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Watershed runoff modeling through a multi-time scale approach by multivariate empirical mode decomposition (MEMD)

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Abstract

Accurate runoff modeling has an important role in water resource management. Attributable to the effects of climate variability and vegetation dynamics, runoff time series is nonstationary, resulting in the difficulty of runoff modeling. Detecting the temporal features of runoff and its potential influencing factors can help to increase the modeling accuracy. Selecting the Yihe watershed in the rocky mountainous area of northern China as a case study, multivariate empirical mode decomposition (MEMD) was adopted to analyze the time scales of the monthly runoff and its influencing factors, i.e., precipitation (P), normalized difference vegetation index (NDVI), temperature (T), relative humidity (RH), and potential evapotranspiration (PE). Using the MEMD technique, the original monthly runoff and its influencing factors were decomposed into six orthogonal and bandlimited functions, i.e., intrinsic mode functions (IMF1-6) and one residue, respectively. Each IMF is a counterpart of the simple harmonic function and represents a simple but general oscillatory mode in the original time series data. The results of the IMF contribution rate showed that the annual cycle had the most important role in runoff, P, NDVI, T and PE change. The contribution of quarterly oscillation was the largest contribution for the month RH variability. The monotonic residue showed that the predominant trends of runoff, P, NDVI, T, RH, and PE were decreasing from 2006 to 2015. Stepwise multiple linear regression (SMLR) was chosen to simulate the runoff IMFs and residue. The modeling results using the IMFs and residue of the potential influencing factors as input variables (R2 ranges from 0.53 to 1.0) were better than those using the original time series of influencing factors as input variables (R2 ranges from 0.17 to 0.6). By summing all the modeled IMFs and residues, the monthly runoff model was obtained, which increased the R2 value by 24.2% compared with the SMLR model using the original time series of influencing factors as input. The results indicated that MEMD was efficient for improving the accuracy of nonstationary runoff modeling.

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Availability of data and materials

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Funding

Financial support for this research was provided by the National Natural Science Foundation of China (Nos. 41701311 and 42077061) and the Project of Introducing and Cultivating Young Talent in the Universities of Shandong Province (LUJIAORENZI20199).

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Authors and Affiliations

  1. Shandong Provincial Key Laboratory of Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi, 276005, People’s Republic of China

    Hanyu Zhang, Wei Jiao, Kai Li, Lizhi Wang & Qianjin Liu

  2. College of Geography and Environment, Shandong Normal University, Jinan, 250014, People’s Republic of China

    Lin Liu

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  1. Hanyu Zhang
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  2. Lin Liu
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  3. Wei Jiao
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  4. Kai Li
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Contributions

Hanyu Zhang and Qianjin Liu contributed to the conception of the study; Hanyu Zhang, Lin Liu, and Wei Jiao contributed significantly to analysis and manuscript preparation; Hanyu Zhang, Kai Li, and Lizhi Wang performed the data analyses and wrote the manuscript. All authors have approved the manuscript and agree with submission to Environmental Science and Pollution Research.

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Correspondence to Qianjin Liu.

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Zhang, ., Liu, L., Jiao, W. et al. Watershed runoff modeling through a multi-time scale approach by multivariate empirical mode decomposition (MEMD). Environ Sci Pollut Res 29, 2819–2829 (2022). https://doi.org/10.1007/s11356-021-13676-1

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