This paper presents a comprehensive spatiotemporal assessment of landslide risk in the Valencian Community (Spain) from 1950 to 2021. The authors propose a multidimensional risk evaluation framework that integrates geological susceptibility, cadastral data, and economic exposure through the Risk Index (RI), Risk Quality Index (RQI), Risk Sensitivity Index (RSI), and Modified Risk Quality Index (mRQI). The topic is timely and relevant, addressing an essential research gap between susceptibility modeling and actionable risk management. The dataset is extensive, and the results have clear implications for regional land-use planning and risk mitigation. 

Recommendation: Major Revision. 

Major Comments：

The manuscript frequently claims that the proposed framework is particularly suitable for large, spatially heterogeneous regions such as the Valencian Community, yet it does not clearly explain why such regions are challenging for landslide risk assessment or how the proposed methodology addresses those challenges. Large and heterogeneous areas are typically characterized by strong geological and geomorphological variability, uneven spatial distribution of landslide inventories, inconsistent resolution of socioeconomic data, and mismatch between geological and administrative boundaries—all of which can undermine comparability and accuracy of regional risk models. The authors should explicitly discuss these challenges and clarify how the RQI, RSI, and mRQI frameworks overcome them, for instance by integrating multi-source datasets, applying normalization to reduce bias among different scales, coupling physical and socioeconomic factors across scales, and using dynamic indicators to capture temporal evolution of risk. Expanding this discussion would convincingly demonstrate the framework’s innovation and justify its claimed applicability to spatially dispersed regions.

Minor comments：

1. The methodological innovation of the RQI, RSI, and mRQI indices relative to existing risk assessment frameworks should be stated more clearly. A concise comparison with earlier studies (e.g., Guzzetti et al., 2005; Pereira et al., 2020; Segoni & Caleca, 2021) would help readers understand the conceptual advancement of this work.

2. The relationships and calculation logic of the indices—particularly the variables Gaj, Faj, LM, and DV—need clearer description. Including a schematic diagram summarizing index derivation, weighting, and normalization would enhance transparency and reproducibility.

3. The study would benefit from a brief uncertainty or sensitivity analysis to evaluate how variations in data inputs (e.g., susceptibility classification or economic valuation) affect risk index results. Even a qualitative discussion would strengthen confidence in the robustness of the findings.

4. Although the dataset spans more than seven decades, temporal changes in landslide risk are not well illustrated. Incorporating a time-series trend, decade-based comparison, or discussion of major shifts in risk drivers would make the “spatiotemporal” aspect of the study more convincing.

5. The discussion of socioeconomic influences such as tourism and urban expansion remains qualitative. Integrating basic quantitative indicators—such as land-use change, population growth, or infrastructure density—would provide stronger empirical support for the interpretation.

6. The manuscript would benefit from editorial refinement. Ensure consistent terminology throughout (e.g., unify “risk zone,” “susceptibility zone,” and “management class”), verify incomplete references, and standardize equation formatting and figure captions for clarity and professionalism.

This study addresses an important topic: the role of (soft) land-use planning regulations in guiding urban growth and reducing landslide risk. It proposes a framework to quantify (and monitor over time) changes in landslide risk by introducing indices that enable comparison of risk evolution across administrative units (e.g., municipalities) and across time for the same unit. The approach is based on risk values (H × E × V) at the building- or dwelling unit-level, aggregated within administrative boundaries, which could be useful as an operational monitoring and decision-support tool for risk management.

Major comments:

While this study does provide an interesting framework for calculating a (decomposed) risk index that enables comparisons across several administrative units and for the same unit over time, the paper, as it stands right now, seems more like an application of the methodology, without a proper evaluation of the strengths and limitations of the method, its implications for the research domain, or a clear indication of how this paper builds on existing research in the field. This is especially the case for the discussion and conclusion sections, which are too tailored toward the interpretation of the case study, without a proper evaluation of what this new index actually means for research and how it could be adopted beyond the case study. Some interesting questions to address would be: how would this framework perform in regions with limited compliance with building standards? Can we consider this approach applicable in such contexts? In which cases would it be applicable, and in which would it not? For instance, the paper mentions in the introduction that proper planning (and thus understanding risk) is especially relevant in developing countries, but from my understanding, the framework developed in the paper would be much more adequate in more “formal” urban contexts (as in the case study in Spain) than in complex, spontaneous settlements in the “developing world.” It may be interesting to address this from a research perspective, as a separate subsection or part of the discussion, to clarify what this research adds and how it generalizes.

Additionally, you may want to consider simplifying the mathematical presentation. The methodology is relatively straightforward (which is a positive feature for applicability in other contexts), but the manuscript currently presents several equations that are somewhat repetitive. A more concise set of key equations, with derivations or intermediate steps moved to an appendix/supplementary material, could improve readability.

Apart from this comment, I have only a few minor comments:

• Line 50: I am not sure whether “permanently” is the most adequate word here, as it is not possible to ensure such permanence.
• Lines 63–64: “Leading to landslides” seems very generic, in the sense of “urban growth leads to landslides.” Maybe explain more clearly whether urban growth leads to increased landslide hazard, exposure, or risk.
• Equation 2 (and others associated, such as Equation 9): If it refers to the sum of (H × E × V) for each dwelling unit (DU), then these (H × E × V) terms should be within parentheses in the equation, correct?
• Lines 176–177: From my understanding, building resistance was assigned only based on building height, correct? Please explain this limitation. Also, I missed information on how the landslide magnitude was obtained.
• Lines 203–206: I understand that assuming risk only at L5 would result in excessively high values. But doesn’t it introduce inaccuracy to disregard buildings constructed in susceptibility level 5? I haven’t taken a look at the map, so there is no way of knowing the representativeness of this class, but I imagine this is an important limitation. Maybe address this choice a bit more, and justify selecting only L4 (and not, for instance, L4+L5 combined).
• Some limitations could be acknowledged. For instance, the implications of assuming homogeneous buildings for all residential structures (both in terms of Dwelling Unit Value (DUV) and construction materials) and, especially, the assumption that land-use regulations will be properly followed (which may not hold in some urban contexts, particularly where enforcement capacity is limited or informality is widespread).