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Water
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Hydraulic, Sediment Transport and Morphological Assessment in Rivers and Reservoirs
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Hydraulic, Sediment Transport and Morphological Assessment in Rivers and Reservoirs

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Erosion and Sediment Transport".

Deadline for manuscript submissions: closed (30 January 2023) | Viewed by 12001

Special Issue Editors

Dr. Sanjay Giri

E-Mail Website
Guest Editor
Inland Water Systems Division, Deltares, Delft, The Netherlands
Interests: rivers; dams and reservoirs; water resources; integrated basin management; sediment transport; fluvial geomorphology; numerical modelling; remote-sensing techniques
Dr. Biswa Bhattacharya

E-Mail Website
Co-Guest Editor
Institute of Hydraulic Engineering-Delft (IHE-Delft), Delft, The Netherlands
Interests: hydraulic and hydrological modelling; flood risk management; flood forecasting and mapping; sediment transport

Special Issue Information

Dear Colleagues,

We invite you to submit your fundamental and applied research works, as well as case studies on hydraulic, sediment transport, and morphological processes and extremes in rivers and reservoirs. The contributions may include numerical modelling, data analysis, field investigation, application of remote-sensing techniques, as well as vision papers on system understanding and processes associated with integrated river and reservoir management considering sediment transport and morphological processes and extremes.

Rivers and reservoirs are integral components of a basin. For proper management of water resources, as well as water-induced/fluvial disasters, it is very important to understand, quantify, and predict flow, sediment transport, and morphological processes in rivers and reservoirs. These reach-scale processes can be both natural and human-induced, such as processes and changes due to extreme (quasi)natural events, as well as due to river management interventions and infrastructures such as river structures, reservoirs, canals, roads, culverts, human settlements, etc.

We anticipate that your contributions will be of interest and use to the communities involved in both research and practices associated with river and reservoir management.

Dr. Sanjay Giri
Dr. Biswa Bhattacharya
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • rivers
  • reservoirs
  • sediment transport
  • morphology
  • numerical modelling
  • remote sensing

Published Papers (5 papers)

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Research

19 pages, 7131 KiB  
Article
Driving Factors and Trend Prediction for Annual Sediment Transport in the Upper and Middle Reaches of the Yellow River from 2001 to 2020
by Jingjing Wu, Jia Tian, Jie Liu, Xuejuan Feng, Yingxuan Wang, Qian Ya and Zishuo Li
Water 2023, 15(6), 1107; https://doi.org/10.3390/w15061107 - 14 Mar 2023
Cited by 3 | Viewed by 1177
Abstract
The Yellow River has long been known for having low water and abundant sediment. The amount of sediment transported in the upper and middle reaches of the Yellow River (UMRYR) has changed significantly in recent years, resulting in an obvious imbalance in the [...] Read more.
The Yellow River has long been known for having low water and abundant sediment. The amount of sediment transported in the upper and middle reaches of the Yellow River (UMRYR) has changed significantly in recent years, resulting in an obvious imbalance in the spatiotemporal distribution of the water resources in the Yellow River Basin (YRB). The changes in the sediment transport in the Yellow River significantly affect ecological security and socioeconomic development in the YRB. In this study, the Google Earth Engine (GEE) platform was used to obtain the potential driving factors influencing the five main gauge stations in the UMRYR: vegetation, soil moisture, population, precipitation, land types, etc. The data on the annual sediment transport (AST) were from the River Sediment Bulletin of China (2001~2020). Linear regression and the Mann–Kendall test were used to study the temporal variation in the AST. The first-order difference was determined from the original data to remove the autocorrelation, and it met the requirement of sample independence. The factors without collinearity were used for the driving force analysis using linear regression (linear model) and random forest regression (nonlinear model). We used the selected driving factors to establish the linear regression, the random forest model for predicting the AST, and cross-validation for verifying the prediction accuracy. Furthermore, the prediction outcomes were compared with the simplest ARIMA time-series model (control model). Our findings showed that the changing trend and the mutation of the AST were different in the UMRYR during the past 20 years. However, after the first-order difference of the AST, the amount of interannual variation in the annual sediment transport (ΔAST) was almost unchanged in the UMRYR. The five driving factors were chosen to establish the prediction models of linear regression and random forest regression, respectively. Compared with the control model, ARIMA, the prediction accuracy of the random forest model was the highest. Full article
(This article belongs to the Special Issue Hydraulic, Sediment Transport and Morphological Assessment in Rivers and Reservoirs)
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27 pages, 5549 KiB  
Article
Prediction of the Amount of Sediment Deposition in Tarbela Reservoir Using Machine Learning Approaches
by Shahzal Hassan, Nadeem Shaukat, Ammar Ahmad, Muhammad Abid, Abrar Hashmi, Muhammad Laiq Ur Rahman Shahid, Zohreh Rajabi and Muhammad Atiq Ur Rehman Tariq
Water 2022, 14(19), 3098; https://doi.org/10.3390/w14193098 - 01 Oct 2022
Cited by 1 | Viewed by 3068
Abstract
Tarbela is the largest earth-filled dam in Pakistan, used for both irrigation and power production. Tarbela has already lost around 41.2% of its water storage capacity through 2019, and WAPDA predicts that it will continue to lose storage capacity. If this issue is [...] Read more.
Tarbela is the largest earth-filled dam in Pakistan, used for both irrigation and power production. Tarbela has already lost around 41.2% of its water storage capacity through 2019, and WAPDA predicts that it will continue to lose storage capacity. If this issue is ignored for an extended period of time, which is not far away, a huge disaster will occur. Sedimentation is one of the significant elements that impact the Tarbela reservoir’s storage capacity. Therefore, it is crucial to accurately predict the sedimentation inside the Tarbela reservoir. In this paper, an Artificial Neural Network (ANN) architecture and multivariate regression technique are proposed to validate and predict the amount of sediment deposition inside the Tarbela reservoir. Four input parameters on yearly basis including rainfall (Ra), water inflow (Iw), minimum water reservoir level (Lr), and storage capacity of the reservoir (Cr) are used to evaluate the proposed machine learning models. Multivariate regression analysis is performed to undertake a parametric study for various combinations of influencing parameters. It was concluded that the proposed neural network model estimated the amount of sediment deposited inside the Tarbela reservoir more accurately as compared to the multivariate regression model because the maximum error in the case of the proposed neural network model was observed to be 4.01% whereas in the case of the multivariate regression model was observed to be 60.7%. Then, the validated neural network model was used for the prediction of the amount of sediment deposition inside the Tarbela reservoir for the next 20 years based on the time series univariate forecasting model ETS forecasted values of Ra, Iw, Lr, and Cr. It was also observed that the storage capacity of the Tarbela reservoir is the most influencing parameter in predicting the amount of sediment. Full article
(This article belongs to the Special Issue Hydraulic, Sediment Transport and Morphological Assessment in Rivers and Reservoirs)
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16 pages, 3941 KiB  
Article
Flushing Capacity of a Stored Volume of Water: An Experimental Study
by Sebastián Guillén-Ludeña, Jorge A. Toapaxi and Luis G. Castillo
Water 2022, 14(17), 2607; https://doi.org/10.3390/w14172607 - 24 Aug 2022
Cited by 4 | Viewed by 1448
Abstract
This paper presents a systematic analysis of the hydraulic flushing capacity of a stored volume of water to remove sediments. This analysis is based on 90 laboratory experiments in which the volume of sediment evacuated was measured for varying initial volumes of water, [...] Read more.
This paper presents a systematic analysis of the hydraulic flushing capacity of a stored volume of water to remove sediments. This analysis is based on 90 laboratory experiments in which the volume of sediment evacuated was measured for varying initial volumes of water, three bed slopes, and three sediment sizes. The experiments consisted of the rapid emptying of a reservoir by means of suddenly opening a tilting gate downstream. This opening produced an accelerated flow which eroded the mobile bed of the reservoir. The efficacy of flushing, herein defined as the ratio of the volume of sediments evacuated to the volume of water released, increased with the initial slope, and decreased as the initial volume of water increased. In relation to the sediment size, while the results obtained for the coarse and medium sands were very similar to each other, the results obtained for the fine sand were affected by the existence of apparent cohesion in the mobile bed. In comparison to the results obtained for the medium and coarse sands, this apparent cohesion reduced the volume of sediment evacuated by a given volume of water and hence, the efficacy of flushing. Full article
(This article belongs to the Special Issue Hydraulic, Sediment Transport and Morphological Assessment in Rivers and Reservoirs)
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13 pages, 2294 KiB  
Article
Experiments on the Drag and Lift Coefficients of a Spinning Sphere
by Shuang Zhou, Genguang Zhang and Xiaoyang Xu
Water 2022, 14(17), 2593; https://doi.org/10.3390/w14172593 - 23 Aug 2022
Cited by 2 | Viewed by 3568
Abstract
The drag and lift coefficients are important parameters that affect the particle motion in a viscous fluid. In the present study, the drag and lift coefficients of a spinning sphere in a water tank were studied experimentally using a high-speed camera. To this [...] Read more.
The drag and lift coefficients are important parameters that affect the particle motion in a viscous fluid. In the present study, the drag and lift coefficients of a spinning sphere in a water tank were studied experimentally using a high-speed camera. To this end, 22 cases were studied to cover a wide range of dimensionless angular speeds (0.149 < Rr < 3.471) and Reynolds numbers (610 < Re < 3472). Based on the present experimental data and the results obtained from the literature, expressions were developed to calculate the lift and drag coefficients. The performed analyses on lift coefficient show that there is a critical Reynolds number (Rec) at each dimensionless angular speed. When 0 < Re < Rec, the lift coefficient decreases with increasing the Reynolds number, while it is constant when Rec < Re< 3500. The constant lift coefficient corresponding to different spin speeds was defined as the limit value of the lift coefficient. It is found that when 1 < Rr < 12, the limit value of the lift coefficient is 0.37, while the limit value of the lift coefficient increases with increasing dimensionless angular speed at 0 < Rr < 1. It is found that the spin increases the drag coefficient of a spinning sphere only when 0 < Rr < 10. Moreover, the performed analyses show that the drag coefficient of a spinning sphere is less than that of a non-spinning sphere when 10 < Rr < 25. Furthermore, the lift-to-drag ratio of a spinning sphere is discussed in this article. Full article
(This article belongs to the Special Issue Hydraulic, Sediment Transport and Morphological Assessment in Rivers and Reservoirs)
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19 pages, 4442 KiB  
Article
Local Scour at Complex Bridge Piers in Bangladesh Rivers: Reflections from a Large Study
by M. Shahjahan Mondal
Water 2022, 14(15), 2405; https://doi.org/10.3390/w14152405 - 03 Aug 2022
Cited by 4 | Viewed by 2081
Abstract
Many small-scale experimental, field, and empirical studies on bridge scour are available, however a large-scale study on local scour at a complex pier with wide variation in design parameters is still lacking. In this study, a country-wide assessment of local scour at complex [...] Read more.
Many small-scale experimental, field, and empirical studies on bridge scour are available, however a large-scale study on local scour at a complex pier with wide variation in design parameters is still lacking. In this study, a country-wide assessment of local scour at complex piers of 239 bridges in Bangladesh is made. The hydrologic, hydraulic, and sediment data required in the assessment are obtained from secondary sources, primary measurements and samples, and numerical model simulations. An incredible number of 239 field visits are made, 1434 km of bathymetric surveys are carried out, and 478 samples of bed soils are collected and analyzed. The local scour depth is estimated using a complex pier configuration with pier, pile, and pile cap dimensions selected in consultation with structural and geotechnical engineers of bridge design. Flood frequency analysis and the HEC-RAS model simulation are used to estimate the hydrologic and hydrodynamic parameters needed in the assessment. A number of empirical formulations are used to estimate and compare the design local scours. The formulae of Melville and Coleman, Jain and Fischer, and Richardson are found to be dominant when deciding the design local scour depth at the bridge piers. Suggestions are provided to include a few additional equations in scour estimation and to develop a unified Bangladesh standard for scour depth estimation. The findings and recommendations of the study would be useful in planning and designing bridges in alluvial deltaic settings, particularly in the selection of empirical methods and mainstreaming of complex pier configuration in bridge scour assessment. Full article
(This article belongs to the Special Issue Hydraulic, Sediment Transport and Morphological Assessment in Rivers and Reservoirs)
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