| 22 Oct 2025
Capturing the complete landslide–debris-rich flood continuum for accurate inventory, susceptibility and exposure mapping – lessons from Cyclone Idai
Abstract. In mountainous regions, intense rainfall can trigger thousands of landslides within hours. The drivers that control the occurrence of such landslides, and the methods for predicting the zones susceptible to their initiation have been extensively studied. Yet, for many of the most severe disasters associated with these landslide events, the main impacts on local communities occurred far from the source areas where most modelling efforts are focused. Sediments mobilized high on slopes by rainfall-triggered landslides can be transported many kilometres downstream, causing significant impacts along their path, while also feeding river systems with large amounts of sediments and consequently increasing flood risks. Such chain of cascading hazards significantly increases the destructive potential of landslides as well as their impact zone. Effective risk mitigation must therefore address not just susceptibility to initiation but also landslide mobility and hazard interactions—yet such studies remain rare.
With this work, we emphasize the importance of capturing what we refer to as the landslide–debris-rich flood continuum (landslide source, runout and related debris-rich floods) for accurate inventory, susceptibility and exposure mapping when landslide mobility is high – as it is often the case for extreme rainfall events. We apply this approach in two districts of eastern Zimbabwe (> 8000 km²), severely impacted by Cyclone Idai in March 2019. Using simple, replicable methods, we mapped over 14,000 (mostly) shallow landslides and 94 km² of debris-rich flood-affected zones. These data informed detailed susceptibility and exposure models that distinguish between the processes involved. Our results show that around 226,000 individuals live in areas of moderate to high susceptibility to landslide or debris-rich floods – closely matching official figures of those affected by the cyclone. Notably, landslide sources account for only about one-fifth of this total exposure. This highlights the need to consider the entire hazard continuum. Our approach also exemplifies how simple, open-access tools and data can be highly effective for hazard and risk analyses across of the globe.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Natural Hazards and Earth System Sciences.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
This study emphasizes the importance of conducting risk assessments over broad areas with consideration of the entire landslide–debris–rich flood continuum, and it estimates the affected population by examining the relationships between past landslide records and various geomorphological parameters. As there are some unclear points regarding the details, I would like to post a few comments below.
-In lines 190–195, you describe the topographic factors calculated using the Copernicus GLO-30 DEM; however, I think it would be easier to understand if the variables used in the calculations were explicitly presented using equations. In addition, it is not clear how rainfall information is considered in the methodology. Is it incorporated through the TWI? If so, please clarify how rainfall data are included as variables and specify what kind of rainfall dataset is used.
-Related to the above point, the DEM used in this study is the Copernicus GLO-30 DEM; however, there are other DEM products with a 30-m spatial resolution, such as SRTM and ASTER GDEM. It would be useful for readers if you could mention whether similar estimations could be achieved using other DEMs, or whether the Copernicus GLO-30 DEM has particular advantages that make it more suitable for this application.
-If this method were applied to other areas, would it become difficult to estimate the impact in locations where the historical records of landslides are insufficient? Alternatively, would it still be possible to estimate the impact in such locations by using a model trained on landslide records from nearby regions? Discussing this point would provide valuable information for potential users who may consider applying the method to other areas.
-From a long-term perspective, it is generally understood that once a landslide occurs, the likelihood of another landslide occurring at the same location may decrease for some time. Since your method estimates hazards based on topographic factors, it appears that the model may implicitly assume that landslides can occur repeatedly without limitation. When the DEM is updated, the affected slope would become gentler, which should alter the estimated results. It would therefore be helpful if you could discuss how updates to the DEM influence the estimation of landslide impacts, as this would improve readers’ understanding of the general applicability and temporal consistency of the method.
-Regarding the leftmost panel of Figure 3 (INV_01-POLY), the area near Chimanimani in the north appears to be mapped in considerable detail, whereas the southern part is represented in a more coarse, rectangular manner. Does this imply that the areas classified as “no landslide/debris-rich flood” do not require detailed spatial information to the same extent?