# Tendency of Runoff and Sediment Variety and Multiple Time Scale Wavelet Analysis in Hongze Lake during 1975–2015

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Research Areas and Data

#### 2.2. Combinatorial Mutation Test Method

#### 2.2.1. M–K Test

_{k}is the cumulative value of the number of values at time i is greater than that at time j, UF

_{1}= 0; E(S

_{k}), Var(S

_{k}) are the mean and variance of cumulative S

_{k}respectively, when x

_{1}, x

_{2}, …, x

_{n}are independent of each other, E(S

_{k}) and Var(S

_{k}) have the same distribution, then:

_{k}is the standard normal distribution, which is a statistical time series calculated by time series X. At a given level of significance α, according to the normal distribution table, when UF

_{k}> U

_{α}, there are obvious trend variations in the time series.

_{k}= −UF

_{k}(k = n, n − 1, …,1), UB

_{1}= 0. To calculate the UF

_{k}and UB

_{k}, if UF

_{k}is greater than 0, the time series shows an upward trend; if the UF

_{k}less than 0, it shows a downward trend. At the significance level α=0.05, the mutation probability increases when UF

_{k}exceeds the critical value ±1.96. Within the confidence interval, if there is an intersection point between curve UF

_{k}and UB

_{k}, it is a possible catastrophe point. However, the M–K test does not applicable to the time series with multiple or multi-scale mutations time series, that is, when there are multiple intersection points in the confidence interval, there may be pseudo catastrophe points, and the clutter points need to be removed [43].

#### 2.2.2. Pettitt Test

_{1}, x

_{2}, …, x

_{to}and x

_{to+1}, x

_{to+2}, …, x

_{T}. If the distributions of the random variables in the two parts are F

_{1}

_{(x)}and F

_{2}

_{(x)}, and F

_{1}

_{(x)}≠ F

_{2}

_{(x)}, the catastrophe point is supposed to happen at t

_{o}. The statistics are defined as the equation [25]:

_{t,T}is obtained by the equation:

#### 2.2.3. Combinatorial Test

#### 2.3. Wavelet Analysis Method

#### 2.3.1. Wavelet Function

^{2}(R) and it satisfies Equation (10).

_{a,b}(t) is the daughter wavelet; $a$ is the scale factor, which can reflect the period length of the wavelet; b is the translation factor, which can reflect the shift in time.

#### 2.3.2. Wavelet Transform

^{2}(R) represents the measurable square-integrable function space defined on the real axis, if the function f(t)∈L

^{2}(R) satisfies Equation (12).

^{2}(R), the continuous wavelet transform is defined as the equation:

_{f}(a,b) is the wavelet transform coefficient; $\overline{\phi}(\frac{t-b}{a})$ is the complex conjugate of $\phi (\frac{t-b}{a})$.

#### 2.3.3. Wavelet Variance

_{j}) is the wavelet transform coefficient with scale factor $a$ and translation factor x

_{j}. For complex wavelet function W

^{2}(a,x

_{j}), it is the square of the wavelet transform coefficient module; n is the total number of wavelet transform coefficients obtained under scale $a$.

^{2}(a,x

_{j}) of each sample at the scale $a$. The wavelet variance graph is the process of wavelet variance changing with time scale $a$, which can reflect the energy distribution of signal fluctuation in different time scales a. Therefore, the wavelet variance diagram can be used to determine the main period of the studied signal.

## 3. Results and Discussion

#### 3.1. Spatial and Temporal Distribution of Runoff and Sediment

#### 3.1.1. Runoff and Sediment Trend Analysis

#### 3.1.2. Analysis of Runoff and Sediment Change Point

^{3}after the mutation, and the reduction is 48.6%. The average outflow sediment decreased by 26.2 billion m

^{3}after the mutation, and the reduction is 56.1%. The sediment of inflow and outflow decreased obviously after the change point.

#### 3.2. Wavelet Analysis

#### 3.2.1. Analysis of Wavelet Transform

#### 3.2.2. Analysis of Wavelet Variance and Periodic Characteristic

#### 3.3. Cause Analysis of Runoff and Sediment Variation

#### 3.3.1. Cause Analysis of Runoff Trend Variation

^{2}= 0.8171). According to the rainfall-runoff relationship, the average annual inflow runoff is 32.75 billion m

^{3}during 1993–2015, the runoff decreased by 1.09 billion m

^{3}compared with 1975–1993. The measured annual average inflow runoff is 27.10 billion m

^{3}during 1993–2015, the runoff decreases by 3.92 billion m

^{3}compared with 1975–1993. Thus, among the major factors influencing the decrease of runoff from 1993 to 2015, the decrease in rainfall accounted for 28%, and the increase in water use for human activities accounted for 72%.

^{3}to 54.02 billion m

^{3}during 1993–2015. The M–K trend was conducted on the water consumption time series, the results showed that the statistic Z = 4.72 is far more than 95% significant level, and the water consumption increased significantly. The economy of the Huaihe River basin increased by 1.35 times and the population increased by 8 million from 1994 to 2004. With the growth of population, the rapid development of domestic water, construction and production water, agricultural irrigation water, aquaculture and other activities, the degree of exploitation and utilization of water resources has been increasing and exceeded 60% [52]. Anthropogenic activities have become the main reason for the decrease of inflow runoff after 1993.

#### 3.3.2. Trend Variation of Sediment and Cause Analysis of Change Point

^{3}. Because the period from 1975–2015 is not the prosperous period of reservoir construction in the Huai River basin, therefore, the research period is divided by the completion time of large and medium-sized reservoirs and storage capacity changing time in the Huaihe River basin. For example, the Banqiao reservoir began to be rebuilt in 1978, and the Huaihe River basin was flooded in 1991. Therefore, the period is divided into 1975–1978, 1979–1991, and 1992–2015. The reservoir capacity increased from 14.75 billion m

^{3}to 15.854 billion m

^{3}, the average inflow sediment decreased from 7.98 million t to 5.22 million t, and the average outflow sediment decreased from 5.24 million t to 2.27 million t. As shown in Figure 11, the inflow sediment and outflow sediment decreased with the increase of the total reservoir capacity, and the silt trapping of the reservoir has a significant effect on the inflow and outflow sediment.

^{3}, part of the sediment was intercepted by the reservoir. At the same time, the implementation of the projects to control the Huaihe River is beneficial to soil and water conservation, since 1991, the upstream of Hongze Lake has been treated with 2184 km

^{2}of soil erosion.

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Rice, J.S.; Emanuel, R.E.; Vose, J.M.; Nelson, S. Continental U.S. Streamflow trends from 1940 to 2009 and their relationships with watershed spatial characteristics. Water Resour. Res.
**2015**, 51, 6262–6275. [Google Scholar] [CrossRef] - Lins, H.F.; Slack, J.R. Streamflow trends in the United States. Geophys. Res. Lett.
**1999**, 26, 227–230. [Google Scholar] [CrossRef] [Green Version] - Lettenmaier, D.P.; Wood, E.F.; Wallis, J.R. Hydro-Climatological Trends in the Continental United States, 1948–1988. J. Climate.
**1994**, 7, 586–607. [Google Scholar] [CrossRef] [Green Version] - Kalra, A.; Ahmad, S.; Nayak, A. Increasing Streamflow Forecast Lead Time for Snowmelt Driven Catchment Based on Large Scale Climate Patterns. Adv. Water Resour.
**2013**, 53, 150–162. [Google Scholar] [CrossRef] - Zhou, Y.; Ma, Z.; Wang, L. Chaotic dynamics of the flood series in the Huaihe River Basin for the last 500 years. J. Hydrol.
**2002**, 258, 100–110. [Google Scholar] [CrossRef] - Lin, C.A.; Wen, L.; Lu, G.; Wu, Z.; Zhang, J.Y.; Yang, Y.; Zhu, Y.F.; Tong, L.Y. Atmospheric-hydrological modeling of severe precipitation and floods in the Huaihe River Basin, China. J. Hydrol.
**2006**, 330, 249–259. [Google Scholar] [CrossRef] - Zhu, Y.; Wang, W.; Liu, Y.; Wang, H.J. Runoff changes and their potential links with climate variability and anthropogenic activities: A case study in the upper Huaihe River Basin, China. Hydrol. Res.
**2015**, 2015, 1019–1036. [Google Scholar] [CrossRef] - Ye, Z.W.; Li, Z.H. Spatiotemporal Variability and Trends of Extreme Precipitation in the Huaihe River Basin, a Climatic Transitional Zone in East China. Adv. Meteorol.
**2017**, 2017, 1–15. [Google Scholar] [CrossRef] - Yu, Z.; Yan, D.; Ni, G.; Do, P.; Yan, D.; Cai, S.; Qin, T.; Weng, B.; Yang, M. Variability of Spatially Grid-Distributed Precipitation over the Huaihe River Basin in China. Water
**2017**, 9, 489. [Google Scholar] [CrossRef] [Green Version] - Jiang, J.H.; Yuan, J.X. Analysis on the historical flood of Hongze Lake (1736–1992). J. Lake Sci.
**1997**, 1997, 231–236. [Google Scholar] [CrossRef] - Yin, Y.; Chen, Y.; Yu, S.; Xu, W.; Wang, W.; Xu, Y. Maximum water level of Hongze Lake and its relationship with natural changes and human activities from 1736 to 2005. Quatern. Int.
**2013**, 304, 85–94. [Google Scholar] [CrossRef] - Duan, H.; Cao, Z.; Shen, M.; Liu, D.; Xiao, Q. Detection of illicit sand mining and the associated environmental effects in China’s fourth largest freshwater lake using daytime and nighttime satellite images. Sci. Total Environ.
**2019**, 647, 606–618. [Google Scholar] [CrossRef] [PubMed] - Milly, P.C.D.; Wetherald, R.T.; Dunne, K.A.; Delworth, T.L. Increasing risk of great floods in a changing climate. Nature (London)
**2002**, 415, 514–517. [Google Scholar] [CrossRef] [PubMed] - Tamaddun, K.A.; Kalra, A.; Ahmad, S. Patterns and Periodicities of the Continental U.S. Streamflow Change. World Environ. Water Resour. Congr.
**2016**, 2016, 658–667. [Google Scholar] [CrossRef] - Mann, H.B. Non-Parametric Test Against Trend. Econometrica
**1945**, 13, 245–259. [Google Scholar] [CrossRef] - Kendall, M.G. Rank Correlation Methods; Charles Griffin: London, UK, 1975. [Google Scholar] [CrossRef]
- Mondal, A.; Kundu, S.; Mukhopadhyay, A. Rainfall trend analysis by Mann-Kendall test: A case study of north-eastern part of Cuttack district, Orissa. Int. J. Earth Sci.
**2012**, 2, 70–78. [Google Scholar] - Nourani, V.; Mehr, A.D.; Azad, N. Trend analysis of hydroclimatological variables in Urmia lake basin using hybrid wavelet Mann–Kendall and Sen tests. Environ. Earth Sci.
**2018**, 77, 207. [Google Scholar] [CrossRef] - Daubechies, I. The wavelet transform, time-frequency localization and signal analysis. IEEE T. Inform. Tteory.
**1990**, 36, 961–1005. [Google Scholar] [CrossRef] [Green Version] - Tamaddun, K.A.; Kalra, A.; Ahmad, S. Wavelet analyses of western US streamflow with ENSO and PDO. J. Water Clim. Chang.
**2017**, 8, 26–39. [Google Scholar] [CrossRef] [Green Version] - Thakur, B.; Pathak, P.; Kalra, A.; Ahmad, S.; Bernardez, M. Using Wavelet to Analyze Periodicities in Hydrologic Variables. World Environ. Water Resour. Congr.
**2017**, 2017, 499–510. [Google Scholar] [CrossRef] - Chong, K.L.; Lai, S.H.; El-Shafie, A. Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment. Water Resour. Manag.
**2019**, 33, 2015–2032. [Google Scholar] [CrossRef] - Mann, H.B.; Whitney, D.R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat.
**1947**, 18, 50–60. [Google Scholar] [CrossRef] - Kruskal, W.H.; Wallis, A.W. Use of ranks in one-criterion variance analysis. J. Am. Stat. Asso.
**1952**, 47, 583–621. [Google Scholar] [CrossRef] - Pettitt, A.N. A Non-Parametric Approach to the Change-Point Problem. J. R. Stat. Soc. C Appl.
**1979**, 28, 126–135. [Google Scholar] [CrossRef] - Chandler, R.E.; Scott, M. Statistical Methods for Trend Detection and Analysis in the Environmental Sciences; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 2011. [Google Scholar] [CrossRef]
- Conte, L.C.; Débora, M.B.; Fábio, M.B. Bootstrap Pettitt test for detecting change points in hydroclimatological data: Case study of Itaipu Hydroelectric Plant, Brazil. Hydrolog. Sci. J.
**2019**, 64, 1312–1326. [Google Scholar] [CrossRef] - Villarini, G.; Smith, J.A. Flood peak distributions for the eastern United States. Water Resour. Res.
**2010**, 46, 1–17. [Google Scholar] [CrossRef] [Green Version] - Wang, W.; Shao, Q.; Yang, T.; Peng, S.; Xing, W.; Sun, F.; Luo, Y. Quantitative assessment of the impact of climate variability and human activities on runoff changes: A case study in four catchments of the Haihe River basin, China. Hydrol. Process.
**2013**, 27, 1158–1174. [Google Scholar] [CrossRef] - Suhaila, J.; Yusop, Z. Trend analysis and change point detection of annual and seasonal temperature series in Peninsular Malaysia. Meteorol. Atmos. Phys.
**2017**, 130, 565–581. [Google Scholar] [CrossRef] - Alexander, L.V.; Zhang, X.; Peterson, T.C.; Caesar, J.; Gleason, B.; Klein Tank, A.M.G.; Haylock, M.; Collins, D.; Trewin, B.; Rahimzadeh, F. Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res. Atmos.
**2006**, 111, 1–22. [Google Scholar] [CrossRef] [Green Version] - Liang, K.; Bai, P.; Li, J.J.; Liu, C. Variability of temperature extremes in the Yellow River basin during 1961–2011. Quat. Int.
**2014**, 336, 52–64. [Google Scholar] [CrossRef] - Klaus, S.; Christian, D.; Matthias, H.; Stötter, J. Assessing potential climate change impacts on the seasonality of runoff in an Alpine watershed. J. Water Clim. Chang.
**2015**, 6, 263–277. [Google Scholar] [CrossRef] - Wang, L.; Liu, H.L.; Bao, A.M.; Pan, X.L.; Chen, X. Estimating the sensitivity of runoff to climate change in an alpine-valley watershed of Xinjiang, China. Hydrol. Sci. J.
**2016**, 61, 1069–1079. [Google Scholar] [CrossRef] [Green Version] - Walling, D.E.; Fang, D. Recent trends in the suspended sediment loads of the world’s rivers. Glob. Planet. Chang.
**2003**, 39, 111–126. [Google Scholar] [CrossRef] - Vorosmarty, C.J.; Green, P.; Salisbury, J.; Lammers, R.B. Global water resources: Vulnerability from climate change and population growth. Science
**2000**, 289, 284–288. [Google Scholar] [CrossRef] [Green Version] - Carlson, T.N.; Arthur, S.T. The impact of land use-land cover changes due to urbanization on surface microclimate and hydrology: A satellite perspective. Glob. Planet. Chang.
**2000**, 25, 49–65. [Google Scholar] [CrossRef] - Ervinia, A.; Huang, J.; Zhang, Z. Land-use changes reinforce the impacts of climate change on annual runoff dynamics in a southeast China coastal watershed. Hydrol. Earth Syst. Sci.
**2015**, 12, 6305–6325. [Google Scholar] [CrossRef] - Dao, N.K.; Tadashi, S. Impact of climate and land-use changes on hydrological processes and sediment yield-A case study of the Be River catchment, Vietnam. Hydrol. Sci. J.
**2014**, 59, 1095–1108. [Google Scholar] [CrossRef] - Apollonio, C.; Balacco, G.; Novelli, A.; Tarantino, E.; Piccinni, A.F. Land Use Change Impact on Flooding Areas: The Case Study of Cervaro Basin (Italy). Sustainability
**2016**, 8, 996. [Google Scholar] [CrossRef] [Green Version] - Bussi, G.; Dadson, S.J.; Prudhomme, C.; Whitehead, P.G. Modelling the future impacts of climate and land-use change on suspended sediment transport in the River Thames (UK). J. Hydrol.
**2016**, 542, 357–372. [Google Scholar] [CrossRef] [Green Version] - Cao, Z.; Duan, H.; Feng, L.; Ma, R.; Xue, K. Climate- and human-induced changes in suspended particulate matter over Lake Hongze on short and long timescales. Remote Sens. Environ.
**2017**, 192, 98–113. [Google Scholar] [CrossRef] - Xing, L.; Huang, L.; Chi, G.; Yang, L.; Li, C.; Hou, X. A Dynamic Study of a Karst Spring Based on Wavelet Analysis and the Mann-Kendall Trend Test. Water
**2018**, 10, 698. [Google Scholar] [CrossRef] [Green Version] - Li, W.W.; Fu, X.D.; Wu, W.Q.; Wu, B.S. Study on runoff and sediment process variation in the lower Yellow River. J. Hydroelectr. Eng.
**2014**, 33, 108–113. [Google Scholar] - Labat, D. Recent advances in wavelet analyses: Part 1. A review of concepts. J. Hydrol.
**2008**, 314, 275–288. [Google Scholar] [CrossRef] - Kovács, J.; Hatvani, I.G.; Korponai, J.; Kovács, I.S. Morlet wavelet and autocorrelation analysis of long-term data series of the Kis-Balaton water protection system (KBWPS). Ecol. Eng.
**2010**, 36, 1469–1477. [Google Scholar] [CrossRef] - Hermida, L.; López, L.; Merino, A.; Berthet, C.; Gercía-Ortega, E.; Sánchez, J.L.; Dessens, J. Hailfall in southwest France: Relationship with precipitation, trends and wavelet analysis. Atmos. Res.
**2015**, 156, 174–188. [Google Scholar] [CrossRef] - Oloruntade, A.J.; Mohammad, T.A.; Ghazali, A.H.; Wayayok, A. Analysis of meteorological and hydrological droughts in the Niger-South Basin, Nigeria. Glob. Planet. Chang.
**2017**, 155, 225–233. [Google Scholar] [CrossRef] - Guignard, F.; Mauree, D.; Kanevski, M.; Telesca, L. Wavelet variance scale-dependence as a dynamics discriminating tool in high-frequency urban wind speed time series. Phys. A Stat. Mech. Its Appl.
**2019**, 525, 771–777. [Google Scholar] [CrossRef] [Green Version] - Sun, P.; Sun, Y.Y.; Zhang, Q.; Wen, Q.Z. Temporal and spatial variation characteristics of runoff processes and its causes in Huaihe Basin. J. Lake Sci.
**2018**, 30, 497–508. [Google Scholar] [CrossRef] [Green Version] - Mario, V.S.; Le, B.Y.; Moussa, R.; Bruno, R. Temporal dynamics of runoff and soil loss on a plot scale under a coffee plantation on steep soil (Ultisol), Costa Rica. J. Hydrol.
**2015**, 523, 409–426. [Google Scholar] [CrossRef] - Jiang, Y.; Peng, Q.D.; Luo, H.H.; Ma, W. Analysis of spatial and temporal variation of water quality in Huaihe River Basin. J. Hydraul. Eng.
**2011**, 42, 1283–1288. [Google Scholar] [CrossRef] - Cai, T.; Li, Q.F.; Yu, M.X.; Lu, G.B.; Cheng, L.P.; Wei, X. Investigation into the impacts of land-use change on sediment yield characteristics in the upper Huaihe River basin, China. Phys. Chem. Earth
**2012**, 53, 1–9. [Google Scholar] [CrossRef]

**Figure 2.**The runoff and sediment time series flowing into and out of Hongze Lake from 1975 to 2015. (

**a**) Runoff time series; (

**b**) Sediment time series.

**Figure 3.**Results of M–K statistics series. (

**a**) Runoff M–K statistics series; (

**b**) Sediment M–K statistics series.

**Figure 4.**Inflow sediment time series combinatorial mutation test. (

**a**) M–K test; (

**b**) First order change point identification; (

**c**) Second order change point identification (1975–1991); (

**d**) Second order change point identification (1991–2015). Where, UF and UB are the statistics, T is the year, P is the significance probability.

**Figure 5.**Outflow sediment time series combinatorial mutation test. (

**a**) M–K test; (

**b**) First order change point identification; (

**c**) Second order change point identification (1975–1991); (

**d**) Second order change point identification (1991–2015).

**Figure 6.**Inflow runoff and sediment anomaly series of wavelet coefficient contour map. (

**a**) Real part contour map of runoff; (

**b**) Real part contour map of sediment; (

**c**) Modular square contour map of runoff; (

**d**) Modular square contour map of sediment. Where, $a$ is the time scale.

**Figure 7.**Wavelet variance diagram of runoff and sediment anomaly series. (

**a**) Runoff wavelet variance; (

**b**) Sediment wavelet variance.

**Figure 8.**Real part variation process of annual runoff anomaly series wavelet coefficients. (

**a**) Time scale of 30$a$; (

**b**) Time scale of 11$a$; (

**c**) Time scale of 6$a$; (

**d**) Time scale of 4$a$.

**Figure 9.**Real part variation process of annual sediment anomaly series wavelet coefficients. (

**a**) Time scale of 30$a$; (

**b**) Time scale of 12$a$; (

**c**) Time scale of 7$a$; (

**d**) Time scale of 3$a$.

**Figure 10.**Rainfall and inflow runoff variation process. (

**a**) Hydrologic time series; (

**b**) M–K statistic analysis.

**Figure 11.**The relationship between sediment flowing into and out of Hongze Lake and the total volume of the upstream reservoir.

**Table 1.**The runoff and sediment (Mann-Kendall) M–K statistic of flowing into and out of Hongze Lake.

M–K test | Inflow Runoff | Outflow Runoff | Inflow Sediment | Outflow Sediment |
---|---|---|---|---|

Statistic | −0.98 | −0.60 | −1.83 | −2.53 |

Inspection criterion | −1.96 ≤ Z ≤ 1.96 | −1.96 ≤Z ≤ 1.96 | −1.96 ≤ Z ≤ 1.96 | −1.96 ≤ Z ≤ 1.96 |

Significance level | No significant downtrend | No significant downtrend | Slightly downtrend | Significantly downtrend |

Sediment | Change Point | Former Mutation | After Mutation | Mean Difference/t | |||||
---|---|---|---|---|---|---|---|---|---|

Year | Significant | Mean/t | Standard Deviation/t | Coefficient of Variation | Mean/t | Standard Deviation/t | Coefficient of Variation | ||

Inflow | 1991 | 0.05 | 860 | 546 | 0.64 | 442 | 406 | 0.92 | 418 |

Outflow | 1991 | 0.05 | 467 | 321 | 0.69 | 205 | 187 | 0.91 | 262 |

Time Scale | Variation Period /Year | Cycle Times | Runoff Variation Trend after 2015 | |
---|---|---|---|---|

30$a$ | 20 | 2 | Maintain in a low water period and will reach the valley floor value | Figure 8a |

11$a$ | 8 | 5 | Enter into a relatively rich water period | Figure 8b |

6$a$ | 4 | 10 | Enter into a relatively rich water period | Figure 8c |

4$a$ | The distribution characteristics are not obvious | Figure 8d |

Time Scale | Variation Period/Year | Cycle Times | Sediment Variation Trend after 2015 | |
---|---|---|---|---|

30$a$ | 20 | 2 | Maintain in a low sediment period and will reach the valley floor value | Figure 9a |

12$a$ | 8 | 5 | Enter into a relatively rich sediment period | Figure 9b |

7$a$ | The distribution characteristics are not obvious | Figure 9c | ||

3$a$ | The distribution characteristics are not obvious | Figure 9d |

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## Share and Cite

**MDPI and ACS Style**

Duan, Y.; Xu, G.; Liu, Y.; Liu, Y.; Zhao, S.; Fan, X.
Tendency of Runoff and Sediment Variety and Multiple Time Scale Wavelet Analysis in Hongze Lake during 1975–2015. *Water* **2020**, *12*, 999.
https://doi.org/10.3390/w12040999

**AMA Style**

Duan Y, Xu G, Liu Y, Liu Y, Zhao S, Fan X.
Tendency of Runoff and Sediment Variety and Multiple Time Scale Wavelet Analysis in Hongze Lake during 1975–2015. *Water*. 2020; 12(4):999.
https://doi.org/10.3390/w12040999

**Chicago/Turabian Style**

Duan, Yu, Guobin Xu, Yuan Liu, Yijun Liu, Shixiong Zhao, and Xianlu Fan.
2020. "Tendency of Runoff and Sediment Variety and Multiple Time Scale Wavelet Analysis in Hongze Lake during 1975–2015" *Water* 12, no. 4: 999.
https://doi.org/10.3390/w12040999