the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Dec 2025
Three decades of snow water equivalent dynamics in the Po River Basin, Italy: Trends and Implications
Abstract. Seasonal snowpack is a key component of the mountain cryosphere, acting as a vital natural reservoir that regulates runoff downstream in snowfed basins. In mid- and low-elevation mountain regions such as the European Alps, snow processes, such as accumulation and ablation, are highly sensitive to climate change, having direct implications for hydrological forecasting and water availability. In this study, we present the analysis of a 30-year (1991–2021) long dataset of snow water equivalent (SWE) in the Po River District, Italy, which includes parts of the Alps and Apennines. The data is available at a 500 × 500 m2 spatial resolution and at a daily temporal scale (Dall’Amico et al., 2025). This data was generated using the “J-Snow” modeling framework, which integrates the physically based GEOtop model with in situ snow height observations and earth observation snow cover products such as MODIS. Our results show that the long-term (30 year) basin-wide mean annual SWE volume equals 3.34 Gm3. The elevation-wise statistical analysis of key snow volume and duration metrics shows that the most pronounced snow water equivalent losses occur below 2000 m a.s.l. Below this threshold, both snow volume metrics and duration metrics show a significant decrease, indicating decrease in snow water storage and earlier melt. Above this elevation, the snow volume metrics show increasing trend while as the duration metrics continue to show a shortened (decreasing trend) snow season except at the highest elevations (> 2500 m). The findings of this study highlight the changes to the mountain seasonal snow storage and the timing of snow disappearance across the Italian Alps. This combined effect highlights a fundamental shift in the hydrological regime of the Po River Basin, with significant implications for water availability and management under ongoing climate change.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-5520', Anonymous Referee #1, 18 Jun 2026
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RC2: 'Comment on egusphere-2025-5520', Anonymous Referee #2, 20 Jun 2026
Summary
This paper analyses a 30-year gridded SWE dataset (500 m, daily) for the Po River District, produced by the J-Snow modelling framework, and examines elevation-dependent trends across six snow phenology metrics. The dataset is genuinely valuable - a continuous, high-resolution SWE record for one of Europe's most water-stressed basins is exactly the kind of product the community needs. The paper does a nice job of showing how the seasonal niveograph is reshaping across the elevation gradient, and I particularly appreciated the Hovmöller plots (Fig. 4).
That said, I think the paper needs more work before it's ready for The Cryosphere. The high-elevation results are presented as findings despite the authors' own doubts about the forcing data, the statistical approach is underdescribed, and a research question on climate drivers is posed but never answered with data.
Main Comments
1. The high-elevation increase undermines itself. The abstract, results, and conclusions all present the increasing SWE trend above 2000 m on equal footing with the low-elevation declines. Yet the authors' own evidence argues against it. Their conceptual figure (Fig. 13) shows snow loss at all elevations under warming - their hypothesis contradicts their result. And independent glaciological work shows ice losses at these same elevations (Sommer et al. 2020).
The authors try to discuss this in Section 5.4.1, but the reasoning doesn't hold together. The passage invokes ERA5's single-layer snow scheme under-melting (Hersbach et al. 2020) as an explanation - but this study doesn't use ERA5's snow scheme; it uses GEOtop. That bias source isn't relevant here. The passage also claims ERA5-Land overestimates high-elevation precipitation, citing gauge-based comparisons (Bandhauer et al. 2022; Dalla Torre et al. 2024), but those reference datasets haven't been corrected for undercatch - and at high elevations, wind-induced gauge losses of 20–50% for solid precipitation are well documented (Kochendorfer et al. 2017). You can't call ERA5 too wet if the benchmark is too dry. The high-elevation increase is suspect, but the paper's attempt to explain why cites an irrelevant bias and an uncertain one. Either correct for the bias or flag the result as uncertain throughout. Direct validation of the SWE output at high-elevation stations (see comment 5) would be far more convincing than arguing about forcing inputs.
2. The trend analysis has methodological gaps. Mann-Kendall is applied to 1000 randomly sampled points per elevation band, but Table 2 reports single Z and τ values per band. How are these aggregated? If each point is tested independently, that's thousands of hypothesis tests with no multiple comparison correction. The authors cite Yue et al. (2002) on autocorrelation but don't indicate whether it was actually tested for or corrected, and SWE time series can have substantial year-to-year persistence. Separately, the anomaly comparison uses a 20-year reference period against a 10-year recent period, making the recent window vulnerable to individual extreme years (the 2016 drought being an obvious case). The aggregation procedure needs spelling out, autocorrelation needs addressing, and the period split needs either a sensitivity test or balanced windows.
3. The third research question goes unanswered. The paper asks "What climate drivers influence these long-term trends?" (L76–78) and then never addresses it with data. The discussion invokes rising temperatures and precipitation phase changes, which is no doubt correct, but it reads as received wisdom rather than analysis. I think this is a missed opportunity. The authors have 30 years of spatially distributed SWE at 500 m; combining this with even a straightforward analysis of ERA5 temperature and precipitation trends by elevation band would let them say something concrete about where the rain–snow transition sits and how it's shifted. It would be quite a powerful addition and would separate this paper from the existing literature that's already documented the general pattern.
4. Novelty needs sharper framing. The elevation-dependent pattern of Alpine snow loss is well established - Matiu et al. (2021), Bozzoli et al. (2024), Gobiet et al. (2014), Colombo et al. (2022), and others, many of which the authors cite. As I read the paper, I kept wondering: what does this analysis tell us that we didn't already know?.
5. The datasets need proper description and high-elevation validation.The paper's entire analysis rests on the WaterJade SWE product, but the reader learns almost nothing about how it was generated - we're pointed to Dall'Amico et al. (2025) but the key methodological choices (GEOtop physics, where ERA5-Land fills in, what gets assimilated) need to be summarised here. The same applies to the IT-SNOW comparison: IT-SNOW uses a completely different model (S3M) with different forcings and different assimilation, yet the paper just reports correlation statistics without explaining what drives the discrepancy. An R² of 0.31 at 0–500 m - where the strongest trends are - and opposite divergence at high elevations (WaterJade higher, IT-SNOW lower) should be explained.
The paper argues high-elevation bias can't be assessed because stations are sparse, but this overstates the gap. Torgnon (2160 m; cosmic-ray and GNSS-based SWE), the Mosso Institute on Monte Rosa (~2900 m; CRS and GNSS SWE), and manual snow course transects above 2000 m at five Aosta Valley hydropower reservoirs are all within the study domain. Direct comparison of WaterJade SWE against these or other observations would do far more than the current approach of citing general ERA5 literature.
Technical and Specific Comments
1. Figure 13 (conceptual diagram). I'm not sure what this figure adds beyond restating textbook expectations. As discussed in main comment 1, it actually undercuts the paper's headline result at high elevations.
2. Decadal non-monotonicity. SWE increasing from decade 1 to 2 at low elevations, then dropping sharply into decade 3, is interesting and unexplained. This feeds directly into the missing climate drivers analysis.
3. Broken cross-references. Several table references appear as "Table ??" (around L234–236). These need fixing.
4. SwS units. The metric is scientifically sound (Aragon and Hill 2024), but mm·day isn't intuitive - it's the area under the SWE curve, combining magnitude and duration into one number. When the paper reports changes in SwS, the reader needs something to anchor to. A statement like "a decline of X mm·day is equivalent to losing Y mm of SWE sustained over Z days" would make the trends interpretable. Without that, it's just big numbers.
5. Language. A number of typos: "dissappearance" (abstract), "responsed" (L74), "suitfed" (L167), "while as" (L13). The phrase "Europe's second most climate-sensitive" (L74) left me puzzled - second to what? A careful language edit would tighten things up.
Citation: https://doi.org/10.5194/egusphere-2025-5520-RC2
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Summary: This work uses a relatively new dataset to examine patterns in snow, including various snow phenology metrics, over time and across elevation gradients for the Po River Basin in Italy. I think this manuscript would be a good contribution to The Cryosphere but only after major changes are made. As currently presented, I found the manuscript incredibly difficult to follow, but I encourage the authors to resubmit.
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