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Safe and sustainable water sources are a serious global concern because of growing population, urbanization, industrialization, and climate change. The conventional water surveillance systems that rely on periodic sampling and laboratory analysis fail to provide time-sensitive and high-resolution data utilized for proactive water management. Artificial Intelligence of Things (AIoT) offers a viable solution, as they can provide tools of constant active monitoring and predictive analytics. The integration of IoT sensor networks with machine learning (ML) methods enables real-time data-driven water resource monitoring and intelligent decision-making, enhances water quality assessment, supports early detection of anomalies, improves predictive capabilities for floods and droughts, and facilitates efficient irrigation and reservoir management, ultimately leading to sustainable and resilient water management systems. The paper presents an extensive overview of AIoT solutions for water quality monitoring and water resource management, including IoT sensor networks for real-time data acquisition, machine learning methods for prediction, classification, anomaly detection, and edge computing platforms for data processing and decision support. This study also highlights existing possibilities, obstacles, and research gaps identified through a review of the recent literature. Key challenges reported across multiple studies include limited data availability, sensor calibration bias, integration of heterogeneous data, and insufficient model interpretability. Advanced paradigms such as digital twin systems, TinyML, federated learning, and explainable AI (XAI) are examined as enabling technologies to enhance system efficiency, flexibility, and transparency. Future research directions are outlined to develop scalable, interpretable, and real-time water management solutions. Full article

Urban areas are increasingly exposed to water-related challenges, including flood risk and water scarcity, amplified by climate change, population growth, and extensive soil sealing. Addressing these pressures requires integrated stormwater management (SWM) strategies that balance hydraulic, environmental, and social objectives. This study introduces a novel, replicable Key Performance Indicator (KPI)-based assessment framework for 36 green–blue and grey sustainable stormwater management systems (SWMSs), designed to enable cross-typology, multiscale comparison. Six KPIs, encompassing flood regulation, water consumption, water quality, air quality, environmental amenity, and biodiversity potential, are derived through a critical synthesis and harmonisation of the literature and complemented with new parameters and sub-parameters to address existing methodological gaps. The framework structures evaluations into six analytical tables and one summary table, ensuring transparent, systematic, and comparative assessment of heterogeneous solutions. Application to a pilot project in Rome demonstrates how integrating KPI evaluation with parametric hydraulic modelling provides actionable insights for solution selection. It also facilitates identification of potential synergies between performance dimensions, enhancing its value as a decision-support tool in preliminary design. Overall, the study demonstrates the research value of multi-scalar, performance-based approaches for urban water planning, highlights the transferability of resilient stormwater strategies in climate-sensitive contexts, and identifies promising avenues for future research, including multi-sectoral integration, trade-off analysis, and cross-platform application. Full article

Most computational studies of vehicle hydroplaning have emphasized structural realism through fluid–structure interaction, tire deformation, tread geometry, and pavement surface characterization. By contrast, the hydrodynamics governing the flow in the tire vicinity, particularly the role of turbulence, have received comparatively limited attention. In a significant number of studies, the flow has been treated as laminar despite turbulent flow conditions, while in a few other studies turbulence modeling has been adopted without an explicit assessment of its impact on hydroplaning predictions. In this study, we present a simplified three-dimensional computational fluid dynamics (CFD) model designed to isolate the flow regimes governing hydroplaning and to quantify the mean effect of the turbulence modeling on the predicted hydroplaning speed. Using a finite-volume formulation with a volume-of-fluid representation of the air–water interface, the flow around and beneath a smooth 0.7 m-diameter tire sliding in locked-wheel mode over a flooded, nominally smooth pavement is simulated. The tire is represented as a rigid body with an idealized rectangular bottom patch whose area is determined from the tire load and inflation pressure, avoiding the need to prescribe a measured or assumed deformed footprint. Steady-state hydroplaning is modeled for a uniform upstream water film thickness of 7.62 mm with a 0.5 mm gap between the tire and the pavement, over tire inflation pressures ranging from approximately 100 to 300 kPa, and predictions are verified against the empirical NASA hydroplaning equation. For these conditions, simulations without turbulence closure exhibit a consistent, systematic underprediction of the hydroplaning speed of approximately 13.5% relative to the NASA relation. Incorporating turbulence effects through Reynolds-averaged closures substantially reduces this bias, with average deviations of about 6% for the realizable k–ε model and 2.4% for the shear stress transport (SST) k–ω model. An analysis of the results indicates that hydrodynamic lift is dominated by pressure buildup associated with stagnation at the lower leading edge of the tire, with a significant contribution from shear-dominated flow in the thin under-tire gap, and that turbulence acts to moderate the integrated lift from these pressure fields. These results demonstrate that explicitly accounting for turbulence in the tire vicinity is essential for reproducing empirical hydroplaning trends and for avoiding systematic bias in CFD-based hydroplaning predictions. Full article

This paper developed a probabilistic framework for system level reliability and risk assessment that coupled hydraulic loading with structural response and explicitly modelled cascading interactions and statistical dependence between components. The contribution is a system-level reliability and risk modelling methodology that integrates dynamic cascading interactions, non-stationary design-life reliability accumulation, and system-level optimisation within a unified Monte Carlo architecture. Dynamic Monte Carlo simulation was used to evaluate individual, joint, conditional, and system-scale probabilities of failure across varying flood magnitudes and design lives. Model verification confirmed that discretisation and sampling errors were small relative to parameter-driven variability. Results showed that long-term system reliability arose from the combined influence of flood frequency, exposure duration, and the strength of interaction between interdependent structures. Frequent loading accelerates the accumulation of failure probability through repeated events, whereas rare events contribute more slowly but dominate extreme outcomes, indicating that cumulative reliability cannot be inferred by the linear extrapolation of annual probabilities. In an examined diversion–levee–basin configuration, strong structural coupling amplified vulnerability by contracting joint stability margins and increasing conditional failure probabilities. The system-level optimisation of structural parameters over the examined design life reduced cumulative system failure probability from 0.305 to 0.153, whereas single-component optimisation redistributed risk within the system without reducing total system risk. The framework advances beyond static risk analysis by integrating time-dependent reliability, cascading dependencies, and design-life optimisation for system-scale mitigation. Full article

Flood and flash flood events can generate severe hydraulic actions on civil structures, requiring modeling strategies able to link flow features to structural damage. This paper proposes a two-scale numerical framework based on advanced finite element modeling to assess the vulnerability of structures subjected to inundation and flood-driven impact. At the macroscale, the flood propagation and the interaction with the built environment are simulated through the depth-averaged Shallow Water Equations, adopting a time-explicit interface treatment to capture the evolution of the free surface. The macroscale model provides time-dependent water depth and flow velocity along the external surfaces of the structure, which are then used to derive hydrostatic and hydrodynamic actions, also in comparison with code-based formulations. At the mesoscale, these actions are transferred to a detailed structural model to investigate the nonlinear mechanical response of the building. Structural components are described through a coupled damage–plasticity constitutive law, enabling the prediction of stiffness degradation, cracking-driven damage patterns, and the identification of the most critical structural zones under flood loading. The proposed workflow is finally applied to a real structure located in the municipality of Cosenza (Italy), demonstrating the capability of the approach to combine hydraulic intensity measures with physics-based structural damage assessment, supporting scenario analyses and risk mitigation evaluations. Full article

This article addresses the regeneration of extractive landscapes through the case study of the abandoned quarry system of Cutrofiano in the Salento region of Southern Italy, positioning the quarry as a critical interface between geology, architecture, and contemporary environmental challenges. The study aims to redefine the quarry landscape not as a residual void, but as a potential ecological and cultural infrastructure. The research adopts an interdisciplinary methodology combining geomorphological and geotechnical surveys, historical and cartographic analysis, spatial interpretation, and a multi-criteria assessment framework to identify vulnerabilities and transformation potentials. The results include a strategic masterplan articulated into three integrated interventions: the conversion of the open-pit quarry into a flood-control basin for hydrogeological risk mitigation and sustainable water management; the transformation of the quarry floor into an energy park; and the design of cultural spaces for public use and territorial enhancement. These strategies demonstrate the feasibility of reconciling environmental safety, renewable energy production, and heritage valorization within a single morphological logic. The study concludes that the quarry can be reinterpreted as a regenerative landscape model, offering transferable tools for Mediterranean contexts characterized by similar geological and socio-economic conditions. Full article

Intensifying hydroclimatic extremes have heightened the need for basin-scale indicators of antecedent wetness that are relevant to flood responses. However, ground-based soil-moisture observations are spatially sparse, and satellite products frequently exhibit temporal gaps. To address this limitation, this study integrated satellite- and ground-soil-moisture observations, hydro-meteorological variables, and observed streamflow data from 2018 to 2024 across 26 standard basins (SBs) within three dam basin regions in South Korea: the Nam River Dam (NGD) and the upstream and downstream regions of the Seomjin River Dam (SJD). Using this integrated dataset, we quantified the relationships among precipitation, basin wetness, and rapid discharge increases, subsequently deriving composite thresholds for flood early warnings. For each SB, we trained a Random Forest regression model using satellite-soil-moisture and basin-representative hydro-meteorological inputs—including 1-day accumulated precipitation (P_1d), 7-day accumulated precipitation (P_7d), the antecedent precipitation index (API), and related meteorological variables—to estimate a continuous, daily basin-representative soil-moisture series (SM_RF). Validation results indicated that the coefficient of determination (R²) ranged from 0.6 to 0.7 for most SBs. Extreme event days were consistently associated with elevated values of SM_RF, P_1d, P_7d, and API, demonstrating that antecedent wetness significantly influences the likelihood of rapid discharge events. Finally, composite threshold scanning yielded candidate rules characterized by high precision, moderate hit rates, and low false-alarm rates, confirming the efficacy of the proposed framework for developing flash-flood early-warning thresholds in South Korean dam basins. Full article

The middle and lower reaches of the Yangtze River Basin occupy a strategically pivotal position in regional development; yet extreme drought-runoff events pose severe threats to water supply and ecological security. Despite this, systematic research gaps persist, including the lack of a unified definition, standardized identification criteria, and clear understanding of formation mechanisms for extreme drought-runoff. To address these limitations, this study focused on extreme drought-runoff in the basin, utilizing 1956–2024 discharge data from four mainstream hydrological stations and meteorological data from 171 stations. Quantitative discrimination criteria were established via Pearson-III frequency analysis; meteorological characteristics were analyzed using the Meteorological Drought Comprehensive Index; and formation mechanisms were explored through partial correlation analysis and multiple linear regression. This study innovatively proposed a basin-wide three-level quantitative discrimination criterion for drought-runoff based on the June–November flow frequency of key mainstream stations, which is distinguished from single-indicator drought identification methods (SPI/SPEI/SSI) by integrating basin-scale hydrological coherence and seasonal drought characteristics. The results revealed basin-wide extreme drought-runoff in 2006 and 2022, severe drought-runoff in 1972 and 2011, and relatively severe drought-runoff in 1959, 1992, and 2024. Typical extreme drought-runoff events were characterized by sustained low precipitation and high temperatures. Meteorological factors emerged as the primary driver during June–September, while reservoir operation and riverine water intake played secondary roles. Notably, the large-scale reservoir group in the Yangtze River Basin (53 key control reservoirs) helped alleviate drought-runoff impacts from December to May (non-flood season) via water supplementation. These findings provide a robust scientific basis for precise drought-runoff prediction and the development of targeted adaptation strategies in the Yangtze River Basin. Full article

The rapid growth of cities and expansion of impervious surfaces have intensified surface runoff problems and urban flooding risk. This scenario, exacerbated by the effects of climate change, demands sustainable and integrated solutions. Thus, this study evaluates the pre-feasibility of implementing sustainable urban drainage systems (SUDS) in the Monteverde neighborhood in Montería, Colombia; an area that is critically affected by floods during rainfall events. Using the storm water management model (SWMM) and hydrological simulations based on design hyetographs for different return periods, the performance of a conventional drainage system was compared with five scenarios using SUDS. To determine the modeling scenarios, a decision-making method through the analytic hierarchy process, AHP, was used to select the most appropriate SUDS. The results showed that implementing storage tanks reduces peak flows at outlets 1 and 2 up to 50%, while bioretention zones and rain gardens in isolation showed reduced effectiveness (<6%). Combining strategies slightly improves overall efficiency, although the impact keeps being dominated by tanks. This study demonstrates that the incorporation of SUDS in vulnerable urban areas lessens water risks, strengthens urban resilience, promotes rainwater harvesting, and eases the transition to a more sustainable infrastructure. In addition, it proposes a methodology that can be replicated in other similar Latin American cities. Full article

Reservoir release induced flooding is a major downstream hazard worldwide, yet most warning systems rely on hydraulic modeling and underuse real time reservoir operation data. This study presents a data driven framework to detect flood discharge events, assess downstream operational severity, and forecast daily discharges using deep learning. The approach was validated at the Ba Ha hydropower reservoir (Vietnam) with inflow, discharge, water level, and CHIRPS rainfall data to represent basin-scale precipitation forcing. More than 160 discharge events were identified using a composite Operational Severity Index (OSI) based on peak discharge, duration, and rise rate; although only ~2% were extreme, they posed the greatest risks. Among three Transformer-based models, Informer achieved the best short-term forecasting performance (RMSE ≈ 78 m³/s, R² ≈ 0.80), while Autoformer showed greater stability at longer horizons (3–7 days). In contrast, all models exhibited reduced skill under abrupt and extreme discharge conditions. These results demonstrate that combining trend and anomaly-aware modeling enables reliable discharge prediction and severity assessment without complex hydraulic simulations. The proposed framework provides a practical foundation for reservoir early warning systems by transforming routine operational data into actionable flood-risk information. Full article

Satellite-based precipitation estimates (SPE) provide essential spatial coverage and near real-time availability for hydrological applications but often exhibit systematic biases in regions characterized by complex terrain and strong climatic variability, limiting their reliability for flood-related studies. To address these limitations, this study proposes an Adaptive Regularization framework integrated within a Long Short-Term Memory (LSTM) model to enhance satellite–gauge rainfall fusion beyond conventional optimization strategies. The framework dynamically adjusts learning rate and weight decay during training based on validation performance and overfitting indicators, improving training stability, data efficiency, and model generalization across diverse precipitation regimes. The proposed approach was applied to refine Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-Final) daily rainfall estimates over the flood-prone east coast of Peninsular Malaysia. Model performance was assessed against ten optimization algorithms using correlation coefficient (CC), mean absolute error (MAE), normalized root mean squared error (NRMSE), percentage bias (PBias), and Kling–Gupta efficiency (KGE). Results show that the Adaptive Regularization framework consistently outperforms all benchmark optimizers, achieving an MAE of 6.87, CC of 0.68, NRMSE of 1.84, and KGE of 0.56. Overall, the proposed framework enhances spatial consistency and robustness across monsoon seasons, offering a scalable solution for improving SPE in flood-prone regions. Full article

Digital elevation models (DEMs) are foundational for critical tasks such as flood inundation simulation, disaster risk assessment, and ecosystem monitoring in coastal zones, yet their vertical accuracy is significantly compromised by complex terrain and surface characteristics. This study quantitatively decomposes the vertical errors of three 30 m global DEMs (COP30, NASADEM, and AW3D30) across the subtropical coastal region of Southeast China using ICESat-2 ATL08 data as a reference. By integrating an eXtreme Gradient Boosting (XGBoost) model with SHapley Additive exPlanations (SHAP), we successfully decoupled systematic biases from random noise. The results show that NASADEM achieved the lowest RMSE (7.775 m), followed by COP30 and AW3D30. While the Terrain Ruggedness Index (TRI) and categorically encoded Land Cover were identified as the universally dominant error drivers across all datasets, explainable analysis revealed distinct secondary mechanisms: X-band COP30 is notably susceptible to canopy height, exhibiting significant positive bias in forests exceeding 15 m; C-band NASADEM shows a systematic bias related to topographic position, typically overestimating ridges and underestimating valleys; and optical AW3D30 is significantly affected by stereo-matching errors. Furthermore, the analysis quantified a systematic error component of ~40%. These findings provide a data-driven basis for DEM selection and highlight that accuracy improvements should prioritize vegetation removal for radar DEMs and enhanced stereo-matching for optical models. Full article

Flooding is an increasingly frequent climate hazard with the potential to mobilize environmental contaminants and elevate human health risks. In this study, we assessed heavy metals and metalloids across five sites arranged along a flood-risk gradient from low to high. Six replicate samples per site (n = 30 per contaminant) were collected in a single sampling event. Contaminants were evaluated using the US Environmental Protection Agency (EPA) risk assessment framework to calculate chronic daily intake (CDI), hazard quotients (HQs), and lifetime cancer risk. Arsenic, chromium, and nickel emerged as the most concerning cancer drivers, with nickel cancer risk consistently exceeding 1 × 10⁻³ (equivalent to one additional cancer case per 1000 exposed individuals) and arsenic at 4.4 × 10⁻⁴ (about 1 in 2250). Lead posed non-cancer risks (HQ = 0.912, near the threshold of concern), while cobalt demonstrated a significant decreasing gradient with increasing flood-risk (p = 0.018). Arsenic and thallium more than doubled in concentration at high-flood sites relative to low-flood sites, while cadmium, cobalt, and nickel decreased. These findings suggest flooding may mobilize arsenic, lead, and thallium, while diluting or displacing other metals such as cadmium, cobalt, and nickel. Organs of concern include the liver and kidneys for arsenic, cadmium, nickel, and cobalt, the brain and bones for lead, and the lungs and liver for chromium. Although statistical significance was limited by the small sample size, effect sizes and fold-changes indicate meaningful flood-related differences. This study highlights the importance of considering flood-risk in contaminant hazard assessments and the need for flood-adaptive risk management strategies in vulnerable communities. Full article

Global climate change continues to intensify, leading to an increase in extreme meteorological disasters characterized by high intensity, frequency, and extensive impact. Chinese cities are facing increasingly severe flood disaster risks. As the fundamental unit of the urban system, scientifically quantifying a community’s post-disaster recovery capacity provides a crucial basis for formulating disaster prevention and mitigation strategies. Existing research has largely focused on either quantitative resilience assessment of communities or the functional recovery of specific systems within communities, falling short of meeting the quantitative needs for assessing community functional recovery after flood disasters. Given this, this paper aims to construct a community functional recovery model based on different land use types to precisely quantify the recovery trajectory of community functions. First, the MIKE 21 two-dimensional hydrodynamic model is employed to simulate 100-year and 200-year flood scenarios, obtaining dynamic inundation data at the community scale. Subsequently, a semi-Markov process is adopted to model the recovery of individual buildings, with the aggregated building functions within the community summarized to derive building recovery curves. A road network topology model is constructed using the Space L method, and network global efficiency is applied to quantify community road functionality. Green space functional loss is quantified based on the percentage of inundated areas. Finally, calculation is performed based on the proposed dual-layer computational framework consisting of a connectivity layer and a functional layer, and the overall community functional recovery curve after the disaster is generated, thereby achieving precise quantification of the recovery process. The research findings indicate that increased disaster intensity significantly amplifies functional losses and recovery delays. Concurrently, distinct land use types exert markedly different impacts on community recovery. This study quantitatively reveals the phased dominant roles of various land use types throughout the community recovery process, providing a scientific basis for formulating phased, prioritized resilience enhancement strategies. Full article

Pythiosis is a neglected infectious disease caused by the aquatic oomycete Pythium insidiosum and remains underrecognized in cattle, particularly in tropical regions. Here, we report the first molecularly confirmed outbreak of bovine pythiosis in the Amazon biome, affecting more than 400 animals raised under extensive production systems and areas with prolonged exposure to standing water. Clinically affected cattle presented ulcerative and exudative cutaneous lesions, predominantly involving the distal limbs. Given the diagnostic challenges associated with pythiosis, etiological confirmation was achieved through quantitative PCR (qPCR) targeting the internal transcribed spacer (ITS) region of P. insidiosum, providing rapid and specific molecular detection during the outbreak investigation. Therapeutic interventions were implemented as part of routine field management, including intramuscular triamcinolone combined with topical copper sulfate; this regimen was associated with clinical improvement in a substantial proportion of affected animals, though treatment efficacy was not formally evaluated. The outbreak occurred in flood-prone pastures during the rainy season, highlighting the role of aquatic environments in pathogen transmission. These findings expand the current understanding of bovine pythiosis in tropical ecosystems and underscore the importance of molecular diagnostics, outbreak surveillance, and a One Health approach for the identification and management of water-associated pathogens in livestock. Full article