the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Nov 2025
Climate and impact attribution of compound flooding induced by tropical cyclone Idai in Mozambique
Abstract. In this study, we investigate the effect of climate change on tropical cyclone (TC) induced compound flooding and impacts for TC Idai, making landfall in Mozambique in 2019. TCs are one of the most damaging extreme events and are challenging to attribute using conventional, probabilistic methods. We develop a storyline attribution framework including a state-of-the-art modelling chain that combines hydrological, coastal, flood and impact models to simulate the changes in flooding and its impact under factual and counterfactual scenarios, with the climate trend removed. For the case of TC Idai, we find that sea level rise and change in wind-driven storm surge lead to the largest increase in flood damage (27 % compared to the counterfactual), while causing a less than 1 % increase in flood volume and flood extent. Climate trends in rainfall lead to the largest increase in flood volume and flood extent (9 % and 2 %, respectively, compared to the counterfactual) but account for a smaller increase in flood damage (4 %). Changes in all drivers combined lead to the same increase in flood volume and flood extent as the rain-only scenario (9 % and 2 %, respectively) but the largest increase in flood damage (31 %). A non-linear relationship between flood hazard and flood damage results in a stronger climate footprint on TC impacts than hazards. Assessing the combination of all climate change-affected flood drivers is crucial for obtaining a comprehensive view on the effect of climate change. The attribution framework presented in this paper is applicable for TC-prone regions across the globe and can be applied in data-poor, yet often highly impacted and vulnerable areas which are currently underrepresented in attribution studies.
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Status: open (until 22 Dec 2025)
- RC1: 'Comment on egusphere-2025-4502', Anonymous Referee #1, 23 Nov 2025 reply
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Climate and impact attribution of TC Idai Vertegaal, Doris M.; Aleksandrova, Natalia; Bovenschen, Tycho; Couasnon, Anaïs; Goulart, Henrique M. D. https://doi.org/10.5281/zenodo.17107289
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Review of egusphere-2025-4502: "Climate and impact attribution of compound flooding induced by tropical cyclone Idai in Mozambique"
General Assessment
This manuscript addresses an important gap in attribution science by developing a storyline framework for tropical cyclone-induced compound flooding in data-sparse regions. The authors demonstrate technical sophistication in coupling multiple state-of-the-art models (SFINCS, wflow, D-Flow FM) to resolve all flood drivers dynamically. The application to TC Idai in Mozambique is particularly valuable given the underrepresentation of African cyclones in attribution literature. The work makes meaningful contributions to understanding how climate signals propagate from hazard to impact through nonlinear damage relationships.
However, the manuscript requires strengthening in several critical areas before publication. The counterfactual design lacks sufficient scientific justification for key parameter choices, the validation strategy needs refinement given data limitations, and the uncertainty quantification is inadequate for the compounding uncertainties inherent in this multi-model framework.
Major Comments
1. Counterfactual Design and Scientific Justification
The manuscript's attribution conclusions depend entirely on robust counterfactual scenarios, yet the justification for key parameters is insufficient:
• Rainfall reduction (8%): While the Clausius-Clapeyron relationship is cited, the manuscript should explicitly demonstrate this calculation rather than only asserting it. Recent literature supports 8% reductions for ~1.1°C warming, but this warrants a dedicated methods subsection showing this applied to the case here and a discussion of uncertainties in this approach, particularly for tropical cyclones, where dynamic effects may deviate from thermodynamic expectations.
• Wind speed reduction (10%): This is more problematic. Knutson et al. (2020) report median projections of 1-10% intensity increases for 2°C warming, suggesting 0.5-5% for current ~1.1°C warming. A 10% reduction appears to overestimate the counterfactual change, potentially inflating the attributed impact from wind-driven processes. The manuscript cites Mester et al. (2023), who used regional observed trends, but doesn't establish why this particular value is appropriate.
• Sea level rise component: The methodology for SLR estimation needs clarification:
o Authors use the Treu et al. (2024) dataset but this contains systematic biases. The manuscript must explicitly address whether such biases affect factual and counterfactual equally (canceling in differences) or differently (amplifying attribution error)
o Figure S10 shows only 2015 data, yet the authors extrapolate to 2019, assuming linear trends.
o Long-term tide gauge observations from the region should be incorporated to validate the 14 cm estimate
• Bankfull discharge assumption: The 2-year return period assumption for bankfull discharge should scale between scenarios. If climate change increases discharge, the effective channel capacity may differ between factual and counterfactual. Holding this constant creates an inconsistent comparison -the counterfactual world would have had different equilibrium channel geometries. This deserves explicit discussion or sensitivity testing.
2. Methodological Concerns
Model simulation duration: How long were the simulations run? The manuscript doesn't specify the total simulation period or spin-up time. For compound flood modeling, the synchronization of multiple drivers is critical. Eilander et al. (2023) found surge peaked 3-5 days before discharge for TC Idai, producing limited compound interaction. Does this framework capture such timing effects?
Boundary condition consistency: How are the multiple drivers synchronized temporally across model domains? The wflow warm-up (365 days) is mentioned, but what about SFINCS, D-Flow FM initialization? Are antecedent soil moisture conditions consistent between scenarios? How is infiltration handled in SFINCS relative to wflow?
Validation metrics: The validation should report separate performance metrics for: coastal zones (surge-dominated), fluvial zones (discharge-dominated) and compound zones (driver interaction). This would strengthen confidence that the model captures the different flooding mechanisms appropriately.
Meteorological forcing quality: ERA5 underestimates TC intensity (appropriately addressed by Holland parametric winds), but the manuscript should clarify: Was the TC parametric wind model used throughout the entire modeling chain? If so, how do biases propagate? The Holland model performs well when fitted to observations or additional empirical relationships, but "out of the box" applications can have significant errors and miss asymmetric TC shapes. The 0.75 TC radius merging also needs explanation—how sensitive are results to this choice?
3. Wave Setup and Coastal Process Assumptions
The assumption that wave setup remains constant across counterfactual scenarios is scientifically invalid and potentially introduces substantial error since wave setup scales with wave breaking intensity. A 10% wind reduction would generate lower wave heights, producing correspondingly lower setup—potentially 15-20% reduction if setup scales as Hs².
Also, the SnapWave approach over transects is problematic since wave conditions aren't properly downscaled (ERA5 offshore waves are coarse), transects are poorly connected to actual water levels, and local nearshore processes (refraction, shoaling, breaking) may be inadequately represented in a transect approach
I strongly recommend: (1) fully integrate SnapWave 2D within SFINCS for dynamic wave-surge coupling in both scenarios, OR (2) remove the wave coupling component entirely, acknowledge this as a limitation, and note it may introduce ~10 to 20% uncertainty in coastal flood depths. The current approach undermines the compound flooding framework's credibility.
4. Flood Damage Modeling
Depth-damage curve validation: The continental curves from Huizinga et al. (2017) assume European-style construction. Snel et al. (2019) showed Ethiopian traditional buildings experience 100% damage at 2m depth versus 5m for concrete structures. Mozambique's post-Idai assessments reported 111,163 completely destroyed houses, but no published studies validate these damage functions against actual losses. I recommend: 1) comparing aggregate model damage against reported sector-specific losses, 2) conducting sensitivity analyses with alternative damage curves for informal/traditional construction and 3) discussing this as a major uncertainty source.
5. Missing Elements: Uncertainty Quantification
This is the review's most critical concern. Every component carries substantial uncertainty:
- Counterfactual parameter choices (rainfall: ±2-3%, wind: ±5%, SLR: ±5cm)
- Meteorological forcing (ERA5 vs. parametric TC model inconsistencies)
- Hydrological model calibration
- Missing river bathymetry (bankfull approximation)
- Wave setup assumptions
- Exposure data completeness
- Damage function transferability
These uncertainties compound multiplicatively, not additively. The total framework uncertainty is substantial.
The manuscript MUST include:
1. Uncertainty quantification at each modeling step
2. Formal uncertainty propagation through the attribution framework
3. Comprehensive sensitivity analysis on key assumptions
4. Probabilistic framing of attribution statements with confidence intervals
Recent attribution papers explicitly report uncertainty ranges. Does climate change contribute 5-15% or 25-35% to damages? Without uncertainty bounds, readers cannot properly interpret the "31% damage attributable to climate change" conclusion.
Minor Comments
Exposure and Vulnerability Treatment (L285-290)
The manuscript states that exposure/vulnerability is held constant, but this critical assumption deserves more prominent discussion. The counterfactual answers: "What would 2019's exposed population experience under a pre-industrial climate?" not "What would the pre-industrial population experience?" This is correct for physical attribution, but should be explicitly stated in Section 2.3.2 rather than buried in the discussion.
Supplementary Figure S2 (Return Period Analysis)
The disconnection around 1-2 years is striking and unexplained. How was the fit performed? Was this a standard GEV/Gumbel distribution? The discontinuity suggests potential issues with the extreme value analysis or the underlying wflow discharge distribution. Please add a methods subsection describing the EVA approach and discuss this feature.
Line 175 (Holland Model Implementation)
"linearly fading the data at 0.75 fraction of the TC radius" - Why 0.75? This appears arbitrary and could significantly affect results. Show sensitivity or cite precedent. Also, how is the TC eye resolved in terms of rainfall distribution? The Holland wind model has asymmetric components—were these included?
Recommendations
Despite the substantial revisions required, this manuscript represents important and novel work. The technical execution is sophisticated, the application to Mozambique addresses a critical gap, and the compound flooding attribution framework is genuinely innovative. With careful attention to the major comments—particularly uncertainty quantification, counterfactual justification, and wave setup treatment—this can become a strong contribution to NHESS and the broader attribution literature.
Priority actions:
1. Add comprehensive uncertainty analysis (Monte Carlo or ensemble approaches)
2. Revise or remove the wave setup coupling
3. Strengthen counterfactual justifications with sensitivity analyses
4. Improve damage model validation against reported losses
The open-source, globally-applicable framework you've developed has significant potential for advancing attribution science in vulnerable, data-poor regions. I look forward to seeing the revised manuscript.