scholarly journals Australian tidal currents – assessment of a barotropic model (COMPAS v1.3.0 rev6631) with an unstructured grid

2021 ◽  
Vol 14 (9) ◽  
pp. 5561-5582
Author(s):  
David A. Griffin ◽  
Mike Herzfeld ◽  
Mark Hemer ◽  
Darren Engwirda
Keyword(s):  
Tidal Current ◽  
Unstructured Grid ◽  
Tide Gauge ◽  
Major Axis ◽  
Absolute Error ◽  
Tidal Currents ◽  
Practical Significance ◽  
Tidal Range ◽  
Validation Dataset ◽  
Velocity Amplitude

Abstract. While the variations of tidal range are large and fairly well known across Australia (less than 1 m near Perth but more than 14 m in King Sound), the properties of the tidal currents are not. We describe a new regional model of Australian tides and assess it against a validation dataset comprising tidal height and velocity constituents at 615 tide gauge sites and 95 current meter sites. The model is a barotropic implementation of COMPAS, an unstructured-grid primitive-equation model that is forced at the open boundaries by TPXO9v1. The mean absolute error (MAE) of the modelled M2 height amplitude is 8.8 cm, or 12 % of the 73 cm mean observed amplitude. The MAE of phase (10∘), however, is significant, so the M2 mean magnitude of vector error (MMVE, 18.2 cm) is significantly greater. The root sum square over the eight major constituents is 26 % of the observed amplitude. We conclude that while the model has skill at height in all regions, there is definitely room for improvement (especially at some specific locations). For the M2 major axis velocity amplitude, the MAE across the 95 current meter sites, where the observed amplitude ranges from 0.1 to 156 cm s−1, is 6.9 cm s−1, or 22 % of the 31.7 cm s−1 observed mean. This nationwide average result is encouraging, but it conceals a very large regional variation. Relative errors of the tidal current amplitudes on the narrow shelves of New South Wales (NSW) and Western Australia exceed 100 %, but tidal currents are weak and negligible there compared to non-tidal currents, so the tidal errors are of little practical significance. Looking nationwide, we show that the model has predictive value for much of the 79 % of Australia's shelf seas where tides are a major component of the total velocity variability. In descending order this includes the Bass Strait, the Kimberley to Arnhem Land, and southern Great Barrier Reef regions. There is limited observational evidence to confirm that the model is also valuable for currents in other regions across northern Australia. We plan to commence publishing “unofficial” tidal current predictions for chosen regions in the near future based on both our COMPAS model and the validation dataset we have assembled.

scholarly journals Australian tidal currents – assessment of a barotropic model (COMPAS v1.3.0 rev6631) with an unstructured grid

2021 ◽  
Author(s):  
David Anthony Griffin ◽  
Mike Herzfeld ◽  
Mark Hemer ◽  
Darren Engwirda
Keyword(s):  
Tidal Current ◽  
Unstructured Grid ◽  
Tide Gauge ◽  
Tidal Currents ◽  
Practical Significance ◽  
Tidal Range ◽  
Validation Dataset ◽  
Validation Data ◽  
Data Set ◽  
Velocity Amplitude

Abstract. While the variations of tidal range are large and fairly well known across Australia (less than 1 m near Perth but more than 14 m in King Sound), the properties of the tidal currents are not. We describe a new regional model of Australian tides and assess it against a validation dataset comprising tidal height and velocity constituents at 615 tide gauge sites and 95 current meter sites. The model is a barotropic implementation of COMPAS, an unstructured-grid primitive-equation model that is forced at the open boundaries by TPXO9v1. The Mean Absolute value of the Error (MAE) of the modelled M2 height amplitude is 8.8 cm, or 12 % of the 73 cm mean observed amplitude. The MAE of phase (10°), however, is significant, so the M2 Mean Magnitude of Vector Error (MMVE, 18.2 cm) is significantly greater. The Root Sum Square over the 8 major constituents is 26 % of the observed amplitude. We conclude that while the model has skill at height in all regions, there is definitely room for improvement (especially at some specific locations). For the M2 major-axis velocity amplitude, the MAE across the 95 current meter sites, where the observed amplitude ranges from 0.1 cm s−1 to 156 cm s−1, is 6.9 cm s−1, or 22 % of the 31.7 cm s−1 observed mean. This nationwide average result is encouraging, but it conceals a very large regional variation. Relative errors of the tidal current amplitudes on the narrow shelves of NSW and Western Australia exceed 100 %, but tidal currents are weak and negligible there compared to non-tidal currents, so the tidal errors are of little practical significance. Looking nation-wide, we show that the model has predictive value for much of the 79 % of Australia’s shelf seas where tides are a major component of the total velocity variability. In descending order this includes the Bass Strait, Kimberley to Arnhem Land and Southern Great Barrier Reef regions. There is limited observational evidence to confirm that the model is also valuable for currents in other regions across northern Australia. We plan to commence publishing ‘unofficial’ tidal current predictions for chosen regions in the near future, based on both our COMPAS model and the validation data set we have assembled.

scholarly journals Australian tidal currents – assessment of a barotropic model with an unstructured grid

2020 ◽  
Author(s):  
David A. Griffin ◽  
Mike Herzfeld ◽  
Mark Hemer
Keyword(s):  
Unstructured Grid ◽  
Tide Gauge ◽  
Major Axis ◽  
Equation Model ◽  
Tidal Currents ◽  
Tidal Range ◽  
Validation Dataset ◽  
Velocity Amplitude ◽  
Shelf Seas ◽  
Relative Errors

Abstract. While the variations of tidal range are large and fairly well known across Australia (less than 1 m near Perth but more than 14 m in King Sound), the properties of the tidal currents are not. We describe a new regional model of Australian tides and assess it against a validation dataset comprising tidal height and velocity constituents at 615 tide gauge sites and 95 current meter sites. The model is a barotropic implementation of COMPAS, an unstructured-grid primitive-equation model that is forced at the open boundaries by TPXO9v1. The Mean Absolute value of the Error (MAE) of the modelled M2 height amplitude is 9.3 cm, or 13 % of the 73 cm mean observed amplitude. The MAE of phase (11°), however, is significant, so the M2 Mean Magnitude of Vector Error (MMVE, 20 cm) is significantly greater. Results for 5 other major constituents are similar. We conclude that while the model has skill at height in all regions, there is definitely room for improvement (especially at some specific locations) before harmonic predictions based on observations are rendered obsolete. For the M2 major-axis velocity amplitude, the MAE across the 95 current meter sites, where the observed amplitude ranges from 0.1 cm s−1 to 144 cm s−1, is 6.5 cm s−1, or 20 % of the 31.7 cm s−1 observed mean. This nationwide average result is not much greater than the equivalent for height, but it conceals a larger regional variation. Relative errors on the narrow shelves of NSW and Western Australia exceed 100 %, but tidal currents are weak and negligible there compared to non-tidal currents. We show that the model has predictive value for much of the 79 % of Australia's shelf seas where tides are a major component of the total velocity variability. In descending order this includes the Bass Strait, Kimberley to Arnhem Land and Southern Great Barrier Reef regions. There is limited evidence the model is also valuable for currents in other regions across northern Australia. We plan to commence publishing unofficial tidal current predictions for chosen regions in the near future, based on both the limited number of observations, and the COMPAS model.

scholarly journals A Study on the Response of the Hydrodynamic Environment to the Morphology of Radial Sand Ridges in the Coastal Waters of Jiangsu

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2190
Author(s):  
Changjun Qi ◽  
Lejun Ma ◽  
Qinggai Wang ◽  
Yuan Zhai ◽  
Jixuan Li ◽  
...  
Keyword(s):  
Tidal Current ◽  
Unstructured Grid ◽  
Tidal Currents ◽  
Current Data ◽  
Material Transport ◽  
Current Field ◽  
Sand Ridges ◽  
Radial Sand Ridges ◽  
Local Current ◽  
Hydrodynamic Environment

A two-dimensional hydrodynamic model for the waters off the coast of Jiangsu, where there are radial sand ridges (RSRs) (hereinafter, the RSR area), was established based on measured topographic, tide level and tidal current data. Considering the complex topographic and geomorphic characteristics of the RSR group in this area, an unstructured grid was used for the calculation. A four-layer refinement was applied to the grid from outside to inside to better fit the complex topography. The simulations were performed to examine the response of the hydrodynamic environment to the morphology of the RSRs in three scenarios, namely, when there are natural RSRs, no RSRs, and partially reclaimed RSRs. When there are no or partially reclaimed RSRs, the tidal current field still exists in a radial pattern in the RSR area. The radial tidal current field is relatively stable and is not controlled by the morphologies of the RSRs. The topographic changes do not alter the distribution pattern of the radial tidal current field but do affect the local current fields. When there are no RSRs, the flood currents can directly reach Jianggang. Under practical conditions, the RSRs block the tidal currents during a flood tide to some extent. This phenomenon is particularly pronounced when the RSRs are partially reclaimed. For example, during an ebb tide, when the tidal currents encounter sand ridges or reclamation areas, their streamlines bend, and they flow around the obstacles. This change will affect the material transport, sediment deposition and seabed erosion.

scholarly journals Morphodynamic Evolution of a Nourished Beach with Artificial Sandbars: Field Observations and Numerical Modeling

2021 ◽  
Vol 9 (3) ◽  
pp. 245
Author(s):  
Cuiping Kuang ◽  
Xuejian Han ◽  
Jiabo Zhang ◽  
Qingping Zou ◽  
Boling Dong
Keyword(s):  
Sediment Transport ◽  
Tidal Current ◽  
Model Simulation ◽  
Morphological Changes ◽  
Current Model ◽  
Beach Nourishment ◽  
Tidal Range ◽  
Coastal Protection ◽  
Field Observations ◽  
One Year

Beach nourishment, a common practice to replenish an eroded beach face with filling sand, has become increasingly popular as an environmentally friendly soft engineering measure to tackle coastal erosion. In this study, three 200 m long offshore submerged sandbars were placed about 200 m from the shore in August 2017 for both coastal protection and beach nourishment at Shanhai Pass, Bohai Sea, northeastern China. A series of 21 beach profiles were collected from August 2017 to July 2018 to monitor the morphological changes of the nourished beach. Field observations of wave and tide levels were conducted for one year and tidal current for 25 h, respectively. To investigate the spatial-temporal responses of hydrodynamics, sediment transport, and morphology to the presence of three artificial submerged sandbars, a two-dimensional depth-averaged (2DH) multi-fraction sediment transport and morphological model were coupled with wave and current model and implemented over a spatially varying nested grid. The model results compare well with the field observations of hydrodynamics and morphological changes. The tidal range was around 1.0 m and the waves predominately came from the south-south-east (SSE) direction in the study area. The observed and predicted beach profiles indicate that the sandbars moved onshore and the morphology experienced drastic changes immediately after the introduction of sandbars and reached an equilibrium state in about one year. The morphological change was mainly driven by waves. Under the influences of the prevailing waves and the longshore drift toward the northeast, the coastline on the leeside of the sandbars advanced seaward by 35 m maximally while the rest adjacent coastline retreated severely by 44 m maximally within August 2017–July 2018. The model results demonstrate that the three sandbars have little effect on the tidal current but attenuate the incoming wave significantly. As a result, the medium-coarse sand of sandbars is transported onshore and the background silt is mainly transported offshore and partly in the longshore direction toward the northeast. The 2- and 5-year model simulation results further indicate that shoreline salient may form behind the sandbars and protrude offshore enough to reach the sandbars, similar to the tombolo behind the breakwater.

scholarly journals ESTIMATION OF ELECTRICITY GENERATED FROM TIDAL ENERGY IN BEDONO VILLAGE OF DEMAK REGENCY

2020 ◽  
Vol 4 (9) ◽  
pp. 13-17
Author(s):  
Agus Margiantono ◽  
Titik Nurhayati ◽  
Wahib Hasbullah
Keyword(s):  
Electric Power ◽  
Current Velocity ◽  
Tidal Current ◽  
Tidal Energy ◽  
Tidal Currents ◽  
Active Power ◽  
Tidal Current Velocity ◽  
The Village

In some places in the village of Bedono Demak Regency there is a location with high tidal current velocity, the coordinates of the Location is 6 ° 55'29.0 "S 110 ° 29'11.4" E. In this study estimated the amount of electric power that can be generated from tidal currents in the village Bedono. Estimates are made by modeling the location and the Darrieus turbine using the CFD (Computating Fluid Dinamyc) Software. From the research that has been done to get the results of electric power that can be produced in the village Bedono highest at 14-16 times 3469.413W and lowest 39.002W at 22-24 hours according to the CFD is the highest active power occurred at 14-16 at 3197.064W and the lowest 35.941W at 22-24 hours.

Wave and tidal power

2007 ◽  
Author(s):  
Dean L. Millar
Keyword(s):  
Wave Energy ◽  
Tidal Current ◽  
High Efficiency ◽  
Energy Resource ◽  
Energy Difference ◽  
Tidal Range ◽  
Tidal Power ◽  
Floating Structures ◽  
Wave Energy Converters ◽  
High Tidal Range

This chapter reviews how electricity can be generated from waves and tides. The UK is an excellent example, as the British Isles have rich wave and tidal resources. The technologies for converting wave power into electricity are easily categorized by location type. 1. Shoreline schemes. Shoreline Wave Energy Converters (WECs) are installed permanently on shorelines, from where the electricity is easily transmitted and may even meet local demands. They operate most continuously in locations with a low tidal range. A disadvantage is that less power is available compared to nearshore resources because energy is lost as waves reach the shore. 2. Nearshore schemes. Nearshore WECs are normally floating structures needing seafloor anchoring or inertial reaction points. The advantages over shoreline WECs are that the energy resource is much larger because nearshore WECs can access long-wavelength waves with greater swell, and the tidal range can be much larger. However, the electricity must be transmitted to the shore, thus raising costs. 3. Offshore schemes. Offshore WECs are typically floating structures that usually rely on inertial reaction points. Tidal range effects are insignificant and there is full access to the incident wave energy resource. However, electricity transmission is even more costly. Tidal power technologies fall into two fundamental categories:1. Barrage schemes. In locations with high tidal range a dam is constructed that creates a basin to impound large volumes of water. Water flows in and out of the basin on flood and ebb tides respectively, passing though high efficiency turbines or sluices or both. The power derives from the potential energy difference in water levels either side of the dam. 2. Tidal current turbines. Tidal current turbines (also known as free flow turbines) harness the kinetic energy of water flowing in rivers, estuaries, and oceans. The physical principles are analogous to wind turbines, allowing for the very different density, viscosity, compressibility, and chemistry of water compared to air. Waves are caused by winds, which in the open ocean are often of gale force (speed >14 m/s).

scholarly journals Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1313 ◽  
Author(s):  
Sunil Saha ◽  
Jagabandhu Roy ◽  
Alireza Arabameri ◽  
Thomas Blaschke ◽  
Dieu Tien Bui
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Gully Erosion ◽  
Machine Learning Techniques ◽  
Weight Of Evidence ◽  
Validation Dataset ◽  
Boosted Regression Tree ◽  
Area Index ◽  
Statistical Measures

Gully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree (GBRT), Naïve Bayes Tree (NBT), and Tree Ensemble (TE). The gully inventory map (GIM) consists of 120 gullies. Of the 120 gullies, 84 gullies (70%) were used for training and 36 gullies (30%) were used to validate the models. Fourteen gully conditioning factors (GCFs) were used for GES modeling and the relationships between the GCFs and gully erosion was assessed using the weight-of-evidence (WofE) model. The GES maps were prepared using RF, GBRT, NBT, and TE and were validated using area under the receiver operating characteristic (AUROC) curve, the seed cell area index (SCAI) and five statistical measures including precision (PPV), false discovery rate (FDR), accuracy, mean absolute error (MAE), and root mean squared error (RMSE). Nearly 7% of the basin has high to very high susceptibility for gully erosion. Validation results proved the excellent ability of these models to predict the GES. Of the analyzed models, the RF (AUROC = 0.96, PPV = 1.00, FDR = 0.00, accuracy = 0.87, MAE = 0.11, RMSE = 0.19 for validation dataset) is accurate enough for modeling and better suited for GES modeling than the other models. Therefore, the RF model can be used to model the GES areas not only in this river basin but also in other areas with the same geo-environmental conditions.

Large bedforms and associated hydraulic conditions within microtidal-inlet channels, southern Gulf of St. Lawrence, Canada

1988 ◽  
Vol 25 (6) ◽  
pp. 916-922 ◽  
Author(s):  
P. N. Matsushita ◽  
S. B. McCann
Keyword(s):  
Tidal Current ◽  
Tidal Cycle ◽  
Average Length ◽  
Tidal Flow ◽  
Tidal Range ◽  
Sand Waves ◽  
Hydraulic Conditions ◽  
Throat Section

A variety of large bedforms, at two scales (sand waves, dunes), occur within the throat section of-small microtidal-inlet channels. The larger forms, which have an average length of 32 m and height of 1.0 m, remain ebb oriented throughout the tidal month but are only actively modified at large tides. The smaller forms, which may be superimposed on the larger, are partially reworked each tidal cycle but are also predominantly ebb oriented. Tidal-current measurements confirm the dominance of ebb-tidal flow in the inlet throats and indicate that despite the small tidal range (maximum 1.1 m), ebb currents may exceed 1.0 m s−1 with associated shear velocities of 0.14 m s−1.

scholarly journals Sea Level Prediction Using Machine Learning

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3566
Author(s):  
Rifat Tur ◽  
Erkin Tas ◽  
Ali Torabi Haghighi ◽  
Ali Danandeh Mehr
Keyword(s):  
Sea Level ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Tide Gauge ◽  
Absolute Error ◽  
Meteorological Factor ◽  
Sea Level Changes ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Cross Correlation Analysis

Sea level prediction is essential for the design of coastal structures and harbor operations. This study presents a methodology to predict sea level changes using sea level height and meteorological factor observations at a tide gauge in Antalya Harbor, Turkey. To this end, two different scenarios were established to explore the most feasible input combinations for sea level prediction. These scenarios use lagged sea level observations (SC1), and both lagged sea level and meteorological factor observations (SC2) as the input for predictive modeling. Cross-correlation analysis was conducted to determine the optimum input combination for each scenario. Then, several predictive models were developed using linear regressions (MLR) and adaptive neuro-fuzzy inference system (ANFIS) techniques. The performance of the developed models was evaluated in terms of root mean squared error (RMSE), mean absolute error (MAE), scatter index (SI), and Nash Sutcliffe Efficiency (NSE) indices. The results showed that adding meteorological factors as input parameters increases the performance accuracy of the MLR models up to 33% for short-term sea level predictions. Moreover, the results contributed a more precise understanding that ANFIS is superior to MLR for sea level prediction using SC1- and SC2-based input combinations.

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