| 08 Dec 2025
Snow Mass Components Analysis: Greater Contribution to Atmospheric Water Vapor than to Water Resources on the Tibetan and Pamir Plateaus
Abstract. Snow in the high-altitude and high-latitude regions is essential for water resources and climate regulation. However, studies on snow mass balance components in alpine areas like the Tibetan and Pamir Plateaus (TPP) are limited. To fill the gap, a novel snow simulation framework was developed, combining in-situ snow depth, satellite snow cover, and point- and grid-scale modelling, supported by sensitivity analysis, automatic calibration, and deep learning. Key snow components—snowfall, snow water equivalent (SWE), refrozen snow, sublimation, evaporation, and snowmelt—were simulated across the TPP from 1962 to 2019 with reliable accuracy. Regionally averaged annual snowfall and refrozen snow—together representing snow pack input—were 70.67 ± 17.32 mm and 16.56 ± 3.85 mm, respectively. On average, 38 % of this input is converted into SWE and snowmelt that contributes 12–19 % of total river discharge over the TPP, while the remaining 62 % is lost to the atmosphere through sublimation and evaporation. Snow contributes less to water resources than to atmospheric moisture over the TPP on annual average. Seasonal snow patterns vary by region: in the Pamirs snow accumulates throughout the winter, making March–April SWE a key water resource indicator; while in the Tibetan Plateau, limited snow accumulation means total annual snowmelt better representing snow water resources. Significant regional declines have been simulated for key snow components though the trends vary spatially, potentially greatly influencing weather and climate both locally and remotely. Precipitation drives SWE changes in the north and west of the TPP, while temperature and wind speed play greater roles in the center and south.
This manuscript attempts to quantify snow mass balance components over the Tibetan and Pamir Plateaus using a combined modeling and machine learning framework, and to assess the relative contributions of snow to water resources and atmospheric moisture. The topic is relevant, and the integration of modeling with parameter regionalization is of some interest.
However, the manuscript has several fundamental issues. The central conclusion—that snow contributes more to atmospheric moisture than to water resources—is not robust. It relies heavily on simulated sublimation, which is highly uncertain, lacks proper validation, and is not accompanied by any uncertainty analysis.
More broadly, the credibility of the modeling framework is insufficient. The evaluation relies largely on model-derived SWE rather than independent observations, and key processes such as sublimation and snowmelt are not rigorously assessed. This weak observational constraint substantially limits confidence in the results.
A critical concern lies in snowfall, which is the fundamental input to the entire snow mass balance. As acknowledged by the authors, there are no direct snowfall observations, and precipitation data themselves carry considerable uncertainties over the TPP. These uncertainties are further amplified by the rain–snow partitioning parameterizations. Given the compounded uncertainties in this key input, it is difficult to see how the study can robustly constrain snow mass balance partitioning or support quantitative conclusions.
Furthermore, this study addresses a large and important scientific question, and the conclusion is potentially far-reaching. However, relying primarily on limited regional observations and a complex modeling chain is insufficient to support such a strong claim. The applicability of model parameterization schemes over the TPP requires careful, component-wise validation. Parameterizations developed and tested in other regions cannot be assumed to be directly transferable, and thus the reported partitioning results cannot be considered reliable.
The overall presentation of the manuscript also requires substantial improvement. The figures and writing are difficult to follow, with redundant descriptions and unnecessary abbreviations (e.g., HAL). The introduction reads more like a literature summary than a clear formulation of the scientific question. In addition, several terms and expressions are unclear (e.g., “standstill snowpack” versus SWE, and “warming-up period”), which further reduces readability. If AI tools are used in the writing process, greater attention should be paid to clarity and precision.
That said, the study shows some technical progress in snow process representation. The work may be more suitable for a model-oriented journal if it is reframed to focus on methodological development rather than broad hydroclimatic conclusions.