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Variation Trend Analysis of Runoff and Sediment Time Series Based on the R / S Analysis of Simulated Loess Tilled Slopes in the Loess Plateau, China

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  • Ju Zhang

    (School of Remote Sensing and Information Engineering, Wuhan University, No. 129, Luoyu Road, Wuhan 430079, China)

  • Qingwu Hu

    (School of Remote Sensing and Information Engineering, Wuhan University, No. 129, Luoyu Road, Wuhan 430079, China)

  • Shaohua Wang

    (School of Remote Sensing and Information Engineering, Wuhan University, No. 129, Luoyu Road, Wuhan 430079, China)

  • Mingyao Ai

    (School of Remote Sensing and Information Engineering, Wuhan University, No. 129, Luoyu Road, Wuhan 430079, China)

Abstract

The objective of this study was to illustrate the temporal variation of runoff and sediment of loess tilled slopes under successive rainfall conditions. Loess tilled slopes with four microtopography types (straight cultivated slope, artificial backhoe, artificial digging, and contour tillage) under five slope gradients (5°, 10°, 15°, 20°, 25°) were simulated and a rainfall intensity of 60 mm/h was adopted. The temporal trends of runoff and sediment yield were predicted based on the Rescaled Range ( R / S ) analysis method. The results indicate that the Hurst indices of runoff time series and sediment time series are higher than 0.5, and a long-term positive correlation exists between the future and the past. This means that runoff and sediment of loess tilled slopes in the future will have the same trends as in the past. The results obtained by the classical R / S analysis method were the same as those of the modified R / S analysis method. The rationality and reliability of the R / S analysis method were further identified and the method can be used for predicting the trend of runoff and sediment yield. The correlation between the microtopography and the Hurst indices of the runoff and sediment yield time series, as well as between the slopes and the Hurst indices, were tested, and the result was that there was no significant correlation between them. The microtopography and slopes cannot affect the correlation and continuity of runoff and sediment yield time series. This study provides an effective method for predicting variations in the trends of runoff and sediment yield on loess tilled slopes.

Suggested Citation

  • Ju Zhang & Qingwu Hu & Shaohua Wang & Mingyao Ai, 2017. "Variation Trend Analysis of Runoff and Sediment Time Series Based on the R / S Analysis of Simulated Loess Tilled Slopes in the Loess Plateau, China," Sustainability, MDPI, vol. 10(1), pages 1-17, December.
  • Handle: RePEc:gam:jsusta:v:10:y:2017:i:1:p:32-:d:124490
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    References listed on IDEAS

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    1. Barunik, Jozef & Kristoufek, Ladislav, 2010. "On Hurst exponent estimation under heavy-tailed distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3844-3855.
    2. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    3. Benoit B. Mandelbrot, 1972. "Statistical Methodology for Nonperiodic Cycles: From the Covariance To R/S Analysis," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 1, number 3, pages 259-290, National Bureau of Economic Research, Inc.
    4. Sergey A. Kamenshchikov, 2014. "Transport catastrophe analysis as an alternative to a fractal description: theory and application to financial crisis time series," Papers 1405.6990, arXiv.org, revised Sep 2014.
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    1. Zhengdong Zhang & Luwen Wan & Caiwen Dong & Yichun Xie & Chuanxun Yang & Ji Yang & Yong Li, 2018. "Impacts of Climate Change and Human Activities on the Surface Runoff in the Wuhua River Basin," Sustainability, MDPI, vol. 10(10), pages 1-21, September.
    2. Mengjing Guo & Tiegang Zhang & Jing Li & Zhanbin Li & Guoce Xu & Rui Yang, 2019. "Reducing Nitrogen and Phosphorus Losses from Different Crop Types in the Water Source Area of the Danjiang River, China," IJERPH, MDPI, vol. 16(18), pages 1-17, September.

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