The research combined a household survey of 385 farmers with seven focus-group discussions, 21 key-informant interviews and satellite-derived vegetation-cover measurements. The study sites were Chilmari in Kurigram, Patnitala in Naogaon, Godagari in Rajshahi, Kishoreganj in Nilphamari, Sundarganj in Gaibandha, Kazipur in Sirajganj and Kaliganj in Lalmonirhat. Districts and upazilas were purposively selected to represent contrasting hazard contexts rather than to create a uniform regional sample. Fifty-five eligible farmers were interviewed in each village, each focus group included eight to 10 participants, and key informants included upazila agriculture officers, assistant agriculture officers and union chairpersons with direct knowledge of local agricultural conditions.

The findings apply to the sampled communities and should not be read as population-representative estimates for every farmer in northern Bangladesh. Respondents were purposively recruited because researchers could not obtain a complete population list with residential addresses, and the design used an equal quota of 55 farmers at each site rather than proportional allocation across the seven unions. Survey questions asked farmers to compare current production with a retrospective baseline extending approximately from 2010 to the 2024 fieldwork. The historical and current response ranges were not identical, making the comparison descriptive rather than a harmonized yield time series. The investigators also identified possible recall bias, attribution bias and selection bias as constraints on the results.

Farmers most frequently named temperature change and rainfall variability as their leading perceived reasons for production change, followed by pest outbreaks, soil fertility or soil conditions, and soil erosion. In a separate set of influence ratings, 67.3% of respondents described pest outbreaks as a major influence, 58.2% said the same of temperature change, and 43.9% did so for rainfall variability. A binary logistic regression associated reported temperature change with an odds ratio of 4.547, rainfall variability with 5.381, high pest and disease losses with 1.749, and drought and heat waves with 3.956 for membership in a higher-production category. These are associations within the survey data, not measured causal effects. The model’s significant Hosmer–Lemeshow test indicated an imperfect fit, and its authors explicitly warned against interpreting the results as evidence that climate variability enhances crop production.

Local accounts clarified the distinction between production level and production resilience. Respondents commonly associated higher output with high-yielding varieties, agrochemical inputs, irrigation, mechanization and intensive crop management. Climate stresses, by contrast, were linked to uncertainty, planting delays, crop loss, pest pressure and the need for additional expenditure. In Chilmari, farmers described an uneven monsoon affecting rice and jute; Kaliganj participants reported drought and pest problems; Kazipur accounts emphasized riverbank erosion and insecure access to cultivable land; and Godagari farmers described substantial irrigation requirements under heat and drought conditions. Patnitala participants reported water scarcity, Kishoreganj accounts referred to cold stress, water constraints and pests, while Sundarganj farmers described recurrent storm and flood damage. These contrasts show why a single adaptation strategy could not represent the needs of all seven locations.

Almost all respondents, 99.2%, reported that production costs had increased compared with the past. The most frequently selected causes were higher prices for fertilizer, pesticide and labor, cited by 86.1%, excessive or inappropriate pesticide use, cited by 82.9%, drought and heat waves, cited by 72.7%, and heavy rainfall, cited by 42.3%. Farmers reported using asset sales, loans, savings and private borrowing in response to these pressures: 65.5%, 62.3%, 57.9% and 40.3%, respectively, reported each response. The survey did not directly measure whether these financial strategies maintained production or their subsequent effects on household welfare. Instead, the results document their use amid substantial financial pressure. The study treated some coping responses as potentially unsustainable because borrowing, savings depletion and asset sales may weaken resilience rather than provide evidence that adaptation has succeeded.

Technology adoption was limited in the sampled population. Only 32.2% of farmers reported adopting a new agricultural practice or technology intended to improve yields, while 67.8% reported none. Tractors or sprayers were the most common adopted measures, selected by 31.17% of the full sample, followed by harvesting machinery at 22.08%. Smaller shares selected crop rotation or changes in planting time, at 9.09%; natural pest controls, at 4.94%; planting machinery, at 4.42%; deep motor pumps, at 3.12%; and disaster-resistant seed, at 2.34%. Across the full sample, 8.6% rated adopted technologies as very effective, 20.3% as effective and 3.4% as somewhat effective. Focus groups and interviews also described high-yielding or climate-resilient varieties, deeper wells, electric pumps, improved irrigation, mulching, organic farming, crop rotation and drip irrigation. Participants identified finance, technical knowledge, market access and extension support as recurring constraints on wider use.

Crop choice reflected interactions among weather, profitability, agronomy and household economics. Overall, 42.6% of respondents reported changing crop varieties. The leading reason was the higher production potential of maize, potato, mustard and other newer varieties, cited by 17.1%, followed by high production costs at 10.1%, declining soil fertility at 6.0%, increased insect infestation at 3.4%, higher temperature combined with water scarcity at 2.3%, unexpected disasters at 2.1%, and shortages of quality seed or fertilizer at 1.6%. Paddy remained dominant at about 90% of reported crop patterns, while maize increased modestly. In Chilmari, Kaliganj, Kazipur and Kishoreganj, qualitative participants described cultivation cycles declining from as many as three crops per year to two or one because of water shortages. Elsewhere, farmers used irrigation to increase cropping frequency or diversify into maize, vegetables, oilseeds, pulses and orchard production, although higher input costs and pest problems remained concerns.

Satellite analysis supplied spatial context but did not establish climatic causes. Using one Landsat image from 2010 and one from 2020 for each region, researchers classified vegetation cover at 30-meter resolution. The classified vegetation-area share decreased by 16.49 percentage points in Kaliganj, 18.73 points in Sundarganj, 14.24 points in Patnitala, 8.92 points in Kishoreganj and 2.02 points in Chilmari. It increased by 10.01 points in Kazipur and 4.71 points in Godagari. These two-date differences may reflect crop phenology, flooding, erosion, irrigation, agricultural expansion, settlement growth or other land-use processes. Because the analysis relied on one image per reference year and included no independent field validation, the mapped changes cannot be assigned to climate change, deforestation or any other single cause. The researchers therefore presented the satellite observations as an additional line of spatial evidence rather than a stand-alone indicator of environmental decline.

Overall, the study identified adaptation as a negotiated response shaped by both hazard exposure and adaptive capacity. Farmers in flood- and erosion-prone locations emphasized flexible crop calendars, early harvest, recovery planting and avoidance of exposed land. Those facing drought and heat relied more on groundwater irrigation, shorter-duration or less water-intensive crops, moisture conservation and, in Godagari, orchard and commercial production. Pest- and cold-affected settings placed greater emphasis on crop timing, tolerant varieties, irrigation and pest management. The evidence supports locally differentiated priorities involving hazard-appropriate seed, water-saving practices, seasonal advisories, integrated pest management, recovery finance, low-interest credit, extension training and accessible insurance. These are planning priorities derived from participant evidence rather than experimentally tested policy effects. Future research would require probability or panel samples, consistent yield measurements, observed meteorological data, growing-season satellite composites and field-based validation of vegetation maps.

Subject of Research: Farmers’ reported crop-production changes and climate adaptation across seven hazard-prone sites in northern Bangladesh

Article Title: Exploring the adaptation strategies of the farmers in Northern Bangladesh under climate change impacts on agricultural crop yield dynamics

Article References: Shahen, M. A., Islam, M. F., Amin, M. A., Arefin, A., Barman, T., Tamanna, R., Khatun, M. S., Afrin, M. N., Nahar, S., Ali, M. I., Rahman, M., & Hossain, M. M. (2026). Exploring the adaptation strategies of the farmers in Northern Bangladesh under climate change impacts on agricultural crop yield dynamics. Environmental Challenges, 25, Article 101686. https://doi.org/10.1016/j.envc.2026.101686

Image Credits: AI Generated

DOI: 10.1016/j.envc.2026.101686

Keywords: Northern Bangladesh, climate adaptation, crop production, flood risk, drought stress, farmer livelihoods, irrigation, crop diversification, mixed methods, NDVI, Exploring, adaptation

The computational study examined flood behavior in an urban river system under breach conditions. Across scenarios of increasing flood magnitude and return period, the model estimated that breach peak discharge increased with mainstream flood magnitude. Higher-return-period floods also began earlier and lasted longer, creating a timing pattern the authors describe as “small floods delay, large floods advance.” These findings are model-based estimates for simulated urban flooding rather than direct measurements from a breached city, limiting the conclusions that can be drawn about individual real-world events.

To analyze the recession after the initial maximum, the researchers introduced an exponential decay model combined with linear regression to estimate recession parameters. The approach represented the modeled evolution of flood discharge following the peak. Smaller floods tended to recede in a nearly linear manner, while larger floods showed delayed recession and, in some scenarios, secondary peaks. The framework provides a quantitative means of distinguishing a straightforward falling limb from a recession interrupted by renewed flow contributions.

Beyond a threshold in the disparity between tributary inflow and mainstream flow, secondary-peak intensity increased in the simulations. The researchers attributed these later crests to lagged replenishment associated with return flows, storage within the channel and backwater effects. Together, those modeled processes can interrupt the expected decline of breach-flow discharge and generate a subsequent maximum. The study therefore identifies a post-peak replenishment period that should be included when assessing flood risk after urban levee failures, while describing the exponential decay model as an extended framework for analyzing breach-flow recession.

Subject of Research: Modeling flood recession and secondary peaks after urban levee breaches

Article Title: Mechanisms of Secondary Peaks and Flood Recession in Urban Levee-Breach Events under Tributary–Mainstream Interactions

Article References: Xiao, S., Hou, J., Ma, L., Xu, X., Li, D., Wang, T., Chen, G., Lv, J., & Wang, Z. (2026). Mechanisms of Secondary Peaks and Flood Recession in Urban Levee-Breach Events under Tributary–Mainstream Interactions. Water Resources Management, 40(14), Article 541. https://doi.org/10.1007/s11269-025-04365-6

Image Credits: AI Generated

DOI: 10.1007/s11269-025-04365-6

Keywords: urban flooding, levee breaches, secondary flood peaks, flood recession, tributary inflows, hydrologic modeling, hydrodynamic modeling, backwater effects, channel storage, flood risk, Mechanisms, Secondary

The literature grew from two qualifying papers in 2016 to 45 in 2025, although part of that increase may reflect the wider adoption of the umbrella term “nature-based solutions.” Older approaches to sustainable drainage, low-impact development, ecosystem-based adaptation and blue-green infrastructure were increasingly described using NbS language. Of the 185 studies, 147, or 79.5%, appeared from 2021 to 2025. This rapid growth coincided with broader interest in climate resilience and sustainable water management, but the review authors cautioned that the publication curve cannot by itself distinguish a genuine expansion of research from a change in terminology.

Unsupervised analysis divided the evidence into nine themes. The largest was urban green infrastructure for runoff mitigation, with 35 papers, followed by spatial planning and decision support with 28 and catchment and hydraulic modeling for flood-risk reduction with 25. Together, these three themes represented 47.6% of the corpus, demonstrating the field’s strong orientation toward the design, placement and hydraulic evaluation of urban interventions. Ecological and hydro-geomorphic processes accounted for 22 studies, while governance, stakeholder engagement and societal uptake comprised 19. Floodplains, wetlands and agricultural landscapes accounted for 11, and 34 studies remained in an “emerging approaches” group. Coastal flood risk and shoreline protection was the smallest theme, with four papers, while systems for urban water services and climate adaptation had seven.

Research was geographically uneven. The United Kingdom, China and the United States were the leading national contributors, followed by Italy and Australia. Europe represented 41.6% of the studies, Asia 29.7%, North America 10.8%, Oceania 5.4%, South America 4.9% and Africa 2.7%; some studies covered several continents or provided no location. The concentration partly reflects different policy environments. Much U.S. research concerns engineered green stormwater infrastructure and regulatory goals, while China’s Sponge City program has driven evaluation and planning studies. European research more often treats green infrastructure as a multifunctional system intended to provide flood mitigation, biodiversity, cooling, recreation and other ecosystem services. Africa, Latin America and parts of South and Southeast Asia, despite substantial flood exposure, were markedly underrepresented.

The review’s implementation analysis indicates that successful deployment depends on more than the performance of an individual measure. Recurrent enablers include strategic siting, hydrological connectivity, coordination with gray and blue infrastructure, institutional support and early stakeholder involvement. Barriers include high costs, limited urban space, competition for land, fragmented governance, uncertain data and difficulties integrating nature-based systems with existing drainage networks. Most research remains local: 69.7% of the included studies operated at the local scale, compared with 16.2% regionally and 14.1% nationally or internationally. The authors describe a gradual move from isolated site-scale projects toward connected blue-green networks, but noted that a systems approach has not yet become firmly established across the field.

Social distribution is another persistent weakness. Upstream landowners, farmers and rural communities may bear the costs of measures whose flood-protection benefits accrue downstream, while vulnerable populations can be disproportionately affected by measures such as managed retreat. Equity considerations appeared in only 30.8% of the studies, community engagement in 44.9% and public acceptance in 30.8%. Public support was generally greater when benefits were visible and communities participated in planning. Conversely, concerns about expense, maintenance and uncertain performance could reduce acceptance, and some coastal respondents viewed nature-based coastal measures as less reliable than conventional infrastructure. The review therefore treats equity, trust and participation as core design conditions rather than optional additions to technically effective projects.

Long-term management emerged as the clearest gap between planning and operational evidence. Modeling studies accounted for 40% of the literature, and only two papers followed implemented interventions for more than five years: a 70-month assessment of blue-green roofs in Hamburg and a ten-year assessment of riverbank bioengineering in Brazil. Monetary implementation costs were reported in only 23.2% of studies, and 50.3% did not report maintenance requirements at all. Where maintenance was discussed, common needs included sediment removal, outlet cleaning, control of clogging, vegetation management and component replacement. These are not incidental details: the hydrological and ecological performance of nature-based systems can change as sediments accumulate, plants develop and watershed conditions shift.

The evidence consistently indicates benefits beyond flood reduction. Reported ecosystem services include runoff retention, infiltration, groundwater recharge, water storage, pollutant reduction, sediment retention, nutrient control, habitat provision and, in floodplains and wetlands, carbon storage. Social co-benefits included recreation, aesthetics, improved public space, walkability, education, community cohesion, protection of livelihoods and greater perceived well-being. Urban cooling and lower building-cooling demand were also reported in some green-infrastructure studies, while gravity-based systems could reduce pumping requirements. These outcomes are usually documented qualitatively or indirectly, however, and the review found little systematic quantification of energy and climate co-benefits. The specific value of a nature-based solution also varies with the flood process and landscape: coastal studies emphasize wave attenuation, erosion control and sediment regulation, whereas agricultural floodplain studies focus more on storage, attenuation and water quality.

Technical gaps converge across nearly every theme. Studies frequently rely on scenario simulations, simplified hydrological assumptions, short observation periods, coarse rainfall and topographic data, and incomplete calibration against observed floods. Governance research uses a different but equally limiting design profile, relying on case studies, expert opinion and perception surveys with small samples and little longitudinal follow-up. Standardized performance metrics are scarce across both technical and social domains, making results difficult to compare across climates, scales and intervention types. The authors identify long-term field validation under extreme and changing conditions, links between local interventions and basin processes, uncertainty-aware decision support, and better treatment of climate, socioeconomic and governance variability as the main research priorities.

AI-assisted methods did not replace the systematic review process, and they carry limitations of their own. The researchers used the OpenAI GPT-5.4 model through an API for screening, extraction, cluster labeling and qualitative synthesis. Prompts instructed the model to report only information explicitly stated in each paper and to return null values for missing details rather than infer them. Team members reviewed full texts, checked every flagged field and all medium-confidence records, and refined prompts after identifying unsupported interpretations or inconsistent categories. In the final extraction dataset, 171 records, or 92.4%, received high confidence, 14 received medium confidence and none received low confidence. Even so, the model extracted clearly reported technical information more consistently than nuanced themes such as equity and institutional conditions, while keyword-driven clustering may have assigned interdisciplinary work to engineering categories or the unclassified group. Combined with the review’s reliance on English-language Web of Science and Scopus records, these limitations mean the map is best understood as a reproducible snapshot of the accessible literature, rather than a complete picture of nature-based flood management worldwide.

Subject of Research: Systematic review of nature-based solutions for flood management and AI-assisted evidence synthesis

Article Title: Water management in flood-prone areas using nature-based solutions: an AI-assisted systematic review and thematic analysis

Article References: Arabi, S., Yaméogo, D. D. S., Rahman, M., Bereded, B., Shibeshi, T., Kwak, N., Eason, M., & Smith, V. (2026). Water management in flood-prone areas using nature-based solutions: an AI-assisted systematic review and thematic analysis. Frontiers in Sustainability, 7, 1966241. https://doi.org/10.3389/frsus.2026.1966241

Image Credits: AI Generated

DOI: 10.3389/frsus.2026.1966241

Keywords: nature-based solutions, flood management, artificial intelligence, systematic review, thematic analysis, green infrastructure, flood risk, evidence gaps, environmental research, Water, management, flood-prone

The researchers divided the approximately 2,093 km² Pampa basin into 31 sub-watersheds and evaluated 15 morphometric, hydrological, and hazard-related criteria. These included drainage-network properties, basin shape, relief, slope, ruggedness, overland-flow length, rainfall variability, and other indicators intended to describe the terrain’s response to water and the occurrence of floods, droughts, and landslides. A 30 m Shuttle Radar Topography Mission digital elevation model was processed in ArcGIS through sink filling, flow-direction and flow-accumulation analysis, stream extraction, stream ordering, and watershed delineation. Daily rainfall records from 2015–2025 were also interpolated in GIS to characterize long-term annual and seasonal variability across the basin.

Relative importance was assigned with AHP, which uses pairwise comparisons to derive criterion weights. Five specialists with more than 10 years of experience in watershed assessment and natural-hazard analysis independently compared the 15 parameters on Saaty’s nine-point scale. Their matrices were combined by geometric averaging, and the resulting consensus matrix was normalized to obtain weights. The consistency ratio was 0.08, below the study’s threshold of 0.10 for acceptable consistency. Each criterion was normalized to a dimensionless scale, then combined through a weighted linear equation to generate a priority score for every sub-watershed. Jenks natural-breaks classification was used to divide the scores into relative priority classes, but the numerical boundaries are specific to this basin and cannot be transferred unchanged to other watersheds.

The terrain analysis described a highly dissected basin with 560 identified stream segments and a Strahler stream-order system extending to sixth order. The drainage network included 405 first-order streams, 103 second-order streams, 37 third-order streams, 13 fourth-order streams, two fifth-order streams, and one sixth-order stream. Mean stream length ranged from 1.5 to 3.8 km, while the basin-wide bifurcation ratio averaged 2.38. Drainage density was below 5.0 km/km² and drainage texture was 8.10, placing the network in an intermediate texture class. Most Rho coefficient values were below 0.2, and more than 60 percent of the basin fell below 0.15, indicating relatively limited storage and a tendency toward rapid overland flow according to the study’s interpretation of the index.

Basin geometry also varied substantially. Pampa’s circularity ratio was 0.36, consistent with an elongated form, while its elongation ratio was 0.65. The highest terrain occurred in sub-watershed W31, and the lowest points appeared in W8 and W9. Sub-watersheds W5 and W18 had comparatively high ruggedness numbers, whereas W31 had a lower value. At the sub-basin scale, infiltration-number values were lowest in W31 and highest in W20, W21, and W24. These contrasts helped differentiate terrain controls on runoff, erosion, slope instability, and water retention, although the researchers cautioned that the AHP model represents these relationships through independent weighted criteria rather than fully coupled physical processes.

The uncertainty experiment began from the baseline AHP weight vector. In each of 10,000 iterations, all 15 weights were randomly perturbed by as much as plus or minus 10 percent using a uniform distribution, then renormalized to sum to one. Priority scores and rankings were recalculated for all 31 sub-watersheds after every perturbation. W10, W14, W7, W4, and W17 repeatedly occupied the highest ranks and were therefore classified as robust. W1, W2, and W22 showed moderate sensitivity. The simulation was designed specifically to examine variability in expert weights; its uniform perturbation range was not intended as a complete probability distribution of uncertainty, and it did not quantify errors in the elevation model, stream-extraction choices, spatial resolution, threshold selection, or model structure.

Household-level records were used as an independent check on the modeled priorities. District Disaster Management Authority data covered flood damage in 2018 and 2019, landslide damage in 2018, and drought impacts reported for the study area. About 75.0 percent of households affected by floods were located in sub-watersheds classified as moderate to very high priority. All 76 households affected by landslides were in moderate- or high-priority areas, while 67.9 percent of the 321 drought-affected households were in moderate- to very-high-priority classes. These results show concentration of reported impacts in relatively high-priority areas, but they do not amount to direct proof of physical susceptibility because household damage also reflects population density, settlement patterns, housing quality, land use, infrastructure, reporting practices, and community resilience.

The quantitative correlation also requires caution. Spearman’s rank correlation between the AHP priority scores and the number of flood-affected households was weak and statistically nonsignificant, with ρ = −0.011 and p = 0.952. This means the composite ranking did not closely track flood damage counts in individual sub-watersheds. The authors attributed the mismatch partly to the framework’s emphasis on intrinsic physical vulnerability, whereas damage records reflect hazard intensity together with exposure, socioeconomic conditions, infrastructure resilience, and post-disaster reporting. The household records were therefore treated as evidence of consistency and practical utility rather than as a direct proxy for susceptibility or a causal validation of the model.

The study’s principal contribution is therefore methodological as much as geographic. Integrating morphometric analysis with AHP creates a transparent way to combine terrain, drainage, rainfall, and hazard indicators when detailed process-model inputs are limited, while Monte Carlo analysis reveals whether the resulting priorities depend heavily on a particular set of expert weights. Even so, the framework remains a first-order regional screening tool. It does not simulate event-scale rainfall intensity, antecedent soil moisture, groundwater behavior, changing land use, cascading hazards, or the full mechanics of floods and slope failures. Transfer to other basins would require recalibrating the natural-breaks classes and examining alternative elevation products, raster resolutions, watershed boundaries, and preprocessing choices. The authors also identify population and infrastructure layers, dynamic land-cover data, climate and land-use scenarios, and process-based flood, drought, and slope-stability models as necessary additions if the approach is to evolve from relative susceptibility mapping into comprehensive risk assessment. The present results provide a reproducible screening tool for comparing 31 tropical monsoon watersheds while explicitly revealing which priorities remain stable and which depend on judgment-based weighting.

Subject of Research: Uncertainty-tested prioritization of 31 Kerala sub-watersheds for flood, drought, and landslide susceptibility

Article Title: Integrating morphometric analysis and AHP–Monte Carlo framework for multi-hazard risk reduction

Article References: Sekar, R., Natarajan, S., & Joseph, N. (2026). Integrating morphometric analysis and AHP–Monte Carlo framework for multi-hazard risk reduction. Natural Hazards, 122(22), Article 689. https://doi.org/10.1007/s11069-026-08468-0

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08468-0

Keywords: multi-hazard susceptibility, watershed prioritization, Monte Carlo simulation, Analytical Hierarchy Process, morphometric analysis, Pampa River basin, Kerala, flood risk, drought risk, landslide risk, GIS, uncertainty analysis

The result comes from a modelling analysis rather than a direct observation of future climate conditions. The authors examined simulated extreme southward swings and investigated the atmospheric and oceanic processes that make such events more likely as the climate warms. The projection is conditional on a high-emissions scenario and the behaviour captured by the model. The available study summary does not quantify projected changes under lower-emissions pathways, so the reported figures should be understood as scenario-specific estimates.

Two principal mechanisms in the analysis link greenhouse warming to more frequent southward displacement of the convergence zone. First, the African continent is projected to warm faster than the Indian Ocean. This strengthens the land–ocean pressure gradient and supports a mean southward geostrophic flow that is conducive to a southward swing. The study’s model results indicate that this faster continental warming intensifies northerly winds, helping establish circulation conditions that favor the unusual movement.

Second, the poleward migration of the Southern Ocean westerlies produces a pattern of faster subtropical Indian Ocean warming to the south. The authors describe this sea-surface-temperature pattern as positive Indian Ocean Dipole-like. It enhances the transport of moisture from the Atlantic to Africa, increasing the moisture available for rainfall as the convergence zone shifts southward. The two mechanisms act on different parts of the climate system, but both make a pronounced southward swing more favorable in the model.

The simulations also show extreme southward swings occurring more often alongside increased rainfall extremes in southern Africa. The estimated average rainfall intensity during these events is 20% higher in the twenty-first-century high-emissions scenario, while the interval between extreme swings falls from 22 to 10 years. The authors therefore suggest that more frequent southward migration of the South Indian convergence zone might signal increasing flooding risk in parts of southern Africa. That conclusion is a model-based implication, not a direct forecast of flood frequency or damage, and the supplied evidence does not provide event-by-event uncertainty estimates or comparisons across emissions scenarios.

Subject of Research: Climate-model projections of more frequent southward South Indian convergence zone swings and heavier rainfall in southern Africa under high emissions

Article Title: Increased southward swings of the South Indian convergence zone under greenhouse warming

Article References: Wang, H., Geng, T., Cai, W., & Lin, X. (2026). Increased southward swings of the South Indian convergence zone under greenhouse warming. Nature Climate Change. https://doi.org/10.1038/s41558-026-02767-x

Image Credits: AI Generated

DOI: 10.1038/s41558-026-02767-x

Keywords: climate modelling, South Indian convergence zone, southward swings, greenhouse warming, high-emissions scenario, southern Africa, heavy rainfall, flood risk, Indian Ocean, African climate, Increased, southward

The research, led by Li Sun and colleagues at Beijing University of Civil Engineering and Architecture, integrates three complementary methodological strands: hydrological simulation, machine learning, and spatial statistics. The team employed the SCS-CN model, a widely used rainfall-runoff framework developed by the United States Soil Conservation Service, to simulate how precipitation is converted into surface runoff across the basin’s varied landscapes. The model’s core logic hinges on a curve number, a parameter that captures how readily land produces runoff depending on soil type, land cover, and antecedent moisture. Paved, compacted, or otherwise impervious surfaces generate far more runoff than forested slopes, and the SCS-CN approach translates those differences into spatially explicit estimates of flood hazard for every corner of the basin.

On top of that hydrological foundation, the researchers deployed XGBoost, a gradient-boosted decision tree algorithm prized for its ability to capture complex, nonlinear relationships among many environmental variables. Flood risk is rarely the product of a single factor in isolation; instead, rainfall depth, elevation, slope, drainage density, vegetation cover, and the extent of impervious surfaces interact in ways that traditional linear models struggle to represent. XGBoost excels at learning these multi-factor couplings from data, but its predictions can be opaque. To open the black box, the team applied SHAP, or SHapley Additive exPlanations, a technique borrowed from cooperative game theory that quantifies each variable’s contribution to every individual prediction. The result is a risk model that is not only accurate but interpretable, revealing exactly how and where each driver pushes risk up or down for the historic buildings in question.

The third pillar of the framework is spatial autocorrelation analysis, a family of statistical methods that tests whether high or low risk values cluster geographically more than random chance would predict. This matters because heritage protection decisions are inherently spatial: resources for flood defenses, drainage improvements, and emergency planning must be allocated to places, and knowing that risk concentrates in identifiable hotspots is far more actionable than a list of individual buildings. By combining hazard, the probability and severity of flooding, with vulnerability, the susceptibility of buildings and their settings to damage, the researchers produced an integrated risk assessment for every one of the 5,482 historic structures in the dataset.

The findings paint a strikingly patterned picture of heritage flood risk across the Minjiang River Basin. Flood hazard, the team found, decreases from downstream to upstream, forming belt-like aggregations that trace the river network itself. This geometry makes intuitive sense once the underlying hydrology is considered: downstream reaches accumulate runoff from the entire upstream catchment, water levels rise higher, and floodplains widen, so the hazard envelope hugs the main channels most heavily in the lower basin. Historic buildings sited along these lowland waterways, often deliberately, since rivers were the arteries of trade and settlement, therefore sit squarely within the zones of greatest hydraulic exposure.

Vulnerability, however, tells the opposite story. The study reveals a spatial mismatch: vulnerability is higher upstream and lower downstream. That inversion matters because risk is not simply hazard; it is the product of how likely a flood is and how much damage it can do. A modest flood striking fragile, poorly protected upstream structures may cause more harm than a larger flood reaching resilient downstream buildings. The mismatch means that hazard maps alone, however sophisticated, cannot guide heritage protection. Planners who prioritized only downstream floodplains would leave the vulnerable upstream clusters, where steep terrain, constrained evacuation routes, and building characteristics may amplify damage, comparatively unguarded.

When hazard and vulnerability are integrated, the resulting risk map follows a southeast-high to northwest-low gradient across the basin, with strong clustering in the estuarine zone and the midstream valleys. These hotspots are precisely where high hydraulic exposure coincides with concentrations of vulnerable historic fabric, making them the natural focal points for intervention. The spatial autocorrelation analysis confirms that these high-risk zones are statistically significant clusters rather than scattered accidents of individual building locations, which strengthens the case for basin-scale, coordinated management rather than building-by-building measures.

Perhaps the most technically interesting contribution lies in what the SHAP analysis uncovered about the drivers of risk. Key factors including impervious surfaces, vegetation cover, and topography exhibit significant nonlinear threshold effects, meaning their influence on flood risk does not increase smoothly but jumps once certain critical values are crossed. Below a threshold of impervious cover, additional paving may contribute only modestly to runoff generation; beyond it, the landscape’s hydrological response can shift abruptly, sharply elevating flood hazard downstream. Vegetation cover operates in the opposite direction, buffering risk, but its protective effect also shows threshold behavior, so incremental loss of canopy in already degraded areas may trigger disproportionate increases in exposure. These nonlinearities are exactly the kind of relationships that linear risk indices miss and that interpretable machine learning is uniquely positioned to reveal.

The study also emphasizes multi-factor coupling, the observation that risk drivers interact rather than act independently. Topography shapes where water collects, but the severity of inundation in any given valley depends on how much of the upstream surface is sealed by development and how much remains vegetated. A steep, forested headwater may shed water quickly yet harmlessly, while the same slope cleared and paved becomes a runoff engine. Understanding these couplings is essential for anticipating how future land-use change and urbanization, layered on top of intensifying rainfall, could redraw the basin’s heritage risk map in ways that static assessments would never foresee.

Beyond its specific findings for the Minjiang, the framework carries broader implications for heritage science and disaster resilience worldwide. River basins across monsoon Asia, and indeed across the globe, host historic buildings whose survival increasingly depends on anticipating climate-driven flood extremes. The study’s combination of physically grounded hydrological simulation, accurate yet interpretable machine learning, and rigorous spatial statistics offers a transferable template: it delivers basin-scale coverage, respects the nonlinear reality of flood mechanisms, and produces outputs that heritage managers can act on directly, from prioritizing estuarine and midstream clusters for protective works to targeting upstream vulnerability reduction. For the thousands of historic buildings lining the Minjiang’s waters, and for countless others in similar basins, the message is clear: effective protection begins with knowing precisely where risk lives, and why.

Subject of Research: Flood risk assessment of historic buildings in the Minjiang River Basin using hydrological simulation, machine learning and spatial analysis

Article Title: Spatial identification and assessment of flood risk for historic buildings in the Minjiang River Basin

Article References: Sun, L., Wang, S., Hu, W., Yun, X., Deng, Q., Zeng, S., Wang, M., & Yang, Z. (2026). Spatial identification and assessment of flood risk for historic buildings in the Minjiang River Basin. npj Heritage Science. https://doi.org/10.1038/s40494-026-03047-9

Image Credits: AI Generated

DOI: 10.1038/s40494-026-03047-9

Keywords: flood risk, historic buildings, heritage science, Minjiang River Basin, SCS-CN model, XGBoost, SHAP, spatial autocorrelation, vulnerability, machine learning, cultural heritage, disaster resilience

The team’s central innovation is a unified spatial framework that links three separate analyses into a single decision-making pipeline. First, they mapped susceptibility to floods, landslides, and urban heat using a Multi-Criteria Evaluation (MCE) model built on the Analytic Hierarchy Process (AHP), a structured method for weighing competing factors such as slope, drainage density, land cover, population density, vegetation indices, and Land Surface Temperature (LST). Second, they overlaid a suitability analysis identifying which parts of the city are physically and socially appropriate for Nature-based Solutions (NbS) — interventions like riparian buffers, slope stabilization plantings, urban forests, and green corridors. Third, they grounded both layers in the lived experience of residents through a socioeconomic survey of 305 households, supplemented by focus group discussions and key informant interviews. The result is not just a hazard map but a blueprint for where green infrastructure can realistically be deployed.

The numbers are striking. According to the study, 34.4 percent of Dessie’s land area falls into the high-susceptibility category for landslides, a figure that reflects the city’s steep topography and rapid, often unplanned, expansion onto fragile slopes. Meanwhile, 57.6 percent of the city is moderately susceptible to flooding, driven by intense seasonal rainfall, degraded upstream catchments, and impervious surfaces that prevent water from infiltrating the soil. Urban heat, the third hazard, is widespread across the city because extensive built-up surfaces absorb and re-radiate solar energy while limited vegetation cover reduces the cooling that trees and green spaces normally provide. Together, these three hazards create a compound risk profile that traditional single-hazard planning approaches systematically underestimate.

What makes this research particularly valuable is its insistence that physical susceptibility alone is not enough to guide action. The authors argue that many urban planning paradigms in the region evaluate environmental hazards in isolation, producing fragmented responses that miss the compound nature of climate risk. By layering hazard susceptibility, NbS suitability, and community perception into one framework, the study bridges a persistent gap between what the landscape can physically support and what residents are actually willing and able to maintain. This integrated approach is especially relevant for rapidly urbanizing, data-scarce cities across sub-Saharan Africa, where comprehensive hazard modeling is often unavailable but the need for evidence-based adaptation is urgent.

The NbS suitability analysis reveals that 42.4 percent of Dessie is highly or very highly suitable for green infrastructure interventions, while 24.2 percent has low suitability due to biophysical constraints such as steep rocky terrain or competing land uses like dense informal settlement. The strategic hotspots for intervention are concentrated in upstream and mid-slope zones, where riparian buffers along drainage channels can slow runoff, slope stabilization plantings can anchor unstable soils, and urban forests and green corridors can break up heat islands while providing recreational and ecological benefits. These are not abstract recommendations: the authors argue that municipal authorities could use the maps to legally enforce blue-green zoning along highly susceptible drainage channels, prioritize structural watershed interventions in upstream catchment zones, and direct resources toward underserved, climate-exposed informal settlements that face the highest combined exposure.

Community voices form the third pillar of the study, and they reveal a nuanced picture of local readiness. The survey of 305 households, along with focus group discussions and key informant interviews, found strong awareness among residents of the environmental, socioeconomic, and health benefits that Nature-based Solutions can deliver — from reduced flood damage to cooler streets and improved wellbeing. Yet awareness does not translate automatically into implementation. Residents and local stakeholders identified several persistent barriers: limited financial resources, high maintenance costs for green infrastructure, weak institutional coordination between municipal departments, and land tenure insecurity that discourages long-term investment in trees and vegetation on land people do not formally own. These findings underscore that successful NbS deployment is as much a governance and equity challenge as it is a technical one.

The study’s methodological rigor deserves attention for anyone interested in how spatial analysis can inform climate adaptation. The AHP-based MCE approach allows researchers to assign explicit weights to different contributing factors — for example, how strongly slope gradient versus drainage density influences landslide susceptibility — and then combine them into composite indices. The inclusion of socio-environmental variables such as population density and vegetation indices alongside purely physical parameters reflects a growing recognition that hazard exposure is fundamentally a function of where people live, not just where the land is unstable. Land Surface Temperature, derived from satellite thermal imagery, provides a direct measure of urban heat rather than relying solely on air temperature stations, which are sparse in many African cities.

For Dessie specifically, the implications are concrete. The city sits in the Amhara region at the edge of the Ethiopian Highlands, where steep escarpments funnel seasonal rains into narrow valleys and where decades of deforestation and agricultural expansion have left upstream catchments degraded. Rapid population growth has pushed informal settlement onto slopes that were once considered too risky to build on, placing low-income households directly in the path of landslides and floodwaters. The study’s maps offer municipal planners a way to prioritize interventions where they will deliver the greatest risk reduction per unit of investment, a critical consideration in a resource-constrained context where every birr spent on infrastructure must be justified.

The research also speaks to a broader global conversation about how cities in the Global South can adapt to climate change without importing expensive, engineering-heavy solutions that may not be maintainable locally. Nature-based Solutions — working with vegetation, soil, and natural hydrology rather than against them — offer a cost-effective alternative, but only if they are sited intelligently and supported by the communities who live alongside them. By demonstrating that hazard susceptibility, NbS suitability, and community perception can be integrated into a single GIS framework, the Dessie study provides a template that other data-scarce, rapidly urbanizing cities could adapt to their own contexts.

The authors acknowledge that their work is a snapshot, not a forecast. Climate change is expected to intensify rainfall variability and heat extremes in the Horn of Africa, potentially shifting the boundaries of susceptibility zones over time. Nevertheless, the framework they have built is designed to be updated as new data becomes available, whether from improved satellite sensors, expanded ground monitoring, or revised land-use plans. For now, the study stands as a practical demonstration that even in cities where comprehensive environmental data is limited, careful spatial analysis combined with genuine community engagement can produce actionable guidance for building a more climate-resilient urban future.

Subject of Research: GIS-based multi-hazard susceptibility and nature-based solution suitability mapping for urban climate resilience in Dessie City, Ethiopia

Article Title: GIS-based multi-hazard susceptibility and nature-based solution suitability analysis for urban climate resilience in Dessie City, Ethiopia

Article References: Kebede, H. A., Ali, A. M., Eshetu, A. A., Moges, G., Desta, M. A., & Mekonen, A. A. (2026). GIS-based multi-hazard susceptibility and nature-based solution suitability analysis for urban climate resilience in Dessie City, Ethiopia. Discover Sustainability. https://doi.org/10.1007/s43621-026-04777-3

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04777-3

Keywords: GIS, multi-hazard susceptibility, nature-based solutions, urban climate resilience, Dessie City, Ethiopia, flood risk, landslide susceptibility, urban heat, Analytic Hierarchy Process, spatial planning, community perception

The Neretva River rises southeast of the Zelengora Mountain in Bosnia and Herzegovina at an elevation of 1,095 meters above sea level and flows approximately 220 kilometers to the Adriatic, of which only the final 22 kilometers lie within Croatia. Its topographic catchment spans roughly 10,975 square kilometers, mostly across the Dinaric Mountain massif, with only 692 square kilometers in Croatian territory. The delta itself developed over an alluvial area of about 190 square kilometers, formed during the last ice age when rising sea levels flooded karst areas along the river. Gravel and sand carried from upstream deposited in the valley, gradually raising the ground level. The delta existed in a largely natural state until the end of the 19th century, when the first regulatory works began, initially focused on maintaining the waterway from the sea to Metković for commercial transport. During the 20th century, numerous embankments, dams, and other hydrotechnical facilities were constructed and wetland reclamation transformed much of the former wetland into agricultural land, significantly altering the natural character of the area.

A defining feature of the Neretva system is its hydropower infrastructure. In the second half of the 20th century, a series of hydroelectric power plants with reservoirs were built in the upstream basin, including Jablanica in 1954 with a dam height of 85 meters and a reservoir volume of 318 million cubic meters, Rama in 1969, Grabovica and Salakovac in 1981, and Mostar in 1985. At the beginning of the 21st century, additional plants such as Peć Mlini on the Tihaljina tributary and Mostarsko Blato were commissioned, while the construction of the Ulog plant has begun and Konjic is planned. The Trebišnjica River basin, interconnected with the Neretva through deep and underexplored underground karst, carries its own substantial hydropower developments, including the large Bileća reservoir with a volume of 1,277 million cubic meters. These structures exert a dominant influence on the frequency, extent, and duration of floods in the downstream delta, and they also reduce sediment delivery to the valley, deepening the riverbed and worsening problems of water and soil salinity.

The hydrological regime of the basin reflects a transition between moderate continental and maritime influences. Average annual precipitation ranges from 1,000 millimeters on the coast to 1,900 millimeters in the mountainous parts of the catchment, with maxima exceeding 2,900 millimeters in elevated areas. The rainiest quarter of the year is November through January, which is also the period when high waters in watercourses are most frequent; the highest recorded monthly precipitation in this quarter at Metković was 400 millimeters in November 2021. Most precipitation in the Dinaric karst quickly sinks underground, accumulating in the subterranean system before reemerging through numerous springs along the edges of the valley, feeding the Neretva, Mala Neretva, and Norin watercourses. This interaction of surface and underground water produces stretched runoff hydrographs and means that floods in the delta are caused primarily by inflow of large waters from the upstream basin, with smaller contributions from karst springs and the sea, and the least impact from precipitation falling directly on the delta area.

Historical measurements at Metković, the furthest upstream hydrological station in Croatia, located approximately 21 kilometers from the river mouth, illustrate how the hydropower construction changed the flood regime. The highest water levels, exceeding 430 centimeters, were recorded in the 1950s before the construction of the Jablanica and Rama plants. After 1961, the highest recorded water level was 414 centimeters in December 2010, an event that flooded a large part of the valley. Because tidal influence prevents unambiguous determination of flows at Croatian stations, the study relied on statistical analysis of maximum annual water levels. Five theoretical distribution functions were tested, and the log-normal function was selected for Metković and Prud, while log-Pearson was chosen for the sea boundary. Calculated high waters for the 1,000-year return period reached 548 centimeters at Metković, 163 centimeters at the sea station, and 182 centimeters at Prud on the Norin River.

For the hydraulic simulation, the researchers built one-dimensional and coupled one-dimensional/two-dimensional hydrodynamic models using the MIKE 11 and MIKE FLOOD program packages from DHI, covering the Neretva River and its banks from the Croatian state border to the mouth. Calibration and verification were carried out separately for four areas due to the complexity of the system, using registered water levels from boundary stations and treating central stations as control points. Manning roughness coefficients ranged from 0.033 for the riverbed to 0.050 for inundation areas. The difference between modelled and measured water levels was mostly within an interval of plus or minus 5 centimeters, with larger deviations occurring only rarely during rapid water level changes. The authors attribute residual inaccuracies to insufficiently precise terrain geometry and to marginal karst springs whose variable inflows were not defined as boundary conditions but were indirectly incorporated through calibration against measured levels.

The simulations classified flood extent into three statistical probability levels. A high-probability flood, corresponding to a 25-year return period, would cover 51 square kilometers; a medium-probability 100-year event would inundate 62 square kilometers; and a low-probability 1000-year flood would spread across 110 square kilometers. The most endangered zone is the Vid–Norin area, where the city of Metković has expanded significantly and many residential, economic, and infrastructure facilities are at risk. The mouth of the Norin is a particularly sensitive location, allowing Neretva flood waters to enter that area. The Kuti area, a swampy zone intersected by numerous canals and influenced by karst springs fed from the underground hinterland, also has a high probability of flooding, though the risk there is moderated by low population density. The Opuzen-Ušće area, protected by numerous embankments following the course of the Neretva and Mala Neretva, carries the lowest risk.

Damage calculations considered seven categories, including populated areas, economic areas, agricultural areas, permanent plantations, green areas, water management facilities, and potential human victims. For the 1,000-year flood, total damage amounted to 250 million Euros, while a 100-year flood would cause 93 million Euros in damage. Populated areas would account for more than 90 percent of the total, with the greatest territorial losses expected in the wider area of Metković on both banks. The estimated delta damage represents approximately 1 percent of the total potential flood damage for the entire Republic of Croatia and about 6 percent of the damage for the Adriatic basin area. Flood risk, defined as the product of probability and adverse consequences, was expressed as an average annual potential number of victims of 0.67 per year and an average annual damage of 11.63 million Euros under conditions of 2015, before the construction of the Jerkovac embankment in Metković.

Climate change simulations modified both boundary conditions, assuming sea level rise at the downstream edge and increased inflow at Metković. The greatest predicted mean sea level increase was 87 centimeters, or 61 percent, for the end of the 21st century at the 1,000-year return period, accompanied by a 23 percent increase in discharge. Under these conditions, the average annual number of potential victims would rise to 0.88 and total potential average damage to 16.5 million Euros, increases of 31 and 41 percent respectively compared to the 2015 baseline. Based on a multi-criteria analysis, the researchers proposed optimal protective measures, including construction of an embankment on the right bank near Metković, a concrete defensive wall with a mobile barrier in the town, embankments on Mislina and near Krvavac, rehabilitation of riverbanks in critical sections, and a self-regulating gate at the junction of the Norin and Neretva rivers. Implementation would reduce the average annual potential number of victims by 70 percent, to 0.2 per year, and average annual damage by 83 percent, to approximately 2 million Euros, with a benefit-cost ratio of 18.04.

The study acknowledges limitations, including reliance on hydrological series ending in 2013 and the use of digital terrain models in certain areas instead of geodetic measurements, which introduces topographic inaccuracy. Extending the input series and defining hydrographs for marginal karst springs are identified as priorities for future work. Nevertheless, the researchers argue that the methodology is universal and can be applied to similar estuary and delta systems worldwide. Beyond flood defense, the quantified hydrological parameters such as water depth, flow rate, flood duration, and salinity can support ecological studies, which is particularly important given recommendations to preserve natural wetlands in the Mediterranean region. Because the Neretva basin spans two countries with separate protocols and professional services, transboundary cooperation in water management remains essential for reducing flood risks and improving conditions across the entire delta.

Subject of Research: Fluvial flood risk assessment and damage modelling in the Neretva River Delta, Croatia

Article Title: Delta flood risk analysis: case study from the Neretva River (Croatia)

Article References: Ljubenkov, I., Kvesić, D., & Erceg, J. (2024). Delta flood risk analysis: case study from the Neretva River (Croatia). Estuarine Management and Technologies, 1, 69-93. https://doi.org/10.3897/emt.1.137829

Image Credits: AI Generated

DOI: 10.3897/emt.1.137829

Keywords: Neretva River Delta, flood risk, hydrodynamic modelling, MIKE FLOOD, return period, climate change, sea level rise, Mediterranean deltas, karst hydrology, flood protection, damage assessment, Croatia

The study, led by Muzhda Qasim Qader of Hawler Medical University in Erbil together with colleagues from institutions across Iraq, Nigeria, and Austria, arrives at a moment when the Eastern Mediterranean and Middle East are recognized as climate change hotspots. Regional assessments have documented rising temperatures, shifting rainfall patterns, and an increase in hydroclimatic extremes across arid and semi-arid lands. In Iraq, the paradox is particularly sharp: the same region that faces intensifying drought also confronts sudden, destructive flood events when rain does fall on hardened urban surfaces and strained drainage systems. Erbil, a rapidly growing city of more than a million people, sits at the intersection of these forces, where urbanization has paved over absorbent ground and concentrated populations in flood-prone districts.

To understand how households respond to this threat, the team conducted a cross-sectional survey of 400 households, collecting data both online and in person. The questionnaire probed three dimensions: how prepared families were, how they perceived the flood risk, and what health impacts they had suffered. The researchers then applied binary logistic regression, a statistical technique that estimates the odds of a household taking preventive measures based on a set of predictor variables. The results were striking in their clarity. Households that had directly experienced flooding were nearly three times as likely to adopt preventive measures as those that had not, with an odds ratio of 2.87 and a 95 percent confidence interval of 1.89 to 4.36, significant at p less than 0.001. Higher education raised the odds by 63 percent, with an odds ratio of 1.63 and a confidence interval of 1.10 to 2.42. And households that attributed flooding to climate change were 87 percent more likely to prepare, with an odds ratio of 1.87 and a confidence interval of 1.15 to 3.04.

These three predictors — flood experience, education level, and climate change attribution — emerged as the strongest drivers of household preparedness, outperforming every other variable the team tested. Chi-square analyses reinforced the pattern, revealing significant associations between age, education, and flood experience. In other words, the demographic profile of a household shaped whether it had encountered floods before, and that encounter, more than any abstract awareness campaign, determined whether it acted. The finding echoes a broader literature on flood risk perception, which has repeatedly documented an unbalanced relationship between what people know about risks and what they actually do about them. Perception alone, studies from Nanjing to Nigeria have shown, rarely translates into preparedness without the catalyst of lived experience or the scaffolding of education.

The methodological rigor of the Erbil survey adds weight to its conclusions. The researchers compared responses collected online with those gathered face to face and found no statistically significant differences between the two modes — a confirmation that the mixed-method approach did not introduce bias. This matters for future research in resource-constrained settings, where online surveys can reach more respondents at lower cost, and it matters for policy, because it suggests the findings are robust rather than an artifact of who happened to answer the phone or the door. The study received ethical approval from the Medical Ethics Committee of Hawler Medical University, and all participants provided informed consent before completing the questionnaire.

But the survey also documented a darker side of flood exposure: the health toll. Respondents reported psychological stress, waterborne illness, and physical injuries as the principal impacts of flooding on their households. The psychological dimension is particularly significant in the Iraqi context, where populations have already endured decades of conflict and trauma, and where researchers have documented rising rates of climate change anxiety symptoms in the Kurdistan Region. Flood-related stress compounds these burdens, and the study’s authors note that the mental health impacts of natural disasters are increasingly recognized as a global concern, with evidence linking disaster exposure to depression, anxiety, and post-traumatic stress. Waterborne illness, meanwhile, reflects the contamination risks that follow flooding in cities where drainage and water treatment infrastructure are strained — a hazard the same research group has documented in earlier work on rainwater quality and atmospheric deposition in Erbil.

Against this backdrop of risk and harm, the study’s most sobering statistic may be its measure of public trust. Only 25 percent of respondents reported satisfaction with the government’s flood response. Three out of four households, in other words, felt that official institutions were not meeting their needs when floods struck. This trust deficit has technical implications. Household preparedness in Erbil is currently driven by private experience and individual education rather than by public risk communication, early warning systems, or coordinated municipal planning. If governments want to close the preparedness gap, the findings suggest, they cannot rely on awareness campaigns alone. They must build institutions that households trust enough to act on — and they must target their interventions at the populations the data identifies as most vulnerable: those without flood experience, without higher education, and without awareness of climate change as a driver of local risk.

The Erbil findings carry implications far beyond Kurdistan. Across the Middle East and North Africa, cities are expanding into flood-prone terrain while climate change intensifies hydroclimatic extremes. Regional reviews have warned that droughts and floods will increasingly coexist in the same urban systems, demanding multi-hazard approaches to planning that many MENA cities have yet to adopt. Studies from Jordan to Bangladesh have shown that community participation and risk communication are essential to building resilience in vulnerable settings, yet resource constraints and institutional weaknesses often prevent these interventions from reaching scale. The Erbil survey offers a template for measuring the problem: a validated household instrument, a mixed-mode survey design, and a statistical framework that isolates the predictors of action. It also offers a warning. In communities where flood experience is the strongest driver of preparedness, the first flood may be the most destructive — because households that have never been flooded have no reason to prepare, and no institutional prompt to do so.

What makes the study resonate beyond the academy is its reframing of climate adaptation as a household decision. Global climate negotiations focus on emissions targets and national plans, but the Erbil data shows that adaptation ultimately happens in kitchens and courtyards, in the choice to elevate an electrical socket, stock emergency supplies, or evacuate early. Those choices depend on experience, education, and attribution — three variables that policy can shape. Education systems that teach climate science raise the odds of preparedness by 63 percent. Risk communication that connects local floods to global warming raises them by 87 percent. And while no government can manufacture flood experience, it can simulate its lessons through drills, warnings, and community planning that translate the memory of one flood into protection against the next. In a region where climate models project intensifying extremes, the households of Erbil are already writing the first draft of adaptation. The question the study leaves open is whether institutions will help them finish it — or whether the next flood will have to teach the lesson again.

Subject of Research: Climate Change

Article Title: Climate-driven flood risks and public preparedness in the Middle East: evidence from Erbil, Kurdistan Region of Iraq

Article References: Qader, M. Q., Ayanlade, O. S., Ayoola, P. O., Fatah, K. K., Oyetunji-Alemede, C. O., Al-Banna, D. A., Ahmed, S. S., Ahmed, S. M., & Ayanlade, A. (2026). Climate-driven flood risks and public preparedness in the Middle East: evidence from Erbil, Kurdistan Region of Iraq. Theoretical and Applied Climatology, 157(11), Article 713. https://doi.org/10.1007/s00704-026-06647-4

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06647-4

Keywords: flood risk, Erbil, Kurdistan Region of Iraq, climate change adaptation, household preparedness, public health, Middle East, logistic regression, Climate-driven, flood, risks, public

The research, led by Man Wang of Wuhan Business University and Zihan Guo of China University of Geosciences, tackles a long-standing weakness in urban flood modeling. Traditional approaches tend to compute the underground one-dimensional pipe network and the two-dimensional surface flow field in isolation. A pipe-network model alone can flag which manholes overflow, but it cannot show where that water spreads once it reaches the street. A surface model alone, conversely, has no way of knowing how much water the buried network can actually convey, and so risks dramatically over- or underestimating local hazard. The mismatch, the authors argue, has hindered deterministic hydraulic support for flood-control engineering in exactly the places that need it most.

To close that gap, the team built an integrated 1D–2D framework linking the U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM) with LISFLOOD-FP, a raster-based surface inundation model. SWMM handled the first half of the problem: transforming rainfall into runoff across 845 sub-catchments and routing it through a reconstructed drainage network of 869 nodes and 890 conduits, solved with the Dynamic Wave scheme that captures pressurized flow, backwater propagation, and even flow reversal when downstream river levels push back. LISFLOOD-FP then took over, receiving time-varying overflow hydrographs from each surcharged node as dynamic water sources and simulating how the escaping water redistributed across a 12.5-meter digital elevation model of the district.

The coupling itself is a technical feat. Sub-catchment boundaries were delineated using Thiessen polygons around each drainage manhole, then cross-checked against terrain slopes derived from the 12.5-meter DEM to weed out geometrically plausible but hydrologically inconsistent assignments. When SWMM detected that a node was overflowing, the overflow volume during each coupling interval was transferred to the corresponding LISFLOOD-FP grid cell according to node coordinates, becoming a time-varying source term in the two-dimensional calculation. The result is a closed loop that connects pipe surcharge to street ponding in a single, physically consistent simulation.

The design storm was no ordinary rain. The team adopted a 120-minute Chicago synthetic hyetograph with a 20-year return period, discretized at 5-minute intervals into 24 rainfall pulses. A peak-position coefficient of r = 0.405 placed the storm’s maximum intensity roughly 48.6 minutes after onset, preserving both the pre-peak accumulation that saturates surfaces and the post-peak recession that reveals how long water lingers. That temporal structure matters: a uniform rainfall input would smooth away exactly the transient surcharge dynamics the researchers wanted to capture.

Under this scenario, the numbers are stark. Ninety-six of the 869 modeled drainage nodes—11.05 percent of the network—overflowed, releasing a cumulative 128,400 cubic meters of water onto the surface. The longest continuous overflow lasted 2.18 hours, and the average was 0.92 hours. But the overflow was far from uniform. Twenty-four overflowing nodes clustered in the Linkonggang Avenue–Wuhuan Road area and twenty-one in the Taxi Road–Fushan Avenue area, together accounting for nearly half of all overflow. Simulated maximum inundation depths at representative road sections ranged from 0.32 to 0.48 meters, with the Taxi Road–Fushan Avenue section reaching 0.48 meters and persisting for an average of 5.5 hours before receding at just 0.07 meters per hour.

What explains this spatial concentration? The answer, the study shows, lies in the coincidence of three factors: runoff generation, local conveyance limits, and downstream hydraulic constraints. Dongxihu’s comprehensive runoff coefficient is 0.56, but highly hardened industrial and road surfaces—industrial and warehousing land alone covers 28.11 percent of the district—push local coefficients to 0.70, compared with just 0.25 for green spaces and squares. When the receiving-water level at the terminal outfall rises, the hydraulic gradient available for gravity drainage shrinks, backwater propagates upstream through the connected pipe network, and surcharge intensifies. In the Taxi Road–Fushan Avenue area, low-lying terrain, a restricted stormwater box-culvert outlet, and pipe-diameter bottlenecks compounded the problem. By contrast, the Huanhu Road section, buffered by surrounding green space and storage areas, produced only seven overflow nodes—7.29 percent of the total—and its inundation receded fastest, at 0.28 meters per hour.

Because continuous observations of pipe discharge, nodal hydraulic head, and surface water depth were unavailable for the study area, conventional calibration was impossible. The researchers instead relied on two complementary lines of evidence. First, they tuned SWMM parameters—surface-flow resistance on impervious areas, depression storage, and the maximum Horton infiltration rate—over five rounds until the simulated comprehensive runoff coefficient reached 0.561, within 0.18 percent of the area-weighted benchmark of 0.56. Two observed rainfall events in June and August 2025, with totals of 130.22 and 101.10 millimeters, yielded event-based runoff coefficients of 0.512 and 0.488, deviations of −8.73 and −13.01 percent from the calibrated reference. Second, the simulated high-inundation zones were cross-checked against historical waterlogging records from the Dongxihu District Emergency Management Bureau and field investigations. Areas with depths exceeding 0.4 meters aligned closely with officially designated high-risk sites along Taxi Road, Jinshan Avenue, and Huanhu Middle Road.

The authors are careful about what this agreement does and does not prove. The runoff coefficient constrains total runoff magnitude but says nothing about where inundation occurs; the spatial check confirms the principal distribution of hotspots but not the accuracy of grid-scale depths or durations. The 12.5-meter terrain resolution, while adequate for district-scale hotspot identification, cannot resolve individual curbs, building thresholds, or stormwater inlets—features that can materially alter local hydraulic connectivity. Operational degradation such as inlet blockage, sediment accumulation, and structural deterioration was not modeled dynamically, meaning local inundation may be underestimated where the real network performs below its assumed capacity. The results, the team emphasizes, should be read primarily as scenario-based identification of waterlogging hotspots rather than fully field-validated hydraulic predictions.

The implications for urban flood policy are concrete. The study argues that fragmented fixes—simply enlarging pipes or building embankments—ignore the subsurface–surface hydraulic connectivity that turns pressurized conduits under river backwater into drivers of street overflow. Instead, mitigation should be spatially prioritized and mechanism-driven: in upstream industrial zones constrained by legacy infrastructure, source-control measures such as green roofs, permeable pavements, and urban wetlands should cut peak runoff before it reaches the network; in low-lying bottleneck sections like Wangan Avenue and Taxi Road, targeted pumping stations and detention facilities are needed to force water out when gravity drainage fails. Future work, the authors suggest, should incorporate drainage-system deterioration explicitly, use field inspection data to define reductions in inlet capture and conduit capacity, and add time-dependent gate and pumping operations—turning a scenario tool into a genuinely predictive instrument for the cities that will need it most.

Subject of Research: Coupled 1D–2D hydrodynamic simulation of urban waterlogging in Dongxihu District, Wuhan, using integrated SWMM and LISFLOOD-FP modeling under a 20-year design storm

Article Title: Hydrodynamic simulation and risk evolution of urban waterlogging based on integrated SWMM–LISFLOOD modeling: a case study of Dongxihu District, Wuhan

Article References: Wang, M., Guo, Z., Zhang, Q., Chen, L., Bao, X., & Zou, J. (2026). Hydrodynamic simulation and risk evolution of urban waterlogging based on integrated SWMM–LISFLOOD modeling: a case study of Dongxihu District, Wuhan. Frontiers in Earth Science, 14, Article 1976240. https://doi.org/10.3389/feart.2026.1976240

Image Credits: AI Generated

DOI: 10.3389/feart.2026.1976240

Keywords: urban waterlogging, SWMM, LISFLOOD-FP, hydrodynamic modeling, drainage network, Wuhan, flood risk, design storm, node overflow, surface inundation, runoff coefficient, 1D-2D coupling