We thank the reviewer for the constructive comments, which have helped us improve the clarity, structure, and scientific contribution of the manuscript. Below, we address each of the concerns raised:

1. Scientific Novelty and Conceptual Contribution

We respectfully disagree with the assessment that the manuscript lacks scientific novelty. While we do not propose a new algorithm or model, we believe the manuscript makes an original contribution by reframing the fragmented field of data-driven multi-hazard and multi-risk modeling through a structured and forward-looking synthesis. Moreover:

• We'll reframe the four core research questions, which collectively reflect a risk-modeling pipeline, from data processing to hazard prediction, risk assessment, and future scenario modeling. This framing provides a clear conceptual lens through which to interpret recent developments in the field.
• We'll focus the synthesis on actionable themes that cut across methods and applications, including:
• The integration of Earth Observation (EO) and textual data for multi-risk modeling;
• The challenge of model transferability and scalability across regions and hazard types;
• The role of model interpretability and explainable AI in machine learning application for multi-risk;
• The use of hybrid models (e.g., physics-informed neural networks) to bridge physical realism and data-driven inference.
• We'll revise the discussion and conclusion sections to more explicitly identify research gaps, including:
• Limited use of ML approaches that incorporate causal reasoning or address cross-sectoral risks;
• A need for better spatiotemporal validation techniques in multi-risk contexts;
• The importance of uncertainty quantification (and communication), particularly under future climate change scenarios.

We believe this review is among the first to explicitly integrate machine learning and statistical approaches (including copulas) through a multi-hazard lens, and to assess their value for both scientific insight and decision support across multiple scales.

1. PRISMA methodology

We appreciate this comment and will add a PRISMA flow diagram and a methodological appendix (now included as supplementary material) to better describe the search strings, screening criteria, and inclusion/exclusion processes, improving the transparency and reproducibility of our literature selection.

1. Poor Language Quality

We fully agree that language quality is essential for a review article. In response, the manuscript will undergo a thorough professional-style language revision. We'll address issues related to grammar, structure, and clarity by:

• Simplifying overly long or technical sentences;
• Removing redundancy across sections;
• Improving flow, particularly in the methods and discussion.

These changes will enhance the readability and accessibility of the manuscript for a broad interdisciplinary audience.

1. Weak Structure and Inconsistent Framing

We will clarify our use of the terms "machine learning" and "data-driven methods" in the introduction. The term "data-driven methods" is explicitly defined as an umbrella term that includes both machine learning techniques and other statistical approaches. This choice is motivated by the key role played by non-ML statistical tools (such as copulas) in multi-hazard modeling. We'll revised section titles, transitions, and content to reduce redundancy and improve coherence across the manuscript. In particular the overlap between Sections 3.1.1 and 3.1.2, will be clarified distinguishing between EO-based data sources and climate reanalysis/model data. We hope these revisions address the reviewer’s concerns and demonstrate our commitment to improving the clarity, rigor, and contribution of the manuscript.

We thank Reviewer 2 for the constructive and detailed feedback, which has helped improve the clarity, scope, and scientific depth of our manuscript. Below, we provide a point-by-point response to each of the reviewer’s comments.

1. Link between the research questions and multi-risk

We have revised the introduction and clarified the framing of the four research questions to better articulate how they address current gaps in the literature and contribute to advancing data-driven multi-hazard and multi-risk assessment.

• RQ1 focuses on the challenge of data preparation and preprocessing in ML-based hazard modeling. We highlight how this includes increasing hazard resolution, bias correction, and the integration of diverse sources of exposure and impact data, such as Earth observation and unstructured textual data (e.g., social media). This step is critical in enabling ML applications where data scarcity and heterogeneity persist.
• RQ2 addresses the modeling of multiple interacting hazards, emphasizing the use of Deep Learning techniques for forecasting compound events and copula models for representing statistical dependencies. This question targets methodological gaps in representing both simultaneous and consecutive hazard interactions.
• RQ3 explores how ML can support risk assessment, in addition to hazard modeling. We discuss the importance of data availability on past impacts and the need for model interpretability to ensure that outputs are actionable and trustworthy in real-world applications.
• RQ4 introduces the integration of future climate scenarios into multi-risk modeling. We have strengthened the revised manuscript by explicitly discussing the role of uncertainty quantification, including ensemble modeling, probabilistic approaches, and interpretability methods.

These questions collectively offer a structured framework that links data engineering, compound hazard detection, multi-risk modeling, and future scenario analysis, contributing both technically and practically to the field.

2. Role of Data Assimilation

We fully acknowledge the importance of data assimilation in climate science and weather forecasting, particularly its role in producing high-quality reanalysis datasets that underpin many hazard models (e.g., ERA5, CHIRPS). However, we consider data assimilation a distinct field of study, more closely tied to physical modeling and numerical weather prediction.

To maintain focus, we have not included a detailed discussion on this topic. That said, we have added a brief reference in the discussion to acknowledge the enabling role of data assimilation in the broader modeling ecosystem and to direct interested readers to relevant literature.

3. Uncertainty Quantification

We agree that uncertainty quantification is a key component of hazard and risk analysis. The revised manuscript now includes a specific discussion that distinguishes between input data uncertainties, such as variability across ensemble climate projections or differences between reanalysis products; model-based uncertainties, stemming from generalization limits and the structure of ML models. We highlight key methods, including probabilistic ML, Bayesian networks (Fan et al., 2021; Salvaña, 2025; Tierz et al., 2017), and Gaussian Processes (Li and Wang, 2025; Tazi et al., 2024), which provide built-in estimates of prediction confidence. We also emphasize how interpretability tools like SHAP contribute to transparency and communication of uncertainty in decision-making.

4. Hybrid Modeling (ML + Physical Constraints)

We appreciate this suggestion and have added a dedicated section on hybrid modeling approaches that integrate physical constraints into ML frameworks. We focus particularly on Physics-Informed Neural Networks and related hybrid architectures that embed conservation laws or differential equations directly into the learning process. These approaches are gaining traction in domains such as climate (Lütjens et al., 2021; Yang et al., 2023), hydrology and compound flooding (Feng et al., 2023; Munoz et al., 2024; Xu et al., 2024). We compare these hybrid approaches with pure ML models, noting that hybrid models often generalize better and produce more interpretable outputs, but can be harder to train and require expert-defined physical constraints (Bonfanti et al., 2024; Cuomo et al., 2022; Farea et al., 2024; Kashinath et al., 2021). We have also linked this topic to RQ1 and RQ2, where the trade-off between physical realism and data-driven flexibility is most relevant.

5. Differences and Complementarities between ML and Copulas

We have revised Section 4.3 to more clearly distinguish the roles of ML and copula techniques. While ML excels in detecting high-dimensional, non-linear patterns, copulas are better suited for modeling explicit dependencies, especially in the tails of distributions, a crucial feature for compound risk assessment. We now include examples where the two approaches are complementary: ML models can generate features or classify compound events, while Copulas can then model the joint probability of those features or events (Couasnon et al., 2018; Jiang et al., 2023). This combination offers interpretable, probabilistic outputs while leveraging the predictive power of ML.

6. Role of Earth Observation (EO) in multi-risk assessments

We have revised the discussion to better highlight the value of EO data, particularly when integrated with ML, in supporting actionable multi-hazard and multi-risk assessments. Rather than emphasizing technical differences, we now focus on operational value and decision support:

1. Timely and spatially explicit indicators: EO enables detection of hazard, exposure, and impact metrics (e.g., vegetation stress, land cover change, water availability), improving early detection and event characterization.
2. Expanded geographic coverage: EO provides access to consistent observations in remote and data-scarce areas, facilitating model scalability and application in developing countries (Chauhan et al., 2025; Kabiru et al., 2023).
3. Recovery monitoring and disaster response: EO data such as nighttime lights and mobility proxies support tracking short-term disaster response and long-term recovery, helping planners assess evolving vulnerabilities (Qiang et al., 2020).

These points are now emphasized in the revised manuscript to align the discussion with practical needs of stakeholders and decision-makers.

Minor Comments

• All citations will be reviewed and updated for consistency and format.
• We'll revise the manuscript to avoid the term “understand” when referring to ML models, replacing it with more appropriate terms such as “model,” “characterize,” or “represent.”
• The manuscript will be subject to a comprehensive language revision, improving grammar, style, and readability.
• Section numbering will be corrected, and “Multi-hazard” is now properly labeled as Section 3.2.

We thank the reviewer again for their valuable and constructive feedback, which has led to a stronger and more focused manuscript. We hope that the revisions address all points satisfactorily.

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