plateau) is expanding towards high altitude or latitude regions under the climate warming[57,58].”

 

Comments on the Quality of English Language

The manuscript suffers from repetitive phrasing, awkward syntax, and grammatical inconsistencies (e.g., “in 2020 year” instead of “in 2020”).

[Response 3.7]: Thank you. Theses grammar issues have now been corrected in lines 217 and 262. And we have also edited the text thoroughly to correct language errors and improve the flow of arguments.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Authors have adequately revised their manuscript. I recommend it can be accepted for publication.

Author Response

Thank you.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

Maybe you can replace “in 2020 year” with “in 2020”; eliminate other grammatical inconsistencies.

If possible, add implications for species, ecosystem services, or alpine biodiversity.

Mention the limitations of temporal mismatch in field validation?

Author Response

Maybe you can replace “in 2020 year” with “in 2020”; eliminate other grammatical inconsistencies.

[Response 2.1]: Thank you. Theses grammar issues have now been corrected in the manuscript. And we have also edited the text thoroughly to correct language errors and improve the flow of arguments.

 

If possible, add implications for species, ecosystem services, or alpine biodiversity.

[Response 2.2]: Indeed, changes in the spatial extent of alpine scree can have significant impacts on species, ecosystem services, or alpine biodiversity. These are explained in the discussion section, as “With ongoing climate warming, the Tibetan Plateau’s alpine scree faces further reduction, posing several ecological risks. First, alpine scree provides critical habitats for endemic plant species, such as Tacheng, Saussurea, Androsace delavayi, and Rhodiola coccinea var. scabrida, which have adapted to extreme environments through traits like dwarfism, dense hair coverings, and rapid growth[40,59,60]. Reductions in alpine scree area could lead to habitat compression, risking population decline or local extinction of these species[61]. Second, the loss of bare rock and permafrost reduces ecosystem resilience to disturbances, heightening the likelihood of natural disasters[62]. Third, the loss of alpine scree would disrupt ecological functions. Although the biomass of alpine scree plants is low, their root systems and microbial activity play a crucial role in soil carbon sequestration[63]. Habitat degradation could diminish carbon storage, exacerbating regional carbon imbalances. Additionally, subsurface flow beneath alpine scree is a vital water source for plateau rivers, which would impact downstream water resources[53,64].”

 

Mention the limitations of temporal mismatch in field validation?

[Response 2.3]: We thank the reviewer for noting the temporal gap between simulation and validation. We acknowledge the temporal mismatch between our simulated scree extent (2020) and field validation campaigns (2023–2024). This limitation arises from logistical constraints in conducting high-altitude fieldwork during optimal seasons. However, the overall change rate of alpine scree was minimal (-0.76% over 45 years; 1975-2020), indicating negligible short-term variations. This has now been explained in the discussion section, as “Fourth, although scree exhibits minimal interannual variation (with a change rate of -0.76% during 1975-2020), temporal mismatches between simulated data and field surveys may introduce some uncertainty. Future verification could employ real-time high-precision remote sensing data to validate these findings.”

Author Response File: Author Response.docx