This study conducts a semi-systematic assessment of scientific literature published between 2015 and 2025 to examine the current state of knowledge on climate hazards in African cities. A broad literature search yielded 1832 records, which were conservatively screened to a final sample of 272 articles. The corpus was analysed using quantitative mapping and a qualitative hazard-based expert review. The analysis reveals a rapidly expanding but uneven knowledge base. Research is geographically concentrated, with Central Africa and small and fast-growing secondary cities underrepresented. Flooding and heat-related topics dominate the literature, whereas sea-level rise, windstorms and urban water stress receive comparatively limited attention. Consideration of multiple hazards remains limited and uneven, while explicit analysis of interactions between hazards is rare. Earth Observation is widely used, but its broader potential remains underexploited, while high-resolution urban climate modelling and observations remain limited. Most studies focus on historical or present-day conditions, and few combine future climate scenarios with projected urban growth. Overall, broader geographic and thematic coverage, improved high-resolution observations and urban-scale modelling, and stronger integration of future climate and urban development are needed to provide a more representative evidence base for adaptation and resilience planning in African cities.
Flood hazard maps and damage estimates guide floodplain management, mitigation planning, and engineering design, but they commonly rely on rainfall frequency analysis to define annual exceedance probability (AEP) storms, commonly referred to as design storms. Current engineering practice typically employs spatially uniform design storms derived from point-based gauge statistics; however, this approach suppresses the spatial organization and intensity gradients present in observed storms. Stochastic storm transposition (SST) offers an alternative by preserving the spatial structure of observed rainfall and stochastically repositioning full storm fields across a watershed. This study compares flood exposure and damage produced by Atlas 14 and SST rainfall representations in the Vermilion River Basin, a low-gradient inland–coastal watershed in south-central Louisiana. Although the TP40-adjusted Atlas 14 rainfall depth was larger than the mean basin-average SST accumulation across the 50 realizations (310 mm vs. 238.1 mm), the SSTmax composite produced substantially greater total structure damage than the baseline Atlas 14 scenario (USD 0.923 billion vs. USD 0.477 billion). Moreover, the comparison identified 11 518 buildings that were inundated in at least one SST realization but not under Atlas 14, representing more than USD 110 million in cumulative structural damage. The divergence between the two approaches is concentrated in mid-elevation urban neighborhoods, where spatial rainfall variability can cross local inundation thresholds that were not crossed under the spatially uniform storm. These results show that, in the VRB, the spatially uniform Atlas 14 design storm missed localized flood exposure that emerged when storm placement and rainfall spatial variability were represented.
Mapping urban flooding in rapidly urbanizing coastal megacities remains challenging due to complex terrain conditions, high population density, and the limited availability of reliable ground observations. This study presents a hydrologically enhanced machine-learning framework for automated urban flood inundation mapping by integrating multi-sensor satellite observations within a scalable decision-support system. A comprehensive predictor dataset was generated using Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 optical imagery, Shuttle Radar Topography Mission (SRTM) digital elevation data, and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall products through Google Earth Engine. To explicitly represent hydrologic controls on flood propagation that are often overlooked in purely data-driven approaches, two hydrologically informed predictors were incorporated: the Relative Elevation Model (REM), which characterizes terrain-controlled flood accumulation, and the Rainfall-Normalized Index (RNI), which represents rainfall-conditioned surface wetness. A flood inventory was generated by integrating SAR backscatter change detection, optical water indices, and topographic constraints, resulting in approximately 2.6 × 105 flooded pixels across the Mumbai Metropolitan Region during the 2019 monsoon season. A balanced stratified sampling strategy was employed to create representative training and testing datasets for supervised classification. Random Forest, optimized Extreme Gradient Boosting (XGBoost), and ensemble models were developed and evaluated using standard classification metrics. The optimized XGBoost model achieved the highest predictive performance, with an overall accuracy of 71.7 % and an area under the receiver operating characteristic curve (AUC) of 0.803, outperforming both the Random Forest and ensemble models. The DeLong test indicated statistically significant differences in AUC for the evaluated hydrologic-predictor ablation comparisons at the 95 % confidence level. Feature ablation analysis further demonstrated the importance of hydrologic-topographic predictors, with REM and RNI contributing an increase of approximately 5 %–6 % in AUC. The inclusion of hydrologic predictors improved model discrimination from approximately 0.66 using SAR-only predictors to approximately 0.80 using the complete predictor framework. The resulting flood maps exhibited spatial patterns that were hydrologically consistent with major drainage networks and low-lying areas across the study region. The proposed framework provides a reproducible, scalable, and hydrologically informed and physically interpretable predictors approach for urban flood inundation mapping and demonstrates considerable potential for operational flood monitoring, hazard assessment, and decision-support applications in data-limited tropical urban environments.
Mountain flash floods pose growing risks under climate and environmental change, yet cross-regional understanding of their triggering mechanisms, cascading impacts and governance remains fragmented. This review synthesises 1967 studies published during 2000–2025 using bibliometric and topic analyses with qualitative synthesis. By integrating these approaches, we reveal pronounced geographical and thematic imbalances, with research concentrated in Europe and Asia. Flash-flood initiation and impacts are strongly state-dependent, emerging from interactions among storm intensity, duration and spatial concentration, antecedent hydrological conditions, surface controls, and hillslope–channel connectivity. These interactions limit the transferability of fixed rainfall thresholds and support dynamic, multi-source early-warning approaches. The review further shows how hazards are transformed into disaster risk through exposure, vulnerability and capacity conditions, leading to realised economic, social and ecological impacts and cascading disasters. Evidence across non-structural, structural and Nature-Based measures indicates that effective risk reduction requires coordinated portfolios rather than isolated interventions. We develop an adaptive governance framework integrating prevention of new risk, reduction of existing risk and management of residual risk, while addressing exceedance uncertainty, warning-to-action effectiveness, and long-term costs and trade-offs. Overall, this synthesis provides a cross-regional basis for more effective mountain flash-flood risk reduction across mountain regions facing uneven evidence and response capacities worldwide.
Using New Zealand's Integrated Data Infrastructure (IDI), we evaluate the effects of the 2010–2011 Canterbury earthquakes on household income and expenditure. We link data from the Household Economic Survey (HES) to administrative address histories and meshblock-level earthquake intensity, and implement a propensity-score-matched difference-in-differences (PSM-DiD) design comparing exposed Canterbury households with matched North Island households. We find that total and regular household income increase by approximately 7000–7900 New Zealand dollars (NZD) in both low- and high-intensity areas. Total expenditure shows no clear average DiD effect, but its composition changes substantially. In high-intensity areas, the magnitude of recorded receipts-and-refunds entries, an HES category that includes insurance reimbursements and related inflows, more than doubles, while spending on general insurance, transportation, telecommunications, diary-recorded day-to-day purchases, fees and subscriptions also increases. Expenditure responses are broader among above-median-income households than among below-median-income households. Households later observed outside Canterbury have housing costs around NZD 25 000 higher and mortgage and loan payments around NZD 9600 lower than households that remain, while conditional income differences are statistically insignificant. These findings show that post-disaster income growth can coexist with heterogeneous recovery costs and substantial changes in household expenditure.
