East China Sea ecosystem under multiple stressors: Heterogeneous responses in the sea surface chlorophyll-a

Deep-Sea Research Part I 151 (2019) 103078

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East China Sea ecosystem under multiple stressors: Heterogeneous responses in the sea surface chlorophyll-a

T

Christina Eunjin Konga,b, Sinjae Yooa,b,∗, Chan Joo Jangb,c a

Jeju Research Institute, Korea Institute of Ocean Science and Technology, 2670 Gujwa-eup, Jeju-si, 63349, Republic of Korea Ocean Science and Technology School, Korea Maritime and Ocean University, Korea Institute of Ocean Science and Technology Joint Program, 727 Taejong-ro, YeongdoGu, Busan, 606-791, Republic of Korea c Korea Institute of Ocean Science and Technology, 385 Haeyangro, Yeongdo-gu, Busan, 49111, Republic of Korea b

ARTICLE INFO

ABSTRACT

Keywords: Multiple stressors East China sea Chlorophyll-a Warming Anthropogenic nutrient enrichment Changjiang river

The East China Sea and southern Yellow Sea ecosystems have undergone drastic changes over the past decades. The changes in the ecosystems are attributable to both natural and anthropogenic stressors. We analyzed the seasonal and interannual variability of the sea surface chlorophyll-a in the East China Sea and the southern Yellow Sea using a suite of remotely sensed data (1998–2012). When seen on a Large Marine Ecosystem level, there seems no trend in the region. However, when seen on a sub-regional scale, heterogeneous responses can be recognized among the subregions. There was an increasing trend of chlorophyll-a in the vicinity of the Changjiang (Yangtze) River mouth, while there was a decreasing trend in the southeastern slope area which can be attributed to anthropogenic nutrient enrichment and warming, respectively. Contrary to some previous studies, our analysis clearly showed that the summer-autumn averaged chlorophyll-a decreased by about 14% in a large area (circa 178,000 km2 ) in the northeastern East China Sea after 2003 coinciding with the initial impoundment of the Three Gorges Dam. Our analysis demonstrates that our ability to detect the trends in response to multiple stressors largely depends on choosing an appropriate spatiotemporal scale.

1. Introduction Marine phytoplankton, the bases of marine food webs, faces a diverse range of environmental conditions arising from local to basinscale changes. In the open ocean, studies have shown that global phytoplankton biomass and productivity have been decreasing over the past several decades (Behrenfeld et al., 2006; Polovina et al., 2008; Boyce et al., 2010). The decreasing trend has been largely attributed to ocean warming possibly with direct and indirect effects (Lewandowska et al., 2014). On the other hand, different trends have been reported in marginal seas, where the impact of anthropogenic forcing is much greater. For example, in the Yellow Sea (YS), the annual primary productivity has continuously increased since the late 1990s (Yoo et al., 2019). The increase is likely driven by anthropogenic nutrient enrichment through rivers (Yuan et al., 2008; Zhou et al., 2010; Li et al., 2015b; Wei et al., 2015), air (Chen et al., 2010; Kim et al., 2011), and submarine groundwater (Tan et al., 2018; Wang et al., 2018), allof which have intensified over the past decades. Surrounded by most rapidly industrialized countries with the immense population, the East China Sea (ECS) and Southern Yellow Sea

∗

(SYS) are recognized as one of the most exploited seas in the world ocean (Yoo et al., 2010). The seas also experienced the fastest warming among the world’s Large Marine Ecosystems (Belkin, 2009, 2016; Yeh and Kim, 2010). The climate as well as anthropogenic pressures alter environmental stressors, which are defined as factors that cause changes in the ecosystem (Rapport et al., 1985). Under multiple stressors, the ecosystem of these marginal seas seem to undergo an abrupt change. Acute symptoms such as an expansion of eutrophication-driven hypoxic zone (Chen et al., 2007; Liu et al., 2010; Wang et al., 2016; Zhu et al., 2017), the occurrence of harmful algal blooms (Tang et al., 2006; Wang and Wu, 2009; Shen et al., 2011; Yu et al., 2018) and macro-algal blooms (Hu et al., 2010; Xing et al., 2015) have been extensively observed in the marginal seas. Despite these signs of ecosystem changes, the primary productivity in the ECS, estimated as a whole, remained relatively stable without any discernible trend during the recent decades (Fig. 6 in Yoo et al., 2019). This is unexpected in that the ECS ecosystem has experienced a considerable amount of pressures from both anthropogenic and largescale climatic forcing, and thereby would be anticipated to undergo substantial changes. Thus it poses an interesting question on how a

Corresponding author. Jeju Research Institute, Korea Institute of Ocean Science and Technology, 2670 Gujwa-eup, Jeju-si, 63349, Republic of Korea. E-mail addresses: [email protected], [email protected] (S. Yoo).

https://doi.org/10.1016/j.dsr.2019.103078 Received 9 April 2019; Received in revised form 3 July 2019; Accepted 6 July 2019 Available online 08 July 2019 0967-0637/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

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Fig. 1. A bathymetric map (in meters) of the study region. The arrows represent the direction and intensity of the major currents; the Changjiang Diluted Water (CDW), Korean Coastal Current (KCC), Kuroshio Current (KC), Taiwan Warm Current (TWC), Tsushima Current (TC), Yellow Sea Coastal Current (YSCC), and Yellow Sea Warm Current (YSWC).

marginal sea ecosystem responds to the local and basin-scale environmental changes. In addition to the multiple stressors, assessing the status of the regional marine ecosystem is complicated by the fact the ECS and SYS lie in highly heterogeneous environmental conditions (Yoo et al., 2010). The ECS and SYS feature a complex bathymetry where the depth ranges from very shallow off the coasts of China and Korea to > 2000 m over the continental slope (Fig. 1). The marginal sea ecosystems are also influenced by a number of ocean currents including the warm and saline Kuroshio Current (KC) and the Taiwan Warm Current (TWC) from the south (Fig. 1). The seas also receive a large amount of nutrient and freshwater discharge from the Changjiang (Yangtze) River known for the fifth largest freshwater discharge in the world. Formed by mixing of the Changjiang River discharge (CRD) with ambient water, the Changjiang Diluted Water (CDW) can reach the sea around Jeju Island or even further offshore regions (Yamaguchi et al., 2012) depending on the seasonal behavior of ocean currents, East Asian monsoon wind, or both (Chang et al., 2014; Lie and Cho, 2016). While the CRD varies by many factors including precipitation and large-scale water transfer (Yang et al., 2015), the operation of the Three Gorges Dam (TGD) can altered the downstream CRD (Zhang et al., 2016; Lai et al., 2016; Tian et al., 2019). The TGD regulates the CRD by storing and releasing water seasonally (Guo et al., 2018). There have been opposing views on its potential impacts on the primary productivity in the adjacent seas (Gong et al., 2006; Yuan et al., 2007). Human regulation of the CRD provides an example of complex interaction of climate and anthropogenic forcing. The environmental heterogeneity can further complicate our understanding of how the multiple stressors affect the regional marine ecosystem. The magnitude of each stressor can be spatially heterogeneous further complicating ecosystem responses. If so, spatially

diverse responses can manifest in these marginal seas as a result of complicated interaction between environmental heterogeneity and multiple stressors. It therefore raises an important question of appropriate scales of observation and how we can disentangle the effect of multiple stressors in a complex marine ecosystem. To cope with these confounding issues, an appropriate scale of observation is a necessity. In this paper, we hypothesize that different subregions in the ECS and SYS ecosystem will respond differently to the multiple stressors. This paper is organized as follows: To disentangle the effect of multiple stressors in the heterogeneous environment conditions, we will classify the whole region into subregions that show a similar consistent chlorophyll-a (chl-a) patterns (1998–2012). We will then compare the seasonal as well as the interannual patterns in each subregion. To unravel the effect such as the operation of the world largest dam, we will also narrow down the seasonal time window where the CDW affects the outer shelf chl-a. From these comparisons, we will try to identify the major causative factor for each of the subregions. 2. Material and methods 2.1. Changjiang River discharge We used monthly CRD data (1961–2016) from Datong gauging station (117.37 °E, 30.46 °N) collected by the Changjiang Water Resources Commission of the Ministry of Water Resources in China (http://www.cjw.gov.cn). The Datong station is the most seaward tidal limit gauging station, located approximately 600 km upstream from the Changjiang river mouth (He et al., 2013; Yang et al., 2015). The CRD data measured at Datong station has been extensively used in the oceanographic community to observe its influence in the adjacent seas (Kim et al., 2009; Chen et al., 2009; He et al., 2013; Jiang et al., 2014). 2

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2.2. Remotely sensed data

2.3. Classification of subregions

Level-3 remotely sensed monthly mean chl-a, photosynthetic active radiation (PAR), euphotic depth (see Section 2.2.2) and remote sensing reflectance (Rrs ( ) ) data (1998–2012) were obtained from NASA’s Ocean Biology Processing Group (http://oceancolor.gsfc.nasa.gov/). To acquire higher temporal coverage, the two satellite products (Reprocessing 2014.0), Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (1998–2007) and MODerate resolution Imaging Spectroradiometer on the AQUA platform (MODIS-Aqua) (2003–2012) data with resolution of 9 km, were merged together using the NASA’s SeaWiFS Data Analysis System (SeaDAS) (http://seadas.gsfc.nasa.gov). For merging, we used an averaging method (IOCCG, 2007). In addition to these key variables, we also obtained the monthly mean sea surface temperature (SST) data from Physical Oceanography Distributed Active Archive Center at NASA’s Jet Propulsion Laboratory (https://podaac.jpl.nasa.gov/). To attain a consistent spatial resolution between the remotely sensed products, we further re-sampled the 4 km SST data to resolution of 9 km using the SeaDAS.

To cope with spatially heterogeneous responses, we divided the study area into a number of subregions with the consistent temporal variability of the chl-a (1998–2012). The method is organized as follows. First, we applied the Data INterpolating Empirical orthogonal function (DINEOF) technique (Beckers and Rixen, 2003) to the monthly logarithmically transformed chl-a anomaly data (1998–2012) to interpolate the missing values caused by clouds or other errors. The DINEOF is a parameter free technique which has been widely used to reconstruct the gappy remotely sensed data (Beckers et al., 2006; Miles and He, 2010; Taylor et al., 2013; Liu and Wang, 2013). We used the logarithmically transformed chl-a to meet the requirement of the empirical orthogonal function (EOF) analysis. Prior to the data reconstruction, the extreme chl-a values (> 80m g m 3) and inland water pixels were removed from the raw chl-a data. We also ensured that the chl-a data followed a log-normal distribution before applying the logarithmic transformation (Campbell, 1995). Using the reconstructed non-gappy chl-a data, we then conducted EOF analysis for data reduction using the singular value decomposition method (Ping et al., 2016). The first 10 principal components (PCs) were retained which accounted for 54.2% of the total variance of the data. The PCs after the first 10 (11–150 PCs) were not taken into account as they only explained less than 1–2% of the variance of the data for each. Lastly, we applied k-mean clustering algorithm to the time series of 10 PCs to classify the study area into a number of subregions with a similar temporal variability of the chl-a. By comparing the outcome of multiple runs with different k values, we finally chose k = 8 as it best describes important oceanographic characteristics of the study area (see Section 3).

2.2.1. Chlorophyll-a algorithm The two in-water empirical algorithms, NASA’s standard OC4/ OC3M version 6 algorithm and the Yellow Sea Large Marine Ecosystem Ocean Color Project (YOC) algorithm, were employed to estimate the chl-a. Using an extensive in situ dataset collected in the YS and ECS, the YOC chl-a algorithm was proposed by Siswanto et al. (2011) to reduce errors in the turbid waters. The YOC chl-a algorithm uses Rrs spectra at four wavelength (λ): 412, 443, 490, and 555 nm.

chl

aYOC = 10 (

2 0.166 2.158 × log10 (X) + 9.345 × log10 (X))

X= (R rs443/R rs555) (R rs412 /R rs490)

0.463

(1) (2)

2.4. Focus periods and seasons

The YOC chl-a algorithm is based on the three-component model of ocean color, originally proposed by Tassan (1994). The coefficients and exponents in the local empirical algorithm were estimated from the in situ chl-a matched with the SeaWiFS’s Rrs data (Siswanto et al., 2011). We further adopted the switching approach as suggested by Yamaguchi et al. (2012), depending on the water turbidity levels in the study regions. Based on the normalized water-leaving radiance at 555 nm (nLw (555)), Yamaguchi et al. (2012) defined three turbidity levels; highly turbid waters (nLw(555) > 2.5mWcm 2µ m 1sr 1), moderately turbid waters (1.5 m W c m 2µm 1sr 1 nLw(555) 2.5 m W c m 2µm 1sr 1), and relatively clear waters (nLw(555) < 1.5 m W c m 2µm 1sr 1). For instance, in the relatively clear waters, the chl-a was estimated using the NASA’s standard OC4/OC3M version 6 algorithm. For the highly turbid waters, the YOC algorithm was applied. In the moderately turbid waters, the chl-a was derived by a linear combination of the standard and local empirical algorithms using the nLw(555).

In the ECS and SYS, our ability to detect the chl-a trend associated with a particular stressor largely depends on choosing an appropriate spatiotemporal scale. One stressor we focused on is the change in the CRD since the operation of the TGD. The TGD has a water storage capacity of 39.3 billion m³ (Guo et al., 2018). Of the total storage capacity, about 56.4% or 22.15 billion m³ can be used for flood-control (Guo et al., 2018). Studies have shown that the impoundment of the TGD can influence the downstream CRD (Zhang et. 2016; Lai et al., 2016; Tian et al., 2019). Fig. 2a and (b) clearly show the impact of the impoundment on the CRD. In 2003, when the initial impoundment began, the reservoir’s water level was raised from < 70 m to 135 m in Jun. (Wang et al., 2013; Lai et al., 2016). Then in 2008, the water level was further raised to 170–175m (Wang et al., 2013; Lai et al. 2016). In reference to the two major impoundment phases, we divided the study period (1998–2012) into three consecutive subperiods. The Period-1 (1998–2002) represents the pre-impoundment or before the operation of the TGD. The Period-2 (2003–2007) and Period-3 (2008–2012) represent the initial and final impoundment phases of the TGD, respectively. To enhance our understanding of the impact of the impoundment on the CRD, ultimately the CDW on the adjacent marine ecosystem, we also calculated the interannual anomalies for two seasonal time windows: for the entire year (Jan–Dec) and for a particular seasonal time window of Aug–Dec, representing the summer-autumn seasons. In each subregion (Fig. 3), the entire year mean anomalies of the key environmental variables (i.e. chl-a) were calculated by subtracting the annual areal mean to the 15-year composite areal mean. The same method was applied to calculate the summer-autumn mean anomalies but using the mean of Aug–Dec. By defining the seasonal time window, we attempted to identify the region that has been significantly influenced by the changes in the CDW during the two post-impoundment phases. As observe in Fig. 2b, the

2.2.2. Photosynthetically active radiation in water column (PARwc) Since the depth-integrated light that the phytoplankton experience within the water column affects the photosynthesis and growth of phytoplankton, we calculated the euphotic depth-integrated PAR in water column (PARwc) from the sea surface PAR and the diffuse attenuation coefficient of down-welling PAR (Kd(PAR)). In calculating the Kd(PAR), we used the inherent optical properties (IOPs) centered semianalytical model introduced by Lee et al. (2002, 2005, & 2007). The performance of the model in the study region has been recently evaluated by Yoo et al., 2019. Through a comparative assessment of different Kd(PAR) models that are available, they suggested that the IOPcentered model performs the best in the study region. We calculated Kd(PAR) from the euphotic depth data which was processed using the IOP-centered algorithm by NASA. We then finally estimated the PARwc using the following equation:PARwc = (0.99x4.605 x PAR)/K d (PAR) 3

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Fig. 2. (a) Interannual CRD (m3s 1) anomaly for 1998 to 2012 measured at Datong gauging station. The bars represent the annual mean CRD anomaly for a particular seasonal time window of Jul–Nov. The dots represent the annual mean CRD anomaly for entire year (Jan–Dec). The vertical line indicates the year of 2003, the beginning of the impoundment of the TGD. (b) Monthly mean CRD (m3s 1) for the three consecutive subperiods; Period-1 (1998–2002), Period-2 (2003–2007), Period-3 (2008–2012). The vertical bars indicate the standard deviation. The grey shade indicates the months when the CRD has been significantly reduced. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

2.5. Statistical test To detect whether there have been any structural changes in the CRD time series that may be related to the operation of the TGD, we used the ‘Fstats’ function in the ‘strucchange’ package in R (https:// cran.r-project.org/web/packages/strucchange/vignettes/strucchangeintro.pdf). The Fstats function is designed to detect a structural change point in a time series by assuming a linear regression model (Zeileis et al., 2002). The Fstats is an extension of the Chow test (Chow, 1960) which does not require a specification of a structural change point. 3. Results The division of 8 subregions (Fig. 3) based on the consistent temporal variability of the chl-a (1998–2012) reflected some of the important oceanographic features of the ECS and SYS (Fig. 1). For example, R1 and R2 both lie in the considerably shallow waters with the average depth of < 25 m and 33 m, respectively. Although the two subregions are located near the Changjiang River mouth, the condition are quite different where R1 is under the direct influence of the CDW and R2 by the southerly Yellow Sea Coastal Current (YSCC). The subregions, R2 and R3, were also well divided. R3 extends from north of the Jeju Island to the southern tip of Korean Peninsula. Although the two contiguous subregions lie in the SYS (32.2–34.8 °N), compared to R2, R3 has water depth of < 100 m. Moreover, R3 is influenced by two major currents: the Yellow Sea Warm Current (YSWC) which generally flows northward (Lie and Cho, 1994; Yuan et al., 2008) and the Korean Coastal Current (KCC) which also flows northward along the west coast of Korea (Koh and Khim, 2014; Lie and Cho, 2016). Located along the contour of 50–200 m, the division of R4, R5, and R6 also well reflected the key oceanographic features of the study area. In the west coast of Taiwan, R5 is predominantly influenced by the TWC which flow northward (Lie and Cho, 1994). In the east along the continental shelf, R6 is influenced by the TWC as well as the KC. The condition in R4 is more complex where it is influenced by a number of different currents which vary seasonally and inter-annually (Chang and Isobe, 2003); the CDW, the Tsushima Current (TC), and the TWC. In the continental slope region (> 200 m), R7 and R8, are under the direct influence of the KC. The two subregions were divided at about 29.8 °N, where the TC branches off the KC (Ichikawa and Beardsley, 2002).

Fig. 3. A map showing the division of eight subregions, R1 to R8, in the study area. The solid black lines represent the bathymetry (in meters) of the study area. The yellow arrow represents the general direction of the Kuroshio Current (KC). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

monthly mean CRD has indeed been decreased during Jul–Nov of Period-2 and Period-3, while there was no clear difference in the other seasons. From this, we hypothesized that the impact of the impoundment on the CRD could be discernible only during this time of the year. However, we purposely employed the seasonal time window of Aug–Dec instead of Jul–Nov, representing the summer-autumn seasons, as it takes about a month or more for the CDW to reach and affect the outer shelf area of the ECS (see Section 4.2). Hereafter we used the term operation of the TGD to describe its overall activities, including impoundment.

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Table 1 Seasonal variability of chlorophyll-a and major hydrographic characteristics in the 8 subregions. Subregion

Depth (m)

R1 R2 R3 R4

< 30 < 50 < 100 50–150

R5 R6 R7 R8

< 150 50–200 > 200 > 200

Major Current (s)

CDW YSCC, CDW YSWC, KCC CDW, TC, TWC TWC TWC, KC KC, TC KC

Chlorophyll-a (1998–2012) Peak time (season)

Mean Winter chl-a ± sd.

Mean Spring chl-a ± sd.

Mean Summer chl-a ± sd.

Mean Autumn chl-a ± sd.

Spring Summer Spring Spring

(mg m 3 ) 0.95 ± 0.37 1.00 ± 0.41 0.88 ± 0.13 0.70 ± 0.13

Spring Spring Spring Winter

0.90 0.57 0.45 0.22

1.09 0.55 0.39 0.14

0.87 0.25 0.22 0.11

0.84 0.40 0.33 0.14

± ± ± ±

(mg m 3 ) 2.29 ± 0.77 1.25 ± 0.31 1.11 ± 0.38 1.02 ± 0.30

0.41 0.25 0.15 0.04

± ± ± ±

0.31 0.39 0.17 0.04

(mg m 3 ) 1.61 ± 0.52 1.42 ± 0.36 1.04 ± 0.21 0.84 ± 0.22 ± ± ± ±

0.12 0.09 0.05 0.02

(mg m 3 ) 0.90 ± 0.30 0.87 ± 0.22 1.10 ± 0.18 0.73 ± 0.14 ± ± ± ±

0.25 0.08 0.07 0.04

*Abbreviation: Changjiang Discharge Water (CDW), Korean Coastal Current (KCC), Kuroshio Current (KC), Taiwan Warm Current (TWC), Tsushima Current (TC), Yellow Sea Coastal Current (YSCC), and Yellow Sea Warm Current (YSWC). *Seasons: Winter (Jan–Mar), Spring (Apr–Jun), Summer (Jul–Sep), and Autumn (Oct–Dec).

Table 1 summarizes the major hydrographic characteristics and seasonal range of the chl-a in the 8 subregions. In general, the chl-a gradually decreased from the coast of China to the continental shelf (Table 1). It should be noted that the chl-a in the study region ranged by more than one order of magnitude. In the following subsections, we present the temporal variability of chl-a in the 8 subregions. We also examine the temporal variability of SST and PARwc (see Section 3.2). Based on these analysis, we will try to identify the major stressors which may have driven the chl-a changes in the 8 subregions (Section 4).

similar trend was observed in R7 and R8, which gradually shifted from the positive to negative chl-a anomaly patterns, with some irregularities among the years. In general, the interannual chl-a anomaly patterns in the ECS and SYS can be summarized by three distinctive trends; an increased trend (R1 and R2); a decreased trend (R4, R7, and R8); and no clear trend (R3, R5, and R6). An increased trend was observed in the coast of China, in R1 and more pronouncedly in R2 (Fig. 4). First, R1 lies in the vicinity of the Changjiang River mouth where the depth is shallower than 30 m. The subregion is mainly influenced by the nutrient-rich CDW (Fan et al., 2011; Kako et al., 2016). Of the 8 subregion, the highest chl-a occurred in R1 with a spring bloom peak of 2.29 ± 0.77 m g m 3during 1998–2012 (Table 1). In R1, the mean of entire year chl-a anomaly gradually increased throughout the three consecutive periods where Period-3 was about 0.24 and 0.06 mg m 3 higher than mean of Period-1 and Period-2, accordingly. Although the changes were less evident during the summer-autumn seasons, the mean of summer-autumn chl-a anomaly pattern also increased where Period-3 was about 0.11 and 0.015 m g m 3 higher than Period-1 and Period-2, respectively. To precisely understand the variation in the interannual chl-a, the seasonal chl-a pattern in respect to the three consecutive subperiods will be

3.1. Variability of chl-a 3.1.1. Interannual variability of chl-a Each subregion has undergone a distinctive spatiotemporal variation in the chl-a during the study period. Fig. 4 displays the interannual variability of chl-a anomaly in the 8 subregions, R1 to R8. As can be seen, both magnitude and general trends in the chl-a substantially varied between the subregions. For example, the interannual chl-a anomaly patterns for R2 and R8 were not only inverse of each other but the magnitude of the change differed substantially. On the other hand, a

Fig. 4. Interannual chl-a anomaly (mg m 3) in the 8 subregions, R1 to R8, during 1998–2012. The bars indicate the mean chl-a anomaly for entire year (Jan–Dec). The black dots indicate the mean chl-a anomaly for summer-autumn (Aug–Dec) seasons. The solid horizontal lines represent the multiannual mean (5-year) of the entire year chl-a anomaly for Period-1 (1998–2002), Period-2 (2002–2007), Period-3 (2008–2012), respectively. The same applies for the dashed horizontal lines but representing the multiannual mean of the summer-autumn chl-a anomaly. 5

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Fig. 5. Monthly mean chl-a in the 8 subregion (R1-R8) during the three consecutive periods; Period-1 (1998–2002), Period-2 (2003–2007), and Period-3 (2008–2012). The vertical bars indicate the standard deviation.

further assessed in Section 3.1.2. Just north to R1, R2 also exhibited a clear increased pattern. Although R1 and R2 lies in close proximity to each other with a similar environment condition (Fig. 1), R2 shows a summer peak pattern while R1 shows a spring bloom pattern (Table 1). Located in the western part of the SYS with an average depth of 33 m, R2 is influenced by the YSCC which continuously flows southward along the coast of China. The subregion is also influenced by the CDW (Chang and Isobe, 2003), depending on the intensity and seasonal behavior of East Asian monsoon wind and other environmental conditions. Since 2005, the interannual chl-a anomaly pattern in R2 shifted from a negative to positive, except for the year of 2011. The mean of entire year chl-a anomaly pattern gradually increased where Period-3 was about 0.33 and 0.14 m g m 3 higher than the Period-1 and Period-2, respectively. In summerautumn seasons, the pattern was less obvious than the entire year chl-a anomaly. For example, the mean of summer-autumn chl-a in Period-3 was lower than the Period-2 while there was a continuously increased pattern in the mean of entire year chl-a. Nevertheless, relative to the Period-1, R2 showed a clear increased trend throughout the study period. While we observed an increased pattern in the coast of China, the interannual chl-a pattern in west coast of Korea (R3) was not clear. In R3, the positive chl-a anomalies appeared in 2000, 2002, 2006–2007 and 2009 in both the entire year and summer-autumn seasons (Fig. 4). However, the difference among the three consecutive periods were less than ± 0.04 m g m 3which clearly indicate that the subregion did not endure much changes in the chl-a over the study period. Similarly, far down in the south, the interannual chl-a trend in R5 was inconspicuous throughout the study period. . For instance, a negative chl-a anomaly exhibited in 2005 (−0.11m g m 3) followed by a positive chl-a anomaly in 2006 (0.04m g m 3 ) and by another negative chl-a anomaly in 2007 (−0.11m g m 3 ), and so on. The interannual chl-a anomaly patterns for the entire year and summer-autumn seasons also differed by a substantial amount. For instance, an opposite trend was observed in 1998–2000, 2002, 2004, 2010, and 2012 between the two seasonal time window. In contrast to R3 and R5, a weak but notable trend exhibited in R6.

With the exception of 2010 and 2012, a negative chl-a anomaly pattern persisted since 2001/2002. A strong positive chl-a anomaly in 2010 (0.19m g m 3) was associated with the high chl-a in Apr (data not shown) which extended from the Changjiang River mouth to the north of R6. Another positive chl-a anomaly in 2012 was associated with the high chl-a in Mar along the coast of China. Because of the strong positive chla anomalies in 2010 and 2012, there was no clear trend in the mean of three consecutive subperiod. While the coastal waters showed an increased trend, a decreased trend was observed in R4, and more pronouncedly in R7 and R8. R4 lies in complex oceanographic conditions where it is influenced by three major currents which vary seasonally and inter-annually; the CDW, TC and TWC. Since 2004, a clear decreased trend was observed in the summer-autumn averaged chl-a (dots in Fig. 4 for R4) whereas the changes in the entire year averaged chl-a was relatively weak. With the exception of some years (i.e. 2007), the interannual chl-a pattern in R4 corresponded to the changes in the CRD (Fig. 2a). We will further discuss the relationship between the CRD and chl-a in R4 in Section 4.4. Since 2001/2002, a clear decreased trend also exhibited in the continental slope regions; R7 and R8. The two subregions are predominantly influenced by the warm and saline KC where average depth spans from 200 to 2000 m (Fig. 1). With the exception of some years (i.e. 2004), the negative chl-a anomaly patterns persisted in both R7 and R8. 3.1.2. Seasonal variability of chl-a To further understand the nature of the interannual variability of the chl-a, we also compared the seasonal variability of the chl-a in each subregion for the three consecutive sub-periods; Period-1 (1998–2002), Period-2 (2003–2007), and Period-3 (2008–2012). In general, the 8 subregions can be characterized by the three seasonal chl-a patterns (Fig. 5 and Table 1); a typical spring bloom pattern of mid-latitude with or without an autumn bloom (R1, R3, R4, R5, R6, and R7), a summer peak pattern (R2), and a winter peak pattern of the subtropical region (R8). With regards to the three consecutive periods, each subregion showed a distinctive seasonal chl-a change. In the coastal waters, R1 6

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Fig. 6. Interannual SST anomaly (°C) in the 8 subregions, R1 to R8, during 1998–2012. The bars indicate the mean SST anomaly for entire year (Jan–Dec). The black dots indicate the mean SST anomaly for summer-autumn (Aug–Dec) seasons. The solid horizontal lines represent the multiannual mean (5-year) of entire year SST anomaly in Period-1 (1998–2002), Period-2 (2002–2007), and Period-3 (2008–2012), respectively. The same applies for the dashed horizontal lines but representing the multiannual mean of the summer-autumn SST anomaly.

autumn seasons appears to coincide with the CRD pattern (Fig. 2b). Since R4 is located on the major pathway of summer CDW transport, such trend in R4 gives a strong hint that the subregion may have been influenced by the changes in the summer CRD (see also Section 4.4). In the most southern part of the ECS, R5, chl-a generally remained high in Mar–May and low in Dec–Feb. Although there was no clear trend in the interannual chl-a (Fig. 4), some differences were observed between the three periods. For instance, in contrast to Period-1, the chla gradually increased in the spring (i.e. Apr) and decreased in autumn (i.e. Oct) during Period-2 and Period-3 (Fig. 5). Such changes in the monthly averaged chl-a has weakened the bimodal pattern, spring and autumn chl-a blooms, as observed in Period-1. Consequently, the monthly mean chl-a in Period-2 and Period-3 showed an unimodal pattern of spring blooms. As described above (see Section 3.2), R7 and R8 showed a similar interannual chl-a trend which gradually decreased since 2001/2002. It also followed a typical subtropical chl-a pattern where it remained relatively low in summer and high in winter (Yoo et al., 2008). For these subregions, the chl-a gradually decreased throughout Period-2 and Period-3, especially in the autumn and winter seasons (Fig. 5).

and R2, we previously observed an increased trend in the interannual chl-a (Fig. 4). However, the source of such trend differed substantially between the subregions. First, the increased trend in R1 (Fig. 4) was largely attributable to the changes in the spring chl-a (Fig. 5). The changes in the spring is critical as a unimodal spring bloom pattern is dominant in the area (Table 1). The monthly averaged chl-a in spring was about 1.4 times greater in Period-2 and Period-3 than Period-1 (Fig. 4). Similar to R1, an increased trend was also observed in R2. With the exception of some years (i.e. 2011), a considerable amount of the chl-a increased throughout the study period (Fig. 5). In general, the seasonal chl-a pattern in R2 can be described by a unimodal distribution pattern; a peak in the summer season. However, some differences were observed between the periods. First, the seasonal chl-a pattern in Period-3 did not clearly trace the summer peak pattern as in Period-1 and Period-2. The chl-a pattern in Period-3 can be described by a long spring to summer peak pattern where a substantial amount of the chl-a increased throughout Feb–Jul. In contrast to the two previous periods, the chl-a in Period-3 increased throughout the year, especially in Feb–May (Fig. 5). In Period-2, the chl-a also increased but it generally followed the unimodal summer peak pattern as in Period-1 and the largest increased was in Jul–Aug. While in the west coast of SYS (R2), we observed an increased trend in the monthly averaged chl-a, the east coast of SYS (R3) did not show a clear change in the chl-a (Fig. 4). In R3, both the magnitude and timing of the chl-a peak were confined to a relatively similar range for all three consecutive periods. In general, the monthly averaged chl-a in R3 showed a bimodal pattern; a spring followed by an autumn chl-a bloom. The variation in the interannual chl-a was largely attributable to the changes in the summer-autumn seasons. However, the chl-a patterns were inconsistent and fluctuated from year to year by a considerable amount. In R4, the monthly averaged chl-a pattern can be described by a typical spring bloom. Prior to mid-summer, there was not much differences between the subperiods. Yet, in contrast to Period-1, the chl-a increased in Jun–Jul and Apr–May during Period-2 and Period-3, respectively. Moreover, from Aug and onwards, the chl-a gradually decreased over the study period. The decreased pattern in the summer-

3.2. Other environmental variables In the following subsections, we present the seasonal and interannual variability in the SST and PARwc in the 8 subregions. The changes in temperature and light availability can substantially affect the phytoplankton abundance and composition (Bopp et al., 2001; Boyce et al., 2010; Lewandowska et al., 2014). By comparing the seasonal and interannual chl-a patterns with these two key environmental variables, we will check whether the chl-a trends in each subregion can be explained by the changes in these variables. 3.2.1. Variability of SST In general, the interannual SST anomaly patterns can abe summarized by three distinctive trends; an increased trend (R1, R5, R6, R7, and R8), a decreased trend (R2), and no clear trend (R3 and R4). While we observed a similar chl-a trend in R1 and R2 (Fig. 4), the interannual SST anomaly for 1998–2012 displayed an opposite trend (Fig. 6). Since 7

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Fig. 7. Monthly mean SST in the 8 subregion (R1-R8) during the three consecutive periods; Period-1 (1998–2002), Period-2 (2003–2007), and Period-3 (2008–2012). The vertical bars indicate the standard deviation.

3.2.2. Variability of PARwc Based on the three consecutive periods, the interannual PARwc anomaly patterns can also be summarized by three major trends; an increased trend (R4, R7, and R8), a decreased trend (R5), and somewhat fluctuating (R1, R2, and R3). First, an increased trend was observed in R4. A positive PARwc anomaly pattern exhibited in 2000–2002, 2004–2005, 2007–2009, and 2011–2012 (Fig. 8). Generally, the interannual PARwc anomaly patterns in R4 is reflected by the changes in May–Sep (Fig. 9). Relative to Period-1, the mean of entire year PARwc anomaly in R4 increased by 4.7 and 9.8 mol photon m 1d 1during Period-2 and Period-3, respectively. The magnitude of changes in the interannual PARwc anomaly were greater during the summer and autumn seasons. Compare to Period-1, the mean of summer-autumn PARwc anomaly in R4 increased by 21.1 and 21.9 mol photon m 1d 1in Period-2 and Period-3, respectively. Such changes in the interannual PARwc were largely attributable to the gradual increase in Aug–Sep, whereas, the PARwc in May–Jul was highly variable throughout the study period. Similarly, an increased trend was also observed in the continental slope regions; R7 and R8. In R7, a positive PARwc anomaly pattern appeared in 2004–2005, 2007–2009, and 2011 (Fig. 8). In general, the magnitude changes were relatively small between Period-2 and Period3. Compare to the Period-1, the mean of entire year PARwc anomaly in R7 increased by 26.9 and 25.6 mol photon m 1d 1in Period-2 and Period-3, respectively. Of the 8 subregions, R8 showed the largest changes in the PARwc. Relative to the Period-1, the mean of entire year PARwc anomaly increased by about 49.4 and 45.9 mol photon m 1d 1during Period-2 and Period-3, respectively. In summer to autumn season, the mean of PARwc anomaly increased about 60.2 and 55.5 mol photon m 1d 1in Period-2 and Period-3 compare to Period-1, respectively (Fig. 9). Potential drivers of the changes in these subregions will be further discussed in Section 4.1 and 4.2. R5 is the only subregion which showed a decreased trend in the interannual PARwc anomaly. Similar to other subregions, the entire year PARwc anomaly in R5 were highly variable throughout May–Jul (Fig. 9). A negative PARwc anomaly pattern was observed in

2004, the SST in R1 gradually increased, especially in Jun, Nov, and Dec (Fig. 7). Compared to Period-1, the mean of entire year averaged SST in R1 increased by 0.70 and 0.84 °C in Period-2 and Period-3, respectively. The magnitude of interannual SST changes was much greater in the summer-autumn seasons. The summer-autumn averaged SST in Period-2 and Period-3 were about 1.14 and 1.30 °C warmer than Period-1, respectively. Similarly to R1, an increased trend also exhibited in R5, R6, R7 and most pronouncedly in R8. Since 2001/2002, the annual SST increased in these subregions, especially in autumn and winter seasons (Fig. 7). For instance, compared to Period-1, the mean of entire year SST anomaly increased by about 0.79 and 0.75 °C in R5, about 0.43 and 0.33 °C in R6, about 0.59 and 0.57 °C in R7, and about 1.01 and 1.2 °C in R8 during Period-2 and Period-3, respectively (Fig. 6). Of these four subregions, R8 showed the largest interannual SST changes throughout Nov–May, especially in Dec–Feb (Fig. 7). In contrast to R1, R2 showed a decreased trend in SST. A negative SST anomaly appeared in 2001, 2003, 2005, 2008, and 2010–2012 (Fig. 6). The cooling trend was evident especially in winter season (Fig. 7). Thus the pattern in summer-autumn SST were less obvious than the entire year SST anomaly. Although R3 and R4 did not show a clear interannual SST trend, some unique patterns were observed. First, in R3, the mean of entire year SST increased by 0.26 and 0.09 °C in Period-2 and Period-3 relative to Period-1, respectively. Generally, the positive SST anomalies in 2004–2009 were largely attributable to the increase in summer and winter SST, yet, the magnitude of SST changes were insignificant and it varied from year to year. Similar to R3, R4 also did not show a clear interannual SST trend. The magnitude of SST changes in R4 were also small and it varied from year to year. For instance, compared with Period-1, the mean of entire year SST anomaly in R4 increased by 0.17 °C in Period-2 while it decreased by 0.16 °C in Period-3. To summarize, the SST anomaly showed a decreased pattern while the chl-a anomaly reversely showed an increased pattern in subregions, R6, R7, and R8. In other subregions, there was no relationship between the two variables suggesting that temperature change had not influenced the chl-a change. 8

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Fig. 8. Interannual PARwc anomaly (mol photon m 1d 1) in the 8 subregions, R1 to R8, during 1998–2012. The bars indicate the mean PARwc anomaly for entire year (Jan–Dec). The black dots indicate the mean PARwc anomaly for summer-autumn (Aug–Dec) seasons. The solid horizontal lines represent the multiannual mean (5-year) of the entire year PARwc anomaly in Period-1 (1998–2002), Period-2 (2002–2007), Period-3 (2008–2012), respectively. The same applies for the dashed horizontal lines but representing the multiannual mean of the summer-autumn PARwc anomaly.

1999–2001, 2005–2006, 2008, and 2010–2012. The mean of entire year PARwc anomaly in Period-1 and Period-2 were relatively similar. Yet, the PARwc anomaly decreased by about 18.7 mol photon m 1d 1 in Period-3 compare to Period-1. In both R1 and R6, the PARwc anomaly trends fluctuated from year to year (Fig. 8). In R1, a positive PARwc anomaly pattern persisted throughout 2003 to 2009 which coincided with the positive chl-a anomaly pattern (Fig. 4). After 2009, the PARwc anomaly pattern shifted to negative, while the chl-a anomaly remained a positive

pattern. The mean of entire year PARwc in R1 during Period-2 and Period-3 were about 14.09 and 8.0 mol photon m 1d 1higher than Period-1, respectively. In R2 and R3, the PARwc anomaly pattern fluctuated more frequently. In summary, the PARwc anomaly was negatively correlated with the chl-a anomaly only in the subregions, R4, R6, R7, and R8, where the chla showed a decreased pattern. We further discuss the nature of this relationship in Section 4.2.

Fig. 9. Monthly mean PARwc in the 8 subregion (R1-R8) during the three consecutive periods; Period-1 (1998–2002), Period-2 (2003–2007), and Period-3 (2008–2012). The vertical bars indicate the standard deviation. 9

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4. Discussion

attributed to the CRD temperature changes, ultimately the CDW. Since the mid-1980s, the mean annual CRD temperature has risen by about 1–1.5 °C (Yang et al., 2015). As R1 lies in the vicinity of Changjiang River mouth, the river temperature will strongly affect the SST variability. Moreover, an inverse relationship is observed between the monthly mean CRD (Fig. 2b) and SST (Fig. 7) during Period-2 and Period-3, considering a time lag of 1-month for the CRD to disperse in R1. This implies that the warming in R1 could be associated with the reduction in CRD. Nevertheless, the persistent warming in R1 did not influence the interannual chl-a trend (see Section 3.2.1). We will argue that the increase in chl-a in R1 is strongly influenced by anthropogenic nutrient enrichment (see Section 4.3). Coastal upwelling has been an important process in R1 (Hu and Wang, 2016). However, since the SST in R1 increased during the study period, upwelling intensity may have not increased or even decreased in the area. Thus the effect of coastal upwelling cannot explain the chl-a increase in R1. In contrast to R1, the warming trend (Fig. 6) in the continental slope and shelf regions are related to changes in mid-autumn to spring seasons (Fig. 7). These subregions are all commonly influenced by the KC, which carries warm and saline tropical water to the ECS (Tan and Cai, 2018). Thus the warming trend in R5-R8 could be strongly associated with anomalous warm advection by the KC (Zhang et al., 2010; Wang et al., 2013; Tan and Cai, 2018). The degree of its influences also varies, depending on the proximity to the KC. For example, of these subregions, the magnitude of SST changes were largest in R8 which lies in the main path of the KC. Since the major source of nutrients in the continental slope and shelf regions are primarily supplied by the vertical mixing and upwelling in winter (Liu et al., 2010; Yatsu et al., 2013), the recent warming may have strengthened the stratification, reducing the winter overturning for nutrient supply to the upper ocean (Behrenfeld et al., 2006; Doney, 2006). The change in the seasonal SST pattern in R8 provides a further evidence for this interpretation. The warming in R8 was strongest in Dec–Feb but negligible in Jun–Oct. This indicates that warming in winter has weakened winter mixing, which has reduced nutrient supply, hence reduced phytoplankton biomass in R8. An alternative explanation that enhanced zooplankton grazing may have reduced the phytoplankton biomass (Sommer and Lewandowska, 2011) is not likely because warming occurred only in winter but the chla was reduced throughout the year (R8 in Figs. 7 and 5). Although there was no clear trend in R5 and R6, the gradual decreased in the chl-a of R7 and R8 (Figs. 4 and 5) during recent warming provides an insight on how future climate change can alter the adjacent marine ecosystem. If the winter sea surface warming continues, the thermal stratification will be fortified which will further reduce mixing and nutrient supply available in the upper ocean, decreasing the primary production and the chl-a there (Doney, 2006). Further study is needed to understand the mechanism of the recent warming in the continental slope and shelf regions of the ECS.

Dividing the ECS and SYS into 8 subregions based on the 15-year chl-a variability (Fig. 3) has revealed that each subregion underwent different changes over the study period. In general, we observed three types of the chl-a patterns (Fig. 4). An increased trend was evident in the coastal waters, R1 and more pronouncedly in R2. A decreased trend was evident in the outer shelf and slope regions; R7 and R8. A decreased trend was also observed in R4 but only during the summerautumn seasons. In the remaining subregions, R3, R5, and R6, there were no clear trend. In the following subsections, we will try to identify the major stressors which drove such distinctive chl-a variations. There are several potential factors that can affect the phytoplankton biomass. For instance, the chl-a can increase if there is an increase in the nutrient supply, solar radiation, water column transparency, or a decrease in grazing, and vice versa. Of these factors, there seems no logical reason to assume that the abundance of grazers is reduced or increased significantly only in a particular subregion. Hence, the effect of grazing will be excluded as a possible cause of the chl-a variations. Of the 8 subregions, we will also exclude the subregions, R3, R5 and R6 in our discussion which did not show a notable chl-a trend over the study period. 4.1. Warming The SST in the ECS has been rising rapidly over the past several decades (Belkin, 2009, 2016; Bao and Ren, 2014; Wang et al., 2019). Yet, the effect of warming on phytoplankton biomass has not been well discussed as the regional sea encompasses a complex environmental condition (Yoo et al., 2010). Multiple shreds of evidence suggested that ocean warming can substantially affect the phytoplankton biomass (Behrenfeld et al., 2006; Polovina et al., 2008; Boyce et al., 2010). The two major mechanisms include a physically mediated effect of warming on vertical stratification which indirectly affects the phytoplankton growth by limiting nutrient supply available in the upper layer of ocean (Behrenfeld et al., 2006; Polovina et al., 2008; Boyce et al., 2010; Belkin, 2016) and a direct effect of SST warming on phytoplankton and zooplankton metabolic rates (Sommer and Lewandowska, 2011; Lewandowska et al., 2014; Marañón et al., 2018). On a subregional scale, a rapid sea surface warming was observed in the coastal (R1), continental shelf (R5 and R6), and slope (R7 and R8) regions of the ECS during 1998–2012 (Fig. 6), which is fairly consistent with recent observation by Bao and Ren (2014) and Wang et al. (2019). In these subregions, the interannual SST anomaly patterns shifted to positive since 2002/2003 (Fig. 6). However, the monthly mean SST patterns were not spatiotemporally homogenous and the magnitude of change varied across the regional sea. For example, a rapid SST warming trend in R1 (Fig. 6) was attributed to the changes in Jun, and Sep–Nov (Fig. 7) while the trend in R5-R8 (Fig. 6) was attributed to the changes in Nov to Apr/May (Fig. 7). Moreover, while R5, R6, R7, and R8 showed a similar warming trend (Fig. 7), the magnitude of change was much more marked in R8 compared to other subregions. These results suggest that the warming trend in the ECS is induced by multiple processes. Thus, the effect of warming on the chl-a will likely vary across the regional sea, depending on the location and its proximity to a dominant driving force of SST variability. Several processes have been suggested to describe the warming trend in the ECS, including the anomalous warm advection by ocean currents (Zhang et al., 2010; Wang et al., 2013; Tan and Cai, 2018), reduced air-sea turbulent heat loss by weakening in the East Asian winter monsoon (Yeh and Kim, 2010; Park et al., 2015; Cai et al., 2017) and strengthening of the western Pacific Subtropical High extension towards the west (Ren et al., 2013; Wang et al., 2019), as well as the effect of major freshwater discharge (Belkin, 2009; Park et al., 2011; Kako et al., 2016). Of these factors, the warming trend in R1 can be

4.2. PAR in the water column PARwc at an instant is determined by the light attenuation (Kd(PAR)) and solar radiation above water (PAR). PARwc could be considered as a direct driver or an outcome of the changes in phytoplankton biomass in the water column. When the change in PARwc acts as a driver, then if the PARwc increased, the chl-a is also expected to increase in response, resulting in similar trends in both variables. On the other hand, an increase in the light attenuation can also be a result of decrease in the phytoplankton biomass. The phytoplankton pigments including chlorophyll-a are strong absorbers of PAR that a small change in the chl-a can change Kd(PAR) and therefore PARwc (Kirk, 2011). The effects would be stronger in Case-1 waters, such as the KC, where the optical properties can be adequately described as a function of phytoplankton (Lee and Hu, 2006). In such cases, if the chl-a decrease, PARwc would increase resulting in opposite trends. The trends in PARwc and chl-a in R4, R7 and R8 can be interpreted this way. The changes in the 10

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two components of PARwc, sea surface PAR and Kd(PAR) support this interpretation. In all the subregions, PAR started to decrease around 2008 (data not shown), which does not show any relationship with the chl-a patterns. On the other hand, Kd(PAR) showed a similar pattern with the chl-a in R4, R7 and R8 (data not shown). The variation in PARwc is a critical factor of the phytoplankton growth, especially in the nutrient-rich turbid water (Alpine and Cloern, 1988). In R1, a positive interannual PARwc anomaly pattern was observed throughout 2003–2009, induced by increase in the PARwc during Jun–Sep. This can be largely attributed to the decrease in the sediment loadings in the Changjiang River catchment. Yang et al. (2015) reported that a substantial amount of sediment flux decreased in Datong station over the last five decades. Compare to 1993–2002, the annual mean sediment flux decreased by 175 Mt yr−1 in 2003–2012 (Yang et al., 2015). Of several potential factors, they argue that the TGD played a major role, about 65% of sediment flux decreased as the dam obstructed the sediment transport. Other factors include the reduction in precipitation, effect of other dams, and water-soil conservation in the downstream catchment (Yang et al., 2015). In light of this, the increase in PARwc from 2003 to 2009 in R1 can be largely attributed to the decrease in sediment loading in the water discharge. Hence, a strong relationship is expected between the chl-a in R1 in relation to the variability of PARwc. However, the variation in PARwc was not strongly correlated with the changes in the chl-a. For instance, while we observed a positive PARwc anomaly in 2004 (+14.8 mol photon m 1d 1), a negative chl-a anomaly (- 0.11 m g m 3) persisted in the same year. In the drought year of 2006, a positive PARwc anomaly (+11.3 mol photon m 1d 1) exhibited in the summer-autumn seasons, yet, a negative chl-a anomaly (- 0.12m g m 3 ) pattern persisted in the area. The general trends in the interannual PARwc and chl-a anomaly in R1 were also inconsistent particularly after 2009 (Figs. 8 and 4). While the chl-a increased in Mar–Jun in Period-2 and Period-3, the PARwc increased in Jun–Sep in the same periods. Therefore, improved light condition might have partially contributed to the increased trend in R1 during 2003–2009 (Fig. 4).

discharge but more importantly, the amount of nutrient loads in the point and nonpoint sources. With the rapid growth of population and increase in human activities (i.e. agricultural and industrial runoff), the anthropogenic nutrient enrichment in the coastal ECS and SYS is a wellestablished fact. As mentioned earlier, R1 receives a large portion of nutrient loads through the river (Kim et al., 2011), air (Chen et al., 2010; Kim et al., 2011), and submarine groundwater discharge (Tan et al., 2018; Wang et al., 2018). Studies have shown that the nutrient level, especially in the CRD, have substantially increased over the last five decades (Liu et al., 2018). The CRD provides about 0.59Tg yr 1 of DIN and 0.02 Tg yr 1 of DIP fluxes to the adjacent seas (Kim et al., 2011). The observations from the Datong station showed that both the total nitrogen (N) and phosphorus (P) continuously increased in the water discharge (Fig. 8A in Ding et al., 2019; Fig. 2k, l and Fig. 3 in Liu et al., 2018) while the volume of CRD remained relatively stable level during 1960–2010 (Liu et al., 2018). In the western SYS (R2), nitrate concentration has been shown to increase for 1985–2012 (Fig. 9A in Li et al., 2015b). The increase in N loads in the river largely attributed to the increase in usage of agricultural and industrial runoff, sewage wastewater, aquaculture, and other human activities (Müller et al., 2008; Liu et al., 2018). The mismatched pattern in the monthly mean CRD and chl-a in R1 also supports this hypothesis. For example, while we observed a decreased pattern in the summer CRD during Period-2 and Period-3 relative to Period-1 (Fig. 5), the chl-a in R1 remained a relatively similar level over the periods. In spring season, there was no clear changes in the CRD but the chl-a increased significantly as the nutrient loads in the water discharge increased over the years. In winter and autumn seasons, the phytoplankton growth strongly depends on the light availability and temperature in the area. Thus, there was no clear changes in the winter and autumn chl-a over the periods. One might ask if the nitrogen level is already high enough in R1, addition of more inorganic nitrogen may not enhance the growth of phytoplankton because of the limitation of phosphates or light energy. We argue that this may not be the case. First, the chl-a level was much lower than those observed in eutrophic waters that it is not likely the phytoplankton in R1 reached their maximum growth potential. Second, as shown in Section 3.2.2, the PARwc anomaly was not correlated with the chl-a anomaly in R1 indicating that light energy may not have limited the phytoplankton growth. Last, not only nitrogen but phosphates have also increased. Ding et al. (2019) observed that phosphates concentration and flux at Datong station have continuously increased in 2003–2016 (their Fig. 9A). A significant increase in nitrate and phosphate concentration was observed in the coastal ECS near the Changjiang River mouth (Zhou et al., 2010). In addition, the submarine groundwater discharge also provides additional phosphates (Wang et al., 2018). Wang et al. (2018) estimated that about 7.32 * 1010 mol yr−1 of dissolved inorganic nitrate and 1.79 * 109 mol yr−1 of dissolved inorganic phosphate fluxes are delivered solely by the submarine groundwater discharge. Even though there is a large riverine input from the CRD, the submarine groundwater derived nutrient fluxes were estimated to be about 0.7 (dissolved inorganic nitrate), 2.2 (dissolved inorganic phosphate), and 1.4 (dissolved silicate) times the corresponding riverine inputs (Wang et al., 2018).

4.3. Anthropogenic nutrient enrichment Of the 8 subregion, R1 and R2 are the only subregions that showed an increased trend in the interannual chl-a anomaly (Fig. 4). Although the two subregions lie close to each other, the conditions in R1 and R2 were quite different. In contrast to R1, a clear increased trend exhibited in R2. The interannual chl-a anomaly pattern in R2 shifted to positive since 2005 (Fig. 4). The intensity of the positive chl-a anomaly pattern in R2 also gradually strengthened over the years, while the changes in R1 were less consistent. Overall, with exception of some years, the chl-a in R2 gradually increased throughout the year while the changes in R1 were only seen in the spring season (Fig. 5). R1 lies in the vicinity of the Changjiang River mouth with an average depth of 25 m (Fig. 1). Thus, it is generally assumed that the variability of chl-a in R1 will correspond to the changes in the nutrientrich CRD. However, the two variables did not show a clear relationship during the study period. For instance, when the monthly chl-a in R1 increased substantially in the spring season for both Period-2 and Period-3 relative to Period-1 (Fig. 5), the CRD remained a relatively similar level throughout the periods (Fig. 2b). On the other hand, when the monthly CRD decreased in the summer season, the chl-a in R1 remained at a relatively similar level over the study period. This clearly indicates that the variability of chl-a in R1 does not strongly corresponds to the change in the volume of CRD. It also suggests that the growth condition of phytoplankton in R1 may not be a function of solely the water discharge. If we exclude the changes in temperature and PARwc as possible drivers of the increase in the chl-a in R1 (see Section 4.1 and 4.2), nutrient enrichment seems the only logically possible cause. The nutrient concentration in R1 not only depends on the volume of the water

4.4. Changes in the CRD The ECS and SYS are river dominated marginal seas (Li et al., 2015a). In addition to the submarine groundwater discharge (Tan et al., 2018; Wang et al., 2018), a large portion of terrestrial materials are transported via the rivers. Of many, the CDW supplies about 90% of the total terrestrial materials to the adjacent seas (Zhou et al., 2010; Chen et al., 2016). As previously discussed, R1 lies in the vicinity of Changjiang River mouth, thus, we expected to see a strong relationship between the chl-a associated with the changes in the CRD (Fig. 2b). However, no clear 11

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relationship was observed between the two variables. As a matter of fact, the result seemed to be quite the opposite. For instance, when the CRD was considerably reduced in the summer-autumn seasons in both Period-2 and Period-3 (Fig. 2b), the chl-a in R1 remained at a relatively similar level for all three consecutive periods (Fig. 5). Whereas the CRD remained relatively at a similar level in the spring season (Fig. 2b), the chl-a increased throughout Period-2 and Period-3 (Fig. 5). This result suggests that volume change of the CRD was not the major driving force for the increase in spring chl-a. Formed by the CRD mixed with the ambient seawater, the nutrientrich CDW extends from the river mouth to the farther offshore waters over the northeastern shelf region (Chang and Isobe, 2003; Kim et al., 2009; Yamaguchi et al., 2012; Kako et al., 2016). To evaluate the impacts of the CRD change on the productivity of the outer shelf area, the transport process of the CDW to the eastern shelf must be considered. Several challenges arise when determining the area where the CDW affected the chl-a variability. The major pathway and extent of the CDW not only depend on the amount of CRD but also by other environmental factors such as the seasonal behavior of the East Asian monsoon winds and ocean currents. In summer, for instance, the CDW tends to flow northeastward toward the Jeju Island by the southwesterly monsoon wind through the Ekman transport (Lie et al., 2003; Chang et al., 2014). While in winter, the CDW generally confines near the river mouth and the coast of China in a narrow band (Chang and Isobe, 2003). In some rare cases, the CDW extends far southeastward and intrudes into the Kuroshio (Sasaki et al., 2014). Understanding the changes in the CDW is more complicated as the variability of the CRD not only depends on the precipitation (Chen et al., 2014) but also depends on the human-regulation of the river discharge. Over the past several decades, the natural flow regime has been markedly altered by the human regulation of the river which includes the operation of numerous dams, large-scale water transfer, and increase in the water preservation and consumption (Yang et al., 2015). Of these factors, we have focused on the effect of the dams, particularly the influence of the TGD to the adjacent seas which began operating in 2003. In general, the TGD stores water during summer and autumn seasons for the flood control and to preserve water for winter hydropower generation, respectively (Guo et al., 2018). The change in the discharge pattern was indeed observed in the monthly averaged CRD where both Period-2 and Period-3 showed lower means than Period-1, especially in Jul–Nov (Fig. 2b). Whether this change in the summerautumn CRD was attributable to the operation of the TGD will be discussed later in this section. Although the concerns on possible impacts of the dam on the adjacent marine ecosystem have been raised in the past (Gong et al., 2006; Yuan et al., 2007; Jiao et al., 2007; Yamaguchi et al., 2012; Wang et al., 2015), no definite conclusion has been drawn yet. In earlier studies, some argued that the operation of the TGD has reduced the supply of nutrients in the vicinity of the Changjiang River mouth and ultimately could reduce the primary productivity in the study area (Gong et al., 2006; Jiao et al., 2007). While others argued that the dam-controlled CRD, especially in summer, could even affect the chl-a in the farther offshore waters of the ECS (Yamaguchi et al., 2012). However, most studies were made by relatively short observations at limited areas (i.e. vicinity of Changjiang estuary) or without providing a clear evidence of the change before and after the operation of the dam. Hence, to evaluate the influences of the dam-controlled CRD on the chl-a, we need to first demarcate the area where the CDW affected the chl-a. By considering the seasonal transport process of the CDW, we will also need to narrow down the seasonal time window to summer-autumn seasons when the CDW affects farther offshore waters. In Fig. 10, we compared the spatiotemporal distribution of crosscorrelation coefficients between the summer-autumn CRD and chl-a anomalies before (Fig. 10a) and after (Fig. 10b) the operation of the TGD. The positive correlation indicates when the CRD increased, the chl-a also increased, vice versa for the negative correlation (p = 0.05).

Fig. 10. Cross-correlation between the summer-autumn CRD (Jun–Nov) and chl-a anomalies (a) before the TGD operation (Period-1; 1998–2002) and (b) after the TGD operation (Period-2; 2003–2007) with time lags of 0–3 months. The color indicates the Spearman correlation coefficient which is significant at p = 0.05. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

The time lags of 0–3 months were taken into account, considering the time for the CDW to reach farther offshore regions of the ECS (Yamaguchi et al., 2012). It has been suggested that it may take a couple of months for the CRD to reach ECS (Delcroix and Murtugudde, 2002) and farther to the northeastern part of the ECS (Yamaguchi et al., 2012). Before the TGD began operation (Period-1), a strong positive correlation was observed from the Changjiang River estuary to the wider shelf areas of the ECS at the same months (Fig. 10a). A month after the discharge, the area of positive correlation expanded and gradually shifted northeastward following the general circulation patterns in the region. Two months after the discharge, the spread of the positive correlation was at its maximum in expanse, reaching the Jeju Island and even farther to the Tsushima Islands. The area with the positive correlation at lag of 1 and 2 months corresponds to R4 (Fig. 3) indicating this variability is well reflected in the classification of the subregions. Note that R4 stretches from 100 km off the Changjiang River mouth to about 880 km downstream in the ECS, covering an area of approximately 178,000 km2. After the TGD began operation (Period-2), the strong positive correlation was reduced at a significant rate, both spatially and temporally. In fact, there seems no coherent spatial structure in positive correlation. For both periods, the strong positive correlation dissipated after three months. 12

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Similarly, Yamaguchi et al. (2012) observed the relationship between the two variables during the flood seasons (Jun–Sep) from 1998 to 2006 with two months lag. Yamaguchi et al. (2012) also observed a relatively strong positive correlation near the river mouth which reached the east of Jeju Island at a lag of two months. Their results were fairly consistent with our observations, but with several differences. For example, our results show a much wider area with a relatively strong positive correlation than they found. Our analysis may be more sensitive because we used monthly anomalies of the CRD and chl-a rather than the values per se. Yamaguchi et al. (2012) also discussed the potential impacts of the operation of the TGD on the adjacent marine ecosystem. In their conclusion, they argued that the dam-controlled CRD may have reduced the magnitude of chl-a in some years (i.e. 2003 and 2006) along the main pathway of the summer CDW but its influence was relatively weak as to that of the natural variability of the CRD. However, this suggestion was drawn from the observations of three small selected areas of less than 700 km2 in the ECS, where factors such as the wind forcing could play a significant role in the CDW distribution (Chang and Isobe, 2003; Lie et al., 2003; Kim et al., 2009; Lee et al., 2018). Their analysis was also quite limited by short observation period after the TGD began operation (2003–2006). Now that we have identified the area where the summer-autumn CDW affects the variability of chl-a in the ECS (Fig. 10), to determine the exact timing of the CDW influence on the chl-a level in R4, we applied a linear regression model with the CRD as the independent and the chl-a in R4 as the dependent variable at lags of 1–2 months (Fig. 11). Our hypothesis is that the summer-autumn chl-a in R4 would be proportional to the CRD. The mechanism behind this is that since the CDW carries nutrients, the greater the CRD, we expect more nutrients to be transported to R4 and the chl-a in R4 would increase proportionately in R4. The change in the coefficient of determination (R2) shows that there was no significant relationship between the CRD and the chl-a in R4 until July (Fig. 11a) but it abruptly increased from Aug (R2 = 0.38) when the CDW arrived in R4 after one month of its transport (Fig. 12b). The same CDW had a greater influence in Sep (R2 = 0.51) arriving in R4 after two months. The R2 at both time lags, one and two-months, reached its maximum in Sep and remained high throughout the end of year. This is because that the CRD showed its maximum in Jul–Aug and

Fig. 12. (a) Time series of the mean summer-autumn CRD (Jul–Nov) for 1961–2016. The dotted line coincides with year of 2003, when the operation of the TGD began. The determination of the change point is based on the F statistics. The red line represents the fitted values. (b) F statistics of the CRD changes. The red line indicates the F value when p = 0.1. The F statistics reached its maximum in 2003, where p value is 0.087. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

the discharged water continued to arrive in Aug–Dec. Note that R2 with a lag of two months was relatively higher than that with one-month time lag (Fig. 11a), suggesting that the major portion of the CRD arrived in R4 about two months after its discharge from the river mouth. The linear regression between the CRD anomalies and the chl-a anomalies in R4 for Aug–Dec at lag of two-months (Fig. 11b) showed an R2 of 0.893, suggesting that the variability of the summer-autumn CRD is the major controlling factor of the summer-autumn chl-a in R4. In other word, about 89% of the summer-autumn chl-a variance in R4 could be explained by the CRD variations. On the other hand, SST anomaly pattern did not match the anomaly pattern of chl-a in R4 (Figs. 4 and 5 vs. Figs. 6 and 7). This may explain why the summer-autumn averaged chla anomaly pattern in R4 (dots in R4 in Fig. 4) well corresponded to the CRD anomaly pattern during Jul–Nov (Fig. 2). It also supports our hypothesis that the phytoplankton abundance in R4 during summerautumn seasons was indeed, greatly influenced by the CRD variations

Fig. 11. (a) The coefficient of determination (R2) of the linear regression between the monthly CRD anomalies with the monthly chl-a anomalies in R4 during 1998–2012. Filled circles indicate R2 with one-month time lag, and triangles indicate R2 with two-month time lag. Shading denotes the duration of the significant influence (Aug–Dec). The numbers indicate the month of corresponding CRD measurements. (b) The linear regression between the CRD anomalies and the chl-a anomalies in R4 with two-month time lag for Aug–Dec season during 1998–2012. 13

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during the study period. To estimate the chl-a changes in relation to the CRD variations during three consecutive periods, we used the linear regression model (R2 = 0.89) from Fig. 11b with two-month time lag. Compared with Period-1, the mean chl-a in R4 for Aug–Dec was reduced by 14.7 and 14.5% in Period-2 and Period-3, respectively. Since these reductions are solely accounted by the CRD variations in Jun–Oct (Fig. 2b), the remaining question is what caused the summer-autumn CRD reduction in both Period-2 and Period-3. Several studies have been conducted in the past regarding the long term changes in the CRD. Yang et al. (2015) reported that the mean annual CRD decreased by 13% from 1993 to 2002 to 2003–2012 and by 7% from 1950 to 2002 to 2003–2012. From a water budget analysis based on empirical models, they estimated that approximately 60 and 70% of the above water discharge reduction was attributable to the decrease in precipitation while the operation of the TGD can only explain about 3 and 6% of the reductions. However, this argument is questionable. First, the monthly precipitation in the Changjiang River catchment has remained at a similar level over the last several decades (1995–2014) (Chen et al., 2016). In both Yichang and Datong stations, the monthly areal mean precipitations were even higher in Jul, Sep, and Oct during 2003–2013 in comparison to 2000-2002, before the operation of the TGD (Mei et al., 2015). Moreover, there was no clear relationship between the monthly areal mean precipitation and CRD pattern during these study periods (Mei et al., 2015). Therefore, the reduction in the summer-autumn CRD patterns during Period-2 and Period-3 (Fig. 2b) cannot be explicitly explained by the natural variability of the precipitation. More importantly, Yang et al. (2015) did not consider a critical aspect of the TGD operation in their water budget analysis, i.e., the seasonal flow regulation of the water discharge. The dam operates according to the intra-annual flood control scheme. Hence, the dam modulates the storing and releasing of the water discharge seasonally (Zheng, 2016; Guo et al., 2018). In general, the TGD releases the water during Jan–Jun and stores during Jul–Aug for floodcontrol and Sep–Nov for hydropower generation in the dry seasons (Wang et al., 2013; Guo et al., 2018). As evident from Fig. 2b, it is likely that the operation of the TGD have selectively reduced the water discharge in Jul–Nov according to the annual flood control scheme. In the following analysis, we also analyzed the long-term CRD data (1961–2016). In contrast to aforementioned studies, we focused on the changes in Jul–Nov because it was the season when the CRD was significantly reduced after the operation of the TGD. As we previously observed, the CDW formed in this season reaches R4 and affects the chla in the area. Fig. 12a displays the time series of the monthly mean CRD in Jul–Nov for 1961–2016. Fig. 12b shows the F statistic estimated by the Chow’s F tests (see Section 2.5). The F statistics remained negligible until the late 1990s but abruptly increased in late 1990s. This increase in F statistics reflects the flood in 1998. It reaches its maximum value in 2003, where the p value is 0.087 which is reasonably significant considering the small sample size and large natural variability. The discontinuity around 2003 separates the whole period into two, which cannot be explained by the changes in precipitation, water preservation and consumption, or the other dam constructions in the Changjiang River unless these factors shifted into a drastically different state around 2003 by pure chance. From this point of view, we find difficulties in accepting the argument by Yang et al. (2015) that the TGD played rather a minor role in the reduction of CRD after 2003. Fitting to a generalized fluctuation model, the means of the two intervals were calculated. The mean CRD for 1961–2002 was 38,187 m3 s−1 and that for 2003–2016 was 33,213 m3 s−1. Overall, there was about 13.0% decrease in the CRD after 2003 which is comparable with Yang et al. (2015). For the shorter time scale, the monthly mean CRD for Jul–Nov was reduced by 23.9% and 22.4% for Period-2 and Period-3 in comparison to Period-1, respectively. Similarly, Guo et al. (2018) also estimated that about 7–20% of CRD was reduced at Datong station during the TGD impounding

periods (varies inter-annually from Aug–Nov) for 2003–2016, again signifying the impact of TGD’s seasonal flow regulation on the river discharge. These results suggest that the impact of the TGD operation was more significant in the Jul–Nov seasonal window. The impact on the CRD would unfold in the chl-a in R4 when the CDW reaches R4 in Aug–Dec. Thus, we conclude that about 14% of the summer-autumn averaged chl-a in R4 (about 178,000 km2 ) was reduced by the CRD reduction during Jul–Nov. Although the precipitation may have played a major role on the interannual CRD variations, the decreased in the summer-autumn water discharge during Period-2 and Period-3 was largely attributable to the TGD operation. Further studies are desirable for a quantitative assessment of the TGD influence in reducing the CRD. 5. Conclusions Dividing the ECS and SYS into the eight subregions based on the fifteen years’ chl-a variability revealed that each subregion underwent different mode and magnitude of changes. The subregions near the Changjiang River mouth showed an increased trend, while the southern subregions showed a decreased trend. Comparing the changes in the interannual and seasonal patterns enabled us to track down the cause of the changes in the region. The increase in the near-shore subregions can largely be attributed to nutrient enrichment while the decrease in the southern subregions can be attributed to warming. Since the TGD could reduce the discharge from the Changjiang River, there has been a concern if the TGD operation could reduce the primary productivity in the adjacent ecosystems. We were able to identify the area and season where and when the CDW influences the variability of chl-a in the outer shelf. Our analysis showed that only one subregion (R4, about 178,000km2 ) was significantly affected after the operation of the TGD during Aug–Dec. The chl-a in R4 was reduced by about 14% during the ten year period (Period-2 and Period-3) in Aug–Dec. Although we conclude that the operation of the TGD played a major role in this reduction, further studies are needed to answer how much can be directly accounted for by the TGD operation itself. Our analysis demonstrates that our ability to detect trends in response to multiple stressors largely depends on choosing the appropriate spatiotemporal scale. The scale would be determined by spatial and temporal heterogeneity of both environmental features and multiple stressors. Acknowledgement We thank the Ocean Biology Processing Group at NASA’s Goddard Space Flight Center for providing access to SeaWiFS and MODIS-Aqua remotely sensed data and the Physical Oceanography Distributed Active Archive Center at NASA’s Goddard Space Flight Center for providing SST remotely sensed data. Y. B. Son helped preparation of the remotely sensed data. We also like to thank six anonymous reviewers and the Editor-in-chief, Prof. Igor Belkin whose comments have helped to improve the manuscript. This research was supported by the “Technology development for Practical Applications of Multi-Satellite data to maritime issues” funded by the Ministry of Ocean and Fisheries, Korea. The research was also partially supported by the Asia-Pacific Network for Global Change Research (APN, CAF2016-RR06-CMY-Siswanto). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.dsr.2019.103078. References Alpine, A., Cloern, J., 1988. Phytoplankton growth rates in a light-limited environment, San Francisco Bay. Mar. Ecol. Prog. Ser. 44, 167–173. https://doi.org/10.3354/ meps044167. Bao, B., Ren, G., 2014. Climatological characteristics and long-term change of SST over

14

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C.E. Kong, et al. the marginal seas of China. Cont. Shelf Res. 77, 96–106. https://doi.org/10.1016/j. csr.2014.01.013. Beckers, J.-M., Rixen, M., 2003. EOF calculations and data filling from incomplete oceanographic data sets. J. Atmos. Ocean. Technol. 20 (12), 1839–1856. https://doi. org/10.1175/1520-0426(2003)020<1839:ECADFF>2.0.CO;2. Beckers, J.M., Barth, A., Alvera-Azcárate, A., 2006. DINEOF reconstruction of clouded images including error maps application to the Sea-Surface Temperature around Corsican Island. Ocean Sci. 2 (2), 183–199. https://doi.org/10.5194/os-2-183-2006. Behrenfeld, M.J., O'Malley, R.T., Siegel, D.A., McClain, C.R., Sarmiento, J.L., Feldman, G.C., Milligan, A.J., Falkowski, P.G., Letelier, R.M., Boss, E.S., 2006. Climate-driven trends in contemporary ocean productivity. Nature 444 (7120), 752–755. https:// doi.org/10.1038/nature05317. Belkin, I.M., 2009. Rapid warming of large marine ecosystems. Prog. Oceanogr. 81 (1–4), 207–213. https://doi.org/10.1016/j.pocean.2009.04.011. Belkin, I., 2016. Chapter 5.2: Sea surface temperature trends in large marine ecosystems. In: IOC-UNESCO and UNEP (2016). Large Marine Ecosystems: Status and Trends. United Nations Environment Programme, Nairobi, pp. 101–109. Bopp, L., Monfray, P., Aumont, O., Dufresne, J.-L., Le Treut, H., Madec, G., Terray, L., Orr, J.C., 2001. Potential impact of climate change on marine export production. Glob. Biogeochem. Cycles 15 (1), 81–99. https://doi.org/10.1029/1999GB001256. Boyce, D.G., Lewis, M.R., Worm, B., 2010. Global phytoplankton decline over the past century. Nature 466, 591–596. https://doi.org/10.1038/nature09268. Cai, R., Tan, H., Kontoyiannis, H., 2017. Robust surface warming in offshore China Seas and its relationship to the East Asian monsoon wind field and ocean forcing on interdecadal time scales. J. Clim. 30 (22), 8987–9005. https://doi.org/10.1175/JCLID-16-0016.1. Campbell, J.W., 1995. The lognormal distribution as a model for bio-optical variability in the sea. J. Geophys. Res. 100 (C7), 13237–13254. https://doi.org/10.1029/ 95JC00458. Chang, P.H., Isobe, A., 2003. A numerical study on the Changjiang diluted water in the Yellow and East China Seas. J. Geophys. Res. 108 (C9), 3299. https://doi.org/10. 1029/2002JC001749. Chang, P.H., Isobe, A., Kang, K.R., Ryoo, S.B., Kang, H.S., Kim, Y.H., 2014. Summer behavior of the Changjiang diluted water to the East/Japan Sea: A modeling study in 2003. Cont. Shelf Res. 81, 7–18. https://doi.org/10.1016/j.csr.2014.03.007. Chen, C.-C., Gong, G.-C., Shiah, F.-K., 2007. Hypoxia in the East China Sea: One of the largest coastal low-oxygen areas in the world. Mar. Environ. Res. 64 (4), 399–408. https://doi.org/10.1016/j.marenvres.2007.01.007. Chen, C.-C., Shiah, F.K., Chiang, K.P., Gong, G.C., Kemp, W.M., 2009. Effects of the Changjiang (Yangtze) river discharge on planktonic community respiration in the East China Sea. J. Geophys. Res.: Oceans 114 (3), 1–15. https://doi.org/10.1029/ 208JC0004891. Chen, H.-Y., Chen, L.-D., Chiang, Z.-Y., Hung, C.-C., Lin, F.-J., Chou, W.-C., Gong, G.-C., Wen, L.-S., 2010. Size fractionation and molecular composition of water-soluble inorganic and organic nitrogen in aerosols of a coastal environment. J. Geophys. Res.: Atmosphere 115 (22), 1–17. https://doi.org/10.1029/2010JD014157. Chen, J., Wu, X., Finlayson, B.L., Webber, M., Wei, T., Li, M., Chen, Z., 2014. Variability and trend in the hydrology of the Yangtze River, China: Annual precipitation and runoff. J. Hydrol. 513, 403–412. https://doi.org/10.1015/j.jhydrol.2014.03.044. Chen, J., Finlayson, B., Wei, T., Sun, Q., Webber, M., Maotian, L., Chen, Z., 2016. Changes in monthly flows in the Yangtze river, China - with special reference to the three Gorges dam. J. Hydrol. 536, 293–301. https://doi.org/10.1016/j.jhydrol.2016.03. 008. Chow, G.-C., 1960. Tests of equality between sets of coefficients in two linear regressions. Econometrica 28 (3), 591–605. https://doi.org/10.2307/1910133. Delcroix, T., Murtugudde, R., 2002. sea surface salinity changes in The east China sea during 1997-2001: Influence of the Yangtze river. J. Geophys. Res.: Oceans 107 (C12) SRF 9-1-SRF 9-11. https://doi.org/10.1029/2001JC000893. Ding, S., Chen, P., Liu, S., Zhang, G., Zhang, J., Felix, S., 2019. Nutrient dynamics in the changjiang and retention effect in the three Gorges reservoir. J. Hydrol. 574, 96–109. https://doi.org/10.1016/j.jhydrol.2019.04.034. Doney, S.C., 2006. Oceanography: Plankton in a warmer world. Nature 444, 695–696. https://doi.org/10.1038/444695a. Fan, X., Zhou, F., Chen, X., Huang, D., Pohlmann, T., 2011. The influence of the ThreeGorges Dam on hydrographic and hydrodynamic conditions of the East China Sea. Acta Oceanol. Sin. 30 (5), 45–55. https://doi.org/10.1007/s13131-011-0146-z. Gong, G.-C., Chang, J., Chiang, K.-P., Hsiung, T.-M., Hung, C.-C., Duan, S.-W., Codispoti, L.A., 2006. Reduction of primary production and changing of nutrient ratio in The east China sea: Effect of the three Gorges dam? Geophys. Res. Lett. 33 (7), 2–5. https://doi.org/10.1029/2006GL025800. Guo, L., Su, N., Zhu, C., He, Q., 2018. How have the river discharge and sediment loads changed in the Changjiang River basin downstream of the Three Gorges Dam? J. Hydrol. 560, 258–278. https://doi.org/10.1015/j.jhydrol.2018.03.035. He, X., Bai, Y., Pan, D., Chen, C.-T.A., Cheng, Q., Wang, D., Gong, F., 2013. Satellite views of the seasonal and interannual variability of phytoplankton blooms in the eastern China Seas over the past 14 yr (1998-2011). Biogeosciences 10 (7), 4721–4739. http://doi.org/10.5194/bg-10-4721-2013. Hu, C.M., Li, D.Q., Chen, C.S., Ge, J.Z., Muller-Karger, F.E., Liu, J.P., Yu, F., He, M.X., 2010. On the recurrent Ulva prolifera blooms in the Yellow Sea and east China sea. J. Geophys. Res.: Oceans 115 (5), 1–8. https://doi.org/10.1029/2009JC005561. Hu, J., Wang, X.H., 2016. Progress on upwelling studies in the China Seas. Rev. Geophys. 54, 653–673. https://doi.org/10.1002/2015RG000505. Ichikawa, H., Beardsley, R.C., 2002. The current system in the yellow and east China sea. J. Oceanogr. 58, 77–92. https://doi.org/10.1023/A:1015876701363. IOCCG, 2007. Ocean-colour data merging. In: Gregg, W. (Ed.), Reports of the International Ocean-Colour Coordinating Group, No. 6. IOCCG, Dartmouth, Canada.

Jiang, Z., Liu, J., Chen, J., Chen, Q., Yan, X., Xuan, J., Zeng, J., 2014. Responses of summer phytoplankton community to drastic environmental changes in the Changjiang (Yangtze River) estuary during the past 50 years. Water Res. 54, 1–11. https://doi.org/10.1016/j.watres.2014.01.032. Jiao, N., Zhang, Y., Zeng, Y., Gardner, W.D., Mishonov, A.V., Richardson, M.J., Hong, N., Pan, X.H., Jo, Y.H., Chen, C.T.A., Wang, P., Chen, Y., Hong, H., Bai, Y., Chen, X., Huang, B., Deng, H., Shi, Y., Yang, D., 2007. Ecological anomalies in The east China sea: Impacts of the three Gorges dam? Water Res. 41 (6), 1287–1293. https://doi. org/10.1016/j.watres.2006.11.053. Kako, S., Nakagawa, T., Takayama, K., Hirose, N., Isobe, A., 2016. Impact of Changjiang river discharge on sea surface temperature in The east China sea. J. Phys. Oceanogr. 46 (6), 1735–1750. https://doi.org/10.1175/JPO-D-15-0167.1. Kim, H.C., Yamaguchi, H., Yoo, S., Zhu, J., Okamura, K., Kiyomoto, Y., Tanaka, K., Kim, S.W., Park, T., Oh, I.S., Ishizaka, J., 2009. Distribution of Changjiang Diluted Water detected by satellite chlorophyll-a and its interannual variation during 1998-2007. J. Oceanogr. 65 (1), 129–135. https://doi.org/10.1007/s10872-009-0013-0. Kim, T.-W., Lee, K., Najjar, R.G., Jeong, H.-D., Jeong, H.J., 2011. Increasing N abundance in the northwestern pacific ocean due to atmospheric nitrogen deposition. Science 334 (6055), 505–509. https://doi.org/10.1126/science.12066583. Kirk, J., 2011. Light and Photosynthesis in Aquatic Ecosystems, 3rd. Cambridge University Press, Cambridge, pp. 1–649. Koh -H, C., Khim, J.S., 2014. The Korea tidal flat of the Yellow Sea: Physical setting, ecosystem. Ocean Coast Manag. 102, 298–414. https://doi.org/10.1016/j. ocecoaman.2014.07.008. Lai, X., Liang, Q., Huang, Q., Jiang, J., Lu, X.X., 2016. Numerical evaluation of flow regime changes induced by the three Gorges dam in middle Yangtze. Nord. Hydrol 57, 149–160. https://doi.org/10.2166/nh.2016.158. Lee, Z.-P., Carder, K.L., Arnone, R.A., 2002. Deriving inherent optical properties from water color: A multiband quasi-analytical algorithm for optically deep waters. Appl. Opt. 41 (27), 5755–5772. https://doi.org/10.1364/AO.41.005755. Lee, Z.-P., Du, K.P., Arnone, R., 2005. A model for the diffuse attenuation coefficient of downwelling irradiance. J. Geophys. Res.: Oceans 110 (C02016), 1–10. https://doi. org/10.1029/2004JC002275. Lee, Z.-P., Hu, C., 2006. Global distribution of case-1 waters: An analysis from SeaWiFS measurements. Remote Sens. Environ. 101 (2), 270–276. https://doi.org/10.1016/j. rse.2005.11.008. Lee, Z.-P., Weidemann, A., Kindle, J., Arnone, R., Carder, K.L., Davis, C., 2007. Euphotic zone depth: Its derivation and implication to ocean-color remote sensing. J. Geophys. Res.: Oceans 112 (3), 1–11. https://doi.org/10.1029/2006JC003802. Lee, Y., Yang, E.-J., Youn, S., Choi, J.K., 2018. Influence of the Changjiang diluted waters on the nanophytoplankton distribution in the northern East China Sea. J. Mar. Biol. Assoc. U. K. 98 (7), 1535–1545. https://doi.org/10.1017/S0025315417001163. Lewandowska, A.M., Boyce, D.G., Hofmann, M., Matthiessen, B., Sommer, U., Worm, B., 2014. Effects of sea surface warming on marine plankton. Ecol. Lett. 17 (5), 614–623. https://doi.org/10.1111/ele.12265. Li, C., Yang, S., Lian, E., Bi, L., Zhang, Z., 2015a. A review of comminution age method and its potential application in the East China Sea to constrain the time scale of sediment source-to-sink process. J. Ocean Univ. China 14 (3), 339–406. https://doi. org/10.1007/s11802-015-2769-8. Li, H.M., Zhang, C.S., Han, X.R., Shi, X.Y., 2015b. Changes in concentrations of oxygen, dissolved nitrogen, phosphate, and silicate in the southern Yellow Sea, 1980-2012: Sources and seaward gradients. Estuar. Coast Shelf Sci. 163, 44–55. https://doi.org/ 10.1016/j.ecss.2014.12.013. Lie, H.-J., Cho, C.-H., 1994. On the origin of the Tsushima warm current. J. Geophys. Res. 99 (C12), 25081–25091. https://doi.org/10.1029/94JC02425. Lie, H.-J., Cho, C.-H., Lee, J.-H., Lee, S., 2003. Structure and eastward extension of the Changjiang river plume in The east China sea. J. Geophys. Res. 108 (C3), 3077. https://doi.org/10.1029/2001JC001194. Lie, H.-J., Cho, C.-H., 2016. Seasonal circulation patterns of the Yellow and East China Seas derived from satellite-tracked drifter trajectories and hydrographic observations. Prog. Oceanogr. 146, 121–141. https://doi.org/10.1016/j.pocean.2016.06.004. Liu, D., Wang, Y., 2013. Trends of satellite derived chlorophyll-a (1997-2011) in the Bohai and Yellow Seas, China: Effects of bathymetry on seasonal and inter-annual patterns. Prog. Oceanogr. 116, 154–166. https://doi.org/10.1016/j.pocean.2013.07. 003. Liu, K.-K., Atkinson, L., Quiñones, R.A., Talaue-McManus, L., 2010. Biogeochemistry of continental margins in a global context. In: Liu, K.-K., Atkinson, L., Quiñones, R.A., Talaue-McManus, L. (Eds.), Carbon and Nutrient Fluxes in Continental Margins. Global Change – The IGBP Series. Springer, Berlin, Heidelberg 10.1007.978-3-54092735-8_1. Liu, X., Beusen, A.H.W., Van Beek, L.P.H., Mogollón, J., M., Ran, X., Bouwman, A.F., 2018. Exploring spatiotemporal changes of the Yangtze river (changjiang) nitrogen and phosphorus sources, retention, and export to The east China Sea and Yellow Sea. Water Res. 142, 246–255. https://doi.org/10.1016/i.watres.2018.06.006. Marañón, E., Lorenzo, M.P., Cermeño, P., Mouriño-Carballido, B., 2018. Nutrient limitation suppresses the temperature dependence of phytoplankton metabolic rates. ISME J. 12 (7), 1836–1845. https://doi.org/10.1038/s41396-018-0105-1. Mei, X., Dai, Z., Van Gelder, P.H.A.J.M., Guo, J., 2015. Linking three Gorges dam and downstream hydrological regimes along the Yangtze river, China. Earth and Space Sci. 1–13. http://doi.org/10.1002/2014EA000052. Miles, T.N., He, R., 2010. Temporal and spatial variability of Chl-a and SST on the South Atlantic Bight Revisiting with cloud-free reconstructions of MODIS satellite imagery. Cont. Shelf Res. 30 (18), 1951–1962. Müller, B., Berg, M., Yao, Z.P., Zhang, X.F., Wang, D., Pfluger, A., 2008. How polluted is the Yangtze river? Water quality downstream from the three Gorges dam. Sci. Total Environ. 402 (2–3), 232–247. https://doi.org/10.1016/j.scitotenv.2008.04.049.

15

Deep-Sea Research Part I 151 (2019) 103078

C.E. Kong, et al.

marchem.2018.05.010. Wang, Q., Li, Y., Li, Q., Liu, Y., Wang, Y., 2019. Changes in means and extreme events of sea surface temperature in The east China seas based on satellite data from 1982 to 2017. Atmosphere 10 (3), 140. https://doi.org/10.3390/atmos10030140. Wei, Q., Yao, Q., Wang, B., Wang, H., Yu, Z., 2015. Long-term variation of nutrients in the southern Yellow Sea. Cont. Shelf Res. 111, 184–196. http://doi.org/10.1016/j.csr. 2015.08.003. Xing, Q., Tosi, L., Braga, F., Gao, X., Gao, M., 2015. Interpreting the progressive eutrophication behind the world’s largest macroalgal blooms with water quality and ocean color data. Nat. Hazards 78 (1), 7–21. https://doi.org/10.1007/s11069-0151694-x. Yatsu, A., Chiba, S., Yamanaka, Y., Ito, S., Shimizu, Y., Kaeriyama, M., Watanabe, Y., 2013. Climate forcing and the Kuroshio/Oyashio ecosystem. ICES (Int. Counc. Explor. Sea) J. Mar. Sci. 70 (5), 922–933. https://doi.org/10.1093/icesjms/fst084. Yamaguchi, H., Kim, H.C., Son, Y.B., Kim, S.W., Okamura, K., Kiyomoto, Y., Ishizaka, J., 2012. Seasonal and summer interannual variations of SeaWiFS chlorophyll a in the Yellow Sea and east China sea. Prog. Oceanogr. 105, 22–29. https://doi.org/10. 1016/j.pocean.2012.04.004. 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. Sci. Rep. 5 (1), 12581. https://doi.org/10.1038/srep12581. Yeh, S.W., Kim, C.H., 2010. Recent warming in the Yellow/East China Sea during winter and the associated atmospheric circulation. Cont. Shelf Res. 30 (13), 1428–1434. https://doi.org/10.1016/j.csr.2010.05.002. Yoo, S., Batchelder, H.P., Peterson, W.T., Sydeman, W.J., 2008. Seasonal, interannual and event scale variation in North Pacific ecosystems. Prog. Oceanogr. 77 (2–3), 155–181. https://doi.org/10.1016/j.pocean.2008.03.013. Yoo, S., An, Y.-R., Bae, S., Choi, S., Ishizaka, J., Kang, Y.-S., Kim, Z.G., Lee, C., Lee, J.B., Li, R., Park, J., Wang, Z., Wen, Q., Yang, E.J., Yeh, S.-W., Yeon, I., Yoon, W.-D., Zhang, C.-I., Zhang, X., Zhu, M., 2010. Status and trends in the Yellow Sea and east China sea region. In: McKinnell, S.M., Dagg, M.J. (Eds.), Marine Ecosystems of the North Pacific Ocean. vol. 4. PICES Special Publication, pp. 393 2003–2008. Yoo, S., Kong, C.E., Son, Y.B., Ishizaka, J., 2019. A critical re-assessment of the primary productivity of the Yellow Sea, east China sea and sea of Japan/east Sea Large marine ecosystems. Deep-Sea Res. Part II Top. Stud. Oceanogr. 163, 6–15. https://doi.org/ 10.1016/j.dsr2.2018.05.021. Yu, R.-C., Lü, S.-H., Liang, Y.-B., 2018. Harmful algal blooms in the coastal waters of China. In: Glibert, P., Berdalet, E., Burford, M., Pitcher, G., Zhou, M. (Eds.), Global Ecology and Oceanography of Harmful Algal Blooms. Ecological Studies (Analysis and Synthesis). vol. 232. pp. 309–316. https://doi.org/10.1007/978-3-319-700694_15. Yuan, J., Hayden, L., Dagg, M., 2007. Comment on “Reduction of primary production of nutrient ratio in the East China Sea: Effect of the Three Gorges Dam?” by Gwo-Ching Gong et al. Geophys. Res. Lett. 34 (LI4609). https://doi.org/10.1029/ 2006GL029036. Yuan, D., Zhu, J., Li, C., Hu, D., 2008. Cross-shelf circulation in the Yellow and East China Seas indicated by MODIS satellite observations. J. Mar. Syst. 70, 134–149. https:// doi.org/10.1016/j.jmarsys.2007.04.002. Zeileis, A., Leisch, F., Hornik, K., Kleiber, C., 2002. strucchange: An R package for testing for structural change in linear regression models. J. Stat. Softw. 7 (2), 1–38. https:// doi.org/10.18637/jsss.v007.i02. Zhang, L., Wu, L., Lin, X., Wu, D., 2010. Modes and mechanisms of sea surface temperature low-frequency variations over the coastal China seas. J. Geophys. Res. 115 (C08031), 1–13. https://dx.doi.org/10.1029/2009JC006025. Zhang, X., Dong, Z., Gupta, H., Wu, G., Li, D., 2016. Impact of the three Gorges dam on the hydrology and ecology of the Yangtze river. Water 8, 1–18. https://doi.org/10. 3390/w8120590. Zheng, S.R., 2016. Reflection on the three Gorges Project since its operation. Engineering 2, 389–397. https://doi.org/10.1016/J.ENG.2016.04.002. Zhou, F., Huang, D.J., Ni, X.B., Xuan, Q.L., Zhang, J., Zhu, K.X., 2010. Hydrographic analysis on the multi-timescale variability of hypoxia adjacent to the Changjiang River Estuary. Acta Ecol. Sin. 30, 4728–4740 (in Chinese with English abstract). Zhu, Z.-Y., Wu, H., Liu, S.-M., Wu, Y., Huang, D.-J., Zhang, J., Zhang, G.-S., 2017. Hypoxia off the Changjiang (Yangtze River) estuary and in the adjacent East China Sea: Quantitative approaches to estimating the tidal impact and nutrient regeneration. Mar. Pollut. Bull. 125 (1–2), 103–114. https://doi.org/10.1016/j.marpolbul. 2017.07.029.

Park, T., Jang, C.J., Jungclaus, J.H., Haak, H., Park, W., Oh, I.S., 2011. Effects of the Changjiang river discharge on sea surface warming in the Yellow and East China Seas in summer. Cont. Shelf Res. 31, 15–22. https://dx.doi.org/10.1016/j.csr2010.10.012. Park, T., Jang, C.J., Kwon, M., Na, H., Kim, K.-Y., 2015. An effect of ENSO on summer surface salinity in the Yellow and East China Seas. J. Mar. Syst. 141, 122–127. https://doi.org/10.1016/j.jmarsys.2014.03.017. Ping, B., Su, F., Meng, Y., 2016. An improved DINEOF algorithm for filling missing values in spatio-temporal sea surface temperature data. PLoS One 11 (5), 1–12. https://doi. org/10.1371/journal.pone.0155928. Polovina, J.J., Howell, E.A., Abecassis, M., 2008. Ocean’s least productive waters are expanding. Geophys. Res. Lett. 35 (3), 2–6. https://doi.org/10.1029/2007GL031745. Rapport, D.J., Regier, H.A., Hutchinson, T.C., 1985. Ecosystem behavior under stress. Am. Nat. 125 (5), 617–640. https://doi.org/10.1086/284368. Ren, X., Yang, X.-Q., Sun, X., 2013. Zonal oscillation of western pacific subtropical high and subseasonal SST variations during Yangtze persistent heavy rainfall events. J. Clim. 26, 8929–8946. https://doi.org/10.1175/JCLI-D-12-00861.1. Sasaki, H., Gomi, Y., Asai, T., Shibata, M., Kiyomoto, Y., Okamura, K., Nishiuchi, K., Hasegawa, T., Yamada, H., 2014. Unique dispersal of the changjiang-diluted water plume in The east China sea revealed from satellite monitoring of colored dissolved organic matter (CDOM). Terr. Atmos. Ocean Sci. 25 (2), 279–287. https://doi.org/10. 3319/TAO.2013.10.03.01. Siswanto, E., Tang, J., Yamaguchi, H., Ahn, Y.H., Ishizaka, J., Yoo, S., Kim, S.W., Kiyomoto, Y., Yamada, K., Chiang, C., Kawamura, H., 2011. Empirical ocean-color algorithms to retrieve chlorophyll-a, total suspended matter, and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas. J. Oceanogr. 67, 627–650. https://doi.org/10.1007/s10872-011-0062-z. Shen, L., Xu, H., Guo, X., Li, M., 2011. Characteristics of large-scale harmful algal blooms (HABs) in the Yangtze River estuary and the adjacent east China sea (ECS) from 2000 to 2010. J. Environ. Prot. 2 (10), 1285–1294. https://doi.org/10.4236/jep.2011. 210148. Sommer, U., Lewandowska, A., 2011. Climate change and the phytoplankton spring bloom: Warming and overwintering zooplankton have similar effects on phytoplankton. Glob. Chang. Biol. 17 (1), 154–162. https://doi.org/10.1111/j.1365-2486. 2010.02182.x. Tan, H., Cai, R., 2018. What caused the record-breaking warming in East China Seas during August 2016? Atmos. Sci. Lett. 19 (10), 1–8. https://doi.org/10.1002/asl.853. Tan, E., Wang, G., Moore, W.S., Li, Q., Dai, M., 2018. Shelf-scale submarine groundwater discharge in the northern south China Sea and east China Sea and its geochemical impacts. J. Geophys. Res. Ocean 123 (4). https://doi.org/10.1029/2017JC013405. Tang, D.L., Di, B.P., Wei, G., Ni, I.H., Im, S.O., Wang, S.F., 2006. Spatial, seasonal and species variations of harmful algal blooms in the South Yellow Sea and East China Sea. Hydrobiologia 568 (1), 245–253. https://doi.org/10.1007/s10750-006-0108-1. Tassan, S., 1994. Local algorithm using SeaWiFS data for the retrieval of phytoplankton, pigments, suspended sediment, and yellow substance in coastal water. Appl. Opt. 33 (12), 2369–2378. https://doi.org/10.1363/AO.33.002369. Taylor, M.H., Losch, M., Wenzel, M., Schröter, J., 2013. On the sensitivity of field reconstruction and prediction using empirical orthogonal functions derived from Gappy data. J. Clim. 26 (22), 9194–9205. https://doi.org/10.1175/JCLI-D-13-00089.1. Tian, J., Chang, J., Zhang, Z., Wang, Y., Wu, Y., Jiang, T., 2019. Influence of three Gorges dam on downstream low flow. Water 11, 1–16. https://doi.org/10.3390/ w11010065. Wang, H., Dai, M., Liu, J., Kao, S.-J., Zhang, C., Cai, W.-J., Wang, G., Gian, W., Zhao, M., Sun, Z., 2016. Eutrophication-driven hypoxia in the east China sea off the changjiang estuary. Environ. Sci. Technol. 50 (5), 2255–2263. https://doi.org/10.1021/acs.est. 5b06211. Wang, J., Wu, J., 2009. Occurrence and potential risks of harmful algal blooms in the East China Sea. Sci. Total Environ. 407 (13), 4012–4021. https://doi.org/10.1016/j. scitotenv.2009.02.040. Wang, J., Shen, Y., Gleason, C.J., Wada, Y., 2013. Downstream Yangtze river levels impacted by three Gorges dam. Environ. Res. Lett. 8, 1–9. https://doi.org/10.1088/ 1748-9326/8/4/044012. Wang, Y., Jiang, H., Jin, J., Zhang, X., Lu, X., Wang, Y., 2015. Spatial-temporal variations of chlorophyll-a in the adjacent sea area of the Yangtze River estuary influenced by Yangtze river discharge. Int. J. Environ. Res. Public Health 12 (5), 5420–5438. https://doi.org/10.3390/ijerph120505420. Wang, X., Baskaran, M., Su, K., Du, J., 2018. The important role of submarine groundwater discharge (SGD) to derive nutrient fluxes into River dominated Ocean Margins - the East China Sea. Mar. Chem. 204 (20), 121–132. https://doi.org/10.1016/j.

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