Search Results (137)

Intensity–Duration–Frequency (IDF) curves are the cornerstone of hydraulic infrastructure design, yet standard methodologies often fail to account for the complex dependence structure of rainfall characteristics and the non-stationary effects of climate change. This study develops a robust Regional Copula Framework for the Kastoria Lake basin, Greece, utilizing sub-hourly rainfall records from four meteorological stations (2007–2024). We employ a forensic data quality control process to pool 277 independent storm events. Unlike traditional approaches, our analysis demonstrates that the Generalized Extreme Value (GEV) distribution (ξ = 0.348) significantly outperforms the standard Lognormal distribution in modeling heavy-tailed rainfall intensities. The dependence between storm duration and intensity was found to be consistently negative (τ = −0.35), a structure best captured by the Rotated Gumbel (90°) copula, which physically reflects the region’s convective storm dynamics. Trend analysis revealed a statistically significant decrease in peak intensity (τ = −0.14) coupled with an increase in storm duration (τ = 0.22), a hydro-climatic shift that contrasts with increasing intensity trends reported in the wider Balkan region. These findings suggest a regime transition from flash-flood dominance to volume-critical events, necessitating updated design criteria that integrate both multivariate dependence and local climatic non-stationarity. Full article

Reduced levels of vitamin D are associated with increased incidence and mortality of sepsis. Nonetheless, the effectiveness of vitamin D supplementation in improving sepsis patients’ outcomes continues to be debated. In this research, which was conducted as a retrospective cohort analysis, data obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV 3.0) were used. The focus of the study was on vitamin D3 administration to sepsis patients while in the ICU. The primary outcome measurement was 28-day ICU mortality, with secondary outcomes of mechanical ventilation duration, percentage of patients receiving mechanical ventilation, and ICU stay length. The Kaplan–Meier curve analysis, Cox regression analysis, and subgroup analyses were performed to explore the link between vitamin D3 supplementation and sepsis prognosis. A 1:1 propensity score matching (PSM) approach was used to strengthen the reliability of the results. Before matching, the cohort comprised 28,524 patients, which was reduced to 4,856 after PSM. The analysis revealed that vitamin D3 supplementation was associated with a lower 28-day ICU mortality rate (HR = 0.71, 95% CI: 0.64–0.78, p < 0.001). Kaplan–Meier curve analysis revealed significantly greater survival probabilities in the group receiving vitamin D3 than in the group not receiving vitamin D3 (p < 0.001). Subgroup analysis showed that total cumulative exposure to vitamin D3 was more strongly associated with 28-day ICU mortality (p < 0.001), whereas daily dose and dosing frequency showed no significant association. The results after PSM and subgroup analysis were consistent with those of the original cohort study, further confirming the robustness of the results. Overall, vitamin D3 supplementation is associated with lower 28-day ICU mortality and better outcomes in patients with sepsis. However, given the retrospective observational design, large-scale prospective randomized controlled trials are warranted to validate these observational associations and establish causal effects. Full article

Floods are among the extreme events associated with climate variability in the Lacramarca River basin, located in the department of Ancash, Peru. Meteorological phenomena such as El Niño during the periods 1982–1983 and 1997–1998, as well as the Coastal El Niño in 2017, constitute key reference events that motivated the development of the present study, based on a case study conducted in the area between the rural settlements of Santa Clemencia and Pampadura. This research is based on maximum precipitation data derived from historical climate records and from the climate scenarios ACCESS 1-3, HadGEM2-ES, and MPI-ESM-MR, as well as the median projected scenario for 2050, obtained from the National Meteorology and Hydrology Service of Peru (SENAMHI) data platform. This information was analyzed considering the spatial location of the basin and its position relative to the area of interest, using Intensity–Duration–Frequency (IDF) curves. To demonstrate the changes in the river hydrological behavior before and after the 2017 Coastal El Niño event, a Random Forest modeling approach was applied using Sentinel-2 satellite imagery. Design peak discharges for return periods of 50, 100, and 140 years were estimated using the HEC-HMS software. Hydraulic simulation of the Lacramarca River basin, carried out using HEC-RAS version 6.7 beta 3 and IBER version 3.3.1 software, made it possible to identify flood-prone areas affecting agricultural land and areas adjacent to population centers, covering 149,000 m² and 172,000 m² for return periods of 100 and 140 years, respectively, based on information from the historical scenario. In contrast, using data from the 2050 projection scenario, affected areas of 242,000 m² and 323,000 m² were estimated for the same return periods. Full article

Climate change is expected to modify precipitation patterns and increase flood hazard in urban areas, potentially affecting critical infrastructures such as wastewater treatment plants (WWTPs), often located in flood-prone zones. This study assesses the impacts of climate-driven changes in extreme rainfall on flood hazard, pedestrian safety, and tangible physical damage at WWTPs in the Metropolitan Area of Barcelona, Spain. Twenty-four future flood scenarios are defined using CMIP6-based downscaled climate projections (SSP126 and SSP585), two time horizons (2041–2070 and 2071–2100), and different climate model percentiles. Climate Change Coefficients derived from updated Intensity–Duration–Frequency curves are applied to hydrodynamic simulations to evaluate flooded and high-hazard areas for plant workers, as well as direct economic damage at the Montcada i Reixac WWTP, used as a case study. Results indicate limited changes under SSP126, while SSP585 leads to systematic increases in hazard extent and damage, particularly for long-term projections (2071–2100) and extreme percentiles (90th). A large dispersion among climate models is also observed, especially for extraordinary flood events. Finally, a site-specific nature-based adaptation measure targeting frequent floods is proposed, demonstrating the potential of integrated assessments to support sustainable adaptation planning and to reduce the Expected Annual Damage in future climate conditions by 93%. Full article

Reliable estimation of extreme rainfall is essential for hydraulic design and flood risk mitigation, particularly in arid regions where rainfall exhibits strong temporal and spatial variability. This study presents a statistical framework for developing rainfall intensity-duration-frequency (IDF) curves, complemented by a machine learning-based assessment of model bias and performance. The analysis was conducted using data from ten rainfall stations located within or near the Wadi Al-Rummah Basin. Annual maximum series (AMS) from 1969 to 2024 were first reconstructed to address missing years using a modified normal ratio method (NRM) combined with nearest-station selection, ensuring spatial consistency while preserving station-specific rainfall characteristics. Six probability distributions (Weibull, Gumbel, gamma, lognormal, generalized extreme value (GEV), and generalized Pareto) were fitted to each station, and the best-fit distribution was identified using multiple goodness-of-fit (GOF) criteria, including the Kolmogorov–Smirnov (K-S) test, Anderson–Darling (A-D) test, root mean square error (RMSE), chi-square (χ²) statistic, Akaike information criterion (AIC), Bayesian information criterion (BIC), and the coefficient of determination (R²). Statistical IDF curves were then developed for durations ranging from 5 to 1440 min and return periods from 2 to 1000 years. To evaluate the robustness of the statistically derived IDF curves, three machine learning (ML) models, multiple linear regression (MLR), regression random forest (RRF), and multilayer feed-forward neural network (MFFNN), were trained as surrogate models using duration, return period, and station geographic attributes as predictor variables. Model performance was evaluated using RMSE, MAE, and mean bias metrics across stations and return periods. The lognormal distribution emerged as the best-fit model for four stations, while the Gumbel and gamma distributions were selected for two stations each. Overall, no single probability distribution consistently outperformed others, indicating station-dependent behavior. Among the machine learning models, the MFFNN achieved the closest agreement with statistical IDF estimates ($RMSE\approx 0.97$, $MAE\approx 0.65$, $bias\approx -0.02$), followed by RRF and MLR based on global average performance across all stations and return periods. The proposed framework offers a reliable approach for rainfall IDF development and evaluation in arid region watersheds. Full article

The November 2025 extreme rainfall event associated with Tropical Cyclone Ditwah caused catastrophic flooding and landslides across Sri Lanka. This study presents a national-scale statistical and Intensity–Duration–Frequency (IDF)-based assessment of the event using long-term rain gauge observations, extreme value analysis, and climate scenario-based projections. The 24-h rainfall data from 46 stations were analyzed for 1-, 2-, and 3-day durations. Historical annual maximum series were extracted and compared with the 2025 event to identify record-breaking extremes. Rainfall volumes were also estimated and compared with the island’s Average Annual Rainfall (AAR) and volumes from major flood events in 2010 and 2016. The November 2025 event exceeded historical maxima at 14 stations, with estimated return periods frequently surpassing 1000 years. The cumulative rainfall volume from 26–28 November accounted for 15.8% of Sri Lanka’s AAR. Updated IDF curves incorporating the event showed marked upward shifts, with intensities at some locations matching or exceeding projections under high-emission climate scenarios. The results highlight the inadequacy of existing design standards in capturing emerging extremes and the need for urgent updates to Sri Lanka’s national IDF relationships to support climate-resilient flood risk management and infrastructure planning. Full article

The intensification of extreme rainfall events under changing climate regimes has heightened concerns over nutrient losses from paddy agriculture, particularly nitrogen (N), a primary contributor to non-point source pollution. Despite advances in drainage management, limited studies have integrated probabilistic rainfall modeling with N transport simulation to evaluate mitigation strategies in rice-based systems. This study addresses this critical gap by coupling the Log-Pearson Type III (LP-III) distribution with the DRAINMOD-N II model to simulate N dynamics under varying rainfall exceedance probabilities and drainage design configurations in the Kunshan region of eastern China. The DRAINMOD-N II showed good performance, with R² values of 0.70 and 0.69, AAD of 0.05 and 0.39 mg L⁻¹, and RMSE of 0.14 and 0.91 mg L⁻¹ for NO₃⁻-N and NH₄⁺-N during calibration, and R² values of 0.88 and 0.72, AAD of 0.06 and 0.21 mg L⁻¹, and RMSE of 0.10 and 0.34 mg L⁻¹ during validation. Using around 50 years of historical precipitation data, we developed intensity–duration–frequency (IDF) curves via LP-III to derive return-period rainfall scenarios (2%, 5%, 10%, and 20%). These scenarios were then input into a validated DRAINMOD-N II model to assess nitrate-nitrogen (NO₃⁻-N) and ammonium-nitrogen (NH₄⁺-N) losses across multiple drain spacing (1000–2000 cm) and depth (80–120 cm) treatments. Results demonstrated that NO₃⁻-N and NH₄⁺-N losses increase with rainfall intensity, with up to 57.9% and 45.1% greater leaching, respectively, under 2% exceedance events compared to 20%. However, wider drain spacing substantially mitigated N losses, reducing NO₃⁻-N and NH₄⁺-N loads by up to 18% and 12%, respectively, across extreme rainfall scenarios. The integrated framework developed in this study highlights the efficacy of drainage design optimization in reducing nutrient losses while maintaining hydrological resilience under extreme weather conditions. Full article

Intensity–Duration–Frequency (IDF) curves are essential tools in the design of stormwater management systems and are often used over long periods without frequent updates. However, the continuous collection of rainfall data and the expansion of monitoring networks call for regular revisions of these curves. In Romania, current engineering and hydrological practices still rely on regionalized IDF graphs developed in 1973. Given the ongoing effects of climate change—particularly the increased frequency and, more significantly, intensity of extreme rainfall events—updating these curves has become critical. Incorporating recent observations is essential not only for methodological accuracy but also to support climate-resilient infrastructure design. This study employs updated IDF curves provided by the National Administration of Meteorology, based on 30 years of precipitation records from 68 meteorological stations across Romania. The main objective is to evaluate alternative regionalization approaches—including clustering methods, geographic proximity analysis, and hourly precipitation isolines for a 1:10 Annual Exceedance Frequency—to develop a new regionalization model and the corresponding nationwide IDF relationships. A comparative analysis using raster-based regional rainfall datasets from both the 1973 and 2025 regionalizations revealed significant changes in precipitation patterns. Short-duration rainfall events (5, 10, and 30 min) have increased in intensity across most regions, while long-duration events (3, 6, 12, and 24 h) have generally decreased in magnitude in several areas. These findings highlight a growing trend toward more intense short-term convective storms, underlining the urgent need for improved flash flood prevention and urban stormwater management strategies. Full article

Intensity–Frequency–Duration Curves (IDF curves) are a tool applied in hydraulic and hydrology engineering to design infrastructure for rainfall management. They express how precipitation, with a defined duration (D) and intensity (I), is frequent in a certain area. They are built from past recorded rainfall series, applying the extreme value statistics, and they are considered invariant in time. However, the current climate change projections are showing a detectable positive trend in temperatures, which, according to Clausius–Clapeyron, is expected to intensify extreme precipitation (higher temperatures bring more water vapour available for precipitation). According to the IPCC (Intergovernmental Panel on Climate Change) reports, rainfall events are projected to intensify their magnitude and frequency, becoming more extreme, especially across “climatic hot-spot” areas such as the Mediterranean basin. Therefore, a sensible modification of IDF curves is expected, posing some challenges for future hydraulic infrastructure design (i.e., sewage networks), which may experience damage and failure due to extreme intensification. In this paper, a methodology for reconstructing IDF curves by analysing the EURO-CORDEX climate model outputs is presented. The methodology consists of the analysis of climatic rainfall series (that cover a future period up to 2100) using GEV (Generalised Extreme Value) techniques. The future anomalies of rainfall height (H) and their return period (RP) have been evaluated and then compared to the currently adopted IDF curves. The study is applied in Lombardy (Italy), a region characterised by strong orographic precipitation gradients due to the influence of Alpine complex orography. The future anomalies of H evaluated in the study show an increase of 20–30 mm (2071–2100 ensemble median, RCP 8.5) in rainfall depth. Conversely, a significant reduction in the return period by 40–60% (i.e., the current 100-year event becomes a ≈40–60-year event by 2071–2100 under RCP 8.5) is reported, leading to an intensification of extreme events. The former have been considered to correct the currently adopted IDF curves, taking into account climate change drivers. A series of applications in the field of hydraulic infrastructure (a stormwater retention tank and a sewage pipe) have demonstrated how the influence of IDF curve modification may change their design. The latter have shown how future RP modification (i.e., reduction) of the design rainfall may lead to systematic under-design and increased flood risk if not addressed properly. Full article

This paper presents an innovative derivation of intensity–duration–frequency (IDF) curves, which play a crucial role in the design of hydraulic infrastructures. IDF curves are herein derived from excess rainfall, that is, the net rainfall obtained by removing abstractions related to hydrological losses from total gross rainfall. When long fine fine-resolution time series of rainfall depth are available at a site, excess rainfall can be obtained by applying a simplified hydrological model of a catchment, including solely the gross-excess rainfall conversion. The application of annual maxima (AM) analysis on excess rainfall intensity data enables the construction of excess rainfall-based intensity–duration–frequency (ERIDF) curves. For assigned rainfall event criticality (return period) and duration, these curves directly provide the associated excess rainfall intensity value. This results in a better preservation of the return period in the rainfall–runoff transformation when used inside the rational formula for estimating peak water discharge, in comparison with the conventional approach adopted by practitioners, based on derivation of IDF curves and on the application of runoff coefficient for gross-excess rainfall conversion inside the rational formula. Full article

With the increasing frequency of extreme rainfall events, pluvial flooding has become a critical challenge to the safety and sustainable development of megacities worldwide. This study proposes a multi-dimensional framework for assessing urban pluvial flood resilience (UPFR) by integrating a coupled hydrological-hydrodynamic model with system performance curves. The framework characterizes the dynamic evolution of resilience across three dimensions: rainfall characteristics, risk thresholds, and spatial scales. Results show that short-duration intense rainfall triggers instantaneous pipe overloading, whereas long-duration storms impose cumulative stress that leads to sustained systemic weakening, with the lowest resilience observed under extreme prolonged rainfall conditions. The specification of risk thresholds strongly influences resilience ranking, with the vehicle stalling risk (VSR) consistently showing the lowest resilience, followed by building inundation risk (BIR) and human instability risk (HIR). Spatially, pipes represent the weakest components, nodes maintain resilience under moderate stress, and the regional system exhibits a pattern of local weakness but overall stability, accompanied by delayed recovery. These findings highlight the importance of incorporating multi-threshold and multi-scale perspectives in flood resilience assessment and management. The proposed framework provides a scientific basis to support staged prevention measures and adaptive emergency response strategies, thereby enhancing urban flood resilience in megacities. Full article

Urban flooding poses an escalating threat to riverine cities in Southern Asia’s tropical regions, primarily driven by rapid urban expansion. This study develops future projections of Intensity–Duration–Frequency (IDF) curves for major urban centers in Punjab, Pakistan, utilizing downscaled satellite-derived precipitation data. Baseline IDF curves were established using historical rainfall records from multiple meteorological stations. Among eight General Circulation Models (GCMs) assessed, EC-Earth3-Veg-LR demonstrated the highest skill in capturing extreme rainfall patterns relevant to the region. Future precipitation projections from this model were downscaled using the Equidistant Quantile Matching (EQM) technique and applied to generate IDF curves under two CMIP6 scenarios: SSP2-4.5 and SSP5-8.5. The results reveal a substantial increase in extreme rainfall intensities, particularly under the SSP5-8.5 scenario, with projected 100-year return period rainfall intensities rising by approximately 30–55% across key cities. The downscaled projections reveal more pronounced variations than the raw GCM outputs, emphasizing the importance of high-resolution climate data for accurate regional hydrological risk evaluation and effective urban flood resilience planning. Full article

Global climate change is intensifying extreme rainfall, exacerbating urban flood risks, and undermining the effectiveness of urban stormwater drainage systems (USDS) designed under stationary climate assumptions. While prior studies have identified general trends of increasing flood risk under climate change, they lack actionable connections between climate projections and practical flood risk assessment. Specifically, quantifiable forecasts of extreme rainfall for defined return periods and integrated frameworks linking climate modeling to hydrological simulation at the watershed scale. This study addresses these gaps by developing an integrated framework to assess USDS resilience under future climate scenarios, demonstrated through a case study in Changsha City, China. The framework combines dynamic downscaling of the MRI-CGCM3 global climate model using the Weather Research and Forecasting (WRF) model to generate high-resolution precipitation data, non-stationary frequency analysis via the Generalized Extreme Value (GEV) distribution to project future rainfall intensities (for 2–200-year return periods in the 2040s and 2060s), and a 1D-2D coupled urban flood model built in InfoWorks ICM to evaluate flood risk. Key findings reveal substantial intensification of extreme rainfall, particularly for long-term period events, with the 24 h rainfall depth for 200-year events projected to increase by 32% by the 2060s. Flood simulations show significant escalation in risk: for 100-year events, an area with ponding depth > 500 mm grows from 1.38% (2020s) to 1.62%, (2060s), and the 300–500 mm ponding zone expands by 21%, with long-return-period events (≥20 years) driving most future risk increases. These results directly demonstrate the inadequacy of stationary design approaches for USDS, which carries substantial applied significance for policymakers and stakeholders. Specifically, it underscores the urgent need for these key actors to update engineering standards by adopting non-stationary intensity-duration-frequency (IDF) curves and integrate Sustainable Urban Drainage Systems (SUDS) into formal flood management strategies. Full article

Background: Microsurgical skill acquisition and development are complex processes, due to the often complex learning curve, limited training possibilities, and growing restrictions on working hours. Simulation-based training programmes, employing various models, have been proposed. Nevertheless, the extent to which these training programmes are supported by scientific evidence is unclear. The aim of this systematic review is to evaluate the extent and quality of the scientific evidence backing validated microsurgical training programmes. Methods: A systematic literature review was conducted, following a study protocol established a priori and in accordance with the PRISMA guidelines. The databases searched were the Web of Science Core Collection (Web of Knowledge), Medline (Ovid), Embase (Embase.com), and ERIC (Ovid). Studies were included if they described microsurgical training programmes and presented a form of validation of training effectiveness. Data extraction included the number of participants, training duration and frequency, validation type, assessment methods, outcomes, study limitations, and a detailed training regimen. The risk of bias and quality were assessed using the Medical Education Research Study Quality Instrument (MERSQI). Validity was assessed using an established validity framework (content, face, construct, and criterion encompassing both concurrent and predictive validity). The Level of Evidence (LoE) and Recommendation (LoR) were evaluated using the Oxford Centre for Evidence-Based Medicine (OCEBM). Results: A total of 25 studies met the inclusion criteria. Training programmes were classified into one-time intensive courses or longitudinal curricula. Face, content, and construct validity were the most commonly assessed aspects, while predictive validity was the least frequently assessed and not properly evaluated. Training models ranged from low-fidelity (silicone tubes, synthetic vessels) to high-fidelity (live animal models). The Global Rating Scale (GRS), the Structured Assessment of Microsurgery Skills (SAMS), and the Objective Structured Assessment of Technical Skills (OSATS) were the most frequently used objective assessment tools for evaluation methods within the programmes. The risk of bias MERSQI score was 12.96, ranging from 10.5 to 15.5, and LoE and LoR scores were moderated. Across the studies, 96% reported significant improvement in microsurgical skills among participants. However, most studies were limited by small sample sizes, heterogeneity in baseline skills, and a lack of long-term follow-up. Conclusions: While validated microsurgical training programmes improve skill acquisition, challenges remain in terms of standardisation and best cost-effective methods. Future research should prioritise evaluating predictive validity, creating standardised objective assessment tools, and focus on skill maintenance. Full article

In semi-arid regions, flood events are often characterized by rapid runoff and high hydrological variability, posing significant challenges for infrastructure safety and flood risk assessment. Traditional flood frequency analysis methods, typically based on univariate models using annual flood peak, may fail to capture the full complexity of such events. This study investigates the limitations of the univariate approach through the analysis of the 2004 flood event in the Jaguaribe River basin (Brazil), which caused the Castanhão Reservoir to receive a discharge of more than 5 hm³ and fill from 4.5% to over 70% of its capacity in just 55 days. Although the peak discharge in 2004 was not an exceptional record, the combination of high flood volume and short duration revealed a much rarer event than suggested by peak flow alone. To improve compound flood risk assessment, a bivariate frequency analysis based on copula functions was applied to jointly model flood peak and average flood intensity. The latter is a variable newly proposed in this study to better capture the short-duration but high-volume flood until peak that can strongly influence dam safety. Specifically, for the 2004 event, the univariate return period of flood peak was only 35 years, whereas the joint return period incorporating both peak flow and average flood intensity reached 995 years—underscoring a potential underestimation of flood hazard when relying solely on peak flow metrics. Our bivariate return periods and the average flood intensity metric provide actionable information for climate adaptation, supporting adaptive rule curves and risk screening during initial impoundment and high-inflow events in semi-arid reservoirs. Collectively, the proposed methodology offers a more robust framework for assessing extreme floods in intermittent river systems and offers practical insights for dam safety planning in climatically variable regions such as the Brazilian Semi-Arid. Full article