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Dead storage of reservoir usually designed by soil erosion rate of catchment area result in certain year without considering land use and precipitation dynamic in the catchment and sedimentation distribution analysis at the bottom of the reservoir as well. Development of protocol is needed for more accurate prediction of reservoir sediment transport to describe the real condition and accommodate data limitations. It will give good perspective in design, operation and maintenance strategy for reservoir. The proposed protocol is consisted of long-term soil erosion prediction, modelling long-term sediment discharge, modelling spatial-temporal reservoir sediment transport and predicting reservoir sediment volume and distribution in the future. The case study is in Wonogiri reservoir, Indonesia. Simulation of the proposed protocol at the case study began by obtaining long-term soil erosion prediction from 1993 – 2019. Sediment discharge was modelled from soil erosion and synthetic long-term hydrographs using the FJ Mock method and validated with sediment rate measured directly in the field. Reservoir sediment transport model was using MIKE software. The simulation gives volume and distribution of sedimentation validated by bathymetry result of the reservoir. Prediction of future sedimentation was using the modular regression method. This protocol promotes spatial reservoir sediment transport model and sedimentation prediction by modular regression. The prediction result of reservoir sediment volume in 2069 is 146.7 million m³.

Among the most characteristic offsite effects of soil erosion, reservoir sedimentation is one of the longlasting problems pertaining to storage capacity and dam operations. In this work, sedimentation rates of the Fortezza reservoir in South Tyrol (Italian Alps) are analysed for nine erosive events over 14 months. Sediment rates are calculated through a modified Universal Soil Loss Equation (USLE) combined with a Sediment Delivery Ratio (SDR) module at 2.5 m resolution. The topographic factor is adjusted to the Alpine terrain, and an additional factor is included to consider the nonerodible parts of the basin. The results are validated against a 14-month sediment yield obtained by high-resolution (0.2 m) reservoir bathymetric analyses. The calculated sediment yield has an absolute deviation of 4.8% from the measured one. The results indicate that these methods can be successfully applied for the estimation of reservoir sedimentation rates in the Alps. [ABSTRACT FROM AUTHOR]

‎In this paper‎, ‎an efficient and robust approach based on the Chebyshev collocation method and Teaching-Learning-Based Optimization (TLBO) is utilized to solve the Optimal Control Problem (OCP) of reservoir sedimentation on Golestan dam in Gonbad Kavous City‎, ‎Iran‎. ‎The discretized method employs Mth degree of Lagrange polynomial approximation for an unknown variable and Gauss-Legendre integration‎. ‎The OCP yields a nonlinear programming problem (NLP)‎, ‎and then this NLP is solved by TLBO‎. ‎Numerical implementations are given to demonstrate this approach yields more acceptable and the accurate results‎. ‎Furthermore‎, ‎it is found that filling the dam with sediment decreases the water storage‎, ‎increases dam maintenance costs‎, ‎and also decreases the stability of the dam over a period of 40 years‎. ‎Our results show that the Golestan dam will gain development with the construction of the new reservoir‎.

Sedimentation processes in reservoirs can jeopardize their functionality and compromise dam safety. Climate change and associated hydrologic uncertainty are introducing additional stressors to US reservoirs, and data-driven indicators of climate impacts on upstream soil erosion and reservoir sedimentation processes are crucial to evaluate their aggradation and life expectancy. The US Army Corps of Engineers developed the Enhancing Reservoir Sedimentation Information for Climate Preparedness and Resilience (RSI) system to consolidate historical information of elevation-capacity surveys. However, the multiple surveying technologies, protocols, and computational analysis methods used over the service life of reservoirs can impact the quality of reservoir survey data in the RSI system. The objective of this study was to develop a methodology to detect anomalous records and identify multivariate relationships between historical sedimentation data for 184 US reservoirs and associated watershed variables. For this purpose, unsupervised machine learning techniques including principal component analysis (PCA), autonomous anomaly detection, and Kolmogorov–Smirnov and Efron anomaly detection were assembled in an anomaly detection protocol that led to the detection of 20 reservoirs with anomalous records. The variables contributing most to anomaly detection were related to elevation characteristics (watershed and channel slopes, and minimum elevation), precipitation (maximum and cumulative monthly precipitation), dam properties (time since dam completion and initial trap efficiency), and curve number (CN). PCA results indicated that reservoirs in the Mediterranean California ecoregion, although experiencing substantial extreme precipitation events, had small basin areas and CN values that reflected in small capacity losses, contrasting with larger capacity losses found at reservoirs in the Great Plains and Eastern Temperate Forests ecoregions. The developed anomaly detection protocol represents a powerful tool for the analysis and monitoring of this large and heterogeneous data set with the potential of providing reliable information on the impacts of historical climate and watershed properties on erosion and sedimentation processes in US reservoirs. Practical Applications: The US Army Corps of Engineers created the Reservoir Sedimentation Information (RSI) system to compile historical reservoir elevation-capacity data collected using various measurement protocols, instruments, and analysis methods. These differences in data collection and analysis methods, in addition to any human error, can result in anomalies that require detection and correction before the dissemination of the data set for further usage. Data anomalies are values that deviate from normal or expected patterns. Apparent erroneous data, related to duplicate records or increases in reservoir capacities, can be flagged through a preliminary analysis. However, the detection of anomalies in an automated and fully data-driven way represents a powerful tool for the maintenance and monitoring of this large and heterogeneous data set. A depurated RSI data set is a potential major data source for large-scale and long-term studies related to sedimentation rates and suspended solid loads in freshwater systems due to the spatial and temporal scale of its records. This kind of data set will allow the development of effective management plans for reservoir operation, maintenance, and upstream erosion control as well as enabling the indirect monitoring of suspended sediment loads in freshwater systems at a nationwide scale. [ABSTRACT FROM AUTHOR]

Abstract Reservoir sedimentation is a serious challenge that reduces reservoir life. Because it decreases the initial capacity of the reservoir and has an impact on drinking water supply, irrigation, and hydropower activities. Inadequate land activities and poor management techniques cause soil erosion and reduce reservoir storage capacity. As a result, accurate sediment estimation was assist in the adoption of sustainable land-use activities and best management practices that lead to effective reservoir operations. The main objective of this study was to evaluate the rate of sedimentation and remaining capacity of Adebra night storage reservoir (NSR) using a bathymetric survey and Arc-GIS 10.8. A comparison of original and current reservoir capacity was used to evaluate the quantity of sediment deposition in the reservoir. The latter was developed using Arc-GIS 10.8 and a bathymetry survey that was used to develop the TIN surface and evaluate reservoir volume. The Adebra NSR reservoir capacity was decreased by the accumulation of sedimentation from 36,902 m3 in 2012 to 27,722 m3 in 2020. The results of this study showed that the Adebra night storage reservoir had lost on average 24.8% of its capacity due to sedimentation, during 8 years of operation. The average deposition rate of sedimentation in Adebra NSR was estimated to be 1147.5 m3/year, with a loss rate of 3.1% per year. The value of sedimentation rates found in live storage of the reservoir area was 1147.5 m3/year. At the current time, the expected life of the night storage reservoir was reduced due to a lack of proper soil conservation practices in the reservoir catchment areas. In general, the study finding showed that the capacity of NSR was reduced by the accumulation of sedimentation year to year throughout the design period. Therefore, to improve the capacity of NSR should be planning and implementing different techniques of sediment control and removal, depending on the estimation of sediment production from watersheds of inlets and outlets of reservoirs.

Several reservoirs across the United States are filling with sediment, which jeopardizes their functionality and increases maintenance costs. USACE developed the Reservoir Sedimentation Information (RSI) system to assess reservoir aggradation and track dam operation suitability for water resource management and dam safety. The RSI data set contains historical elevation-capacity data for approximately 400 dams (excluding navigation structures), which correspond to less than 1% of dams across the United States. Thus, there is a critical need to develop methods for estimating reservoir sedimentation for unmonitored sites. The goal of this project was to create a generalized method for estimating reservoir sedimentation rates using reservoir design information and watershed data. To meet this objective, geospatial tools were used to build a refined composite data set to complement the RSI system's data with precipitation and watershed characteristics. Nine deep learning models were then used on the benchmark data set to determine its accuracy at predicting capacity loss for the RSI reservoirs: four supervised machine learning models, four deep neural network (DNN) models, and a multilinear power regression model. A DNN model, containing a progressively increasing node and layer construction, was deemed the most accurate, with R2 values from its calibration and validation data sets being 0.83 and 0.70, respectively. The best model was recalibrated over the entire data set, which showed greater accuracy on the prediction of the RSI reservoir's capacity loss, with an R2 of 0.81. This predictive model could be used to evaluate the capacity loss of unmonitored reservoirs, forecast sedimentation rates under future climate conditions, and identify reservoirs with the highest risk of losing functionality. Practical Applications: Many communities depend on reservoirs for a variety of socioeconomic benefits, such as providing reliable water sources and flood mitigation. Rivers entering reservoirs are a constant source of silt, sand, and gravel particles (i.e., sediments) that deposit slowly, filling the reservoirs over time, thus reducing their volume capacity and effectiveness. Surveys to measure reservoir capacities are labor intensive and expensive and many sites go unmonitored. This study provides prediction tools to estimate capacity loss over time using data derived from publicly available data sources (e.g., digital elevation models, monthly precipitation data, and the National Inventory of Dams). A key role of this tool is to forecast reservoir capacity loss over time under varying climate change conditions and relate the associated sedimentation processes to local and regional conditions. The tool can also be applied broadly to hindcast capacity loss for reservoirs with or without prior surveys for identifying high-risk sites that should be investigated further. USACE plans to use these prediction tools with the RSI database to conduct a national assessment of reservoir impacts, which will inform distributions of federal resources to address water security concerns related to reservoir sedimentation. [ABSTRACT FROM AUTHOR]

Abstract Global change alters hydro-climatic conditions, affects land use, and contributes to more frequent droughts and floods. Large artificial reservoirs may effectively alleviate hydro-climatic extremes, but their storage capacities are threatened by sedimentation processes, which in turn are exacerbated by land use change. Envisioning strategies for sustainable reservoir management requires interdisciplinary model chains to emulate key processes driving sedimentation under global change scenarios. Therefore, we introduce a model chain for the long-term prediction of complex three-dimensional (3d) reservoir sedimentation considering concurrent catchment, hydro-climatic, and land-use conditions. Applied to a mountainous Mediterranean catchment, the model chain predicts increased sediment production and decreased discharge for high and medium emission pathways. Increased winter precipitation, accompanied by a transition from snowfall to rainfall, is projected to aggravate reduced summer precipitation, emphasizing a growing need for reservoirs. Additionally, higher winter precipitation proliferates sediment production and reservoir sedimentation. Land use change can outweigh the increased reservoir sedimentation originating from hydro-climatic change, which highlights the significance of localized actions to reduce sediment production. Finally, a 3d hydro-morphodynamic model provides insights into interactions between global change and reservoir sedimentation with spatially explicit information on future sedimentation patterns facilitating the implementation of management strategies.

In nearly all reservoirs, storage capacity is steadily lost due to trapping and accumulation of sediment. Despite critical importance to freshwater supplies, reservoir sedimentation rates are poorly understood due to sparse bathymetry survey data and challenges in modeling sedimentation sequestration. Here, we proposed a novel approach to estimate reservoir sedimentation rates and storage capacity losses using high‐resolution Sentinel‐2 satellites and daily in situ water levels. Validated on eight reservoirs across the central and western United States, the estimated reservoir bathymetry and sedimentation rates have a mean error of 4.08% and 0.05% yr−1, respectively. Estimated storage capacity losses to sediment vary among reservoirs, which overall agrees with the pattern from survey data. We also demonstrated the potential applications of the proposed approach to ungauged reservoirs by combining Sentinel‐2 with sub‐monthly water levels from recent satellite altimeters. Plain Language Summary: Reservoir storage capacity is steadily lost due to sediment filling, which threatens freshwater supplies both now and in the future. Yet, lost reservoir storage capacities to sediment are largely unknown. Here, we develop a generic method to estimate capacity losses and reservoir sedimentation rates by leveraging remote sensing techniques. We tested on eight reservoirs across the central and western United States and found capacity losses and sedimentation rates vary across reservoirs. The proposed method offers a promising alternative to evaluate and predict capacity losses in reservoirs nationwide and globally, and thus supports effective water managements and planning for sustainable freshwater supplies in the future. Key Points: High‐resolution Sentinel‐2 images and daily in situ water levels were used to estimate reservoir sedimentation rates and capacity lossesEstimated reservoir sedimentation rates and storage capacity losses have a mean error of 0.05% yr−1 of full storage capacityPotential applications of this method to ungauged reservoirs are feasible with sub‐monthly level data from recent satellite altimeters [ABSTRACT FROM AUTHOR]

The extent of reservoir sedimentation is an important index related to the functional operation of reservoirs. Therefore, it is vital to accurately conduct sedimentary assessment. In this paper, the analytic hierarchy process was used to determine subjective weights, gray correlation analysis and entropy weight method were used to determine objective weights. The combination weights obtained using optimized combination weighting method based on genetic algorithm were more suitable for the comprehensive analysis of the impact of reservoir sedimentation. This was then used to constructed a multi-level fuzzy comprehensive evaluation model based on improved cloud model. A reservoir was selected as the study object, and its sedimentation impact level was evaluated: the numerical characteristics of the stratus cloud of the comment on the impact of the reservoir sedimentation were (0.6372, 0.0664, 0.0795). The results showed that the reservoir sedimentation is considered as severe influence, and the sedimentation has become a major problem that needed to be solved urgently. The results of this paper could provide insight for reservoir research domestically and abroad. Furthermore, it could also enable managers to more accurately grasp the severity of reservoir sedimentation. HIGHLIGHTS The combination of the cloud model and genetic algorithm in computing was used to study and evaluate the influence of reservoir sedimentation. The calculation result was proved to be much accurate than that of the traditional method, and is more in line with the actual purpose of the project.; The multi-level fuzzy comprehensive evaluation model was constructed based on the improved cloud model for the sole purpose of analyzing the influence of sedimentation on reservoir function.; The improved combination weighting method based on genetic algorithm was used to solve the combination weight issues.;

Surface water change is a very important indicator for environmental, climatic, and anthropogenic activities. The sediment brought by the stream into the reservoir starts settling down and gets deposited on the bed of the reservoir at all levels. The finer particles are carried in suspension and settle down on the reservoir floor while some of these are passed over the spillway towards the downstream of a dam. If the concentration is high, density current occurs along the floor of the reservoir. The purpose of this study is to assess the sedimentation of the Poondi Reservoir using Satellite Remote-Sensing (SRS). This helps in providing full utilization capacity of reservoir by providing more storage and supply of fresh water to the catchment area. The sedimentation assessment was carried out using satellite data and reservoir water level data from 2000 to 2020. Water spread area was analyzed from satellite data. The NDWI has been used to delineate open water features and to enhance the presence of water surface and linear interpolation/extrapolation technique has been applied for water spread area at different elevation. Further, these areas were used to compute live storage capacity of reservoir between two elevations by Prismoidal formula.

Sediment accumulation is a major factor in reducing the useful life of irrigation reservoirs. As a result, information on any changes in storage capacity is required to reduce the reservoir's risk. Therefore, the goal of this research was to identify the amount of sediment accumulated in the reservoir using a bathymetric survey. The bathymetric survey method involves subtracting the two-period capacities (before dam construction and after dam construction). The bathymetric survey of the reservoir was carried out by analyzing volume and surface area using ArcGIS 10.8, and developing the Triangulated Irregular Network (TIN) surface from topo-sheet data. The bathymetric survey showed that 297,000 m3 (362,340 tons) of sediment had accumulated in the reservoir over six years of operation. This means the reservoir's total capacity has been reduced by 7.52% over the last six years. The average rate of sedimentation was assessed to be 49,500 m3/y (60,390 tons/year) and the annual capacity reduction of the reservoir was 1.25 percent. The proportion of dead storage capacity to sediment deposition level was used to calculate the reservoir's useful life. Hence; the sedimentation rates of the dead storage and live storage regions were 31,666.67 m3/year and 48,333.33 m3/year, respectively. Finally, this indicates that the reservoir will not be functional for more than 15 years if the sediment deposition rate remains the same as the previous six years throughout the operation periods.

Effective management of reservoir sedimentation requires models that can predict sedimentation of the reservoirs. In this study, linear regression, non-linear exponential regression and artificial neural network models have been developed for the forecasting of annual storage capacity loss of reservoirs in the Guinea Savannah Ecological Zone (GSEZ) of Ghana. Annual rainfall, inflows, trap efficiency and reservoir age were input parameters for the models whilst the output parameter was the annual sediment volume in the reservoirs. Twenty (20) years of reservoirs data with 70% data used for model training and 30% used for validation. The ANN model, the feed-forward, back-propagation algorithm Multi-Layer Perceptron model structure which best captured the pattern in the annual sediment volumes retained in the reservoirs ranged from 4-6-1 at Karni to 4-12-1 at Tono. The linear and nonlinear exponential regression models revealed that annual sediment volume retention increased with all four (4) input parameters whilst the rate of sedimentation in the reservoirs is a decreasing function of time. All the three (3) models developed were noted to be efficient and suitable for forecasting annual sedimentation of the studied reservoirs with accuracies above 76%. Forecasted sedimentation up to year 2038 (2019–2038) using the developed models revealed the total storage capacities of the reservoirs to be lost ranged from 13.83 to 50.07%, with 50% of the small and medium reservoirs filled with sediment deposits if no sedimentation control measures are taken to curb the phenomenon. HIGHLIGHTS The study developed two mathematical models using linear regression.; The study developed non-linear exponential regression.; The study developed an artificial neural network (ANN) model.; The study forecasted sedimentation up to year 2038 using the developed models.; The study revealed that the total storage capacities of the reservoirs to be lost ranged from 13.83 to 50.07%.;

The accumulation of sediment in reservoirs poses a major challenge that impacts the storage capacity, quality of water, and efficiency of hydroelectric power generation systems. Geospatial methods, including Geographic Information Systems (GIS) and Remote Sensing (RS), were used to assess Dukan Reservoir sediment quantities. Satellite and reservoir water level data from 2010 to 2022 were used for sedimentation assessment. The satellite data was used to analyze the water spread area, employing the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) to enhance the water surface in the satellite imagery of Dukan Reservoir. The cone formula was employed to calculate the live storage capacity of the reservoir within two elevations. According to the study results, the live storage capacity of Dukan Reservoir at elevation 511.78 m had decreased from 8000 MCM to 7007.77MCM and 6923.53 MCM using NDWI and MNDWI respectively, due to sedimentation, resulting in a capacity loss of 14.59% and 15.83% for NDWI and MNDWI respectively. The annual sedimentation was 13.78 MCM and 14.95 MCM for NDWI and MNDWI, respectively. Joglekar's equation and Khosla's formula have demonstrated that the sedimentation rate in the Dukan reservoir exceeds the critical rate. The findings of this study will inform the development of sediment management strategies aimed at preserving the reservoir's capacity.

Soil loss (SL) and its related sedimentation in mountainous areas affect the lifetime and functionality of dams. Darbandikhan Lake is one example of a dam lake in the Zagros region that was filled in late 1961. Since then, the lake has received a considerable amount of sediments from the upstream area of the basin. Interestingly, a series of dams have been constructed (13 dams), leading to a change in the sedimentation rate arriving at the main reservoir. This motivated us to evaluate a different combination of equations to estimate the Revised Universal Soil Loss Equation (RUSLE), Sediment Delivery Ratio (SDR), and Reservoir Sedimentation (RSed). Sets of Digital Elevation Model (DEM) gathered by the Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), Harmonized World Soil Database (HWSD), AQUA eMODIS NDVI V6 data, in situ surveys by echo-sounding bathymetry, and other ancillary data were employed for this purpose. In this research, to estimate the RSed, five models of the SDR and the two most sensitive factors affecting soil-loss estimation were tested (i.e., rainfall erosivity (R) and cover management factor (C)) to propose a proper RUSLE-SDR model suitable for RSed modeling in mountainous areas. Thereafter, the proper RSed using field measurement of the bathymetric survey in Darbandikhan Lake Basin (DLB) was validated. The results show that six of the ninety scenarios tested have errors <20%. The best scenario out of the ninety is Scenario #18, which has an error of <1%, and its RSed is 0.46458 km3·yr−1. Moreover, this study advises using the Modified Fournier index (MIF) equations to estimate the R factor. Avoiding the combination of the Index of Connectivity (IC) model for calculating SDR and land cover for calculating the C factor to obtain better estimates is highly recommended. [ABSTRACT FROM AUTHOR]

Despite studies showing that dams have significant effects on the sediment dynamics and evolution of a river upstream of a dam, the knowledge on relationships between river topography and sediment transport in the dam’s backwater zone has been poorly applied in reservoir sedimentation management. Therefore, this study evaluated the benefits that topographic control might have on reservoir sedimentation. To do this, three test scenarios were applied to explore the hydraulic and sediment transport regimes in the Middle Yuba River. A two-dimensional hydrodynamic model was employed to simulate the stream hydraulic response to topographic controls. The results indicate that adding a topographic constrictions and expansion sequence can change a river’s capability of entraining sediment and control the spatial distribution of sediment transport regimes by either accelerating sediment flushing near the dam or ponding the sediment farther upstream from dam. Topographic controls perform best for the low and medium flow.

Reservoir sedimentation is a serious challenge in many regions of the world and has severe consequences for water management, flood control, and generation of energy. The purpose of this paper is providing a valuable source of information on the reservoir sedimentation problem and reviewing the existing control strategies utilized globally against it. A wide range of sedimentation related problems were outlined. Different sediment management strategies were investigated with their main advantages and disadvantages. Special attention was devoted to the flushing technique as the most effective way of preserving the reservoirs’ storage capacity. As the main novelty of this study, a series of innovative complementary methods to improve the efficiency of sediment removal during the flushing operation was introduced. Based on the analysis, each sediment management strategy may be advantageous under certain circumstances, and successful implementation of such strategies needs regular monitoring and comprehensive recognition of the effective factors. The use of initiative structural methods, including those cited in this article, although they improve the flushing efficiency, they may come with some limitations that should be considered in practice. It is strongly recommended that such structural methods be taken into account in the first phases of dams’ design because they may influence the layout of dam structures. As conclusion, it was found that the progress is being made in successfully reducing and managing of sedimentation at reservoirs. New methods in combination with the flushing operation demonstrated significant performance in sediment removal from the reservoirs and restoring the corresponding storage. The study of this paper is recommended to interested researchers, dam owners, and water resources authorities.

Abstract In nearly all reservoirs, storage capacity is steadily lost due to trapping and accumulation of sediment. Despite critical importance to freshwater supplies, reservoir sedimentation rates are poorly understood due to sparse bathymetry survey data and challenges in modeling sedimentation sequestration. Here, we proposed a novel approach to estimate reservoir sedimentation rates and storage capacity losses using high‐resolution Sentinel‐2 satellites and daily in situ water levels. Validated on eight reservoirs across the central and western United States, the estimated reservoir bathymetry and sedimentation rates have a mean error of 4.08% and 0.05% yr−1, respectively. Estimated storage capacity losses to sediment vary among reservoirs, which overall agrees with the pattern from survey data. We also demonstrated the potential applications of the proposed approach to ungauged reservoirs by combining Sentinel‐2 with sub‐monthly water levels from recent satellite altimeters.

The hydro-energetic potential assessment of Sebeş River from the Southern Carpathians through the construction of several dams led to the appearance of Oaşa, Tău, Obreja de Căpâlna and Petreşti Reservoirs. The study of quantitative and qualitative characteristics of lake water was the subject of many articles. This study wants to identify the particularities of the lake sedimentation because the sedimentation rate influences the water quality and the lake’s water volume, in fact the electric energy produced in the lake. For this were analysed the sedimentation sources and the indicators of the sedimentation rate, the sedimentation pace, the dynamic of sedimentation process and the characteristic volumes of each reservoir. Using the measurements made, sedimentation areas with maximum development sectors were highlighted. At the end, was analysed the influence of sedimentation process on the lake’s morphometric elements.