the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Potential improvements of landslide prediction by hydro-meteorological thresholds: an investigation based on reanalysis soil moisture data and principal component analysis
- 1Department of Civil Engineering and Architecture, University of Pavia, Italy
- 2Department of Civil Engineering and Architecture, University of Catania, Catania, 95123, Italy
- 3Department of Civil Engineering and Architecture, University of Pavia, Pavia, 27100, Italy
- anow at: Department of Civil Engineering and Architecture, University of Catania, Catania, 95123, Italy
- 1Department of Civil Engineering and Architecture, University of Pavia, Italy
- 2Department of Civil Engineering and Architecture, University of Catania, Catania, 95123, Italy
- 3Department of Civil Engineering and Architecture, University of Pavia, Pavia, 27100, Italy
- anow at: Department of Civil Engineering and Architecture, University of Catania, Catania, 95123, Italy
Abstract. In recent times, several efforts have been addressed to understand the extent to which soil moisture estimations may improve the performance of landslide early warning systems (LEWSs). These systems have been traditionally based on rainfall intensity-duration thresholds. Still a limited number of studies explore the possible enhancement of the performance of LEWSs through the identification of hydro-meteorological thresholds. In this study, we propose a methodology for developing regional hydro-meteorological landslide triggering thresholds coupling mean rainfall intensity and soil moisture information. To test the potential improvements in prediction we use ERA5-Land reanalysis soil moisture data, available at four depth levels and hourly resolution. Two different instances are investigated, namely the identification of triggering thresholds using rainfall intensity and the soil moisture at each of four depth levels, and the identification of triggering thresholds using rainfall intensity and a combination of soil moisture at the four depths as obtained by principal component analysis (PCA). We propose thresholds in the form of a piece-wise linear equation. The equation’s parameters are optimized in order to maximize the ROC True Skill Statistic (TSS) prediction performance metric. The proposed hydro-meteorological thresholds are tested on the case of Sicily Island (south Italy) and the performance is compared with those obtained through the traditional rainfall intensity-duration (ID) power-law thresholds. Overall, the results show that the soil moisture information adds a considerable value to the improved thresholds’ performance since the ROC True Skill Statistic increases from 0.50 to 0.71. A similar performance is obtained when the first principal component derived from the PCA is used, proving PCA to be a valuable support tool for the identification of the proposed hydro-meteorological thresholds, as it allows to take into account the multi-layer information while keeping the thresholds two-dimensional.
Nunziarita Palazzolo et al.
Status: open (until 15 Aug 2022)
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RC1: 'Comment on nhess-2022-175', Anonymous Referee #1, 27 Jul 2022
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General Comments:
This is a mostly well-written preprint that I feel only needs minor revisions. The use of PCA to reduce the dimensionality of the hydrometeorological space is novel, and the thresholds produced are an improvement over other methods. There are some missing details regarding the landslide inventory selection process, described below. These missing details constitute the bulk of my concerns and if addressed, I feel that the paper will tell a more complete story of the authors' methodology.
Specific Comments:
Regarding the landslide selection process described near the end of section 2.1:
The selection of ground truth is a critical decision for this type of analysis, especially if that ground truth is partially derived from other algorithms or datasets, such as what you are doing with CTRL-T. I believe this section needs two additions to help convince readers that what you're doing is scientifically sound.
Firstly, for the "adjustable parameters" of CTRL-T, I would like to see some description of how and why you chose the final parameter values. I believe you briefly mention the separating length of time for rainfall events in wet and dry periods later on in the paper. There is also this sentence in line 135: "Rainfall event parameters were calibrated adopting the monthly soil water balance model and evapotranspiration analysis." But I'm unclear on if this calibration process was done automatically by the program or manually by the authors. A final list of adjustable parameter values, with some brief defense of their selection, would help readers understand what parts of CTRL-T are automated and which are tuned by hand.
Secondly, I would describe briefly in greater detail how you decided that landslides did not have identifiable or uncertain landslide conditions. I presume some threshold on the weights was used. If so, what what were those thresholds values and how did you decide them? Or if some other metric was used to quantify the landslide cause as being uncertain, briefly provide and defend those decisions.
Technical Comments and Optional Suggestions:
See attached PDF for grammar corrections and other suggestions for additional figures or figure revisions that I do not feel are mandatory.
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RC2: 'Comment on nhess-2022-175', Anonymous Referee #2, 29 Jul 2022
reply
The authors compare classical intensity-duration power law thresholds with hydrometeorological thresholds that combine mean intensity with ERA5-Land reanalysis soil moisture. They consider both the soil moisture at 4 different depths and a combination of them obtained with principal component analysis. They find that adding soil moisture information improves the prediction of a rainfall-based threshold.
The manuscript is generally clear and well organized. I only have one major concern with the publication. In fact, the novelty in the work is the use of PCA for combining the soil moisture information at the different depths and keeping the problem 2D (rainfall intensity and soil moisture derived component). While I see the potential of such a methodology and agree with the advantage of keeping the dimensionality of the problem small, I believe some further analyses are required to demonstrate that it is indeed advantageous.
The TSS obtained using intensity and soil moisture at the first or second shallowest depth is pretty much identical to the one using the first component of the PCA. This means that the overall performances are not sufficient to demonstrate the advantage of PCA. The authors should find some alternative way of either demonstrating the advantages directly (with data/results) or demonstrating that the most influential soil layers changes depending on some properties. Can they observe any pattern in which one is more influential than the others? Those could be spatial patterns (e.g., in certain regions the shallower/deeper soil moisture is more important) or relative to landslide properties (e.g., for certain types of landslides the deeper/shallower soil moisture is more important) or relative to the time of the year (e.g., over certain times of the year deeper/shallower soil moisture is more important). Because the PCA piece is the novel piece in this manuscript, I believe it is important to show and demonstrate the advantage compared to just using the shallowest soil moisture.
Besides this, I also have some minor comments:
- I think the method for the definition of rainfall events should be better explained. The definitions of parameters P1-P2-P3-P4 are unclear and so is the sentence in line 148. What does it mean that rainfall conditions were not identified? What are the uncertainties? In rainfall? In landslide properties. Does this mean they were incorrectly identified as “rainfall triggered” in the database ()? Also, what are the values of the parameters chosen (e.g., the maximum radius Rb)? Only information about the interarrival times was provided.
- On soil moisture: could you provide more information about soil moisture? The only information I could find is “association of SM data to the beginning of each rainfall event”, but what does this mean? The first hour of the rainfall event? The hour before its beginning?
- Do rainfall events end when the hour of landslide occurrence or whenever they ended (which could be N hours before or even after the landslide)?
- Would be good to report in Figure 5 also the timing of each landslide (maybe as a vertical black line)
- In Figure 6 you could consider using different plotting techniques to make the figure more readable. In fact, it is impossible from a scatter plot to understand where/how many points there are in the different parts of the plot. You should consider plotting the 2D histogram instead (e.g., see Leonarduzzi et al., 2017). This would allow the reader to better see where most of the events are and which groups of events are “driving” the thresholds. Based on more visible (because it’s less events) distribution of the triggering events, it looks like the not triggering ones are “driving” the threshold. In other words, just by looking at the triggering events, a steeper threshold would improve the performances, which seems to suggest there are a lot of not triggering events in the 20-300h 0.1-1 mm/h region. Would be nice if that could be looked at! What I am suggesting is something similar to what Leonarduzzi et al., (2017) did in Figure 3.
- Would be nice to have information also about TPR and FPR also for the ID threshold (maybe as an additional entry in Table 2)
- I have a similar suggestion for Figure 8 as for Figure 6, allowing the reader to see the distribution of the no-triggering events (the triggering sample is small enough that they are visible without much overlapping)
Finally, in the supplementary pdf, some grammar/rewording suggestions are provided.
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Leonarduzzi, E., Molnar, P., and McArdell, B. W. (2017),  Predictive performance of rainfall thresholds for shallow landslides in Switzerland from gridded daily data, Water Resour. Res.,  53,  6612– 6625, doi:10.1002/2017WR021044.
Nunziarita Palazzolo et al.
Nunziarita Palazzolo et al.
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