Search Results (34)

Hydrological simulation of large, transboundary water systems like the Laurentian Great Lakes remains challenging. Although deep learning has advanced hydrologic forecasting, prior efforts are fragmented, lacking a unified basin-wide model for daily streamflow. We address this gap by developing a single Entity-Aware Long Short-Term Memory (EA-LSTM) model, an architecture that distinctly processes static catchment attributes and dynamic meteorological forcings, trained without basin-specific calibration. We compile a cross-border dataset integrating daily meteorological forcings, static catchment attributes, and observed streamflow for 975 sub-basins across the United States and Canada (1980–2023). With a temporal training/testing split, the unified EA-LSTM attains a median Nash–Sutcliffe Efficiency (NSE) of 0.685 and a median Kling–Gupta Efficiency (KGE) of 0.678 in validation, substantially exceeding a standard LSTM (median NSE 0.567, KGE 0.555) and the operational NOAA National Water Model (median NSE 0.209, KGE 0.440). Although skill is reduced in the smallest basins (median NSE 0.554) and during high-flow events (median PBIAS −29.6%), the performance is robust across diverse hydroclimatic settings. These results demonstrate that a single, calibration-free deep learning model can provide accurate, scalable streamflow prediction across an international basin, offering a practical path toward unified forecasting for the Great Lakes and a transferable framework for other large, data-sparse watersheds. Full article

Climate change has intensified extreme hydrological events in cold regions, threatening the stability of river ecosystems. The traditional monthly frequency method for calculating ecological flow assumes equal guarantee rates across all months, overlooking the complex nonlinear dependencies between interannual and intermonthly flows. This approach may result in flow values for certain months during low-flow years exceeding those of corresponding months in high-flow years, failing to align with actual hydrological patterns. This study integrates Copula functions with the monthly frequency method to establish an improved ecological flow calculation framework, accurately characterizing the statistical correlation between interannual and intermonthly flow variability. The Hailang River basin in Northeast China was selected as the study area. First, the SWAT model was employed to simulate natural runoff processes from 1956 to 1965. The calibration phase demonstrated excellent performance (R² = 0.84, NSE = 0.83), and the validation phase also met standards (R² = 0.82, NSE = 0.81). The improved method selected optimal Copula functions for each month through rigorous statistical tests (AIC, BIC, RMSE, and K-S test), establishing joint probability distributions for annual and monthly average flows. The results indicate that different Copula types better align with monthly hydrological seasonal characteristics: Gaussian Copula suits February, May, and July; t-Copula suits August; Clayton Copula from September to December; Gumbel Copula for January, March, April, and June. Through conditional probability relationships (P(X₀≥x₀, 90%) = 0.9), the monthly guarantee rate range determined by the improved method spans 81.83% to 90.08%, significantly outperforming the uniform 90% guarantee rate employed by traditional methods. Verification using the Tennant method confirmed that ecological flows throughout the year met “excellent” or higher standards. Ecological flows exhibited pronounced seasonal variation, ranging from 6.2 m³/s during winter to spring to 96.93 m³/s during summer to autumn, providing scientific basis for basin-scale ecological water management. This study establishes a reliable methodological framework for ecological flow management in cold-region rivers. Full article

Hydrological drought indices, while critical for monitoring, are often limited by their reliance on single variables, failing to capture the multidimensional complexity of water scarcity, particularly in data-scarce and climate-sensitive regions. This study addresses this critical gap by introducing a Composite Hydrological Drought Index (CHDI) for a northern watershed in Thailand, a region where drought risk is intensified by climatic shifts and intensive land use. The proposed methodology integrates multiple outputs from the Variable Infiltration Capacity (VIC) hydrological model, including precipitation, runoff, evapotranspiration, baseflow, and soil moisture layers, and employs Principal Component Analysis (PCA) to synthesize the dominant drivers of water-level variability. The first principal component (PC1), which accounted for over 50% of the total variance, served as the basis for the CHDI, and was strongly correlated with precipitation, surface runoff, and surface soil moisture. The performance of CHDI was rigorously evaluated against observed data from eight hydrological stations. The index demonstrated significant predictive skill, with Pearson’s correlation coefficients (R) ranging from 0.49 to 0.79 (p < 0.05), a maximum Nash–Sutcliffe Efficiency (NSE) of 0.63, and F1-scores for drought detection as high as 0.92. It effectively captured seasonal and interannual variability, including the accurate identification of low-flow events reported by the National Hydro Informatics Data Center (NHC). While the CHDI showed robust performance, particularly under high-flow conditions and in drought classification, some limitations were observed in complex or anthropogenically influenced sub-catchments. These findings highlight the potential of CHDI as a reliable and integrative tool for hydrological drought monitoring and for supporting water resource management in data-scarce and climate-sensitive regions. Full article

This study presents the first investigation into the distribution of microplastics (MPs) in Sasebo City, Japan, using principal component analysis (PCA) in conjunction with water flow velocity and salinity variables. The mean MP abundance was 82.4 ± 47.7 items/m³ (SSB1–SSB4), showing no significant difference among sampling points. The fragment-to-fiber ratio was 76:24, and polypropylene and polyethylene (each 41%) were the main polymers. Fragment abundance increased with decreasing particle size, while fibers were rare below 700 μm. PCA indicated distinct MP polymer and shape distributions corresponding to stagnant water (SSB1), high-flow conditions (SSB2 and SSB3), and seawater (SSB4). Based on the literature, the study area represents a case of a small-scale aquatic system with limited anthropogenic influence due to moderate population, short river length, efficient effluent discharge, minimal industry, good water quality, and the absence of significant spatial variation in MP abundance. The infrequent precipitation during the sampling event supports the findings of the present study as a reliable baseline for objectively assessing MP contamination. Compared to aquatic systems of varying scales and anthropogenic influence, this baseline is applicable to both small-scale and large-scale aquatic systems with significant influences. This will serve as a valuable reference for future MP studies across diverse freshwater environments. Full article

Climate change is projected to significantly alter hydrological conditions across the Northern Hemisphere, with increased precipitation variability, more intense rainfall events, and earlier, rain-driven spring floods in regions like northern Sweden. These changes will affect both natural ecosystems and hydropower-regulated rivers, particularly during ecologically sensitive periods such as the grayling spawning season in late spring. This study examines the impact of extreme spring flow conditions on grayling spawning habitats by analyzing historical runoff data and simulating high-flow events using a 2D hydraulic model in Delft3D FM. Results show that previously suitable spawning areas became too deep or experienced flow velocities beyond ecological thresholds, rendering them unsuitable. These hydrodynamic shifts could have cascading effects on aquatic vegetation and food availability, ultimately threatening the survival and reproductive success of grayling populations. The findings underscore the importance of integrating ecological considerations into future water management and hydropower operation strategies in the face of climate-driven flow variability. Full article

Background: Arteriovenous malformations (AVMs) are high-risk cerebrovascular anomalies that can lead to devastating complications, especially when associated with intracranial aneurysms. Their coexistence poses unique challenges in diagnosis and management due to heightened hemodynamic stress and rupture risks. This case presents a 35-year-old woman with a giant unruptured left frontal AVM and a bilobed posterior communicating artery (PComA) aneurysm, highlighting the critical role of advanced imaging, meticulous surgical planning, and individualized care in addressing complex cerebrovascular conditions. Methods: The patient presented with a generalized tonic–clonic seizure, her first-ever neurological event. Advanced imaging, including digital subtraction angiography and 3D rotational imaging, revealed a 3–4 cm AVM supplied by the left middle and anterior cerebral arteries, with venous drainage into the superior sagittal sinus. Additionally, an unruptured bilobed PComA aneurysm was identified. Given the AVM’s large size, high-flow dynamics, and significant rupture risk, surgical resection was prioritized. The aneurysm, being stable and anatomically distinct, was managed conservatively. Microsurgical techniques were employed to ensure complete AVM resection while preserving critical vascular and neurological structures. Results: Postoperative angiography confirmed the complete removal of the AVM without residual nidus or abnormal vascular connections. The patient recovered without complications, achieving seizure freedom and preserved neurological function. At the three-month follow-up, imaging showed a stable resection cavity and a hemodynamically stable aneurysm. Conclusions: This case demonstrates the power of multidisciplinary care and advanced neurosurgical techniques in achieving curative outcomes for complex cerebrovascular anomalies. It underscores the importance of risk-prioritized strategies and highlights emerging directions for the field, including AI-integrated imaging, hybrid treatment approaches, and long-term studies on hemodynamic stability post-resection. This case contributes valuable insights into optimizing outcomes for patients with coexisting AVMs and aneurysms, offering hope for those facing similarly challenging diagnoses. Full article

Background: There is a notable lack of protocols addressing extubation techniques in transoral robotic surgery (TORS) for obstructive sleep apnea (OSA). Methods: This retrospective cohort study enrolled patients who underwent TORS for OSA between March 2015 and December 2021 and were managed with different extubation approaches. The patients were divided into two groups: high-flow nasal cannula (HFNC) therapy and conventional oxygen therapy. The use of an airway exchange catheter (AEC) was investigated. Results: The application of HFNC use versus conventional oxygen therapy led only to a statistical reduction in extubation time (p = 0.024); length of stay in the intensive care unit (ICU) and the episodes of desaturation below 95% were reduced, but data are non-statistically significant. Similarly, the application of an AEC led to a reduction in extubation time in hours (p = 0.008) and length of stay in the ICU (p = 0.024). Conclusions: In patients with OSA who underwent TORS, the use of an HFNC, with or without an AEC, resulted in a significant reduction in extubation time without major adverse events. Additionally, HFNC utilization may decrease desaturation episodes during extubation. Despite limitations, based on the findings of this preliminary investigation, the combination of an HFNC and an AEC emerges as a promising strategy for enhancing the safety and efficacy of extubation protocols in this patient population. Full article

Runoff forecasting is crucial for water resource management and flood safety and remains a central research topic in hydrology. Recent advancements in machine learning provide novel approaches for predicting runoff. This study employs the Competitive Adaptive Reweighted Sampling (CARS) algorithm to integrate various machine learning models into a data-driven rainfall–runoff simulation model. We compare the forecasting performance of different machine learning models to improve rainfall–runoff prediction accuracy. This study uses data from the Maduwang hydrological station in the Bahe river basin, which contain 12 measured flood events from 2000 to 2010. Historical runoff and areal mean rainfall serve as model inputs, while flood data at different lead times are used as model outputs. Among the 12 flood events, 9 are used as the training set, 2 as the validation set, and 1 as the testing set. The results indicate that the CARS-based machine learning model effectively forecasts floods in the Bahe River basin. Under the prediction period of 1 to 6 h, the model achieves high forecasting accuracy, with the average NSE ranging from 0.7509 to 0.9671 and the average R² ranging from 0.8397 to 0.9413, though the accuracy declines to some extent as the lead time increases. The model accurately predicts peak flow and performs well in forecasting high flow and recession flows, though peak flows are somewhat underestimated for longer lead times. Compared to other machine learning models, the SVR model has the highest average RMSE of 0.942 for a 1–6 h prediction period. It exhibits the smallest deviation among low-, medium-, and high-flow curves, with the lowest NRMSE values across training, validation, and test sets, demonstrating better simulation performance and generalization capability. Therefore, the machine learning model based on CARS feature selection can serve as an effective method for flood forecasting. The related findings provide a new forecasting method and scientific decision-making basis for basin flood safety. Full article

The optimal performance of a hydroelectric power plant depends on accurate monitoring and well-functioning sensors for data acquisition. This study proposes the use of artificial neural networks (ANNs) to estimate the Pelton turbine shaft power of a 10 kW micro-hydropower plant. In the event of a failure of the sensor measuring the torque and/or rotational speed of the Pelton turbine shaft, the synthetic turbine shaft power data generated by the ANN will allow the turbine output power to be determined. The experimental data were obtained by varying the operating conditions of the micro-hydropower plant, including the variation of the input power to the electric generator and the variation of the injector opening. These changes consequently affected the flow rate and the pressure head at the turbine inlet. The use of artificial neural networks (ANNs) was deemed appropriate due to their ability to model complex relationships between input and output variables. The ANN structure comprised five input variables, fifteen neurons in a hidden layer and an output variable estimating the Pelton turbine power. During the training phase, algorithms such as Levenberg–Marquardt (L–M), Scaled Conjugate Gradient (SCG) and Bayesian were employed. The results indicated an error of 0.39% with L–M and 7% with SCG, with the latter under high-flow and -energy consumption conditions. This study demonstrates the effectiveness of artificial neural networks (ANNs) trained with the Levenberg–Marquardt (L–M) algorithm in estimating turbine shaft power. This contributes to improved performance and decision making in the event of a torque sensor failure. Full article

Background: Early post-operative airway management after laryngo-tracheal surgery is crucial. Acute respiratory failure due to glottis’ edema may occur, requiring reintubation. This can prolong ventilatory assistance, jeopardizing anastomosis. To date, only judicious steroid administration and fluid management are available to avoid more invasive procedures. High-flow oxygen therapy (HFOT) is a noninvasive O₂ support method providing humidification, warmed air, and Positive End-Expiratory Pressure (AIRVO2). No data about HFOT use to prevent early complications after laryngo-tracheal surgery are reported in the literature. Methods: Between September 2020 and September 2022, 107 consecutive patients who underwent laryngo-tracheal surgery received HFOT (Group A). Data and long-term results were compared with those of 80 patients operated between September 2018 and August 2020 (Group B), when HFOT was not available. All patients were operated in a single center. No pre- or post-operative settings changed, except for HFOT introduction. We analyzed and compared the risk for “delayed” reintubation (unexpected reintubation within the first 24–48 h after extubating/laryngeal mask removal) in the two groups. Results: No patients reported HFOT-related adverse events. The control group (B) presented “delayed” reintubation in 37% (p = 0.027), intensive care unit admission in 67% (p = 0.005) and longer hospital stay (p = 0.001) compared to the HFOT group (A). The minor complications’ rate was 3% in both group and overall mortality was 0%. Re-stenosis was described in 4.6% of the HFOT group, without a statistically significant difference (p = 0.7006). Conclusions: Our study is the first to investigate HFOT use in patients undergoing laryngo-tracheal surgery, potentially representing a consistent innovation in the peri-operative management of these patients. With the limitation of a retrospective series, we would suggest HFOT use for preventing post-operative reintubation rate, possibly reducing ICU admissions and hospital stays. Full article

Vascular access (VA) flow suppression surgery augments VA flow resistance and can increase other circulation flows hindered by high-flow VA. However, whether VA flow suppression surgery affects cervical circulation has rarely been reported. We aimed to determine the effect of VA flow suppression surgery on the cervical circulation in patients with high-flow VA. This single-center, retrospective, observational study included 85 hemodialysis patients who underwent VA flow suppression surgery at the Kanno Dialysis and Access Clinic between 2009 and 2018. Blood flow in the VA, bilateral vertebral arteries, and common carotid artery was measured before and after VA flow suppression surgery. The VA flow decreased from 1548 mL/min to 693 mL/min postoperatively. The flow of the vertebral artery on the VA side increased from 55 mL/min to 81 mL/min. The flow in the bilateral common carotid arteries also increased. Patients whose symptoms improved postoperatively showed better improvement in the vertebral artery on the VA side. VA flow suppression surgery in patients with high-flow VA increases the flow of the vertebral artery on the VA side and of the bilateral common carotid arteries. High-flow VA can hinder the vertebral and common carotid circulation. Full article

Background: When heart transplantation and myocardial recovery are unlikely, patients presenting with biventricular cardiogenic shock initially treated with extracorporeal membrane oxygenation (ECMO) may benefit from a mechanical support upgrade. In this scenario, a micro-invasive approach is proposed: the combination of the double-lumen ProtekDuo cannula (Livanova, London, UK) and the Impella 5.5 (Abiomed, Danvers, MA) trans-aortic pump that translates into a hybrid BiVAD. Methods: All consecutive ECMO patients presenting with biventricular cardiogenic shock and ineligibility to heart transplantation from August 2022 were prospectively enrolled. The clinical course, procedural details, and in-hospital events were collected via electronic medical records. Results: A total of three patients, who were temporarily not eligible for heart transplantation or durable LVAD due to severe acute pneumonia and right ventricular (RV) dysfunction, were implanted with a hybrid BiVAD. This strategy provided high-flow biventricular support while pulmonary function ameliorated. Moreover, by differentially sustaining the systemic and pulmonary circulation, it allowed for a more adequate reassessment of RV function. All the patients were considered eligible for isolated durable LVAD and underwent less invasive LVAD implantation paired with a planned postoperative RVAD. In all cases, RV function gradually recovered and the RVAD was successfully removed. Conclusions: The Hybrid BiVAD represents an up-to-date micro-invasive mechanical treatment of acute biventricular failure beyond ECMO. Its rationale relies on more physiological circulation across the lungs, the complete biventricular unloading, and the possibility of including an oxygenator in the circuit. Finally, the independent and differential control of pulmonary and systemic flows allows for more accurate RV function evaluation for isolated durable LVAD eligibility reassessment. Full article

Climate change and extreme weather events have the potential to increase the occurrences of flooding and hydrological droughts. Dam reservoir operation can mitigate or aggravate this impact. This study aims to evaluate the influence of the planned Kamieniec Ząbkowicki dam reservoir on the flow patterns of the Nysa Kłodzka river in the context of changing hydrological conditions and climate change. In the study, a 40-year observational series of hydrological data was used to simulate changes in water flow through the river valley in a numerical model. This simulation was conducted both for the natural river valley and for the same river valley but with the added reservoir dam. Flow simulations revealed that dam operation increased downstream flow values, reducing variability in extreme high-flow events. Addition, the mixture log-normal distribution shows that the operation of the dam resulted in a reduction in the variability of both low flows and extreme high-flow events. Furthermore, the model illustrates that moderate-flow conditions remain relatively stable and similar before and after dam construction. The Mann–Kendall trend test, Sen slope trend test and Innovative Trend Analysis indicated that the dam had a significant impact on flow trends, reducing the negative trend. This hydrotechnical structure stabilizes and regulates flows, especially in response to climate-induced changes. These findings highlight the effectiveness of the dam in mitigating flood risk and supporting water resource management. It is essential to consider the role of the dam in adapting to changing hydrological conditions influenced by climate change. For practical application, efficient flow regulation by reservoir administration is crucial. Full article

Flood modeling and forecasting are crucial for managing and preparing for extreme flood events, such as those in the Tropical Andes. In this context, assimilating streamflow data is essential. Data Assimilation (DA) seeks to combine errors between forecasting models and discharge measurements through the updating of model states. This study aims to assess the applicability and performance of streamflow DA in a sub-daily forecasting system of the Peruvian Tropical Andes using the Ensemble Kalman Filter (EnKF) and Particle Filter (PF) algorithms. The study was conducted in a data-sparse Andean basin during the period February–March 2022. For this purpose, the lumped GR4H rainfall–runoff model was run forward with 100 ensemble members in four different DA experiments based on IMERG-E and GSMaP-NRT precipitation sources and assimilated real-time hourly discharges at the basin outlet. Ensemble modeling with EnKF and PF displayed that perturbation introduced by GSMaP-NRT’-driven experiments reduced the model uncertainties more than IMERG-E’ ones, and the reduction in high-flow subestimation was more notable for the GSMaP-NRT’+EnKF configuration. The ensemble forecasting framework from 1 to 24 h proposed here showed that the updating of model states using DA techniques improved the accuracy of streamflow prediction at least during the first 6–8 h on average, especially for the GSMaP-NRT’+EnKF scheme. Finally, this study benchmarks the application of streamflow DA in data-sparse basins in the Tropical Andes and will support the development of more accurate climate services in Peru. Full article

This paper presents a comprehensive river discharge analysis to estimate past and future hydrological extremes across Morocco. Hydrological simulations with historical forcing and climate change scenario inputs have been performed to better understand the change in magnitude and frequency of extreme discharge events that cause flooding. Simulations are applied to all major rivers of Morocco, including a total of 16 basins that cover the majority of the country. An ensemble of temperature and precipitation input parameter sets was generated to analyze input uncertainty, an approach that can be extended to other regions of the world, including data-sparse regions. Parameter uncertainty was also included in the analyses. Historical simulations comprise the period 1979–2021, while future simulations (2015–2100) were performed under the Shared Socioeconomic Pathway (SSP) 2–4.5 and SSP5–8.5. Clear patterns of changing flood extremes are projected; these changes are significant when considered as a proportion of the land area of the country. Two types of basins have been identified, based on their different behavior in climate change scenarios. In the Northern/Mediterranean basins we observe a decrease in the frequency and intensity of events by 2050 under both SSPs, whereas for the remaining catchments higher and more frequent high-flow events in the form of flash floods are detected. Our analysis revealed that this is a consequence of the reduction in rainfall accumulation and intensity in both SSPs for the first type of basins, while the opposite applies to the other type. More generally, we propose a methodology that does not rely on observed time series of discharge, so especially for regions where those do not exist or are not available, and that can be applied to undertake future flood projections in the most data-scarce regions. This method allows future hydrological hazards to be estimated for essentially any region of the world. Full article