the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 May 2026
Impact attribution of compound flooding from Tropical Cyclone Idai: Assessing the influence of land cover change and underlying socio-economic drivers using a mixed-methods approach
Abstract. In this study, we investigate the influence of socio-economic drivers on the impacts of compound flooding induced by tropical cyclone (TC) Idai. Making landfall close to the city of Beira in Mozambique in 2019, TC Idai was one of the most devastating TC’s to have hit the Southern Hemisphere. Attribution studies generally quantify the contribution of climate change to extreme events and their societal impacts; however, few studies assess how socio-economic drivers amplify or attenuate those impacts. We develop a mixed-methods approach, combining qualitative data from Key Informant Interviews (KIIs) and Causal Loop Diagrams (CLDs) with quantitative data from a physics-based modelling chain to assess how land use and land cover (LULC) changes over 20 years prior to TC Idai plausibly influenced the compound flooding impacts from TC Idai. Results from the quantitative approach show that land use changes (irrespective of climate change) potentially worsened the flood hazard from TC Idai. Results from the qualitative approach explain the underlying drivers of these land use changes such as deforestation driven by charcoal production and informal urban expansion. By integrating two methodologies, we find that the impacts of TC Idai were not only the result of intense climatic hazards but were amplified by complex, deeply rooted socio-economic processes that create reinforcing cycles of vulnerability and exposure. This research demonstrates the value of an interdisciplinary, mixed-methods approach, using localised contextual information to advance impact attribution in data-scarce settings.
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Status: open (until 02 Oct 2026)
- RC1: 'Comment on egusphere-2026-2454', Anonymous Referee #1, 28 Aug 2026 reply
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RC2: 'Comment on egusphere-2026-2454', Anonymous Referee #2, 04 Sep 2026
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Review of manuscript egusphere-2026-2454
Impact attribution of compound flooding from Tropical Cyclone Idai: Assessing the influence of land cover change and underlying socioeconomic drivers using a mixed-methods approach.
by Poppy E. Webb et al.
This manuscript investigates how climate change, land-use/land-cover change, and underlying socio-economic processes influenced compound flooding during TC Idai in Mozambique using a mixed-method approach. The manuscript would benefit from a clearer distinction between flood-hazard attribution and impact attribution, as the quantitative analysis estimates changes in flood depth, and extent. However, it does not quantify impacts through exposure and vulnerability analysis. The integration between the qualitative and quantitative components should also be explained more explicitly. These revisions would strengthen the methodological part and interpretation of the results. I therefore recommend major revisions before the paper can be considered for publication.
Introduction
1. The definition of compound flooding in terms of fluvial, pluvial and coastal flooding is appropriate for the focus of this study. However, I wonder whether it would be useful to briefly clarify at this point that these represent the flooding-related drivers considered in the study, rather than the complete set of physical hazards associated with a tropical cyclone. TC Idai also involved strong winds, as the authors themselves acknowledge later in the Introduction (lines 74-76). Previous studies on tropical cyclones in Mozambique, including TC Idai, have shown that wind and flooding may contribute differently to direct and indirect impacts (e.g. Mühlhofer et al., 2023; Espejo et al., 2025). A short clarification here would help distinguish the broader physical drivers of TC impacts from the specific compound-flooding processes investigated in this study, without changing the scope of the analysis.
References
Espejo GG, Stalhandske Z, Mühlhofer E, Röösli T, Brönnimann S, Bresch DN and Zischg AP (2025) From hazard to disruption: forecasting direct and indirect tropical cyclone impacts on infrastructure in Mozambique. Front. Clim. 7:1666586. doi: 10.3389/fclim.2025.1666586
Link: https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2025.1666586/full
Link: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=2TrzxOQAAAAJ&citation_for_view=2TrzxOQAAAAJ:eQOLeE2rZwMC
2. In line 69 in the introduction. There is a typo error probably. “This can be achieved by using a mixed-method approach that combines qualitative and qualitative methods”. It is maybe quantitative?
Methods
The quantitative framework itself is clear: The combination of KIIs/causal mapping with a SFINCS- workflow compound-flood chain and four factual/counterfactual scenarios. But I see the following points worth checking.
3. In Section 2.2.2, the authors describe the use of the 30 m Lisboa et al. (2024) LULC dataset and use the 2020 map to represent factual land-cover conditions for TC Idai, which occurred in 2019. I assume this choice is related to the lack of a comparable regional dataset at that resolution for 2019. However, the current description raises some questions, since the objective is to represent land-cover conditions before the TC Idai 2019. Could the authors clarify why the 2020 dataset is considered representative of conditions during TC Idai, and whether land-cover changes between March 2019 and 2020, including changes caused by Idai itself or post-event recovery/reconstruction, could affect the factual scenario?
The manuscript also states that the Lisboa et al. dataset was “minorly” merged with the 2019 Buchhorn et al. global LULC product to cover the full model domain. Could the authors clarify which parts of the domain required this additional dataset and approximately what proportion of the model area it represents? If feasible, a brief sensitivity check comparing the results with and without this merged area would help demonstrate whether the merging has a significant influence on the results and would support the statement that the merge is minor.
4. After line 105 the Figure1; I suggest reviewing it and modified
Figure 1 may be slightly misleading in the way the qualitative and quantitative components are linked. The arrow from the causal loop diagram directly to “Flood hazard (extent & volume)” suggests that the qualitative analysis is used as an input to the flood-hazard calculation. However, as I understand the methodology, the quantitative modelling chain assesses how the LULC and climate scenarios influence flood hazard, while the KIIs and CLD are used to identify and interpret the socio-economic drivers underlying the observed LULC changes.
I therefore suggest revising Figure 1, or adding a complementary schematic, to make the sequence of the mixed-method approach clearer. For example, were the KIIs conducted and analysed first to develop the CLD, and was the CLD then used to interpret the observed differences between the 2000 and 2020 LULC maps? Or were the LULC changes identified first and then used to guide the qualitative analysis? Clarifying this sequence would help the reader understand how the two components are integrated and avoid the impression that the CLD directly feeds into the quantitative flood model.
Explain it for steps maybe to understand the sequence
5. Figure 4 effectively shows the spatial differences in maximum flood depth between the factual and counterfactual scenarios. However, the interpretation of the positive and negative differences, particularly in the LULC-change panel, could be explained more explicitly in the text. For example, it would be useful to clarify why some areas experience increased flood depth while others show decreases under the factual LULC scenario, and to relate these spatial patterns to the main LULC transitions identified earlier (e.g. deforestation, cropland expansion, urbanization or reforestation). This would help connect the mapped differences more directly to the physical mechanisms discussed in the study.
For example:
Please clarify the physical mechanism proposed for the local decrease in flood depth northwest of Beira. The statement that reforestation reduces land-cover roughness and therefore decreases water velocity appears counterintuitive. Could you please review it. Is right the description?
6. In line 260 there is a part to edit maybe: of “socioeconomic deprivation … was. Those points .........
7. Section: Impacts of changes in climate and LULC on flood hazard.
The combined climate + LULC scenario results in a 12% increase in flood volume, compared with 9% and 2% for the individual effects. Could the authors clarify whether this difference is due to rounding?
8. In lines 377–382, the manuscript refers to assessing impacts and integrating exposure and vulnerability. However, these components are identified qualitatively rather than quantitatively assessed. I suggest using more cautious wording and clearly distinguishing flood-hazard attribution from qualitative interpretation of exposure, vulnerability, and socio-economic drivers. If the authors wish to claim impact assessment more explicitly, this could be strengthened in future work by combining the hazard results with exposure data such as buildings, population, or crops.
I am referring to this part: “We combine a quantitative attribution framework with KIIs visualised in a CLD to assess cascading drivers, impacts, their interactions and consequently reinforcing socio-economic feedback cycles in rural and urban areas. We propose a mixed-methods approach to truly understand real-word impacts and identify drivers of vulnerability and exposure for compound flooding after TC Idai. By doing so, we consider all risk components (Simpson et al., 2021), particularly integrating exposure and vulnerability as they drive the experienced impacts”
10. The dataset referenced at https://doi.org/10.5281/zenodo.19328562 could not be accessed. Please check that all the data is available and code. You could add also add the repository in GitHub for the modelling chain, as it is open source so far I understand.
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- 1
The paper investigates compound flooding associated with Tropical Cyclone Idai in Mozambique, combining a qualitative research based on eight key informant interviews on which are constructed causal-loop diagrams. The quantitative scenarios compare observed land cover (in 2000 and 2020) and climate conditions (with and without anthropogenic climate change), while the qualitative component is used in parallel to identify socio-economic drivers such as charcoal-related deforestation, informal urban expansion, poverty, and limited government capacity, as drivers of the flood risk.
The paper currently conflates flood-hazard attribution with impact attribution. The quantitative model estimates changes in flood depth, extent, volume, and discharge under selected counterfactuals; it does not quantify changes in damages, displacement, affected population, livelihoods, or other societal impacts.
Separately, the qualitative analysis provides interesting (if unsurprising) interpretation, but it is not integrated at all into the quantitative estimates; the two parts are not integrated but are conducted in parallel, ostensibly to shed light on each other, but with little evidence of that.
Maybe most importantly, the paper compares the actual impact of the storm against three counterfactuals: (1) without climate change; (2) with 2000 LULC rather than 2020 LULC; (3) combining (1) and (2). Counterfactual (1) was already investigated in a previous paper by a some of the same authors; (2) is possibly interesting but has nothing to do with attribution or climate change; and the relevance of this is not very clear, as we don’t really know much about impacts (see comment below); and it is not entirely clear why (3) is an interesting question. Clearly, if the changes in LULC made the flooding more severe, they would have made the attributable flooding more severe as well. Why the question which one is more important is a worthwhile question to ask is not made clear at all. I think the reader deserves to know that. This is not, as far as I can tell, a policy-relevant question, so what is it useful for?
Major Comments 1. Reframe or substantiate the claim of impact attribution
The title, abstract, and discussion frame the paper as an impact-attribution study. Yet the quantitative results are flood-hazard results: maximum flood depth, flood volume, flood extent, and discharge responses under climate and LULC counterfactuals. These are important hazard metrics, but they are not socio-economic, cultural, or environmental impact metrics. The qualitative interviews identify mechanisms of vulnerability and exposure, but the manuscript does not propagate the modelled hazard changes through exposed population, exposed assets, displacement caused, livelihoods lost, or damage experienced.
This matters because the strongest conclusion in the manuscript is that LULC changes amplified the impacts of TC Idai. The evidence supports the more cautious claim that LULC changes plausibly affected vulnerability/exposure (though this is not investigated) and that LULC changes modified flood hazard locally. We still don’t know if impacts were amplified.
I would further recommend to use the terms hazard, exposure, vulnerability, risk, and impact consistently throughout the manuscript, following the IPCC or UNDRR definitions of these terms (both have glossaries where these are defined).
2. The LULC counterfactual requires stronger justification and sensitivity testing
The study uses a 2020 LULC map to represent factual conditions during TC Idai, which occurred in March 2019, and a 2000 LULC map as the counterfactual. This choice is driven by data availability, but it is a potential weakness of the paper. The 2020 map may include post-Idai land-cover changes, storm damage, reconstruction, or recovery signals; it is difficult to believe that such a big event did not lead to significant LULC changes. The 2000 map represents a historical landscape in an arbitrary point in time rather than a clearly defined counterfactual based on policy changes, demographic shifts, or any other change related to this exact timing.
The manuscript also attributes observed LULC changes to socio-economic drivers identified in interviews. That is plausible, but the causal link between the mapped 2000-2020 land-cover changes and the interview-derived drivers is not established (other than by the claims from the 8 KIIs). The higher resolution is used to justify the 2000-2020 choice, but this decision involves a significant trade-off. Is it possible to use remote sensing products to construct LULC maps that would be better timed (and many higher frequency), even if they will be less spatially detail
3. The hydrological interpretation of LULC effects needs more explanation
I wasn’t able to follow the hydrological modelling (I am not a hydrologist), but since this is based on a previous paper, and there is nothing new here, maybe this is not an issue.
4. The qualitative component needs more methodological transparency
The qualitative strand is central to the claimed contribution of the paper (since the quantitative part is very similar to the previous paper published in the same journal). The authors acknowledge the limited number of key informants and the lack of local community and government perspectives. That limitation is important because the paper makes claims about poverty, charcoal livelihoods, informal settlement dynamics, urban planning, and humanitarian access. The paper should make clearer which claims are based on direct interview evidence, which are supported by the literature, and which are interpretive syntheses by the authors.
I would probably also argue that using only 8 KII, neither of them, as far as I can tell, a resident of the affected area, and all of them with limited experience there (and possibly even limited experience in Mozambique) is a significant weakness, even if it is acknowledged. Why not enlarge the number and variety of KIIs?
The text of the paper should also mention whether the research received ethics approval (or did the authors decide one is not required for the KIIs interviews).