Wavelet transform-based trend analysis of streamflow and precipitation in Upper Blue Nile River basin

Abebe, Sintayehu A.; Qin, Tianling; Zhang, Xin; Yan, Denghua

doi:10.1016/j.ejrh.2022.101251

Cited by 19 publications

(15 citation statements)

References 50 publications

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“…Given the discrete nature of hydrological time series, the former is typically preferred [20]. The formulation of the DWT equation [21,22] is as follows:…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

Yang,

Li,

Liu

2024

Water

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Neural networks have become widely employed in streamflow forecasting due to their ability to capture complex hydrological processes and provide accurate predictions. In this study, we propose a framework for monthly runoff prediction using antecedent monthly runoff, water level, and precipitation. This framework integrates the discrete wavelet transform (DWT) for denoising, variational modal decomposition (VMD) for sub-sequence extraction, and gated recurrent unit (GRU) networks for modeling individual sub-sequences. Our findings demonstrate that the DWT–VMD–GRU model, utilizing runoff and rainfall time series as inputs, outperforms other models such as GRU, long short-term memory (LSTM), DWT–GRU, and DWT–LSTM, consistently exhibiting superior performance across various evaluation metrics. During the testing phase, the DWT–VMD–GRU model yielded RMSE, MAE, MAPE, NSE, and KGE values of 245.5 m3/s, 200.5 m3/s, 0.033, 0.997, and 0.978, respectively. Additionally, optimal sliding window durations for different input combinations typically range from 1 to 3 months, with the DWT–VMD–GRU model (using runoff and rainfall) achieving optimal performance with a one-month sliding window. The model’s superior accuracy enhances water resource management, flood control, and reservoir operation, supporting better-informed decisions and efficient resource allocation.

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“…Given the discrete nature of hydrological time series, the former is typically preferred [20]. The formulation of the DWT equation [21,22] is as follows:…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

Yang,

Li,

Liu

2024

Water

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“…In the application of DWT, the original time-series signal is passed through low-pass and high-pass filters and emerge as Approximation (A) and Detail (D) components, respectively. While component D represents the small scale, high-frequency series, component A comprises the high scale, low-frequency series [34]. The decomposition process can continue iteratively, where component A from the first decomposition is further divided into new A and D components [33,49,54,60].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

“…One such tool is the wavelet transform (WT). WT is a relatively recent development in the field of signal processing [31,32], and has, in recent times, emerged as an effective tool to analyze trends in hydroclimatic series especially in the atmospheric and hydrological science space [29,[33][34][35][36][37][38]. It can be thought of as a 'mathematical microscope' with the ability to zoom in and out of the signal (or time series) to pull out the patterns.…”

Section: Introductionmentioning

confidence: 99%

Hydroclimatic Trends and Streamflow Response to Recent Climate Change: An Application of Discrete Wavelet Transform and Hydrological Modeling in the Passaic River Basin, New Jersey, USA

Oteng Mensah,

Alo,

Ophori

2024

Hydrology

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The exigency of the current climate crisis demands a more comprehensive approach to addressing location-specific climate impacts. In the Passaic River Basin (PRB), two bodies of research—hydroclimatic trend detection and hydrological modeling—have been conducted with the aim of revealing the basin’s hydroclimate patterns as well as the hydrologic response to recent climate change. In a rather novel application of the wavelet transform tool, we sidelined the frequently used Mann–Kendal (MK) trend test, to identify the hidden monotonic trends in the inherently noisy hydroclimatic data. By this approach, the use of MK trend test directly on the raw data, whose results are almost always ambiguous and statistically insignificant in respect of precipitation data, for instance, no longer poses a challenge to the reliability of trend results. Our results showed that, whereas trends in temperature and precipitation are increasing in the PRB, streamflow trends are decreasing. Based on results from the hydrological modeling, streamflow is more sensitive to actual evapotranspiration (ET) than it is to precipitation. In periods spanning decades with sufficient water availability, energy governs actual evapotranspiration rates, rendering streamflow more sensitive to increases in precipitation. Conversely, during meteorologically stressed decades, water availability dictates actual evapotranspiration, consequently amplifying streamflow sensitivity to fluctuations in actual evapotranspiration. We found that the choice of baseline condition constitutes an important source of uncertainty in the sensitivities of streamflow to precipitation and evapotranspiration changes and should routinely be considered in any climate impact assessment.

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“…where n represents the quantity of points within the time scale [40,41]. The global coherence coefficient is a statistical tool that can measure the correlation between two time series, with the added benefit of being able to measure on multiple scales.…”

Section: Global Coherencementioning

confidence: 99%

The Effects of Climate Variation and Anthropogenic Activity on Karst Spring Discharge Based on the Wavelet Coherence Analysis and the Multivariate Statistical

2023

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This study focused on the impact of anthropogenic activity on magnitude, frequency, and minima of spring discharge. Niangziguan Springs (NS), China, was selected as an example, as its discharge is decreasing due to the combined effects of climate variation and human activity. For exploring the impact of human activity on the spring discharge from climate change, the spring discharges from 1959 to 2015 were divided into two periods: pre-development period (i.e., 1959–1980) and post-development period (i.e., 1981–2015). A polynomial regression model of the spring discharge was developed for the pre-development period. We deduced the model in the post-development period, compared the results with the observed spring discharge, and concluded that the climate variation and human activity caused 6.93% and 32.38% spring discharge decline, respectively. The relationships of spring discharge with Indian Summer Monsoon (ISM), East Asian Summer Monsoon (EASM), E1 Niño Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO) were analyzed by wavelet analysis during the two periods. The results illustrated that the monsoons (i.e., ISM and EASM) were dominated by climate factors that affect the NS discharge versus climate teleconnections (i.e., ENSO and PDO). According to different time scales, human activities have had an impact on the periodicity of NS discharge, which altered the periodicities of the spring discharge at inter-annual time scales, but the periodicities at intra-annual and annual time scales have remained the same between the two periods. Under the effects of human activity, the local parameter of non-stationary general extreme value (NSGEV) distribution varied with time. The predicted spring discharge minimum value is supposed to be 4.53 m3/s with a 95% confidential interval with an upper boundary of 6.06 m3/s and a lower boundary of 2.80 m3/s in 2020. The results of this study would benefit the management of spring discharge and water resources.

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Wavelet transform-based trend analysis of streamflow and precipitation in Upper Blue Nile River basin

Cited by 19 publications

References 50 publications

Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

Hydroclimatic Trends and Streamflow Response to Recent Climate Change: An Application of Discrete Wavelet Transform and Hydrological Modeling in the Passaic River Basin, New Jersey, USA

The Effects of Climate Variation and Anthropogenic Activity on Karst Spring Discharge Based on the Wavelet Coherence Analysis and the Multivariate Statistical

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