1. Introduction

2. Sediment Transport

3. Process of Reservoir Sedimentation

Figure 2. Illustrates sediment deposit zones in reservoirs, as outlined by Morris and Fan (1998).

Figure 2. Illustrates sediment deposit zones in reservoirs, as outlined by Morris and Fan (1998).

4. Approaches in Sedimentation Studies

4.2. Models for Sediment Transportation

To utilize sediment transport models and hydrodynamic models, solving one or more governing differential equations of fluid continuity, momentum, and energy, as well as numerically solving the sediment continuity equation, is necessary (Zang, 2014). These models encompass a wide range of applications, including distinctions between suspended load and bed load, physical and chemical transport, and their formulation across spatial and temporal continua, such as one-dimensional (1D), two-dimensional (2D), or three-dimensional (3D) models, as well as steady-state versus unsteady conditions. The selection of a specific model for a given problem hinges on factors such as the problem’s nature and complexity, the model’s accuracy in simulating the problem, data availability for model calibration and verification, as well as time and budget constraints. Numerical sediment transport models enable the simulation of flows in one, two, and three dimensions. These models prove invaluable for scenario assessments and optimizing sediment Best Management Practices (BMPs), as they require extensive simulation techniques. Coupled models have shown promise as a valuable tool for integrated sediment modeling and management planning in such scenarios (Shin et al., 2019). Computational models offer a distinct advantage over physical models in their adaptability to multiple physical domains, a feature particularly useful for representing a variety of site-specific conditions

4.2.1. One Dimensional (1D) Models

Since the early 1980s, one-dimensional (1D) models have been effectively employed in both research and engineering applications. Typically utilizing a rectilinear coordinate system, these models employ finite-difference techniques to solve the differential conservation equations governing mass and momentum of flow (St. Venant flow equations) as well as the sediment mass continuity equation (Exner equation). Primarily used for long-term simulation of extensive river reaches, 1D models often require minimal field data for calibration and testing. Their numerical solutions are particularly suited for narrow reservoirs, prolonged simulations, and diverse analytical approaches due to their stability and lower computational demands (Anari et al., 2020; Garcia, 2008). These models can accurately predict essential channel characteristics such as bulk velocity, water surface elevation, bed-elevation changes, and sediment transport loads (Delaney and Adhikari, 2020; Martínez-Salvador and Conesa-García, 2020). Employed across vast river expanses or reservoirs, 1D mathematical models facilitate sediment transport analysis, effectively replicating significant transport processes within a one-dimensional flow field. These models are commonly utilized to address various sediment-related issues, including sediment pass-through, dam scour, and sediment accumulation in reservoirs (HEC-RAS, 2016; Shin et al., 2019)

4.2.2. Two Dimensional (2D) Models

There has been a movement in computational research toward 2D models since the early 1990s. The majority of the 2D models are now available to the hydraulic engineering community as interface-based software, allowing for simple data input and visualization of results.

2D models give depth-averaged information regarding water depth and bed elevation within rivers, lakes, and estuaries, as well as the amplitude of depth-averaged streamwise and transverse velocity components. Both suspended load and bedload can be individually modeled (Nones, 2019). Most 2-D models use finite difference, finite element, or finite volume methods to solve the depth-averaged continuity and Navier-Stokes equations, as well as the sediment mass balance equation.

4.2.3. Three Dimensional (3D) Models

When two-dimensional (2D) models prove insufficient to capture certain hydrodynamic and sediment transport phenomena, the utilization of three-dimensional (3D) models becomes necessary in hydraulic engineering applications. Instances where 3D flow structures are prevalent, and 2D models fail to accurately represent physics, include flows near piers and hydraulic systems (Bhattacharjee and Akter, 2018).Recent advancements in computing technology, such as increased processing speed, parallel computing capabilities, and improved data storage, have made 3D hydrodynamic and sediment transport models more attractive to use (Chao et al., 2022; Wang et al., 2018). The majority of 3D models employ finite difference, finite element, or finite volume methods to solve the continuity, Navier-Stokes, and sediment mass balance equations. These governing equations are typically solved using the Reynolds-Averaged Navier-Stokes (RANS) method (Burkow and Griebel, 2016; Lai and Wu, 2019; Paik et al., 2009).

Table 1. Summary of selected sediment transport models.

Table 1. Summary of selected sediment transport models.

Model Acronym	Model Name	Type	Reference
HEC-RAS HEC-HMS HEC-6	HydraulicEngineering Center HydraulicEngineering Center HydraulicEngineering Center	1D 1D 1D	Thomas and Prashum,(1977) Sathya et al., (2023) Joshi et al., (2019)
S.WAT Model	Soil & Water Assessment Tool	Model spanning from small watershed to river basin scale	MRC, (2010)
SEDNET	European Sediment Network	1D	Brils, (2020)
MOBED	MObile BED;	1D	Krishnappan, (1981)
MAST-1D		1D	Lauer, (2016)
GSTARS	Generalized sediment transport models for alluvial River simulation	1D	Molinas and Yang, (1986)
SEDICOUP	S.EDIment COUPled	1D	Holly &Rahuel, (1990)
3STD1	steep stream sediment Transport 1D model	1D	Papanicolaou et al. (2004)
OTIS HEC-RAS	One--dimensional transport and inflow and storage Hydraulic Engineering Center	1D 2D	Runkel and Broshears, (1991) Shabani et al. (2021)
MIKE 21	Danish acronym of the word microcomputer	2D	Danish Hydraulic Institute, (1993)
SEDIMORPH CCHE2D	Sediment Morphology The National Center for Computational Hydroscience and Engineering	2D 2D	Gundlach et al., (2023) Jia and Wang, (1999)
FAST2D:	Flow Analysis Simulation Tool	2D	Minh Duc et al. (1998)
UNI.BEST-- TC	UNI.form BEach Sediment Transport-Transport Cross-shore;	2D	Bosboom et al. (1997)
USTARS	Unsteady Sediment Transport Models for Alluvial Rivers Simulations	2D	Lee et al. (1997)
Telemac-2D		2D	Hervouet, (2007)
D.elft -2D		2D	Walstra et al. (1998)
CST-3D SCHISM	Wave average area morphologic change numerical modeling system Semi-implicit Cross-Scale	3D 3D	Specht et al., (2021) Yoo et al., (2021)
ECOMSED	Estuarine, Coastal, and Ocean Model-SEDiment transport	3D	Blumberg and Mellor, (1987)
CH.3D-SED	Computational Hydraulics 3D-SEDiment;	3D	Spasojevic and Holly, (1994)
FAST3D	Flow Analysis Simulation Tool	3D	Landsberg et al. (1998)
TEL.EMAC;		3D	Hervouet and Bates _2000
SSIIM	Sediment Simulation In Intakes with Multiblock options	3D	Olsen, (1994)

In comparison to physical models, numerical models have several significant advantages, including lower cost, the ability to be easily rerun to simulate a variety of different conditions, the ability to numerically simulate some types of problems that are inappropriate for physical modeling due to the scaling laws involved (e.g., sediment cohesion), portability, and reproducibility.

A physical model is a scaled physical depiction of the prototype system that simulates prototype behavior using fluid and sediment. Water is typically utilized as a fluid, while air models have been employed in rare cases to analyze difficulties such as groyne positions for river training. Natural grains or manufactured materials such as plastics or walnut shells can be used to create model sediment. Physical models are broadly categorized into four types: undistorted fixed-bed model in which both vertical and horizontal scales are equal, distorted fixed-bed model with a larger vertical than horizontal scale, undistorted movable-bed model, and distorted movable-bed model.

4.3. Field Application of Models

4.3.1. Sediment Transport Models

Numerous mathematical models have been developed by researchers to forecast reservoir sedimentation. Many of these models utilize one-dimensional (1D) software tools, such as Hec-Ras (Kim and Shin 2013; Mohammad et al. 2016; Ghimire and DeVantier 2016) and Gstars (Yang et al. 1989; Yang and Simões 2002). Conversely, some models employ two-dimensional (2D) approaches, such as Mike21 (DHI 1993; de Villiers and Basson 2007) and CCHE2D (Zhang 2005; Moussa 2013). While most 1D models are based on the equilibrium concept for sediment entrainment (Papanicolaou et al. 2008), 2D sediment transport models typically consider the logarithmic distribution of sediment across depth rather than assuming uniformity. Moreover, these models address the impact of sediment concentration towards the top of the bed load-layer on the net deposition rate (van Rijn 1987; Wu et al. 2000).

Three-dimensional (3D) models are rarely employed in dam reservoir sediment investigations due to their substantial computational requirements, particularly for long-term sediment studies. To ensure model stability, a minimal computational time step must be selected, and a large number of cells are essential due to the domain’s size and the complexity of various processes involved, including flow dynamics, bed load transport, suspended load concentrations, sediment deposition, and soil erosion (Omer et al., 2015). In numerical models, sediment transport can be simulated using one of two methods: the “Non-Equilibrium Capacity” concept, which considers suspended load and incorporates time lag and adjustment length effects on sediment adaptation with flow, or the “Equilibrium Capacity” concept, which accounts for bed material (Juez et al., 2016; Langendoen et al., 2016). Khorrami and Banihashemi (2020) devised a non-coupled algorithm for long-term simulation of dam reservoir sedimentation in 2D/3D. Over a span of 30 years, the model was utilized to simulate sedimentation in the Zonouz dam reservoir in Iran. The non-coupled algorithm integrates the flow, sediment transport, and bed modules by solving the depth-averaged Navier-Stokes equations for the flow module, the 3D equation of sediment mass conservation for the sediment module, and the Exner equation for bed deformations. This approach reduced simulation time and demonstrated the feasibility of using a 3D sediment model for long-term modeling

The numerical model comprised flow, sediment, and bed modules. Below are the Governing Equations:

2D Shallow water flow equations

$$\frac{\partial h}{\partial t}+\frac{\partial \left(Uh\right)}{\partial x}+\frac{\partial \left(Vh\right)}{\partial y}=0$$

(i)

$$\frac{\partial \left(hU\right)}{\partial t}+\frac{\partial \left(hU²\right)}{\partial x}+\frac{\partial \left(hUV\right)}{\partial y}+gh\frac{\partial ŋ}{\partial x}=\frac{\partial }{\partial x}\left(\epsilon \frac{\partial \left(hU\right)}{\partial x}\right)+\frac{\partial }{\partial x}\left(\epsilon \frac{\partial \left(hU\right)}{\partial y}\right)+\frac{1}{\rho }{\tau }_{bx}$$

(ii)

$$\frac{\partial \left(hV\right)}{\partial t}+\frac{\partial \left(hUV\right)}{\partial x}+\frac{\partial \left(hV²\right)}{\partial y}+gh\frac{\partial ŋ}{\partial y}=\frac{\partial }{\partial x}\left(\epsilon \frac{\partial \left(hV\right)}{\partial x}\right)+\frac{\partial }{\partial x}\left(\epsilon \frac{\partial \left(hV\right)}{\partial y}\right)+\frac{1}{\rho }{\tau }_{by}$$

(iii)

where η = h + zb, and Zb is the bed level; η and h are water surface elevation and the water depth, respectively; ρ is fluid density; U and V refer to depth-averaged velocity components in the x and y directions, respectively; g is the gravitational acceleration; ε is the depth-averaged turbulent eddy viscosity; τbx and τby refer to the bed shear stresses in the x and y directions, respectively, which are calculated as follows:

3D sediment transport equations

$$\frac{\partial C}{\partial t}+\frac{\partial \left(uC\right)}{\partial x}+\frac{\partial \left(vC\right)}{\partial y}+\frac{\partial \left(WsC\right)}{\partial z}=\frac{\partial }{\partial x}\left({\epsilon }_x\frac{\partial C}{\partial x}\right)+\frac{\partial }{\partial y}\left({\epsilon }_x\frac{\partial C}{\partial y}\right)+\frac{\partial }{\partial z}\left({\epsilon }_x\frac{\partial C}{\partial z}\right)$$

(iv)

where C is the suspended sediment concentration; ws is the fall velocity of the sediment particles; u and v are the local fluid velocities at elevation z. ε_i is the turbulent diffusivity in direction i (where i = x, y, z).

Bed evolution equation (van Rijn 1987)

$$\left(1-P\right)\frac{\partial z_b}{\partial t}+\frac{\partial q{b}_x}{\partial x}+\frac{\partial q{b}_y}{\partial y}+Db-Eb=0$$

(v)

Bilal et al. (2017) employed a 1D numerical model (HEC-RAS) to conduct a sediment dynamics investigation of the Tenryu River stretch upstream of Sakuma Dam in Japan, aiming to forecast future bathymetric alterations. Their simulations also aimed to anticipate potential changes in the riverbed profile and estimate the point at which Sakuma Dam might no longer fulfill all its intended purposes. The study showcased the feasibility of conducting sediment dynamic analyses even with limited data, albeit such inquiries may only provide qualitative insights into sediment movements.

In another study, Yao Li (2020) devised a series of methodologies for generating high-resolution 3-D bathymetry maps and long-term storage records on a global scale, applicable for diverse purposes including global hydrological modeling and local water resource assessments. The initial research effort involved creating a 3D reservoir bathymetry by integrating elevations obtained from the Ice, Cloud, and Land Elevation Satellite (ICESat-2) airborne prototype with area data derived from Landsat-based water classifications spanning from 1982 to 2017. Subsequently, the bathymetry generating algorithm was employed to construct a global dataset encompassing 347 reservoirs worldwide using satellite altimetry and imaging observations. In the latter study, a combination of multi-source remote sensing data and an enhanced area-depth-storage database were utilized to estimate monthly storage fluctuations for 7245 reservoirs globally from 1999 to 2018.Olsson et al. (2011) introduced an integrated modeling strategy that merges a basic water budget model (THC-model) with a 3D reservoir sedimentation model (MOHIDWater) to adjust reservoir operations and showcase their impacts on sediment deposition. The results from the modeled scenarios affirm the efficacy of the revised operation rules for THC reservoirs and highlight the potential for altering the operation of significant dams to facilitate integrated sediment-water management.

Governing equation (Sorokin and Ikramova 2005):

$$∆V{t}_r+Q{S}_{t\left(r\right)}+Q{G}_{t\left(r\right)}-QS{Q}_{t\left(r\right)}-QG{E}_{t\left(r\right)}+B_{t\left(r\right)}-E_{t\left(r\right)}+P_{t\left(r\right)}=0$$

(vi)

where,

ΔV      Change in water storage volume (m³)

QS       Reservoir inflow (m³)

QG      Groundwater inflow (m³)

QSO    Reservoir release (m³)

QGE        Ground-water exchange (m³)

B        Run-off of reservoir catchment (m³)

E        Evaporation (m³)

P        Precipitation (m³)

t         Time step

r         Modelled reservoir or lake

Faghihirad et al., (2015) created a revised numerical model of a three-dimensional layer-integrated turbulence model and applied it to a scaled physical model of Iran’s Hamidieh Reservoir. The study utilized a numerical model to analyze the flow dynamics and sediment transport mechanisms in an Iranian reservoir featuring a dam, water intakes, and sluice gates, particularly focusing on the vicinity of hydraulic structures. By employing the numerical model, various scenarios were simulated, offering insights into conditions that would pose challenges for physical experimentation.

Governing Equations (Lin and Falconer, 1997):

$$\frac{\partial \zeta }{\partial t}+{\sum }_{n=k}^k\{\frac{\partial \left(hu\right)}{\partial x}+\frac{\partial \left(hv\right)}{\partial y}\}$$

(vii)

$$\frac{\partial S}{\partial t}+\frac{\partial }{\partial x}\left(uS\right)+\frac{\partial }{\partial y}\left(vS\right)+\frac{\partial }{\partial z}⌊\left(w-Ws\right)S⌋-\frac{\partial }{\partial x}\left({\epsilon }_x\frac{\partial S}{\partial x}\right)-\frac{\partial }{\partial x}\left({\epsilon }_y\frac{\partial S}{\partial y}\right)-\frac{\partial }{\partial x}\left({\epsilon }_x\frac{\partial S}{\partial z}\right)$$

(viii)

Qiu and Li (2021) devised a method to calculate the rate of suspended sediment transport within reservoirs, which formed the basis for developing a multi-objective model. This model aimed to optimize both sediment transportation efficiency and power production efficiency simultaneously. The Pareto front of the model was identified employing the non-dominated sorting genetic algorithm (NSGA-II) and principles of Pareto optimality. The study applied this approach to the Three Gorges project in China, revealing not only the Pareto front of the reservoir operation scheme but also indicating a 6% increase in maximum annual sediment runoff at the expense of a 9% reduction in annual power generation.

Wang et al. (2020) developed a numerical model in three dimensions (3D) to investigate both the thermodynamic dynamics and sediment processes occurring within a subtropical drinking water reservoir. Data-driven models were established to generate inflow conditions. The modeling results illustrated that climatic factors such as storms and winds significantly influence the stratification and mixing processes within the lake. Storm events drive sediment transport, and amorphous flux dominates sediment delivery to the reservoir. Figure 3 illustrates the flow-chart used in the development of their 3D Numerical Model.

Zeleke et al. (2013) employed the River Hydraulics and Sedimentation one-dimensional model (SRH-1D), version 2.6, to simulate and forecast the sedimentation pattern in the Angereb Reservoir, Ethiopia, up to the year 2015. The observed trends were effectively replicated, demonstrating satisfactory alignment between measurements and model predictions. However, the model tended to overestimate sediment deposition volumes in certain upstream areas of the reservoir, except for one instance.

Governing equations (U.S. Department of Interior User’s Manual SRH-1D, 2010):

$$\frac{\partial \left(A+A_d\right)}{\partial t}+\frac{\partial Q}{\partial x}=q_{lat}$$

(ix)

$$\frac{\partial Q}{\partial t}+\frac{\partial \left(\beta \frac{Q^2}{A}\right)}{\partial x}+gA\frac{\partial z}{\partial x}=-g{A}_{St}$$

(x)

$$\epsilon \frac{\partial A_s}{\partial t}+\frac{\partial Q_{sv}}{\partial x}=q_{Slat}$$

(xi)

In their study, Bai et al. (2019) employed a multi-objective model alongside two single-objective models to optimize water-sediment regulation in the Longyangxia and Liujiaxia cascade reservoirs. They introduced a novel approach, FSS-MOPSO, which combines the Feasible Search Space (FSS) with the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to seek optimal solutions for the joint operation of cascade reservoirs. By investigating various optimization strategies for five objectives—hydropower generation, sediment transport, water supply, flood control, and ice control—the study uncovered a significant trade-off between hydropower generation and sediment transport objectives. Bronstert et al. (2014) developed a modeling framework to quantify water and sediment fluxes in catchments, river systems, and reservoirs. Their system included hydrological components adapted to dryland characteristics, hill slope erosion, sediment transport, and reservoir deposition processes. They employed a hierarchical spatial discretization using a multi-scale concept to account for specific process scales, improving understanding of hydro-sedimentological systems characterized by flashy runoff generation and erosion pulses.

Ahn and Yen (2015) developed numerical modeling approaches for long-term reservoir sedimentation using the Generalized Stream Tube computer models for Alluvial River Simulation version 3.0 (GSTARS3). They demonstrated that semi-two-dimensional modeling incorporating non-equilibrium sediment transport equations and the theory of minimum stream power could predict reservoir sedimentation over time.

Figure 4. Flow Chart of Conventional One-Dimensional Sediment Transport Model (Ahn and Yen, 2015).

Figure 4. Flow Chart of Conventional One-Dimensional Sediment Transport Model (Ahn and Yen, 2015).

In their study, Sukhinov et al. (2014) utilized a non-stationary spatial two-dimensional model to examine sediment transport in water reservoir coastal zones. They considered various physical parameters and processes, including soil porosity, critical shear stress for sediment movement, turbulent exchange, changing bottom elevation levels, and factors like wind currents and bottom friction. The study also incorporated a hydrodynamic spatial two-dimensional model to analyze sediment transport in reservoir coastal zones.

On the other hand, Li et al. (2019) investigated waves and sediment transport resulting from landslides impacting reservoirs using a two-dimensional coupled double-layer averaged shallow water hydro-sediment-morphodynamic model. Unlike previous models, this model represents a significant advancement by fully addressing sediment transport. It was validated against laboratory studies of landslide-induced waves and compared favorably. The model’s ability to simulate sediment transport in terms of concentration and bed deformation was also demonstrated. The findings suggest that sediment transport speed influences wave characteristics and landslide-to-wave momentum transfer, affecting landslide efficiency, defined as the ratio of horizontal runout distance to vertical fall height.

Tarar et al. (2019) utilized sediment rating curves (SRC) alongside wavelet artificial neural networks (WA-ANNs) to establish sediment load boundary conditions for the HEC-RAS 1D numerical model, This initiative was directed towards replicating sediment transportation dynamics within Tarbela Reservoir. The model underwent morphodynamic calibration by incorporating more precisely reconstructed sediment load boundary conditions in HEC-RAS, resulting in significant enhancements, with R2 = 0.980 and NSE = 0.979. Given that the WA-ANN-based sediment load boundary conditions effectively captured the intricacies of sediment transport, this modeling approach presents a promising avenue for exploring variations in bed levels across reservoirs and rivers globally. I mean try to paraphrase this inside the text.

4.3.2. Bathymetric Surveys

Bathymetric surveying is a direct approach for assessing sedimentation distribution and bed profile in dam reservoirs. The bathymetric survey is a significant method for gaining an understanding of sediment storage in reservoirs. The fieldwork needs a relatively short time investment and does not entail continuous monitoring and/or maintenance of sediment yield/runoff equipment.

Bathymetric data for three reservoirs along the lower Susquehanna River were acquired using HYPACK software, incorporating a 210 kHz echo sounder, single beam transducer, and DGPS unit. This technological setup facilitated an estimation of the remaining sediment-storage capacity (Langland, 2015). Earlier studies indicated that the upper two reservoirs had reached a state of equilibrium concerning long-term sediment storage, while the downstream reservoir retained the capacity for sediment trapping. Depth data were systematically recorded through transects within HYPACK at predefined intervals for each reservoir and overlaid on geo-referenced aerial photographs. To ensure data accuracy, post-processing of raw data sets was conducted to eliminate any erroneous spikes identified from analog data recorded on thermal paper. Notably, diagonal transects were employed to validate depth observations. The analysis revealed a total deposition of approximately 14,700,000 tons of silt across the three reservoirs between 1996 and 2008, with the majority, around 12,000,000 tons, accumulated in Conowingo Reservoir.

USGS in partnership with the city of Santa Cruz, assessed Loch Lomond Reservoir to evaluate its current storage capacity and sedimentation rates (Whealdon-Haught et al., 2019). This assessment integrated various survey techniques, including sonar soundings for bathymetric analysis, lidar scans with GPS data for near-shore topography assessment, and sediment bed sampling to analyze reservoir bed-material size. The collected data facilitated the creation of a bare-earth digital elevation model (DEM) with a resolution of 1 square meter or better up to the spillway elevation, enabling accurate estimation of storage capacity.

In another collaboration with the San Francisco Public Utilities Commission (SFPUC), the US Geological Survey conducted a similar evaluation of San Antonio Reservoir in 2018 to assess its storage capacity and sedimentation characteristics (Marineau et al., 2018). The bathymetric survey employed depth soundings obtained through a multi-beam echo sounder mounted on a boat. Findings from the reservoir capacity study revealed a storage capacity of 53,266 (plus or minus 140) acre-ft, representing a 2,766 acre-ft (or 5.5 percent) increase from the original 1965 estimate, attributed to enhanced survey methodologies.

For Morse and Geist Reservoirs, bathymetric data were collected using a high-resolution multibeam echosounder, supplemented by acoustic Doppler current profiler data for shallow areas (Boldt and Martin, 2020). These surveys aimed to update bathymetric maps, determine storage capacities at specified water-surface elevations, and compare current conditions with historical surveys.

Iradukunda and Bwambale (2021) conducted a study on the Murera reservoir dam, focusing on assessing reservoir sedimentation and its impact on storage loss. They utilized the Bathymetric Survey System (BSS), which employed a navigation twin boat system equipped with a built-in Global Positioning System (GPS). By employing a multi-frequency acoustic system, they determined the reservoir bed level, identified sediment layers, and evaluated deposited sediments relative to pre-impoundment levels. Data processing and analysis were conducted using Depthpic 5.0.2, Surfer 15.5, and ArcGIS 10.3 applications. The findings revealed a depth increase from north to south within the reservoir, with the southern half exhibiting more sediment deposition.

Additionally, in collaboration with the City of Santa Cruz, the U.S. Geological Survey (USGS) conducted a bathymetric and topographic survey to assess water storage capacity and quantify capacity loss due to sedimentation. Utilizing bathymetric scanning with multibeam-sidescan sonar and topographic surveying with laser scanning (LiDAR), the USGS determined the reservoir’s maximum storage capacity as of March 2009 to be 8,646.85 acre-ft at the spillway altitude of 577.5 ft, with a 99 percent confidence level.

Foxgrover et. al., (2014) reported that bathymetry surveys were conducted on Coyote Creek and Alviso Slough, South San Francisco Bay, California using the state-of-the-art research vessel R/V Parke Snavely outfitted with an interferometric side scan sonar for swath mapping in extremely shallow water. We provide high-resolution bathymetric data collected by the USGS. For the 2010 baseline survey we have merged the bathymetry with aerial lidar data that were collected for the USGS during the same time period to create a seamless, high-resolution digital elevation model (DEM) of the study area. The series of bathymetry datasets are provided at 1 m resolution and the 2010 bathymetric/topographic DEM at 2 m resolution. The data are formatted as both X, Y, and Z text files and ESRI Arc ASCII files that are accompanied by FGDC-compliant metadata.

The US Geological Study conducted a bathymetric study of the lower Sixmile Creek reservoir in Tompkins County, New York, in collaboration with the City of Ithaca, New York, and the New York State Department of State, in November 2015. The reservoir’s useful storage capacity was evaluated using the data gathered (Wernly et al., 2016). This assessment could assist municipal management in making decisions about the disposition of the reservoir’s 30-foot (ft) dam and the reservoir’s potential usage as a flood control structure. The US Geological Survey used an acoustic Doppler current profiler to acquire bathymetric data. Depths were manually measured in areas where aquatic vegetation interfered with the acoustic Doppler current profiler signal. The reservoir’s maximum depth of 16.6 feet was determined 130 feet upstream from the dam. A lot shallower than the original 30-ft depth, the depths closest to the dam were just about 13.5 feet.

Bathymetric data for Loch Raven and Prettyboy reservoirs were acquired during the fall of 1998 and early summer of 1998, respectively. This data collection involved the use of a differential global positioning system (DGPS) and digital echo-sounding technology. More than 32,000 individual soundings were compiled to develop the present bathymetric model of L-och-Raven Reservoir (Ortt et al., 2000).A bathymetry model of Prettyboy Reservoir was created using over 48,000 discrete soundings gathered. The bathymetric models reveal that Loch Raven and Prettyboy reservoirs presently possess storage capacities of 19.1 billion gallons equivalent to 72.3 MCM and 18.4 billion gallons equivalent to 69.7 Mcm, respectively.

4.3.3. Remote Sensing and Geographic Information System (GIS)

Liu et al., (2020) carried out a remote sensing-based modeling of the bathymetry and water storage for channel-type reservoirs worldwide. The study proposed a novel approach for determining the bathymetry and water storage of channel-type Reservoirs, referred to as ADBAR, for which only an open-access digital elevation model (DEM) and satellite images are required.

Al-Mamari et al., (2023) applied the Revised Universal Soil Loss Equation (RUSLE) along with remote sensing information to estimate the annual soil erosion within the upstream catchment area of a reservoir. They conducted UAV surveys combined with photogrammetry analysis to assess the sediment buildup in the Assarin Dam reservoir in Oman, aiming to measure siltation levels. Data from seven previous sediment trapping events were collected to investigate the relationship between soil erosion at the catchment scale and sediment deposition volumes in the reservoir. By considering factors such as rainfall erosivity, soil erodibility, topography, land cover management, and conservation practices, the RUSLE method was utilized to estimate soil erosion rates. The study utilized remote sensing data and GIS platforms to analyze the quantitative and spatial distribution patterns of the RUSLE parameters.

4.3.4. Artificial Neural Network (ANN) in Sediment Modeling

Artificial Neural Networks (ANN) are data processing and modeling tools that are frequently used in estimation, forecasting, pattern recognition, optimization, and the establishment of links between intricately detailed variablesHaykin and Lippmann (1994) discuss an artificial neural network (ANN), which is characterized as a highly parallelized and distributed information processing system as having some performance traits in common with biological neural networks found in the human brain.

Many tasks in hydrology, hydraulics, and water resources management have effectively used ANN models, including forecasting floods, groundwater levels, and rainfall-runoff ( Noori and Kalin, 2016; Yaseen et al., 2016).

The utilization of artificial neural networks (ANN) has expanded to include the estimation of suspended sediment, leading to numerous studies focusing on sediment transport. Several investigations have applied ANN in this context (Afan et al., 2015; Jain, 2001; Mustafa et al., 2012; Rai and Mathur, 2008; Singh et al., 2013; Van Maanen et al., 2010). Generally, these AI models exhibit satisfactory performance, accurately predicting suspended discharge concentrations with an accuracy of up to 90% (Afan et al., 2015).

5.0. Strategies for Reservoir Sedimentation Management

6.0. Challenges and Prospects

7.0. Conclusion

Statements and Declarations

Competing Interest

References

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