scholarly journals Multi-temporal runoff-sediment discharge relationships

2022 ◽  
Author(s):  
Honglin Xiao ◽  
Jinping Zhang ◽  
Hongyuan Fang
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Annual Runoff ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Sediment Discharge ◽  
Discharge Data ◽  
Temporal Correlations ◽  
Mode Decomposition ◽  
Multi Temporal ◽  
Runoff Sediment

To understand the runoff-sediment discharge relationship , this study examined the annual runoff and sediment discharge data obtained from the Tangnaihai hydrometric station. The data were decomposed into multiple time scales through Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN). Furthermore, double cumulative curves were plotted and the cointegration theory was employed to analyze the microscopic and macroscopic multi-temporal correlations between the runoff and the sediment discharge and their detailed evolution.

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

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.

Application of MEEMD-ARIMA combining model for annual runoff prediction in the Lower Yellow River

2019 ◽  
Vol 11 (3) ◽  
pp. 865-876 ◽  
Author(s):  
Xianqi Zhang ◽  
Wei Tuo ◽  
Chao Song
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Yellow River ◽  
Arima Model ◽  
Ensemble Empirical Mode Decomposition ◽  
Annual Runoff ◽  
Lower Yellow River ◽  
The Lower Yellow River ◽  
Mode Decomposition ◽  
Runoff Prediction

Abstract The prediction of annual runoff in the Lower Yellow River can provide an important theoretical basis for effective reservoir management, flood control and disaster reduction, river and beach management, rational utilization of regional water and sediment resources. To solve this problem and improve the prediction accuracy, permutation entropy (PE) was used to extract the pseudo-components of modified ensemble empirical mode decomposition (MEEMD) to decompose time series to reduce the non-stationarity of time series. However, the pseudo-component was disordered and difficult to predict, therefore, the pseudo-component was decomposed by ensemble empirical mode decomposition (EEMD). Then, intrinsic mode functions (IMFs) and trend were predicted by autoregressive integrated moving average (ARIMA) which has strong ability of approximation to stationary series. A new coupling model based on MEEMD-ARIMA was constructed and applied to runoff prediction in the Lower Yellow River. The results showed that the model had higher accuracy and was superior to the CEEMD-ARIMA model or EEMD-ARIMA model. Therefore, it can provide a new idea and method for annual runoff prediction.

Different Circadian Rest-Active Rhythms in Kleine-Levin Syndrome: A prospective and case-control study

SLEEP ◽  
2021 ◽  
Author(s):  
Chen Lin ◽  
Wei-Chih Chin ◽  
Yu-Shu Huang ◽  
Kuo-Chung Chu ◽  
Teresa Paiva ◽  
...  
Keyword(s):  
Circadian Rhythm ◽  
Young Patients ◽  
Recovery Phase ◽  
Multiple Time Scales ◽  
Fluctuation Analysis ◽  
Multiple Time ◽  
Temporal Correlations ◽  
Significant Difference ◽  
Attack Phase ◽  
Physiological Markers

Abstract Study objectives Kleine-Levin-syndrome (KLS) is a rare recurrent hypersomnia. Our study aimed at monitoring the movements of patients with KLS using actigraphy and evaluating their circadian rhythm. Methods Twenty young patients with KLS and 14 age-matched controls were recruited. Each individual wore an actigraphy for more than 6 months to monitor at least two attacks. Controls kept wearing the device for at least 7 days. The activity counts were averaged in hourly basis and the day-to-night amplitude was quantified by the differences of the averaged activity counts during daytime and nighttime. The hourly activities of different days were aligned and averaged to construct the circadian profile. Parametric and nonparametric estimation of circadian rhythm was calculated. We applied detrended fluctuation analysis to evaluate the temporal correlations beneath the activity fluctuations at multiple time scales. Results Circadian rhythm in asymptomatic period showed no significant difference compared to the controls. During hypersomnia attack, the amplitude of the circadian rest-active rhythms drastically decreased and decreased inter-daily stability (IS) was found, as well as significant decreased M10 and short-time fractal correlation (α1). Drastically decreased mean and standard deviation of activity were noted, compared to the pre-attack phase and recovery phase.α1 and M10 increased during the late attack phase, and overcompensated IS was noted in the recovery phase. Conclusions This study confirmed that circadian rest-active rhythms was affected when KLS hypersomnia attack. Several parameters including M10, IS and α1 may be physiological markers of KLS, which can help to predict the end of hypersomnia episodes.

scholarly journals Trend and Variance of Continental Fresh Water Discharge over the Last Six Decades

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3556
Author(s):  
Chen Wang ◽  
Hui Zhang
Keyword(s):  
Time Series ◽  
Water Discharge ◽  
Linear Method ◽  
Surface Air Temperature ◽  
Ensemble Empirical Mode Decomposition ◽  
Discharge Data ◽  
Nonlinear Trend ◽  
Mode Decomposition ◽  
Eemd Method ◽  
Fresh Water Discharge

Trend estimation of river discharge is an important but difficult task because discharge time series are nonlinear and nonstationary. Previous studies estimated the trend of discharge using a linear method, which is not applicable to nonstationary time series with a nonlinear trend. To overcome this problem, we used a recently developed wavelet-based method, ensemble empirical mode decomposition (EEMD), which can separate nonstationary variations from the long-term nonlinear trend. Applying EEMD to annual discharge data of the 925 world’s largest rivers from 1948–2004, we found that the global discharge decreased before 1978 and increased after 1978, which contrasts the nonsignificant trend as estimated by the linear method over the same period. Further analyses show that precipitation had a consistent and dominant influence on the interannual variation of discharge of all six continents and globally, but the influences of precipitation and surface air temperature on the trend of discharge varied regionally. We also found that the estimated trend using EEMD was very sensitive to the discharge data length. Our results demonstrated some useful applications of the EEMD method in studying regional or global discharge, and it should be adopted for studying all nonstationary hydrological time series.

Multi-scale Atmospheric CO2 Variabilities over Southern Africa

2020 ◽  
Author(s):  
Boipelo B Thande ◽  
Gizaw Mengistu Tsidu ◽  
Anteneh Getachew Mengistu
Keyword(s):  
Southern Africa ◽  
Anthropogenic Activities ◽  
Statistical Technique ◽  
Ensemble Empirical Mode Decomposition ◽  
Reanalysis Data ◽  
In Situ Data ◽  
Mode Decomposition ◽  
Multi Temporal ◽  
Year 2000

<p>Carbon sinks play an important role in absorbing almost half of the CO<sub>2</sub> emissions emanating from anthropogenic activities. However, regional contributions of atmospheric CO<sub>2</sub> are not well known in Southern Africa. This is partly attributed to a shortage of in-situ data, data gaps, and limitation in the theory in modeling atmospheric CO<sub>2</sub> dynamics. The shortage of in-situ observations and poor model skills have created a need for assimilation of observations into models to assess the variability of atmospheric levels in near real-time globally. <span>In this study, we investigated the variabilities of XCO</span><sub><span>2</span></sub><span> at multi-temporal scales based on reanalysis data from the carbon tracker (CT) assimilation model over Southern Africa from the year 2000 to 2016. The ensemble empirical mode decomposition (EEMD) statistical technique was used to decompose the CO</span><sub><span>2</span></sub><span> time series into signals with different periodicities.</span><span> The results demonstrate that the different component signals are driven by</span><span> atmospheric, surface and oceanic forcings (e.g., rainfall, temperature, soil moisture, and SST)</span><span>.</span></p>

scholarly journals Spatio-Temporal Correlation Analysis of Air Quality in China: Evidence from Provincial Capitals Data

2020 ◽  
Vol 12 (6) ◽  
pp. 2486
Author(s):  
Qingchen Liu ◽  
Xinyi Li ◽  
Tao Liu ◽  
Xiaojun Zhao
Keyword(s):  
Air Quality ◽  
Temporal Correlation ◽  
Temporal Analysis ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Capital Cities ◽  
Temporal Correlations ◽  
Agglomeration Effects ◽  
Provincial Capital ◽  
Spatio Temporal

In China, public health awareness is growing as people get more concerned about the air quality. Based on the air quality index (AQI) of 31 provincial capital cities (2015–2018) in China, we studied the spatio-temporal correlations of air quality between cities. With spatial, temporal and spatio-temporal analysis, we systematically obtained many interesting results where the traditional analyses may be lacking. Firstly, the air quality of cities has spatial spillover and agglomeration effects and further the spatial correlation becomes higher with time. Secondly, there exists temporal correlation between the current AQI and its past values on multiple time scales, which shows certain periodicity. Thirdly, due to the changing characteristics of time, social activities and other factors affect the air quality positively. However, with the panel data model, the coefficients of spatio-temporal correlation vary for different cities.

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