Search Results (34)

Focusing the over simplification of existing models in simulating river flow evolution process and lack of coordination of hydropower generation and export, this paper proposes a hydropower generation and export coordinated optimal operation model that, at the same time, incorporates dynamic water flow delay by finely modeling the water flow evolution process among cascade hydropower stations within a river basin. Specifically, firstly, a dynamic water flow evolution model is built based on the segmented Muskingum method. By dividing the river into sub-segments and establishing flow evolution equation for individual sub-segments, the model accurately captures the dynamic time delay of water flow. On this basis, integrating cascade hydropower systems and the transmission system, a hydropower generation and export coordinated optimal operation model is proposed. By flexibly adjusting the power export, the model balances local consumption and external transmission of hydropower, enhancing the utilization efficiency of hydropower resources and achieving high economic performance. A case study verified the accuracy of the dynamic water flow evolution model and the effectiveness of the proposed hydropower generation and export coordinated optimal operation model. Full article

Accurate water hammer mitigation simulation is crucial for designing and protecting pipeline systems. This study draws on the principles of the Muskingum method to develop an improved discrete vaporous cavity model (DVCM) that enhances computational accuracy. The key improvements include significantly reducing numerical dissipation by optimizing the Courant number (C_n) and adjusting the friction coefficient (f) to balance numerical and physical dissipation. Specifically, the predictive accuracy of the node water head was improved by 69.93%, and the accuracy of the liquid–column separation time was enhanced by 77.21%. These enhancements were achieved by proposing an equivalent treatment method for numerical and physical dissipation, ensuring that the model’s total dissipation matches actual physical conditions. The validation process involved simulating the liquid column separation process using data from the Simpson experiment. The results demonstrated a high degree of consistency with the experimental data, confirming the effectiveness of the proposed method. Full article

Climate change-induced alterations in monsoon patterns have exacerbated flooding challenges in Balochistan, Iran. This study addresses the urgent need for improved flood prediction methodologies in data-scarce arid regions by integrating the Muskingum–Cunge model with advanced optimization techniques. Particle swarm optimization (PSO) and harmony search (HS) algorithms were applied and compared across eight major rivers in Balochistan, each with distinct hydrological characteristics. A comprehensive multi-metric evaluation framework was developed to assess the performance of these algorithms. The results demonstrate PSO’s superior performance, particularly in complex terrain conditions. For instance, at the Kajou station, PSO improved the Coefficient of Residual Mass (CRM) by 0.01, efficiency (EF) by 0.92, Agreement Index (d) by 0.98, and Normalized Root Mean Square Error (NRMSE) by 0.10 compared to HS. Correlation coefficients ranging from 0.6558 to 0.9645 validate the methodology’s effectiveness in data-scarce environments. This research provides valuable insights into algorithm performance under limited data conditions and offers region-specific parameter optimization guidelines for similar geographical contexts. By advancing flood routing science and providing a validated framework for optimization algorithm selection, this study contributes to improved flood management in regions vulnerable to climate change. Full article

Due to the complexity of terrain and climate in the mountain–plain transition zone, it is difficult to simulate and forecast the flow discharge of river basins accurately. The poor regularity of the river thus leads to uncertain flood control scheduling. Meanwhile, reservoirs and flood detention areas are constructed to store and divert water when severe floods threaten the safety of the basin. In order to improve the accuracy of flood forecasts and the effectiveness of flood control, a hydrological and 1D/2D hydrodynamic coupling model was developed to enable the joint computation of multiple objects, including mountainous streams, plains river networks, hydraulic control structures, and flood detention areas. For the hydrological component, the Xin’anjiang model with the Muskingum module is employed to simulate mountainous flow discharge. For the hydrodynamic component, the Saint–Venant equations and shallow water equations are applied to estimate flood processes in rivers and on land surfaces, respectively. The Dongtiaoxi River Basin in Zhejiang Province, China, serves as the case study, where river flow is influenced by both upstream mountainous floods and downstream backwater effects. Using the integrated model, flood routing and scheduling are simulated and visualized. Both the Xin’anjiang model and the 1D hydrodynamic model demonstrate over 80% acceptability in calibration and validation, confirming their robustness and reliability. Meanwhile, inundation in flood detention areas can be effectively estimated by coupling the 1D and 2D hydrodynamic models with a flood diversion scheduling model. The coupled model proves capable of simulating flood routing in complex river basins that include mountains, plains, and hydraulic control structures, accounting for the interactions between hydrological elements. These findings provide a new perspective on flood simulation in other similarly complex river basins. Full article

The computation of hydrographs in large watersheds necessitates utilizing channel routing, which calculates the movement of hydrographs along channel branches. Routing methods rely on an implicit scheme to facilitate numerical resolution, which requires more computational time than the explicit scheme. This study presents an explicit scheme channel routing model that offers a versatile approach to open channel flow analysis. The model is based on mass conservation principles and Manning equations, and it can accommodate varying bed slopes, making it highly adaptable to diverse hydraulic scenarios. In addition, the proposed model considers backwater effects, which enhances its applicability in practical scenarios. The model was tested in a practical application on a rectangular channel with a width of 7 m, and the results showed that it can accurately predict outflow hydrographs and handle different flow conditions. Comparative analyses with existing models revealed that the proposed model’s performance in generating water flow oscillations was competitive. Moreover, sensitivity analyses were performed, which showed that the model is highly responsive to parameter variations, such as Manning’s coefficient, bed slope, and channel width. The comparison of peak flows and peak times between the proposed model and existing methods further emphasized the model’s reliability and efficiency in simulating channel routing processes. This research introduces a valuable addition to the field of hydrology by proposing a practical and effective channel routing model that integrates essential hydraulic principles and parameters. The results of the proposed model (lumped routing) are comparable with the solution provided by the Muskingum–Cunge method (distributed routing). It is of utmost importance to note that the proposed model applies to channel branches with bed slopes below 6°. Full article

The flow routing process plays a crucial role in underpinning the execution of real-time operations within interbasin water transfer projects (IWTPs). However, the water transfer process within the supplying area is significantly affected by the time lag of water flow over extended distances, which results in a misalignment with the water demand process in the receiving area. Hence, there is an imperative need to investigate the flow routing patterns in long-distance water transfer processes. While MIKE11(2014 version) software and the Muskingum method are proficient in simulating flow routing within a water transfer network, they fall short in addressing issues arising from mixed free-surface-pressure flows in water transfer pipelines. This study enhanced the capabilities of the MIKE11(2014 version) software and the Muskingum method by introducing the Preissmann virtual narrow gap method to tackle the challenge of simulating mixed free-surface-pressure flows, a task unattainable by the model independently. This approach provides a clear elucidation of hydraulic characteristics within the water transfer network, encompassing flow rates and routing times. Furthermore, this is integrated with the Muskingum inverse method to compute the actual water demand process within the supplying area. This methodology is implemented in the context of the Han River to Wei River Diversion Project (HTWDP). The research findings reveal that the routing time for the Qinling water conveyance tunnel, under maximum design flow rate conditions, is 12.78 h, while for the south and north main lines, it stands at 15.85 and 20.15 h, respectively. These results underscore the significance of the time lag effect in long-distance water conveyance. It is noteworthy that the average errors between simulated and calculated values for the south and north main lines in the flow routing process are 0.45 m³/s and 0.51 m³/s, respectively. Compared to not using the Preissmann virtual narrow gap method, these errors are reduced by 59.82% and 70.35%, indicating a significant decrease in the discrepancy between simulated and calculated values through the adoption of the Preissmann virtual narrow gap method. This substantially improves the model’s fitting accuracy. Furthermore, the KGE indices for the flow routing model are all above 0.5, and the overall trend of the reverse flow routing process closely aligns with the simulated process. The relative errors for most time periods are constrained within a 5% range, demonstrating the reasonability and precision of the model. Full article

In order to further improve the accuracy of flood routing, this article uses the Variable Exponential Nonlinear Muskingum Model (VEP-NMM), combined with the Artificial Rabbit Optimization (ARO) algorithm for parameter calibration, to construct the ARO-VEP-NMM flood routing model. Taking Wilson’s (1974) flood as an example, the model calculation results were compared and analyzed with the Muskingum model constructed with seven optimization algorithms. At the same time, six measured floods in the Zishui Basin were selected for model applicability testing. The results show that the ARO algorithm exhibits stronger robustness and search ability compared with other optimization algorithms and can better solve the parameter optimization problem of the Muskingum model. The use of the ARO-VEP-NMM model for flood routing accurately reflects the movement patterns of floods. The Nash coefficient of the Wilson section reached 0.9983, and the average Nash coefficient during the flood validation period in the Zishui Basin was 0.9, further verifying the adaptability and feasibility of the ARO-VEP-NMM model in flood routing. The research results can provide certain references and a theoretical basis for improving the accuracy of flood forecasting. Full article

In this study, a framework to circumvent the difficulties in selecting a proper flood routing method was established by employing two different multi-criteria decision analysis (MCDA) tools, namely, TOPSIS and PROMETHEE, with definite decisive criteria such as the error metrics, the number of model parameters, and the model background, under three scenarios. For eight distinct flood datasets, the parameters of 10 different Muskingum models were determined using the water cycle optimization algorithm (WCOA) and the performance of each model was ranked by both MCDA tools considering the hydrograph types of flood datasets, labeled as smooth single peak, non-smooth single peak, multi-peak, and irregular. The results indicate that both tools were compatible by giving similar model results in the rankings of almost all scenarios that include different weights in the criteria. The ranking results from both tools also showed that the routing application in single-peak hydrographs was examined better with empirical models that have a high number of parameters; however, complex hydrographs that have more than one peak with irregular limps can be assessed better using the physical-based routing model that has fewer parameters. The proposed approach serves as an extensive analysis in finding a good agreement between measured and routed hydrographs for flood modelers about the estimation capabilities of commonly used Muskingum models considering the importance of correlation, model complexity, and hydrograph characteristics. Full article

After the Paris Agreement was signed in 2015, many countries worldwide focused on the hydrogen economy, aiming for eco-friendly and renewable energy by moving away from the existing carbon economy, which has been the primary source of global warming. Hydrogen is the most common element on Earth. As a light substance, hydrogen can diffuse quickly; however, it also has a small risk of explosion. Representative explosion accidents have included the Muskingum River Power Plant Vapor Cloud Explosion accident in 2007 and the Silver Eagle Refinery Vapor Cloud Explosion accident in 2009. In addition, there was an explosion in a hydrogen tank in Gangneung, Korea, in May 2019, and a hydrogen refueling station (HRS) in Norway exploded in 2018. Despite this risk, Korea is promoting the establishment of HRSs in major urban centers, including downtown areas and public buildings, by using the Regulatory Sandbox to install HRSs. This paper employed the Hydrogen Risk Assessment Model (HyRAM) of Sandia National Laboratories (SNL), a quantitative risk assessment (QRA) program specialized in hydrogen energy for HRSs installed in major urban hubs. A feasibility evaluation of the site conditions of an HRS was conducted using the French land use planning method based on the results obtained through evaluation using the HyRAM and the overpressure results of PHAST 8.0. After a risk assessment, we confirmed that an HRS would be considered safe, even if it was installed in the city center within a radius of influence of jet fires and overpressure. Full article

Design flood hydrograph estimation is a key problem in hydrology and is necessary for a variety of applications from the design of hydraulic structures to flood risk mapping processes. Furthermore, in large ungauged basins (>1000 km²), design flood estimation methods mainly rely on single-event theories using digital elevation models, land use/land cover and soil type data, and relevant meteorological information (temperature and rainfall data). The single event-based deterministic approach was adopted based on three modelling components: (i) a synthetic storm generator; (ii) a hydrological simulation model; and (iii) a hydrological routing model. In this study the 100-year design flood (which is assumed equal to 100-year extreme rainfall) was estimated for the Pinios River Basin, Thessaly, Greece, at Larissa outlet station (upstream of the area by about 6500 km²). The hydrological approach is based on semi-distributed modelling of the rainfall–run-off process (at the sub-basin scale) using HEC-HMS v.4.10 software and the SCS-CN method for estimating rainfall excess, as well as the unit hydrograph theory and the Muskingum hydrological flow routing method for propagating the surface run-off to the sub-basin outlets. Full article

Water engineering problems are typically nonlinear, multivariable, and multimodal optimization problems. Accurate water engineering problem optimization helps predict these systems’ performance. This paper proposes a novel optimization algorithm named enhanced multioperator Runge–Kutta optimization (EMRUN) to accurately solve different types of water engineering problems. The EMRUN’s novelty is focused mainly on enhancing the exploration stage, utilizing the Runge–Kutta search mechanism (RK-SM), the covariance matrix adaptation evolution strategy (CMA-ES) techniques, and improving the exploitation stage by using the enhanced solution quality (IESQ) and sequential quadratic programming (SQP) methods. In addition to that, adaptive parameters were included to improve the stability of these two stages. The superior performance of EMRUN is initially tested against a set of CEC-17 benchmark functions. Afterward, the proposed algorithm extracts parameters from an eight-parameter Muskingum model. Finally, the EMRUM is applied to a practical hydropower multireservoir system. The experimental findings show that EMRUN performs much better than advanced optimization approaches. Furthermore, the EMRUN has demonstrated the ability to converge up to 99.99% of the global solution. According to the findings, the suggested method is a competitive algorithm that should be considered in optimizing water engineering problems. Full article

The interest in the use of mathematical models for the simulation of hydrological processes has largely increased especially in the prediction of runoff. It is the subject of extreme research among engineers and hydrologists. This study attempts to develop a simple conceptual model that reflects the features of the arid environment where the availability of hydrological data is scarce. The model simulates an hourly streamflow hydrograph and the peak flow rate for any given storm. Hourly rainfall, potential evapotranspiration, and streamflow record are the significant input prerequisites for this model. The proposed model applied two (2) different hydrologic routing techniques: the time area curve method (wetted area of the catchment) and the Muskingum method (catchment main channel). The model was calibrated and analyzed based on the data collected from arid catchment in the center of Jordan. The model performance was evaluated via goodness of fit. The simulation of the proposed model fits both (a) observed and simulated streamflow and (b) observed and simulated peak flow rate. The model has the potential to be used for peak discharges’ prediction during a storm period. The modeling approach described in this study has to be tested in additional catchments with appropriate data length in order to attain reliable model parameters. Full article

Under a changing environment, the current hydrological design values derived from historical flood data for the Three Gorges Reservoir (TGR) might be no longer applicable due to the newly-built reservoirs upstream from the TGR and the changes in climatic conditions. In this study, we perform a multivariate dam-site flood frequency analysis for the TGR considering future reservoir regulation and summer precipitation. The Xinanjiang model and Muskingum routing method are used to reconstruct the dam-site flood variables during the operation period of the TGR. Then the distributions of the dam-site flood peak and flood volumes with durations of 3, 7, 15, and 30 days are built by Pearson type III (PIII) distribution with time-varying parameters, which are expressed as functions of both reservoir index and summer precipitation anomaly (SPA). The multivariate joint distribution of the dam-site flood variables is constructed by a 5-D C-vine copula. Finally, by using the criteria of annual average reliability (AAR) associated with the exceedance probabilities of OR, AND and Kendall, we derive the multivariate dam-site design floods for the TGR from the predicted flood distributions during the future operation period of the reservoir. The results indicate that the mean values of all flood variables are positively linked to SPA and negatively linked to RI. In the future, the flood mean values are predicted to present a dramatic decrease due to the regulation of the reservoirs upstream from the TGR. As the result, the design dam-site floods in the future will be smaller than those derived from historical flood distributions. This finding indicates that the TGR would have smaller flood risk in the future. Full article

Flood routing can be subclassified into hydraulic and hydrologic flood routing; the former yields accurate values but requires a large amount of data and complex calculations. The latter, in contrast, requires only inflow and outflow data, and has a simpler calculation process than the hydraulic one. The Muskingum model is a representative hydrologic flood routing model, and various versions of Muskingum flood routing models have been studied. The new Muskingum flood routing model considers inflows at previous and next time during the calculation of the inflow and storage. The self-adaptive vision correction algorithm is used to calculate the parameters of the proposed model. The new model leads to a smaller error compared to the existing Muskingum flood routing models in various flood data. The sum of squares obtained by applying the new model to Wilson’s flood data, Wang’s flood data, the flood data of River Wye from December 1960, Sutculer flood data, and the flood data of River Wyre from October 1982 were 4.11, 759.79, 18,816.99, 217.73, 38.81 (m³/s)², respectively. The magnitude of error for different types of flood data may be different, but the error may be large if the flow rate of the flood data is large. Full article

The Muskingum method is one of the widely used methods for lumped flood routing in natural rivers. Calibration of its parameters remains an active challenge for the researchers. The task has been mostly addressed by using crisp numbers, but fuzzy seems a reasonable alternative to account for parameter uncertainty. In this work, a fuzzy Muskingum model is proposed where the assessment of the outflow as a fuzzy quantity is based on the crisp linear Muskingum method but with fuzzy parameters as inputs. This calculation can be achieved based on the extension principle of the fuzzy sets and logic. The critical point is the calibration of the proposed fuzzy extension of the Muskingum method. Due to complexity of the model, the particle swarm optimization (PSO) method is used to enable the use of a simulation process for each possible solution that composes the swarm. A weighted sum of several performance criteria is used as the fitness function of the PSO. The function accounts for the inclusive constraints (the property that the data must be included within the produced fuzzy band) and for the magnitude of the fuzzy band, since large uncertainty may render the model non-functional. Four case studies from the references are used to benchmark the proposed method, including smooth, double, and non-smooth data and a complex, real case study that shows the advantages of the approach. The use of fuzzy parameters is closer to the uncertain nature of the problem. The new methodology increases the reliability of the prediction. Furthermore, the produced fuzzy band can include, to a significant degree, the observed data and the output of the existent crisp methodologies even if they include more complex assumptions. Full article