Discrete wavelet‐based trend identification in hydrologic time series

Sang, Yan‐Fang; Wang, Zhonggen; Liu, Changming

doi:10.1002/hyp.9356

Cited by 45 publications

(21 citation statements)

References 37 publications

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“…Nowadays, investigators interested in trend analysis emphasize that the trend component having the lowest frequency [2] should be properly distinguished not only from short-term persistence (STP), but also from long-term persistence (LTP) effects. Both types of persistence cause most known trend tests to reject the null hypothesis of no trend too often.…”

Section: Trend Analysismentioning

confidence: 99%

Freely available mean daily discharge series from Czechia: what can be inferred from them?

Ledvinka

2017

E3S Web Conf.

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Abstract. Most hydrometeorological data from Czechia are still provided for a fee. This especially applies to time series with a finer step than monthly. The fact that the data must be paid with respect to an expert's appraisement and that the proper licence agreement ratification has to be performed causes a considerable delay in the data transfer to potential customers, unfortunately including scientists as well. Naturally, this timeconsuming process is unpleasant to the experts on both sides. Due to a substantial rise of university students' requests for data, the Czech Hydrometeorological Institute's hydrologists launched a website from which long mean daily discharge series representing ten selected watergauging stations can be downloaded. Besides other assessments, the series may play an important role when studying climate change impacts on water resources in Czechia. Therefore, the objective here was to extract from these series some preliminary information on long-term changes such as abrupt and gradual trends caused either by the construction of reservoirs or by climate variability itself. The main tool used was nonparametric trend analysis. Mainly different months were of interest so as to determine if there have been recorded some changes in seasonality. The results may be easily expanded by students.

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Section: Trend Analysismentioning

confidence: 99%

Freely available mean daily discharge series from Czechia: what can be inferred from them?

Ledvinka

2017

E3S Web Conf.

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“…Sang et al [24] elaborated wavelet based test for trend identification based on comparison of the energy difference between hydrologic time series and the noise. Furthermore, influence of the mother wavelet choice, decomposition level choice and noise content on trend identification method is investigated.…”

Section: Multi-scale Trend Analysis Of Discharge and Suspended Sedimementioning

confidence: 99%

A review on the applications of wavelet transform for streamflow and suspended sediment analysis

2017

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Preliminary communicationAnalysis and modelling of discharge and suspended sediment time series is of great importance in hydrology and water management. Discharge and suspended sediment time series are the result of complex physical processes and characterized by non-stationarity. Wavelet transform enables representation of non-stationarities in time-frequency domain, decomposition and reconstruction of series and de-noising of series, and therefore represents powerful tool for analysis of hydrological time series. Overview of mentioned wavelet transform advantages is given in this paper with focus on discharge and suspended sediment time series, presented through: (i) multi-temporal scale analysis of series variability; (ii) multi-scale trend analysis; (iii) prediction and forecasting of series with wavelet based hybrid black-box models, and (iv) wavelet-aided simulation of synthetic series. Keywords: discharge; suspended sediment; wavelet transform Pregled primjene valićne transformacije u analizi protoka i suspendiranog nanosaPredhodno priopćenje Analiza i modeliranje vremenskih serija protoka i suspendiranog nanosa je vrlo važan zadatak u hidrologiji i gospodarenju vodama. Vremenske serije protoka i suspendiranog nanosa uvjetovane su složenim fizikalnim procesima i karakterizirane su nestacionarnošću. Valićna transformacija omogućava prikaz nestacionarnosti u vremensko-frekvencijskoj domeni, rastavljanje i rekonstrukciju serija te potiskivanje šuma u serijama, te time predstavlja moćan alat za analizu hidroloških vremenskih serija. U ovom radu je dan pregled primjene spomenutih prednosti valićne transformacije s fokusom na vremenske serije protoka i suspendiranog nanosa prikazan kroz: (i) viševremensku skalenu analizu varijabilnosti serija (ii) analizu trenda na više skala; (iii) predviđanje i prognoziranje serija hibridnim modelima crne-kutije baziranim na valićima te (iv) simulaciju sintetičkih serija potpomognutu valićnom transformacijom.

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“…For multiple time-scales issues with non-stationarity and non-linearity variables, many time-scale decomposition approaches have been introduced to separate the different time scales in hydrological series for hydrological prediction and to provide important support for system analysis and runoff prediction. For example, the wavelet transform (WT) has been adopted by many researchers for analyzing hydrological time series with multiple scales due to its excellence in situations with multiple resolutions in time and frequency domains [22][23][24][25]. Essentially, a wavelet transform is a Fourier transform with an adjustable window, and the signal should be stable in the WT window.…”

Section: Introductionmentioning

confidence: 99%

Forward Prediction of Runoff Data in Data-Scarce Basins with an Improved Ensemble Empirical Mode Decomposition (EEMD) Model

Zhang

Singh

2018

Water

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Data scarcity is a common problem in hydrological calculations that often makes water resources planning and engineering design challenging. Combining ensemble empirical mode decomposition (EEMD), a radial basis function (RBF) neural network, and an autoregression (AR) model, an improved EEMD prediction model is proposed for runoff series forward prediction, i.e., runoff series extension. In the improved model, considering the decomposition-prediction-reconstruction principle, EEMD was employed for decomposition and reconstruction and the RBF and AR model were used for component prediction. Also, the method of tracking energy differences (MTED) was used as stopping criteria for EEMD in order to solve the problem of mode mixing that occurs frequently in EEMD. The orthogonality index (Ort) and the relative average deviation (RAD) were introduced to verify the mode mixing and prediction performance. A case study showed that the MTED-based decomposition was significantly better than decomposition methods using the standard deviation (SD) criteria and the G. Rilling (GR) criteria. After MTED-based decomposition, mode mixing in EEMD was suppressed effectively (|Ort| < 0.23) and stable orthogonal components were obtained. For this, annual runoff series forward predictions using the improved EEMD-based prediction model were significantly better (RAD < 11.1%) than predictions by the rainfall-runoff method and the AR model method. Thus, this forward prediction model can be regarded as an approach for hydrological series extension, and shows promise for practical applications.

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Discrete wavelet‐based trend identification in hydrologic time series

Cited by 45 publications

References 37 publications

Freely available mean daily discharge series from Czechia: what can be inferred from them?

Freely available mean daily discharge series from Czechia: what can be inferred from them?

A review on the applications of wavelet transform for streamflow and suspended sediment analysis

Forward Prediction of Runoff Data in Data-Scarce Basins with an Improved Ensemble Empirical Mode Decomposition (EEMD) Model

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