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Located on the south-facing slope of the Himalayas, Nepal receives intense, long-lasting precipitation during the Asian summer monsoon, making Nepal one of the most susceptible countries to flood and landslide hazards in the region. However, sparse gauging and irregular measurement constrain the vulnerability assessments of floods and landslides, which rely highly on the accuracy of precipitation. Therefore, this study evaluates the performance of Satellite-based Precipitation Products (SPPs) in the Himalayas region by comparing different datasets and identifying the best alternative of gauge-based precipitation for hydro-meteorological applications. We compared eight SPPs using statistical metrics and then used the Multi-Criteria Decision-Making (MCDM) technique to rank them. Secondly, we assessed the hydrological utility of SPPs by simulating them through the GR4J hydrological model. We found a high POD (0.60–0.80) for all SPPs except CHIRPS and PERSIANN; however, a high CC (0.20–0.40) only for CHIRPS, IMERG_Final, and CMORPH. Based on MCDM, CMORPH and IMERG_Final rank first and second. While SPPs could not simulate daily discharge (NSE < 0.28), they performed better for monthly streamflow (NSE > 0.54). Overall, this study recommends CMORPH and IMERG_Final and improves the understanding of data quality to better manage hydrological disasters in the data-sparse Himalayas. This study framework can also be used in other Himalayan regions to systematically rank and identify the most suitable datasets for hydro-meteorological applications. [ABSTRACT FROM AUTHOR]

Located on the south-facing slope of the Himalayas, Nepal receives intense, long-lasting precipitation during the Asian summer monsoon, making Nepal one of the most susceptible countries to flood and landslide hazards in the region. However, sparse gauging and irregular measurement constrain the vulnerability assessments of floods and landslides, which rely highly on the accuracy of precipitation. Therefore, this study evaluates the performance of Satellite-based Precipitation Products (SPPs) in the Himalayas region by comparing different datasets and identifying the best alternative of gauge-based precipitation for hydro-meteorological applications. We compared eight SPPs using statistical metrics and then used the Multi-Criteria Decision-Making (MCDM) technique to rank them. Secondly, we assessed the hydrological utility of SPPs by simulating them through the GR4J hydrological model. We found a high POD (0.60–0.80) for all SPPs except CHIRPS and PERSIANN; however, a high CC (0.20–0.40) only for CHIRPS, IMERG_Final, and CMORPH. Based on MCDM, CMORPH and IMERG_Final rank first and second. While SPPs could not simulate daily discharge (NSE < 0.28), they performed better for monthly streamflow (NSE > 0.54). Overall, this study recommends CMORPH and IMERG_Final and improves the understanding of data quality to better manage hydrological disasters in the data-sparse Himalayas. This study framework can also be used in other Himalayan regions to systematically rank and identify the most suitable datasets for hydro-meteorological applications.

Natural disasters, including droughts and floods, have caused huge losses to mankind. Hydrological modelling is an indispensable tool for obtaining a better understanding of hydrological processes. The DEM-based routing methods, which are widely used in the distributed hydrological models, are sensitive to both the DEM resolution and the computational cell size. Too little work has been devoted to the joint effects of DEM resolution and computational cell size on the routing methods. This study aims to study the joint effects of those two factors on discharge simulation performance with two representative routing methods. The selected methods are the improved aggregated network-response function routing method (I-NRF) and the Liner-reservoir-routing method (LRR). Those two routing methods are combined with two runoff generation models to simulate the discharge. The discharge simulation performance is evaluated under the cross combination of four DEM resolutions (i.e., 90 m, 250 m, 500 m, and 1000 m) and fifty-six computational cell sizes (ranging from 5 arc-min to 60 arc-min). Eleven years of hydroclimatic data from the Jianxi basin (2000–2010) and the Shizhenjie basin (1983–1993) in China are used. The results show that the effects of the DEM resolution and the computational cell size are different on the I-NRF method and the LRR method. The computational cell size has nearly no influence on the performance of the I-NRF methods, while the DEM resolution does. On the contrary, the LRR discharge simulation performance decreases with oscillating values as the computational cell size increases, but is hardly affected by the DEM resolution. Furthermore, the joint effects of the DEM resolution and the computational cell size can be ignored for both routing methods. The results of this study will help to establish the appropriate DEM resolution, computational cell size, and routing method when researchers build hydrological models to predict future disasters.

Spontaneous ignition caused by material discharge inside a silo causes considerable economic damage. To prevent this, we developed a silo hotspot detector that can be installed inside the silo to monitor the temperature according to the depth of the silo. However, if the silo hotspot detector located inside the silo is destroyed because of the pressure and load generated during material discharge, it could lead to a larger accident. Therefore, the structural safety of the silo hotspot detector should be evaluated based on material discharge; currently, there is no particular method to achieve this. Therefore, in this study, the theoretical formula is obtained through Eurocode, and the pressure and tensile force acting on the silo hotspot detector are predicted through the finite element method (FEM) using the Coupled Eulerian–Lagrangian(CEL) method. These result were verified by comparing the load measurement data acting on the silo hotspot detector when the silo material was discharged. It was confirmed that simulation using the CEL method can sufficiently simulate the behavior of the silo according to material discharge. Additionally, we confirmed that the structural safety of the silo hotspot detector inside the silo can be evaluated through FEM. [ABSTRACT FROM AUTHOR]

The occurrence of preferential flow in the subsurface has often been shown in field experiments. However, preferential flow is rarely included in models simulating the hydrological response at the catchment scale. If it is considered, preferential flow parameters are typically determined at the plot scale and then transferred to larger‐scale simulations. Here, we successfully used the optimization algorithm DiffeRential Evolution Adaptive Metropolis (DREAM) to calibrate a 3D physics‐based dual‐permeability model directly at the catchment scale. In order to keep computational costs of the optimization routine at a reasonable level, we limited the number of parameters to be calibrated to the ones that had been shown before to be most influential for the simulation of discharge. We also calibrated parameters of the matrix domain and the macropore domain with a fixed parameter ratio between soil layers instead of calibrating every layer separately. These ratios reflected observed depth profiles of soil hydraulic properties at our study site. The dual‐permeability parameter sets identified during calibration were able to simulate observed discharge time series satisfactorily but did not outperform a calibrated single‐domain reference model scenario. Saturated hydraulic conductivities of the macropore domain were calibrated such that they became very similar to matrix saturated hydraulic conductivities, thereby effectively removing the effect of macropores. This suggests that the incorporation of vertical preferential flow as represented by the dual‐permeability approach was not relevant for reproducing the hydrometric response reasonably well in the studied catchment. We also tested the scale‐invariance of the calibrated dual‐permeability parameter sets by using the parameter sets performing best at catchment scale to simulate plot‐scale bromide depth profiles obtained from tracer irrigation experiments. This parameter transfer proved to be not successful, indicating that soil hydraulic parameters are scale‐variant, independent of the direction of parameter transfer. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the performance of the Soil and Water Assessment Tool (SWAT), a simple Auto-Regressive with eXogenous input (ARX) model, and a gene expression programming (GEP)-based model in one-day-ahead discharge prediction for the upper Kentucky River Basin. Calibration of the models were carried out for the period of 2002–2005 using daily flow at a stream gauging station unaffected by the flow regulation. Validation of the calibrated models were executed for the period of 2008–2010 at the same gauging station along with another station 88 km downstream. GEP provided the best calibration (coefficient of determination (R) value 0.94 and Nash-Sutcliffe Efficiency (NSE) value of 0.88) and validation (R values of 0.93 and 0.93, NSE values of 0.87 and 0.87, respectively) results at the two gauging stations. While SWAT performed reasonably well in calibration (R value 0.85 and NSE value 0.72), its performance somewhat degraded in validation (R values of 0.85 and 0.82, NSE values of 0.65 and 0.65, for the two stations). ARX performed very well in calibration (R value 0.92, NSE value 0.82) and reasonably well in validation (R values of 0.88 and 0.92, NSE values of 0.76 and 0.85) at the two stations. Research results suggest that sophisticated hydrological models could be outperformed by simple data-driven models and GEP has the advantage to generate functional relationships that allows investigation of the complex nonlinear interrelationships among the input variables.

Spontaneous ignition caused by material discharge inside a silo causes considerable economic damage. To prevent this, we developed a silo hotspot detector that can be installed inside the silo to monitor the temperature according to the depth of the silo. However, if the silo hotspot detector located inside the silo is destroyed because of the pressure and load generated during material discharge, it could lead to a larger accident. Therefore, the structural safety of the silo hotspot detector should be evaluated based on material discharge; currently, there is no particular method to achieve this. Therefore, in this study, the theoretical formula is obtained through Eurocode, and the pressure and tensile force acting on the silo hotspot detector are predicted through the finite element method (FEM) using the Coupled Eulerian–Lagrangian(CEL) method. These result were verified by comparing the load measurement data acting on the silo hotspot detector when the silo material was discharged. It was confirmed that simulation using the CEL method can sufficiently simulate the behavior of the silo according to material discharge. Additionally, we confirmed that the structural safety of the silo hotspot detector inside the silo can be evaluated through FEM.

As a revolutionary tool leading to substantial changes across many areas, Machine Learning (ML) techniques have obtained growing attention in the field of hydrology due to their potentials to forecast time series. Moreover, a subfield of ML, Deep Learning (DL) is more concerned with datasets, algorithms and layered structures. Despite numerous applications of novel ML/DL techniques in discharge simulation, the uncertainty involved in ML/DL modeling has not drawn much attention, although it is an important issue. In this study, a framework is proposed to quantify uncertainty contributions of the sample set, ML approach, ML architecture and their interactions to multi-step time-series forecasting based on the analysis of variance (ANOVA) theory. Then a discharge simulation, using Recurrent Neural Networks (RNNs), is taken as an example. Long Short-Term Memory (LSTM) network, a state-of-the-art DL approach, was selected due to its outstanding performance in time-series forecasting, and compared with simple RNN. Besides, novel discharge forecasting architecture is designed by combining the expertise of hydrology and stacked DL structure, and compared with conventional design. Taking hourly discharge simulations of Anhe (China) catchment as a case study, we constructed five sample sets, chose two RNN approaches and designed two ML architectures. The results indicate that none of the investigated uncertainty sources are negligible and the influence of uncertainty sources varies with lead-times and discharges. LSTM demonstrates its superiority in discharge simulations, and the ML architecture is as important as the ML approach. In addition, some of the uncertainty is attributable to interactions rather than individual modeling components. The proposed framework can both reveal uncertainty quantification in ML/DL modeling and provide references for ML approach evaluation and architecture design in discharge simulations. It indicates uncertainty quantification is an indispensable task for a successful application of ML/DL.

This study assesses the impact of hydroelectric dams on the discharge and total suspended solids (TSS) concentration in the Huong River basin in Vietnam. The analysis is based on hydrologic and sediment transport simulations by the Soil and Water Assessment Tool (SWAT) model driven by the Tropical Rainfall Measuring Mission (TRMM) 3B42V6 rainfall data, from January 2003 through December 2010. An upstream sub-basin not affected by the hydroelectric dams was used for model calibration. The calibration results indicate good agreement between simulated and observed daily data (0.67 Nash-Sutcliffe efficiency, 0.82 Pearson correlation coefficient). The calibrated model for discharge and TSS simulation is then applied on another major sub-basin and then the whole Huong River basin. The simulation results indicate that dam operation in 2010 decreased downstream discharge during the rainy season by about 35% and augmented it during the dry season by about 226%. The downstream TSS concentration has decreased due to the dam operation but the total sediment loading increased during the dry season and decreased during the rainy season. On average, the dam construction and operation affected the pattern of discharge more than that of the sediment loading. Results indicate that SWAT, driven by remotely sensed inputs, can reasonably simulate discharge and water quality in ungauged or poorly gauged river basins and can be very useful for water resources assessment and climate change impact studies in such basins.

Water resources management requires an integrated approach between sustainability and catchment management to provide an enhanced understanding of usage options and environmental impacts. Discharge contributes significantly to the lake water and hence its accurate assessment would help in designing the management policies. This study simulates river discharge contributing to a lake using different physical and data-based modeling approaches. Models such as support vector machine (SVM); extreme learning machine (ELM); MIKE SHE; and a combination of MIKE SHE and the Soil and Water Assessment Tool (SWAT), the hybrid MIKE SHE-SWAT model, are used to simulate discharge for three subcatchments of Loktak Lake. Discharge simulation at different sections of a river using physically based models is cumbersome because it requires data such as river geometry, hydrologic time series, channel roughness coefficients, and hydraulics of existing control structures. In developing countries such as India, quality and quantity of data become the limitation because of poor monitoring and record keeping. Therefore, a data-based modeling approach can also be used as an alternative tool in hydrologic simulation studies. The selected methods are evaluated using statistical indicators, and results show that both physical and data-based methods satisfactorily simulate the observed hydrograph at the outlet of each subcatchments. This study concludes that using the hybrid MIKE SHE-SWAT model simulates the river discharge with the highest Nash-Sutcliffe model efficiency (NSE), 0.854 for the Nambul subcatchment, 0.890 for the Iril subcatchment, and 0.845 for the Thoubal subcatchment. Subsequently, hydrological models for the other six subcatchments are developed using the hybrid MIKE SHE-SWAT model. Furthermore, water balance components of the Loktak Lake are also analyzed, which suggest that of the total inflows to the lake, discharge from the nine subcatchments accounts for about 91%, and the remainder is contributed by direct rainfall onto the lake area. Among outflows, Ithai barrage (69%) accounts for the largest fraction, whereas abstractions for hydropower generation, evaporation from the lake surface not covered by phumdis, evapotranspiration from the area of phumdis on the lake, irrigation, and domestic consumption account for 22, 2, 5, 1, and 1%, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Physics is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hetao Oasis is located in a typical piedmont alluvial plain bounded by the Langshan Mountain Range in the north, desert in the west, and the Yellow River in the south. Agricultural activities within the oasis significantly impact the hydrological cycle and water quality in downstream locations. The research uses the Soil and Water Assessment Tool (SWAT) for a piedmont plain by defining the watershed boundary as coinciding with the natural mountain ridge, the border between the oasis and the desert, and the Yellow River. The model simulates water discharge with coefficient of determination and a Nash-Sutcliffe model efficiency of 0.78 and 0.62 during model calibration, and 0.75 and 0.69 during model validation, suggesting that delineation of the watershed as carried out in this research is suitable for piedmont plain topography. From the results, the mountains contribute 28.4% to the water discharge at the outlet of the watershed, and water-use efficiency of irrigated water is about 40%, which is consistent with field-based measurements. Methodologies used in delineating watershed boundaries and parameterizing SWAT provide a solid foundation for water balance studies in other regions of the world with similar topography. [ABSTRACT FROM AUTHOR]

The address discharge simulation and the light waveform are investigated when the additional pulses are applied to the common electrode during an address period in AC plasma display panel. The wall charges in a cell are accumulated on three electrodes during a reset period and those are utilized to produce the address discharge triggering in addition to the externally applied voltage between the scan and address electrodes during an address period. As the accumulated wall charges should be utilized on the overall electrodes to improve the address discharge triggering, the additional pulses are applied to the common sustain electrodes when the scan and address pulses are driven. In this study, the Xe3P2energy state to generate the vacuum ultraviolet are simulated when the scan and address pulses on two electrode are applied including the additional pulses on the common electrodes. In addition, the address discharge light waveforms in the conventional and the proposed driving method are measured and compared. [ABSTRACT FROM AUTHOR]

Spatial variability of input data for distributed and physically based models has a significant impact on the results of model simulations. The objective of this study was to compare the methods of precipitation spatial interpolation, namely, the method of Thiessen polygons (THI), Inverse Distance Weighting (IDW), and Universal Kriging (UNK) using the physically based and spatially distributed hydrological WetSpa model. The paper also illustrates how these methods allow more accurate estimation of river discharge simulated with the WetSpa model for the Gharesou basin in Iran. Daily data of 22 precipitation gaging stations were used in this study. In order to simulate hydrological processes for the basin, especially river runoff, we used the precipitation data interpolated with THI, IDW, and UNK as well as digital information on topography, the land use and soil type of the basin. For comparing three interpolation methods, they were applied to the simulation of river discharge using the WetSpa model. Simulated daily values of water discharge in the basin were compared with observational data. The quality of data simulated using these three methods was very good or excellent. According to the efficiency criterion of Aggregated Measure (0.879), the application of THI method provided more accurate forecast of river discharge than that of IDW and UNK methods. [ABSTRACT FROM AUTHOR]

This study assesses the impact of hydroelectric dams on the discharge and total suspended solids (TSS) concentration in the Huong River basin in Vietnam. The analysis is based on hydrologic and sediment transport simulations by the Soil and Water Assessment Tool (SWAT) model driven by the Tropical Rainfall Measuring Mission (TRMM) 3B42V6 rainfall data, from January 2003 through December 2010. An upstream sub-basin not affected by the hydroelectric dams was used for model calibration. The calibration results indicate good agreement between simulated and observed daily data (0.67 Nash-Sutcliffe efficiency, 0.82 Pearson correlation coefficient). The calibrated model for discharge and TSS simulation is then applied on another major sub-basin and then the whole Huong River basin. The simulation results indicate that dam operation in 2010 decreased downstream discharge during the rainy season by about 35% and augmented it during the dry season by about 226%. The downstream TSS concentration has decreased due to the dam operation but the total sediment loading increased during the dry season and decreased during the rainy season. On average, the dam construction and operation affected the pattern of discharge more than that of the sediment loading. Results indicate that SWAT, driven by remotely sensed inputs, can reasonably simulate discharge and water quality in ungauged or poorly gauged river basins and can be very useful for water resources assessment and climate change impact studies in such basins. [ABSTRACT FROM AUTHOR]

In the current study, two regional climate models (MM5 and REMO) driven by different global boundary conditions (the ERA40 reanalysis and the ECHAM5 model) are one-way coupled to the uncalibrated hydrological process model PROMET to analyze the impact of global boundary conditions, dynamical regionalization and subsequent statistical downscaling (bilinear interpolation, correction of subgrid-scale variability and combined correction of subgrid-scale variability and bias) on river discharge simulation. The results of 12 one-way coupled model runs, set up for the catchment of the Upper Danube (Central Europe) over the historical period 1971-2000, prove the expectation that the global boundaries applied to force the RCMs strongly influence the accuracy of simulated river discharge. It is, however, noteworthy that all efficiency criteria in case of bias corrected MM5 simulations indicate better performance under ERA40 boundaries, whereas REMO-driven hydrological simulations better correspond to measured discharge under ECHAM5 boundaries. Comparing the hydrological results achievable with MM5 and REMO, the application of bias-corrected MM5 simulations turned out to allow for a more accurate simulation of discharge, while the variance in simulated discharge in most cases was better reflected in case of REMO forcings. The correction of subgrid-scale variability within the downscaling of RCM simulations compared to a bilinear interpolation allows for a more accurate simulation of discharge for all model configurations and all discharge criteria considered (mean monthly discharge, mean monthly low-flow and peak-flow discharge). Further improvements in the hydrological simulations could be achieved by eliminating the biases (in terms of deviations from observed meteorological conditions) inherent in the driving RCM simulations, regardless of the global boundary conditions or the RCM applied. In spite of all downscaling and bias correction efforts described, the RCM-driven hydrological simulations remain less accurate than those achievable with spatially distributed meteorological observations. [ABSTRACT FROM AUTHOR]

Natural disasters, including droughts and floods, have caused huge losses to mankind. Hydrological modelling is an indispensable tool for obtaining a better understanding of hydrological processes. The DEM-based routing methods, which are widely used in the distributed hydrological models, are sensitive to both the DEM resolution and the computational cell size. Too little work has been devoted to the joint effects of DEM resolution and computational cell size on the routing methods. This study aims to study the joint effects of those two factors on discharge simulation performance with two representative routing methods. The selected methods are the improved aggregated network-response function routing method (I-NRF) and the Liner-reservoir-routing method (LRR). Those two routing methods are combined with two runoff generation models to simulate the discharge. The discharge simulation performance is evaluated under the cross combination of four DEM resolutions (i.e., 90 m, 250 m, 500 m, and 1000 m) and fifty-six computational cell sizes (ranging from 5 arc-min to 60 arc-min). Eleven years of hydroclimatic data from the Jianxi basin (2000–2010) and the Shizhenjie basin (1983–1993) in China are used. The results show that the effects of the DEM resolution and the computational cell size are different on the I-NRF method and the LRR method. The computational cell size has nearly no influence on the performance of the I-NRF methods, while the DEM resolution does. On the contrary, the LRR discharge simulation performance decreases with oscillating values as the computational cell size increases, but is hardly affected by the DEM resolution. Furthermore, the joint effects of the DEM resolution and the computational cell size can be ignored for both routing methods. The results of this study will help to establish the appropriate DEM resolution, computational cell size, and routing method when researchers build hydrological models to predict future disasters. [ABSTRACT FROM AUTHOR]

In order to clarify the influence of the addition of nitrogen on the fundamental properties of plasma display panel (PDP) discharges in a Ne/Xe (10%) gas mixture, the PDP discharges in Ne/Xe/N2 gas mixtures have been simu- lated using a one-dimensional fluid model that is based on the continuity equations for electrons, ions, excited atoms and molecules, with the Poisson equation and the electron energy balance equation. Based on the simulation results, we found that a pulsed discharge with a short time (-100 ns) occurs once during every half cycle. At a nitrogen concentration of more than 100 ppm, a phase delay of the gap voltage and discharge current in Ne/Xe gas mixture PDP discharges began to appear. [ABSTRACT FROM AUTHOR]

This study investigated the sensitivity of a dynamic downscaling atmospheric model system coupled with a rainfall-runoff model to hindcast an example of reservoir water management in the semi-arid region of Northeast Brazil (NEB). A regional atmospheric spectral model (RSM) is driven by the outputs of an atmospheric general circulation model (AGCM), itself forced by the observed sea surface temperature over the World Ocean. Daily precipitation simulated by the RSM was then used as the input to a hydrological rainfall-runoff model for the Upper Jaguaribe River Basin to estimate inflows at the Orós Reservoir in the state of Ceará. A hindcast analysis of precipitation was performed during the rainy season over NEB (January to June) from 1971 to 2000. The RSM captured the precipitation variability relatively well when a probability density function (PDF) was used to correct the numerical bias. Three hindcast series of inflow using (i) the observed rainfall, (ii) the simulated rainfall before the PDF correction, and (iii) the simulated rainfall after the PDF correction were performed during the study period and then compared to the series of observed inflow. The atmospheric-rainfall-runoff 'cascade' model efficiency was evaluated by comparing the Orós Reservoir release decisions from different scenarios based on observed, simulated (RSM, RSM-PDF), and mean historical reservoir inflows. The cascade model has the potential, relatively well balanced during dry, normal or wet years, to be a useful tool to correctly forecast the decision managements of reservoirs in the semi-arid region of NEB. Additional progress in the numerical simulation is however necessary to improve the performance. [ABSTRACT FROM AUTHOR]

Abstract: The Xitiaoxi catchment is one of the most important catchments in the Taihu Basin in southeastern China. It contributes a significant amount of surface runoff and nutrient to Taihu Lake. Understanding the nutrient cycling and identification of critical non-point source pollution in this catchment are therefore of primary importance. In this paper, the Xinanjiang-Nitrogen (XAJ-N) model, a conceptual model of nutrient mobilization and transport is developed by integration of the Xinanjiang rainfall–runoff model, the Integrated Nitrogen CAtchment (INCA) model and the Modified Universal Soil Loss Equation (MUSLE). It is implemented with the environmental modelling language PCRaster and estimates the water fluxes and nutrient loadings on a cell-by-cell basis in daily time steps. The model includes the nitrogen cycling processes of mineralization, leaching, fixation, volatilization, nitrification, denitrification and plant uptake. Nitrogen is assumed to be mobilized by surface runoff and groundwater. The model performance was verified by comparing simulated and measured daily discharge and nutrient loadings. The results showed a fairly good relationship between predicted and observed values. Due to the scarce observed data, the simulation results were also validated using an internal mass balance method and values from the literature. It showed that the modelling approach can be used as a tool to estimate the export of nutrient with a daily resolution at a catchment scale. [ABSTRACT FROM AUTHOR]

Abstract: In this work, we simulated the whole processes of plasma-enhanced selective catalytic reduction for nitrogen oxides removal with hydrocarbon additive. The simulation model consists of plasma simulation and catalysis simulation. First, single filamentary microdischarge in dielectric barrier discharge was calculated to evaluate the radical production yield as a function of specific input energy. Second, the chemical reaction process in the discharge reactor was simulated to find the gas reforming property by the plasma. This plasma simulation was applied to NO oxidation process in atmospheric pressure N2/O2/NO/C3H6 mixtures under various discharge conditions. Finally, the catalytic reaction process was modeled using simple mass balance equations in gas-phase and on catalyst surface. The catalytic reaction simulation was tested for the reduction of nitrogen oxides on γ-Al2 O 3 catalyst in N2/O2/NO/C3H6 and in N2/O2/NO2/C3H6. [Copyright &y& Elsevier]

Sunwoo, W.; Lee, G.; Nguyen, H.H.; Shin, H., and Jun, K.S., 2021. Parameter optimization of a conceptual rainfall-runoff model in the coastal urban region. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 261–265. Coconut Creek (Florida), ISSN 0749-0208. The rainfall-runoff simulation based on local conditions is crucial for flood monitoring and mitigation. Although numerous rainfall-runoff models were widely used, only few studies have been applied in coastal regions in South Korea. The major reasons are that model parameters are incorrectly determined, and the peak flooding is overestimated due to extreme rainfall events. In this study, a conceptual rainfall-runoff model, the Probability Distributed Model (PDM), was employed to simulate discharge with an incorporation of combined satellite-, model-, and ground-based soil moisture as an input data. The combined soil moisture data was generated using two renormalization strategies. In this study, we aimed to compare the performances of two optimization techniques associated in the PDM model, including a traditional non-linear and a newly metaheuristics algorithms, for the discharge prediction. The simulation was conducted in a coastal urban region, Hongseong, using the optimized parameter sets for flood events. The simulated results from the PDM model revealed that parameters obtained with the metaheuristics algorithm indicated a superior performance when comparing against the observed runoff data. Moreover, parameter sets obtained by the metaheuristics algorithm and non-linear technique showed significant differences. The method of this study can provide more improvements in rainfall-runoff simulation results for flood management. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the performance of the Soil and Water Assessment Tool (SWAT), a simple Auto-Regressive with eXogenous input (ARX) model, and a gene expression programming (GEP)-based model in one-day-ahead discharge prediction for the upper Kentucky River Basin. Calibration of the models were carried out for the period of 2002–2005 using daily flow at a stream gauging station unaffected by the flow regulation. Validation of the calibrated models were executed for the period of 2008–2010 at the same gauging station along with another station 88 km downstream. GEP provided the best calibration (coefficient of determination (R) value 0.94 and Nash-Sutcliffe Efficiency (NSE) value of 0.88) and validation (R values of 0.93 and 0.93, NSE values of 0.87 and 0.87, respectively) results at the two gauging stations. While SWAT performed reasonably well in calibration (R value 0.85 and NSE value 0.72), its performance somewhat degraded in validation (R values of 0.85 and 0.82, NSE values of 0.65 and 0.65, for the two stations). ARX performed very well in calibration (R value 0.92, NSE value 0.82) and reasonably well in validation (R values of 0.88 and 0.92, NSE values of 0.76 and 0.85) at the two stations. Research results suggest that sophisticated hydrological models could be outperformed by simple data-driven models and GEP has the advantage to generate functional relationships that allows investigation of the complex nonlinear interrelationships among the input variables. [ABSTRACT FROM AUTHOR]

The paper presents the results of a numerical investigation of a fast ionization wave in a non-preionized gas as the initial stage of a nanosecond capillary discharge. The wave was created in a 5 cm long narrow capillary filled with nitrogen at a pressure p = 2 Torr by applying a nanosecond voltage pulse of negative polarity, which was supplied by an electric circuit consisting of a preliminary charged capacitor, a thyratron switch and a cable. Propagation of the wavefront along the capillary and formation of a conducting plasma channel were simulated using the fluid approach to description of processes in low-temperature plasma. Including electrical circuit into consideration allowed obtaining realistic voltage pulse shapes as well as current rise-rates in the system immediately after the ionization wave has reached the grounded electrode. The latter was used as a parameter indicating the efficiency of the consequent initiation of a capillary discharge. Obtained dynamics of wave propagation and structure of the wavefront are discussed. Influence of dielectric permittivity of the capillary material on the wave properties in general and on the capillary discharge initiation is analyzed. • Numerical simulations of the initial stage of a fast capillary discharge created without preionization were carried out. • Fluid approach coupled with the equations describing the power circuit was used. • Estimates of current rise rate that takes place after the ionization wave has reached the grounded electrode were made. • A considerable dependence of simulation results on the dielectric permittivity of the capillary was observed. [ABSTRACT FROM AUTHOR]

As a revolutionary tool leading to substantial changes across many areas, Machine Learning (ML) techniques have obtained growing attention in the field of hydrology due to their potentials to forecast time series. Moreover, a subfield of ML, Deep Learning (DL) is more concerned with datasets, algorithms and layered structures. Despite numerous applications of novel ML/DL techniques in discharge simulation, the uncertainty involved in ML/DL modeling has not drawn much attention, although it is an important issue. In this study, a framework is proposed to quantify uncertainty contributions of the sample set, ML approach, ML architecture and their interactions to multi-step time-series forecasting based on the analysis of variance (ANOVA) theory. Then a discharge simulation, using Recurrent Neural Networks (RNNs), is taken as an example. Long Short-Term Memory (LSTM) network, a state-of-the-art DL approach, was selected due to its outstanding performance in time-series forecasting, and compared with simple RNN. Besides, novel discharge forecasting architecture is designed by combining the expertise of hydrology and stacked DL structure, and compared with conventional design. Taking hourly discharge simulations of Anhe (China) catchment as a case study, we constructed five sample sets, chose two RNN approaches and designed two ML architectures. The results indicate that none of the investigated uncertainty sources are negligible and the influence of uncertainty sources varies with lead-times and discharges. LSTM demonstrates its superiority in discharge simulations, and the ML architecture is as important as the ML approach. In addition, some of the uncertainty is attributable to interactions rather than individual modeling components. The proposed framework can both reveal uncertainty quantification in ML/DL modeling and provide references for ML approach evaluation and architecture design in discharge simulations. It indicates uncertainty quantification is an indispensable task for a successful application of ML/DL. [ABSTRACT FROM AUTHOR]

Chemical nonthermal plasma reactors in air at atmospheric pressure are often built by numerous filamentary discharges, each of them being similar to that launched in a point-to-plane discharge in atmospheric air. The filamentary discharge during its crossing through the gas will trigger sets of chemical reactions, as well as hydrodynamic gas dynamic around the cylindrical path used by the discharge. In this paper, the role of the excited species on the formation of nitric oxide NO and atomic oxygen O is presented. [ABSTRACT FROM AUTHOR]