Author(s)

Yun Xiao

Affiliation(s)

College of Humanities and Education, Shaanxi Energy Institute, Xianyang 712000, Shaanxi, China.

Corresponding Author

Yun Xiao

ABSTRACT

Studying the variation law of water and sediment fluxes in the Yellow River is of great significance for environmental protection, adaptation to climate change and improvement of the quality of life of residents in the Yellow River Basin. this paper established a cubic spline interpolation model to supplement the missing sediment content data. By using the known data and interpolation data for plotting, it was found that the interpolation effect was good. Subsequently, the total drainage volume and total sediment discharge volume from 2016 to 2021 were calculated respectively using the interpolation data. In order to further study the variability of water and sediment content, this paper adopts the Mann-Kendall non-parametric test method to analyze the variability of water and sediment flux, revealing its inherent law. Through specific data analysis, the important role of sand regulation and water control in ensuring the ecological health of the Yellow River has been further demonstrated.

KEYWORDS

Cubic spline interpolation; Mann-Kendall non-parametric test method; Residual analysis; MATLAB 

CITE THIS PAPER

Yun Xiao. Spatiotemporal variability of Yellow River water-sediment fluxes: a hybrid approach using cubic spline interpolation and Mann-Kendall test. Frontiers in Environmental Research. 2025, 3(2): 1-6. DOI: https://doi.org/10.61784/fer3023.

REFERENCES

[1] Z Cao, Y Zhang, H Chen, et al. Understanding the influencing factors and evolving trends of the Yellow River Water-Sediment Regulation System from a system perspective. Frontiers of Engineering Management, 2024, 11(3): 528–541.

[2] C Habermann, F Kindermann. Multidimensional spline interpolation: Theory and applications. Computational Economics, 2007, 30: 153–169.

[3] H A Abdulmohsin, H B Abdul Wahab, A M Jaber Abdul Hossen. A Novel Classification Method with Cubic Spline Interpolation. Intelligent Automation & Soft Computing, 2022, 31(1).

[4] Y He, H Li. Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression. Neurocomputing, 2021, 430: 121–137.

[5] C Liu, Z Zhu, W Hao, et al. Heterogeneous multivariate time series imputation by transformer model with missing position encoding. Expert Systems with Applications, 2025, 271: 126435.

[6] G T Patle, A Libang, S Ahuja. Analysis of rainfall and temperature variability and trend detection: A non parametric Mann Kendall test approach. 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), IEEE, 2016: 1723–1727.

[7] J B Cabral Júnior, R L Lucena. Analysis of precipitation using Mann-Kendall and Kruskal-Wallis non-parametric tests. Mercator (Fortaleza), 2020, 19: e19001.

[8] Y F Sang, Z Wang, C Liu. Comparison of the MK test and EMD method for trend identification in hydrological time series. Journal of Hydrology, 2014, 510: 293–298.

[9] J Yaméogo. Annual rainfall trends in the Burkina Faso Sahel: a comparative analysis between Mann–Kendall and innovative trend method (ITM). Discover Applied Sciences, 2025, 7(3): 1–17.

[10] J B C Júnior, R L Lucena. Analysis of precipitations by non-parametric tests of mann-kendall and kruskal-Wallis. Mercator-Revista de Geografia da UFC, 2020, 19(1): 19001.

[11] V Gaddikeri, A Sarangi, D K Singh, et al. Trend and change-point analyses of meteorological variables using Mann–Kendall family tests and innovative trend assessment techniques in New Bhupania command (India). Journal of Water and Climate Change, 2024, 15(5): 2033–2058.