Differential Quadrature Method For Solving Bed Load Sediment Transport

Sediment transport is crucial for designing, and operating hydraulic structures. Hence, itsprediction has forced researches to study it through experimental and mathematical modeling works. Mathematical modeling has gained importance especially with the advent of powerfulcomputers. These modeling studies are mostly based on the numerical solutions of transport equations of the partial differential equations with finite difference, finite element or finite volume methods. This study, as an alternative to existing methods, has developed a numerical technique, called differential quadrature method (DQM). The DQM expresses a differential at apoint as a function of products of weight coefficients and the functional values at each point ofthe domain. The weight coefficients are determined using one of the several algorithms such asthe Langrangian, depending upon the spacing intervals. In this study, bed load sediment transport equation, coupled with flow equations of the continuity and momentum, is solved using the DQM. The performance of the model is tested against that of the finite difference method and aswell as the experimental data. The results revealed that DQM can also be employed in modeling bed load sediment transport

Double Decomposition Method for the Solution of Sediment Wave Equation

Transient sediment waves are solved by the double decomposition (DD) method. The method solves the parabolic partial differential equation by decomposing the solution function into summation of M number of components. The solution is approximated by considering the first three terms. The performance of the model in simulating experimental data is satisfactory.The hypothetical case study reveals that the model can mimic the sediment transport in nature

Modelling Rainfall-Runoff Process of Kabul River Basin in Afghanistan Using ArcSWAT Model

Kabul River Basin is the most populated and an important source of water resources in Afghanistan. The Soil and Water Assessment Tool (SWAT) model, together with the ArcGIS and SWAT-CUP, is employed to predict the runoff in the basin. Nine years of meteorological and hydrological data are employed in the study. The DEM, the soil cover, and the land use/cover data are downloaded from the available global database. The ArcGIS based soil classification, the land use/cover, the elevation, the drainage, and the slope distribution maps of the basin are generated. The meteorological data from 18 different stations and the hydrological data from 7 different stations are obtained from the Ministry of Energy and Water of Afghanistan. The basin is divided into 48 sub-basins with a total number of 770 hydrological response units (HRUs). The sensitivity analysis results revealed that the flow characteristics of KRB are highly influenced by the groundwater and snowmelt.  The model is calibrated using the data from 2010 to 2014 and validated employing the data from 2015 to 2017 at seven different hydrological stations. The SWAT-CUP is successfully used to calibrate the model for predicting monthly and daily runoffs. The calibrations and validations for the seven stations are achieved, on the average, with the correlation coefficient (R) of 0.78 (for daily flows) and 0.82 (for monthly flows), respectively. Total water yield in the basin is estimated to be 432.9 mm/year, corresponding to 31 176 Mm3/year, hardly meeting the demand of 26 512 Mm3/year in the basin. &nbsp

Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Neural Network

An artificial neural network (ANN) model was developed to predict the longitudinal dispersion coefficient in natural streams and rivers. The hydraulic variables [flow discharge (Q), flow depth (H), flow velocity (U), shear velocity (u*), and relative shear velocity (U/u*)] and geometric characteristics [channel width (B), channel sinuosity (σ), and channel shape parameter (β)] constituted inputs to the ANN model, whereas the dispersion coefficient (Kx) was the target model output. The model was trained and tested using 71 data sets of hydraulic and geometric parameters and dispersion coefficients measured on 29 streams and rivers in the United States. The training of the ANN model was accomplished with an explained variance of 90% of the dispersion coefficient. The dispersion coefficient values predicted by the ANN model satisfactorily compared with the measured values corresponding to different hydraulic and geometric characteristics. The predicted values were also compared with those predicted using several equations that have been suggested in the literature and it was found that the ANN model was superior in predicting the dispersion coefficient. The results of sensitivity analysis indicated that the Q data alone would be sufficient for predicting more frequently occurring low values of the dispersion coefficient (Kx < 100 m²/s). For narrower channels (B/H<50) using only U/u* data would be sufficient to predict the coefficient. If β and σ were used along with the flow variables, the prediction capability of the ANN model would be significantly improved

ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff

This study presents the development of artificial neural network _ANN_ and fuzzy logic _FL_ models for predicting event-based rainfall runoff and tests these models against the kinematic wave approximation _KWA_. A three-layer feed-forward ANN was developed using the sigmoid function and the backpropagation algorithm. The FL model was developed employing the triangular fuzzy membership functions for the input and output variables. The fuzzy rules were inferred from the measured data. The measured event based rainfall-runoff peak discharge data from laboratory flume and experimental plots were satisfactorily predicted by the ANN, FL, and KWA models. Similarly, all the three models satisfactorily simulated event-based rainfall-runoff hydrographs from experimental plots with comparable error measures. ANN and FL models also satisfactorily simulated a measured hydrograph from a small watershed 8.44 km2 in area. The results provide insights into the adequacy of ANN and FL methods as well as their competitiveness against the KWA for simulating event-based rainfall-runoff processes

Proposing a Popular Method for Meteorological Drought Monitoring in the Kabul River Basin, Afghanistan

This paper investigates meteorological drought in one of Afghanistan's most important socio-economic river basins called Kabul River Basin (KRB) using a 38 years monthly precipitation data. Several drought indices such as Standardized Precipitation Index (SPI), Percent of Normal Precipitation Index (PNPI), Deciles Index (DI), and China-Z Index (CZI) were applied for the first time on the basin in order to observe the correlation among the indices in the basin for drought, and to see which method is suitable for drought monitoring in KRB. Due to the concerns that climate is changing and especially the rapid snowmelt that accounts for 80% of the precipitation in Afghanistan, it was essential to carry such a study in order to warn the responsible bodies in the country for a better drought management. Moreover, the rapid population increase and USAge of more water for both drinking and agricultural purposes in the basin with a possible decrease in the annual precipitation make it necessary to undertake such a study. The results of the investigation show that KRB area experienced drought conditions continuously from 2000 to 2004 with a peak extreme drought in 2001 which confirm to the reported worst drought in the region. It is noted that log-SPI, gamma-SPI, and Deciles captured the historical extreme and severe drought periods successfully, therefore, these methods are recommended to be applied to this region as drought assessment tools

Case Study: Finite Element Method and Artificial Neural Network Models for Flow through Jeziorsko Earthfill Dam in Poland

A finite element method (FEM) and an artificial neural network (ANN) model were developed to simulate flow through Jeziorsko earthfill dam in Poland. The developed FEM is capable of simulating two-dimensional unsteady and nonuniform flow through a nonhomogenous and anisotropic saturated and unsaturated porous body of an earthfill dam. For Jeziorsko dam, the FEM model had 5,497 triangular elements and 3,010 nodes, with the FEM network being made denser in the dam body and in the neighborhood of the drainage ditches. The ANN model developed for Jeziorsko dam was a feedforward three layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water levels on the upstream and downstream sides of the dam were input variables and the water levels in the piezometers were the target outputs in the ANN model. The two models were calibrated and verified using the piezometer data collected on a section of the Jeziorsko dam. The water levels computed by the models satisfactorily compared with those measured by the piezometers. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM model. This case study offers insight into the adequacy of ANN as well as its competitiveness against FEM for predicting seepage through an earthfill dam body

Kinematic wave model for transient bed profiles in alluvial channels under nonequilibrium conditions

An edited version of this paper was published by AGU. Copyright 2007 American Geophysical Union.Transient bed profiles in alluvial channels are generally modeled using diffusion (or dynamic) waves and assuming equilibrium between detachment and deposition rates. Equilibrium sediment transport can be considerably affected by an excess (or deficiency) of sediment supply due to mostly flows during flash floods or floods resulting from dam break or dike failure. In such situations the sediment transport process occurs under nonequilibrium conditions, and extensive changes in alluvial river morphology can take place over a relatively short period of time. Therefore the study and prediction of these changes are important for sustainable development and use of river water. This study hence developed a mathematical model based on the kinematic wave theory to model transient bed profiles in alluvial channels under nonequilibrium conditions. The kinematic wave theory employs a functional relation between sediment transport rate and concentration, the shear-stress approach for flow transport capacity, and a relation between flow velocity and depth. The model satisfactorily simulated transient bed forms observed in laboratory experiments

Kinematic wave model of bed profiles in alluvial channels

An edited version of this paper was published by AGU. Copyright 2006 American Geophysical Union.A mathematical model, based on the kinematic wave (KW) theory, is developed for describing the evolution and movement of bed profiles in alluvial channels. The model employs a functional relation between sediment transport rate and concentration, a relation between flow velocity and depth and Velikanov's formula relating suspended sediment concentration to flow variables. Laboratory flume and field data are used to test the model. Transient bed profiles in alluvial channels are also simulated for several hypothetical cases involving different water flow and sediment concentration characteristics. The model‐simulated bed profiles are found to be in good agreement with what is observed in the laboratory, and they seem theoretically reasonable for hypothetical cases. The model results reveal that the mean particle velocity and maximum concentration (maximum bed form elevation) strongly affect transient bed profiles