| 04 Dec 2025
Climate change may increase landslide frequency despite generally drier conditions in the Mediterranean area
Abstract. This study presents a methodological framework to investigate the impacts of climate change on rainfall-triggered landslides at the subregional scale. Focusing on a ~ 170 km² area in the Partenio Mountains in southern Italy, we employed regional rainfall projections (CORDEX) under moderate (RCP4.5) and high (RCP8.5) emission scenarios for 2006–2070. Rainfall data were bias-corrected with observations from 2006–2023 and benchmarked against a synthetic dataset generated through stochastic reproduction of currently observed conditions. Physically based simulations of hydrological processes, coupled with slope stability analyses that account for unsaturated soil conditions, enabled event-by-event identification of landslides throughout the period. Statistical comparisons between scenarios were conducted across three rainfall homogeneous subregions. Results show a general tendency toward drier soil conditions, consistent with regional-scale climate studies, but with increasing rainfall variability across subregions. Despite this drying trend, projections indicate a significant rise in landslide occurrence, with a faster increase under RCP4.5 when compared to RCP8.5. This counterintuitive outcome reflects shifts in rainfall dynamics: under RCP8.5, landslides are mainly linked to more intense triggering rainfall, while under RCP4.5 they result from a combination of wetter antecedent conditions and more intense early-peak rainfall events. These findings emphasize the critical role of antecedent soil moisture in landslide initiation by showing its stable influence on landslide occurrence despite the rapid evolution of climate change. Overall, the methodology provides a transferable framework to assess local climate change impacts on geohazards by integrating bias-corrected climate projections with physically based hydrological–geomechanical modeling.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Natural Hazards and Earth System Sciences.
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In this article, Roman Quintero et al. project changes in landslide frequency due to climate change for a small region of mountainous Italy. They combine a previously developed physics-based landslide hazard model with ensemble forecasts of bias-corrected rainfall time series to produce their projections and analyze their results with multiple statistical methods. They found that the frequency of landsliding in their study area was generally projected to increase, although to varying degrees based on locality, future scenario, and season, with changes to the timing of rainfall having strong control on predicted frequencies. As part of their workflow, they also found high ensemble confidence that shallow soils in the region will be drier across seasons by 2070 than today.
Overall, I find that the article fits the journal scope well, is written fairly well, and advances the science with a novel approach at a relatively small scale. My comments to improve the manuscript are minor and focused on clarifying aspects of the paper that I didn’t fully understand. This includes some additional descriptions of the methods, explanation of the results, and further discussion of a few points. I look forward to seeing this interesting and important article in print.
Specific comments
Line 214: I’d like to see just a bit more methodological description here: what is the spatial structure and resolution of the FS predictions (i.e., raster grid cells? resolution?); is the failure plane also 2 m below the surface?
Line 251: 10 ensemble members per scenario? 9 EURO-CORDEX members (lines 242-243) + 1 VHR-PRO_IT member?
Lines 258-259: “limited by data availability in the VHR-PRO_IT dataset” do you mean that this data is only available through 2070? I didn’t see this mentioned before, consider moving it to the previous paragraph (lines 244-250)
Line 304: don’t hyphenate “bias-correction”
Line 338: “Rainfall hyetograph is known to be related to slope instabilities” I’m confused by this sentence, could you please expand on what is meant
Lines 355-357: a bit more description here, please. For instance, am I correct in assuming that only 1 landslide can be predicted per storm event? Or is it 1 storm can be predicted in every 3-hour timestep such that a storm can produce more than one landslide at a given location? Or something else?
Lines 358-359: Equation (1) is just an ensemble average, correct? i.e., the summation argument could be 𝜆𝐿𝑆,i(t), the landslide frequency in year t for ensemble member i (equivalent to NLS,i(t) when measured over a time period of one year)
Line 399: could you provide a sentence or two more on the patterns in CTRL displayed in Figure 6? Unless I'm missing something, some of them are surprising given that the current climate rainfall time series was drawn from a static distribution based on historical observations. For example, why does CTRL storm duration increase over time in panel (b)? Why does soil water content decline when PET is also kept the same?
Line 402: In Figure 6, what does the red and blue shading in each plot signify? I would guess the output range of the climate change ensemble, but it isn’t stated clearly.
Line 413-414: Please move the sentence describing the use of Voronoi polygons to the Methods section, probably lines 149-154.
Line 417: Making sure I understand Figure 7 correctly, would –(53%) mean that the remaining 47% of the ensemble found a positive trend? Or would some fraction of that also include members that found no trend?
Line 443: In Figure 8, I would appreciate if you added the ensemble shading of Figure 6 to these plots, too, in order to understand the spread in model predictions (and thus inform our confidence). Also applies to Figures 9 and 11 (10 would probably be too busy with shading).
Line 466: Can you please introduce the use of Φ in the methods that describe the rainfall type (i.e., near Figure 5).
Lines 474-475: the colors described in these sentences differ from the shades of red (negative) to green (positive) presented in Table 1. This error is also present in the table caption.
Line 479: In Table 1, there are some very strong correlations between Δ𝜆𝐿𝑆 and the rainfall types, which I take to mean that the model results are largely determined by the trend in rainfall timing. I was hoping to see more in the Discussion on this point with ties to the literature, e.g., has a trend in rainfall timing been detected in observations, so far?
Line 556: One question that comes to mind is the role of sediment supply in this region/environment. Soil lost due to more frequent landsliding would not be replenished on the decadal timescales of this study (are there any studies of soil production rate for the area?). How do you think this might affect your results?
Cheers.