Abstract. Empirical evidence from the humid tropics shows that informal housing can increase the occurrence of rainfall-triggered landslides. However, informal housing is rarely accounted for in landslide hazard assessments at community or larger scales. We include informal-housing influences (vegetation removal, slope cutting, house loading, and point water sources) in a slope stability analysis. We extend the mechanistic model CHASM (Combined Hydrology and Stability Model) to include leaking pipes, septic tanks, and roof gutters. We apply this extended model (CHASM+) in a region of the humid tropics using a stochastic framework to account for uncertainties related to model parameters and drivers (including climate change). We find slope cutting to be the most detrimental construction activity for slope stability, and we quantify its influence and that of other destabilising factors. When informal housing is present, more failures (+85 %) are observed in slopes that would otherwise have had low landslide susceptibility and for high-intensity, short-duration precipitations. As a result, the rainfall threshold for triggering landslides is lower when compared to non-urbanised slopes and comparable to those found empirically for similar urbanised regions. Finally, low cost-effective “low regrets” mitigation actions are suggested to tackle the main landslide drivers identified in the study area. The proposed methodology and rainfall threshold calculation are suitable for data-scarce contexts, i.e. when limited field measurements or landslide inventories are available.
Rainfall-triggered landslides are increasing in the humid tropics, and Small Island Developing States are disproportionately affected. Frequent shallow slides in hillside cuttings along roads and in communities hinder sustainable development. Larger, less frequent storms cause hundreds of landslides that block lifeline roads, impede disaster response and reverse economic growth. Top-down Disaster Risk Reduction (DRR) policies and approaches aiming to transfer conventional landslide assessment science and engineering practices are not always suitable in these data-and resourcelimited contexts. This paper recognises the emergence of co-production approaches as part of the resilience paradigm response to DRR science-policy-practice gaps. We present a case study from Saint Lucia, Eastern Caribbean, in which government engineers and policymakers have partnered with the authors to co-produce landslide hazard assessment data and prototype decision support tools to strengthen landslide hazard management along lifeline roads.
<p>Machine learning models that automatically delineate river geomorphic features on Sentinel 2 (S2) images have the potential to provide a weekly monitoring of their dynamics and a better understanding of the underlying river channel processes. The accuracy (e.g. 95%) of these feature delineations is generally assessed by quantifying the percentage of pixels of known nature correctly classified by the model. However, the pixels used for such calculations are often sampled within the classified satellite image (with a resolution of 10m of larger) laying shadow on the real, relative extent of the misclassified pixels (e.g. the remaining 5%) usually located at the borders between features, which unavoidably lead to the under or overestimation of one feature for another. This issue raises questions on the real extent of the geomorphic features measured or on the true geomorphic temporal change that can be detected. In this work, we identified the nature and extent of the misclassified pixels on S2 images of a section of the river Sesia (North Italy) by comparing the classes of water, sediment and vegetation automatically delineated by a machine learning model with those manually delineated in higher resolution images: Planet at 3m, and aerial orthophotos at 0.3m resolution. Assuming the orthophoto as error-free, we found that: (1) in both S2-based automatic classification and Planet-based manual classification, water is underclassified and that (2) the error of the misclassified area is insensitive to the spatial resolution, with the water class ~20% underestimated in both the S2 (10m) and the Planet (3m). By considering the period between 2018 and 2022, we also demonstrated that the active channel (water + sediment) trajectory assessed by using the S2 images on a weekly basis is comparable to the trajectory determined using the Planet or aerial orthophotos on a yearly basis. However, the frequent image acquisition of the S2 was able to capture the river corridor abrupt response and prompt recovery to a major flood in 2019, overlooked in the other two image sources. This work therefore shows that once the spatial uncertainty is quantified (e.g. 20% for the water class), the frequent image acquisition of the S2 provides a robust reconstruction of the river geomorphic trajectories as well as a better interpretation of the river processes, in particular recognising transient states in between significant events.</p>
We thank Reviewer 1 for taking the time to read our paper. We think the Reviewers' comments can be addressed in a revised manuscript as follow.Comment (1): The study site map should be added to the C1 NHESSD Interactive comment Printer-friendly version Discussion paper reviewed manuscript. Meanwhile, the basic geological setting and rainfall information may be helpful to readers.The methodology applied allows evaluation of the probability of failure of slopes for which there is scarce and/or uncertain data. Rather than referring to a specific site with C1
We thank Reviewer 2 for taking the time to read our paper, for the positive comments and for recognising the contribution this research makes. We think we can revise our manuscript and address the specific comments as follow. Comment (1): L 83 The MS "promises" that somehow the modelling exercise will take into account climate change. I think this is quite weak in the analysis presented. The authors should discuss a little if climate change projections could be used to define future values of rainfall based on Representative concentration scenarios and simulations by Regional/Global climate models, and mention literature on the subject.
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