scholarly journals Application of the EMD Method to River Tides

2018 ◽  
Vol 35 (4) ◽  
pp. 809-819 ◽  
Author(s):  
Haidong Pan ◽  
Zheng Guo ◽  
Yingying Wang ◽  
Xianqing Lv
Keyword(s):  
Water Level ◽  
River Flow ◽  
Internal Tides ◽  
Analysis Tool ◽  
Tidal Analysis ◽  
Mode Decomposition ◽  
Mean Water Level ◽  
Nonstationary Tides ◽  
Tidal Processes ◽  
Emd Method

AbstractA lot of tidal phenomena, including river tides, tides in ice-covered bays, and internal tides in fjords, are nonstationary. These tidal processes present a severe challenge for the conventional tidal analysis method. The empirical mode decomposition (EMD) method is useful for nonstationary and nonlinear time series and has been used for different geophysical data. However, application of EMD to nonstationary tides is rare. This paper is meant to demonstrate a new tidal analysis tool that can help study nonstationary tides, in this case river tides. EMD is applied to a set of hourly water level records on the lower Columbia River, where the tides are greatly influenced by the fluctuating river flow. The results show that the averaged period of any EMD mode almost exactly doubles that of the previous one, suggesting that EMD is a dyadic filter. The highest and second highest frequency modes of EMD represent the semidiurnal (D2) and diurnal (D1) tides, respectively. The sum of the EMD modes except for the first two is the mean water level (MWL). The study finds that the EMD method successfully captured the nonstationary characteristics of the D1 tides, the D2 tides, and the MWL induced by river flow.

scholarly journals Analytical approach for determining the mean water level profile in an estuary with substantial fresh water discharge

2016 ◽  
Vol 20 (3) ◽  
pp. 1177-1195 ◽  
Author(s):  
Huayang Cai ◽  
Hubert H. G. Savenije ◽  
Chenjuan Jiang ◽  
Lili Zhao ◽  
Qingshu Yang
Keyword(s):  
Water Level ◽  
Fresh Water ◽  
River Discharge ◽  
River Flow ◽  
Water Discharge ◽  
Value Theory ◽  
Velocity Amplitude ◽  
Mean Water Level ◽  
The Mean ◽  
Fresh Water Discharge

Abstract. The mean water level in estuaries rises in the landward direction due to a combination of the density gradient, the tidal asymmetry, and the backwater effect. This phenomenon is more prominent under an increase of the fresh water discharge, which strongly intensifies both the tidal asymmetry and the backwater effect. However, the interactions between tide and river flow and their individual contributions to the rise of the mean water level along the estuary are not yet completely understood. In this study, we adopt an analytical approach to describe the tidal wave propagation under the influence of substantial fresh water discharge, where the analytical solutions are obtained by solving a set of four implicit equations for the tidal damping, the velocity amplitude, the wave celerity, and the phase lag. The analytical model is used to quantify the contributions made by tide, river, and tide–river interaction to the water level slope along the estuary, which sheds new light on the generation of backwater due to tide–river interaction. Subsequently, the method is applied to the Yangtze estuary under a wide range of river discharge conditions where the influence of both tidal amplitude and fresh water discharge on the longitudinal variation of the mean tidal water level is explored. Analytical model results show that in the tide-dominated region the mean water level is mainly controlled by the tide–river interaction, while it is primarily determined by the river flow in the river-dominated region, which is in agreement with previous studies. Interestingly, we demonstrate that the effect of the tide alone is most important in the transitional zone, where the ratio of velocity amplitude to river flow velocity approaches unity. This has to do with the fact that the contribution of tidal flow, river flow, and tide–river interaction to the residual water level slope are all proportional to the square of the velocity scale. Finally, we show that, in combination with extreme-value theory (e.g. generalized extreme-value theory), the method may be used to obtain a first-order estimation of the frequency of extreme water levels relevant for water management and flood control. By presenting these analytical relations, we provide direct insight into the interaction between tide and river flow, which will be useful for the study of other estuaries that experience substantial river discharge in a tidal region.

scholarly journals Adaptation of Classical Tidal Harmonic Analysis to Nonstationary Tides, with Application to River Tides

2013 ◽  
Vol 30 (3) ◽  
pp. 569-589 ◽  
Author(s):  
Pascal Matte ◽  
David A. Jay ◽  
Edward D. Zaron
Keyword(s):  
Time Series ◽  
Harmonic Analysis ◽  
River Flow ◽  
Water Levels ◽  
Spectral Lines ◽  
Noise Model ◽  
Analysis Tool ◽  
Tidal Analysis ◽  
Nonstationary Signals ◽  
Analysis Methods

Abstract One of the most challenging areas in tidal analysis is the study of nonstationary signals with a tidal component, as they confront both current analysis methods and dynamical understanding. A new analysis tool has been developed, NS_TIDE, adapted to the study of nonstationary signals, in this case, river tides. It builds the nonstationary forcing directly into the tidal basis functions. It is implemented by modification of T_TIDE; however, certain concepts, particularly the meaning of a constituent and the Rayleigh criterion, are redefined to account for the smearing effects on the tidal spectral lines by nontidal energy. An error estimation procedure is included that constructs a covariance matrix of the regression coefficients, based on either an uncorrelated or a correlated noise model. The output of NS_TIDE consists of time series of subtidal water levels [mean water level (MWL)] and tidal properties (amplitudes and phases), expressed in terms of external forcing functions. The method was tested using records from a station on the Columbia River, 172 km from the ocean entrance, where the tides are strongly altered by river flow. NS_TIDE hindcast explains 96.4% of the signal variance with a root-mean-square error of 0.165 m obtained from 288 parameters, far better than traditional harmonic analysis (38.5%, 0.604 m, and 127 parameters). While keeping the benefits of harmonic analysis, its advantages compared to existing tidal analysis methods include its capacity to distinguish frequencies within tidal bands without losing resolution in the time domain or data at the endpoints of the time series.

APPLICATION OF THE EMPIRICAL MODE DECOMPOSITION METHOD TO GROUND-ROLL NOISE ATTENUATION IN SEISMIC DATA

2013 ◽  
Vol 31 (4) ◽  
pp. 619 ◽  
Author(s):  
Luiz Eduardo Soares Ferreira ◽  
Milton José Porsani ◽  
Michelângelo G. Da Silva ◽  
Giovani Lopes Vasconcelos
Keyword(s):  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Low Frequency ◽  
High Amplitude ◽  
Seismic Line ◽  
Seismic Processing ◽  
Mode Decomposition ◽  
Ground Roll ◽  
Fortran 90 ◽  
Emd Method

ABSTRACT. Seismic processing aims to provide an adequate image of the subsurface geology. During seismic processing, the filtering of signals considered noise is of utmost importance. Among these signals is the surface rolling noise, better known as ground-roll. Ground-roll occurs mainly in land seismic data, masking reflections, and this roll has the following main features: high amplitude, low frequency and low speed. The attenuation of this noise is generally performed through so-called conventional methods using 1-D or 2-D frequency filters in the fk domain. This study uses the empirical mode decomposition (EMD) method for ground-roll attenuation. The EMD method was implemented in the programming language FORTRAN 90 and applied in the time and frequency domains. The application of this method to the processing of land seismic line 204-RL-247 in Tacutu Basin resulted in stacked seismic sections that were of similar or sometimes better quality compared with those obtained using the fk and high-pass filtering methods.Keywords: seismic processing, empirical mode decomposition, seismic data filtering, ground-roll. RESUMO. O processamento sísmico tem como principal objetivo fornecer uma imagem adequada da geologia da subsuperfície. Nas etapas do processamento sísmico a filtragem de sinais considerados como ruídos é de fundamental importância. Dentre esses ruídos encontramos o ruído de rolamento superficial, mais conhecido como ground-roll . O ground-roll ocorre principalmente em dados sísmicos terrestres, mascarando as reflexões e possui como principais características: alta amplitude, baixa frequência e baixa velocidade. A atenuação desse ruído é geralmente realizada através de métodos de filtragem ditos convencionais, que utilizam filtros de frequência 1D ou filtro 2D no domínio fk. Este trabalho utiliza o método de Decomposição em Modos Empíricos (DME) para a atenuação do ground-roll. O método DME foi implementado em linguagem de programação FORTRAN 90, e foi aplicado no domínio do tempo e da frequência. Sua aplicação no processamento da linha sísmica terrestre 204-RL-247 da Bacia do Tacutu gerou como resultados, seções sísmicas empilhadas de qualidade semelhante e por vezes melhor, quando comparadas as obtidas com os métodos de filtragem fk e passa-alta.Palavras-chave: processamento sísmico, decomposição em modos empíricos, filtragem dados sísmicos, atenuação do ground-roll.

Presentation and assessment of tides and water level records for geophysical investigations

1970 ◽  
Vol 7 (2) ◽  
pp. 607-625 ◽  
Author(s):  
G. C. Dohler ◽  
L. F. Ku
Keyword(s):  
Water Level ◽  
Power Spectra ◽  
Rate Of Change ◽  
Regression Technique ◽  
Level Data ◽  
Water Level Data ◽  
Geophysical Investigations ◽  
Water Level Variations ◽  
Mean Water Level ◽  
Change In Mean

The methods and problems involved in collecting water level data are explained, and the processing and formats of the data are illustrated. The trend of the change in mean water level is plotted and the corresponding rate of change is estimated by the regression technique. The power spectra of the water level variations are plotted to illustrate these variations in terms of frequencies.

scholarly journals Output-Only Modal Analysis Based on Improved Empirical Mode Decomposition Method

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Shiqiang Qin ◽  
Qiuping Wang ◽  
Juntao Kang
Keyword(s):  
Modal Analysis ◽  
Empirical Mode Decomposition ◽  
Modal Parameters ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Random Decrement Technique ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Output Only ◽  
Emd Method

The output-only modal analysis for bridge structures based on improved empirical mode decomposition (EMD) is investigated in this study. First, a bandwidth restricted EMD is proposed for decomposing nonstationary output measurements with close frequency components. The advantage of bandwidth restricted EMD to standard EMD is illustrated by a numerical simulation. Next, the modal parameters are extracted from intrinsic mode function obtained from the improved EMD by both random decrement technique and stochastic subspace identification. Finally, output-only modal analysis of a railway bridge is presented. The study demonstrates the mode mixing issues of standard EMD can be restrained by introducing bandwidth restricted signal. Further, with the improved EMD method, band-pass filter is no longer needed for separating the closely spaced frequency components. The modal parameters extracted based on the improved EMD method show good agreement with those extracted by conventional modal identification algorithms.

scholarly journals Dynamics of fortnightly water level variations along a tide-dominated estuary with negligible river discharge

2021 ◽  
Author(s):  
Erwan Garel ◽  
Ping Zhang ◽  
Huayang Cai
Keyword(s):  
Water Level ◽  
Phase Difference ◽  
River Discharge ◽  
Water Levels ◽  
Level Increase ◽  
Upstream Direction ◽  
Water Level Variations ◽  
Mean Water Level ◽  
The Mean ◽  
Guadiana Estuary

Abstract. Observations indicate that the fortnightly fluctuations in mean water level increase in amplitude along the lower half of a tide-dominated estuary (The Guadiana estuary) with negligible river discharge but remain constant upstream. Analytical solutions reproducing the semi-diurnal wave propagation shows that this pattern results from reflection effects at the estuary head. The phase difference between velocity and elevation increases from the mouth to the head (where the wave has a standing nature) as the high and low water levels get progressively closer to slack water. Thus, the tidal (flood-ebb) asymmetry in discharge is reduced in the upstream direction. It becomes negligible along the upper estuary half, as the mean sea level remains constant despite increased friction due to wave shoaling. Observations of a flat mean water level along a significant portion of an upper estuary, easier to obtain than the phase difference, can therefore indicate significant reflection of the propagating semi-diurnal wave at the head. Details of the analytical model shows that changes in the mean depth or length of semi-arid estuaries, in particular for macrotidal locations, affect the fortnightly tide amplitude, and thus the upstream mass transport and inundation regime. This has significant potential impacts on the estuarine environment.

scholarly journals Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models

2021 ◽  
Author(s):  
Karim Amininia ◽  
Seyed Mahdi Saghebian
Keyword(s):  
Data Processing ◽  
Uncertainty Analysis ◽  
Mississippi River ◽  
River Flow ◽  
Ensemble Empirical Mode Decomposition ◽  
Multivariate Adaptive Regression Splines ◽  
Applied Model ◽  
Flood Warning ◽  
Mode Decomposition ◽  
Artificial Intelligence Models

Abstract The flow assessment in a river is of vital interest in hydraulic engineering for flood warning and evacuation measures. To operate water structures more efficiently, models that forecast river discharge are desired to be of high precision and certain degree of accuracy. Therefore, in this study, two artificial intelligence models, namely kernel extreme learning machine (KELM) and multivariate adaptive regression splines (MARS), were applied for the monthly river flow (MRF) modeling. For this aim, Mississippi river with three consecutive hydrometric stations was selected as case study. Using the previous MRF values during the period of 1950–2019, several models were developed and tested under two scenarios (i.e. modeling based on station's own data or previous station's data). Wavelet transform (WT) and ensemble empirical mode decomposition (EEMD) as data processing approaches were used for enhancing modeling capability. Obtained results indicated that the integrated models resulted in more accurate outcomes. Data processing enhanced the model's capability up to 25%. It was observed that the previous station's data could be applied successfully for MRF modeling when the station's own data were not available. The best-applied model dependability was assessed via uncertainty analysis, and an allowable degree of uncertainty was found in MRF modeling.

scholarly journals Determination of variation uncertainty in runoff time series at multi-temporal scales

2021 ◽  
Author(s):  
Yan Ye ◽  
Jinping Zhang ◽  
Xunjian Long ◽  
Lihua Ma ◽  
Yong Ye
Keyword(s):  
Time Series ◽  
Annual Runoff ◽  
Important Variable ◽  
Temporal Scales ◽  
Flood Season ◽  
Mode Function ◽  
Mode Decomposition ◽  
Multi Temporal ◽  
Emd Method

Abstract In order to survey the possible periodic, uncertainty and common features in runoff with multi-temporal scales, the empirical mode decomposition (EMD) method combined with the set pair analysis (SPA) method was applied, with data observed at Zhangjiashan hydrological station. The results showed that the flood season and annual runoff time series consisted of four intrinsic mode function (IMF) components, and the non-flood season time series exhibited three IMF components. Moreover, based on the different coupled set pairs from the time series, the identity, discrepancy, and contrary of different periods at multi-temporal scales were determined by the SPA method. The degree of connection μ between the flood season and annual runoff periods were the highest, with 0.94, 0.77, 0.7 and 0.73, respectively, and the μ between the flood periods and the non-flood periods were the lowest, with 0.66, 0.46, 0.24 and 0.24, respectively. Third, the maximum μ of each SPA appeared in the first mode function. In general, the different extractive periods decomposed by EMD method can reflected the average state of Jinghe River. Results also verified that runoff suffered from seasonal and periodic fluctuations, and fluctuations in the short-term corresponded to the most important variable. Therefore, the conclusions draw in this study can improve water resources regulation and planning.

scholarly journals Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach

2021 ◽  
Vol 13 (19) ◽  
pp. 10720
Author(s):  
Muhammad Ali Musarat ◽  
Wesam Salah Alaloul ◽  
Muhammad Babar Ali Rabbani ◽  
Mujahid Ali ◽  
Muhammad Altaf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Level ◽  
River Flow ◽  
Early Stage ◽  
Moving Average ◽  
Weather Prediction ◽  
Maximum Level ◽  
Information Criterion ◽  
Water Levels ◽  
Kabul River

The water level in a river defines the nature of flow and is fundamental to flood analysis. Extreme fluctuation in water levels in rivers, such as floods and droughts, are catastrophic in every manner; therefore, forecasting at an early stage would prevent possible disasters and relief efforts could be set up on time. This study aims to digitally model the water level in the Kabul River to prevent and alleviate the effects of any change in water level in this river downstream. This study used a machine learning tool known as the automatic autoregressive integrated moving average for statistical methodological analysis for forecasting the river flow. Based on the hydrological data collected from the water level of Kabul River in Swat, the water levels from 2011–2030 were forecasted, which were based on the lowest value of Akaike Information Criterion as 9.216. It was concluded that the water flow started to increase from the year 2011 till it reached its peak value in the year 2019–2020, and then the water level will maintain its maximum level to 250 cumecs and minimum level to 10 cumecs till 2030. The need for this research is justified as it could prove helpful in establishing guidelines for hydrological designers, the planning and management of water, hydropower engineering projects, as an indicator for weather prediction, and for the people who are greatly dependent on the Kabul River for their survival.

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