Decomposition of Water Level Time Series of a Tidal River into Tide, Wave and Rainfall-Runoff Components

There are numerous streamwater parameters that exhibit a diurnal cycle. However, the shape of this cycle has a huge variation from one parameter to another and from one monitoring point to another on the same river. Important variations also occur at the same point during some events, such as high waters. Water level, specific conductivity, dissolved oxygen, oxidation reduction potential, and pH of the Suceava River were monitored for 365 days (2018–2019, hourly sampling frequency) in order to assess the upstream-downstream changes in the diurnal cycle of these parameters, some of these changes being caused by the impact of Suceava city, which is located between the selected monitoring points. The multiresolution analysis of the maximal overlap discrete wavelet transform and the wavelet coherence analysis were combined in a flexible methodology that helped in comparing the upstream and downstream shapes of the diurnal cycle. The methodology allowed for a fast comparison of diurnal profiles during periods of high waters or baseflow. Notable changes were observed in the moments of diurnal maxima and minima.

Section: Analysis Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Methodology for the Fast Comparison of Streamwater Diurnal Cycles at Two Monitoring Points

Briciu

Graur

Oprea

et al. 2019

“…Methods to assess the influence of the local geology and environment on groundwater tide characteristics, such as phase shifts, range from analyzing special conditions [39] to applying various mathematical techniques. The decomposition of water-level time series containing tidal oscillations might be a useful tool for simplifying the data analysis [40]. The characteristics of the groundwaters in various aquifers are responsible for the delayed orthotidal signal found in rivers.…”

Section: Resultsmentioning

confidence: 99%

Changes in Physical Properties of Inland Streamwaters Induced by Earth and Atmospheric Tides

Briciu

2019

Earth and atmospheric tides create oscillations in water parameters of inland rivers, mainly due to the similar behavior of groundwaters. Tidal oscillations of inland rivers were termed orthotides and were detected in fluctuations of water level and specific conductivity of some rivers. However, few things are understood about orthotides because of their recent discovery. Here, we show that orthotidal signals exist in streamwater temperature too. Wavelet and T_TIDE analyses are used to study streamwater temperature and specific conductivity. We found solar and lunar semidiurnal orthotides (S2 and M2) in Alapaha River (USA) water temperature and Wybong River (Australia) water specific conductivity with amplitudes of up to 0.6 °C and 11.3 µS/cm. We demonstrate that the tidal semidiurnal cycles have statistical significance and are caused by similar cycles in groundwater. Oscillations found in water temperature time series for some new moon time intervals have shapes that correlate with the gravitational tides. Diurnal and fortnightly tidal cycles were found and overlapped with other natural cycles with similar periodicities. The inclusion of more water parameters to the list of orthotidally sensitive parameters indicates the wider than expected environmental impact of the small periodic natural changes.

“…The hydrological cycle is a particularly complex global system driven by solar radiation and gravity [1,2]. Hydrological time series (HTS) contain the influences of numerous processes involved in the transfer of water in the hydrological cycle [3,4], and they are influenced by many physical factors that are often interrelated, especially large-scale fluctuations in atmospheric circulation, the Earth's rotation and revolution, and sunspots [5][6][7][8]. Therefore, to reveal the variability of hydrological processes, it is essential to be able to analyze HTS that have complicated stochastic characteristics [9,10].…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Wang

Lian

2021

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.