The Effects of Nourishments Using the Grain-Size Trend Analysis on the Intertidal Zone at a Sandy Macrotidal Beach

Choi, Taejin; Choi, Jinyong; Park, Jun-Young; Um, Han-Yong; Choi, Jung-Hoon

doi:10.2112/si85-086.1

Cited by 3 publications

(2 citation statements)

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“…In estuarine and marine environments, the combined effects of waves and currents govern sediment processes (Seymore, 1989). Anthropogenic activities also contribute to sediment processes, in the form of construction (Aoula et al, 2021), dredging (Pastor et al, 2020), propeller wash (Yew et al, 2017), and beach nourishment (Choi et al, 2018), to name a few.…”

Section: Introductionmentioning

confidence: 99%

Suspended sediment concentration estimation in the Sacramento‐San Joaquin Delta of California using long short‐term memory networks

Kim¹,

He²,

Sandhu³

2022

Hydrological Processes

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Sedimentation is an important aspect of water resources management with many implications. Often, process-based methods are employed to predict and assess the amount of sediment in water, but there are still challenges because the mechanisms that govern sediment transport are not yet fully understood. Furthermore, complex domains make model calibration difficult. Thus, as a complementary tool, a machinelearning model was developed in the present study to emulate an existing processbased model in simulating suspended sediment concentration (SSC). It employs the long short-term memory (LSTM) networks, which are a type of artificial neural networks (ANNs) designed for supervised learning of a sequence of data (e.g., time series). The model was applied to the Sacramento-San Joaquin Delta (the Delta) of California, USA, which is characterized by an interconnected system of sloughs, waterways, and a tidal outlet. The model training was performed with historical records of flow, stage and SSC at various locations within the Delta. The study period was 2010 through 2016, but the training period (i.e., range of observed data used to train the model) was varied to assess the model's sensitivity to the inputs and to determine the optimum model setup. Comparison between the model-estimated SSC and the observation at 12 key locations within the Delta showed that the estimation accuracy of the LSTM model during the study period is comparable or superior to that of the Delta Simulation Model II-General Transport Model (DSM2-GTM), a process-based operational hydrodynamics and water quality model for the Delta. In terms of the ratio of the root-mean-square error to the standard deviation (RSR), LSTM models generally showed higher predictability than DSM2-GTM in all test cases investigated, with the lowest (most desirable) and highest (least desirable) LSTM-based RSR being 0.21 and 1.14, respectively. In comparison, the lowest and highest RSR values with DSM2-GTM were 0.26 and 3.70, respectively. The median LSTM-based RSR of all study locations is around 0.7 while its DSM2-GTM counterpart is about 1.0. LSTM models also yielded remarkably higher (more desirable) Nash-Sutcliffe Efficiency values. Moreover, visual inspection found that LSTM models better captured the timing and magnitude of peaks as well as the temporal variations in the SSC time series. The LSTM model's performance was further analysed with hydro-meteorological data (precipitation and wind speed) incorporated in

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Section: Introductionmentioning

confidence: 99%

Suspended sediment concentration estimation in the Sacramento‐San Joaquin Delta of California using long short‐term memory networks

Kim¹,

He²,

Sandhu³

2022

Hydrological Processes

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“…Sediment properties of sandy beaches and sand dunes, including grain size, carbonate content, moisture content, organic content, magnetic susceptibility and grain mineralogy, are most commonly measured and quantified based on field observations or field sampling, and then laboratory analysis of these samples using different analytical equipment [1][2][3][4]. Following this, a range of statistical techniques (e.g., calculation of moment measures, multivariate analyses) can be used on the grain size data in particular, in order to characterise sediment properties and to interpret depositional processes and environments and their changes over time and space, e.g., [5][6][7][8][9][10][11]. This standard methodology has been undertaken on many beaches and dunes worldwide, resulting in an understanding of spatial patterns of different sediment properties (based mainly on grain size) across different coastal depositional environments, e.g., [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Characterisation of Coastal Sediment Properties from Spectral Reflectance Data

Knight

Elbasit

2022

Applied Sciences

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Remote sensing of coastal sediments for the purpose of automated mapping of their physical properties (grain size, mineralogy and carbonate content) across space has not been widely applied globally or in South Africa. This paper describes a baseline study towards achieving this aim by examining the spectral reflectance signatures of field sediment samples from a beach–dune system at Oyster Bay, Eastern Cape, South Africa. Laboratory measurements of grain size and carbonate content of field samples (n = 134) were compared to laboratory measurements of the spectral signature of these samples using an analytical spectral device (ASD), and the results interrogated using different statistical methods. These results show that the proportion of fine sand, CaCO3 content and the distributional range of sediment grain sizes within a sample (here termed span) are the parameters with greatest statistical significance—and thus greatest potential interpretive value—with respect to their spectral signatures measured by the ASD. These parameters are also statistically associated with specific wavebands in the visible and near infrared, and the shortwave infrared parts of the spectrum. These results show the potential of spectral reflectance data for discriminating elements of grain size properties of coastal sediments, and thus can provide the baseline towards achieving automated spatial mapping of sediment properties across coastal beach–dune environments using hyperspectral remote sensing techniques.

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Validation of Nadir SWH and Its Variance Characteristics from CFOSAT in China’s Offshore Waters

et al. 2023

Remote Sensing

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The offshore waters of China are a typical monsoon−affected area where the significant wave height (SWH) is strongly influenced by the different seasonal mean flow in winter and summer. However, limited in situ validations of the SWH have been performed on the China–France Oceanography Satellite (CFOSAT) in these waters. This study focused on validating CFOSAT nadir SWH data with SWH data from in situ buoy observations for China’s offshore waters and the Haiyang−2B (HY−2B) satellite, from July 2019 to December 2021. The validation against the buoy data showed that the relative absolute error has a seasonal cycle, varying in a narrow range near 35%. The RMSE of the CFOSAT nadir SWH was 0.29 m when compared against in situ observations, and CFOSAT was found to be more likely to overestimate the SWH under calm sea conditions. The sea−surface winds play a key role in calm sea conditions. The spatial distributions of the CFOSAT and HY−2B seasonal SWHs were similar, with a two−year mean SWH−field correlation coefficient of 0.98. Moreover, the coherence between the two satellites’ SWH variance increased with SWH magnitude. Our study indicates that, in such typical monsoon−influenced waters, attention should be given to the influence of sea conditions on the accuracy of CFOSAT SWH, particularly in studies that combine data from multiple, long−duration space−based sensors.

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The Effects of Nourishments Using the Grain-Size Trend Analysis on the Intertidal Zone at a Sandy Macrotidal Beach

Cited by 3 publications

References 0 publications

Suspended sediment concentration estimation in the Sacramento‐San Joaquin Delta of California using long short‐term memory networks

Suspended sediment concentration estimation in the Sacramento‐San Joaquin Delta of California using long short‐term memory networks

Characterisation of Coastal Sediment Properties from Spectral Reflectance Data

Validation of Nadir SWH and Its Variance Characteristics from CFOSAT in China’s Offshore Waters

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