the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Apr 2024
Regional-scale analysis of weather-related rockfall triggering mechanisms in Norway, and its sensitivity to climate change
Abstract. This paper evaluates the relation between rockfall events and weather conditions for two regions in Norway – Romsdalen and Gudbrandsdalen and explores how rockfall frequency might change with future climate conditions. Our analysis focuses on understanding the relationship between rockfall occurrence and effective water inputs, including rainfall and snow melt, and temperature oscillations both in cold conditions (freeze-thaw cycles) and in warm conditions (hot-cold cycles). To accomplish this, regional weather data and rockfall information in the Norwegian Mass Movement Database have been employed. Our results indicate that temperature oscillations might be better suited than effective water input to depict the occurrence of rockfalls in the two study areas in Norway. Freeze-thaw cycles are most frequent during winter and spring, and hot-cold cycles are most frequent during summer. Climate change will affect rockfall seasonality and the frequency in which freeze-thaw cycles and hot-cold cycles are observed. Thus, altering the exposure of population and infrastructures to rockfalls.
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Status: open (until 22 May 2024)
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RC1: 'Comment on nhess-2024-46', Anonymous Referee #1, 26 Apr 2024
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Review on "Regional-scale analysis of weather-related rockfall triggering mechanisms in Norway, and its sensitivity to climate change" by
Rosa M. Palau, Kjersti Gleditsch Gisnås, Anders Solheim, and Graham Lewis GilbertGeneral comments:
The authors aim at identifying the weather pre-conditions that promote rockfall in two Norwegian regions. The study is within the scope of NHESS. The figures are of good quality and the language is clear.
However, there is a fundamental problem with the method used in the analysis. The relationship between the hot-cold/freeze-thaw cycles and the rockfall events, the authors believe to find, is just a result of the analysis set up. The same holds for the analysis of the relationship between effective water input accumulation and rockfall events.
The method includes a pre-selection of days that enter the analysis. This introduces a bias on the results. Basically, a number of days with certain weather pre-conditions are selected (e.g. 2 temperature cycles in 3 days). Then the pre-conditions for these days are compared to the pre-conditions of all other days. Of course the probability to find the pre-conditions is higher in the selection (100%), compared to the other days. It is completely irrelevant if the selected days are also associated with rockfall events. One would get the same result if a number of non-rockfall days with these pre-conditions would be used as the reference against which all other days are compared.
That the number of temperature cycles increases, when the pre-condition period is extended, is also a result of the analysis design (it cannot decrease).
A fair test if the distribution for these pre-conditions differs between rockfall and non-rockfall days would be the following boot-strap test:
n-pre-ev=number of days with rockfall event and selected pre-conditions
n-pre=number of days with selected pre-conditions
Randomly draw n-pre=n-pre-ev days with the same preconditions but without an event. Repeat this very often (e.g. 1000 times). Compare the distribution from the random days with the distribution for the event days. It there a statistically significant difference (e.g. median of distribution of pre-conditioned rockfall-event days is outside 95% of distribution obtained with the pre-conditioned random days)?
Depending on the results of a statistically sound new analysis, sections 6 and 7 need to be redone.
I am not sure if the required work can be finished within the mayor-revision time frame. It might be necessary to withdraw the paper and resubmit it again later.Major Comments:
- L172 a) Generally means before all events? b) Is this really a special condition before rockfall events or just climatology. How often do these conditions occur when no rockfall event follows?
- L174 Please give the exact definition of how you define a hot-cold cycle. What is a significant temperature oscillation?
- L205 Before (L172) you identified one cycle in the previous 7 days. Why do you now choose two cycles in 3 days as a threshold?
- Figure 6 and 7: By definition there should not be any events in pannels a) and c) with numbers <2 and a period length of 3 days. The boxes, however, indicate that such events exist in the data subset. Why?
- L237 Is the daily temperature cycle in Gudbrandsdalen more pronounced than in Romsdalen? This can be expected because of the larger distance from the ocean.
- L281 "and at least two freeze-thaw cycles have been observed" This is not a result but just reflects the initial event-selection threshold.
- L309 The temperature increase depends on the analysed climate projection. Which greenhouse gas concentration pathway 1.6°C corresponds to?
- L314 Please explain "recommended climate factors"
- L333 Please give more information on the climate simulations and the downscaling. The reviewer was not able to access Hanssen-Bauer et al., 2015 (too little information in references to find it)
Minor comments:
- L 495 Where is this published? Please provide more information
Citation: https://doi.org/10.5194/nhess-2024-46-RC1
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