Deltas occupy only about one percent of the Earth’s land surface, yet they are home to more than seven percent of the global population. These low-lying coastal zones, favored for their fertile soils and abundant water, host intensive agriculture and dense human settlement, which makes them particularly exposed when rivers swell. Since 2000, roughly 85 percent of the world’s coastal deltas have experienced severe flooding linked to shifting precipitation patterns, and research suggests that about half of all deltaic areas are vulnerable to inundation driven by rising sea levels. As projected sea level rise in the twenty-first century exceeds the inundation thresholds of many deltas, the urgency of accurate flood hazard assessment has never been greater.

The Danube Delta covers approximately 5,640 square kilometers where the Danube River meets the Black Sea, splitting into three main branches: Chilia, Sulina and Sfântu Gheorghe. The delta evolved under a natural regime until 1860, but the twentieth century brought profound changes. The Sulina branch was straightened for navigation, navigation channels were cut through the interior, and large areas were removed from the natural floodplain. These interventions altered the delta’s hydrological dynamics and increased its susceptibility to flooding. Major floods struck in 1942, 1970, 1975, 1981, 1988, 2005, 2006 and 2010, and the frequency of such events has risen in recent decades, a trend the study attributes to the region’s geography, its hydrological specificity and the intensification of extreme meteorological phenomena under climate change.

To quantify this vulnerability, the research team turned to HEC-RAS, the River Analysis System developed by the US Army Corps of Engineers, in its two-dimensional configuration. Unlike earlier approaches in the delta, which relied on hydrograde analysis, satellite imagery or one-dimensional hydraulic models, this study is the first to apply 2D hydraulic modeling across an extended area of the Danube Delta. The model solves the equations describing the conservation of mass and momentum in a plane, capturing how water velocity and depth evolve across the landscape under the forces of gravity, friction at the riverbed, surface stress and even the Coriolis effect. The finite volume method used in the software is valued for its conservative nature, geometric flexibility and conceptual simplicity, making it well suited to the delta’s complex mosaic of channels, lakes and marshes.

The foundation of the model is a high-resolution digital terrain model built from LiDAR flight data. Sectors between the Sfântu Gheorghe and Sulina branches were mapped at 2-meter resolution, while areas between Sulina and Chilia, bounded by the Romanian-Ukrainian border, were captured at 5-meter resolution. Gaps were filled with 25-meter EU-DEM data, producing a terrain model covering the entire delta, including the Chilia Arm on Ukrainian territory. Because water reflects LiDAR pulses poorly, the researchers corrected the riverbed elevations using bathymetric measurements and refined the thalwegs of major lakes and channels with 1:25,000 topographic maps. Terrain roughness, expressed through the Manning coefficient, was assigned at an average of 0.0035 for the main branches, with higher values over lake and marsh surfaces, which slows water spread outside the main channels.

The flood scenarios were anchored in the hydrology of the 2006 event, the largest flood on record at the Ceatal Izmail station, where discharge reached 15,900 cubic meters per second, corresponding to a return period of roughly 200 years. Following European Commission recommendations for flood hazard mapping, the team simulated three probability scenarios: a 10-year return period flood with discharges above 13,250 cubic meters per second, a 100-year event at 15,055 cubic meters per second, and a rare 1,000-year flood at 16,160 cubic meters per second. The durations of these discharges during the 2006 flood, roughly two months, twenty days and one day respectively, were replicated in the model runs. The computational mesh covered approximately 3,509 square kilometers with a base resolution of 400 meters, refined to 50 or even 25 meters near levees, yielding around 186,000 computation points.

The simulations revealed a stark escalation of flood exposure as the return period lengthens. Under the 10-year scenario, flooding covered about 1,792 square kilometers of the modeled area. The 100-year flood expanded this to roughly 2,102 square kilometers, while the 1,000-year event pushed the inundated surface to approximately 2,203 square kilometers. For the 100-year scenario, often the benchmark in hazard analysis, the flooded area during the reference period from 1971 to 2005 stood at around 2,100 square kilometers, a figure the authors describe as a significant increase in the area covered by water. These numbers confirm that rarer events do not merely deepen existing floods but extend them across substantially larger portions of the delta.

The depth analysis adds a crucial layer of insight. Shallow flooding between 0 and 0.1 meters dominated the 10-year scenario, accounting for 19.3 percent of the modeled area, but its share fell steadily to 4.3 percent under the 1,000-year flood, suggesting that marginal zones are exposed frequently but with limited intensity. In contrast, the intermediate depth class of 0.2 to 0.5 meters was the most affected by 100-year events, covering 38.5 percent of the area, or about 809 square kilometers. Rare 1,000-year floods shifted the impact toward greater depths, with the 0.5 to 0.75 meter class jumping from 7.7 percent under the 10-year scenario to 21.1 percent under the 1,000-year event, and the 0.75 to 1 meter class rising from 5.0 to 8.6 percent. Even deeper classes expanded, with the 2 to 3 meter range growing from 5.0 to 7.9 percent, indicating that severe flooding reaches into areas normally spared.

The hazard maps generated by the study are more than academic outputs; they are practical tools for flood risk management, enabling authorities to identify critical areas and prioritize adaptation strategies. Among the solutions the researchers propose are the ecological reconstruction of wetlands, which can absorb and slow floodwaters, the implementation of effective climate change adaptation policies, and improved water management strategies across the basin. These measures, the study argues, are vital for protecting both local communities and the delta’s exceptional biodiversity, which depends on the delicate balance between river flow and marine influence.

The authors are candid about the limitations of their approach. The simulations excluded the influence of Black Sea level, which can compound riverine flooding through backwater effects, and the variable resolution of the terrain model introduces uncertainty in some sectors. Future work, they suggest, should integrate sea level variations, climate projections and socio-economic analyses to refine risk anticipation and optimize protective measures. Even so, the study demonstrates that two-dimensional hydraulic modeling in HEC-RAS is an efficient and powerful instrument for hydrological hazard assessment, providing a solid scientific basis for policy decisions and the sustainable protection of one of Europe’s most iconic and imperiled landscapes.

Subject of Research: Flood hazard assessment in the Danube Delta using two-dimensional HEC-RAS hydraulic modeling

Article Title: Flood hazard assessment in the Danube Delta using 2D hydraulic modeling

Article References: Ciobotaru, N., Crăciun, A., & Costache, R.-D. (2025). Flood hazard assessment in the Danube Delta using 2D hydraulic modeling. Scientific Annals of the Danube Delta Institute, 30, 79-90. https://doi.org/10.3897/saddi.30.160526

Image Credits: AI Generated

DOI: 10.3897/saddi.30.160526

Keywords: Danube Delta, flood hazard, 2D hydraulic modeling, HEC-RAS, climate change, return period, floodplain, Black Sea, wetland restoration, risk management, LiDAR terrain model, hydrology

The scale of the problem is enormous. According to research cited in the review, approximately 1.81 billion people, or 23 percent of the global population, are exposed to substantial flood risk, with projections indicating increasing flood frequency and severity across Asia, Latin America and Africa. Records from the Emergency Events Database maintained by the Centre for Research on the Epidemiology of Disasters show that floods account for the largest proportion of recorded disasters between 1900 and 2024, ahead of storms. Climate change intensifies the hazard by increasing atmospheric moisture and heavy precipitation, while rapid urbanisation, land surface modification and inadequate drainage infrastructure alter runoff generation and place more people and assets in harm’s way.

To build its evidence base, the research team followed the PRISMA 2020 guidelines for systematic reviews, searching Web of Science and Google Scholar for studies combining flood-related terms with geospatial, meteorological, hydrological and artificial intelligence terminology. The initial search retrieved 2,150 records; after duplicate removal, 1,720 were screened, 500 full-text articles were assessed for eligibility, and 190 studies made the final thematic synthesis. Each eligible study was mined for publication year, geographical region, methodological approach, data sources, model type, validation metrics and reported limitations, allowing the authors to compare approaches across four decades of methodological development.

Four principal methodological domains emerged from the evidence. The first and most widely adopted is geospatial technology, particularly geographic information systems and remote sensing. Satellite observations provide consistent, large-area coverage that substantially improves flood monitoring, especially where ground-based hydrological observations are sparse or absent. Geographic information systems support terrain analysis, watershed delineation, multi-criteria evaluation and the spatial integration of environmental and socio-economic variables. Compared with physically based hydraulic models, these tools offer greater spatial scalability, rapid data acquisition and lower computational demands, although they represent flood processes only indirectly and work best as foundational components within integrated frameworks rather than as standalone solutions.

The second domain, hydrological and hydraulic modelling, remains indispensable for simulating runoff generation, channel routing and floodplain inundation. Three tools dominate the reviewed literature: the Hydrologic Engineering Centre’s River Analysis System, known as HEC-RAS, the LISFLOOD-FP model, and the Height Above Nearest Drainage terrain index. HEC-RAS delivers physically realistic one- and two-dimensional simulations suited to detailed local-scale inundation modelling, while LISFLOOD-FP balances computational efficiency with acceptable performance over extensive regional floodplains. HAND offers a computationally cheap terrain-based alternative for rapid floodplain delineation in ungauged catchments, though its simplified hydraulics limit its use for detailed analysis. The review stresses that these models are not interchangeable; selection should reflect the desired balance between hydraulic realism, computational cost, data availability and operational constraints.

Meteorological information forms the third domain and the primary driver of flood generation. Rainfall intensity, precipitation duration, antecedent soil moisture, atmospheric circulation patterns and climate variability consistently emerge as the principal hydroclimatic controls on flooding. High-resolution reanalysis products and satellite-derived precipitation datasets improve hydroclimatic representation where rain gauges are sparse, but their predictive value depends strongly on spatial resolution, temporal consistency, bias correction and successful coupling with hydrological models. The review concludes that meteorological data contribute most when embedded within coupled hydrometeorological modelling systems rather than analysed in isolation.

The fastest-growing domain is artificial intelligence. Machine learning methods including Random Forest, Support Vector Machines, Extreme Gradient Boosting, Convolutional Neural Networks and Long Short-Term Memory networks repeatedly demonstrate strong capability in modelling the nonlinear interactions among climatic, hydrological and environmental variables, and generally outperform conventional statistical approaches when extensive training datasets are available. Yet the review also flags persistent weaknesses: limited interpretability, uncertain transferability across regions, heavy data dependency and poorly quantified uncertainty. Hybrid frameworks that combine physically based hydrological understanding with data-driven prediction are increasingly seen as offering greater robustness and transparency than purely black-box approaches.

A crucial caveat runs through the entire synthesis: reported performance metrics such as AUC, accuracy and F1-score should be read as study-specific evidence rather than directly comparable measures of model superiority. Differences in datasets, sample sizes, spatial resolution, flood inventories, predictor variables, validation strategies and geographical conditions make direct quantitative comparison across studies unreliable. The authors also highlight stark regional disparities. In Africa, sparse hydrometeorological monitoring networks, fragmented observational records and constrained early warning systems restrict the operational use of advanced methods, even as changing precipitation regimes, sea level rise and rapid urbanisation drive risk upward. Satellite precipitation estimates from NASA’s Global Precipitation Measurement mission during the April 2022 floods in eastern South Africa illustrate how Earth observation can partially compensate where ground observations are limited.

From this evidence the authors derive a conceptual framework that explicitly connects methodological capability with operational readiness and decision support. Rather than proposing a new predictive algorithm, the framework links multisource data acquisition, preprocessing, modelling, validation and uncertainty assessment to practical applications such as early warning, hazard mapping, emergency planning and policy development, while incorporating continuous monitoring and adaptive feedback. It recognises that data availability, monitoring infrastructure, computational capacity, validation practices, model transferability and institutional capability all determine whether sophisticated science actually reaches the communities that need it.

Looking forward, the review identifies clear priorities: explainable artificial intelligence, hybrid physics-informed machine learning, interoperable Earth observation systems and cloud-native geospatial platforms, alongside expanded open-access datasets, FAIR data principles and strengthened hydrometeorological monitoring networks, especially in underrepresented and data-constrained regions. The authors acknowledge limitations, including their restriction to English-language literature and the rapid evolution of GeoAI technologies, but their conclusion is unambiguous. No single analytical approach can address the complexity of contemporary flood hazards. Effective flood risk assessment will depend on integrating complementary methodologies within scalable, transparent and operationally feasible systems capable of supporting climate adaptation and disaster risk reduction under increasingly uncertain hydroclimatic conditions.

Subject of Research: Systematic review of geospatial, meteorological, hydrological and AI-based methods for flood risk assessment and monitoring

Article Title: A systematic review of geospatial and meteorological approaches to flood risk assessment and monitoring

Article References: Sibandze, P., Kalumba, A. M., & Afuye, G. A. (2026). A systematic review of geospatial and meteorological approaches to flood risk assessment and monitoring. Environmental Earth Sciences, 85(16), Article 425. https://doi.org/10.1007/s12665-026-13155-1

Image Credits: AI Generated

DOI: 10.1007/s12665-026-13155-1

Keywords: flood risk assessment, remote sensing, GIS, machine learning, deep learning, GeoAI, hydrological modelling, HEC-RAS, meteorology, early warning systems, PRISMA, climate adaptation

The problem the researchers set out to solve is one that haunts hydraulic engineers across the developing world. Bridge scour, the removal of sediment from around foundations by flowing water, is the leading cause of bridge collapse worldwide, and its severity depends on river discharge, sediment load, and channel geometry. Yet most design codes still assume that the statistics of future floods will resemble those of the past, an assumption famously challenged in 2008 when hydrologists declared that stationarity is dead. In data-scarce tropical basins like the Rupnarayan, where gauge records are short and sparse, quantifying how climate change will alter the forces acting on critical infrastructure has remained poorly constrained. The team’s answer was to build a modelling pipeline that squeezes usable engineering numbers out of limited observations.

At the top of the pipeline sits a high-resolution global climate model, the Max Planck Institute Earth System Model version 1.2 Higher Resolution, known as MPI-ESM1-2-HR. Raw global model output is too coarse and too biased to drive a local river model, so the researchers downscaled and bias-corrected the projections before feeding them into the Soil and Water Assessment Tool, a widely used watershed model that simulates how rainfall becomes runoff and sediment. The calibration was strong: the coupled model achieved a Nash-Sutcliffe efficiency of 0.90 and a coefficient of determination of 0.86 for discharge, figures that indicate the simulated river flows track the observed record closely. That performance matters, because every downstream estimate of scour depends on the credibility of the flow and sediment series entering the hydraulic model.

To bracket the range of possible futures, the team ran three Shared Socioeconomic Pathways: the moderate SSP245, the intermediate SSP370, and the fossil-fuel-intensive SSP585. The observed record from 1960 to 2020 shows no significant trend in the basin’s hydrology, but the projections diverge sharply from that historical baseline. Under SSP585, mean annual temperature rises by 1.31 degrees Celsius and mean annual rainfall increases by 22.1 percent by 2100. Those shifts translate into altered river flow regimes and sediment delivery, which the researchers propagated through a two-dimensional Hydrologic Engineering Center River Analysis System model, HEC-RAS 2D, to compute water-surface elevations, energy gradients, and bed shear stress for floods with return periods ranging from 2 to 100 years.

The hydraulic results reveal a consistent intensification of the forces that move sediment. Across the three-scenario comparison, critical shear stress and the energy gradient at the Kolaghat Bridge increase by 11 to 18 percent, with the MPI-ESM1-2-HR-driven projections clustering around 15 percent. Bed shear stress is the fundamental quantity in sediment transport: it describes the drag exerted by moving water on the channel bed, and when it exceeds the critical threshold for the bed material, grains begin to move. A higher energy gradient means steeper, more energetic flow, and a higher critical shear stress at the site means the river is pushing harder against its bed precisely where the bridge foundations are anchored.

The most striking number in the study comes from applying the HEC-18 procedure, the standard method of the U.S. Federal Highway Administration for evaluating scour at bridges. Total pier scour at the Kolaghat Bridge, projected under the high-emissions pathway, grows from approximately 6.07 metres to approximately 10.93 metres, an increase of about 80 percent. Pier scour develops when flowing water accelerates around a pier, forming horseshoe vortices at the bed that excavate a hole around the foundation. The deeper that hole becomes, the less of the pile remains embedded in competent material, and the smaller the reserve of safety against collapse. According to the study, the projected scour would diminish the foundation safety margin below the code threshold, a finding that converts an abstract climate statistic into a concrete engineering liability.

Paradoxically, the same simulations point to a second, opposite hazard. The projected water-surface elevations show reduced high-frequency variability, which the researchers interpret as a signal of increased sedimentation, channel narrowing, and a rise in overall channel sinuosity from 1.08 to 1.67. A sinuosity increase of that magnitude describes a river meandering more sharply through its floodplain, reshaping its course over decades. Sedimentation and narrowing can raise flood levels for a given discharge, while the shifting thalweg, the deepest line of the channel, can direct erosive flow toward piers that were designed for a different channel alignment. The bridge, in other words, faces threats from both directions: stronger scour during floods and a progressively less stable channel around it.

What makes the study methodologically significant is that it accomplishes this assessment in a basin with almost no dedicated monitoring infrastructure. The authors combined openly available bias-corrected CMIP6 climate projections, published by Mishra and colleagues on Zenodo, with satellite-derived terrain data and standard public modelling tools, creating a replicable template for other data-scarce rivers. The three-scenario, three-model comparison also gives engineers a sense of uncertainty: the 11 to 18 percent spread in shear stress intensification reflects how different scenario pathways and model configurations propagate through the chain. Similar physics-based approaches have been applied to regional bridge scour risk in the United States and to national scour assessments in Sweden, but applications to tropical, monsoon-dominated, peri-urban basins in South Asia have been rare, despite the region’s dense inventory of aging river crossings.

The practical implications extend well beyond a single bridge. The authors argue that their results make a clear case for climate-adjusted hydraulic design loads, meaning that new structures and retrofits should be sized not on historical flood statistics but on projected future flows and shear stresses. They also recommend regular bathymetric monitoring, repeated surveys of the riverbed depth around foundations, so that scour can be tracked as it develops rather than discovered after the fact, together with planned dredging to manage sediment accumulation. For the Kolaghat Bridge, which carries traffic across a working river in a densely populated corridor of West Bengal, such monitoring could mean the difference between a scheduled intervention and an emergency.

More broadly, the study is a reminder that climate change does not only arrive as floods and droughts; it arrives as altered physics in the beds of rivers, working silently on the foundations of the built world. A warming atmosphere loads more moisture into monsoon systems, reshaping discharge regimes and sediment budgets in ways that standard design manuals, written for a stationary climate, never anticipated. As extreme rainfall intensifies across South Asia, the modelling chain demonstrated here, from downscaled Earth system projections through watershed simulation to two-dimensional hydraulics and scour analysis, offers a way for engineers in data-poor regions to see that hidden stress coming, and to reinforce their infrastructure before the river quietly undermines it.

Subject of Research: Climate change impacts on bridge pier scour and river channel morphology in the data-scarce Rupnarayan basin, India

Article Title: Climate-induced hydraulic stress and geomorphological changes: an assessment of vulnerability of critical river infrastructure in the data-scarce basin, India

Article References: Ghosh, A., Kundu, M., & Padhi, E. (2026). Climate-induced hydraulic stress and geomorphological changes: an assessment of vulnerability of critical river infrastructure in the data-scarce basin, India. Environmental Monitoring and Assessment, 198(11), Article 1175. https://doi.org/10.1007/s10661-026-16013-6

Image Credits: AI Generated

DOI: 10.1007/s10661-026-16013-6

Keywords: bridge scour, climate change, HEC-RAS, SWAT, CMIP6, Rupnarayan River, bed shear stress, hydraulic modelling, infrastructure vulnerability, SSP585, India, geomorphology

The team, led by Ankita Ghoke, Priyom Roy and colleagues, combined three complementary lines of evidence to track the slope’s evolution. Eighteen cloud-free optical images from Indian Resourcesat-2 and 2A satellites, the European Sentinel-2 mission and the high-resolution Cartosat-3 sensor allowed them to map morphological changes between 2013 and 2025. They then applied multi-temporal interferometric synthetic aperture radar, or MT-InSAR, using the Small Baseline Subset technique on Sentinel-1 radar data to measure millimeter-scale ground deformation over four distinct time windows. Finally, field campaigns after the 2024 reactivation documented the structural and geological weaknesses that make the slope so prone to failure. Together, these datasets reveal a landslide that is not a single event but a slow-motion, retrogressive collapse that keeps climbing uphill and swallowing new ground.

The optical record tells a striking story of escalation. In March 2013, the unstable zone was a modest, elongated feature roughly 500 to 600 meters long. A major reactivation in June 2013, coinciding with the disastrous Kedarnath cloudburst that devastated much of Uttarakhand, stretched the slide to about 965 meters and carved the first prominent head scarp while blocking the Shambhu River. Between 2014 and 2016 the failure continued to retreat upslope, damming and re-damming the river. Around October 2018, an entirely new landslide initiated to the southwest, and by late 2019 its scar had merged with the main slide mass, enlarging the failure zone still further. By 2024, four separate head scarps were visible on the slope, evidence of multi-scarp retrogression still underway. The landslide now covers approximately 0.95 square kilometers, and imagery shows that several settlements in its path were destroyed as it grew.

The radar measurements add a crucial dimension: they show the slope moving before it visibly fails. In the first analysis phase, from October 2014 to October 2016, monitoring points on the flanking slopes recorded average line-of-sight velocities of roughly 40 millimeters per year, with deformation accelerating during the 2015 and 2016 monsoons before complete failure and scarp formation. In the second phase, a point tracking the nascent secondary landslide crept at 13.6 millimeters per year before a rapid acceleration culminating in the October 2018 collapse. Later phases recorded velocities between about 5 and 12 millimeters per year, each acceleration episode closely following periods of intense rainfall. The pattern is classic transient landslide behavior: stability, then primary creep, then accelerating motion, then failure, with monsoon precipitation acting as the trigger on a slope already predisposed to collapse by its geology.

That geology is unfavourable in almost every respect. The slope lies near the Main Central Thrust in the seismically active Lesser Himalaya, underlain by highly sheared talc schist, carbonaceous phyllite, quartzite and slate of the Lameri Formation, all draped in thick, loose colluvium. Fieldwork revealed foliation planes dipping at 45 to 50 degrees, parallel to the slope face, creating ready-made planar failure surfaces. The team also mapped transverse cracks, active groundwater seepage channels exuding along those cracks, and linear discontinuities that appear to be faults or joints. Particularly worrying is the village of Kuwari itself, which sits on a previously stabilized portion of the landslide body that field evidence now shows is bulging and deforming anew. A road constructed across the secondary landslide zone in early 2022 has further destabilized that section, a reminder that human activity can amplify an already fragile situation.

Meanwhile, the lake behind the debris dam keeps growing. The first impoundment formed in June 2013 and drained in 2015; a second formed in November 2016, partially drained in October 2017 and re-formed in 2018. From November 2021 onward the water body expanded relentlessly: roughly 885 meters long by April 2022, about 900 meters by October 2022, 1,050 meters by October 2023, and approximately 1,130 meters long and 100 meters wide by October 2024, the largest extent on record. Each enlargement reflects fresh debris choking the valley and raises the volume of water poised above downstream communities. History offers little comfort: previous research indicates that 80 to 90 percent of landslide-dammed lakes fail within their first year, and the Kuwari dam has already been partially breached by the river cutting a narrow channel through the debris.

To assess how stable that dam actually is, the researchers applied two geomorphic indices. The Morphological Obstruction Index, calculated at 3.82, places the dam in the uncertain evolution domain, while the Hydro-morphological Dam Stability Index, computed from the dam volume, upstream catchment area of about 2.46 square kilometers and channel gradient, falls squarely within the instability domain. The dam itself is a type III barrier in the classification of Costa and Schuster, meaning debris has moved both upstream and downstream of the valley and completely obstructs it. With an estimated height of around 40 meters and a surface area near the crest of roughly 10,000 square meters, the structure is an unconsolidated pile of landslide debris, not engineered material, and it is already being eroded by the river flowing through it.

The most dramatic part of the study is the flood simulation. Using HEC-RAS version 6.7, the team modeled a complete overtopping breach of the dam, drawing breach geometry from an empirical relationship derived from historical dam failures. Assuming the full 3.7 million cubic meter reservoir and a 17-meter breach height, the model predicts an average breach width of 48.3 meters forming in just 0.64 hours, less than forty minutes. The resulting flood would race down the narrow valley at a peak velocity of 15 meters per second near the breach, with overbank flooding extending about six kilometers downstream to Choting village, where water could rise up to 10 meters above normal levels and inundate agricultural terraces. Beyond Choting, the flow would remain channelized but still dangerous, reaching the villages of Melkhet, Deosari and Dewal at average velocities of 6 to 8 meters per second, fast enough to threaten anything along the riverbanks.

The authors are careful to note that the simulation is a predictive, end-member scenario that cannot yet be validated, and that its results carry uncertainties in breach width, formation time, erosion rates and dam material properties. But the broader message is hard to escape. The Kuwari landslide is an active, compound hazard in one of the world’s most landslide-prone countries, which a global analysis by Froude and Petley ranked highest in landslide fatalities. The study’s authors call for continuous monitoring, an early warning system for downstream communities, slope stabilization measures, and strict controls on construction across already scarped slopes. As climate change intensifies monsoon extremes across the Himalaya and infrastructure development pushes further into fragile terrain, the integrated approach demonstrated here, pairing satellite radar with optical imagery, field geology and hydrodynamic modeling, may become an essential template for spotting the next Kuwari before its lake bursts.

Subject of Research: Reactivation dynamics of the Kuwari landslide in Uttarakhand, India, and assessment of landslide lake outburst flood hazard using remote sensing and hydrodynamic modelling.

Article Title: Reactivation dynamics of the Kuwari landslide (Uttarakhand, India) and assessment of LLOF hazard using optical imagery, MT-InSAR and hydrodynamic modelling

Article References: Ghoke, A., Gaur, A., Roy, P., Jalan, P., Martha, T. R., & Das, I. C. (2026). Reactivation dynamics of the Kuwari landslide (Uttarakhand, India) and assessment of LLOF hazard using optical imagery, MT-InSAR and hydrodynamic modelling. Discover Geoscience, 4(1), Article 345. https://doi.org/10.1007/s44288-026-00717-w

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00717-w

Keywords: Kuwari landslide, Uttarakhand, Himalaya, MT-InSAR, landslide-dammed lake, LLOF, HEC-RAS, Sentinel-1, dam breach, natural hazards, remote sensing, monsoon rainfall

The core insight of the study is that flood resilience cannot be assessed by hydrology alone. A city’s vulnerability to flooding depends not only on where water will go during an extreme event, but also on which facilities matter most when disaster strikes. Hospitals, emergency service centers, utilities and administrative hubs form the backbone of a city’s response capacity, and when these facilities are disrupted, the consequences ripple far beyond the flooded footprint. Operational efficiency drops, public dissatisfaction rises, and the continuity of essential services is impaired precisely when citizens depend on them most. The researchers therefore framed their investigation around a two-part question: where will the water go, and what will it hit that the city cannot afford to lose?

To answer the first half of that question, the team turned to HEC-RAS, the Hydrologic Engineering Center’s River Analysis System, a widely used one-dimensional hydraulic modeling platform developed by the US Army Corps of Engineers. The researchers investigated the hydrological conditions of Hamedan City and its surrounding areas, then simulated river behavior under multiple flood scenarios defined by different return periods. Return periods, expressed in years, describe the average interval between flood events of a given magnitude; a 100-year flood, for example, has roughly a one percent chance of occurring in any single year. By running the model across this spectrum of scenarios, the team captured how inundation extents and water depths change as floods grow rarer and more severe.

The hydraulic output from HEC-RAS was then integrated with a Geographic Information System, or GIS, to delineate flood-prone areas across the city. This integration is where the technical power of the approach emerges. Hydraulic models produce spatially distributed estimates of water surface elevations along river reaches, and when those elevations are draped over high-resolution terrain data within a GIS environment, they generate precise floodplain boundaries. Planners can overlay any layer of urban information, from land parcels to road networks to building footprints, onto these boundaries to see exactly what lies within the inundation zone. The resulting maps translate abstract hydraulic computations into actionable spatial intelligence for zoning and emergency planning.

In parallel, the team built a vulnerability classification system for urban assets. They established a set of criteria and sub-criteria designed to capture the multiple dimensions along which urban facilities can be evaluated, ranging from operational characteristics to economic significance. To determine the relative importance of these factors, the researchers designed a structured questionnaire and distributed it to selected experts, whose judgments were then aggregated. The arithmetic mean of the expert responses fed into the Analytic Hierarchy Process, a well-established multi-criteria decision-making method, implemented in the Expert Choice software. AHP works by decomposing a complex decision into a hierarchy of criteria and alternatives, deriving weights through pairwise comparisons, and producing a ranked prioritization that is transparent and reproducible.

The AHP results revealed a clear hierarchy of concerns. The quantitative level of operation, a measure of how intensively and measurably a facility functions, received the greatest weight among all evaluated indicators, signaling that experts view operational capacity as the dominant factor in urban vulnerability. At the other end of the scale, economic value was assigned the lowest weight, suggesting that the sheer monetary worth of a facility matters less to resilience than its functional role during a crisis. This finding carries practical weight for municipal decision-makers: protecting a modest but operationally critical service center may deliver more resilience per unit of investment than safeguarding a high-value property with limited emergency significance.

With critical urban centers identified and prioritized through the AHP framework, and flood-prone zones delineated through the HEC-RAS and GIS pipeline, the final analytical step was a spatial overlay. By intersecting the layer of prioritized urban facilities with the mapped flood extents in the GIS environment, the researchers identified and enumerated the vulnerable centers, those critical assets that fall within areas likely to be inundated under the modeled scenarios. This intersection transforms two separate analyses into a single, decision-ready product: a ranked list of the facilities whose flooding would inflict the greatest damage on the city’s capacity to function and respond.

The framework’s architecture is deliberately transferable. The authors note that the approach can potentially be applied to other urban areas, provided that appropriate local data are available and that the hydraulic component undergoes site-specific calibration and validation. That caveat is important. Hydraulic models are only as reliable as the terrain data, flow measurements and roughness coefficients that feed them, and a model calibrated for Hamedan’s river channels and topography cannot simply be transplanted elsewhere without adjustment. The vulnerability weighting, too, depends on expert panels whose composition and judgments will vary between cities, meaning that the specific rankings are context-dependent even if the methodology is universal.

The study is also candid about its boundaries. The assessment rests on current hydrological conditions and conventional flood return periods; explicit future climate projections are not incorporated into the modeling. This is a meaningful limitation in an era when climate change is altering precipitation patterns and intensifying extreme rainfall events in many regions, including arid and semi-arid landscapes like western Iran. The authors recommend that future studies integrate climate projections and scenario-based precipitation changes to evaluate how urban flood risk evolves under future climate conditions. Such an extension would allow planners to stress-test their resilience strategies against the wetter extremes that a changing climate may deliver, rather than against the historical record alone.

For Hamedan, the practical implications are immediate. The identification of vulnerable critical centers gives municipal authorities a concrete starting point for protective investment, whether through physical flood defenses, facility hardening, relocation of the most exposed assets, or revised zoning that restricts new critical infrastructure from flood-prone land. For the broader field of urban flood management, the study demonstrates how hydraulic simulation, geospatial analysis and structured expert judgment can be woven into a single coherent workflow. As cities worldwide grapple with the compounding pressures of growth and climate volatility, frameworks of this kind, which ask not just where the water will flow but what the city can least afford to lose, are likely to become an increasingly standard part of the flood resilience toolkit.

Subject of Research: GIS-integrated HEC-RAS flood simulation and AHP-based vulnerability assessment of critical urban centers in Hamedan City

Article Title: Enhancing Flood Resilience: a GIS-Integrated Approach to River Flow Modelling and Urban Zoning using HEC-RAS Simulation

Article References: Naseri, A., Estelaji, F., Yari, M. H., Asl, B. R., Zahedi, R., & Abedi, B. (2026). Enhancing Flood Resilience: a GIS-Integrated Approach to River Flow Modelling and Urban Zoning using HEC-RAS Simulation. Water Resources Management, 40(12), Article 528. https://doi.org/10.1007/s11269-026-04893-9

Image Credits: AI Generated

DOI: 10.1007/s11269-026-04893-9

Keywords: flood resilience, HEC-RAS, GIS, AHP, urban zoning, Hamedan, hydraulic modeling, flood vulnerability, river flow simulation, urban planning, Water Resources Management, multi-criteria decision making

The research, led by Hai Sun of the Ocean University of China together with colleagues at Qingdao University of Technology, Clemson University, and Western Sydney University, tackles one of the most persistent blind spots in flood management: the pathways linking land-use patterns to hydrological responses. Urbanization reshapes basin hydrology by replacing soils and vegetation with impervious surfaces such as roads, rooftops, and parking lots. These surfaces prevent infiltration, accelerate runoff generation, and amplify flood peaks, sending more water into rivers faster and overwhelming channels and drainage systems. Green infrastructure — permeable pavements, rain gardens, wetlands, and vegetated storage areas — counteracts this by improving infiltration, storage, and surface roughness. But until now, planners have lacked a rigorous, basin-scale framework for measuring how these benefits vary across space and under different storm conditions.

To close that gap, the team constructed three urban expansion scenarios for the year 2044 in the Poyang Lake Basin: a no-green-infrastructure baseline, a storage-oriented green infrastructure scenario, and a permeability-oriented scenario. The Poyang Lake Basin, China’s largest freshwater lake system and a critical node in the Yangtze River’s hydrology, is a natural laboratory for this question. Its low-lying floodplains, dense tributary network, and rapidly expanding urban platforms make it acutely sensitive to changes in land cover.

Land-use changes under each scenario were simulated using a cellular automaton–Markov (CA–Markov) model, a technique that combines transition probabilities derived from historical land-change data with spatial neighborhood constraints to project how urban footprints evolve. This allowed the researchers to generate realistic maps of where impervious surfaces would spread by 2044 and where green infrastructure would be deployed under each strategy. The hydrological consequences were then evaluated through a coupled one-dimensional and two-dimensional modeling framework, chaining HEC-HMS, a rainfall-runoff model, to HEC-RAS, a river hydraulics and flood inundation model. The coupling is significant: it enables a continuous simulation chain from land-use evolution to runoff generation to flood dynamics, rather than treating each link in isolation.

The baseline results are sobering. Under unconstrained urban expansion, impervious surface coverage in the basin rises from 3.93 percent to 7.37 percent — nearly a doubling of sealed ground. Correspondingly, the basin-averaged curve number, a standard parameter in the Soil Conservation Service runoff method that encapsulates how readily a landscape converts rainfall into runoff, increases from 68 to 72. That seemingly modest shift carries heavy consequences: the researchers calculate an approximately 18 percent decrease in potential maximum retention, the landscape’s capacity to absorb and store rainfall before it becomes floodwater. In plain terms, by mid-century the basin could surrender nearly a fifth of its natural buffering capacity to concrete and asphalt.

Green infrastructure partially blunts this trajectory, but the two strategies do so very differently. Permeability-oriented green infrastructure — designed to restore infiltration across the urban surface — achieves the strongest reduction in impervious coverage, limiting the rise to 6.68 percent, and effectively reverses the degradation of infiltration and storage capacity captured by the curve number. Storage-oriented green infrastructure, which concentrates water retention in discrete facilities, shows only limited improvement in these landscape-scale parameters. The reason is structural: storage works locally, behind berms and inside basins, while permeability works everywhere, beneath every street and rooftop it touches.

Those parameter-level differences propagate directly into flood behavior. Under small to moderate rainfall events, permeability-oriented green infrastructure reduces peak discharge by 6.2 percent and total runoff volume by 3.54 percent, and — critically — it maintains its effectiveness even under extreme conditions, because infiltration capacity does not fill up the way a storage basin does. Storage-oriented measures, by contrast, remain constrained by finite capacity: once a retention facility fills, additional rainfall passes through unattenuated. Yet the picture is not one-sided. The analysis reveals clear spatial heterogeneity: in areas with sufficient storage capacity, storage-based strategies actually outperform infiltration-based measures during large rainfall events, when rainfall intensity outpaces the soil’s ability to absorb water and detention volume becomes the deciding factor. The catch is that this advantage is limited by spatial and capacity constraints at the basin scale — there is simply not enough suitable land and storage volume to deploy it everywhere.

The two-dimensional hydraulic component of the framework sharpens this spatial story further. Permeability-oriented green infrastructure reduces the extent of high-depth and high-velocity flood hotspots, with the strongest benefits concentrated along river corridors and urban platforms — precisely the locations where people and assets cluster. This leads the authors to a practical siting logic built around the curve number itself: boost permeability and roughness on the slopes, maintain them through the channels, and add storage capacity at confluences where flows converge and backwater effects amplify. In other words, read the landscape’s hydrological fingerprints and match the intervention to the terrain.

The study’s most consequential recommendation is that no single strategy suffices. Because green infrastructure performance varies with location, terrain, and storm magnitude, the authors argue for differentiated strategies aimed at residual high-risk areas: infiltration-dominated measures for moderate rainfall, enhanced storage and conveyance capacity for extreme events, and spatially targeted deployment that combines infiltration and storage synergies in low-lying zones, storage-control combinations along flow corridors, and strict land-use regulation in high-risk areas. This is a departure from the one-size-fits-all deployments that have characterized much green infrastructure planning, including China’s high-profile sponge city program, and it provides theoretical support and decision-making guidance for coordinated planning and refined flood risk management at the basin scale.

The timing could hardly be more urgent. Recent global research has documented rapid urban growth inside flood zones since 1985 and projected substantial increases in future fluvial flood risk across China’s major urban agglomerations, with the Global South bearing disproportionately higher exposure. As climate change intensifies extreme rainfall and cities continue to seal their surfaces, the Poyang Lake findings offer a template that travels: simulate the land, couple it to the water, and let the spatial heterogeneity of performance — not generic best practice — dictate where every permeable meter and every storage basin goes. The difference, this research suggests, may be measured not just in percentage points of peak discharge, but in neighborhoods that stay dry.

Subject of Research: Spatial variability in the flood mitigation performance of green infrastructure under future urban expansion in the Poyang Lake Basin

Article Title: Spatial heterogeneity of green infrastructure performance in flood mitigation under urban expansion

Article References: Sun, H., Wang, H., Chu, Y., Yao, W., Fan, C., Chu, Z., & Liang, B. (2026). Spatial heterogeneity of green infrastructure performance in flood mitigation under urban expansion. Natural Hazards, 122(19), Article 637. https://doi.org/10.1007/s11069-026-08312-5

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08312-5

Keywords: green infrastructure, flood mitigation, urban expansion, Poyang Lake Basin, CA-Markov model, HEC-HMS, HEC-RAS, curve number, impervious surfaces, runoff reduction, spatial heterogeneity, flood risk management