Abstract

The discharge at Datong station may be not entirely representative of net discharge into the East China Sea due to water abstraction and precipitation along the lower Yangtze River. Therefore, Xuliujing station, which is much nearer to the river mouth, is chosen to accurately estimate the net discharge in this paper. The Multi-Profile Method that considers the cross-sectional variability of flow is applied at Xuliujing station to estimate the tidal discharge. Then, the low-pass filter is used to remove the effects of the tide and the net discharge is obtained. Lastly, the net discharge in Datong and that in Xuliujing are compared. The results show that the net discharge of Xuliujing is obviously larger than that of Datong in the rainy season due to precipitation, but the net discharge of Xuliujing is slightly smaller than that of Datong in the dry season due to persistent abstraction and less precipitation. Therefore, in some degree, the net discharge of Xuliujing is more representative than that of Datong regarding the discharge into the East China Sea.

NOMENCLATURE

     
  • A

    cross-sectional area

  •  
  • ADCP

    Acoustic Doppler Current Profiler

  •  
  • a0, a1, a2

    u-U model coefficients

  •  
  • b

    constant in u-U model

  •  
  • c0, c1, c2

    polynomial coefficients in G-A relationship model

  •  
  • FPADCP

    Fixed-Profile ADCP

  •  
  • G

    water level

  •  
  • H

    depth of water

  •  
  • IVM

    Index Velocity Method

  •  
  • MPM

    Multi-Profile Method

  •  
  • Qdata

    discharge measured by VMADCP

  •  
  • Qfit

    estimated discharge

  •  
  • RMSE

    Root Mean Square Error

  •  
  • RSD

    Relative Standard Deviation

  •  
  • U

    mean channel velocity

  •  
  • Udata

    Qdata / A

  •  
  • Ufit

    estimated mean channel velocity

  •  
  • u

    index velocity

  •  
  • ui

    index velocity in profile Ci

  •  
  • VMADCP

    Vessel-Mounted ADCP

  •  
  • WN

    number of depth bins

  •  
  • λi

    coefficient of ui in u-U model

INTRODUCTION

Net discharge of estuaries is a key parameter in providing a reliable basis for water resources protection and planning (Kawanisi et al. 2009). The Yangtze Estuary, the largest estuary in China, is world-famous for its great abundance and development intensity of soil and water, water transport and fishery resources. With an annual average discharge rate of 3.0 × 104 m3/s, this river accounts for 90% of the total freshwater discharge into the East China Sea (Beardsley et al. 1985; Chen et al. 2001b). Datong is the last permanent hydrometric station with long discharge records on the main Yangtze River, located at the tidal limit of the Yangtze Estuary (Figure 1(a)). Downstream from this point, there is no large tributary, and the discharge is difficult to observe owing to the tidal influence. Thus, the discharge in Datong is commonly regarded as the net discharge into the East China Sea.

Figure 1

(a) Location of Datong and Xuliujing in Yangtze River (by Google Earth). (b) Topographic map of study section.

Figure 1

(a) Location of Datong and Xuliujing in Yangtze River (by Google Earth). (b) Topographic map of study section.

However, Datong is situated over 500 km away from the estuary (Figure 1(a)). On the one hand, the region along the lower Yangtze River is one of the most developed areas in China, containing nine cities and 13 countries. Thus, water abstraction and drainage could reduce the discharge into the estuary substantially during a dry year or a dry period (Chen et al. 2002; Zhang et al. 2012). In addition, agricultural irrigation and water transfer projects have become most important for seasonal changes in water discharge to the sea (Yang et al. 2015). On the other hand, the tributaries of the lower Yangtze River may transfer a considerable amount of runoff discharge to the main stream during the rainy season. In a word, cultivated fields accelerate the speed of runoff from agricultural fields into the Yangtze River during the flood season, while during droughts they take up a tremendous amount of water to the agricultural fields and decrease the water discharge into the estuary (Chen et al. 2001a). Therefore, the discharge measured in Datong may not be regarded as the net discharge into the East China Sea (Liu 2016).

The combined effects of water abstraction, agricultural irrigation and precipitation along the lower Yangtze River are likely to decrease, increase or not change the discharge into the sea, the key reference being the net discharge measured in the Yangtze Estuary. Xuliujing is about 110 km from the river mouth (Figure 1(a)) with a cross-sectional width of nearly 6 km (Figure 1(b)), and it is the demarcation point of the South and North tributaries and the starting point of multi-level branching in the Yangtze Estuary. Therefore, Xuliujing is an ideal section for determining the net discharge of the Yangtze River into the East China Sea.

According to the previous study, the flow in Xuliujing is complex and a non-synchronized current exists in the whole section (Zhao et al. 2016). Due to the intertidal variations of flow between thalweg and shallow water areas, the Multi-Profile Method (MPM) proposed by Zhao et al. (2016) is used for estimating the tidal discharge firstly in this paper. Then, the estimated instantaneous discharge in Xuliujing is low-pass filtered to remove the effects of the tide and the net discharge in Xuliujing can be obtained. Lastly, the net discharge in Datong and that in Xuliujing are compared and the difference between them is also discussed.

DATA AND METHODS

Data collection

To obtain the instantaneous discharge, a discharge monitoring system was designed and operated at the Xuliujing section by Zhao et al. (2016). Three upward-looking ADCPs with 300 kHz were mounted on the river bed of locations C1, C2 and C3 (Figure 1(b)), named the Fixed-Profile ADCP (FPADCP). The length of depth cell and sampling interval were set as 1.0 m and 0.5 hours in the FPADCP measurement. The tidally averaged depths of the three fixed profiles are 14.9 m, 51.5 m and 9.7 m, respectively. Three groups of FPADCP data in 2011 were extracted for calibrating mean channel velocity models (Table 1). Another three groups of data were also extracted in 2012 for validating the models (Table 2). In addition, a stage station was laid in the south end of the section to estimate the cross-sectional area (Figure 1(b)).

Table 1

Parameters of three sets of the half-hourly data extracted in 2011 for calibration

Time of data collectionNGmax (m)Gmin (m)Amax (104 m2)Amin (104 m2)Qmax,ebb (104 m3/s)Qmax,flood (104 m3/s)
Mar. 17 08:00, 2011 to Mar. 18 14:30, 2011 62 3.65 1.10 8.39 7.28 5.72 −8.48 
July 02 11:00, 2011 to July 03 16:00, 2011 (spring tide) 59 4.78 1.82 9.27 7.82 10.80 −9.71 
Oct. 14 11:00, 2011 to Oct. 15 16:30, 2011 60 4.27 1.42 8.94 7.59 8.06 −10.80 
Time of data collectionNGmax (m)Gmin (m)Amax (104 m2)Amin (104 m2)Qmax,ebb (104 m3/s)Qmax,flood (104 m3/s)
Mar. 17 08:00, 2011 to Mar. 18 14:30, 2011 62 3.65 1.10 8.39 7.28 5.72 −8.48 
July 02 11:00, 2011 to July 03 16:00, 2011 (spring tide) 59 4.78 1.82 9.27 7.82 10.80 −9.71 
Oct. 14 11:00, 2011 to Oct. 15 16:30, 2011 60 4.27 1.42 8.94 7.59 8.06 −10.80 

N means the number of records, Gmax, Gmin, Amax, Amin, Qmax,ebb and Qmax,flood means maximum water level, minimum water level, maximum cross-sectional area, minimum cross-sectional area, maximum discharge of ebb tide and maximum discharge of flood tide during the data collection, respectively.

Table 2

Parameters of three sets of the half-hourly data extracted in 2012 for validation

Time of data collectionNGmax (m)Gmin (m)Amax (104 m2)Amin (104 m2)Qmax,ebb (104 m3/s)Qmax,flood (104 m3/s)
Mar. 10 12:00, 2012 to Mar. 11 17:00, 2012 59 4.28 1.46 8.82 7.48 8.74 −9.68 
July 04 12:00, 2012 to July 05 16:00, 2012 (spring tide) 57 5.12 1.98 9.16 7.71 11.20 −9.65 
Oct. 16 11:00, 2012 to Oct. 17 16:00, 2012 (spring tide) 59 5.38 1.37 9.39 7.48 9.61 −13.80 
Time of data collectionNGmax (m)Gmin (m)Amax (104 m2)Amin (104 m2)Qmax,ebb (104 m3/s)Qmax,flood (104 m3/s)
Mar. 10 12:00, 2012 to Mar. 11 17:00, 2012 59 4.28 1.46 8.82 7.48 8.74 −9.68 
July 04 12:00, 2012 to July 05 16:00, 2012 (spring tide) 57 5.12 1.98 9.16 7.71 11.20 −9.65 
Oct. 16 11:00, 2012 to Oct. 17 16:00, 2012 (spring tide) 59 5.38 1.37 9.39 7.48 9.61 −13.80 

Meaning of the symbols is the same as in Table 1.

Meanwhile, Vessel-Mounted Acoustic Doppler Current Profiler (VMADCP) measurements were also implemented at every 0.5 hour in corresponding periods (Table 1) to obtain the discharge in the measured zone of the cross-section. The discharge of the unmeasured zone at the top and bottom was extrapolated by a best velocity model (Chen et al. 2016). Lastly, the discharge defined as Qdata can be obtained by VMADCP measurements of discharge plus extrapolated discharge of the unmeasured zone. With the calculated cross-sectional area (elaborated in the following section), Qdata can be used for calibrating and validating mean channel velocity models and evaluating discharge estimation methods.

In order to compare and verify the difference of net discharge between Datong and Xuliujing, the precipitation data in Nanjing and Shanghai cities (Figure 1(a)) on the lower Yangtze are also obtained from the national hydrometric stations. In addition, information about water abstractions and drainages along the river banks are based on documented material from local government agencies and the work of Liu (2016).

Multi-profile discharge estimation

The following paragraphs will briefly outline the MPM; more details can be found in Zhao et al. (2016).

Mean channel velocity estimation

ADCP can get velocity vi at each of the depth bins of a profile. So, the depth-mean velocity u used as index velocity can be represented as the following:  
formula
(1)
where WN is the number of depth bins. Similarly, index velocity can also be obtained by ultrasonic velocity meters which are set in both sides of the river (Ruhl & Simpson 2005) or Horizontal-ADCP (Simpson & Bland 2000). Then, the relationship between mean channel velocity U and u can be expressed in a linear form (Chen & Chiu 2002) or a polynomial form (Ruhl & Simpson 2005), namely, the u-U model:  
formula
(2)
where a0, a1 and a2 are the model coefficients. U can be solved by VMADCP discharge measurement and bathymetry measurement. Once U and u are acquired, these coefficients can be solved by least squares.
For a large estuary, the cross-sectional flow velocity variation is obvious (Chen et al. 2016), so the above Index Velocity Method (IVM) by using a single ADCP profile set at the thalweg cannot get a good estimation of discharge. Then, the relationship between mean channel velocity U with the three index velocities u in Xuliujing can be expressed as the following:  
formula
(3)
where ui is the depth-mean velocity of the Ci profile, λi is the coefficient of ui, and b is a constant. The principle of the multi-profile model depicted in Equation (3) can be understood as being that the mean channel velocity U is the result of the weighted average of all the three depth-mean velocities. The method represents a multiple linear regression with ui as independent variables. In theory, the stronger the representativeness of the ith profile is, the larger λi should be.

From the above depiction, MPM is an expansion of the traditional IVM by which the mean channel velocity U is estimated by the multiple ADCP profiles. The u-U model in the IVM is calibrated by only one depth-mean velocity as shown in Equation (2), but the u-U model in the MPM is calibrated by two or more depth-mean velocities as shown in Equation (3). In addition, on the one hand, for the IVM, the sole ADCP must be mounted at the thalweg of the section to obtain the velocity of the main stream. However, on the other hand, the multiple ADCPs can be mounted on the thalweg and river banks for the MPM, which can take the flow velocity variation near the bank into account.

Cross-sectional area estimation

Due to the quickly changing water level G in the tidal reaches, real-time measurement of cross-sectional area A is impossible. The G-A relationship model provides an available way to solve the problem. The general G-A model can be expressed as a polynomial form (Ruhl & Simpson 2005):  
formula
(4)
where c0, c1 and c2 are polynomial coefficients, and they can also be solved by least squares.

Calculation of discharge

After obtaining the mean channel velocity U and the cross-sectional area A, the instantaneous discharge Qfit can be calculated as U multiplied by A. If the discharge Qdata measured by VMADCP is the true value, the accuracy of the estimated discharge Qfit can be assessed by the Root Mean Square Error (RMSE) or Relative Standard Deviation (RSD):  
formula
(5)
where N is the number of discharge data. Once the time series of the tidal discharge has been calculated, a low-pass Butterworth filter can be applied to the data to remove the high-frequency tidal signals and then the net discharge can be obtained (Roberts & Roberts 1978). By comparing the net discharge at Xuliujing with that at Datong, the impact of water abstraction and precipitation along the lower Yangtze River on the change of net discharge into the East China Sea can be found.

RESULTS AND DISCUSSION

Calculation of tidal discharge

After obtaining the mean channel velocity models and cross-sectional area models which are calibrated by data from 2011, the corresponding three sets of data of cross-sectional area A and the mean channel velocities U in 2012 can also be calculated. Then, the instantaneous discharges Qfit can be estimated. Comparing with the discharge Qdata measured at the same periods, the RMSE and RSD can be calculated to assess the accuracy of discharge estimated by the IVM and MPM (Figure 2).

Figure 2

Discharge estimated by the IVM (left panel) and MPM (right panel). The mean channel velocity estimation models of the IVM and MPM are both calibrated by data from 2011 and validated by data from 2012.

Figure 2

Discharge estimated by the IVM (left panel) and MPM (right panel). The mean channel velocity estimation models of the IVM and MPM are both calibrated by data from 2011 and validated by data from 2012.

Theoretically, the accuracies of U, A and Q are all influenced by the errors in flow velocity u and water level G. The influence can be estimated by the law of error propagation. Supposed that the U is about 1.0 m/s and the mean A is about 78,000 m2, the accuracies of u and G are less than 0.05 m/s and 0.02 m in our experiment, which will induce about 0.04 m/s of U error according to the u-U models shown in Equation (3) and 96 m2 of A error according to Equation (4), and finally will induce 3,141 m3/s of Q error and 4.0% of relative estimation error. From this perspective, although the mean channel velocity estimation accuracy of the MPM was only improved by 0.02 m/s relative to that of the IVM (the calculation procedure of mean channel velocity is omitted), the discharge estimation accuracy can be improved by about 1,260 m3/s and 1.3% of relative discharge estimation. However, from Figure 2 we can see, the discharge estimation accuracy of the MPM is improved by 3,750 m3/s and the relative discharge estimation is improved by 5.1% relative to that of the IVM, which are far greater than the theoretical values of 1,260 m3/s and 1.3%, respectively. Therefore, for the Yangtze River with an annual average discharge of 3.0 × 104 m3/s, the improvement in the MPM is necessary and meaningful.

Figure 3 shows the time series of discharges Qfit (red line) estimated by the MPM and the measured discharges Qdata (blue line) in the three periods shown in Table 2. It can be found that both of them have a higher consistency in nearly all the tide cycles except for some peak values. This further proves that the MPM is valid in the tidal discharge estimation of a large estuary.

Figure 3

Time series of measured discharge Qdata (blue line) and the discharge Qfit estimated by the MPM (red line). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2017.181.

Figure 3

Time series of measured discharge Qdata (blue line) and the discharge Qfit estimated by the MPM (red line). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2017.181.

Calculation of net discharge

After the data of tidal discharge have been calculated, a low-pass Butterworth filter is applied to the data to remove the high-frequency tidal signals. A stopband period of 30 hours and a passband period of 40 hours are used: signals with periods less than 30 hours are not transmitted to the filtered data; signals with periods greater than 40 hours are transmitted to the filtered data with minimal loss; and signals that fall in the transition between the stopband and the passband are damped, but some fraction of them are transmitted to the filtered data. Filter ringing causes erroneous data at the beginning and end of a continuous data set; therefore, data for 2 days at the beginning and end of the time series and on either side of a data gap are rejected as part of this process.

Figure 4 shows the tidal discharge and filtered (net) discharge in March, July and October of 2011, and Figure 5 shows the monthly precipitation of 2011 at Shanghai and Nanjing, which are located on the lower Yangtze River (Figure 1(a)). It is clear to see that the averaged net discharge in summer (about 42,000 m3/s) is much larger than that in spring (about 10,000 m3/s) and autumn (about 22,000 m3/s). The reason for this is that rainfall along the Yangtze drainage basin directly controls the pattern of annual discharge distribution (Figure 5). The rainy season along the entire Yangtze drainage basin usually occurs from May to September. Precipitation along the lower basin is primarily between March and August, amounting to nearly 73% of the annual total (Chen et al. 2001b). In contrast, precipitation in the winter season (December and January) usually represents <1%. In this sense, the calculated net discharge is highly consistent with the precipitation, which indicates its rationality.

Figure 4

Time series of tidal discharge (blue line) and filtered (net) discharge (red line). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2017.181.

Figure 4

Time series of tidal discharge (blue line) and filtered (net) discharge (red line). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2017.181.

Figure 5

Monthly precipitation of 2011 in Shanghai (blue bar) and Nanjing (red bar). The data were obtained from the national hydrometric stations.

Figure 5

Monthly precipitation of 2011 in Shanghai (blue bar) and Nanjing (red bar). The data were obtained from the national hydrometric stations.

Comparison between the net discharge in Datong and Xuliujing

To quantify the difference of net discharge between the upstream and downstream of the Yangtze Estuary, net discharge in Datong (blue solid line), Xuliujing (red solid line) and the difference between them (green solid line) in 2011 are calculated and shown in Figure 6. It can be seen that the net discharge in Datong is basically consistent with that in Xuliujing during the whole year. However, in summer and autumn, especially in June, July, August and September, the net discharge in Xuliujing is obviously larger than that in Datong. In contrast, the net discharge of Xuliujing is slightly smaller than that of Datong in spring.

Figure 6

Time series of net discharge in Datong (blue solid line), Xuliujing (red solid line) and the difference between them (green solid line). In addition, the red dotted line means the 6-day-advanced net discharge in Xuliujing and the green dotted line means the difference between it and the net discharge in Datong. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2017.181.

Figure 6

Time series of net discharge in Datong (blue solid line), Xuliujing (red solid line) and the difference between them (green solid line). In addition, the red dotted line means the 6-day-advanced net discharge in Xuliujing and the green dotted line means the difference between it and the net discharge in Datong. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2017.181.

In order to explain the inconsistency of net discharge in some months of 2011, Figure 7 presents the monthly abstraction (blue bar), drainage (red bar) and the difference between them (green bar) from Datong to Xuliujing in 2011. According to Figure 7, it can be found that there is not much difference in abstraction through the whole year with all of them in the range 15 × 108–40 × 108 m3. However, the difference in drainage between the 12 months is remarkable due to precipitation and the values range from 5 × 108 m3 (dry season) to 95 × 108 m3 (rainy season). As a result, the difference of net discharge between Xuliujing and Datong (drainage minus abstraction) is positive in the rainy season and the net discharge is negative in the dry season. In other words, the net discharge of Xuliujing is more than that of Datong in the rainy season due to precipitation, but the net discharge of Xuliujing is slightly smaller than that of Datong in the dry season due to persistent abstraction and less precipitation.

Figure 7

Abstraction (blue bar), drainage (red bar) and the difference between them (green bar) from Datong to Xuliujing in 2011. The data are based on documented material from local government agencies and the work of Liu (2016). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2017.181.

Figure 7

Abstraction (blue bar), drainage (red bar) and the difference between them (green bar) from Datong to Xuliujing in 2011. The data are based on documented material from local government agencies and the work of Liu (2016). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2017.181.

Moreover, it is also worth noting that the discharge peak in Xuliujing is obviously delayed compared to that in Datong. According to a previous study (Zhang et al. 2012), 6 days are usually regarded as the time required for water to travel from Datong to the estuary. Therefore, the net discharge in Xuliujing is advanced by 6 days (red dotted line), and the difference between it and the net discharge in Datong is also calculated and shown in Figure 6 (green dotted line). From the figure, we can see that the advanced net discharge curve of Xuliujing is in good agreement with that of Datong, especially at the peak values. However, the conclusion remains unchanged that the net discharge of Xuliujing is larger than that of Datong in the rainy season and less than that of Datong due to the huge abstraction. The most likely explanation is that the deviation can be justified by the fact of flood (or generally hydrograph) routing.

CONCLUSIONS

  • (1)

    The discharge estimation accuracy of the MPM is improved by 3,750 m3/s and relative discharge estimation is improved by 5.1% relative to that of the IVM in our experiments, which are far greater than the theoretical values of 1,260 m3/s and 1.3%, respectively. Therefore, for large rivers like the Yangtze River, Nile River, Mississippi River and Amazon etc., the improvement is necessary and meaningful.

  • (2)

    Rainfall along the Yangtze drainage basin directly controls the pattern of annual discharge distribution so that the averaged net discharge in summer is much larger than that in spring and winter.

  • (3)

    The net discharge of Xuliujing is more than that of Datong in the rainy season due to precipitation, but the net discharge of Xuliujing may be less than that of Datong in the dry season due to persistent abstraction and less precipitation. Therefore, in some degree, the net discharge of Xuliujing is more representative than that of Datong for regarding discharge into the East China Sea.

ACKNOWLEDGEMENTS

The authors are grateful to the Yangtze Estuary Hydrology and Water Resources Survey Bureau for their generosity of providing the valuable observation data. This work would also not have been possible without the funding support of NSFC (Natural Science Foundation of China) (coded by 41404026), the Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG (coded by WE2016008), Research Funding of East China University of Technology (coded by DHBK2015312), Research Foundation of Education Bureau of Jiangxi Province (coded by GJJ150559), Natural Science Foundation of Jiangxi Province, China (coded by 20161BAB206163) and Director Foundation of the Second National Oceanic Research Institute of the State Oceanic Administration (coded by JG-1508).

REFERENCES

REFERENCES
Beardsley
,
R. C.
,
Limeburner
,
R.
,
Yu
,
H.
&
Cannon
,
G. A.
1985
Discharge of the Changjiang (Yangtze River) into the East China Sea
.
Continental Shelf Research
4
(
1–2
),
57
76
.
Chen
,
Z.
,
Li
,
J.
,
Shen
,
H.
&
Zhanghua
,
W.
2001b
Yangtze River of China: historical analysis of discharge variability and sediment flux
.
Geomorphology
41
(
2–3
),
77
91
.
Chen
,
Y.-C.
&
Chiu
,
C.-L.
2002
An efficient method of discharge measurement in tidal streams
.
Journal of Hydrology
265
(
1–4
),
212
224
.
Chen
,
Z.
,
Wang
,
Z.
,
Liu
,
Y.
,
Wang
,
S.
&
Leng
,
C.
2016
Estimating the flow velocity and discharge of ADCP unmeasured area in tidal reach
.
Flow Measurement and Instrumentation
52
,
208
218
.
Kawanisi
,
K.
,
Watanabe
,
S.
,
Kaneko
,
A.
&
Abe
,
T.
2009
River acoustic tomography for continuous measurement of water discharge
. In:
Proceedings – 3rd International Conference and Exhibition on Underwater Acoustic Measurements: Technologies and Results
,
Institouto Ypologistikōn Mathēmatikōn
,
Nafplion, Greece
, pp.
613
620
.
Liu
,
W.
2016
Water Abstraction along the Yangtze River Downstream from Datong to Estuary and Its Impact on Water Discharge into Estuary
.
Master's Thesis
,
East China Normal University
,
Shanghai, China
(in Chinese)
.
Roberts
,
J.
&
Roberts
,
T. D.
1978
Use of the Butterworth low-pass filter for oceanographic data
.
Journal of Geophysical Research: Oceans
83
(
C11
),
5510
5514
.
Ruhl
,
C. A.
&
Simpson
,
M. R.
2005
Computation of Discharge Using the Index-Velocity Method in Tidally Affected Areas
.
US Department of the Interior, US Geological Survey
,
Denver, CO, USA
.
Simpson
,
M.
&
Bland
,
R.
2000
Methods for accurate estimation of net discharge in a tidal channel
.
IEEE Journal of Oceanic Engineering
25
(
4
),
437
445
.
Yang
,
S. L.
,
Xu
,
K. H.
,
Milliman
,
J. D.
,
Yang
,
H. F.
&
Wu
,
C. S.
2015
Decline of Yangtze River water and sediment discharge: impact from natural and anthropogenic changes
.
Scientific Reports
5
,
12581
.
Zhang
,
E.
,
Savenije
,
H. H. G.
,
Chen
,
S.
&
Chen
,
J.
2012
Water abstraction along the lower Yangtze River, China, and its impact on water discharge into the estuary
.
Physics and Chemistry of the Earth, Parts A/B/C
47–48
,
76
85
.
Zhao
,
J.
,
Chen
,
Z.
,
Zhang
,
H.
&
Wang
,
Z.
2016
Multiprofile discharge estimation in the tidal reach of Yangtze Estuary
.
Journal of Hydraulic Engineering
142
(
12
),
04016056
.