Abstract

As the largest hydropower project in the world, the Three Gorges Dam (TGD) has drawn extensive concern in terms of its impact on downstream areas. In this study, an improved time delay estimation and wavelet analysis were used to investigate the influence of the TGD on the streamflow and sediment in the middle and lower Yangtze River, using time series of the daily discharge and sediment concentration data from three hydrological stations downstream of the dam. The results indicated that all of the time series at the three stations have prominent annual cycles, but the cycle of daily mean sediment concentration was nearly nonexistent after the impoundment of the TGD. Changes in discharge and sediment between the Yichang and the Hankou stations are larger than those between the Hankou and the Datong stations, which is mainly attributed to the streamflows of tributaries and Dongting Lake and the flood diversion area of Jingjiang. The transmission time of discharge for the whole Yichang–Datong river section is approximately 6 days. In addition, the attenuation of discharge from the Yichang station to the Datong station is 20%–30%. In contrast, the transmission of suspended sediment is slower than that of discharge, which takes 7–7.5 days to move from the Yichang station to the Datong station. The attenuation of sediment is approximately 30% in the Yichang–Datong river section and shows a clear increasing trend after 2006, mainly because a large amount of sediment was trapped by the TGD, and the dynamic balance of sediment was disturbed.

1. Introduction

The Three Gorges Dam (TGD), the world’s largest dam, was completed and began storing water in 2003. Large dams disrupt river continuity and unavoidably induce alterations in flow, sediment, and water temperature regimes (Chen et al. 2016; Li et al. 2011; Syvitski et al. 2005; Wang et al. 2016). With the construction of the TGD, considerable attention has been focused on how the dam impacts the river regime, especially the environment downstream in the middle and lower Yangtze River (Chen et al. 2016; Stone 2008). The middle and lower Yangtze River basin is one of the most developed and densely populated areas in China. The flow regime, changed by the TGD, may have a great influence on water supply and economic development. Furthermore, sediment deposition in the reservoir has reduced the amount of sediment in the middle and lower reaches and has even resulted in significant topographic changes. Therefore, it is necessary to understand the impact of the TGD on the flow and sediment regime downstream.

Variations in discharge and sediment at stations in the main stream or tributaries of the Yangtze River during recent decades have been documented in previous studies. Dai et al. (2008) examined the impacts of the TGD impoundment and serious droughts on river discharge reduction in 2006. A sharp decrease of sediment load after the impoundment of the TGD was reported by many studies (Li et al. 2011; Q. Zhang et al. 2012, 2013), and the significant downward trends in sediment flux were likely to be dominated by human activities, especially dam construction (Zhao et al. 2012). Gao et al. (2013) investigated flow regime changes in the middle and lower Yangtze River between the pre- and the postimpoundment periods and found that the TGD significantly reduced the mean flow in October, while river discharge increased in February. Compared to climate variability impacts on the catchment, the TGD has a much greater influence on the seasonal (September–October) dryness of Poyang Lake and has further altered the relationship between the river and the lake (Guo et al. 2012; Zhang et al. 2014). Mei et al. (2015) analyzed hydrological data from the Yichang, Hankou, and Datong stations and noted that the hydrology of the Yangtze River is mainly controlled by the TGD. Luan and Jin (2016) calculated and analyzed the impacts of the TGD impoundment on sediment based on a water-sediment transport numerical algorithm. These studies imply that the TGD may have a significant influence on river discharge and sediment transport in the middle and lower Yangtze River. Although the seasonality of the altered levels prevailed throughout the downstream reaches of the Yangtze River, the time of occurrence and magnitude of level changes varied among the stations (Wang et al. 2013). Understanding the lag time and transmission factors of river discharge and sediment between upstream and downstream is critical for building precise models and evaluating and improving control strategies for pollutants transported by the river. Nevertheless, there are insufficient studies on the lag time, river discharge transmission factor estimation (TFE), and suspended sediment in the Yangtze River.

Several methods have been used to investigate the impacts of dam construction, such as the Mann–Kendall test (Assani 2016; Chen et al. 2016), the continuous wavelet transform (CWT) technique (White et al. 2005; Zhang et al. 2012), time series analysis (Li et al. 2011), scanning t test (Q. Zhang et al. 2013), indicators of hydrologic alteration (IHA), and the range of variability approach (RVA; Alrajoula et al. 2016; Wang et al. 2016). However, previous studies on the Yangtze River focused more on analyzing individual stations, and few studies have evaluated the quantitative relationships between upstream and downstream regions. A more detailed investigation is required into the alterations of discharge and sediment regimes resulting from the TGD with more accurate methods. Time delay estimation (TDE) between the transmissions of an array of sensors has been utilized in different areas. For example, Hocking and Kelly (2016) quantified the time lag between rainfall and recharge in groundwater, and the results showed that the significant delay should be incorporated into numerical groundwater models or that it was a source of a calibration error. DeWalle et al. (2016) estimated the lag times between atmospheric deposition and stream chemistry by cross-correlating monthly data from four pairs of stream and deposition monitoring sites, noting that understanding lag times between changes in chemical inputs and watershed responses is critical to evaluating and refining pollutant control strategies. Xia et al. (2016) proposed a high-resolution time delay estimation scheme to obtain the complete time sequence structure of the geometric scattering of an underwater target. TDE is also used in optics, seismology, fault location, and biomedical engineering to evaluate lag time and attenuation (Liao et al. 2013; Ling et al. 2015; Yuan et al. 2014; Zhou et al. 2014). However, TDE is seldom applied in hydraulic engineering analysis at present.

Using the time series of daily mean discharge and sediment concentration data from the Yichang, Hankou, and Datong hydrological stations, the present study was conducted with the following objectives: 1) to assess the periodic variation in discharge and sediment concentration caused by the TGD and investigate the dependencies among stations and 2) to quantify the lag time and attenuation of discharge and sediment in the main stream.

2. Materials and methods

a. Description of the study area

The Yangtze River, the longest river in China, plays a vital role in Chinese economic development and environmental conservation. The expansive Yangtze River basin is divided into the upper, middle, and lower Yangtze reaches, though we concentrate on the middle and lower Yangtze River in this paper. The upper reaches extend from the river source to the Yichang station, which is located just downstream of the TGD. The middle reaches flow from the Yichang to Hankou stations with a length of 950 km. The lower reaches are 600 km long, flowing from Hankou to the river mouth, though the river section between the Hankou and Datong stations is defined as being within the lower reach in this study to avoid the influence of tides (Fig. 1). There are two large lakes in the middle and lower Yangtze River: Dongting Lake and Poyang Lake. Exchanges of water and material between the two lakes and the main channel support flood control, the ecosystem, and water supply in the middle and lower basin (Gao et al. 2013). The Yangtze River basin is characterized by a subtropical monsoon climate, and the monsoon-driven precipitation causes seasonal variability in the river flow, with high water and sediment discharge in the wet season from May to October (Li et al. 2011). The construction of the TGD has resulted in a series of changes in flow and sediment regimes. For example, after the impoundment of the TGD, a sharp decrease in the sediment load was widely reported. Therefore, understanding the impact of the TGD on downstream areas is necessary. In this paper, we use wavelet analysis to assess the periodic variation of hydrological features and the dependencies of discharge and sediment regimes among stations. We improve the time delay estimation algorithm to quantify the lag time and attenuation of discharge and sediment between upstream and downstream sites.

Fig. 1.

Locations of the selected hydrological stations in the Yangtze River basin.

Fig. 1.

Locations of the selected hydrological stations in the Yangtze River basin.

b. Data source

Data were sourced from the hydrological yearbook of China, consisting of discharges and sediments at the Yichang, Hankou, and Datong gauging stations in the main stream of the Yangtze River. These stations represent the hydrological regime of the upper, middle, and lower Yangtze reaches, respectively. The Yichang station, constructed in 1877, is located just downstream of the TGD with a distance of 46 km and can record direct responses to such a large project. The Hankou station, constructed in 1865, is a site in the middle reaches of the river, representing the mainstream hydrological regime change after the inflow of the Han River. The Datong station, constructed in 1922, is approximately 640 km away from the estuary and is the upstream limit of tidal influence. The test section for each station has a straight river channel. The dataset of daily mean discharges (m3 s−1) for the three stations ranges from 1951 to 2009, although the dataset of daily mean sediment concentrations (kg m−3) for these three stations ranges from 1980 to 2009. Based on the raw data, the daily mean sediment flux of each station is estimated, as follows:

 
formula

where is the daily mean sediment flux (kg s−1), is the daily mean sediment concentration (kg m−3), and is the daily mean discharge (m3 s−1).

Measurements and quality control were carried out by the Changjiang Water Resources Commission (CWRC; available at www.cjh.com.cn) of the Ministry of Water Resources. The details about measurement can be found in Yang et al. (2014) and Zhou et al. (2016), arraying 10–30 vertical profiles for a specific gauging station (cross section), and for each profile, depth and flow velocity were measured using a velocimeter at the surface and at 0.2, 0.4, 0.6, 0.8, and 1 water column depth. At the same depth, where the flow velocity was determined, water samples were collected using horizontally oriented water bottles. Suspended sediment concentrations were obtained after samples dry (at 105°C) and reach constant weights. Water discharge was determined as the product of cross-section area and mean velocity. Before these data were released to the public, they had gone through rigorous verification (e.g., eliminating outliers and correcting random errors) and uncertainty analysis following government protocols to ensure that the systemwide confidence level is above 95% (Dai and Liu 2013). Data published by CWRC have been widely used in geomorphology and hydrology studies at home and abroad (Gao et al. 2014; Xu and Milliman 2009). The quality control imposed by the surveying agencies ensured the reliability of the data, and the published work that used these data all attest to the trustworthiness of the data (Dai and Liu 2013).

Because of the matching of original datasets and the accuracy requirements of the wavelet analysis method, the daily mean discharges, daily mean sediment concentrations, and sediment flux used in the wavelet analysis were selected from 1990 to 2009. In the time delay estimation and transmission factor estimation, the daily mean discharges for the period 1951–2009 and the daily mean sediment flux for the period 1980–2009 were used.

Three approaches based on the wavelet transform method were applied in our study to assess the periodic variation of hydrological features and the dependencies of discharge and sediment regimes between stations. Meanwhile, the time delay estimation algorithm was improved to quantify the lag time and attenuation of discharge and sediment between upstream and downstream sites. The study procedure is shown in Fig. 2.

Fig. 2.

The study procedure.

Fig. 2.

The study procedure.

c. Description of wavelet analysis

Wavelet analysis has been extensively applied in the area of time series analysis and prediction due to its multiresolution and localization capabilities both in the time and frequency domains (Maheswaran and Khosa 2012). Put simply, wavelet transforms provide useful mathematic decompositions of original time series data at various resolutions by controlling the scaling and shifting factors of a single wavelet: the mother wavelet (Nalley et al. 2012). Torrence and Compo (1998) explained the wavelet transform method in detail.

Three wavelet transform approaches were applied in our study. We employed the continuous wavelet transform to assess the variations in the daily mean discharges and sediment concentrations at three stations along the Yangtze River. In addition, the cross wavelet transform and wavelet coherence analysis were used to assess the dependencies of the daily mean discharges and sediment flux among upstream and downstream stations along the river.

1) Continuous wavelet transform

The continuous wavelet transform of a time-dependent variable for a specific location is defined as (Fang et al. 2015)

 
formula

where is the complex conjugate of the mother wavelet , which can be selected from a variety of functions, t is the time, is the translation factor (time shift), and (>0) is the scale factor corresponding to different scales of observation. The Morlet wavelet, which we used as the wavelet function in this study, is defined as

 
formula

where is the central frequency of the wavelet (Torrence and Compo 1998; Yang et al. 2016)

2) Cross-wavelet transform

The cross-wavelet transform (XWT), as introduced by Hudgins et al. (1993), was developed to identify the cross-wavelet spectrum of two time series and understand how the phase angle represents mechanisms in the causal and physical relationships between the time series (Grinsted et al. 2004; Yu and Lin 2015). The XWT defined for two time series X and Y, with wavelet transforms WX and WY, respectively, is

 
formula

where * denotes a complex conjugate. Torrence and Webster (1999) defined the cross-wavelet energy as . The larger is, the more closely related the two time series are, and vice versa. The statistical significance of the cross wavelet is estimated using Monte Carlo methods with red noise to determine the 5% significance level (Torrence and Webster 1999).

3) Wavelet coherence analysis

As in the study conducted by Torrence and Webster (1999), the wavelet coherence (WTC) can be estimated using the squared absolute value of the smoothed cross-wavelet power spectrum of each selected time series. Hence, the squared wavelet coefficient is defined as

 
formula

where S is a smoothing operator. In Eq. (5), the value of squared wavelet coherence ranges between 0 and 1, indicating no dependencies and close dependencies, respectively. In general, XWT can better reveal the time–frequency areas with common high power, and WTC is appropriate for identifying the time–frequency correlations at the time–frequency areas with the low powers or in the nonprimary period (Maraun and Kurths 2004; Yang et al. 2016).

d. Analysis methods for time delay and transmission factor estimation

Time delay estimation between signals received by different sensors has been widely utilized in different areas, including radar, optics, seismology, fault location, and biomedical engineering (Liao et al. 2013; Yu and Lin 2015; Yuan et al. 2014; X. Zhang et al. 2013; Zhou et al. 2014). In this paper, we introduce TDE to obtain high precision and accuracy in the analysis of hydrological time series. The hydrological time series from the upper-stream stations are used as the source signals, those from the downstream stations are used as the received signals, and variations between upstream and downstream sites (such as the inflow of branches, exchanges of water and material between lakes and river, etc.) are considered to be noise. We improve the TDE algorithm to estimate the transmission time of discharge and sediment between upstream and downstream sites. Further, we then use the algorithm to extract components of the upstream stations from the downstream stations to calculate the loss along the main stream, which is a process called transmission factor estimation.

Suppose and are two independent received signals, and they satisfy equations

 
formula

where and where is the original source signal. The transmission factor is a . Both and are unknown additive background noise. The task is to find D using the N samples of and . In this paper, the cross-correlation method based on a maximum likelihood window is proposed. The cross function is defined as

 
formula

where is the correlation function of noise, is the autocorrelation function of the source signal, and A is the transmission factor, which is defined as

 
formula

If noise is an independent random process, will reach a peak value when is equal to and the estimated value of D is obtained. However, the peak value is often not clear in reality for a variety of reasons. Thus, preprocessing the raw data is needed, and the most common method is to multiply by a window function. Selecting various window functions has a great influence on the results of TDE. The maximum likelihood window function is the most common window function :

 
formula

where is the cross spectrum of two time series, and and are autocorrelation spectrums of these two time series.

To eliminate high-frequency noise and enhance the stability of the algorithm, we chose the Hamming window to process the intercept segments of time series after analyzing different types of filtering window functions. The Hamming window is essentially a low-pass filter and is defined as (Kumar et al. 2011)

 
formula

where N is the total length of all windowed time series. In this study, the length of the intercept segments of time series is relatively short, and the high-frequency noise cannot be completely eliminated just by using the Hamming window. Therefore, we proposed an improved maximum likelihood window function: , the peak value of which is clearly decreased. When the cross spectrum is multiplied by the improved maximum likelihood window function, certain frequencies of will not be strengthened excessively.

The comparison of cross-spectrum power is shown in Fig. 3. The cross spectrum without the window function cannot display the time delay correctly, while time delays are just peak values in these with window functions. However, there is interference from n = 20 to n = 70 in Fig. 3b, which is mainly because the magnitudes of the unimproved maximum likelihood window function have a sudden increase in this range while the extreme values of the improved function differ by an order of magnitude. Thus, interference suppression is achieved effectively through the improved maximum likelihood window function without affecting the main peak value.

Fig. 3.

The comparison of cross-spectrum power: (a) without the window function, (b) with the maximum likelihood window function, and (c) with the improved maximum likelihood window function.

Fig. 3.

The comparison of cross-spectrum power: (a) without the window function, (b) with the maximum likelihood window function, and (c) with the improved maximum likelihood window function.

To verify the improved algorithm, a random time series is generated, from which two new time series are intercepted at [1, n] and [1 + D, n + D] (n = 1024, D = 16), respectively. There is no attenuation in these two new time series; that is to say, the transmission factor equals 1.

The improved algorithm is used to estimate the time delay and attenuation of two time series. The results are shown in Fig. 4.

Fig. 4.

The result of the (left) TDE and (right) TFE of two time series.

Fig. 4.

The result of the (left) TDE and (right) TFE of two time series.

As seen in Fig. 4a, the result of the TDE is 16.00 ± 0.00, which is very precise and not very affected by high-frequency noise. The result of the TFE is 89.15% ± 0.63%, as shown in Fig. 4b, which is clearly lower than the expected value (100%).

3. Results and discussion

a. Hydrologic features of individual stations

1) Characteristics of daily mean discharge at individual stations

Figure 5 illustrates the raw data variations in the daily mean discharges at the three stations based on the CWT. The red area at the bottom (top) of the continuous power spectra represents strong variation at low (high) frequencies, while the red area on the left-hand (right hand) side indicates significant variation at the beginning (end) of the study period. According to Fig. 5, the daily mean discharges at all three stations show significant high variations at an annual scale (12 months), indicating that the daily mean discharges have a prominent annual cycle. There is no obvious change in power spectra at all three stations around the year 2003, implying that the TGD has not caused considerable variation in the period of daily mean discharge in the middle and lower reaches of the river. According to water-level scheduling, water is stored in the TGD in winter and released from the TGD in summer. Yang et al. (2015) argued that the seasonal effects of impoundment and release affect short-term discharge but not overall annual flow. It is therefore possible that there are changes in monthly or seasonal flows that are not detectable at the annual scale. Streamflow changes at the three hydrological stations might be attributed to climate change, such as precipitation, but not to anthropogenic factors, such as the construction of water reservoirs (Q. Zhang et al. 2013). In addition, Fig. 5 shows relatively high frequencies at a half-year scale (6 months) around 1998, which might be related to the large flood in the Yangtze River during that year. It is inferred that the intense flood pulse impacted the original hydrological pulse and resulted in an abnormal half-year scale. Additionally, half-year scales are shown in other years to some degree, but were neither clear nor continuous. Further, regarding the three-wavelet spectrum around 1998, it can be seen that the power of the half-year scale at the Yichang, Hankou, and Datong stations decreased stepwise, which means that the impacts of extremely large flows decreased successively between the three stations rather than increasing in sequence with the influence of inflows of tributaries and lakes. This indicates the important role of flood control and the distributary system in the middle and lower Yangtze River, weakening the effect of flooding on the hydrological period.

Fig. 5.

CWTs of the daily mean discharges. Shown are the continuous power spectra of the daily mean discharges from 1 Jan 1990 to 31 Dec 2009 at the (a) Yichang, (b) Hankou, and (c) Datong stations.

Fig. 5.

CWTs of the daily mean discharges. Shown are the continuous power spectra of the daily mean discharges from 1 Jan 1990 to 31 Dec 2009 at the (a) Yichang, (b) Hankou, and (c) Datong stations.

2) Characteristics of daily mean sediment concentrations at individual stations

Figure 6 illustrates the raw data variations of the daily mean sediment concentrations at the three stations based on the CWT.

Fig. 6.

As in Fig. 5, but for the daily mean sediment concentrations.

Fig. 6.

As in Fig. 5, but for the daily mean sediment concentrations.

Before 2003, similar to the daily mean discharges (Fig. 5), sediment concentrations (Fig. 6) also had a prominent annual cycle (12 months) at the three stations. However, since the TGD began to impound water, the daily mean sediment concentrations show abrupt changes at all three stations. Specifically, the annual cycle of sediment concentration at the Yichang station was nearly nonexistent in 2003, while this also occurred around the years 2003–04 at the Hankou and Datong stations. The difference between these stations might be attributed to the increasing of response time with distance from the TGD. The decrease in the average annual streamflow was less than 10% at the Yichang, Hankou, and Datong stations, mostly resulting from climate change (decreasing precipitation). In comparison, there was an abrupt reduction in sediment flux at the three stations after the construction of the Three Gorges Dam, with drastic reduction rates of approximately 88%, 70%, and 65% from pre-TGD to post-TGD at the Yichang, Hankou, and Datong stations, respectively (Zhao et al. 2012). It is possible that the sharp and massive reductions of sediment break the dynamic balance of sediment and hugely diminish the time series signal, resulting in abrupt changes in the annual variation laws. Changes in the variation laws are gradually postponed from upstream to downstream stations, indicating a time delay in the transmission of the impacts of the TGD along the main stream, mainly due to the dredging process in the Yichang–Hankou and Hankou–Datong river sections. The decreased sediment load from the upper Yangtze River might cause the erosion of the riverbed (Gao et al. 2014; Q. Zhang et al. 2013). From 2003 to 2006, channel erosion occurred, and an average of 70 Mt yr−1 of sediment was eroded along the river section between Yichang and Datong (Xu and Milliman 2009). Because channel-eroded sediment was generally coarser than suspended sediment, the median grain size increased from 5 μm at Yichang to 11 μm downstream at Hankou in 2005 (Bulletin of Yangtze Sediment 2005). Because of the active channel erosion downstream of the Three Gorges Reservoir (TGR) after June 2003, the bed level on the mainstem channel was lowered, and the process of sediment escaping from the main stem to Dongting Lake essentially ceased in 2004 (0 Mt; Xu and Milliman 2009). The erosion of the river channel, with considerable amounts of sediment received from tributaries merging at Poyang Lake and the Han River, supplements the sediment concentrations at the Hankou and Datong stations to some degree and contributes to a moderate decrease. This erosion, however, does not offset the loss of sediment in the TGR (Yang et al. 2007). Consequently, the annual variation laws of the daily mean sediment concentrations at all stations along the main stream change consequently, except for the differences in time. The spatial variations in the effects of the TGD may have complicated the sediment transport processes along the river (Guo et al. 2012). Studies that provide insight into the sediment change patterns may not be sufficient until a balance in this dynamic is achieved in the future. Thus, the question of how the TGD could have affected the stability of the river morphology deserves further investigation in the future.

Along the middle and lower reaches, the Yangtze River exchanges water and sediment with numerous tributaries and lakes (e.g., Poyang Lake and the Han River), which are rather complicated interactions. The river has lost some sediment (due to siltation and reclamation) through several passages but has received considerable sediment from tributaries. After the impoundment of the TGD, huge amounts of sediment built up behind dams, which led to sediment starvation in the mainstem, significantly changed sediment dynamics, and may have even caused erosion of the riverbed. It should be noted that sediment deposition was generally most serious in the initial years of the reservoir operation, and a gradually decreasing sediment deposition rate can be anticipated as time goes (Li et al. 2011). The influence of water reservoirs on the hydrological regime, particularly the sediment load and subsequent effects, can be far reaching (Q. Zhang et al. 2013). The TGD has been in operation for less than 20 years, while the dynamic balance of erosion and deposition between the reservoir and catchment and the interactions between the river and its tributaries and lakes needs considerably more time. Therefore, the balance of sediment is not achieved at this time.

b. Relationship of discharges between stations

To investigate the relationship of the daily mean discharges among upstream and downstream stations along the Yangtze River, the XWTs for the pairs are summarized on the left-hand side of Fig. 7, and the WTCs for the pairs are summarized on the right-hand side.

Fig. 7.

(a),(c) XWTs and (b),(d) WTCs of the daily mean discharge from 1 Jan 1990 to 31 Dec 2009.

Fig. 7.

(a),(c) XWTs and (b),(d) WTCs of the daily mean discharge from 1 Jan 1990 to 31 Dec 2009.

According to Fig. 7a, the common period of the daily mean discharges at the Yichang and Hankou stations is one year (except 1998), as both stations have a prominent annual cycle. There is an apparent high-frequency area around 1998; however, the interference in the XWT for the Yichang–Hankou station (Fig. 7a) is slightly larger than that in the CWT for the Hankou station (Fig. 5b). This is because the influence of flooding was smaller at the Hankou station than at the Yichang station. The interpretation of Fig. 7c is similar to Fig. 7a. Dependencies of the daily mean discharges at the Hankou and Datong stations are evidently similar to those at the Yichang and Hankou stations in terms of hydrological cycles. Specifically, the high-frequency interference around 1998 in the XWT for the Hankou–Datong stations is weaker than that at the Yichang–Hankou station because the impacts of extremely large flows decrease stepwise from upstream to downstream stations. More tributaries and lakes inflow into the main stream, and the influence of upstream input is increasingly moderate; thus, the change in the hydrological cycle is not so extreme at downstream stations. The WTC plot shown in Fig. 7b reveals the clear associations at a period of 12 months for the daily mean discharges at the Yichang and Hankou stations, with the R2 being mostly larger than 0.9 throughout all the study periods. However, some breakdowns in coherence can be observed for time scales more than 12 months, indicating a great difference between these two stations. It is inferred that the result is caused by streamflow from the Hanjiang River and Dongting Lake and the flood diversion area of Jingjiang between the Yichang and Hankou stations, not by the construction of the TGD. Comparatively, Fig. 7d demonstrates a good agreement between the Hankou and Datong stations, indicating a similarity of daily mean discharges at the two stations, which might be attributed to the fact that no large tributaries and no massive streamflow inputs are present in the lower Yangtze River basin (Q. Zhang et al. 2013). There is no obvious interference in all power spectra around the year 2003 in Fig. 7, implying that the TGD did not cause significant variation in the period of daily mean discharge in the middle and lower Yangtze River.

In this study, the time series of daily mean discharges at the Yichang, Hankou, and Datong stations between 1951 and 2009 are used to estimate the time delay and attenuation among these three stations.

According to Fig. 8a, it generally takes approximately 3 days for discharge to flow from the Yichang station to the Hankou station, except for the abnormally long time (up to 6 days) in 1956 and 2002. The abnormal values might be caused by the instability of the algorithm. Figure 8b illustrates the results of TFE for discharge flows from the Yichang station to the Hankou station and demonstrates that the attenuation of discharge is approximately 15% from the Yichang station to the Hankou station. Specifically, before 1963, the attenuation was more than 20% then rapidly dropped to approximately 15%. Decreasing attenuation can be observed from 1980 to 1985. The attenuation was characterized as increasing from 2000 to 2005, up to 20%, while a decreasing trend was found after 2005. Figure 8c displays the result of the TDE for the discharge flow from the Hankou station to the Datong station. We can see that the transmission time of flow for this segment is approximately 3 days, except in certain years, including 1993 and 1998, in which the time delay decreased to 1.5 days. The outliers appearing in 1953 indicate that the algorithm used in this study still needs further enhancement. The result of the TFE for the discharge flows from the Hankou station to the Datong station is presented in Fig. 8d. It can be seen from Fig. 8d that the attenuation first increased and then decreased before 1963, reaching a stable level of approximately 8% during the late 1960s and 1970s. After 1980, there was large fluctuation in individual years (such as in 1990 and 1996). The total discharge transmission time in the Yichang–Datong river section is approximately 6 days, which is calculated by adding the transmission times of both river sections together. This result basically agrees with the findings of Wang et al. (2013), who showed a time lag of 7–8 days from the TGD to the Datong station by calculating the cumulative flow-path travel time. In addition, our results indicated that the attenuation of discharge from Yichang to Datong is 20%–30%, which was acquired by multiplying the attenuation of both river sections together.

Fig. 8.

(a),(c) TDEs and (b),(d) TFEs of the daily mean discharge from 1951 to 2009.

Fig. 8.

(a),(c) TDEs and (b),(d) TFEs of the daily mean discharge from 1951 to 2009.

c. Relationship of the sediments between stations

Figure 9a demonstrates that the common primary period of the daily mean sediment flux at the Yichang and Hankou stations is 1 year (12 months) before 2003 (except the year 1998) because both stations show annual cycles. The primary-period energy attenuates sharply after 2003, clearly showing the effects of the Three Gorges Dam on sediment load. Moreover, the apparent high-frequency area around 1998 shows an expanding phenomenon, which is clearly the coupling of the daily mean sediment flux in Fig. 9a from 1997 to 2001. In addition to the rainy reason (from June to September), precipitation in the Yangtze River basin has a second peak in spring (from March to May). Therefore, the discharges of the tributaries and main stream increase considerably and even exhibit bimodality in the XWT for sediment flux, which reflects the semiannual cycle. This phenomenon is especially noticeable in dry years when river flow is relatively low in the rainy reason. Although the semiannual energy is not typically statistically significant at the 0.05 level, the coupling of this cycle with the high-frequency interference in 1998 strengthened the energy in this specific period, leading to a clear half-year period in Fig. 9a. The interpretation of Fig. 9c is similar to that of Fig. 9a. With regard to the common primary period, the Hankou and Datong stations also have an annual period, similar to the Yichang and Hankou stations. However, abrupt energy attenuation at the Hankou and Datong stations occurred around 2005, which is two years later than that at the Yichang and Hankou stations and one year later than that of their respective changes in period. This result indicates that, although sediment concentrations and sediment flux at the Hankou and Datong stations decreased quickly after the TGD commenced operations (2003), the transitive relation of these two stations was not broken until 2005. There is an evident time lag between upstream and downstream stations. The response time increased with the distance between the TGD and the study site because the sediment regime of the station at a distance from the reservoir was not only affected by sediment from upstream but also by the sediment from the local catchment (Li et al. 2011). Channel erosion downstream of the TGD plays a key role in sediment transfer in the middle and lower reaches (Xu and Milliman 2009). The WTC for the daily mean sediment flux is similar to that for the daily mean discharges. Although sediment flux at the Yichang and Hankou stations decreased sharply after the operation of the TGD, there is still a strong correlation between the two stations. Figure 9b demonstrates the clear associations with a period of 12 months for the daily mean sediment flux at these stations throughout all study periods. However, as to longer time scales of more than 12 months, there is a great difference between these two stations, which is independent of the construction of the TGD. Compared to Fig. 9b, Fig. 9d shows a good agreement between the Hankou and Datong stations, indicating a similarity in the daily mean sediment flux at the two stations on both annual and longer scales.

Fig. 9.

As in Fig. 7, but for the daily mean sediment flux.

Fig. 9.

As in Fig. 7, but for the daily mean sediment flux.

In this study, the time series of daily mean sediment flux at the Yichang, Hankou, and Datong stations between 1980 and 2009 are used to estimate the time delay and attenuation among these three stations.

According to Fig. 10a, the time delay of the suspended sediment concentration from the Yichang station to the Hankou station is approximately 3.5 days, longer than the transmission time of discharge. Moreover, the fluctuation of suspended sediment is larger than that of the discharge from the same period. The maximum TDE for suspended sediment appeared in 1994. It is possible that the start of construction on the TGD in 1994 disturbed the relatively stable transmission of sediment. The minimum value was observed between 2003 and 2004, which may have been because the TGD was completed and began storing water in 2003. After a period of time, the regular transmission of sediment began to recover. The outliers occurring in 2006 may have been caused by the instability of the algorithm. Figure 10b shows the result of the TFE for suspended sediment from the Yichang station to the Hankou station. The year 2006 was identified as the significant changepoint for this value. Before 2006, attenuation was steady at 15%–20%, but the attenuation has increased sharply to 30% since this changepoint, which might be the result of the massive sediment impoundment effect of the TGD and because the dynamic balance of sediment downstream of the dam is disturbed. After 2006, though the transmission factor increases a little, the attenuation of suspended sediment still exceeds 25%. Figure 10c illustrates the TDE for suspended sediment from the Hankou station to the Datong station. Overall, the time delay gradually decreased from 4 to 3.5 days. An abrupt drop appeared in 1989, but the cause of this is not readily clear. An abrupt drop also appeared in 1998, when an extreme flood occurred in the Yangtze River, and a third abrupt drop occurred in approximately 2006 after the second-phase water storage began in the Three Gorges Reservoir. The instability of the algorithm still caused outliers in 1984. The TFE plot shown in Fig. 10d demonstrates the obvious change in the attenuation of suspended sediment from the Hankou station to the Datong station over time. Specifically, from 1981 to 1986, the attenuation decreased from 20% to 5%. Later, it increased to approximately 10%. A decreasing trend can be observed again from 1995 to 2001, while after 2001, attenuation increased from 5% to 15%. The transmission of suspended sediment is slower than the discharge, taking approximately 7–7.5 days from the Yichang to Datong stations. The attenuation of suspended sediment is approximately 30% in the Yichang–Datong river section, which is more severe than the discharge.

Fig. 10.

As in Fig. 8, but for the daily mean sediment flux from 1980 to 2009.

Fig. 10.

As in Fig. 8, but for the daily mean sediment flux from 1980 to 2009.

4. Conclusions

In this paper, we analyzed the daily river discharge and sediment load data, and major findings are as follows:

  1. The annual cycle of daily mean sediment concentrations was nearly nonexistent after the impoundment of the TGD. Both daily mean discharges and sediment fluxes in the Yichang–Hankou river section exhibit larger changes than in the Hankou–Datong river section, a finding that is not the result of the impacts of the TGD.

  2. The transmission time of discharge in both river sections is approximately 3 days in preimpoundment periods and postimpoundment periods. Throughout the Yichang–Datong river section, the attenuation of discharge is 20%–30%. In addition, the transmission time of suspended sediment is slower than that of discharge. It takes approximately 3.5 days for sediment to flow from Yichang to Hankou station, and the fluctuation of sediment is larger than the fluctuation of discharge during the same period. From Hankou to Datong stations, the time delay exhibits a decreasing trend overall, decreasing from 4 to 3.5 days.

The attenuation of suspended sediment is approximately 30% in the Yichang–Datong river section and increased significantly after 2006 (up to 40%), a greater increase than discharge. The inflow of branches and exchanges of water and material between lakes and river are not included in the estimation of time delay and transmission factor.

This study ascertains the river discharge and sediment relationships between upstream and downstream stations, especially the lag time and attenuation thereof between stations, which provides an important contribution to the river management and restoration and improvement of pollutant control strategies in the middle and the lower Yangtze River basin. However, any assessment of the full impacts (including short- and long-term influence) of the dam should be based on large amounts of long-term data (Zhang et al. 2012), while the Three Gorges Dam has only been in operation for a short time. Because of the scarcity of historical data, there is some inevitable uncertainty in the present study. In addition, the dynamic balance of erosion and deposition in the river channel is the result of a complex superposition of many factors. Further analyses based on more extensive postdam data are needed to investigate the dam’s impact on the middle and lower Yangtze River.

Acknowledgments

This research was funded by the Fund for Innovative Research Group of the National Natural Science Foundation of China (51421065), the National Basic Research Program of China (Grant 2010CB429003), the National Natural Science Foundation of China (Grants 51409004 and 51409003), and the Interdiscipline Research Funds of Beijing Normal University.

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Footnotes

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