the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Dec 2024
Brief communication: Visualizing uncertainties in landslide susceptibility modeling using bivariate mapping
Abstract. Effectively communicating uncertainties inherent to statistical models is a challenging yet crucial aspect of the modeling process. This is particularly important in applied research, where output is used and interpreted by scientists and decision makers alike. In disaster risk reduction, susceptibility maps for natural hazards are vital for spatial planning and risk assessment. We present a novel type of landslide susceptibility map that jointly visualizes the estimated susceptibility and the corresponding prediction uncertainty, using an example from a mountainous region in Carinthia, Austria. We also provide implementation guidelines to create such maps using popular free and open-source software packages.
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RC1: 'Comment on nhess-2024-213', Anonymous Referee #1, 13 Dec 2024
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Using the Central Eastern Alps as an example, this brief communication presents a unique method that integrates the vulnerability and uncertainty of landslides into a single bivariate map. Compared to the conventional approach of examining landslide risk through separate maps, this method offers several advantages. First, by integrating two maps into one, readers can avoid the hassle of cross-referencing, which greatly improves efficiency and reduces the possibility of errors. Secondly, the bivariate map provides a more holistic and intuitive understanding of the complex interplay between vulnerability and uncertainty, enhancing the overall assessment of landslide risk.
The manuscript is well-structured and professionally written. Therefore, the reviewer has no issues with agreeing to publication in its current form.
Citation: https://doi.org/10.5194/nhess-2024-213-RC1 -
RC2: 'Comment on nhess-2024-213', Anonymous Referee #2, 16 Dec 2024
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The authors present a bivariate mapping method to spatially visualize both prediction values and uncertainty within the same map. They also provide supplemental material to enable others to apply this approach using free and open-source software packages. The authors discuss and present an approach for estimating uncertainties and classifying susceptibility levels required to build a bivariate map.
This brief communication is well-written and provides a generally significant contribution to the landslide science community, as it highlights a method for improving the communication of uncertainties in hazards predictions. I would recommend minor revisions to address some literature gaps in the introduction and discussion.
The paper fails to acknowledge previously peer-reviewed research on visualization methods for geospatial predictions. The only reference on this topic is from a blog post, which is problematic given that the main contribution of this paper is the visualization of spatial prediction uncertainties. In particular, it is missing references to highly cited research by MacEachren et al (2013 in Cartography and Geographic Information Science), earlier applications to slope stability (Davis and Keller 1997 in Computers & Geosciences), and others who have applied bivariate mapping for communicating spatial prediction and uncertainties (Cola 2013 in Cartography and Geographic Information Science and Nelson 1999 in Cartographic Perspectives).
Another major drawback of this paper is its heavy reliance on reference to works by its own co-authors (Steger and Spiekermann) for landslide susceptibility modelling and data quality, while failing to acknowledge other important contributions in the field.
Other comments:
Introduction
The introduction provides a good overview of methods applied for spatially estimating uncertainties of landslide susceptibility predictions. However, it is lacking background on general methods for visualizing and communicating uncertainties in spatial predictions. Existing research on this topic should be incorporated to help position the authors’ approach within the context of prior work.
L20. The authors rely heavy on citing the co-authors’ prior contributions on data quality. However, there are many different researchers with significant contributions in this field, and these should be acknowledged.
L40. The paper should also reference Heckmann et al. (2014 in NHESS), who used repeated resampling and combined (100) susceptibility maps to estimate the interquartile range (IQR) in spatially predicted probabilities – a similar approach to the one used in this submission.
Methods
Section 2.1
The authors cite a blog post as the source of their methods but fail to reference earlier peer-reviewed contributions using a similar approach (e.g., Cola et al. 2013).
While the authors clarify that their uncertainty calculations are based solely on variations in the sampling of absence (non-landslide) points, given the large number of landslide samples (~2000), they could have also resampled landslides (e.g., using a cross-validation approach). At the very least, they should acknowledge that more robust approaches, which account for variations in landslide presence data, are available.
Section 2.2.1
L87. I think it’s good that you acknowledge the source of your inspiration for your bivariate approach; however, existing peer-review on bivariate mapping approaches to communicate prediction and uncertainty should also be acknowledged.
Results
L133. The authors mention the term “geomorphic plausibility” in the introduction, but don’t define it. I think it would be useful to the reader to define it.
Discussion
L159. “In addition, the type of uncertainty conveyed should be kept in mind” – what are you trying to communicate with this sentence? It’s not clear.
L164. “In landslide susceptibility modeling, results are commonly discretized into three classes signifying low, medium and high susceptibility” – this statement is highly debatable. There are a wide range of approaches in practice to classify landslide susceptibility levels.
L166. The authors again cite only co-authors’ works (Spiekermann and Steger), even though there are other well-cited approaches for calculating breaks (e.g. Chung and Fabbri 2003) including accounting for proportion of landslides covered (e.g. Petschko et al 2014), as applied in this submission.
L175. The authors discuss the importance of color considerations but should expand on how their chosen color palette addresses issues like color impairment (e.g., for colorblind readers). Providing recommendations for alternative palettes or best practices would be helpful for readers.
L185. The authors should reference other approaches for quantifying uncertainties in landslide susceptibility models to provide a more balanced discussion.
Citation: https://doi.org/10.5194/nhess-2024-213-RC2 -
CC1: 'Comment on nhess-2024-213', Kamal Serrhini, 26 Dec 2024
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The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2024-213/nhess-2024-213-CC1-supplement.pdf
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