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Investigation of the complexity of streamflow fluctuations in a large heterogeneous lake catchment in China

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Abstract

The occurrence of flood and drought frequency is highly correlated with the temporal fluctuations of streamflow series; understanding of these fluctuations is essential for the improved modeling and statistical prediction of extreme changes in river basins. In this study, the complexity of daily streamflow fluctuations was investigated by using multifractal detrended fluctuation analysis (MF-DFA) in a large heterogeneous lake basin, the Poyang Lake basin in China, and the potential impacts of human activities were also explored. Major results indicate that the multifractality of streamflow fluctuations shows significant regional characteristics. In the study catchment, all the daily streamflow series present a strong long-range correlation with Hurst exponents bigger than 0.8. The q-order Hurst exponent h(q) of all the hydrostations can be characterized well by only two parameters: a (0.354 ≤ a ≤ 0.384) and b (0.627 ≤ b ≤ 0.677), with no pronounced differences. Singularity spectrum analysis pointed out that small fluctuations play a dominant role in all daily streamflow series. Our research also revealed that both the correlation properties and the broad probability density function (PDF) of hydrological series can be responsible for the multifractality of streamflow series that depends on watershed areas. In addition, we emphasized the relationship between watershed area and the estimated multifractal parameters, such as the Hurst exponent and fitted parameters a and b from the q-order Hurst exponent h(q). However, the relationship between the width of the singularity spectrum (Δα) and watershed area is not clear. Further investigation revealed that increasing forest coverage and reservoir storage can effectively enhance the persistence of daily streamflow, decrease the hydrological complexity of large fluctuations, and increase the small fluctuations.

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References

  • Bashan A, Bartsch R, Kantelhardt JW, Havlin S (2008) Comparison of detrending methods for fluctuation analysis. Physica A 387:5080–5090

    Article  Google Scholar 

  • Bonacci O, Buzjak N, Rojebonacci T (2016) Changes in hydrological regime caused by human intervention in karst: a case of the Rumin Springs. Hydrological Sciences Journal/journal Des Sciences Hydrologiques 61(13):2387–2398

    Article  Google Scholar 

  • Bond N, Lake PS, Arthington AH (2008) The impacts of drought on fresh water ecosystems: an Australian perspective. Hydrobiologia 600:3–16

    Article  Google Scholar 

  • Bunde A, Eichner JF, Havlin S, Kantelhardt JW (2003) The effect of long-term correlations on the return periods of rare events. Physica A 330:1–7

    Article  Google Scholar 

  • Chianca CV, Ticona A, Penna TJP (2005) Fourier-detrended fluctuation analysis. Physica A 357:447–454

    Article  Google Scholar 

  • Dahlstedt K, Jensen HJ (2005) Fluctuation spectrum and size scaling of river flow and level. Physcia A 348:596–610

    Article  Google Scholar 

  • Guo H, Hu Q, Jiang T (2008) Annual and seasonal stream flow responses to climate and land-cover changes in the Poyang Lake basin, China. J Hydrol 33:172–186

    Google Scholar 

  • Hirpa FA, Mekonnen G, Over TM (2010) River flow fluctuation analysis: effect of watershed area. Water Resour Res 46(12):65–74

    Article  Google Scholar 

  • Hu K, Ivanov PC, Chen Z, Carpena P, Stanley HE (2001) Effect of trends on detrended fluctuation analysis. Phys Rev E 64(1):011114

    Article  Google Scholar 

  • Hurst HE (1951) Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineering 116:770–808

    Google Scholar 

  • Ihlen EA (2012) Introduction to multifractal detrended fluctuation analysis in Matlab. Front Physiol 3(3):141. doi:10.3389/fphys.2012.00141

    Google Scholar 

  • IPCC (2013) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK

    Google Scholar 

  • Kantelhardt J, Zschiegner WSA, Koscielny-Bunde E, Havlin S, Bunde A, Stanley HE (2002) Multifractal detrended fluctuation analysis of nonstationary time series. Physica A 316:87–114

    Article  Google Scholar 

  • Kantelhardt JW, Koscielny-Bunde E, Rybski D, Bunde A, Havlin S (2006) Long-term persistence and multifractality of precipitation and river runoff records. J Geophys Res 111(D1):93–108

    Article  Google Scholar 

  • Koscielny-Bunde E, Kantelhardt JW, Braun P, Bunde A, Havlin S (2006) Long-term persistence and multifractality of river runoff records: detrended fluctuation studies. J Hydrol 322(1–4):120–137

    Article  Google Scholar 

  • Labat D, Masbou J, Beaulieu E, Mangin A (2011) Scaling behavior of the fluctuations in stream flow at the outlet of karstic watersheds, France. J Hydro 410(3–4):162–168

    Article  Google Scholar 

  • Li YL, Tao H, Yao J, Zhang Q (2016) Application of a distributed catchment model to investigate hydrological impacts of climate change within Poyang Lake catchment (China). Hydro Res 47(S1):120–135. doi:10.2166/nh.2016.234

    Article  Google Scholar 

  • Lovejoy S, Schertzer D (1991) Nonlinear variability in geophysics: scaling and fractals. Kluver Academic Publ, Dordrecht, Netherlands

    Google Scholar 

  • Menzel L, Bürger G (2002) Climate change scenarios and runoff response in the Mulde catchment (Southern Elbe, Germany). J Hydrol 267:53–64

    Article  Google Scholar 

  • Min Q, Zhan L (2012) Characteristics of low-water level changes in Lake Poyang during 1952–2011. J Lake Sci 24(5):675–678 (in Chinese, with English abstract)

    Article  Google Scholar 

  • Movahed MS, Jafari GR, Ghasemi F, Rahvar S, Tabar MRR (2006) Multifractal detrended fluctuation analysis of sunspot time series. J Stat Mech: Theory Exp 2006(02):P02003–P02003

    Article  Google Scholar 

  • Mudelsee M (2007) Long memory of rivers from spatial aggregation. Water Resour Res 43(1):129–137

    Article  Google Scholar 

  • Pandey G, Lovejoy S, Schertzer D (1998) Multifractal analysis of daily river flows including extremes for basins five to two million square kilometers, one day to 75 years. J Hydrol 208:62–81

    Article  Google Scholar 

  • Rego CRC, Frota HO, Gusmão MS (2013) Multifractality of Brazilian rivers. J Hydrol 495(495):208–215

    Article  Google Scholar 

  • Vicuña S, Gironás J, Meza FJ, Cruzat ML, Jelinek M, Bustos E, Poblete D, Bambach N (2013) Exploring possible connections between hydrological extreme events and climate change in central south Chile. Hydrological Sciences Journal/journal Des Sciences Hydrologiques 58(8):1598–1619

    Article  Google Scholar 

  • Wang H, Chen Y, Li W (2014) Hydrological extreme variability in the headwater of Tarim River: links with atmospheric teleconnection and regional climate. Stoch Env Res Risk A 28(2):443–453

    Article  Google Scholar 

  • White MA, Schmidt JC, Topping DJ (2005) Application of wavelet analysis for monitoring the hydrologic effects of dam operation: Glen Canyon Dam and the Colorado River at Lees Ferry, Arizona. River Res Appl 21(5):551–565

    Article  Google Scholar 

  • Xu C-Y, Singh VP (2005) Evaluation of three complementary relationship evapotranspiration models by water balance approach to estimate actual regional evapotranspiration in different climatic regions. J Hydrol 308:105–121

    Article  Google Scholar 

  • Yao J, Zhang Q, Li Y, Li M (2016) Hydrological evidence and causes of seasonal low water levels in a large river-lake system: Poyang Lake, China. Hydro Res 47(S1):24–39. doi:10.2166/nh.2016.044

    Article  Google Scholar 

  • Ye XC, Zhang Q, Bai L, Hu Q (2011) A modeling study of catchment discharge to Poyang Lake under future climate in China. Quatern Int 244:221–229

    Article  Google Scholar 

  • Ye XC, Zhang Q, Liu J, Li XH (2013) Distinguishing the relative impacts of climate change and human activities on variation of streamflow in the Poyang Lake catchment, China. J Hydrol 494(12):83–95

    Article  Google Scholar 

  • Zhang Q, Xu C-Y, Chen YD, Yu Z (2008a) Multifractal detrended fluctuation analysis of streamflow series of the Yangtze River basin, China. Hydrol Process 22(26):4997–5003

    Article  Google Scholar 

  • Zhang Q, Xu C-Y, Zhang Z, Chen YD, Liu CL, Lin H (2008b) Spatial and temporal variability of precipitation maxima during 1960–2005 in the Yangtze River basin and possible association with large-scale circulation. J Hydrol 353(3–4):215–227

    Article  Google Scholar 

  • Zhang Q, Xu C-Y, Yu Z, Liu CL, Chen YD (2009) Multifractal analysis of streamflow records of the East River basin (Pearl River), China. Physica A 5(388):927–934

    Article  Google Scholar 

  • Zhang Q, Sun P, Jiang T (2011) Changing properties, causes and impacts of extreme streamflow in Lake Poyang basin, China. J Lake Sci 23(3):445–453 (in Chinese, with English abstract)

    Article  Google Scholar 

  • Zhang Q, Li L, Wang Y-G, Werner AD, Xin P, Jiang T, Barry DA (2012) Has the Three-Gorges Dam made the Poyang Lake wetlands wetter and drier? Geophys Res Lett. doi:10.1029/2012GL053431

    Google Scholar 

  • Zhou X, Persaud N, Wang HG, Lin HS (2007) Multifractal scaling of daily runoff time series in agricultural watersheds. J Am Water Resour Assoc 42(6):1659–1670

    Article  Google Scholar 

  • Zhou Y, Zhang Q, Singh VP (2014) Fractal-based evaluation of the effect of water reservoirs on hydrological processes: the dams in the Yangtze River as a case study. Stoch Env Res Risk A 28(2):263–279

    Article  Google Scholar 

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Authors and Affiliations

  1. State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Hohai University, Nanjing, 210098, China

    Xuchun Ye & Chong-Yu Xu

  2. Department of Geosciences, University of Oslo, 0316, Oslo, Norway

    Xuchun Ye & Chong-Yu Xu

  3. Key Laboratory of Watershed Geographic Science, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China

    Xuchun Ye, Xianghu Li & Qi Zhang

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  1. Xuchun Ye
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  2. Chong-Yu Xu
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  3. Xianghu Li
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  4. Qi Zhang
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Correspondence to Xuchun Ye.

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Ye, X., Xu, CY., Li, X. et al. Investigation of the complexity of streamflow fluctuations in a large heterogeneous lake catchment in China. Theor Appl Climatol 132, 751–762 (2018). https://doi.org/10.1007/s00704-017-2126-5

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  • DOI: https://doi.org/10.1007/s00704-017-2126-5

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