the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multitemporal characterisation of a proglacial system: a multidisciplinary approach
Elisabetta Corte
Andrea Ajmar
Carlo Camporeale
Alberto Cina
Velio Coviello
Fabio Giulio Tonolo
Alberto Godio
Myrta Maria Macelloni
Stefania Tamea
Andrea Vergnano
Abstract. The recession of Alpine glaciers causes an increase in the extent of proglacial areas that leads to changes in the water and sediment balance morphodynamics and sediment transport. Although the processes occurring in proglacial areas are relevant not only from a scientific point of view but also for the purpose of climate change adaptation, there is a lack of studies on the continuous monitoring and multitemporal characterization of these areas. This work offers a multidisciplinary approach that merges the contributions of different scientific disciplines such as hydrology, geophysics, geomatics and water engineering to characterise the Rutor glacier and its proglacial area. We surveyed the glacier and its proglacial area since 2020 with both uncrewed (drone) and crewed aerial photogrammetric flights; we determined the bathymetry of the most downstream proglacial lake and the thickness of the sediments deposited on its bottom. Water depth at four different locations within the hydrographic network of the proglacial area and the bedload at the glacier snout were continuously monitored. The synergy of our approach enables the characterisation, monitoring and understanding of a set of complex and interconnected processes occurring in a proglacial area.
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Elisabetta Corte et al.
Status: open (until 11 Oct 2023)
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RC1: 'Comment on essd-2023-94', Anonymous Referee #1, 09 Jun 2023
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The authors present a multi-temporal and multi-disciplinary characterization of the pro-glacial margin of the Rutor Glacier. The dataset that comes out from this characterization is remarkable, and shows the importance of using a multi-disciplinary approach to appreciate the functioning of pro-glacial systems in full. The paper is well written, and I enjoyed reading it. The paper fits within the aims and scope of ESSD, and it is worth publishing but I would like to suggest some modifications that may improve the overall quality of the manuscript.
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General comments:
Section 2.2.1 Geomatic survey: The section does not provide a detailed explanation of the photogrammetric processing, and it is not supported by the relevant literature. In my opinion, this section needs to be improved. For instance, the name "SfM-MVS photogrammetry" is never mentioned throughout the article and no effort is made to explain the steps needed in a rigorous photogrammetric study. A few examples: the authors do not mention how the images were collected (e.g., drone flight geometries), how many ground control points they used, or which software or freeware they used to process the images. Finally, the author do not provide any information on the quality of the DSMs, therefore questioning if their results are reliable or not. This important weakness needs to be addressed.
Section 2.2.4 Bedload monitoring: As per the geomatics, I believe there is the need of providing a more detailed explanation of the bedload monitoring since the use of seismometers in bedload studies is relatively recent. It would be very useful to know in more detail how the data were processed and the steps required to go from the raw signal to the results presented here.
Data availability: The 2020 orthophoto and DSM are not available on Zenodo (https://zenodo.org/record/7713299). Are you going to include them in future?
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Detailed comments:
1. Lines 26 – 28: “Alpine glacier retreat is leading to increased exposure of formerly glaciated terrain, entailing the colonization of plants and animals, and changes in morphodynamics and sediment transfer.”
Consider adding one or more citations here.
2. Lines 29 – 30: “Little Ice Age (LIA)”
You already defined the acronym; perhaps just use LIA instead of “Little Ice Age (LIA)”.
3. Lines 35 – 36: “On the one hand, plant colonization stabilizes glacial sediment and reduces sediment fluxes; on the other hand, geomorphic processes disturb and limit vegetation succession.”
Consider adding one or more citations here.
4. Lines 54 – 55: “Sediment yield depends on water discharge and sediment availability which are both highly variable in space and time.”
Consider adding one or more citations here.
5. Line 148: “manned photogrammetric flights”
I would move away from “manned” and describe those as crewed or airborne.
6. Lines 154 – 159:
How many targets did you use in total? Did you deploy the targets only in 2021? Why did you not consider collecting independent checkpoints for quality assessment?
7. Line 160: “Unlike drone flights which were oriented exploiting a direct georeferencing approach”
What do you mean with “direct georeferencing”? Did you use the camera positions alone? If yes, why? The GPS onboard of the DJI P4 is of poor quality for high-precision photogrammetric surveys, and it is a standard practice to use ground targets in SfM-MVS studies (particularly to reduce the occurrence of systematic deformations in DEMs).
8. Lines 163 – 166: “Due to a large number of well-distributed ground control points, the 2021 aerial survey was considered the reference model (referred to as ’Model” Zero’) to be used for multitemporal analyses. The 2020 survey was, therefore, co-registered (i.e., georeferenced in the same reference system, enabling the overlap of all the derivative products) with the 2021 survey.”
This suggests that you did not use any target in 2020 (see comment 6), am I right? How did you co-register the 2020 survey? Could you please explain the co-registration procedure? Could you provide statistics on the quality of the co-registration?
9. Lines 282 – 284: “The aerial DSMs were preliminary compared to the LiDAR DSM as of 2008 available on Valle d’Aosta Geoportal to verify the consistency of the produced model, checking the stability of the periglacial rocky areas. Subsequently, 2021 and 2020 DSMs were subtracted to quantify glacier ablation and displacement”
I would move this section into the methods, and explain how you compared the DSMs. In the results, it would be more informative to provide the statistics of such a comparison (e.g., mean error, std of error – not the RMSE) in order to demonstrate that your DSMs were free of systematic (mean error close to 0) and random (std of error close to 0) errors.
10. Figure 8a:
I am a little concerned about the way you presented the DSM of difference. First, why did you not use a bivariate scale (from –X to +X)? A bivariate scale would help a lot in my opinion. Second, why did you not apply a Limit of Detection? The use of a Limit of Detection is a common practice in DoDs, and allows showing changes that are statistically significant (e.g., at 68 or 95% confidence limits). Lastly, from the DoD presented in Fig. 8a it seems that the whole study area experienced at least some movements in Z, is that really possible? Did you check for systematic deformations (e.g., doming, datum shift) in your DSMs? There is the need of providing statistics that illustrate the quality of your DSMs, e.g. the mean error in Z (i.e., systematic errors) and the std of error in Z (i.e., random errors) in respect to reference point altitudes or independent check points.
11. Line 289: “Additionally, a comparison with the 2008 DSM shows a lowering of glacier surface up to 50 meters in glacial front areas”
Where is this result presented?
12. Lines 290 – 293: “As far as very high-resolution satellite stereo pairs are concerned, they enable the extraction of 3D information with a lower vertical accuracy (metric level) with respect to aerial and drone data. Nevertheless, the coverage of a much larger area (in the range of hundreds of square kilometres) enables a multiscale and multiplatform approach to identify the most critical areas where to focus the monitoring activities in the field”
You do not present these results nor discuss them later, what is the point of including such a thing?
13. Lines 300 – 303: “The x-y-z locations of the first interface, representing the lake bottom, detected in all the GPR sections, were interpolated to produce a bathymetry map (Figure 10, which also displays the sediment thickness distribution and the electrical conductivity measurements). The perimeter of the lake, retrieved from the 6-cm-resolution orthophoto, was useful to fix the 0-depth in the interpolation process.”
This section reads like methods. I would move it to the methods.
14. Lines 326 – 334: “The ecoLog1000 and CTDs instruments were first installed in July 2021 and June 2022, respectively. The measuring periods of each sensor are shown in a time: measured-quantity diagram in Table 1. At the L4 gauging station, a set of velocity-based discharge measurements (Q) taken in the summer of 2021 and 2022 were related to the corresponding water depth measured at the gauge (h), in order to plot the stage-discharge diagram (Fig. 11(a); details of the procedure followed to determine the stage-discharge relationship are given in Appendix A). Discharge measurements were also used to calibrate the lake outflow curve, i.e., the relationship between the hydraulic head (H) in the lake and the flowing discharge (see Fig. 11(c)). For this purpose, a linear fitting between the water depth at the gauge (h) and the Hydraulic head in the lake (H) was also calibrated (Fig. 11(b), R2 ∼ 0.98), since the water levels in the lake and in the control cross-section in the stream are strictly related but not equal, due to the head-dependant outflow process and water speed.”
This section reads like methods, I would therefore re-arrange this part.
14. Line 357: “permits the identification of time intervals characterized by intense transport”
Although bed load would easily occur at high discharges, is the signal you see necessarily related to intense transport events? Or, could the peaks be related to flow turbulence instead?
15. Lines 358 – 359: “Raw seismic signals were filtered in the band 5-95 Hz and then the envelope was calculated as the average of the absolute value of the filtered signal over a time window of 1 min”
The sentence reads like methods, consider moving it in the appropriate section.
16. Lines 363 – 364: “In 2021, we directly observed the absence of bedload transport in three days (10 July, 20 July and 13 September).”
What does “directly observed” mean here? Could you be more precise?
17. Lines 364 – 371: “During the 2022 season, we performed direct measurements of bedload transport at the glacier mouth by means of portable samplers on the occasion of one day of intense glacier melt (14 July) and at the end of the monitoring season (16 September). Bedload traps (4 mm mesh size, 20 × 30 cm opening, (Bunte et al., 2004)) were deployed simultaneously at 2 positions. Measured unit bedload rates feature a large variability ranging from 0.02 to 16.2 kg/m/min in a few hours, as already observed in glacierized basins (Coviello et al., 2022). Bedload samples were sieved and weighed to obtain the grain size distribution. The total bedload transport rate Qs (kg/min above 4 mm) for each sampling period (ranging 370 from 2 to 30 min) was estimated as width-weighted averages based on the available positions sampled.”
This section really is about methods, I would therefore move into the methods.
18. Lines 416 – 417: “It is important to stress that the accurate georeferencing of all the acquired data with respect to the same Datum plays a crucial role in the data integration phase and in enabling the multitemporal analyses.”
This is right, but what about systematic deformations that could well lead to erroneous multitemporal analysis? See comment 10.
Citation: https://doi.org/10.5194/essd-2023-94-RC1 -
AC1: 'Reply on RC1', Elisabetta Corte, 11 Sep 2023
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We sincerely thank Referee #1 for the thorough review, which provided valuable insights that have enhanced our manuscript. In the attached PDF document, we outline the pivotal revisions made in accordance with the reviewer's suggestions and provide a comprehensive point-by-point elucidation of each query raised.
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AC1: 'Reply on RC1', Elisabetta Corte, 11 Sep 2023
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Elisabetta Corte et al.
Data sets
Bathymetry, sediment thickness, and geotechnical-geophysical properties of sediments of Lake Seracchi in Rutor proglacial area E. Corte, A. Ajmar, C. Camporeale, A. Cina, V. Coviello, F. Giulio Tonolo, A. Godio, M. M. Macelloni, C. Oggeri, S. Tamea, and A. Vergnano https://doi.org/10.5281/zenodo.7682072
Rutor glacier fronts footprints E. Corte, A. Ajmar, C. Camporeale, A. Cina, V. Coviello, F. Giulio Tonolo, A. Godio, M. M. Macelloni, S. Tamea, and A. Vergnano https://doi.org/10.5281/zenodo.7713146
Hydrometric data in the Rutor proglacial area E. Corte, A. Ajmar, C. Camporeale, A. Cina, V. Coviello, F. Giulio Tonolo, A. Godio, M. M. Macelloni, S. Tamea, and A. Vergnano https://doi.org/10.5281/zenodo.7697100
Geophone data in the Rutor proglacial area (Valle d’Aosta, Italy) E. Corte, A. Ajmar, C. Camporeale, A. Cina, V. Coviello, F. Giulio Tonolo, A. Godio, M. M. Macelloni, S. Tamea, A. Vergnano, M. Bonfrisco, and F. Comiti https://doi.org/10.5281/zenodo.7708800
Orthophoto and DSM Rutor Glacier E. Corte, A. Ajmar, C. Camporeale, A. Cina, V. Coviello, F. Giulio Tonolo, A. Godio, M. M. Macelloni, S. Tamea, and A. Vergnano https://doi.org/10.5281/zenodo.7713299
Elisabetta Corte et al.
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