Submarine groundwater discharge (SGD) has been widely recognized as a significant source of water and dissolved material transport from land to ocean. To quantify SGD into the northern Bohai Bay, China, naturally occurring radium isotope (226Ra) was measured in water samples collected along two transects in September 2012. Based on a tidal prism model, two different flushing times of the coastal water were determined to be 9.1 d and 11.5 d with respect to the different return flow factor (b) obtained from a physical model and a mass balance model of 226Ra and salinity, respectively. Using the derived flushing time, we developed a 226Ra mass balance model to estimate the SGD into the bay, which includes mixing, sedimentary input and SGD. The 226Ra budget indicated the 226Ra input from SGD accounted for 99% of the total tracer input to the northern Bohai Bay. We arrived at an average flux from SGD of 4.83 × 107 m3/d. The large volume of SGD confirms its importance in supplying a considerable quantity of nutrients to the bay.

INTRODUCTION

Interaction between fresh groundwater and coastal seawater in the aquifer results in two complementary processes: seawater intrusion and submarine groundwater discharge (SGD). The definition of SGD, defined by Burnett et al. (2003), is any and all flow of water on continental margins from the seabed to the coastal ocean, regardless of fluid composition or driving force. The driving forces of SGD are complicated, and include the hydraulic gradients, density gradients and a variety of oceanic processes such as waves and tidal pumping (Burnett et al. 2006; Geng & Boufadel 2015). As an important natural component of the hydrological cycle, SGD has been widely recognized as a significant source of water and an important pathway for nutrients and chemicals transport from land to the ocean (Burnett et al. 2006; Moore 2010).

Due to spatial and temporal variations in SGD, it is not easy to measure and evaluate its effect accurately. Currently, the main methods of measuring SGD include direct physical measurement (seepage meters), tracer techniques (radium isotopes, radon) and modelling methods. Among these methods, geochemical tracers are particularly useful for calculating SGD on large temporal and spatial scales (Kim et al. 2005). 226Ra, a naturally occurring isotope of the 238U-series with a half-life of 1,600 years, is a tracer of considerable interest in studies of SGD since it is conservative chemically and widely enriched in groundwater compared to surface waters (Moore 2003; Burnett et al. 2006).

Although the SGD has been quantified using radium and radon isotope methods in many coastal areas of China (Kim et al. 2005; Peterson et al. 2008; Wu et al. 2013; Xu et al. 2013; Wang et al. 2015), SGD in Bohai Bay is seldom estimated, particularly using geochemical tracers. Bohai Bay, surrounded by the Bohai Economic Ring, is considered to be one of the most polluted marine areas in China (Gao & Chen 2012). In recent years, Bohai Bay has encountered an increasing number of algal blooms due to its poor physical self-clean capacity and the continuous increase of the pollution inputs (Feng et al. 2011). In order to promote sustainable development and management in the coastal regions, the research on groundwater–seawater circulation in Bohai Bay must be paid much attention. Thus, our study aimed to evaluate the dynamics of groundwater discharge and associated impacts on the coastal ecosystem in northern Bohai Bay. We used 226Ra to estimate SGD into Bohai Bay, Bohai Sea, China.

METHODS

Field sampling

Our field work was conducted on September 21–23, 2012 in northern Bohai Bay, China. We collected 6 coastal groundwater samples along the shoreline and 10 surface seawater samples (1–2 m below the seawater surface). Ten surface seawater stations were oriented along two shore-perpendicular transects within ∼20 km distance offshore (Figure 1). 226Ra was collected from waters using the methods established by Moore (1976). Large volume water samples (∼30 L for seawater, ∼15 L for groundwater) were pumped and filtered through 1 μm filter for 226Ra extraction. 226Ra was extracted from the samples by passing the water slowly through a cartridge containing about 25 g of Mn-fibers (Moore 1976). The flow rate was controlled not to exceed 1.0 L min−1 to ensure complete 226Ra adsorption on the Mn-fibers. The extracting efficiency of 226Ra determined by two Mn-fibers connected in series was found to be ∼95.2%. The salinity, temperature, and pH of the water samples were measured in situ using a handheld HI9828 Model probe (HANNA).
Figure 1

Map of the study area and sampling stations. The dots and pentagrams represent sampling stations for seawater (S) and groundwater (GW), respectively. The numbers 1, 2, and 3 in the inset denote Bohai Bay, Liaodong Bay and Laizhou Bay, respectively.

Figure 1

Map of the study area and sampling stations. The dots and pentagrams represent sampling stations for seawater (S) and groundwater (GW), respectively. The numbers 1, 2, and 3 in the inset denote Bohai Bay, Liaodong Bay and Laizhou Bay, respectively.

226Ra analysis

In the laboratory, 226Ra adsorbed on Mn-fibers was analyzed using a radon-in-air monitor (RAD7, Durridge Co., Inc., USA) proposed by Kim et al. (2001). The 226Ra activity for the portable system is determined based on the equilibrium between 226Ra and 222Rn. Our procedure for 226Ra analysis in water samples can be summarized into the following steps. (1) The fibers were sealed for 20 d to allow 222Rn and its daughter nuclides to equilibrate with 226Ra. The weight ratios of water/fiber may be maintained between 0.7 and 2.5, and in this case the escape efficiency of Rn from Mn-fiber is optimal (Sun & Torgersen 1998; Kim et al. 2001). (2) Before the measurement, helium was circulated for 5 min through the detector chamber to sweep the residual 222Rn (218Po), creating a low background value and a dry environment. (3) The Mn-fiber column was connected to a desiccant column and a radon-in-air monitor through a closed loop. In order to quickly reach equilibration between 222Rn and 226Ra and reach the optimal detection efficiency, the system was allowed to run for 15 min before data collection. (4) To improve the accuracy of measurement, a long counting time of 8 h was set on every sample and the humidity in the closed loop was controlled not to exceed 10%.

RESULTS AND DISCUSSION

Hydrographic distribution

The results of 226Ra measurements are listed in Table 1, together with the latitude, longitude, pH, and salinity for all samples. 226Ra activity in coastal groundwater ranged from 8.38 to 79.07 dpm 100 L−1 with a large spatial variation (dpm means disintegrations per minute). 226Ra activity in surface seawater varied from 32.97 to 69.43 dpm 100 L−1, with an average of 52.20 dpm 100 L−1. Saline groundwater usually has high activities of Ra, because it contacts more sediment surfaces and gains large supply from decay of their insoluble parent isotopes in solid phase and desorption from the adsorbed phase (Moore et al. 2006; Luo et al. 2014). However, 226Ra activities in groundwater samples in this study were not high. In particular, some were lower than those in surface seawater. This is because the seawater samples were pumped at a depth of ∼2.5 m below the surface and they contained shallow groundwater from the surficial coastal aquifer. Surficial aquifers that are continuously flushed by seawater have low activities of 226Ra because there is not enough time for this isotope to regenerate from its parent (230Th).

Table 1

226Ra activities, salinity and pH for all water samples

Station Longitude Latitude Salinity pH 226Ra (dpm 100 L−1
Seawater 
S-1 118.878 39.148 26.70 8.11 46.59 
S-2 118.879 39.125 27.76 8.04 69.43 
S-3 118.879 39.080 27.48 8.07 58.29 
S-4 118.891 39.025 28.18 8.14 32.97 
S-5 118.899 39.011 28.29 8.09 55.02 
S-6 118.571 39.138 24.62 7.89 51.60 
S-7 118.598 39.104 27.30 8.01 45.67 
S-8 118.646 39.076 26.92 7.97 56.94 
S-9 118.669 39.035 27.57 7.99 51.09 
S-10 118.665 38.990 28.32 7.97 54.36 
Groundwater 
GW-1 118.957 39.155 19.90 7.56 21.80 
GW-2 118.872 39.156 12.50 7.12 79.07 
GW-3 118.568 39.148 5.54 7.93 55.57 
GW-4 118.476 39.055 14.57 7.24 8.38 
GW-5 118.226 39.058 0.27 8.26 18.51 
GW-6 118.138 39.204 23.44 6.79 36.53 
Station Longitude Latitude Salinity pH 226Ra (dpm 100 L−1
Seawater 
S-1 118.878 39.148 26.70 8.11 46.59 
S-2 118.879 39.125 27.76 8.04 69.43 
S-3 118.879 39.080 27.48 8.07 58.29 
S-4 118.891 39.025 28.18 8.14 32.97 
S-5 118.899 39.011 28.29 8.09 55.02 
S-6 118.571 39.138 24.62 7.89 51.60 
S-7 118.598 39.104 27.30 8.01 45.67 
S-8 118.646 39.076 26.92 7.97 56.94 
S-9 118.669 39.035 27.57 7.99 51.09 
S-10 118.665 38.990 28.32 7.97 54.36 
Groundwater 
GW-1 118.957 39.155 19.90 7.56 21.80 
GW-2 118.872 39.156 12.50 7.12 79.07 
GW-3 118.568 39.148 5.54 7.93 55.57 
GW-4 118.476 39.055 14.57 7.24 8.38 
GW-5 118.226 39.058 0.27 8.26 18.51 
GW-6 118.138 39.204 23.44 6.79 36.53 

Figure 2(a) and 2(b) show the variation in salinity and pH along the two transects, respectively. The surface water samples collected from transect I had salinity ranging from 24.6 to 28.3 with an average of 27, and the samples from transect II had salinity varying from 26.7 to 28.3 with an average of 28. The salinity of the surface seawater along two transects showed similar variation trend. The pH ranged from 7.89 to 8.01 with an average 7.95 for transect I and it varied from 8.04 to 8.14 with an average of 8.09 for transect II (Figure 2(b)). The pH in each transect was relatively stable and showed a lower value in Transect I compared with transect II. In general, both the salinity and pH of the samples from transect I were slightly lower than those collected from transect II. Based on the analysis of the two parameters, one can conclude that the freshwater discharge along transect I may be higher than that of transect II. This is likely because transect I includes longer shoreline.
Figure 2

Distribution of (a) salinity and (b) pH versus distance offshore for surface seawater samples along two transects based on surveys in September, 2012.

Figure 2

Distribution of (a) salinity and (b) pH versus distance offshore for surface seawater samples along two transects based on surveys in September, 2012.

Determination of flushing time

To explore the dynamics of coastal water in the northern Bohai Bay, the flushing time related to the timescale of material transport is introduced. Flushing time is used to describe the general exchange characteristics of a water body and is defined as the ratio of the mass or volume of a constituent (V) to its renewal rate (Q), or (Geyer et al. 2000). One can estimate the flushing time based on the tidal prism method (Sanford et al. 1992): 
formula
1a
 
formula
1b
where is the flushing time; V is the volume of water in the system; T is the tidal period; P is the tidal prism (total volume of seawater entering the system during a rising tide); b is the return flow factor (percentage of the tidal prism that returns from outside of the bay during the flood tide); A is the water surface area of the region; z is the water depth over tidal range (H = 1.73 m).

The above equations are based on the following assumptions: (1) the influence of wind is neglected; (2) the system should be well mixed (Sanford et al. 1992); (3) flushing is related exclusively to the tidal prism; (4) river flow and groundwater discharge must be small compared to tidal flow; (5) the tidal prism must be large enough to dilute the water in the system so that offshore water quality does not change.

To calculate the flushing time, the tidal prism (P), water volume (V) and return flow factor (b) should be determined firstly. The water volume of the system was defined as the product of the average area and water depth, which turned out to be approximately 2.30 × 109 m3. The tidal prism was determined by multiplying the average surface area by the tidal range during the sampling period to be 3.3 × 108 m3. The tidal period was equal to 0.52 d in the study area. The return flow factor (b) is the most difficult parameter to obtain. Here we used two methods for estimating the return flow factor.

Van de Kreeke (1983) defined the return flow factor using the outflowing ebb velocity and the incoming flood velocity as: 
formula
2
where v is the average value of the flood and ebb velocity; U is the net velocity (the difference between incoming flood velocity and the outflowing ebb velocity). The average velocities of the rising tide and falling tide are 28.3 cm s−1 and 25.0 cm s−1, respectively. Using the parameters above, we estimated v= 26.6 cm s−1 and U= 3.3 cm s−1. Solving this equation yields a return flow factor of b = 0.78.
To identify whether the return flow factor was applicable, we used another method to determine the flushing time in the region. Moore et al. (2006) developed a three-end-member mixing model for estimating the return flow factor. This mixing model was used to estimate the fraction of every end-member (groundwater, rivers, Bohai Sea): 
formula
3a
 
formula
3b
 
formula
3c
where , , and are the fractions of the open seawater (Bohai Sea), river and groundwater end-members, respectively; , and are the activities of 226Ra in open seawater, river and groundwater end-members, respectively; , and are the salinity in the open seawater, river and groundwater end-members, respectively; and are the 226Ra activity and salinity measured in surface seawater, respectively. The parameter was equal to zero, because no river discharged into the study area during our sampling time. We used the sample S4 as the seawater end-member and used the sample GW-2 as the groundwater end-member. Based on Equation (3), the average fractions of water from the open sea and groundwater were 0.83 and 0.17, respectively. The model calculated the amount of water from the open sea, which is simply the return flow. Thus we can treat as the return flow b. This is comparable with the physical estimate of b = 0.78.

Based on Equation (1a), we can derive the flushing time of 9.1 d and 11.5 d with respect to the return flow factor 0.78 and 0.83. The derived flushing time will be used for estimating SGD in the 226Ra mass balance model.

SGD estimate based on 226Ra mass balance model

In order to quantify the fluxes of SGD into the northern Bohai Bay, a 226Ra mass balance model was developed. The variation of 226Ra in a coastal water body is a balance of inputs into the system and losses from the system. The sources of 226Ra include SGD, rivers (dissolved 226Ra) and desorption from suspended particulate matter (SPM), diffusion from bottom sediments and ingrowth from parent isotopes. The loss of 226Ra is primarily mixing with open sea, because the decay term of 226Ra is not significant on the time scale of flushing time. We assumed that the water column was well mixed vertically; 226Ra fluxes from rivers and desorption from suspended particles were neglected since the discharge of rivers was very low during the sampling period. In addition, the ingrowth for 226Ra in seawater was neglected since the activity of 230Th in seawater would be negligible. Thus, 226Ra in this system was mainly influenced by SGD, diffusion from bottom sediments and mixing loss with the open sea, and the equation of 226Ra mass balance model can be written as follows: 
formula
4
where is the flux of groundwater (m3 d−1); , and are the 226Ra activity in groundwater, surface seawater along two transects and offshore water (the background activity of 226Ra), respectively; is the area of bottom sediments; is the regeneration rate of 226Ra from sediments (dpm m−2 d−1); is the flushing time (d).

The average flux of 226Ra from fine-grained sediments is very low compared with other three radium isotopes. Hancock et al. (2006) suggested the highest regeneration rate of 226Ra from fine-grained sediments was 0.66 dpm m−2 d−1 in the Great Barrier Reef. The value of this rate used by Hancock et al. (2006) was applied approximately to estimate 226Ra input from sediment diffusion. The area of the water–sediment interface of the study area is estimated to be 3.84 × 108 m2, based on which a 226Ra input from sediment diffusion of 2.53 × 108 dpm/d can be calculated (Table 2). To obtain the mixing loss, we used the lowest activity of 226Ra measured at station S-4 as the seawater end-member, i.e. . Using the flushing time of 11.5 d, 226Ra mixing loss was calculated to be 4.86 × 1010 dpm d−1. Given a representative value of 79.07 dpm 100 L−1 for 226Ra activity in coastal groundwater, we can determine a conservative SGD flux into the bay to be 4.83 × 107 m3 d−1 or 0.13 m3 m−2 d−1. All parameters needed in the model were listed in Table 2.

Table 2

Parameter values used in SGD mass balance model

Parameters Values 
226Ra in groundwater end-member  79.07dpm 100L−1 
226Ra in seawater end-member  32.97dpm 100L−1 
226Ra activity in surface seawater  52.20dpm 100L−1 
Area of bottom sediments 3.84 × 108m2 
The regeneration rate of 226Ra  0.66dpmm−2d−1 
Mean volume  2.30 × 109m3 
Flushing time 11.5d 
SGD flux  4.83 × 107m3d−1 
Parameters Values 
226Ra in groundwater end-member  79.07dpm 100L−1 
226Ra in seawater end-member  32.97dpm 100L−1 
226Ra activity in surface seawater  52.20dpm 100L−1 
Area of bottom sediments 3.84 × 108m2 
The regeneration rate of 226Ra  0.66dpmm−2d−1 
Mean volume  2.30 × 109m3 
Flushing time 11.5d 
SGD flux  4.83 × 107m3d−1 

Uncertainty analysis

Estimates of the SGD fluxes are always subject to inherent uncertainties associated with sampling and analytical measurements. In the 226Ra model, the uncertainties of the SGD calculation mainly depend on the groundwater end-member and flushing time in the study area. Because the contribution of the diffusion from bottom sediments to the total 226Ra flux was less than 1%, we can neglect the uncertainty caused by this term.

Defining the groundwater end-member activity is a crucial step in groundwater tracer studies and has been recognized as a major source of uncertainty (Peterson et al. 2008; Xu et al. 2013). The large groundwater end-member activity used in the model will cause a comparatively low SGD. Here, to assess the model uncertainty induced by this end-member selection, we conducted the model assuming 10% decrease of the end-member. In this case, the average SGD fluxes would increase by 11.1%.

The flushing time was based on the tidal prism mode via two independent estimates of the return flow factor. The short flushing time determined by scaling the water volume may result in high SGD rates. If a short flushing time was used (9.1 d) in our study, the corresponding modeled SGD flux would increase by 26.5%. Although there were large uncertainties in the groundwater end-member and flushing time, these values allowed us to estimate a conservative SGD flux into the bay. This value is comparable to the SGD fluxes in the regions of China's northern coast (Table 3).

Table 3

Comparison of SGD fluxes with previous studies on the coast of China

Sites Methods SGD (cm/d) Reference 
Yellow Sea 226Ra 0.3–1.7 Kim et al. (2005)  
Yellow River Delta 224,223,226Ra,222Rn 4.5–13.9 Peterson et al. (2008)  
Seepage meters 12.3–16.3 Taniguchi et al. (2008)  
Changjiang effluent plume 226Ra 3.1–14.6 Gu et al. (2012)  
Dongshan Bay 226Ra 24–230 Ji et al. (2012)  
Jiaozhou Bay 222Rn 6.4–8.3 Guo et al. (2013)  
Xiangshan Bay, East China Sea 222Rn 23–69 Wu et al. (2013)  
Laizhou Bay 226Ra 8.9–10.3 Wang et al. (2015)  
Northern Bohai Bay 226Ra 13 This study 
Sites Methods SGD (cm/d) Reference 
Yellow Sea 226Ra 0.3–1.7 Kim et al. (2005)  
Yellow River Delta 224,223,226Ra,222Rn 4.5–13.9 Peterson et al. (2008)  
Seepage meters 12.3–16.3 Taniguchi et al. (2008)  
Changjiang effluent plume 226Ra 3.1–14.6 Gu et al. (2012)  
Dongshan Bay 226Ra 24–230 Ji et al. (2012)  
Jiaozhou Bay 222Rn 6.4–8.3 Guo et al. (2013)  
Xiangshan Bay, East China Sea 222Rn 23–69 Wu et al. (2013)  
Laizhou Bay 226Ra 8.9–10.3 Wang et al. (2015)  
Northern Bohai Bay 226Ra 13 This study 

CONCLUSIONS

In this study, the flushing time and SGD flux in northern Bohai Bay were estimated for the first time using radium isotope and salinity as geochemical tracers. We can draw the following conclusions. (1) The salinity and pH of the samples from transect I were slightly lower than those collected from transect II. This showed that freshwater discharge along transect I may be higher than that of transect II. (2) The estimated return flow factor (b) was 0.78 and 0.82 based on two independent methods. This yielded flushing times of 9.1 d and 11.5 d, respectively. (3) A SGD flux of 4.83 × 107 m3/d into northern Bohai Bay was obtained based on a 226Ra mass balance model. It demonstrated that the primary input of radium to this system was SGD. The SGD, mentioned here, is a mixture of the terrestrial freshwater and the re-circulated seawater in the nearshore aquifer. The large volume of SGD confirms its importance in delivering nutrients to northern Bohai Bay. With new understanding of our assessment of SGD, the management of the bay related to water resources, ecology and environment in coastal and offshore areas should be reviewed. We hope that our results stimulate future work on these problems in this area.

ACKNOWLEDGEMENTS

This research was supported by the National Natural Science Foundation of China (Grant No. 41025009 and 41272267). The authors thank Tao Zheng, Zhenfei Xu, Long Xi, Shengtao Zheng and Zhigang Cheng for their field laboratory work.

REFERENCES

REFERENCES
Burnett
W. C.
Bokuniewicz
H.
Huettel
M.
Moore
W. S.
Taniguchi
M.
2003
Groundwater and pore water inputs to the coastal zone
.
Biogeochemistry
66
(
1–2
),
3
33
.
Burnett
W. C.
Aggarwal
P. K.
Aureli
A.
Bokuniewicz
H.
Cable
J. E.
Charette
M. A.
Kontar
E.
Krupa
S.
Kulkarni
K. M.
Loveless
A.
Moore
W. S.
Oberdorfer
J. A.
Oliveira
J.
Ozyurt
N.
Povinec
P.
Privitera
A. M. G.
Rajar
R.
Ramassur
R. T.
Scholten
J.
Stieglitz
T.
Taniguchi
M.
Turner
J. V.
2006
Quantifying submarine groundwater discharge in the coastal zone via multiple methods
.
Science of the Total Environment
367
(
2–3
),
498
543
.
Feng
H.
Jiang
H.
Gao
W.
Weinstein
M. P.
Zhang
Q.
Zhang
W.
Yu
L.
Yuan
D.
Tao
J.
2011
Metal contamination in sediments of the western Bohai Bay and adjacent estuaries, China
.
Journal of Environmental Management
92
(
4
),
1185
1197
.
Geng
X. L.
Boufadel
M. C.
2015
Numerical study of solute transport in shallow beach aquifers subjected to waves and tides
.
Journal of Geophysical Research: Oceans
120
(
2
),
1409
1428
.
Geyer
W. R.
Morris
J. T.
Pahl
F. G.
Jay
D. A.
2000
Interaction between physical processes and ecosystem structure: A comparative approach
. In:
Estuarine Science: A Synthetic Approach to Research and Practice
(
Hobbie
J. E.
, ed.).
Island Press, Washington, DC
, pp.
177
206
.
Guo
Z.
Ma
Z.
Zhang
B.
Yuan
X.
Liu
H.
Liu
J.
2013
Tracing Submarine groundwater discharge and associated nutrient fluxes into Jiaozhou Bay by continuous 222Rn measurements
.
Earth Science-Journal of China University of Geosciences
38
(
5
),
1073
1080
(in Chinese)
.
Hancock
G. J.
Webster
I. T.
Stieglitz
T. C.
2006
Horizontal mixing of Great Barrier Reef waters: Offshore diffusivity determined from radium isotope distribution
.
Journal of Geophysical Research
111
(
C12
),
doi:10.1029/2006jc003608
.
Ji
Z. Q.
Hu
D.
Weng
H. X.
Zhang
F.
Han
Z. D.
2012
Temporal and spatial variations of 226Ra in coastal sea and the estimation of submarine groundwater discharge (SGD)
.
Geochimica
42
(
1
),
15
22
(in Chinese)
.
Kim
G.
Burnett
W. C.
Dulaiova
H.
Swarzenski
P. W.
Moore
W. S.
2001
Measurement of Ra-224 and Ra-226 activities in natural waters using a radon-in-air monitor
.
Environmental Science & Technology
35
(
23
),
4680
4683
.
Moore
W. S.
1976
Sampling 228Ra in the deep ocean
.
Deep Sea Research and Oceanographic Abstracts
23
(
7
),
647
651
.
Moore
W. S.
2010
The effect of submarine groundwater discharge on the ocean
.
Annual Review of Marine Science
2
,
59
88
.
Moore
W. S.
Blanton
J. O.
Joye
S. B.
2006
Estimates of flushing times, submarine groundwater discharge, and nutrient fluxes to Okatee Estuary, South Carolina
.
Journal of Geophysical Research-Oceans
111
(
C9
),
doi:10.1029/2005jc003041
.
Peterson
R. N.
Burnett
W. C.
Taniguchi
M.
Chen
J.
Santos
I. R.
Ishitobi
T.
2008
Radon and radium isotope assessment of submarine groundwater discharge in the Yellow River delta, China
.
Journal of Geophysical Research
113
(
C9
),
doi:10.1029/2008jc004776
.
Sanford
L. P.
Boicourt
W. C.
Rives
S. R.
1992
Model for estimating tidal flushing of small embayments
.
Journal of Waterway Port Coastal and Ocean Engineering-Asce
118
,
635
654
.
Taniguchi
M.
Ishitobi
T.
Chen
J.
Onodera
S. I.
Miyaoka
K.
Burnett
W. C.
Peterson
R.
Liu
G.
Fukushima
Y.
2008
Submarine groundwater discharge from the Yellow River Delta to the Bohai Sea, China
.
Journal of Geophysical Research
113
(
C6
),
doi:10.1029/2007jc004498
.
Van de Kreeke
J.
1983
Residence time – application to small boat basins
.
Journal of Waterway Port Coastal and Ocean Engineering-Asce
109
(
4
),
416
428
.
Wang
X. J.
Li
H. L.
Jiao
J. J.
Barry
D. A.
Li
L.
Luo
X.
Wang
C. Y.
Wan
L.
Wang
X. S.
Jiang
X. W.
Ma
Q.
Qu
W. J.
2015
Submarine fresh groundwater discharge into Laizhou Bay comparable to the Yellow River flux
.
Scientific Reports
5
,
8841
.
Xu
B.
Burnett
W.
Dimova
N.
Diao
S.
Mi
T.
Jiang
X.
Yu
Z.
2013
Hydrodynamics in the Yellow River Estuary via radium isotopes: Ecological perspectives
.
Continental Shelf Research
66
,
19
28
.