Search Results (759)

Green roofs are used as nature-based solutions for urban stormwater management and for improving the thermal performance of buildings. Their hydrological performance depends on structural properties and rainfall characteristics, but the relative importance of these factors has not been fully quantified. Therefore, this study aimed to identify the key variables controlling the hydrological effectiveness of a green roof. A conceptual model of a flat roof representing a typical single-family building in south-eastern Poland was developed in the Storm Water Management Model (SWMM), with a modeled roof area of 232 m² and 100% of the roof surface covered by the green roof LID system. A total of 24,576 simulation cases were analyzed, considering different values of soil thickness, berm height, initial saturation, vegetation-related storage, rainfall duration, rainfall probability, and rainfall temporal distribution. The hydrological response was evaluated using peak runoff reduction and cumulative runoff volume ratio determined at selected times after rainfall. Predictive models based on the eXtreme Gradient Boosting (XGBoost) algorithm were developed, and their interpretation was performed using the SHapley Additive exPlanations (SHAP) method. The main novelty of the study is its application-oriented framework combining SWMM simulations, XGBoost modeling, and SHAP explainability to distinguish the factors controlling peak runoff reduction and delayed runoff release from a green roof. The results showed that peak runoff reduction ranged from 10.97% to 100.00%, with a median of 99.91%, indicating a generally high capacity of the analyzed system to attenuate peak flow. In contrast, the cumulative runoff volume ratio increased over time, with median values rising from 0.05% immediately after rainfall to 7.91% after 24 h, confirming the significant retention and detention potential of the green roof. SHAP analysis revealed that peak runoff reduction was governed primarily by berm height, whereas cumulative runoff volume was controlled mainly by initial substrate saturation. The results confirm that different mechanisms control short-term and long-term green roof performance. Full article

Indian metropolitan cities such as Mumbai grapple with rapid urbanisation, extreme urban density, high built-up areas, loss of green cover, and shrinking open spaces, resulting in increased impermeable surfaces, urban heat island effects, and frequent flooding occurrences. Modern stormwater management has increasingly been characterised by integrated grey-green approaches; however, cities in the Global North benefit from established policies, technical expertise, and financial resources that enable the systematic and large-scale integration of Blue-Green Infrastructure (BGI) through district-wide geospatial assessment frameworks, unlike many cities in the Global South. Despite growing interest in nature-based stormwater solutions, there remains a dearth of geospatial empirical research from India examining the placement, distribution, performance, and functionality of BGI integrated with existing stormwater management systems in cities such as Mumbai. Furthermore, hydrological modelling using tools such as the Storm Water Management Model (SWMM) for the design, planning, and implementation of BGI in Indian cities remains largely unexplored. This study explores the role of BGI strategies in improving urban stormwater management within high-density Indian cities under a 25-year return period extreme rainfall scenario. Using an integrated approach that combines QGIS-based spatial analysis with EPA-SWMM hydrologic-hydraulic modelling, the research examines runoff behaviour, identifies flooding hotspots, and evaluates the effectiveness of Low Impact Development (LID)-based BGI measures such as permeable pavements, infiltration trenches, and green roofs applied at the ward level in Mumbai’s F/North and G/North Wards. Detailed land use classification, spatial mapping, and rainfall simulation corresponding specifically to a 25-year return period rainfall event was used to assess pre- and post-intervention conditions. The findings indicate that the applied BGI measures led to a 12.6% reduction in peak runoff (137.6 m³/s to 120.2 m³/s) and a 5.5% decrease in total runoff volume (783,510 m³ to 740,410 m³). More importantly, the peak flooding flow rate decreased by 45% (94.1 m³/s to 51.7 m³/s), demonstrating that BGI measures can efficiently reduce peak flooding flows by extending runoff hydrographs during extreme rainfall events. These findings are specifically applicable to the simulated 25-year return period extreme rainfall scenario and may vary under different rainfall intensities or return periods. Less extreme events could potentially experience even greater relative reductions or prevent flooding altogether, while also easing downstream hydraulic loads. Overall, strategically placed BGI interventions can significantly reduce surface runoff and peak flow, thereby enhancing stormwater resilience within spatially constrained urban environments. This study provides a replicable, data-driven framework for catchment-scale stormwater planning in dense Indian cities under extreme rainfall conditions, offering practical insights into methods, local contextual considerations, and spatial planning strategies for policymakers and urban planners seeking to retrofit and adapt existing infrastructure under increasing hydrologic stress and climate variability. Full article

Environmental risk management in urban areas has become increasingly important in recent decades, mainly due to climate change and the anticipated rise in the frequency and severity of extreme rainfall events. In highly urbanized environments, these conditions can intensify hydraulic stress on drainage systems, leading to flooding and surcharge within combined sewer networks. Continuous simulations (2008–2018) were performed using coupled hydrological–hydraulic modeling. Discharge outputs and rainfall series were aggregated at hourly resolution and segmented into independent events. Results show marked seasonality: ~86 events/year and ~118 events/year were identified, with higher occurrence in autumn and winter and fewer events in summer. Event duration tends to be longer from late autumn to spring, whereas summer events are generally shorter. Conversely, peak rainfall and peak discharge exhibit higher median values and variability during summer and early autumn, consistent with intense convective Mediterranean storms. Full article

Coastal urban environments are increasingly exposed to natural hazards, including storm surges, tsunamis, coastal erosion, and flooding, which threaten lives, livelihoods, and infrastructure. Despite their widespread use, conventional hard and soft engineering measures have often proved insufficient to address the escalating risks posed by climate change and rapid urbanisation. This study explores the potential of Nature-Inspired Solutions (NiS) as a complementary pathway to advance resilience in architecture, urban design, and planning. Unlike Nature-Based Solutions that utilise existing ecosystems directly, NiS draw design principles from both biotic and abiotic natural systems, offering innovative models for resilient settlements, coastal infrastructure, and adaptive urban planning. Using a mixed-methods approach that includes systematic and narrative reviews, semi-structured expert interviews, analysis of urban development plans, a panel discussion, and expert brainstorming, this research examines how natural coastal systems inform design interventions. Sri Lanka was selected as the primary case study context due to its exceptional coastal vulnerability, significant climate adaptation policy gaps, and status as a small island developing state representative of the coastal challenges faced by similar contexts globally. Furthermore, Sri Lanka was selected as the case study in accordance with the original research proposal submitted to the University of Huddersfield, which identified the country as a suitable context due to its significant vulnerability to coastal hazards, as outlined above. Field investigations in the Lunawa coastal area documented community-based adaptive practices emerging from multi-generational environmental observation. Analysis reveals how dune morphologies, root structures, living shorelines, and rock pool formations translate into architectural and engineering applications. Findings identify critical implementation challenges, including context-specific requirements, technical knowledge gaps, insufficient policy frameworks, limited practitioner awareness, and uncertainties about economic feasibility, as well as key enablers such as demonstrated ecological effectiveness and the potential of multifunctional infrastructure. The study demonstrates that embedding NiS into risk-informed planning and resilient urban design contributes to climate change adaptation, ecological sustainability, and inclusive governance, while highlighting persistent barriers that require strategic intervention. By bridging ecological wisdom and architectural innovation, NiS offers transformative opportunities to reimagine resilient coastal cities and communities facing escalating climate-induced hazards. Full article

Human-induced climate change has rendered urban areas highly vulnerable to extreme events such as heatwaves, droughts, and floods. This study conducts a scoping review of extreme and compound climate hazards in Iranian urban areas under global warming conditions. Mapping the available literature, 92 authoritative scientific works published between 1999 and 2025 were analyzed. The review synthesizes evidence on the spatiotemporal patterns of heatwaves, drought, torrential rainfall, sea-level rise, and compound hazards across Iran. The results indicate that central, northwestern, eastern, and southern Iran experience the highest heatwave intensity and frequency, with short-duration heatwaves being more common than prolonged ones. Western Iran faces a high risk of torrential rainfall, but urbanization amplifies flood consequences by expanding impervious surfaces and accelerating surface runoff. Coastal areas show high vulnerability to compound flooding due to sea-level rise and storms. The review further reveals that Iran is experiencing hydroclimate whiplash (abrupt transitions between drought and flood) driven by global warming. The study concludes by presenting management suggestions and future research directions for integrated compound hazard management in Iran. Full article

Xinjiang experiences frequent dust storms, posing significant challenges to regional ecological security and public health. Based on the China High-resolution and High-quality Near-surface Air Pollutants (CHAP) dataset and ground monitoring data, this paper adopts the Potential Source Contribution Function (PSCF) to analyze the spatiotemporal characteristics of atmospheric particulate matter across Xinjiang and typical cities and to identify potential source regions and contribution intensities. The results show that (1) PM_2.5 and PM₁₀ concentrations are elevated in southern Xinjiang but reduced in the north, and particulate pollution in most areas has generally decreased. (2) Northern Xinjiang cities have high PM_2.5 in winter, while southern Xinjiang cities keep persistently high PM₁₀ levels. (3) The PM_2.5/PM₁₀ ratio is above 0.35 in northern cities, where pollution is dominated by fine particles affected mainly by human activities; southern Xinjiang is dominated by coarse particles from natural sources. (4) Particulate matter in Urumqi mainly comes from the northern Tianshan Mountains, with winter WPSCF over 0.9. Pollutants in Kashgar originate from both long-distance cross-border dust transmission and local emissions. These findings are of great significance for the sustainable development of Xinjiang and urban agglomerations along the Belt and Road. Full article

Configuring urban hydrological models, such as the Storm Water Management Model (SWMM), for operational use remains onerous for many modellers. We propose aiswmm, a SWMM-specialized agentic runtime, together with an Agentic SWMM workflow that embeds (Agent) Skills and Model Context Protocol (MCP) tools to automate QGIS preprocessing, SWMM configuration, execution, and postprocessing. We demonstrate this natural-language triggered workflow on the Tod Creek watershed (located on the Saanich Peninsula, British Columbia). We also validate the proposed Agentic SWMM workflow at three levels: (i) a QGIS-based watershed-pour-point detection that agrees with the commercial PCSWMM^® method to within 0.88% of the watershed perimeter (approximately 7.5 pixels in the digital elevation model); (ii) byte-identical SWMM output files (Secure Hash Algorithm 256-bit identical) between the command-line execution and the MCP paths across 60 paired simulations, and (iii) peak inflow at the watershed outlet matching to three significant digits between the manual SWMM interface and Agentic SWMM workflows. The results confirm that Agentic SWMM workflow can produce the same outputs with the manual SWMM interface, as they are designed to use the same computational engine. We also propose a verification-first contract and byte-level audit chain that record the inputs, parameters, and outputs of each run, thereby supporting the auditability and reproducibility. The aiswmm runtime, Skills, MCP servers, and byte-level audit chain are released as open source and remain compatible with mainstream agentic runtimes (Codex, Claude Code, Hermes, and OpenClaw) to support reproducible SWMM modelling driven by natural language. Full article

Under climate warming, frequent short-duration extreme precipitation events in coastal megacities exacerbate urban waterlogging, whereas the associated convective mechanisms over complex underlying surfaces remain poorly understood. On 21 July 2023, an extreme short-duration rainfall event (14:00–19:00 LST, peak intensity 127.3 mm h⁻¹) struck Shanghai under weak vertical wind shear (VWS) conditions that cannot be fully explained by classic storm dynamics. Based on multi-source observations, this study shows that the middle and lower troposphere was controlled by warm, moist southwesterly flows, with highly favorable thermodynamic conditions (CAPE ~3300 J kg⁻¹, CIN near zero) that only required weak local lifting to trigger convection. Both 0–1 km and 0–6 km VWS were below 7 m s⁻¹, maintaining stable, upright updrafts that favored high precipitation efficiency. The formation and maintenance of the quasi-linear convective system and the resultant extreme precipitation depended critically on the southerly sea breeze, local mesoscale convergence, and cold pool feedback. Convergence induced by the complex underlying surface (urban friction, high-rise building blocking) played important roles in initiating convective cells, while the interaction between cold pool outflows and the sea breeze from the East China Sea and Hangzhou Bay sustained the system, which evolved into a unique “fish-shaped” rainstorm. Driven by dominant convective propagation toward unstable inland areas, the system moved west–southwestward across the coastal zone into central urban Shanghai. This mechanism differs from both the cold pool–VWS balance under strong shear and the urban convective relay propagation mode under weak VWS documented in previous studies. These findings provide new observational insights into the formation and maintenance of weak-shear, short-duration extreme rainfall in coastal megacities, and carry important implications for identifying convectively prone zones, optimizing spatial development patterns, and improving climate-resilient land management and urban planning practices. Full article

Urban separate sewer systems face significant challenges from rainfall-derived infiltration and inflow (RDII) during the wet season. To achieve the integrated optimization of operational safety, energy consumption, and carbon emissions, this study proposes a dynamic optimal control method. A real-time regulation framework was developed by coupling a Storm Water Management Model (SWMM) hydraulic model with a Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization algorithm within a Model Predictive Control (MPC) structure. Based on real-time water level risks, the framework adaptively adjusts the priority among three objectives: overflow reduction, pumping station energy consumption, and methane emission potential. Using a real separate sewer network in CZ city as a case study, the method was evaluated under light, moderate, and heavy rainfall scenarios. Results show that, compared with traditional rule-based control (RBC) and fixed-weight static model predictive control (SMPC), the proposed dynamic model predictive control (DMPC) strategy reduces overflow by 37.2% during heavy rain, and achieves 16.5% energy savings and a 15.8% reduction in methane emission potential during light rain. The strategy also balances network storage utilization, mitigates local overload, and demonstrates enhanced robustness to rainfall forecast errors, providing an effective technical solution for safe, energy-efficient, and low-carbon urban drainage operation. Full article

This study estimates rainfall disaggregation coefficients for the State of Rio de Janeiro and for the Rio de Janeiro Metropolitan Region (RMRJ) based on automatic rain gauges from the CEMADEN network. A Python-based workflow collected time series, selected stations according to record length, extracted annual extreme events (10 min to 48 h), and calculated sub-daily to daily rainfall ratios for return periods of 2–100 years. The formulations proposed by Pfafstetter and Chen were evaluated through a case study to guide the model selection. In the RMRJ, 109 stations were analyzed and aggregated by municipality, resulting in the metropolitan mean disaggregation coefficient (COERM). The COERM values are close to those proposed by CETESB up to the 30 min–1 h duration range. However, the coefficients were up to 18.8% higher in the duration range between 1 h and 3 h relative to the 24 h rainfall, indicating a stronger temporal concentration of precipitation precisely in durations critical for urban drainage design. Full article

Climate change amplifies urban sustainability challenges, with intensifying sand and dust storm (SDS) hazards highlighting the important role of Ecosystem wind erosion prevention (EWEP) as an ecosystem service (ES). In northern China, a region prone to wind erosion, EWEP mitigates aeolian processes at sand sources and reduces downwind dust transport to urban centers. This study employs the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to simulate diffusion dynamics of EWEP and to assess its hazard mitigation effects for cities in northern China. The findings are as follows: (1) EWEP capacity increased consistently from 2000 to 2024; (2) Aggregated preventive benefits rose, which aligns with the interpretation that systemic ecological restoration reduces dust dispersion; (3) Preventive benefits exhibit stratification across different urban agglomerations. These findings can inform SDS risk management and climate adaptation strategies to support urban sustainability. Full article

Efficient storm drainage systems (SDSs) play a pivotal role in sustainable urban development. In arid regions, urban SDS often underperform during prolonged dry periods, leaving them inoperable due to sediment buildup and clogging from the intrusion of sprawling waste. Municipalities either rely on emergency response to flooding complaints or inspect storm sewers individually to handle flash floods and conserve high-value rainwater. The present study developed a risk-based decision-analysis framework for resource-efficient inspection and maintenance (I&M) planning of SDS to prioritize geographically clustered sub-zones. The study applied the framework to a case study of three urban zones with varying population densities and land use distributions in Buraydah, Qassim, Saudi Arabia. The framework integrates fuzzy synthetic evaluation (FSE) to address data limitations and subjective expert knowledge, with geographic information system (GIS)-based spatial analysis to assess three risk factors: likelihood, consequences, and detectability of sewer clogging potential. In addition to traditional likelihood-based evaluation of the susceptibility of smaller sewers to sediment accumulation due to performance anomalies, the consequence analysis augmented the process by considering land-use characteristics, exemplified by commercial areas exhibiting higher socio-economic losses than open spaces that buffer excess runoff. The detectability factor consolidated the decision analysis by incorporating the impacts of past delayed inspections, deep manholes, and scattered construction-related waste on clogging potential. The analysis identified sub-zones with aged sewers, deep manholes, long-awaited inspections, and high population densities, resulting in a high risk. GIS maps showing distinct impacts of the three factors on overall flood risk facilitate municipalities facing unique urban flooding challenges arising from sediment accumulation during long dry periods, followed by short-duration, high-intensity rainfall. Full article

With the increasing frequency of desilting in urban drainage systems, the safe disposal and resource reuse of sewer sediments have become a prominent practical challenge. Screened sand, the most promising component for resource recovery from sewer sediments, still lacks systematic insight into its pollution risks and the necessity of pretreatment. In this study, 120 raw sewer sediment samples were collected from sanitary, storm, and illicitly connected (IC) storm sewers in Shanghai, alongside seasonal screened sand samples. We systematically characterized their physicochemical properties and heavy metal and antibiotic pollution profiles, and evaluated the purification performance of ultrasonic treatment, sodium hexametaphosphate (SHMP) washing, and their coupled processes. Results revealed significant differences in sediment properties across pipeline types. Screened sand, dominated by SiO₂ and CaO, shows preliminary potential for reuse as a low-grade bulk building material, but its organic loss on ignition (LOI) of 5.29–13.42% exceeded the reuse limit. Concentrations of heavy metals and antibiotics were generally higher in screened sand than in raw sediments, with further enrichment in the fine sand fractions, indicating that screening only redistributed contaminants rather than eliminated them. The coupled ultrasonic–SHMP process, applied for the first time to screened sand from sewer sediments, achieved optimal performance, with a maximum LOI reduction and over 85% removal of certain antibiotics, without damaging the sand’s mineral skeleton. This study provides a scientific basis for the safe resource reuse of screened sand. Full article

Urban drainage systems (UDSs) are critical built assets increasingly challenged by short-duration extreme rainfall, aging infrastructure, and rising surcharge risk. Physics-based hydrodynamic models are widely used for system assessment, but their high computational cost limits real-time operational prediction. Existing data-driven prediction approaches improve computational efficiency, but often rely mainly on sensor inputs and provide limited asset-level interpretation. This study develops an explainable digital twin for real-time prediction of storm-driven water level response in a separate sewer network in the Yangtze River Delta, China. The framework integrates 5 min monitoring and SCADA data, including water level, flow, pump status, and rainfall, with GIS and as-built asset information, including pipe geometry, hydraulic capacity, catchment characteristics, and network connectivity. A hybrid TCN-LSTM model was developed to predict water level and surcharge risk probability at 15–60 min lead times. A surrogate-based SHAP module was used to explain model predictions at the node and subcatchment scales. Multi-source fusion reduced the RMSE by approximately 18% compared with sensor-only baselines. The SHAP results showed that the pipe capacity-related variables and upstream contributing area were the main drivers of surcharge onset. The framework provides interpretable, operationally relevant predictions to support the resilience-oriented management of urban drainage systems. Full article

Best Management Practices (BMPs) are key instruments for improving the resilience of urban environments to climate change and land-use pressures. They mitigate pluvial flooding and heat waves by restoring natural soil processes and providing multiple co-benefits at both the building and urban scale. Urban planning increasingly requires comprehensive assessments of the multiple benefits provided by BMPs, which extend beyond their hydrological function. Traditional hydrological models such as SWMM5 are robust and widely used for simulating drainage performance, but they are not designed to evaluate wider co-benefits or to be easily applied in planning contexts. For this reason, simplified tools have been developed to offer quicker and more accessible assessments, although their reliability, especially in reproducing hydrological outcomes, remains uncertain. This study examines the Green Values Stormwater Management Calculator (GVC), which has been developed to combine hydrological and co-benefit evaluations within a single, easy-to-use framework. In this preliminary analysis, we tested the hydrological module of the GVC on a 290-hectare mixed-land-use catchment in the metropolitan area of Milan, where two calibrated SWMM5 drainage models were available as benchmarks: one representing current conditions and another including a retrofitting design with BMPs. The scenarios were simulated with the GVC and compared under selected storm events in terms of total runoff volumes. The results show that the GVC reproduces current-condition runoff with good accuracy, but tends to underestimate BMP efficiency. Full article