Predicting coastal morphological changes with empirical orthogonal function method

In order to improve the accuracy of prediction when using the empirical orthogonal function (EOF) method, this paper describes a novel approach for two-dimensional (2D) EOF analysis based on extrapolating both the spatial and temporal EOF components for long-term prediction of coastal morphological changes. The approach was investigated with data obtained from a process-based numerical model, COAST2D, which was applied to an idealized study site with a group of shore-parallel breakwaters. The progressive behavior of the spatial and temporal EOF components, related to bathymetric changes over a training period, was demonstrated, and EOF components were extrapolated with combined linear and exponential functions for long-term prediction. The extrapolated EOF components were then used to reconstruct bathymetric changes. The comparison of the reconstructed bathymetric changes with the modeled results from the COAST2D model illustrates that the presented approach can be effective for long-term prediction of coastal morphological changes, and extrapolating both the spatial and temporal EOF components yields better results than extrapolating only the temporal EOF component

Mapping of Flood Dynamics Due to Sea Level Rise in Dyfi Estuary

Water Qualit

Effect of hydrodynamics factors on sediment flocculation processes in estuaries

Purpose Cohesive sediment is able to flocculate and create flocs, which are larger than individual particles and less dense. The phenomenon of flocculation has an important role in sediment transport processes such as settling, deposition and erosion. In this study, laboratory experiments were performed to investigate the effect of key hydrodynamic parameters such as suspended sediment concentration and salinity on floc size and settling velocity. Results were compared with previous laboratory and field studies at different estuaries. Materials and methods Experimental tests were conducted in a 1-L glass beaker of 11-cm diameter using suspended sediment samples from the Severn Estuary. A particle image velocimetry system and image processing routine were used to measure the floc size distribution and settling velocity. Results and discussion The settling velocity was found to range from 0.2 to 1.2 mm s−1. Settling velocity changed in the case of increasing suspended sediment concentration and was controlled by the salinity. The faster settling velocity occurred when sediment concentration is higher or the salinity is lower than 2.5. On the other hand, at salinities higher than 20, in addition to increasing SSC, it was found that the situation was reversed, i.e. the lower the sediment concentration, the faster the settling velocity. Conclusions Sediment flocculation is enhanced with increasing sediment concentration but not with increasing salinity

Simulating flood hazard in Wye Valley during Storm Dennis using Hec-Ras

A 2D Hec-Ras flood model is established at the middle reach of the River Wye, from Ross-on-Wye to Monmouth, along the English-Wales border. The flow conditions during Strom Dennis, which occurred on 15-16 Feb 2020 are used to simulate the flood extents in the study area. The flood hazard rate, suggested by EA, is analysed to assess the risk level of flooding in the area. The results show the extensive flood extents at the study site. The flood hazard rate map shows that large part of Wye Valley is at high risk of flooding

Modelling buildings during flood inundation using TELEMAC-2D

Global climate change significantly increases flood risk. Inundation around buildings in both urban and rural areas can pose significant risk to life and property. Accurate and precise modelling is the key to a better understanding and quantification of flood risk. This study applies three different methods for modelling buildings within TELEMAC-2D from a dyke breach scenario: a) buildings excluded from the mesh; b) buildings modelled as elevated bathymetry; and c) buildings modelled as vegetation friction. The maximum flood hazard rating for each method is then calculated from the hydrodynamics generated by the model and compared. The results show that using vegetation friction to represent the buildings in the model is the most effective and accurate approach in evaluating the flood risk

Application of the Variational Mode Decomposition (VMD) method to river tides

Tides in fluvial estuaries are distorted by non-stationary river discharge, which makes the analysis of estuarine water levels less accurate when using the conventional tidal analysis method. As a powerful and widely-used method for non-stationary and nonlinear time series, the application of Variational Mode Decomposition (VMD) method to non-stationary tides is nonexistent. This paper aims to illustrate and verify the suitability of the VMD method as a new tidal analysis tool for river tides. The efficiency of VMD is validated by the measurements from the Columbia River Estuary. VMD strictly divides different tidal species into different modes, and thus avoids mode mixing. Compared to VMD, Ensemble Empirical Mode Decomposition (EEMD), which is another commonly-used method, fails to completely solve the problem of mode mixing. The observed water levels at Longview station are decomposed into 12 modes via VMD. Based on the mean periods and amplitudes of each VMD mode, the 12 VMD modes sequentially correspond to the tidal species from the sub-tides (D0), diurnal tides (D1), semi-diurnal tides (D2), and up to D11 tides. The non-stationary characteristics of tides influenced by river discharge are accurately captured by VMD without mode mixing. The results also show that the EEMD and VMD modes can capture the subtidal signals better than the nonstationary tidal harmonic analysis tool (NS_TIDE). As a general method, the VMD model can also be used for other research purposes related to non-stationary tides, such as detiding

Impact of river discharge on hydrodynamics and sedimentary processes at Yellow River Delta

During the Anthropocene, regulating river discharge by high dams may have met the need for water demands in river basins, but resulted in carrying less freshwater and sediment to the sea, inducing land degradation and shoreline retreat in worldwide mega-river deltas. In land-ocean interaction, tide response to water discharge changes plays an important role and is crucial for the river-laden sediment transfer and dispersal, affecting both nearshore and estuarine deposits. The Yellow River Delta (YRD), which is under an increasing pressure of the new discharge regime of the Yellow River, has undergone drastic changes in terms of sediment dynamics and morphologic evolution. To gain a better understanding of the overall fluvial and marine hydrodynamics and morphodynamic processes in the YRD, in this study, a full-scale numerical model is built to investigate the interaction and impacts of changing environmental forcing and dynamics on flow and sediment transport in the estuary of YRD and its adjacent coasts. The results show that the river discharge strongly affects the tidal dynamics and morphology of the delta, particularly in the close vicinity of the outlet and the intertidal zone. Tidal constituents M2 and K1, which are the most significant ones in the YRD, are found to be noticeably affected with a decreasing trend when the river discharge increases. The model results also indicate that river discharge affects the location and intensity of the shear front that occurs in the nearshore areas of the YRD. Increasing the river discharge can induce a seaward movement of the shear front, reduce its width and concentrate its shear intensity. It is found that the reverse of the flow direction at each side of the shear front and strong longshore tidal current can act as a barrier for the sediment dispersal process by keeping suspended sediment in the inner zone, thus to form a particular sediment deposition zone and the depo-center

Optimization of multi-model ensemble forecasting of typhoon waves

Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles). The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the optimization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to implement and practical for real-time wave forecasting

Air entrainment and air demand in the spillway tunnel at the Jinping-I dam

Artificial air entrainment has been widely used to avoid cavitation damage in spillways where high-velocity flow occurs, and its performance is very important for spillway safety. In order to evaluate the performance of the aeration system in the spillway tunnel of the Jinping-I Dam, which is the highest arched dam in the world to date, systematic prototype observation was conducted. Ventilation characteristics of the air supply system and aeration-related characteristics of the aeration devices were examined at the prototype scale. The results showed that air flows smoothly in the air intake well and the real effect of air entrainment of the aeration device was desirable. In contrast with results from laboratory tests with a physical model at a scale of 1/30 following the gravity similarity, it was found that air demand in the prototype is much greater, clearly indicating the scale effect. By summing up and analyzing the air demand ratio of the prototype to the model in some projects, the scale effect was found to be ignorable when the model scale was greater than 1/10. In addition, based on a series of prototype data on air demand, a brief evaluation of present calculation methods for air demand was conducted and a new form of calculation method for air demand related to unit width flow rate was established. The present prototype results can be used as a reference for similar engineering design, and to validate and verify numerical simulations as well as model tests

Artificial neural network-based storm surge forecast model: Practical application to Sakai Minato, Japan

The present study describes a novel way of a systematic and objective selection procedure for the development of an Artificial Neural Network-based storm Surge Forecast Model (ANN-SFM) with the 5, 12 and 24 h-lead times and its application to Sakai Minato area on the Tottori coast, Japan. The selection procedure guides how to determine the superiority of the best performing model in terms of the appropriate combination of unit number in the hidden layer and parameter in the input layer. In the application of ANN-SFM to Sakai Minato, it is found that the best 5 and 12 h-forecast ANN-SFMs are established with the most suitable set of 70 units (the number of hidden neurons) and the input components of surge level, sea level pressure, the depression rate of sea level pressure, longitude, latitude, central atmospheric pressure and highest wind speed. The best 24 h-forecast ANN-SFM is determined with 160 units and the input parameters of surge level, sea level pressure, the depression rate of sea level pressure, longitude and latitude. The proposed method of the selection procedure is able to be adaptable to other coastal locations for the development of the artificial neural network-based storm surge forecast model as establishing the superiority of the most relevant set combining unit numbers and input parameters