Tidal Level Data Research Articles

Extracting waterline and inverting tidal flats topography based on Sentinel-2 remote sensing images: A case study of the northern part of the North Jiangsu radial sand ridges

Muddy tidal flats with landforms vary widely over space and time. The evolution of tidal flats topography has had a significant impact on the region. Therefore, there is a need to develop methods for rapidly and accurately characterizing tidal flats topography. This study applied thresholding segmentation (TS), machine learning, and two image classification methods to Sentinel-2 remote sensing images to extract the instantaneous waterline of the northern part of the North Jiangsu Radial sand Ridges (NJRSR). Tidal level data at specific time intervals were obtained using the Delft3D and TPXO9 models, after which tidal flats topography inversion was conducted. As a result, varied among the different classification methods, with the K-Nearest Neighbors (KNN) algorithm producing the optimal waterline extraction. Furthermore, the accuracy of tidal level simulation was improved by employing Delft3D software and constructing a scaled-down model of the study area. Comparisons with Delft3D model simulations of tidal levels with observations at three tide gauge stations yielded accuracies of 17.5 cm, 18.5 cm and 17.9 cm, this result showed that the establishment of the tidal level model can enhance the accuracy of tidal flats Digital Elevation Model (DEM) inversion. In fact, the combined application of the KNN algorithm and Delft3D model within retrieval of tidal level provided the most accurate topographic inversion, with an average error of 0.297 m, which was reduced to 0.292 m after median filtering, and the results indicated a trend of southward erosion in the tidal flats in the study area, with a turning point roughly located in the southern part of Doulong Port. The results of this study can help improve the future monitoring of dynamic changes in intertidal wetland topography and the conservation and development of tidal flats areas.

Relative contributions of main factors to water levels in Wuchengxiyu Region, China

The rapid urbanization and frequent occurrence of extreme weather have made flooding more frequent; tide-sensitive areas are more prone to flooding due to tide level. However, the extent of their respective impacts on flood variability in the context of the combined effects remains to be explored. Therefore, analysis of the driveability of the water level is important for regional flood control. Land use data from 1985 to 2015 was used in this study to analyze the characteristics of the changes. General trends, abrupt variability, and persistence of rainfall, tide, and water level data from 1957 to 2018 were analyzed. The influence of rainfall, tide level factors and underlying surface on water level variation was analyzed qualitatively and the contribution of the three factors to water level variation was quantified. The results indicate that the most important directions of land-type transformation were the construction land and arable land. Within the rainfall sequence, only the 3d rainfall sequence exhibited a significant increasing trend, while both tide level and water levels showed significant increasing trends. The 3d rainfall sequence and tide level sequence are significantly positively correlated with water level sequences, whereas among the underlying surface factors, only construction land and unused land are significantly positively correlated with water levels. Furthermore, the contribution rates of driving factors vary across different regions. In the Changzhou region, the underlying surface factors dominated with a relative influence of 62%., whereas for other regions, the combined relative influences of rainfall and tide level are 57.2%, 52.8%, 60.4%, and 66.9%, The methods and results of this study contribute to regional flood regulation.

Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting

Tide level forecasting plays an important role in environmental management and development. Current tide level forecasting methods are usually implemented for solving single task problems, that is, a model built based on the tide level data at an individual location is only used to forecast tide level of the same location but is not used for tide forecasting at another location. This study proposes a new method for tide level prediction at multiple locations simultaneously. The method combines nonlinear autoregressive moving average with exogenous inputs (NARMAX) model and recurrent neural networks (RNNs), and incorporates them into a multi-task learning (MTL) framework. Experiments are designed and performed to compare single task learning (STL) and MTL with and without using non-linear autoregressive models. Three different RNN variants, namely, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are employed together with non-linear autoregressive models. A case study on tide level forecasting at many different geographical locations (5 to 11 locations) is conducted. Experimental results demonstrate that the proposed architectures outperform the classical single-task prediction methods.

An adaptive real-time modular tidal level prediction mechanism based on EMD and Lipschitz quotients method

Real-time prediction of tidal level is vital for on-the-spot activities such as marine transportation and ocean surveys. Aiming at the complex characteristics of nonlinearity, time-varying dynamics, and uncertainty generated by celestial bodies' movements and influenced by geographical as well as hydrometeorological factors, an adaptive real-time modular tidal level prediction mechanism is proposed based on empirical mode decomposition (EMD) and Lipschitz quotients method. An adaptive modular tidal level prediction mechanism is proposed by combining the harmonic analysis method with a variable structure neural network. The order of time series decomposition and the prediction input model order of the neural network are automatically determined based on EMD and the Lipschitz quotients method, respectively. The adaptability of the prediction mechanism is further enhanced with the network dimension, hidden units’ locations, and connecting parameters of the variable neural network being online adjusted in a sequential learning scheme. While the extraction of harmonic components alleviates the difficulty in prediction, the multi-resolution decomposition of residual series provides further insight into the time-varying tide dynamics caused by environmental disturbances, thus enabling precise predictions for tidal levels. The feasibility and effectiveness of the proposed adaptive modular tidal prediction mechanism are demonstrated based on the real-measured tidal level data.

Exploring the relationship between flood insurance claims, crowdsourced rainfall, and tide levels for coastal urban communities: Case study for the mid-Atlantic United States

The Federal Emergency Management Agency (FEMA) recently released a large dataset (∼2.5 million records) of flood insurance claim transactions from the National Flood Insurance Program (NFIP) that are geolocated by census tract. In this study, we present a methodology for using these NFIP flood claims along with agency rainfall and tide level data, combined with high-resolution rainfall data collected from growing networks of crowdsourced Personal Weather Stations (PWSs), to investigate the characteristics of storm events that result in flood insurance claims. Using 25 cities/counties in five metropolitan areas in the Mid-Atlantic region as case studies for testing the methodology, we find that the majority of flood claim events in this region were associated with rainfall below a 1-year return period and a tide level below the NOAA minor flood risk tide level. When storm events exceeded these rainfall and tide thresholds, the probability of a flood claim occurring could reach 100% for rainfall only events and 33% for tidal only events, depending on the region. While compound events of both high rainfall and high tide were rare, when seen in Virginia Beach, the probability of insurance claims being made during these events far exceeded the probability that would be expected if rainfall and tide were independent, showing how compound events in this region exacerbate flood impacts. Additionally, analysis of 110 heavy rainfall events showed that crowdsourced PWSs, can better capture rainfall extremes associated with insurance claims due to their higher spatial density compared to federal agency rainfall networks. This suggests that the PWS networks, once quality controlled, can offer greater insights into which rainfall events are likely to contribute to insurance claims. These region-specific findings and the general methodology presented in this study can benefit multiple stakeholders including researchers, risk management officials, insurers as well as the general public in understanding the impacts of rainfall, tidal, and compound flooding, along with the value of crowdsourced data in assessing flood risk.

Evaluation of the Stability of Muddy Coastline Based on Satellite Imagery: A Case Study in the Central Coasts of Jiangsu, China

Monitoring the coastline dynamic can provide the basis for the balance of sediment erosion and deposition. The evaluation of coastal stability is beneficial to decision makers for the rational development and ecological conservation of coastal resources. The present study first collected 61 scenes of remote sensing images and extracted the multi-temporal coastlines from the years 1990–2020 in Jiangsu Province, China using an improved waterline method. Given the characteristics of gentle slopes of our study area, we modified the coastlines using actual tidal level data to avoid the influence from different tidal regimes. Finally, the coastal stability analysis was conducted on the central coast of Jiangsu, which experiences frequent changes in erosion and siltation. The results showed that the coastline has changed significantly; the natural coastline decreased by 116 km, while the artificial coastline increased by 108 km. the area of tidal flats decreased by 1152 km2, and the average width of the tidal flats decreased from 8.83 km to 3.55 km. In general, the coastline advanced seawards for many years, mainly due to sediment siltation and tidal flat reclamation, with annual average rates of siltation and reclamation of 9.67 km/a and 40.75 km/a, respectively. The node of siltation and erosion migrated 1.8 km southwards, moving from the Sheyang Estuary to the Doulong Port. The coastal stability gradually decreased from north to south, by values of 88.5 km (40%) for stable coast and 63.97 km (28.9%) for extremely unstable coast. The most unstable coast came from frequent reclamation areas. The method in this study is expected to provide a reference for evaluating the stability of typical muddy coasts, and our results can provide a basis for the sustainable development, utilization, and protection of coastal areas.

Bridging the Gap: Enhancing Storm Surge Prediction and Decision Support with Bidirectional Attention-Based LSTM

Accurate storm surge forecasting is vital for saving lives and avoiding economic and infrastructural damage. Failure to accurately predict storm surge can have catastrophic repercussions. Advances in machine learning models show the ability to improve accuracy of storm surge prediction by leveraging vast amounts of historical and realtime data such as weather and tide patterns. This paper proposes a bidirectional attention-based LSTM storm surge architecture (BALSSA) to improve prediction accuracy. Training and evaluation utilized extensive meteorological and tide level data from 77 typhoon incidents in Hong Kong and Macao between 2017 and 2022. The proposed methodology is able to model complex non-linearities between large amounts of data from different sources and identify complex relationships between variables that are typically not captured by traditional physical methods. BALSSA effectively resolves the problem of long-term dependencies in storm surge prediction by the incorporation of an attention mechanism. It enables selective emphasis on significant features and boosts the prediction accuracy. Evaluation has been conducted using real-world datasets from Macao to validate our storm surge prediction model. Results show that accuracy and robustness of predictions were significantly improved by the incorporation of attention mechanisms in our models. BALSSA captures temporal dynamics effectively, providing highly accurate storm surge forecasts (MAE: 0.0126, RMSE: 0.0003) up to 72 h in advance. These findings have practical significance for disaster risk reduction strategies, saving lives through timely evacuation and early warnings. Experiments comparing BALSSA variations with other machine learning algorithms consistently validate BALSSA’s superior predictive performance. It offers an additional risk management tool for civil-protection agencies and governments, as well as an ideal solution for enhancing storm surge prediction accuracy, benefiting coastal communities.

A multi-site tide level prediction model based on graph convolutional recurrent networks

Predicting regional tide levels is vital for engineering and catastrophe avoidance along the shore. Data-driven method is capable of fast prediction of tide levels. However, current data-driven algorithms only make predictions for individual tide stations, rather than aiming to a regional network system of tide stations. This paper proposes a model based on graph convolutional recurrent networks to predict tidal levels at regional multiple tide stations. The model captures spatial and temporal features from historical tide level and meteorological data. Future tidal levels for multiple tide stations are the model's output. In this work, 48-year historical data from five tidal stations in Pearl River Delta were utilized for model training and evaluation. The results show that: (1) The model outperforms five commonly used baseline models in terms of evaluation metrics RMSE and MAE, and is able to predict future tide levels at multiple tide stations; (2) Short-term forecasts (1 and 3 h) are more accurate than long-term forecasts (12 h); (3) The model retains a high degree of accuracy for short-term predictions and satisfactory accuracy for long-term prediction during typhoons. The method provides a new instrument for regional prediction of tide levels and forecasting of storm surges.

Advance prediction of coastal groundwater levels with temporal convolutional and long short-term memory networks

Abstract. Prediction of groundwater level is of immense importance and challenges coastal aquifer management with rapidly increasing climatic change. With the development of artificial intelligence, data-driven models have been widely adopted in hydrological process management. However, due to the limitation of network framework and construction, they are mostly adopted to produce only 1 time step in advance. Here, the temporal convolutional network (TCN) and models based on long short-term memory (LSTM) were developed to predict groundwater levels with different leading periods in a coastal aquifer. The initial data of 10 months, monitored hourly in two monitoring wells, were used for model training and testing, and the data of the following 3 months were used as prediction with 24, 72, 180, and 360 time steps (1, 3, 7, and 15 d) in advance. The historical precipitation and tidal-level data were incorporated as input data. For the one-step prediction of the two wells, the calculated R2 of the TCN-based models' values were higher and the root mean square error (RMSE) values were lower than that of the LSTM-based model in the prediction stage with shorter running times. For the advanced prediction, the model accuracy decreased with the increase in the advancing period from 1 to 3, 7, and 15 d. By comparing the simulation accuracy and efficiency, the TCN-based model slightly outperformed the LSTM-based model but was less efficient in training time. Both models showed great ability to learn complex patterns in advance using historical data with different leading periods and had been proven to be valid localized groundwater-level prediction tools in the subsurface environment.

Analysis of Sea Level Changes in Eastern Taiwan Coast—Case of Yilan Area

This study considers the impact of extreme climates on most of the world’s land and seas, which may cause the average sea level to rise due to global climate change. Affected by the rise of sea level, it will change the distribution characteristics of sea tides, waves and currents. When the distribution of tides, waves and currents in the sea area changes, it may cause changes in the overall environmental characteristics of the estuary and coastal areas, and further affects the function of coastal protection facilities. This study takes the sea area of Yilan as an example, and analyzes the problem of sea level rise in the three-in-one climate change (including sea level rise, typhoon and heavy rain). For the analysis of sea level rise, this study calculates tide level data in three ways, the first is simple moving average (SMA), then there are empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) in the hilbert-huang transform (HHT) for sea level rise analysis. The use of ensemble empirical mode decomposition (EEMD) is because when modal confusion occurs in empirical mode decomposition (EMD), the occurrence of modal confusion can be reduced by adding the decomposed signal to the white noise sequence. Comparing the analysis results of the three respectively, the result obtained by using simple moving average (SMA) is the smallest (2.78 mm/yr), and the result obtained by using ensemble empirical mode decomposition (EEMD) calculation is the largest (4.11mm/yr). By analyzing the results obtained, it can be provided for assessing future hydrological and environmental changes based on climate change conditions.

The Classification to Stationary Process of Tidal Motion Observed at the Time of Kuroshio’s Meandering

The tide level displays information about the state of the sea current and the tidal motion. The tide level of the southern coast of Japan Island is affected strongly by Kuroshio Current flowing in the western part of North Pacific Ocean. When Kuroshio takes the straight path and flow along the Japan Islands, the tide level increases, and it is calculated from two tide level data observed at Kushimoto and Uragami in the southern part of Kii Peninsula. In contrast, the tide level decreases at the time when Kuroshio leaves from the Japan Islands. In this paper, the hourly tidal data are analyzed using the Autocorrelation Function (ACF) and the Mutual Information (MI) and the phase trajectories at first. We classify the results into 5 types of tidal motion. Each categorized type is investigated and characterized precisely using the mean tide level and the unit root test (ADF test) next. The frequency of the type having unstable tidal motion increases when the Kuroshio Current is non-meandering or in a transition state or the tide level is high, and the type shows a non-stationary process. On the other hand, when the Kuroshio Current meanders, the tidal motion tends to take a periodical and stable state and the motion is a stationary process. Though it is not frequent, we also discover a type of stationary and irregular tidal motion.

Shoreline Change Along Raigarh Coast, Maharashtra- a Bi-decadal Scenario

The dynamic nature of the shoreline makes the coastal areas vulnerable to the impacts of climate change and sea level rise. Changes in any shoreline concerning erosion and deposition affect the coastal landforms and population. Delineating the shoreline and mapping the areas of erosion/deposition would undoubtedly augment the knowledge base in the coastal region and help the environmental planners better maintain the coastal tracts. Shoreline delineation is challenging; however, remote sensing data combined with the Geoinformatics technique helps extract and monitor shorelines. The present paper attempts to delineate and analyze the shoreline changes over the bi-decade time frame for the coastal tracts of Raigarh. Satellite data (Landsat) and tide level data were employed in this work. Short-time shoreline change has been analyzed for five-time sections starting from 2000, 2004, 2008, 2012, 2016, and 2019, and long-term shoreline change encompasses the 2000 to 2019 scenario. The methodology adopted for this study involved image processing techniques for delineating the shoreline. The automated Shorelines were extracted using the Digital Shoreline Analysis System (DSAS) for the images prepared by employing a few indices using the ISODATA unsupervised classification technique. The present analysis results portray the Raigarh coast's dynamic nature. A cyclic pattern of erosion and deposition has been observed in the short-time analysis. Over twenty years, the Raigarh shoreline exhibits an overall erosion of -0.12 m/yr. A detailed analysis had done on the small section depicting mangrove patches, and this is to understand the response of mangroves at the zone of erosion and deposition. Mangrove patches with anthropogenic stress represent a zone of erosion, and patches with mature growth, protected from human interference, represent a zone of deposition. The mangrove patches, specifically over the northernmost parts like Kamothe, Khandeshwar, and Vaghivali, which are close to urban agglomeration or other allied anthropogenic activities like industry, exhibit an active erosion process. Mangrove patches with mature trees have well-developed root systems representing the deposition zone. Navedarbeli well represents such cases. Patches like Kalinje reported deposition, which is the result of a successful conservation program by the mangrove cell of Maharashtra. Overall, the headland regions of Alibag, Murud, and Srivardhan talukas are more susceptible to erosion. This type of study will help in monitoring the shoreline as well as will help in restoring and preserving the mangrove habitat.

Wave–current interaction by Typhoon Fongwong on saline water intrusion and vertical stratification in the Yangtze River Estuary

Saline water intrusion and vertical stratification are two important phenomena in estuaries, and thereby inextricably related to water mixing. In this study, a coupled 3D current-wave-salinity transport modeling is used to evaluate the effects of strong wind, wave and current on salt intrusion and water mixing in the Yangtze River Estuary (YRE) during typhoon Fongwong (2014). The model is validated by the measured wave height and period, tidal level, current velocity and salinity data. The model results show that: 1) wave-current interaction (WCI) during the typhoon period increased the current velocity and tidal level while decreased the air pressure, causing a storm surge by 1.2 m in the YRE; 2) the strong waves and winds strengthened the salinity intrusion in the YRE and the North Branch (NB) presented evident changes. After typhoon passage, the high saline water did not flush out immediately, indicating the typhoon effects were a fast process but the recovery of two weeks was slow; 3) The effect of the WCI showed a significant increase of water mixing and destratification during the typhoon event. Similar to the salinity intrusion, the process of regaining stratification needed a relatively long time. This research highlights that the WCI effects are important in the typhoon period on the hydrodynamics and salinity variation in the horizontal and vertical directions.

Oil spill modeling of Chengdao oilfield in the Chinese Bohai Sea

Offshore oil exploration and development is faced with complex operational conditions and harsh environments. Once an oil spill occurs, it will cause serious damage to the marine ecological environment. The Chengdao oilfield is located in the south of the Chinese Bohai Sea, which is an important oil production area of the Shengli oilfield. At present, the trajectories and fate of spilled oil under complex marine conditions are still unclear and the potential impacts in different areas of the Chengdao oilfield are unknown. Aiming to solve the above problems, this paper carries out numerical simulations of oil spills in the Chengdao oilfield. The hydraulic model of the Chengdao oilfield is constructed and its accuracy is evaluated against tide level and tidal current data from different ports along the coast. By coupling the hydrodynamic field and wind field, an oil spill model of the Chengdao oilfield considering weathering effects could be constructed. The effects of hydrodynamic factors and environmental factors on an oil spill in the Chengdao oilfield are studied. Weathering processes such as evaporation and emulsification are considered in the simulations. The research results can provide theoretical and technical support for the emergency responses to the oil spills in the Chengdao oilfield.

Meteotsunamis in Japan Associated with the Tonga Eruption in January 2022

Large-amplitude meteotsunamis were observed in many areas in Japan, following the arrival of barometric Lamb waves emitted by an underwater volcanic eruption of Hunga Tonga-Hunga Ha‘apai in January 2022. We modeled the power spectra of the tidal level data obtained from 12 tide stations of the Geospatial Information Authority of Japan, based on a method of transfer function which converts the barometric pressure pulse spectra into the meteotsunami spectra. The obtained transfer functions are similar at 12 stations. The pressure pulse spectra are obtained from the ensemble average of ∼1500 Soratena weather sensors of Weathernews Inc. distributed over Japan. The observed meteotsunami spectra can be characterized by the enhanced seiche eigenmodes at each station excited by the mesoscale pressure pulse within the amplitude error of 50%, which contributes for accumulating the necessary knowledge to understand the potential dangers in various different areas over Japan.

Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method

Purpose: Prigi Beach has the largest fishing port in East Java, but the topography of this beach is quite gentle, so it is prone to disasters such as tidal flooding. The tides of seawater strongly influence the occurrence of this natural event. Therefore, information on tidal level data is essential. This study aims to provide information about tidal predictions.Methods: In this case using the SVE method. Input data and time were examined using PACF autocorrelation plots to form input data patterns. The working principle of SVR is to find the best hyperplane in the form of a function that produces the slightest error.Result: The best SVR model built from the linear kernel, the MAPE value is 0.5510%, the epsilon is 0.0614, and the bias is 0.6015. The results of the tidal prediction on Prigi Beach in September 2020 showed that the highest tide occurred on September 19, 2020, at 10.00 PM, and the lowest tide occurred on September 3, 2020, at 04.00 AM. Value: After conducting experiments on three types of kernels on SVR, it is said that linear kernels can predict improvements better than polynomial and gaussian kernels.

Simulation and design of joint distribution of rainfall and tide level in Wuchengxiyu Region,China

Flood control schemes and the scale of drainage facilities are closely related to the occurrence of rainstorms and the tide level of an outer river, so it is necessary to study the probability of the occurrence of rainstorms and the tide levels. Based on the daily rainfall and tide level data of Jiangyin station from 1956 to 2018 in the Wuchengxiyu region of Taihu Lake basin, copula functions were used to establish the joint distribution function of annual 1-day, 3-day, 7-day, and 15-day maximum rainfall and the highest tide level of the Yangtze River for different return periods. Based on this information, a risk probability model was constructed to evaluate the flood and tidal risks.It was found that when the design criteria of rainfall and tide level were similar, the probability of the simultaneous occurrence of rainfall and tide level greater than their respective magnitudes was significantly less than their respective design frequencies. The encounter combination analysis method proposed in this study has a wide application prospect for flood control and drainage design.

Three-dimensional Hydrodynamic Analysis of Yushan Petrochemical Reclamation

With the help of Mike 3 software, a three-dimensional hydrodynamic mathematical model was built for Daxiaoshan sea area. The model was calibrated and verified by the measured tidal current and tidal level data. The influence of Daxiaoshan reclamation project on the surrounding hydrodynamic conditions was analyzed by the numerical simulation of tidal current. The results show that in the model verification, the tide level only has a little deviation in the neap tide stage, and the velocity error is controlled within 0.4m/s. The influence of reclamation projects is mainly concentrated in the vicinity of reclamation areas and has no influence on the hydrodynamic conditions in a large area of sea. The three-dimensional effect of the positions of the southeast and southwest dikes is obvious, and the vertical velocity distribution of the south dike is the most uneven because of the complex terrain conditions. Using the Mike 3 hydrodynamic module can better simulate the flow field distribution in the engineering area and the surrounding sea area of the project, and can better guide the construction of related projects.

Analysis of hydrodynamics and sediment conditions around East Coast Sea Area in Bay of Bengal

The bay of Bengal is an important sea area of the 21st century Maritime Silk Road. Systematic analysis of the hydrodynamic and sediment characteristics of the bay of Bengal is conducive to the development and implementation of offshore and coastal projects in the bay of Bengal. In this paper, based on the historical reanalysis data of ECMWF and the observed data of several hydrological stations along the east coast of Bangladesh, including wave, tide level, tidal current and sediment data are verified and assimilated, and the characteristics of wind wave, water level, tide and sediment along the east coast of Bengal are analysed. The hydrodynamic and sediment conditions in the bay of Bengal are generally characterized by “strong tide, strong wave and high suspended sediment concentration”. The bay of Bengal is easily affected by cyclonic storms, which causes a great increase in storm surge. The above situation will lead to the complicated sediment erosion and deposition of storm surge, strong wave and strong tide and high suspended sediment concentration. At present, there has no deep-water port and coastal power plant located in the bay of Bengal. Whether to build a deep-water port or how to transfer at sea should be paid attention to or studied in depth.

Design Water Level Calculation And Analysis Of Chittagong Power Plant Project In Bangladesh

The design water level calculation methods in the Chinese and foreign codes are different. The design high and low water levels and extreme high and low water levels in the Chinese norms are calculated using historical cumulative frequencies with tide level data of not less than 1 year and 20 years, respectively. In foreign norms, the average high and low tide levels of spring tides are mostly used as the design high and low water levels, and for the sea areas lacking long-term measured data, the water level combination method is mostly used to determine the extreme water levels. This paper compares the results of different methods used to calculate the design water level using the Chittagong Power Plant in Bangladesh as an example. The calculations show that it is safer to use the tidal eigenvalue method to determine the design high and low water levels, and that the Water Level Combination Method is more suitable for extreme water level determination than Bangladesh National Building Code.