the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Pre-collapse motion of the February 2021 Chamoli rock-ice avalanche, Indian Himalaya
- 1St Anthonys Falls laboratory, University of Minnesota, Minneapolis, MN, USA
- 2Department of Earth and Environmental Sciences, University of Minnesota, Minneapolis, MN, USA
- 3Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
- 4Laboratory for Hydraulics, Hydrology, and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
- 5Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL), Birmensdorf, Switzerland
- 6CESBIO, Université de Toulouse, CNRS, CNES, IRD, INRAE, UPS, Toulouse, France
- 7LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
- 8Water, Sediment, Hazards, and Earth-surface Dynamics (waterSHED) Lab, Department of Geoscience, University of Calgary, Canada
- 9Department of Geosciences, University of Oslo, Oslo, Norway
- 1St Anthonys Falls laboratory, University of Minnesota, Minneapolis, MN, USA
- 2Department of Earth and Environmental Sciences, University of Minnesota, Minneapolis, MN, USA
- 3Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
- 4Laboratory for Hydraulics, Hydrology, and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
- 5Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL), Birmensdorf, Switzerland
- 6CESBIO, Université de Toulouse, CNRS, CNES, IRD, INRAE, UPS, Toulouse, France
- 7LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
- 8Water, Sediment, Hazards, and Earth-surface Dynamics (waterSHED) Lab, Department of Geoscience, University of Calgary, Canada
- 9Department of Geosciences, University of Oslo, Oslo, Norway
Abstract. On the 7th of February 2021, a large rock-ice avalanche triggered a debris flow in Chamoli district, Uttarakhand, India, leaving over 200 dead or missing. The rock-ice avalanche originated from a steep, glacierized north-facing slope. In this work, we assess the precursory signs exhibited by this slope prior to the catastrophic collapse. We evaluate monthly slope motion from 2015 to 2021 through feature tracking of high-resolution optical satellite imagery. We then combine these data with a time series of pre- and post-event DEMs, which we use to evaluate elevation change over the same area. Both datasets show that the 26.9 Mm3 collapse block moved over 10 m horizontally and vertically in the five years preceding collapse, with particularly rapid motion occurring in the summers of 2017 and 2018. We propose that the collapse results from a combination of snow-loading in a deep headwall crack and permafrost degradation in the heavily jointed bedrock. Our observation of a clear precursory signal highlights the potential of satellite imagery for monitoring the stability of high-risk slopes. We find that the timing of the Chamoli rock-ice avalanche could likely not have been forecast from satellite data alone.
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Maximillian Van Wyk de Vries et al.
Status: final response (author comments only)
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RC1: 'Comment on nhess-2021-333', Anonymous Referee #1, 29 Jan 2022
Dear Authors,
The manuscript is written well and address the research questions defined by you. However, I have some suggestions/comments before the manuscript is accepted for publication in NHESS.
- Himalayan terrain many times induces decorrelations. due to its topography as well as vegetation. The authors have tried to implement simple DInSAR methodology and were unable to obtain good interferograms. Loss of coherence is the main challenge to the InSAR application in the area.
I am afraid that simple PSI technique will hardly yield any significant result. Even the techniques such as SBAS, SqueeSAR etc may also fail to produce anything. As a suggestion you can try A-DinSAR techniques such as SBAS or techniques based on distributed scatterers such as Quasi PS (Perissin & Wang, 2011; Razi et al., 2018) or SqueeSAR (Ferretti et al., 2011) in this region.
Perissin, D., & Wang, T. (2011). Repeat-pass SAR interferometry with partially coherent targets. IEEE Transactions on Geoscience and Remote Sensing, 50(1), 271-280.
Razi, P., Sumantyo, J. T. S., Perissin, D., Febriany, F., & Izumi, Y. (2018, August). Multi-temporal land deformation monitoring in V shape area using quasi-persistent scatterer (Q-PS) interferometry technique. In 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama) (pp. 910-915). IEEE.
Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., & Rucci, A. (2011). A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE transactions on geoscience and remote sensing, 49(9), 3460-3470.
- A study has been done like this on the same study area. They have claimed that they have used PSI on the regional level.
Kothyari, G. C., Joshi, N., Taloor, A. K., Malik, K., Dumka, R., Sati, S. P., & Sundriyal, Y. P. (2021). Reconstruction of active surface deformation in the Rishi Ganga basin, Central Himalaya using PSInSAR: a feedback towards understanding the 7th February 2021 Flash Flood. Advances in Space Research.
- For Figure 6b, I suggest the authors check all the available interferograms to confirm that the two areas marked with “Rock glacier motion” are always moving. From only one interferogram, it is hard to say the motion. The area marked with “Atmospheric noise” seems not so evident because there are some strange values in the surrounding area possibly caused by unwrapping errors due to the low coherence. If these areas can’t be confirmed from other interferograms, I suggest writing these areas are possible rock glacier motion or possible atmospheric noise. I suggest also writing “2.8 cm” in the caption of Figure 6 after “wrapped phase”.
- In figure 6b, I’m not completely sure about the “atmospheric noise”. I suggest to check the displacement time series, if available, because atmospheric noise can be identified as strange peaks.
- AC1: 'Reply on RC1', Maximillian Van Wyk de Vries, 02 May 2022
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RC2: 'Reviewer comment on nhess-2021-333', Anonymous Referee #2, 24 Mar 2022
The manuscript focuses on investigating pre-failure surface displacement for the case study of the 2021 Chamoly avalanche. The case is undoubtedly interesting but, in my opinion, the manuscript should not be considered for publication in NHESS. In the aims and scope of the journal it is stated that "The following are generally considered out-of-scope or we do not encourage: [...] Localised case studies with no broader implications (in other words, ask yourself, what would someone else in another region learn from the case study that you have done; what is the broader context?)." While I recognise that the authors' work could have broader implications, these are not discussed at all in the manuscript. The manuscript is indeed completely focused on the case study and the authors make indeed conclusions (e.g., the unpredictability of the timing of collapse) only for the case study and do not discuss what they learned in terms of general implications. Could the timing have been predicted if more images were available (e.g., 24 prior to the collapse, 8 hours prior to the collapse, etc.)? Is it just a matter of noise or, even without noise, no trend to failure could be seen? Is it a matter of resolution, instead? Is it a matter of mechanism of failure (e.g., a very steep tertiary creep that causes orders of magnitude of acceleration in a matter of minutes/hours?). Is this mechanism rare or typical in such a type of failures? What is the general conclusion in terms of remote sensing capability in predicting failures/timing of failures? What types of landslides could be predicted, instead? Are there examples in the literature of successful/unsuccessful predictions of other case studies based on similar data sources? Will we ever be able to predict the timing of failure based on satellite remote sensing alone?
In addition, with reference to the specific case study, I found the discussion speculative, for instance, when it came to the safety factor as all discussions on driving and resisting forces were based on general knowledge/speculation and not supported by, e.g., geomechanical data of the case study. Also, the introduction is too generic, describing information that is very well known to researchers in the field. Perhaps the introduction could have focused only on recent advances in remote sensing techniques for natural hazards that are perhaps closing a knowledge gap and enabling the type of analysis conducted by the authors, albeit with remaining limitations.
- AC2: 'Reply on RC2', Maximillian Van Wyk de Vries, 02 May 2022
Maximillian Van Wyk de Vries et al.
Maximillian Van Wyk de Vries et al.
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