Sediment classification in a Brazilian reservoir: Pros and cons of parametric low frequencies

Abstract. Numerical models are an important tool for simulating temperature, hydrodynamics and water quality in lakes and reservoirs. Existing models differ in dimensionality by considering spatial variations of simulated parameters (e.g., flow velocity and water temperature) in one (1D), two (2D) or three (3D) spatial dimensions. The different approaches are based on different levels of simplification in the description of hydrodynamic processes and result in different demands in computational power. The aim of this study is to compare three models with different dimensionality and to analyze differences between model results in relation to model simplifications. We analyze simulations of thermal stratification, flow velocity, and substance transport by density currents in a medium-sized drinking water reservoir in the subtropical zone, using three widely used open-source models: GLM (1D), CE-QUAL-W2 (2D) and Delft3D (3D). The models were operated with identical initial and boundary conditions over a one-year period. Their performance was assessed by comparing model results with measurements of temperature, flow velocity and turbulence. Results show that all models were capable of simulating the seasonal changes in water temperature and stratification. Flow velocities, only available for the 2D and 3D approaches, were more challenging to reproduce, but 3D simulations showed closer agreement with observations. With increasing dimensionality, the quality of the simulations also increased in terms of error, correlation and variance. None of the models provided good agreement with observations in terms of mixed layer depth, which also affects the spreading of inflowing water as density currents, and the results of water quality models that build on outputs of the hydrodynamic models.

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“…The reservoir is around 11 km long, has 9 km 2 of surface area and a maximum depth of 16.5 m close to its dam (Sotiri et al, 2019).…”

Section: Field Datamentioning

confidence: 99%

Effects of dimensionality on the performance of hydrodynamic models

Ishikawa

González

Golyjeswski

et al. 2021

Preprint

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“…The GP was deployed at 134 points in the Passaúna reservoir (Figure 10). To determine the sediment magnitude, the information from the dynamic cone penetration resistance (DCPR) at some locations in the reservoir was related to the sediment magnitude data from core samples [12,66]. Characteristic changes in the sediment density/composition were identified and then related to the changes in the DCPR from the GP.…”

Section: Sediment In the Reservoirmentioning

confidence: 99%

“…In total, a stock of 3.4•× 10 6 m 3 of sediment could be measured in the reservoir. According to [66], the sediment has an average density of 1.12 g/cm 3 . Therefore, the total mass of sediment in the reservoir is approximately 3.8•× 10 6 tons.…”

Section: Reservoir Sediment Stockmentioning

confidence: 99%

To What Extent Can a Sediment Yield Model Be Trusted? A Case Study from the Passaúna Catchment, Brazil

Sotiri

Hilgert

Durães

et al. 2021

Water

Self Cite

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Soil degradation and reservoir siltation are two of the major actual environmental, scientific, and engineering challenges. With the actual trend of world population increase, further pressure is expected on both water and soil systems around the world. Soil degradation and reservoir siltation are, however, strongly interlinked with the erosion processes that take place in the hydrological catchments, as both are consequences of these processes. Due to the spatial scale and duration of erosion events, the installation and operation of monitoring systems are rather cost- and time-consuming. Modeling is a feasible alternative for assessing the soil loss adequately. In this study, the possibility of adopting reservoir sediment stock as a validation measure for a monthly time-step sediment input model was investigated. For the assessment of sediment stock in the reservoir, the commercial free-fall penetrometer GraviProbe (GP) was used, while the calculation of sediment yield was calculated by combining a revised universal soil loss equation (RUSLE)-based model with a sediment delivery ratio model based on the connectivity approach. For the RUSLE factors, a combination of remote sensing, literature review, and conventional sampling was used. For calculation of the C Factor, satellite imagery from the Sentinel-2 platform was used. The C Factor was derived from an empirical approach by combining the normalized difference vegetation index (NDVI), the degree of soil sealing, and land-use/land-cover data. The key research objective of this study was to examine to what extent a reservoir can be used to validate a long-term erosion model, and to find out the limiting factors in this regard. Another focus was to assess the potential improvements in erosion modeling from the use of Sentinel-2 data. The use of such data showed good potential to improve the overall spatial and temporal performance of the model and also dictated further opportunities for using such types of model as reliable decision support systems for sustainable catchment management and reservoir protection measures.

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“…Additionally, the resolution decreases with the increase of range. Based on the nonlinear properties of sound propagation, the parametric sub-bottom profiler [5,6] can generate a low frequency via the difference between two transmitted signals. A new signal is used to detect the buried sediment in the sea bottom.…”

Section: Introductionmentioning

confidence: 99%

“…By using the Mills cross configuration of multibeam sonar and the synthetic aperture technique of the SAS system, this paper introduces a SAS framework called the SAS sub-bottom profiler. This is different from traditional multibeam sonar [6], in that the receiver array is mounted along the keel of a ship, while the transmitter array is mounted perpendicular to the receiver array. Based on this new configuration, the along-track resolution can be obtained for high resolution images.…”

Section: Introductionmentioning

confidence: 99%

A Novel Sub-Bottom Profiler and Signal Processor

Tan

Zhang

Yang³

et al. 2019

Sensors

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In this paper, we introduce a novel sub-bottom profiler, making good use of the Mills cross configuration of multibeam sonar and synthetic aperture techniques of the synthetic aperture sonar system. The receiver array is mounted along the ship keel, while the transmitter array is mounted perpendicular to the receiver array. With the synthetic aperture technique, the along-track resolution can be greatly improved. The system often suffers from motion error, which severely degrades the imaging performance. To solve this problem, the imaging algorithm with motion compensation (MC) is proposed. With the presented method, the motion error is first estimated based on overlapped elements between successive pulses. Then, the echo data is processed by using the range migration algorithm based on the phase center approximation (PCA) method, which simultaneously performs the MC with the estimated motion error. In order to validate the proposed sub-bottom profiler and data processing method, some simulations and lake trial results are discussed. The processing results of the real data further indicate that the presented configuration has great potential to find buried objects in seabed sediments.

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Sediment classification in a Brazilian reservoir: Pros and cons of parametric low frequencies

Cited by 10 publications

References 16 publications

Effects of dimensionality on the performance of hydrodynamic models

Effects of dimensionality on the performance of hydrodynamic models

To What Extent Can a Sediment Yield Model Be Trusted? A Case Study from the Passaúna Catchment, Brazil

A Novel Sub-Bottom Profiler and Signal Processor

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