| 28 Jan 2026
Greek mountain snow cover halved in past four decades due to regional warming
Abstract. Snowpacks in mountain regions with Mediterranean climates are exceptionally sensitive to climate warming. However, these marginal snowpacks are sparsely monitored, limiting our understanding of recent snow losses and constraining our ability to anticipate and manage future changes in mountain water supply. Here we present snowMapper v1.0, a modular, physics-informed, machine-learning-based model for reconstructing daily snow cover at high spatial resolution using satellite imagery and gridded climate products. snowMapper is fully configurable and features dedicated modules for masking, preprocessing, snow binarization, snow reconstruction, spatiotemporal aggregation, and validation. It performs with exceptionally high skill. Using snowMapper, we generate a monthly snow-cover climatology for ten of Greece’s highest mountain massifs for the period 1984–2025. Our results reveal a rapid and widespread decline in snow cover area (SCA), amounting to a ~58 % reduction relative to the 1984–2025 mean. We identify sustained warming throughout the snow season as the primary driver of this decline. Precipitation changes correlate with SCA only in early and mid-winter, underscoring the dual role of air temperature in controlling both accumulation (via snowfall fraction) and ablation processes. The North Atlantic Oscillation exerts only a modest influence on mid-winter SCA, and primarily when acting in conjunction with the Arctic Oscillation, representing a stark contrast to patterns observed in western Mediterranean mountain ranges. Finally, the absence of a strong relationship between SCA and the Atlantic Multidecadal Oscillation reinforces the conclusion that the observed trends lie outside the bounds of natural climate variability.
Overall Evaluation
The manuscript presents a valuable and well-structured reconstruction of snow cover using a hybrid gap-filling framework that combines decision-tree and machine learning approaches. The long-term dataset and the integration of multiple satellite sources represent a significant contribution to snow monitoring and hydrological applications.
The methodology is generally sound, and the results are relevant and promising. However, several methodological aspects require clarification to improve transparency and reproducibility, particularly regarding model configuration, data processing choices, and evaluation procedures. In addition, some figures and descriptions would benefit from clearer explanations to facilitate interpretation.
Overall, the manuscript is of good quality and suitable for publication after minor revisions addressing the points raised below.
Detailed Comments and Suggestions
Comment on Section 2.2 (snowMapper model overview):
The model overview is clear and well structured, and Figure 2 is informative. However, this section remains largely descriptive and would benefit from additional clarification. Specifically:
Comment on Section 2.3.1 (Satellite imagery and MODIS processing):
The satellite data processing is generally well described; however, several methodological choices require further justification:
Comment on Section 2.3.4 (In situ data):
The training data are derived from stations in the Alps and Pyrenees rather than from Greece. Please justify the transferability of the model to Mediterranean snow conditions, which may differ significantly.
Comment on Section 2.4.1 (Machine learning classifier):
The Random Forest hyperparameters (e.g., number of trees = 30, minimum leaf size = 1, bag fraction = 0.5) are specified, but their selection is not justified. Please clarify how these values were chosen (e.g., cross-validation, sensitivity analysis, or empirical testing).
Comment on Section 2.4.5 (Final output):
The computation of monthly aggregates is not clearly described. Please clarify how daily snow cover is aggregated to monthly values (e.g., mean, maximum, or fraction of snow-covered days). In addition, the method used to convert daily binary snow maps into monthly fractional snow cover (FSC) should be explicitly defined.
Comment on Figure 4:
Figure 4 is not easy to interpret. The definition of “fraction of pixels” is unclear, and it is not specified how these monthly proportions are computed. Please provide additional information in the figure caption. In addition, the machine learning contribution appears relatively constant over time; please clarify how this fraction is computed and whether it varies across years.
Comment on Figure 5:
Although Figure 5 describes the temporal aggregation of the metrics, the evaluation methodology is not fully clear. Please clarify what datasets are being compared (e.g., model outputs vs. observations) and whether the evaluation is performed at the pixel level over the study area.