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Investigation of the Complexity of Streamflow Fluctuations in a Large Heterogeneous Lake Catchment in China

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Investigation of the Complexity of Streamflow Fluctuations in a Large Heterogeneous Lake Catchment in China Theor Appl Climatol DOI 10.1007/s00704-017-2126-5 ORIGINAL PAPER Investigation of the complexity of streamflow fluctuations in a large heterogeneous lake catchment in China Xuchun Ye1,2,3 & Chong-Yu Xu1,2 & Xianghu Li3 & Qi Zhang3 Received: 13 December 2016 /Accepted: 7 April 2017 # Springer-Verlag Wien 2017 Abstract The occurrence of flood and drought frequency is as the Hurst exponent and fitted parameters a and b from the highly correlated with the temporal fluctuations of streamflow q-order Hurst exponent h(q). However, the relationship be- series; understanding of these fluctuations is essential for the tween the width of the singularity spectrum (Δα)andwater- improved modeling and statistical prediction of extreme shed area is not clear. Further investigation revealed that in- changes in river basins. In this study, the complexity of daily creasing forest coverage and reservoir storage can effectively streamflow fluctuations was investigated by using multifractal enhance the persistence of daily streamflow, decrease the hy- detrended fluctuation analysis (MF-DFA) in a large heteroge- drological complexity of large fluctuations, and increase the neous lake basin, the Poyang Lake basin in China, and the small fluctuations. potential impacts of human activities were also explored. Major results indicate that the multifractality of streamflow fluctuations shows significant regional characteristics. In the 1 Introduction study catchment, all the daily streamflow series present a strong long-range correlation with Hurst exponents bigger River flow series represents a valuable historical record that than 0.8. The q-order Hurst exponent h(q)ofallthe carries lots of mechanism information about the hydrological hydrostations can be characterized well by only two parame- cycle. Investigation of inherent disciplinarian of streamflow se- ters: a (0.354 ≤ a ≤ 0.384) and b (0.627 ≤ b ≤ 0.677), with no ries is one of the basic ways to understand the dynamic charac- pronounced differences. Singularity spectrum analysis point- teristics of the hydrological cycle. According to well-evidenced ed out that small fluctuations play a dominant role in all daily global warming processes, atmospheric circulation and global streamflow series. Our research also revealed that both the hydrological cycle have shown a tendency towards being accel- correlation properties and the broad probability density func- erated gradually (Menzel and Bürger 2002;XuandSingh2005; tion (PDF) of hydrological series can be responsible for the IPCC 2013). According to this change, floods and/or droughts multifractality of streamflow series that depends on watershed may occur at modified severity and frequency, causing consider- areas. In addition, we emphasized the relationship between able socioeconomic loss and extensive degradation of the aquatic watershed area and the estimated multifractal parameters, such ecosystem (Bond et al. 2008; Zhang et al. 2008b; Vicuña et al. 2013;Wangetal.2014;Lietal.2016). In addition, the contin- uous intensified anthropogenic stresses in some areas increased * Xuchun Ye the complexity of the hydrological system and the risk of water [email protected] utilization (e.g., Zhang et al. 2012; Bonacci et al. 2016). Therefore, increasing attention has been paid to the investigation 1 State Key Laboratory of Hydrology–Water Resources and Hydraulic of hydrological variability and modeling for sustainable hydro- Engineering, Hohai University, Nanjing 210098, China environmental protection and disaster prevention. 2 Department of Geosciences, University of Oslo, 0316 Oslo, Norway Temporal fluctuations of streamflow are directly correlated 3 Key Laboratory of Watershed Geographic Science, Nanjing Institute with the occurrence of flood and drought frequency; under- of Geography and Limnology, Chinese Academy of Sciences, standing of these fluctuations can improve the statistical pre- Nanjing 210008, China diction and modeling of floods and droughts in river systems X. Ye et al. (Bunde et al. 2003;Regoetal.2013). Since the mid-twentieth width of the singularity spectrum is not well related to the century, scientists have realized the existence of scaling be- watershed area, although significant differences can be found havior in nature for hydrological and climatological time se- in the spectrum width of different rivers all over the world. ries (Lovejoy and Schertzer 1991; Zhang et al. 2008a; Labat These multifractal differences may come from the heteroge- et al. 2011). The complex behavior of these series can be neity of river basins with different climates and landscapes. characterized by the so-called Hurst exponent (or scaling ex- Furthermore, studies reported two possible sources of the ponent), which was first proposed by Hurst in studying the multifractality of streamflow records: one is due to a broad long-range correlation of storage capacity of reservoirs in the probability density function (PDF) of the time series, and the Nile River (Hurst 1951). The Hurst exponent is a dimension- other one is due to different correlations in small- and large- less estimator of the self-similarity of a time series, which can scale fluctuations (e.g., Movahed et al. 2006). Although both be used as a measure indicator of time series data to follow a of the sources can be affected by basin factors, no studies have random walk or biased random walk process (Ihlen 2012). examined the effect of the watershed area on the source of Therefore, the exponent provides a feasible way to quantify streamflow multifractality. the correlation properties of time series and makes it possible As an important influencing factor, human activities have a to identify similarities between different phenomena (Chianca potential to exert tremendous influences on the hydrological et al. 2005). It has been reported that the property of persis- processes of a river and so the multifractality of streamflow tence widely exists in hydrological and climatological series series. The study of White et al. (2005) indicated that the water over different time scales (Hurst 1951; Zhang et al. 2008a; reservoirs had evident modification to the periodicity proper- Rego et al. 2013). However, this long-range dependence was ties of streamflows during the operation of dams in the described by a single spectral scaling exponent at earlier ap- Colorado River in the Grand Canyon. Zhang et al. (2009) plications. Due to the highly nonlinear characteristics of pointed out that the water reservoirs could obviously alter streamflow processes, a multifractal description is required the scaling properties at a larger time scale in the East River, to fully characterize this complexity (e.g., Pandey et al. a tributary of the Pearl River. Zhou et al. (2014)investigated 1998). In recent years, multifractal analysis has drawn consid- the effect of dam construction on hydrological processes in the erable attention, and a number of studies have reported the Yangtze River and revealed that the fractal dimension spec- long memory and multifractal properties of streamflow for trum showed a significant difference during the construction various rivers (e.g., Kantelhardt et al. 2006;Regoetal. of the Gezhouba Dam. In addition, after the construction of 2013). To describe the complex behavior of the time series, the Gezhouba Dam, the minimal multifractal dimension at the the multifractal detrended fluctuation analysis (MF-DFA) pro- Yichang station started to be less than that at the Cuntan sta- posed by Kantelhardt et al. (2002) from a modified version of tion, suggesting that the streamflow becomes less fluctuated. DFA has been applied to detect the multifractal properties of These studies mainly focused on the effect of huge water nonstationary time series that are related to geophysical reservoirs. Furthermore, these studies ignored the effect of phenomena. other human activities, such as land cover changes, and some- Temporal fluctuation of streamflow series is mainly con- times, different human activities may accumulate or counter- trolled by local climate condition (mainly precipitation) and act each other and their specific effect cannot be well highly impacted by other basin factors, such as watershed recognized. area, river network, plant coverage, and water conservancy Up to date, although numerous studies have reported the project. Previous studies have examined the effect of the multifractal properties of streamflows for various rivers, the watershed area on the multifractality of streamflow link between these properties and the physical processes/ fluctuation. For example, Zhou et al. (2007) analyzed the daily factors that influence the streamflow is not generally under- streamflows of four small agricultural watersheds ranging stood (Hirpa et al. 2010). For example, how will the changes from 0.01 to 334 km2 and reported that the multifractal pa- in the watershed area affect the strength of the multifractal rameters do not change with watershed areas. Mudelsee effect of streamflow series and the source of streamflow (2007) investigated the relationship between long memory multifractality? In addition, the effect of human activities, and basin size of 28 stations located in six major river basins such as changes in land use and reservoir storage, on around the world and got the result that larger basins have streamflow fluctuations and on the multifractal properties is stronger memory than small basins. Similarly, Hirpa et al. not clear. It is also necessary to understand the integrity and (2010) revealed
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