Applicability Study of Hydrological Period Identification Methods: Application to Huayuankou and Lijin in the Yellow River Basin, China

Abstract

Identifying implicit periodicities in hydrological data is significant for managing river–basin water resources and establishing flood forecasting systems. However, the complexity and randomness of hydrological systems make it difficult to detect hidden oscillatory characteristics. This study discusses the performance and applicability of five period identification methods, namely periodograms, autocorrelation analysis (AA), maximum entropy spectral analysis (MESA), wavelet analysis (WA), and the Hilbert–Huang transform (HHT). The annual and monthly runoff data are sampled from two stations (Huayuankou and Lijin on the Yellow River in China) in the years 1949–2015. The conclusions are as follows: (i) All methods identify the significant periods of 6 months, 12 months, and 18–19 months, which have relatively high energy of peaks; (ii) WA and HHT perform best when dealing with nonstationary time series, but they are ineffective for identifying large-scale periods; (iii) MESA has high resolution and stability but is prone to oscillate at small-scale periods when applied to monthly series; and (iv) periodograms and AA are relatively simple, but their results lack stability and are significantly affected by the data length—the resolution of AA is too low when applied to annual data, and periodograms can easily produce “false peaks”. Generally, it is better to apply multiple methods comprehensively than each method singularly, and this can be effective in reducing subjective influences.

Improving the visualization of rainfall trends using various innovative trend methodologies with time–frequency-based methods

“…It has made very significant progress compared in the area of hydrology. The application in the hydrology field has shown its success since the beginning of the last decade, in particular in the hydrological time series analysis and forecasting (e.g., Chen et al 2021;Zhang et al 2021;Shi et al 2021;Saraiva et al 2021;Abda et al 2021;Freire et al 2019;Abda and Chettih 2018;Reddy and Adarsh 2016;Huang et al 2014;. It is worth highlighting that although CWT could give information and solution for our requirements, DWT and HHT are more flexible.…”

Use of singular spectral analysis to reconstruct average monthly flow time series with complex behavior

“…For example, Pham et al (2021) highlights that the main reason for data pre-processing is to eliminate possible noise in the time series data and, thus, avoid unwanted model training. Chen et al (2021) highlights that useful information hidden in the flow series can be extracted effectively through data pre-processing, thus improving prediction accuracy. Baydaroğlu et al (2018) found, that hybrid modeling of SSA with support vector regression (SVR), on monthly flow data from the Kızılırmak River in Turkey proved successful, yielding values of coefficient of determination (0.…”

“…The identification of periodicities in hydrological data is important for the management of water resources in hydrographic basins and for the establishment of flood forecasting systems. However, the complexity and randomness of hydrological systems make it difficult to detect hidden oscillatory features (Chen et al, 2021;Pham et al, 2021). Therefore, decomposition techniques, such as SSA, are important tools for helping watercourse hydrological behavior understanding.…”

Determination of Discharge Cycles of the South Morava River (Serbia)

“…A similar study was conducted by Roshani et al [9] who determined the existence of a certain water cycle of the Rudhan River (Iran), especially during the summer and autumn seasons. Some research used the same methodology to investigate the correlation between water level changes and climate changes [13], [14]. In order to determine the discharge cycles and their repetition, the first step include trend analysis and registration of changes of the hydrological parameter.…”