the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Jul 2025
Estimation of annual discharge using geometric parameters of the UAV-based supraglacial channel over the Qiyi Glacier, northern Tibetan Plateau
Abstract. Due to difficulties in direct field observation and uncertainties in glacier runoff models, accurately estimating the glacier runoff remains one of the foremost challenges in cryospheric science. Using a digital elevation model (DEM) and orthophotos (both with a resolution of 5 cm) obtained from an unmanned aerial vehicle (UAV), this study developed a remote sensing novel method for estimating the annual discharge volume of the supraglacial channel over Qiyi Glacier in the northern Tibetan Plateau, which contributes to the majority of the annual glacier runoff. Our results showed that the catchment areas of the six main supraglacial channels covered 92.02 % of the total glacier area and transported 89.43 % of the annual surface meltwater yield (each ranging from 0.07 × 106 to 0.66 × 106 m3). Some geometric parameters of the supraglacial channels (including lateral deviation, gradient, and width) were selected to predict the annual discharge using a stepwise regression model, which explained ~78.2 % of the variance in the measurement-based glacier annual discharge, with the explained variance increasing to 81.8 % after five-point moving filtering. In comparison, a nonlinear regression model incorporating only the lateral deviation and specific gradient, which were more easily obtained practically, performed somewhat less well, accounting for 66.2 % of the discharge variation; however, the explained variance increased to 81.4 % after five-point filtering. Our regression models, based solely on the UAV-derived supraglacial channel network, will be a promising solution for monitoring changes in annual glacier discharge if satellite remote sensing data have centimeter-class spatial resolution.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-2940', Rob Storrar, 04 Aug 2025
- AC1: 'Reply on RC1', Longjiang Xie, 10 Sep 2025
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RC2: 'Comment on egusphere-2025-2940', Anonymous Referee #2, 15 Aug 2025
In the manuscript “Estimation of annual discharge using geometric parameters of the UAV-based supraglacial channel over the Qiyi Glacier, northern Tibetan Plateau” Xie and coauthors describe a UAV survey of Qiyi Glacier which was used to produce a high-precision (5cm) DEM of the glacier. This DEM was used to determine supraglacial drainage pathways which were then analyzed for geometric parameters and compared to estimated annual discharge from the glacier. While I find the manuscript generally well written, I have several major concerns regarding the how the results and the claims made about the significance of the findings which I elaborate on below. I therefore recommend major revisions prior to publication.
My main concern with this manuscript is that the authors claim they develop a “novel method for estimating annual discharge” of supraglacial channels which is not supported by work presented in the manuscript. Specifically,
- The authors calculation of annual glacial discharge (which per the title is the pivotal contribution of this work) relies on data from two weather stations and nine ablation stakes. These 9 ablation stakes are used to calculate mass balance (constraining MB in equ 1) at a 5 cm resolution. There is a brief discussion of some errors in the discussion but they cite errors from other studies and not an error analysis for this work. Moreover, the manuscript lacks details on the methodology for ablation stake measurements (what dates and at what frequency were ablation stakes measured?, what is the associated error?) And the data itself is not presented in the manuscript.
- Estimating annual discharge into a supraglacial stream is not a novel method, and even if it were, it would need to be validated on actual discharge measurements. Intuitively, streams with larger catchment areas should convey a greater proportion of annual discharge (e.g., Yang and Smith 2016), however, this isn’t specifically described and therefore makes it seem like that is a novel finding of this work. I recommend revising the manuscript to take care to properly cite and describe known physical relationships and by potentially adding a section to the discussion which distinguishes between new findings (stream morphology relationships with discharge) vs. findings that align with previous studies.
The main contribution of this work seems to be the geometric analysis performed on the very high resolution DEM created for Qiyi Glacier in 2023. By framing the analysis in the context of annual discharge, which has significant yet undiscussed uncertainties, undercuts confidence in the results. I suggest revisiting the annual discharge calculation, including a more robust error analysis and discussion (in addition to increasing the detail in the methods section), and shifting the main findings of the manuscript to what can be confidently argued by your results.
Other (non-exhaustive) comments below:
- Methodology on the Automatic determination of supraglacial streams and “manual correction” should be elaborated on. How many streams were originally identified by the algorithm? How many needed to be corrected visually? Only 11 streams are shown in Fig 4a so it is unclear why an automatic method needed to be employed in the first place.
- Section 3.4 belongs in the methods section
L54: Yes, not every study uses weather station data (as cited in this sentence) but many studies do use weather station data, this sentence is quite misleading in this regard.
Tables, what do the ** mean? I can’t find a description for this in the text.
Fig 9. Move to methods
Citation: https://doi.org/10.5194/egusphere-2025-2940-RC2 - AC2: 'Reply on RC2', Longjiang Xie, 10 Sep 2025
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- 1
This paper presents the results of a detailed UAV survey of Qiyi glacier, with the intention of taking morphometric measurements of supraglacial channels that can, in turn, be used to estimate runoff. The analysis reveals relationships between sinuosity and lateral deviation (similar metrics), gradient and discharge. This relationship is promising as a means of deriving runoff information from parameters that are measurable by high-resolution satellite remote sensing. Other parameters (height/width) also showed strong(er) relationships, though these are dependent on UAV data and so less widely applicable to satellite remote sensing methods.
This is a neatly conceptualized and well-executed study and the manuscript is well-written. The model has the potential to be very useful for estimating runoff across many glaciers, subject to some caveats. The data and methods appear sound, although some of the morphometric data requires more explanation. The discussion should explain more about exactly how applicable this method is to other glaciers, since I think this is over-stated a bit in the text. I expand on these points below, but otherwise I think this paper is a very useful contribution and I enjoyed reading it.
More information is needed on how exactly some of the morphometric data were calculated. Channel height and width is not straightforward to measure due to the topographic complexity of glacier surfaces, and so it is important that a consistent method is used to represent this (which I am guessing is what was done). How were the points h1 and h2 derived? This has a very significant implication for the measurement of both height and width. Please add some explanation of this to the methods section (2.3).
Since the premise of this study is that supraglacial channel morphometry may be a better way of estimating runoff than existing modelling techniques, it would be good to see some comparison of the data generated here with modelled runoff data. This is hinted at in the conclusions (line 365). I don’t know how feasible this is, but if possible would be a useful addition.
The discussion of applicability (section 4.4) should be extended to discuss further exactly how typical this glacier is, and other types of glacier to which this model probably does not apply besides tidewater glaciers. For example, temperate glaciers, glaciers with strong surface structures (e.g. crevasses) and debris-covered glaciers, all of which will be very common, are likely too complex to be represented by this model. This section should also note that to derive similar relationships for other glaciers requires mass balance data at the very least (because the scaling relationships are likely different for each glacier), so unless I am mistaken it can’t be applied using remote sensing alone.
I suggest rewording the title to make it clearer to something like: “Estimation of annual runoff using supraglacial channel geometry derived from UAV surveys of Qiyi Glacier, northern Tibetan Plateau”
Minor comments:
Line 14-15: Re-order words (novel remote sensing method)
Line 15: Not sure what is meant by ‘discharge volume’? I think you just mean discharge here?
Lines 24-25: I don’t understand the point here. We don’t have centimetre-resolution satellite imagery?
Line 59: Suggest ‘uncrewed’ instead of ‘unmanned’ to remove gender bias.
Lines 68-71: Are these findings published? If so, please provide a reference.
Line 72: Discharge?
Line 129: I assume from the equation that mass balance is expressed with negative values indicating mass loss specifically? It would be good to clarify this in the text below the equation (as well as stating the units).
Line 155: Pre-existing ice structure (e.g. fractures) also exerts a strong influence on channel morphology (e.g. Rippin et al. 2015: https://onlinelibrary.wiley.com/doi/full/10.1002/esp.3719?casa_token=5KxM3AvAxIYAAAAA%3AAODpVkdle62Mntm4D44VdOLPP_C7as8R1utImLL7u3vRhY6XGGyBfY0zhIDLpP5UlRx0olELoZHKRCU)
Line 185: Median and mean?
Line 292: ‘especially for mountain glaciers’ needs to be in a separate sentence because it is not what Smith said.
Line 296-7: Change glaciers to glacier (it has only been done at one!).
Line 308-9: Yes, sinuosity and lateral deviation could be determined from high-resolution satellite imagery, but you also need gradient, which can be taken from DEMs, which will be at significantly lower resolution where no UAV data are available. That is not to say that it is not useable, but perhaps worth stating.