We thank the Reviewer for their careful reading and constructive comments. We apologize for the delay in responding - we waited for a second review before engaging. We appreciate that the Reviewer finds the core concept of the landslide–debris-rich flood continuum sound, and raises only points of clarification.

On topographic factors and rainfall  

We deliberately chose not to reproduce equations for the topographic variables, as these are now standard in the literature and are fully detailed in the references provided. Given the manuscript's length, we preferred to keep the methods section concise and on the most novel aspects while directing readers to the relevant sources. We are of course happy to include equations if the Reviewer or Editor considers this necessary.

Regarding rainfall: it is not used as a predictor variable, as doing so would bias the model toward the specific landscape conditions of the Idai event rather than capturing underlying morphological susceptibility. Rainfall data (IMERG-GPM) are instead used in the event description (Section 1.2) and in the interpretation of results (Section 3.1).

On the DEM choice    

A growing body of literature supports the use of the Copernicus GLO-30 DEM over older products such as SRTM (now over 20 years old) for geomorphological applications (e.g., Meadows et al., 2024). That said, at 30 m resolution, we expect differences between equivalent-resolution DEMs to have a minor influence on model outputs. We will add one sentence acknowledging that while Copernicus GLO-30 is preferred, results would likely be similar with other 30 m DEMs.

Meadows, M., Jones, S. and Reinke, K., 2024. Vertical accuracy assessment of freely available global DEMs (FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM) in flood-prone environments. International Journal of Digital Earth, 17(1), p.2308734.

On transferability to data-scarce regions     

This is a valid and practically important question. The methodology was intentionally built around simple morphological and hydrological variables available globally, with replicability in data-scarce environments explicitly in mind. While the model as trained here should be transferable to other regions as relying on predictors that apply to any landslide (and related debris-rich flood) anywhere on Earth (simply capturing where mass is likely to be mobilized, transported, or deposited), accuracy would likely improve when retrained on locally collected inventories, potentially supplemented by region-specific variables reflecting local environmental conditions. We will add a short paragraph in the discussion addressing transferability and the trade-offs involved.

On temporal dynamics and DEM updates                                                                               

The Reviewer raises an interesting point. We fully agree that following a landslide, the likelihood of recurrence at the exact same location decreases over time as mobilizable material is depleted — a dynamic analogous to the one described in post-fire debris flow systems in e.g., McGuire et al., (2024). Yet, such geomorphic considerations have implications when studying the background (historical) susceptibility. Here, while the produced models have clear predictive power for other future events, we aim primarily at mapping the susceptibility (and especially the related population exposure) linked to a specific (extreme) event. We will make sure to better stress this point in the revised version of the manuscript.

With respect to the topographic data, a DEM update would indeed capture slope modifications and alter model outputs accordingly. However, three considerations limit the practical impact of this caveat in our framework. First, neighbouring slopes with similar morphological conditions remain susceptible even when a specific location has been destabilized. Second, landslides triggered by Idai being in vast majority shallow, working with 30-m resolution DEM, the impact of landslides on topography is barely visible, and we believe would not really influence the result. Third, our framework explicitly extends beyond landslide initiation to downstream impacts — a domain where sediment remobilization from prior landslides can persist for years (as illustrated in Fig. 1b–d, taken 2.5 years post-event).

McGuire, L.A., Ebel, B.A., Rengers, F.K., Vieira, D.C. and Nyman, P., 2024. Fire effects on geomorphic processes. Nature reviews earth & environment, 5(7), pp.486-503.

On Figure 3 (INV_01-POLY)     

The apparent contrast in spatial detail between the northern and southern portions of the panel simply reflects the distribution of landslides and debris-rich floods triggered by Cyclone Idai — concentrated around Chimanimani and east of Chipinge — rather than any difference in mapping protocol or resolution. All four classes were mapped with consistent detail throughout the zones shown in panel a. Inventory polygons were selected to be broadly representative of the range of processes observed across both districts, together covering a very large 15% of the total study area (Section 2.1.1).

We thank the Reviewer for their careful reading and constructive comments.

On reproducibility and transferability with/without new data           

This is indeed an important aspect that should be clarified in the text. The methodology was intentionally designed around simple morphological and hydrological variables available globally, with replicability in data-scarce environments explicitly in mind. While the model, as trained here, should be transferable to other regions—relying on predictors applicable to any landslide (and related debris-rich flood) anywhere on Earth (simply capturing where mass is likely to be mobilized, transported, or deposited)—its accuracy would likely improve if retrained on locally collected inventories, potentially supplemented by region-specific variables reflecting local environmental conditions.

Regarding reproducibility within the same area, it should remain high for events of similar magnitude to Idai, on which the models were trained and evaluated (i.e., quite extreme). However, it would likely be lower for smaller-scale events (zones with the highest risk for landslide initiation remain similar, but with a much more constrained extent for runout and debris-rich floods). We will add a short paragraph in the Discussion to address these aspects of reproducibility and transferability.

On the use in regional disaster risk reduction frameworks

This study was conducted as part of the UNESCO project BE-RESILIENT Zimbabwe, funded by the World Bank and managed by the UNOPS Zimbabwe Idai Recovery Project with disaster risk management directly in mind. The landslide hazard and exposure maps identified critical educational and health institutions that were potentially at risk. This result led to a more in-depth analysis of 15 schools in the region, with respect to their multi-hazard risk, leading to risk profiles for each school and clear investment criteria. Additionally, this pilot case will be further upscaled to a larger area in the following months, covering headwater catchments in the transboundary Busi, Pungwe and Save Catchments, providing support to more operational landslide awareness and preparedness, and providing the building block for a future landslide early warning system in the region.

On the Figures

Regarding Fig 3 and Fig Appendix A1 the slight overlay of the "km" label with the map was a deliberate visual choice. While we do believe it does not hinder the reader’s understanding that the unit is kilometers, we will adjust the figure layout in the revision to minimize overlap if the Reviewer prefers. As explained in the method section 2.1.1. and in the Fig 3a legend, the polygons represent “the manual polygon-based inventory (INV_01-POLY) used for training and validating the logistic regression model (cf. sections 2.1.3 and 2.2)”. The green rectangles (which indicate areas manually checked for landslides/debris-rich floods but where none were found) may have caused some confusion.