We would like to thank the referee for his positive comments about our paper. Regarding the issues that are raised, we propose the following answers (after recalling each remark/question between quotes):

"1) The necessity & novelty of the manuscript should be presented and stressed in the “Introduction” section, despite seven paragraphs."

Thank you for the remark, we are sorry if the necessity and novelty were not clear enough. The introduction will be modified to make them more explicit: in short, they can be explained as a need for continuous improvement in tools towards landslide monitoring on the one hand, and a use of combined mechanical model with Kalman prediction tools on the other hand. More precisely, an appropriate observer-based reconstruction and forecasting scheme is exhibited, a novel tuning methodology is proposed, and an illustration with a set of real landslide data is provided. We will highlight even more clearly that the main focus of the study being on demonstrating the applicability of the Kalman filter methodology to the case of landslides, the developments are purposedly based on a simple mechanical model with a low number of parameters.

"2) The discussion of previous studies on Kalman filter method in landslide prediction should be strengthened in the introduction or methodology section to clarify its advantages and applicable conditions."

Thank you for this emphasis on Kalman filter, more comments on such a tool will be added, both from a general point of view and for landslide applications: in short, the interest of Kalman results are basically their optimality features, and when used with a mechanical model, the powerful prediction they can provide thanks to their feedback connection with real data. A difficult issue with such tools is the tuning, and in the present work we provide a novel algorithmic method towards an appropriate tuning. Regarding landslides, there are not so many previous studies with a Kalman approach, and those we can found will be added in the references.

"3) The methodology is just tested on a series of 16-days real data measured in the Super-Sauze landslide, France. How do you think that it would be extended to a scope of “slow-moving landslides”, as presented in the Manuscript title?"

Thank you for pointing this important aspect, we will address it via some additional comment in the paper. In short, we are indeed dealing with landslides characterized by slow motions (on average between 1 and 30 mm/day for Super-Sauze landslide), but focusing rather on periods of acceleration induced by increased water pressure, for which our simple mechanical model is applicable. This is what is indeed illustrated by the results included in the paper, based on data representative of such a case (with varying water pressure, and quite significant motion). This 16-days dataset was chosen as it corresponds to a phase of landslide motion that our mechanical model can represent. Dealing with periods when the landslide is almost not moving may reach the limitations of our model (for which no motion is assumed whenever the water pressure becomes too low).

"4) The displacement forecasting in this work is based on surface or ground displacement, which often fails to reflect the actual landslide kinematics. So your method would be extended to forecast subsurface deformation, especially for displacement or strain around slip surface? The authors should add a section on this topic."

Thank you for this interesting comment, we will definitely add a remark about it in the paper. In short, our approach indeed focuses on surface displacement, because this is the configuration for which we do have data. In case information about subsurface behaviour can be available, our claim is that the approach we propose is highly versatile and could be used as well if an appropriate mechanical model is available.

Thank you very much for the careful reading and the constructive comments. Our answers are as follows (after recalling each comment/question between quotes):

"First, the introduction should provide a more focused review of the state of the art related to the specific topic, ensuring that the novelty of the proposed method is clearly emphasized in comparison to previous studies. This would allow the authors to better articulate the added value of their work."

Sorry for the lack of clarity in the novelty, the introduction will be modified so as to remedy to this: in short the main contribution is a combined mechanical model with Kalman prediction tools.  More precisely, an appropriate modelling is exhibited, a novel tuning methodology is proposed, and an illustration with a set of real landslide data is provided. More information about the interest of using Kalman tools, and our contribution in that specific area as compared to existing results, will be provided as well.

"Furthermore, the method is applied to a mudflow, which does not fully align with the assumptions of the proposed model (Figure 1). I recommend explicitly addressing the limitations of the selected model in the context of this case study."

Thank you for the remark. In fact, the Super-Sauze landslide is a slow-moving landslide (average velocities between 1 and 30 mm/day  in that case) showing phases of accelerating motion (up to 400mm/day during spring season), rather than a mudflow. As also mentioned in our replies to the first Referee, the main focus of our study is on the Kalman filter methodology. As such, the study is based on a simple mechanical model that does indeed demonstrate strong limitations : block motion, halted motion during phases of low water pressure, etc. These limitations will be more explicitly mentioned in the paper. We claim however that the proposed Kalman filter approach, including the specific calibration procedure, can be viewed as generic and may be coupled to more complex and realistic mechanical models in the future.

"Lastly, I suggest extending the observation period of the landslide displacement data to enhance the robustness of the predictions."

This remark about robustness is of course important, but we do feel that with the dataset we have considered, we cover quite a representative period where there is some displacement, with varying water table, and that extending the period would not bring much more information (except if going beyond our model validity). Besides, as mentioned in our replies to the first Referee, this 16-days dataset was chosen as it corresponds to a phase of landslide motion that our simple mechanical model can adequately represent.

We are grateful to the referee for the positive comments, and for pointing a list of possible improvements in the presentation. We will carefully take all of them into account.