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A numerical study of the Ulva prolifera biomass during the green tides in China - toward a cleaner Porphyra mariculture
Marine Pollution Bulletin 161 (2020) 111805
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A numerical study of the Ulva prolifera biomass during the green tides in China - toward a cleaner Porphyra mariculture Ke Sun a, c, 1, Junchuan Sun b, d, 1, Qing Liu e, Zhan Lian b, d, Jeffrey S. Ren c, f, Tao Bai g, Yitao Wang a, c, Zexun Wei b, d, * a
Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China Key Laboratory of Marine Science and Numerical Modeling, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology, Qingdao, 266200, China d Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China e College of Environmental Science and Engineering, Yangzhou University, Yangzhou 225100, China f National Institute of Water and Atmospheric Research, 10 Kyle Street, PO Box 8602, Christchurch 8440, New Zealand g North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266033, China b c
A R T I C L E I N F O
A B S T R A C T
Keywords: Macroalgal bloom Ulva prolifera Yellow Sea Porphyra Biophysical model
The green tides caused by Ulva prolifera have become a recurrent phenomenon in Yellow Sea, China. Investi gating the factors governing the biomass of green tides is important for developing management strategies. In this study, an U. prolifera growth model was combined with a hydrodynamic model. This biophysical model can reasonably reproduce the spatiotemporal variation of the green tides in 2012. Among three zones (northern, central, and southern-zones) of Porphyra mariculture region, the northern and central zones were more important in controlling the bloom intensity, and the central zone was the key area in controlling the amount of biomass landed on beaches. Due to the limitation of temperature and nutrients, an earlier or postponed facility recycling might effectively reduce the magnitude of green tides in 2012. This study provides useful information for mitigation of green tides and management of Porphyra mariculture.
1. Introduction The seaweed Porphyra is one of the most important mariculture species in China and its mariculture area has been increased dramati cally, particularly since 2007 (China Fishery Statistical Yearbook, 19822019). The rapid expansion of Porphyra mariculture, especially off the coast of Jiangsu Province (Fig. 1), has caused the occurrence of the world’s largest green tide (Wang et al., 2015). Since 2007, the green tides caused by the green macroalgae, Ulva prolifera, have become a recurrent phenomenon in Yellow Sea, China (Zhang et al., 2019a). Recent studies have suggested that the green tides may originate from the attached green macroalgae on the Porphyra mariculture rafts off the coast of Jiangsu Province (Keesing et al., 2011; Wang et al., 2015; Zhang et al., 2019a). As wastes of Porphyra mariculture, large amount of attached green macroalgae, including U. prolifera, are scraped from the rafts and discarded into the culture area during facility recycling (Zhang et al., 2019b). The discarded U. prolifera grow rapidly under favorable
environment and drift from the coast off Jiangsu Province to the Shan dong Peninsula (Son et al., 2015; Liu and Zhou, 2018). The floating biomass can reach up to 1 million tonnes (Liu et al., 2013; Wang et al., 2015), and massive landings of U. prolifera impose multiple deleterious effects on human activities and ecosystems (Ye et al., 2011; Lyons et al., 2014). It is with a great urgency to mitigate the intensity of green tides. Developing effective mitigation strategies requires understanding of the proximate processes that are responsible for individual green tide events (Smetacek and Zingone, 2013). The Porphyra mariculture rafts are usually deployed in September and recycled in the following April (Wang et al., 2015). It has been reported that an earlier or postponed Porphyra-facility recycling may reduce the biomass of green tides (Keesing et al., 2016; Xing et al., 2019). However, the underlying bio logical mechanism remains unclear. Investigating the contribution of U. prolifera disposed at the different locations and times could help to mitigate the intensity of green tides at the origin. In addition, it has been reported that the intensity of the green tides in the Yellow Sea is
* Corresponding author at: The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China. E-mail address: [email protected] (Z. Wei). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.marpolbul.2020.111805 Received 7 July 2020; Received in revised form 15 October 2020; Accepted 20 October 2020 Available online 14 November 2020 0025-326X/© 2020 Elsevier Ltd. All rights reserved.
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collectively determined by the initial biomass of U. prolifera on Porphyrafacility, temperature and nutrient availability (Wang et al., 2015; Liu et al., 2016; Wei et al., 2018). Therefore, the roles of temperature and nutrient dynamics in the spatiotemporal distribution of the biomass should also be investigated. A numerical tool is one of necessary means for understanding the biological and physical processes of green tides. In the past decades, numerical models have been applied in the investigation of the green tides in the Yellow Sea (Lee et al., 2011; Bao et al., 2015; Son et al., 2015; Xu et al., 2016). However, most of numerical studies focused on the hydrodynamic processes. Several zero-dimensional biological models for U. prolifera have been developed to simulate its physiological behavior in response to changes in environmental conditions (Wang et al., 2019a, 2019b; Sun et al., 2020). Those studies provide biological components which can be incorporated into spatially explicit frame works (e.g., hydrodynamic models) for analyzing and predicting the biomass of green tides on a fine spatiotemporal scale. The purpose of the present study is to combine a dynamic growth model for U. prolifera with a hydrodynamic model to simulate the biomass dynamics of green tides. The performance of this biophysical model was validated with field observations and remote sensing data. The model was then used to evaluate the effects of Porphyra mariculture, temperature and nutrients on the development of green tides.
2.1. Hydrodynamic model and particle tracking A numerical model based on Regional Ocean Modeling System (ROMS http://www.myroms.org/), a free-surface, hydrostatic and primitive-equation model (Song and Haidvogel, 1994), was used in this study. The model domain (28◦ N-41◦ N, 117.5◦ E-127◦ E) covered the Bohai Sea, the Yellow Sea, and parts of the East China Sea (Fig. 1). The horizontal grid spacing was 1/12 degrees with 26 s-coordinate levels in the vertical. Bottom topography data were derived from ETOPO1 (http://www.ngdc.noaa.gov/mgg/global/) with a few corrections in the coastal area. For the climatology run, the model was initialized with current, temperature and salinity data obtained from a northwestern Pacific model (Yang et al., 2011; Sun, 2015). After spun up for 20 years, the model was run for one additional year with the 2012 atmospheric forcing acquired from ERA-interim (http://apps.ecmwf.int/datasets/). The forcing factors included the daily mean wind, net heat flux, net fresh water fluxes and surface solar shortwave radiation. The open boundary data were interpolated from the simulation results obtained from the Hybrid Coordinate Ocean Model (HYCOM, http://hycom.rsmas.miami. edu/dataserver). Eight major tidal constituents from the Global In verse Tide Model data set (Egbert and Erofeeva, 2002) were applied along the boundaries. In addition, daily mean discharges of the Changjiang River (http://yu-zhu.vicp.net/) were considered in the model as freshwater input. The spatiotemporal distributions of the dis solved inorganic nitrogen (DIN), dissolved inorganic phosphate (DIP), dissolved organic nitrogen (DON), and dissolved organic phosphorus (DOP) were analyzed through field surveys during the green tides in 2012 (Shi et al., 2015). The regions not covered by the field surveys were populated with data from the Marine Atlas (Editorial Board for Marine Atlas, 1993). The merged nutrient data were linearly interpolated into each time step of the model simulation. A Lagrangian particle-tracking model was included in ROMS, which could record the location and environmental variables along the particle trajectories. The major farming region of Porphyra is located in the intertidal zones of the Jiangsu Province (Xing et al., 2019). Around this area, 75 model particles per day were continuously released from 1 April to 30 May 2012 (as shown in Fig. 1). Considering the horizontal model
2. Materials and methods Intensive field surveys, supported by a project (CEOHAB II) in the National Basic Research Priority Program, were undertaken in 2012 (Zhou et al., 2015). These data collected during 2012 bloom season were adopted in our model configuration. The modeling system consists of a hydrodynamic model and an offline biological model for U. prolifera. The hydrodynamic model simulated the temperature, currents and subsequent particle tracking. The environmental variables along the particle trajectories were recorded and input to the biological model, which calculated the biomass of U. prolifera.
Fig. 1. Illustration of the model domain, provincial boundary (blue lines), bottom topography (contour; unit: meter) and the stations of nutrients (black dots) sampled during the green tide in 2012 (source: Shi et al., 2015). A sketch map of the main Porphyra mariculture region is shown with brown dots (source: Liu et al., 2014; Fan et al., 2015). The locations of the initial release points for particle tracking are shown with red dots. The three zones marked as N, C, and S represent the northern, central, and southern parts, respectively, of the Porphyra mariculture region. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 2
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resolution, the arrangement of release points is not exactly consistent with the Porphyra mariculture region. The release time was determined considering the period of Porphyra-facility recycling (Wang et al., 2015). All particles were released and migrated within the surface layer (the top sigma level). The Porphyra mariculture region was artificially segmented into three zones (northern-N, central-C, and southern-S zones) according to the latitude (as shown in Fig. 1), and the contributions of the particles released in the different zones to the floating and beached biomass were calculated. Similarly, the particles released at the different times were traced to analyze the effects of facility recycling timing on the intensity of green tides. During the simulation, the location, temperature and the concentrations of nutrients along the trajectory of each particle were recorded. Those data were used to force the offline growth model (see Section 2.2).
floating green algae as follows: NDAI =
Rrc(745) − Rrc(660) Rrc(745) + Rrc(660)
where, Rrc(745) and Rrc(660) represent the Rayleigh-corrected reflec tance in wavelength of 745 nm and 660 nm, respectively. A positive value (NDAI>0) indicates the presence of floating green algae, and a negative value (NDAI< 0) indicates their absence. Most cloud pixels were removed according to the method of Qiu et al. (2018). Some cloud edge and land adjacent pixels might be falsely treated as macroalgae, and these pixels were manually removed. To visualize the maximum coverage of green tides, the satellite image of 13 June, which was merged from satellite HJ-1A/B and Synthetic Aperture Radar (SAR) images, was adopted from MONR (2013). As a supplement, the results of the in situ trawling bioassays (Wang et al., 2015) were also adopted to validate the coverage of green tides.
2.2. Growth model and biomass calculation A dynamic growth model has been established to simulate the growth of U. prolifera in response to environmental factor variation (Sun et al., 2020). In this model, three state variables (carbon, nitrogen and phosphorus) were adopted to describe the physiological behavior of U. prolifera. The simulated growth rate (GR) was calculated as follows:
3. Results 3.1. Temperature and current The distribution of the simulated sea surface temperature agreed well with the observations (Fig. 2), in which the rising temperature from April to July was clear. The cold waters located east of the Subei Shoal were also suitably reproduced (Fig. 2F, H). We noted that the temper ature in the Subei Shoal was overestimated by the model, which will be discussed in Section 4.1. In the coastal area, the currents were mainly northward in April (Fig. 2B), and the velocity increased gradually with its direction turning northeast (Fig. 2B, F, H).
GR = (P − Rd) × fT × min(fN, fP) where, P is the photosynthesis, fT, fN, and fP are the limitation functions due to temperature, nitrogen, and phosphate, respectively. The only loss term of biomass was caused by the dark respiration (Rd). Both dissolved inorganic and organic nutrients can be utilized by U. prolifera for growth. The potential inhibition effects of high irradiance and selfshading on the growth of U. prolifera were not considered. Instead of an explicit mortality, the duration of vegetative growth was considered that the growth of each U. prolifera particle was stopped on the 35th day after releasing. It was based on the investigation that the maturation periods of sporophytes and gametophytes were 35.7 and 31.3 days, respectively (Cui et al., 2018). Model equations were solved using a fourth-order Runge-Kutta numerical integration scheme with a time step of 1 h. The detailed description about the growth model refers to Sun et al. (2020). The initial biomass of U. prolifera on Porphyra rafts was approxi mately 6500 tonnes in 2012 (Wang et al., 2015), and an average wet weight (WW) of 108 tonnes were assumed to be released into coastal waters each day. The initial biomass of each particle was calculated according to the size of mariculture zones (Xing et al., 2019), assuming a homogeneous distribution in each zone. The biomass of each particle increased with the variable GR, and the total biomass was calculated once a day. According to the hydrodynamic model grid, the particles reaching the land were marked as beached, and their growth and loca tion were no longer updated in the following steps.
3.2. Spatial variation of the green tides Since there were no floating green algae in the simulations and ob servations in March, only the simulation results from April to July are shown. Compared to the observations, the spatial distribution of the simulated green tides occurred approximately 4 days later, and the reason will be discussed in Section 4.1. On 14 April, only small patches with lower biomass appeared north of the mariculture region (Fig. 3A). On 6 May and 16 May, patches with higher biomass occurred in the coastal waters of Jiangsu Province (Fig. 3B, C). Part of patches drifted toward the northeast and became parallel to the coastline of the Shan dong Peninsula on 28 May (Fig. 3D). The simulated biomass peaked on 13 June (Fig. 3E) and then decreased during drifting northeastward (Fig. 3F, G). Overall, the simulated coverage of green tides agreed well with the satellite and in situ observations, which further validates the hydrodynamic model. Nevertheless, small patches drifted eastward on 28 May and 25 July (Fig. 3D, G), which was not captured by the model and will be discussed in Section 4.1.
2.3. Model validation
3.3. Temporal variation of the green tides
Monthly averaged sea surface temperature from the Moderate Res olution Imaging Spectroradiometer (MODIS, available at https://oceanc olor.gsfc.nasa.gov/) were adopted to validate the simulated tempera ture. Because of the patchy distribution, there is no standard method to quantify the floating biomass of green tides. In the present study, both satellite images and in situ trawling bioassays were adopted to validate the coverage of green tides. Daily Geostationary Ocean Color Imager (GOCI) data (level 1b) from April to August of 2012 were collected from the Korea Ocean Satellite Center (KOSC) (http://kosc.kiost.ac.kr/). Four clear images without heavy cloud interference were processed. These images were corrected by removing the molecular scattering effects and converted to the Rayleigh-corrected reflectance Rrc(λ) as a function of the wavelength. The Normalized Difference Algae Index (NDAI) sug gested by Shi and Wang (2009) was employed for the detection of
The floating and beached biomass were calculated (Fig. 4). The total simulated biomass started to increase after particles were released and peaked on 15 June with a maximum value of 0.138 million tonnes. Similarly, the floating biomass increased with the total biomass and peaked on 13 June with a maximum value of 0.113 million tonnes. The beached biomass increased over the whole simulation period, and the ultimate beached biomass was approximately 0.06 million tonnes. Recently, a method was established to convert MODIS images to Ulva biomass (Hu et al., 2019). In the present study, merged data from the results of the in situ trawling bioassays (Liu et al., 2014) and data retrieved from satellite images (Hu et al., 2019) were adopted to validate the temporal variation of the biomass. The variation trend of the simulated floating biomass closely agreed with the observed variation trend, but the maximum biomass was underestimated by the model. This 3
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Fig. 2. Observed (A, C, E, G) and simulated (B, D, F, H) monthly mean sea surface temperatures in 2012. The observed sea surface temperature is derived from MODIS, and the simulated surface currents are shown with gray arrows. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 4
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Fig. 3. Spatiotemporal distribution of the simulated floating biomass of the green tides in 2012. The biomass distribution was re-gridded according to the location of each particle. The main Porphyra mariculture region is shown with brown dots. The black and red dots denote the coverage of the floating macroalgae in field surveys (source: Wang et al., 2015) and satellite images (source: GOCI and State Oceanic Administration), respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
discrepancy will be discussed in Section 4.1.
(Fig. 6A). At that time, 15% of the particles released in the N-zone increased in biomass by more than 50 times (Fig. 5A), while the pro portion increased to 41% for the particles released in the C-zone (Fig. 5B). All particles released in the S-zone increased in biomass by less than 40 times (Fig. 5C). The contributions of the particles released in the N-, C-, and S-zones to the maximum floating biomass were approxi mately 35.6%, 61.7%, and 2.7%, respectively (Fig. 6B). At the end of the green tides (31 August), approximately 2800 particles had been beached on the coast of China (Fig. 5D, E, F). Among them, more particles (62.5%) had been beached in Jiangsu Province and mostly stemmed from the S-zone (Fig. 6C). In contrast, the particles released in the N-
3.4. Distribution of the particles released at the different locations Driven by the northward currents (Fig. 2), all particles (4500 in this study) tended to drift northward after releasing, and only 3000 particles still floated on 15 June. Since the latitude of 35◦ N is close to the coastal boundary between Jiangsu and Shandong Provinces, it was chosen to evaluate the northward drift of green tides. More particles (59.7%) drifted across the 35◦ N latitude and mostly originated in the N-zone, whereas the particles released in the S-zone tended to stay south of 35◦ N 5
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Fig. 4. Comparison of the simulated and observed biomass of the green tides in 2012. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 5. Location and biomass multiple of the particles released in the different zones (red dots) at two different times. The maximum biomass occurred on 15 June, and 31 August represents the end of the green tides. The floating, beached and total biomass are also shown (unit: tonne). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
zone tended to beach in Shandong Province. The contributions of the particles from the N-, C-, and S-zones to the beached biomass were approximately 10.2%, 81.2%, and 8.6%, respectively (Fig. 6D). These results imply that the N- and C-zones are more important in controlling the intensity of green tides and that the C-zone is the key area in con trolling the amount of biomass landed on beaches.
3.5. Growth of the particles released at the different times Based on the temperature and nutrients recorded along the trajec tories of the particles (Fig. 7), the GR of particles were calculated (Fig. 8A, C, E). The limitations of environmental variables were calcu lated as fT, fN, and fP (see Section 2.2). For all of the three zones, the 6
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Fig. 6. Contribution of the particles released in the different zones to the floating and beached biomass. A, B: the floating particles and biomass on 15 June. The slices with the black edges represent the portions across 35◦ N; C, D: the beached particles and biomass on 31 August. The slices with the black edges represent the portions beached in Shandong Province. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
particles released on 1 April attained the lowest GR, and their biomass increased less than 20 times over 35 days (Fig. 8A, C, E). Thereafter, the GR increased gradually. The particles released on 1 May, 10 May, and 20 April exhibited the highest GR for the N-, C-, and S-zones, respectively. Then, the GR decreased and the biomass of the particles released on 30 May increased only 25, 20, and 5 times for N-, C-, and S-zones, respec tively. The limitation of temperature (fT) gradually increased from 1 April to 10 May. Thereafter, fT remained high until 30 May, followed by a tiny decreased. Over the entire simulation period, the limitation of phosphorus (fP) remained around 0.9. The limitation of nitrogen (fN) started with a sharp decrease over the first 10 days after releasing, fol lowed by an increase, and then decreased again from 10 May to 30 May for the particles released in N- and C-zones.
trajectory (Lee et al., 2011; Bao et al., 2015; Son et al., 2015; Xu et al., 2016). In contrast to previous model studies, a growth model of U. prolifera was incorporated into a hydrodynamic model in this study. The biomass dynamics during the development of green tides were reasonably reproduced with the model. However, there were some mismatches between the simulations and observations. There are several factors could cause these disagreements, one of which could be the contribution of micro-propagules. In 2012, the quantity of micropropagules steadily increased up to the development phase of green tides (Li et al., 2014), which might play an important role in the outbreak of green tides (Zhang et al., 2015). However, micro-propagules were not considered in the present study, which could partially explain the underestimated maximum biomass in Fig. 4. By comparing the simulated and observed floating biomass (see Fig. 4), the contribution of micro-propagules to biomass was estimated to be approximately 25%. In addition, although the ROMS model includes wind-driven com ponents in the surface flow, the direct frictional drag by winds (leeway drift) on the patches of floating macroalgae was not considered. Son et al. (2015) and Xu et al. (2016) pointed out that leeway drift is a crucial forcing factor in floating green algae tracing. However, other numerical studies have also generated acceptable results without considering leeway drift (Lee et al., 2011; Bao et al., 2015). There was a delay of approximately 4 days in the occurrence of the simulated green tides in our study, which could be partially explained by the lack of leeway drift. In addition, this could also be caused by the model resolution. The horizontal resolution (1/12 degrees) might not provide enough details
4. Discussion 4.1. Model advantages and improvement The green tides in the Yellow Sea are characterized by massive freefloating macroalgae (Liu and Zhou, 2018), which are similar with the golden tides caused by the floating brown macroalgae Sargassum (Smetacek and Zingone, 2013). A coupled modeling approach that in tegrates hydrodynamic, biogeochemical and Sargassum growth models has been applied to investigate the seasonal distribution of Sargassum (Brooks et al., 2018). For the green tides in the Yellow Sea, various hydrodynamic models have been established to investigate their 7
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Fig. 7. Environmental variables along the trajectory of particles released every 10 days, with uncertainty ranges shaded (±1 s.d.). A: temperature; B: dissolved inorganic nitrogen; C: dissolved organic nitrogen; D: dissolved inorganic phosphorus; E: dissolved organic phosphorus. The red dashed line represent the optimal temperature for Ulva prolifera growth. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
for the transport of floating macroalgae. This can also explain the mismatch between the simulated and observed temperatures in the Subei Shoal. A model with a higher resolution should be adopted for simulations of this region. We noted that part of the macroalgae drifted eastward in the images of 28 May and 25 July (Fig. 3), which was not captured by our model. We tentatively rearranged the release locations of particles and found that the particles released at further south of Porphyra mariculture region could drift to the east. It has been reported that some patches of U. prolifera appeared at south of mariculture region (Hu et al., 2010), which might contribute to the patches moving to the east. In this study, we only simulated and analyzed the green tides in 2012. To our knowledge, the biomass of green tides in the Yellow Sea varies in different years (Hu et al., 2019; Xiao et al., 2019) with the lowest biomass appeared in 2012. We noted that more particles and higher biomass beached on the coast of Jiangsu Province (Fig. 6), which is unusual but might partially explain the lowest bloom scale in 2012. In addition, there was some variability in the sea surface wind and
temperature between years (Fig. 9), which was also found in previous studies (Keesing et al., 2011; Lee et al., 2011). The effects of these interannual variation on the biomass of green tides should be considered in the future. 4.2. Contribution of the U. prolifera released at the different locations and times It has been hypothesized that the expansion of the Porphyra mari culture area, especially the northern part (N- and C-zones in this study), along Jiangsu Province is the most likely cause of the green tides in the Yellow Sea (Liu et al., 2013; Xing et al., 2019; Zhang et al., 2019b). This hypothesis was confirmed in the present study, and two reasons were concluded. (i) Depending on the locations further to the north and the sustained northward currents (Fig. 2), the particles released in the Nand C-zones were more easily transported northward than those released in the S-zone (Fig. 5). (ii) During drifting, the particles released in the N- and C-zones exhibited higher GR than those in the S-zone 8
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Fig. 8. Biomass variation and environmental limitation to the particles released every 10 days, with the uncertainty ranges shaded (±1 s.d.). A, C, E: multiples of the biomass increased for the particles released in N-, C-, and S-zones, respectively; B, D, F: limitations of the temperature, nitrogen and phosphorus for the particles released in N-, C-, and S-zones, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
(Fig. 8). U. prolifera occurred on the Porphyra rafts from March to May 2012, and its relative abundance increased from April to mid-May (Fan et al., 2015; Zhang et al., 2015). An earlier harvest of Porphyra and earlier facility recycling may reduce the biomass of U. prolifera attached on the rafts and lower the magnitude of green tides (Keesing et al., 2016). In our study, the GR of the particles released before 20 April was relatively lower (Fig. 8). This can be explained by the lower temperature and lower level of N-related nutrients along the trajectories of these particles (Fig. 8). This result further proves that an earlier facility recycling can reduce the intensity of green tides. However, the reduction of Porphyra production caused by an earlier harvest should also be considered in practice. Based on remote sensing observations, the postponed facility recycling may also reduce the biomass of green tides (Xing et al., 2019).
In the present study, the GR of the particles released after 10 May gradually decreased, and we found that the lower nutrients and higher temperature were not suitable for their growth (Fig. 8). In 2012, the period of Porphyra-facility recycling was from mid-April to mid-May (Wang et al., 2015). Therefore, considering the limitation of tempera ture and nutrients, an earlier or postponed-facility recycling might effectively reduce the magnitude of green tides. Nevertheless, the most effective way to mitigate green tides is to dispose all attached green macroalgae on land (Liu et al., 2013), which is highly recommended. Since the N- and C-zones are far away from the coastal line and the transportation cost of attached macroalgae in these zones are relatively higher, farmers are more likely to remove macro algae in situ rather than collect them (Xing et al., 2019). Considering the importance of N- and C-zones in governing the intensity of green tides, 9
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Fig. 9. Monthly averaged sea surface temperature and wind in April, May, June, and July from 2010 to 2014. The temperature and wind data are obtained from MODIS and ERA-interim, respectively.
governmental policy makers should provide economic incentives for the collecting of macroalgae especially in these two zones to keep farmers positive. Before these policies are implemented, a risk map and an advice for Porphyra facility recycling were proposed in this study. Generally, high risk appeared first in the S-zone, followed by the N-zone and then by the C-zone (Fig. 10). In the S- and C-zones, high risk appeared first in nearshore waters and then in offshore waters. In order to control the biomass, farmers should avoid facility recycling in the Nand C-zones from late April to early May (Table 1). Since the distribution of particle release points was not in complete agreement with the Por phyra mariculture region, results of this study could be used to evaluate the impact of further expansion of Porphyra mariculture region, and the risk map could be used as reference for the early intercept and salvage of
floating macroalgae. 5. Conclusions In this study, an U. prolifera growth model was integrated with a hydrodynamic model through particle tracking. The biophysical model achieved a satisfactory performance in reproducing the biomass spatiotemporal variation during the green tides in 2012. The contribu tions of the macroalgae released at the different locations and times were analyzed. The results imply that the northern and central zones are more important in controlling the intensity of green tides and that the central zone is also the key area in controlling the amount of biomass landed on beaches. Considering the limitation of temperature and 10
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review & editing. Tao Bai: Visualization. Yitao Wang: Writing - review & editing. Zexun Wei: Supervision, Resources. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements We deeply appreciate the outstanding efforts of scientists who pub lished the experimental data and those who took part in the cruises in 2012. This work was supported by the National Key Research and Development Program of China [grant numbers 2018YFD0900705, 2018YFD0900906]; the National Natural Science Foundation of China [grant numbers 41606038, 41606040]; the Natural Science Foundation of Jiangsu Province [grant number BK20180940]; the Integration and Application of Global Ocean Dynamic Environmental Forecasting Sys tem [grant number 2016YFC1401409]; the Central Public-interest Sci entific Institution Basal Research, CAFS & Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture and Rural Affairs of the People’s Republic of China [grant number 2019HYXKQ01]; and the Central Public-interest Scientific Institution Basal Research Fund, YSFRI, CAFS [grant number 20603022018003]. References Bao, M., Guan, W., Yang, Y., Cao, Z., Chen, Q., 2015. Drifting trajectories of green algae in the western Yellow Sea during the spring and summer of 2012. Estuar. Coast. Shelf Sci. 163, 9–16. https://doi.org/10.1016/j.ecss.2015.02.009. Brooks, M.T., Coles, V.J., Hood, R.R., Gower, J.F.R., 2018. Factors controlling the seasonal distribution of pelagic Sargassum. Mar. Ecol. Prog. Ser. 599, 1–18. https:// doi.org/10.3354/meps12646. China Fishery Statistical Yearbook (in Chinese), 1982-2019. Bureau of Fisheries of the Ministry of Agriculture. Beijing, China. Cui, J., Shi, J., Zhang, J., Wang, L., Fan, S., Xu, Z., Huo, Y., Zhou, Q., Lu, Y., He, P., 2018. Rapid expansion of Ulva blooms in the Yellow Sea, China through sexual reproduction and vegetative growth. Mar. Pollut. Bull. 130, 223–228. https://doi. org/10.1016/j.marpolbul.2018.03.036. Editorial Board for Marine Atlas, 1993. Marine Atlas of Bohai Sea, Yellow Sea, East China Sea: Hydrology (in Chinese). China Ocean Press. Egbert, G.D., Erofeeva, S.Y., 2002. Efficient inverse modeling of barotropic ocean tides. J. Atmos. Ocean. Tech. 19 (2), 183–204. https://doi.org/10.1175/1520-0426(2002) 019<0183:EIMOBO>2.0.CO;2. Fan, S., Fu, M., Wang, Z., Zhang, X., Song, W., Li, Y., Liu, G., Shi, X., Wang, X., Zhu, M., 2015. Temporal variation of green macroalgal assemblage on Porphyra aquaculture rafts in the Subei Shoal, China. Estuar. Coast. Shelf Sci. 163, 23–28. https://doi.org/ 10.1016/j.ecss.2015.03.016. Hu, C., Li, D., Chen, C., Ge, J., Muller-Karger, F.E., Liu, J., Yu, F., He, M., 2010. On the recurrent Ulva prolifera blooms in the Yellow Sea and East China Sea. J. Geophys. Res. 115, C05017. https://doi.org/10.1029/2009JC005561. Hu, L., Zeng, K., Hu, C., He, M., 2019. On the remote estimation of Ulva prolifera areal coverage and biomass. Remote Sens. Environ. 223, 194–207. https://doi.org/ 10.1016/j.rse.2019.01.014. Keesing, J.K., Liu, D., Fearns, P., Garcia, R., 2011. Inter- and intra-annual patterns of Ulva prolifera green tides in the Yellow Sea during 2007–2009, their origin and relationship to the expansion of coastal seaweed aquaculture in China. Mar. Pollut. Bull. 62, 1169–1182. https://doi.org/10.1016/j.marpolbul.2011.03.040. Keesing, J.K., Liu, D., Shi, Y., Wang, Y., 2016. Abiotic factors influencing biomass accumulation of green tide causing Ulva spp. on Pyropia culture rafts in the Yellow Sea, China. Mar. Pollut. Bull. 105, 88–97. https://doi.org/10.1016/j. marpolbul.2016.02.051. Lee, J.H., Pang, I., Moon, I., Ryu, J., 2011. On physical factors that controlled the massive green tide occurrence along the southern coast of the Shandong peninsula in 2008: a numerical study using a particle-tracking experiment. J. Geophys. Res. 116, C12036. https://doi.org/10.1029/2011JC007512. Li, Y., Song, W., Xiao, J., Wang, Z., Fu, M., Zhu, M., Li, R., Zhang, X., Wang, X., 2014. Tempo-spatial distribution and species diversity of green algae micro-propagules in the Yellow Sea during the large-scale green tide development. Harmful Algae 39, 40–47. https://doi.org/10.1016/j.hal.2014.05.013. Liu, D., Zhou, M., 2018. Green tides of the Yellow Sea: Massive free-floating blooms of Ulva prolifera. 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. Springer, Cham, pp. 317–326. https://doi.org/ 10.1007/978-3-319-70069-4_16.
Fig. 10. Distribution of the time with highest risk. The time was defined as the date when the particle with the highest GR released in each release point. The main Porphyra mariculture region is shown with brown dots. The three zones marked as N, C, and S represent the northern, central, and southern parts, respectively, of the Porphyra mariculture region. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Table 1 Advised schedule for the facility recycling in each zone. April
May
N-zone
1st
23th
18th
30th
C-zone
1st
15th
16th
30th
S-zone
1st
30th
Note: The periods with the averaged biomass multiple lower than 35 were shaded.
nutrients, an earlier or postponed facility recycling might effectively reduce the magnitude of green tides in 2012. Nevertheless, the most effective way to mitigate green tides is to dispose attached green mac roalgae on land, and an advice for Porphyra facility recycling was pro vided. Considering the variability in the sea surface wind and temperature between years, the biomass interannual variation should be investigated in the future. CRediT authorship contribution statement Ke Sun: Writing - original draft, Conceptualization, Validation. Junchuan Sun: Methodology, Software. Qing Liu: Writing - review & editing. Zhan Lian: Writing - review & editing. Jeffrey S. Ren: Writing 11
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Marine Pollution Bulletin 161 (2020) 111805 the green tide in Yellow Sea. Limnol. Oceanogr. 60 (4), 1105–1117. https://doi.org/ 10.1002/lno.10083. Wang, C., Su, R., Guo, L., Yang, B., Zhang, Y., Zhang, L., Xu, H., Shi, W., Wei, L., 2019a. Nutrient absorption by Ulva prolifera and the growth mechanism leading to greentides. Estuar. Coast. Shelf Sci. 227, 106329. https://doi.org/10.1016/j. ecss.2019.106329. Wang, W., Zhang, G., Sun, X., Zhang, F., Zhang, X., 2019b. Temporal variability in zooplankton community in the western Yellow Sea and its possible links to green tides. PeerJ 7, e6641. https://doi.org/10.7717/peerj.6641. Wei, Q., Wang, B., Yao, Q., Fu, M., Sun, J., Xu, B., Yu, Z., 2018. Hydro-biogeochemical processes and their implications for Ulva prolifera blooms and expansion in the world’s largest green tide occurrence region (Yellow Sea, China). Sci. Total Environ. 645, 257–266. https://doi.org/10.1016/j.scitotenv.2018.07.067. Xiao, Y., Zhang, J., Cui, T., Gong, J., Liu, R., Chen, X., 2019. Remote sensing estimation of the biomass of floating Ulva prolifera and analysis of the main factors driving the interannual variability of the biomass in the Yellow Sea. Mar. Pollut. Bull. 140, 330–340. https://doi.org/10.1016/j.marpolbul.2019.01.037. Xing, Q., An, D., Zheng, X., Wei, Z., Wang, X., Li, L., Tian, L., Chen, J., 2019. Monitoring seaweed aquaculture in the Yellow Sea with multiple sensors for managing the disaster of macroalgal blooms. Remote Sens. Environ. 231, 111279 https://doi.org/ 10.1016/j.rse.2019.111279. Xu, Q., Zhang, H., Cheng, Y., Zhang, S., Zhang, W., 2016. Monitoring and tracking the green tide in the yellow sea with satellite imagery and trajectory model. IEEE JSTARS 9 (11), 5172–5181. https://doi.org/10.1109/JSTARS.2016.2580000. Yang, D., Yin, B., Liu, Z., Feng, X., 2011. Numerical study of the ocean circulation on the East China Sea shelf and a Kuroshio bottom branch northeast of Taiwan in summer. J. Geophys. Res. 116, C05015. https://doi.org/10.1029/2010JC006777. Ye, N., Zhang, X., Mao, Y., Liang, C., Xu, D., Zou, J., Zhuang, Z., Wang, Q., 2011. ‘Green tides’ are overwhelming the coastline of our blue planet: taking the world’s largest example. Ecol. Res. 26, 477–485. https://doi.org/10.1007/s11284-011-0821-8. Zhang, Q., Liu, Q., Yu, R., Kang, Z., Guo, W., Ding, Y., Chen, J., Wang, Y., Li, Y., Yan, T., Zhou, M., 2015. Application of a fluorescence in situ hybridization (FISH) method to study green tides in the Yellow Sea. Estuar. Coast. Shelf Sci. 163, 112–119. https:// doi.org/10.1016/j.ecss.2014.09.005. Zhang, J., Shi, J., Gao, S., Huo, Y., Cui, J., Shen, H., Liu, G., He, P., 2019a. Annual patterns of macroalgal blooms in the Yellow Sea during 2007-2017. PLoS One 14 (1), e0210460. https://doi.org/10.1371/journal.pone.0210460. Zhang, Y., He, P., Li, H., Li, G., Liu, J., Jiao, F., Zhang, J., Huo, Y., Shi, X., Su, R., Ye, N., Liu, D., Yu, R., Wang, Z., Zhou, M., Jiao, N., 2019b. Ulva prolifera green-tide outbreaks and their environmental impact in the Yellow Sea, China. Natl. Sci. Rev. 6, 825–838. https://doi.org/10.1093/nsr/nwz026. Zhou, M.J., Liu, D.Y., Anderson, D.M., Valiela, I., 2015. Introduction to the special issue on green tides in the yellow sea. Estuar. Coast. Shelf Sci. 163, 3–8. https://doi.org/ 10.1016/j.ecss.2015.06.023.
Liu, D., Keesing, J.K., He, P., Wang, Z., Shi, Y., Wang, Y., 2013. The world’s largest macroalgal bloom in the Yellow Sea, China: formation and implications. Estuar. Coast. Shelf Sci. 129, 2–10. https://doi.org/10.1016/j.ecss.2013.05.021. Liu, X., Li, Y., Wang, Z., Zhang, Q., Cai, X., 2014. Cruise observation of Ulva prolifera bloom in the southern Yellow Sea, China. Estuar. Coast. Shelf Sci. 163, 17–22. https://doi.org/10.1016/j.ecss.2014.09.014. Liu, X., Wang, Z., Zhang, X., 2016. A review of the green tides in the Yellow Sea. China. Mar. Environ. Res 119, 189–196. https://doi.org/10.1016/j. marenvres.2016.06.004. Lyons, D.A., Arvanitidis, C., Blight, A.J., Chatzinikolaou, E., Guy-Haim, T., Kotta, J., Orav-Kotta, H., Queir´ os, A.M., Rilov, G., Somerfield, P.J., Crowe, T.P., 2014. Macroalgal blooms alter community structure and primary productivity in marine ecosystems. Glob. Chang. Biol. 20 (9), 2712–2724. https://doi.org/10.1111/ gcb.12644. MONR (Ministry of Natural Resources of the People’’s Republic of China), 2013. Bulletin of China marine disaster. http://www.mnr.gov.cn/sj/sjfw/hy/gbgg/zghyzhgb/. (Accessed 17 April 2020). Qiu, Z., Li, Z., Bilal, M., Wang, S., Sun, D., Chen, Y., 2018. Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: case study of Yellow Sea using GOCI images. Opt. Express 26 (21), 26810. https://doi.org/10.1364/ OE.26.026810. Shi, W., Wang, M., 2009. Green macroalgae blooms in the Yellow Sea during the spring and summer of 2008. J. Geophys. Res. 114, C12010. https://doi.org/10.1029/ 2009JC005513. Shi, X., Qi, M., Tang, H., Han, X., 2015. Spatial and temporal nutrient variations in the Yellow Sea and their effects on Ulva prolifera blooms. Estuar. Coast. Shelf Sci. 163, 36–43. https://doi.org/10.1016/j.ecss.2015.02.007. Smetacek, V., Zingone, A., 2013. Green and golden seaweed tides on the rise. Nature 504, 84–88. https://doi.org/10.1038/nature12860. Son, Y.B., Choi, B., Kim, Y.H., Park, Y., 2015. Tracing floating green algae blooms in the Yellow Sea and the East China Sea using GOCI satellite data and Lagrangian transport simulations. Remote Sens. Environ. 156, 21–33. https://doi.org/10.1016/ j.rse.2014.09.024. Song, Y., Haidvogel, D., 1994. A semi-implicit ocean circulation model using a generalized topography-following coordinate system. J. Comput. Phys. 115 (1), 228–244. https://doi.org/10.1006/jcph.1994.1189. Sun, J., 2015. Numerical Study on the Circulation in the Yellow Sea in Winter. The University of Chinese Academy of Sciences (Doctoral dissertation (in Chinese with English abstract)). Sun, K., Ren, J.S., Bai, T., Zhang, J., Liu, Q., Wu, W., Zhao, Y., Liu, Y., 2020. A dynamic growth model of Ulva prolifera: application in quantifying the biomass of green tides in the Yellow Sea, China. Ecol. Model. 428, 109072 https://doi.org/10.1016/j. ecolmodel.2020.109072. Wang, Z., Xiao, J., Fan, S., Li, Y., Li, X., Liu, D., 2015. Who made the world’s largest green tide in China? - An integrated study on the initiation and early development of
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Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
A numerical study of the Ulva prolifera biomass during the green tides in China - toward a cleaner Porphyra mariculture Ke Sun a, c, 1, Junchuan Sun b, d, 1, Qing Liu e, Zhan Lian b, d, Jeffrey S. Ren c, f, Tao Bai g, Yitao Wang a, c, Zexun Wei b, d, * a
Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China Key Laboratory of Marine Science and Numerical Modeling, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology, Qingdao, 266200, China d Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China e College of Environmental Science and Engineering, Yangzhou University, Yangzhou 225100, China f National Institute of Water and Atmospheric Research, 10 Kyle Street, PO Box 8602, Christchurch 8440, New Zealand g North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266033, China b c
A R T I C L E I N F O
A B S T R A C T
Keywords: Macroalgal bloom Ulva prolifera Yellow Sea Porphyra Biophysical model
The green tides caused by Ulva prolifera have become a recurrent phenomenon in Yellow Sea, China. Investi gating the factors governing the biomass of green tides is important for developing management strategies. In this study, an U. prolifera growth model was combined with a hydrodynamic model. This biophysical model can reasonably reproduce the spatiotemporal variation of the green tides in 2012. Among three zones (northern, central, and southern-zones) of Porphyra mariculture region, the northern and central zones were more important in controlling the bloom intensity, and the central zone was the key area in controlling the amount of biomass landed on beaches. Due to the limitation of temperature and nutrients, an earlier or postponed facility recycling might effectively reduce the magnitude of green tides in 2012. This study provides useful information for mitigation of green tides and management of Porphyra mariculture.
1. Introduction The seaweed Porphyra is one of the most important mariculture species in China and its mariculture area has been increased dramati cally, particularly since 2007 (China Fishery Statistical Yearbook, 19822019). The rapid expansion of Porphyra mariculture, especially off the coast of Jiangsu Province (Fig. 1), has caused the occurrence of the world’s largest green tide (Wang et al., 2015). Since 2007, the green tides caused by the green macroalgae, Ulva prolifera, have become a recurrent phenomenon in Yellow Sea, China (Zhang et al., 2019a). Recent studies have suggested that the green tides may originate from the attached green macroalgae on the Porphyra mariculture rafts off the coast of Jiangsu Province (Keesing et al., 2011; Wang et al., 2015; Zhang et al., 2019a). As wastes of Porphyra mariculture, large amount of attached green macroalgae, including U. prolifera, are scraped from the rafts and discarded into the culture area during facility recycling (Zhang et al., 2019b). The discarded U. prolifera grow rapidly under favorable
environment and drift from the coast off Jiangsu Province to the Shan dong Peninsula (Son et al., 2015; Liu and Zhou, 2018). The floating biomass can reach up to 1 million tonnes (Liu et al., 2013; Wang et al., 2015), and massive landings of U. prolifera impose multiple deleterious effects on human activities and ecosystems (Ye et al., 2011; Lyons et al., 2014). It is with a great urgency to mitigate the intensity of green tides. Developing effective mitigation strategies requires understanding of the proximate processes that are responsible for individual green tide events (Smetacek and Zingone, 2013). The Porphyra mariculture rafts are usually deployed in September and recycled in the following April (Wang et al., 2015). It has been reported that an earlier or postponed Porphyra-facility recycling may reduce the biomass of green tides (Keesing et al., 2016; Xing et al., 2019). However, the underlying bio logical mechanism remains unclear. Investigating the contribution of U. prolifera disposed at the different locations and times could help to mitigate the intensity of green tides at the origin. In addition, it has been reported that the intensity of the green tides in the Yellow Sea is
* Corresponding author at: The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China. E-mail address: [email protected] (Z. Wei). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.marpolbul.2020.111805 Received 7 July 2020; Received in revised form 15 October 2020; Accepted 20 October 2020 Available online 14 November 2020 0025-326X/© 2020 Elsevier Ltd. All rights reserved.
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collectively determined by the initial biomass of U. prolifera on Porphyrafacility, temperature and nutrient availability (Wang et al., 2015; Liu et al., 2016; Wei et al., 2018). Therefore, the roles of temperature and nutrient dynamics in the spatiotemporal distribution of the biomass should also be investigated. A numerical tool is one of necessary means for understanding the biological and physical processes of green tides. In the past decades, numerical models have been applied in the investigation of the green tides in the Yellow Sea (Lee et al., 2011; Bao et al., 2015; Son et al., 2015; Xu et al., 2016). However, most of numerical studies focused on the hydrodynamic processes. Several zero-dimensional biological models for U. prolifera have been developed to simulate its physiological behavior in response to changes in environmental conditions (Wang et al., 2019a, 2019b; Sun et al., 2020). Those studies provide biological components which can be incorporated into spatially explicit frame works (e.g., hydrodynamic models) for analyzing and predicting the biomass of green tides on a fine spatiotemporal scale. The purpose of the present study is to combine a dynamic growth model for U. prolifera with a hydrodynamic model to simulate the biomass dynamics of green tides. The performance of this biophysical model was validated with field observations and remote sensing data. The model was then used to evaluate the effects of Porphyra mariculture, temperature and nutrients on the development of green tides.
2.1. Hydrodynamic model and particle tracking A numerical model based on Regional Ocean Modeling System (ROMS http://www.myroms.org/), a free-surface, hydrostatic and primitive-equation model (Song and Haidvogel, 1994), was used in this study. The model domain (28◦ N-41◦ N, 117.5◦ E-127◦ E) covered the Bohai Sea, the Yellow Sea, and parts of the East China Sea (Fig. 1). The horizontal grid spacing was 1/12 degrees with 26 s-coordinate levels in the vertical. Bottom topography data were derived from ETOPO1 (http://www.ngdc.noaa.gov/mgg/global/) with a few corrections in the coastal area. For the climatology run, the model was initialized with current, temperature and salinity data obtained from a northwestern Pacific model (Yang et al., 2011; Sun, 2015). After spun up for 20 years, the model was run for one additional year with the 2012 atmospheric forcing acquired from ERA-interim (http://apps.ecmwf.int/datasets/). The forcing factors included the daily mean wind, net heat flux, net fresh water fluxes and surface solar shortwave radiation. The open boundary data were interpolated from the simulation results obtained from the Hybrid Coordinate Ocean Model (HYCOM, http://hycom.rsmas.miami. edu/dataserver). Eight major tidal constituents from the Global In verse Tide Model data set (Egbert and Erofeeva, 2002) were applied along the boundaries. In addition, daily mean discharges of the Changjiang River (http://yu-zhu.vicp.net/) were considered in the model as freshwater input. The spatiotemporal distributions of the dis solved inorganic nitrogen (DIN), dissolved inorganic phosphate (DIP), dissolved organic nitrogen (DON), and dissolved organic phosphorus (DOP) were analyzed through field surveys during the green tides in 2012 (Shi et al., 2015). The regions not covered by the field surveys were populated with data from the Marine Atlas (Editorial Board for Marine Atlas, 1993). The merged nutrient data were linearly interpolated into each time step of the model simulation. A Lagrangian particle-tracking model was included in ROMS, which could record the location and environmental variables along the particle trajectories. The major farming region of Porphyra is located in the intertidal zones of the Jiangsu Province (Xing et al., 2019). Around this area, 75 model particles per day were continuously released from 1 April to 30 May 2012 (as shown in Fig. 1). Considering the horizontal model
2. Materials and methods Intensive field surveys, supported by a project (CEOHAB II) in the National Basic Research Priority Program, were undertaken in 2012 (Zhou et al., 2015). These data collected during 2012 bloom season were adopted in our model configuration. The modeling system consists of a hydrodynamic model and an offline biological model for U. prolifera. The hydrodynamic model simulated the temperature, currents and subsequent particle tracking. The environmental variables along the particle trajectories were recorded and input to the biological model, which calculated the biomass of U. prolifera.
Fig. 1. Illustration of the model domain, provincial boundary (blue lines), bottom topography (contour; unit: meter) and the stations of nutrients (black dots) sampled during the green tide in 2012 (source: Shi et al., 2015). A sketch map of the main Porphyra mariculture region is shown with brown dots (source: Liu et al., 2014; Fan et al., 2015). The locations of the initial release points for particle tracking are shown with red dots. The three zones marked as N, C, and S represent the northern, central, and southern parts, respectively, of the Porphyra mariculture region. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 2
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resolution, the arrangement of release points is not exactly consistent with the Porphyra mariculture region. The release time was determined considering the period of Porphyra-facility recycling (Wang et al., 2015). All particles were released and migrated within the surface layer (the top sigma level). The Porphyra mariculture region was artificially segmented into three zones (northern-N, central-C, and southern-S zones) according to the latitude (as shown in Fig. 1), and the contributions of the particles released in the different zones to the floating and beached biomass were calculated. Similarly, the particles released at the different times were traced to analyze the effects of facility recycling timing on the intensity of green tides. During the simulation, the location, temperature and the concentrations of nutrients along the trajectory of each particle were recorded. Those data were used to force the offline growth model (see Section 2.2).
floating green algae as follows: NDAI =
Rrc(745) − Rrc(660) Rrc(745) + Rrc(660)
where, Rrc(745) and Rrc(660) represent the Rayleigh-corrected reflec tance in wavelength of 745 nm and 660 nm, respectively. A positive value (NDAI>0) indicates the presence of floating green algae, and a negative value (NDAI< 0) indicates their absence. Most cloud pixels were removed according to the method of Qiu et al. (2018). Some cloud edge and land adjacent pixels might be falsely treated as macroalgae, and these pixels were manually removed. To visualize the maximum coverage of green tides, the satellite image of 13 June, which was merged from satellite HJ-1A/B and Synthetic Aperture Radar (SAR) images, was adopted from MONR (2013). As a supplement, the results of the in situ trawling bioassays (Wang et al., 2015) were also adopted to validate the coverage of green tides.
2.2. Growth model and biomass calculation A dynamic growth model has been established to simulate the growth of U. prolifera in response to environmental factor variation (Sun et al., 2020). In this model, three state variables (carbon, nitrogen and phosphorus) were adopted to describe the physiological behavior of U. prolifera. The simulated growth rate (GR) was calculated as follows:
3. Results 3.1. Temperature and current The distribution of the simulated sea surface temperature agreed well with the observations (Fig. 2), in which the rising temperature from April to July was clear. The cold waters located east of the Subei Shoal were also suitably reproduced (Fig. 2F, H). We noted that the temper ature in the Subei Shoal was overestimated by the model, which will be discussed in Section 4.1. In the coastal area, the currents were mainly northward in April (Fig. 2B), and the velocity increased gradually with its direction turning northeast (Fig. 2B, F, H).
GR = (P − Rd) × fT × min(fN, fP) where, P is the photosynthesis, fT, fN, and fP are the limitation functions due to temperature, nitrogen, and phosphate, respectively. The only loss term of biomass was caused by the dark respiration (Rd). Both dissolved inorganic and organic nutrients can be utilized by U. prolifera for growth. The potential inhibition effects of high irradiance and selfshading on the growth of U. prolifera were not considered. Instead of an explicit mortality, the duration of vegetative growth was considered that the growth of each U. prolifera particle was stopped on the 35th day after releasing. It was based on the investigation that the maturation periods of sporophytes and gametophytes were 35.7 and 31.3 days, respectively (Cui et al., 2018). Model equations were solved using a fourth-order Runge-Kutta numerical integration scheme with a time step of 1 h. The detailed description about the growth model refers to Sun et al. (2020). The initial biomass of U. prolifera on Porphyra rafts was approxi mately 6500 tonnes in 2012 (Wang et al., 2015), and an average wet weight (WW) of 108 tonnes were assumed to be released into coastal waters each day. The initial biomass of each particle was calculated according to the size of mariculture zones (Xing et al., 2019), assuming a homogeneous distribution in each zone. The biomass of each particle increased with the variable GR, and the total biomass was calculated once a day. According to the hydrodynamic model grid, the particles reaching the land were marked as beached, and their growth and loca tion were no longer updated in the following steps.
3.2. Spatial variation of the green tides Since there were no floating green algae in the simulations and ob servations in March, only the simulation results from April to July are shown. Compared to the observations, the spatial distribution of the simulated green tides occurred approximately 4 days later, and the reason will be discussed in Section 4.1. On 14 April, only small patches with lower biomass appeared north of the mariculture region (Fig. 3A). On 6 May and 16 May, patches with higher biomass occurred in the coastal waters of Jiangsu Province (Fig. 3B, C). Part of patches drifted toward the northeast and became parallel to the coastline of the Shan dong Peninsula on 28 May (Fig. 3D). The simulated biomass peaked on 13 June (Fig. 3E) and then decreased during drifting northeastward (Fig. 3F, G). Overall, the simulated coverage of green tides agreed well with the satellite and in situ observations, which further validates the hydrodynamic model. Nevertheless, small patches drifted eastward on 28 May and 25 July (Fig. 3D, G), which was not captured by the model and will be discussed in Section 4.1.
2.3. Model validation
3.3. Temporal variation of the green tides
Monthly averaged sea surface temperature from the Moderate Res olution Imaging Spectroradiometer (MODIS, available at https://oceanc olor.gsfc.nasa.gov/) were adopted to validate the simulated tempera ture. Because of the patchy distribution, there is no standard method to quantify the floating biomass of green tides. In the present study, both satellite images and in situ trawling bioassays were adopted to validate the coverage of green tides. Daily Geostationary Ocean Color Imager (GOCI) data (level 1b) from April to August of 2012 were collected from the Korea Ocean Satellite Center (KOSC) (http://kosc.kiost.ac.kr/). Four clear images without heavy cloud interference were processed. These images were corrected by removing the molecular scattering effects and converted to the Rayleigh-corrected reflectance Rrc(λ) as a function of the wavelength. The Normalized Difference Algae Index (NDAI) sug gested by Shi and Wang (2009) was employed for the detection of
The floating and beached biomass were calculated (Fig. 4). The total simulated biomass started to increase after particles were released and peaked on 15 June with a maximum value of 0.138 million tonnes. Similarly, the floating biomass increased with the total biomass and peaked on 13 June with a maximum value of 0.113 million tonnes. The beached biomass increased over the whole simulation period, and the ultimate beached biomass was approximately 0.06 million tonnes. Recently, a method was established to convert MODIS images to Ulva biomass (Hu et al., 2019). In the present study, merged data from the results of the in situ trawling bioassays (Liu et al., 2014) and data retrieved from satellite images (Hu et al., 2019) were adopted to validate the temporal variation of the biomass. The variation trend of the simulated floating biomass closely agreed with the observed variation trend, but the maximum biomass was underestimated by the model. This 3
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Fig. 2. Observed (A, C, E, G) and simulated (B, D, F, H) monthly mean sea surface temperatures in 2012. The observed sea surface temperature is derived from MODIS, and the simulated surface currents are shown with gray arrows. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 4
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Fig. 3. Spatiotemporal distribution of the simulated floating biomass of the green tides in 2012. The biomass distribution was re-gridded according to the location of each particle. The main Porphyra mariculture region is shown with brown dots. The black and red dots denote the coverage of the floating macroalgae in field surveys (source: Wang et al., 2015) and satellite images (source: GOCI and State Oceanic Administration), respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
discrepancy will be discussed in Section 4.1.
(Fig. 6A). At that time, 15% of the particles released in the N-zone increased in biomass by more than 50 times (Fig. 5A), while the pro portion increased to 41% for the particles released in the C-zone (Fig. 5B). All particles released in the S-zone increased in biomass by less than 40 times (Fig. 5C). The contributions of the particles released in the N-, C-, and S-zones to the maximum floating biomass were approxi mately 35.6%, 61.7%, and 2.7%, respectively (Fig. 6B). At the end of the green tides (31 August), approximately 2800 particles had been beached on the coast of China (Fig. 5D, E, F). Among them, more particles (62.5%) had been beached in Jiangsu Province and mostly stemmed from the S-zone (Fig. 6C). In contrast, the particles released in the N-
3.4. Distribution of the particles released at the different locations Driven by the northward currents (Fig. 2), all particles (4500 in this study) tended to drift northward after releasing, and only 3000 particles still floated on 15 June. Since the latitude of 35◦ N is close to the coastal boundary between Jiangsu and Shandong Provinces, it was chosen to evaluate the northward drift of green tides. More particles (59.7%) drifted across the 35◦ N latitude and mostly originated in the N-zone, whereas the particles released in the S-zone tended to stay south of 35◦ N 5
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Fig. 4. Comparison of the simulated and observed biomass of the green tides in 2012. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 5. Location and biomass multiple of the particles released in the different zones (red dots) at two different times. The maximum biomass occurred on 15 June, and 31 August represents the end of the green tides. The floating, beached and total biomass are also shown (unit: tonne). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
zone tended to beach in Shandong Province. The contributions of the particles from the N-, C-, and S-zones to the beached biomass were approximately 10.2%, 81.2%, and 8.6%, respectively (Fig. 6D). These results imply that the N- and C-zones are more important in controlling the intensity of green tides and that the C-zone is the key area in con trolling the amount of biomass landed on beaches.
3.5. Growth of the particles released at the different times Based on the temperature and nutrients recorded along the trajec tories of the particles (Fig. 7), the GR of particles were calculated (Fig. 8A, C, E). The limitations of environmental variables were calcu lated as fT, fN, and fP (see Section 2.2). For all of the three zones, the 6
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Fig. 6. Contribution of the particles released in the different zones to the floating and beached biomass. A, B: the floating particles and biomass on 15 June. The slices with the black edges represent the portions across 35◦ N; C, D: the beached particles and biomass on 31 August. The slices with the black edges represent the portions beached in Shandong Province. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
particles released on 1 April attained the lowest GR, and their biomass increased less than 20 times over 35 days (Fig. 8A, C, E). Thereafter, the GR increased gradually. The particles released on 1 May, 10 May, and 20 April exhibited the highest GR for the N-, C-, and S-zones, respectively. Then, the GR decreased and the biomass of the particles released on 30 May increased only 25, 20, and 5 times for N-, C-, and S-zones, respec tively. The limitation of temperature (fT) gradually increased from 1 April to 10 May. Thereafter, fT remained high until 30 May, followed by a tiny decreased. Over the entire simulation period, the limitation of phosphorus (fP) remained around 0.9. The limitation of nitrogen (fN) started with a sharp decrease over the first 10 days after releasing, fol lowed by an increase, and then decreased again from 10 May to 30 May for the particles released in N- and C-zones.
trajectory (Lee et al., 2011; Bao et al., 2015; Son et al., 2015; Xu et al., 2016). In contrast to previous model studies, a growth model of U. prolifera was incorporated into a hydrodynamic model in this study. The biomass dynamics during the development of green tides were reasonably reproduced with the model. However, there were some mismatches between the simulations and observations. There are several factors could cause these disagreements, one of which could be the contribution of micro-propagules. In 2012, the quantity of micropropagules steadily increased up to the development phase of green tides (Li et al., 2014), which might play an important role in the outbreak of green tides (Zhang et al., 2015). However, micro-propagules were not considered in the present study, which could partially explain the underestimated maximum biomass in Fig. 4. By comparing the simulated and observed floating biomass (see Fig. 4), the contribution of micro-propagules to biomass was estimated to be approximately 25%. In addition, although the ROMS model includes wind-driven com ponents in the surface flow, the direct frictional drag by winds (leeway drift) on the patches of floating macroalgae was not considered. Son et al. (2015) and Xu et al. (2016) pointed out that leeway drift is a crucial forcing factor in floating green algae tracing. However, other numerical studies have also generated acceptable results without considering leeway drift (Lee et al., 2011; Bao et al., 2015). There was a delay of approximately 4 days in the occurrence of the simulated green tides in our study, which could be partially explained by the lack of leeway drift. In addition, this could also be caused by the model resolution. The horizontal resolution (1/12 degrees) might not provide enough details
4. Discussion 4.1. Model advantages and improvement The green tides in the Yellow Sea are characterized by massive freefloating macroalgae (Liu and Zhou, 2018), which are similar with the golden tides caused by the floating brown macroalgae Sargassum (Smetacek and Zingone, 2013). A coupled modeling approach that in tegrates hydrodynamic, biogeochemical and Sargassum growth models has been applied to investigate the seasonal distribution of Sargassum (Brooks et al., 2018). For the green tides in the Yellow Sea, various hydrodynamic models have been established to investigate their 7
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Fig. 7. Environmental variables along the trajectory of particles released every 10 days, with uncertainty ranges shaded (±1 s.d.). A: temperature; B: dissolved inorganic nitrogen; C: dissolved organic nitrogen; D: dissolved inorganic phosphorus; E: dissolved organic phosphorus. The red dashed line represent the optimal temperature for Ulva prolifera growth. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
for the transport of floating macroalgae. This can also explain the mismatch between the simulated and observed temperatures in the Subei Shoal. A model with a higher resolution should be adopted for simulations of this region. We noted that part of the macroalgae drifted eastward in the images of 28 May and 25 July (Fig. 3), which was not captured by our model. We tentatively rearranged the release locations of particles and found that the particles released at further south of Porphyra mariculture region could drift to the east. It has been reported that some patches of U. prolifera appeared at south of mariculture region (Hu et al., 2010), which might contribute to the patches moving to the east. In this study, we only simulated and analyzed the green tides in 2012. To our knowledge, the biomass of green tides in the Yellow Sea varies in different years (Hu et al., 2019; Xiao et al., 2019) with the lowest biomass appeared in 2012. We noted that more particles and higher biomass beached on the coast of Jiangsu Province (Fig. 6), which is unusual but might partially explain the lowest bloom scale in 2012. In addition, there was some variability in the sea surface wind and
temperature between years (Fig. 9), which was also found in previous studies (Keesing et al., 2011; Lee et al., 2011). The effects of these interannual variation on the biomass of green tides should be considered in the future. 4.2. Contribution of the U. prolifera released at the different locations and times It has been hypothesized that the expansion of the Porphyra mari culture area, especially the northern part (N- and C-zones in this study), along Jiangsu Province is the most likely cause of the green tides in the Yellow Sea (Liu et al., 2013; Xing et al., 2019; Zhang et al., 2019b). This hypothesis was confirmed in the present study, and two reasons were concluded. (i) Depending on the locations further to the north and the sustained northward currents (Fig. 2), the particles released in the Nand C-zones were more easily transported northward than those released in the S-zone (Fig. 5). (ii) During drifting, the particles released in the N- and C-zones exhibited higher GR than those in the S-zone 8
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Fig. 8. Biomass variation and environmental limitation to the particles released every 10 days, with the uncertainty ranges shaded (±1 s.d.). A, C, E: multiples of the biomass increased for the particles released in N-, C-, and S-zones, respectively; B, D, F: limitations of the temperature, nitrogen and phosphorus for the particles released in N-, C-, and S-zones, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
(Fig. 8). U. prolifera occurred on the Porphyra rafts from March to May 2012, and its relative abundance increased from April to mid-May (Fan et al., 2015; Zhang et al., 2015). An earlier harvest of Porphyra and earlier facility recycling may reduce the biomass of U. prolifera attached on the rafts and lower the magnitude of green tides (Keesing et al., 2016). In our study, the GR of the particles released before 20 April was relatively lower (Fig. 8). This can be explained by the lower temperature and lower level of N-related nutrients along the trajectories of these particles (Fig. 8). This result further proves that an earlier facility recycling can reduce the intensity of green tides. However, the reduction of Porphyra production caused by an earlier harvest should also be considered in practice. Based on remote sensing observations, the postponed facility recycling may also reduce the biomass of green tides (Xing et al., 2019).
In the present study, the GR of the particles released after 10 May gradually decreased, and we found that the lower nutrients and higher temperature were not suitable for their growth (Fig. 8). In 2012, the period of Porphyra-facility recycling was from mid-April to mid-May (Wang et al., 2015). Therefore, considering the limitation of tempera ture and nutrients, an earlier or postponed-facility recycling might effectively reduce the magnitude of green tides. Nevertheless, the most effective way to mitigate green tides is to dispose all attached green macroalgae on land (Liu et al., 2013), which is highly recommended. Since the N- and C-zones are far away from the coastal line and the transportation cost of attached macroalgae in these zones are relatively higher, farmers are more likely to remove macro algae in situ rather than collect them (Xing et al., 2019). Considering the importance of N- and C-zones in governing the intensity of green tides, 9
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Fig. 9. Monthly averaged sea surface temperature and wind in April, May, June, and July from 2010 to 2014. The temperature and wind data are obtained from MODIS and ERA-interim, respectively.
governmental policy makers should provide economic incentives for the collecting of macroalgae especially in these two zones to keep farmers positive. Before these policies are implemented, a risk map and an advice for Porphyra facility recycling were proposed in this study. Generally, high risk appeared first in the S-zone, followed by the N-zone and then by the C-zone (Fig. 10). In the S- and C-zones, high risk appeared first in nearshore waters and then in offshore waters. In order to control the biomass, farmers should avoid facility recycling in the Nand C-zones from late April to early May (Table 1). Since the distribution of particle release points was not in complete agreement with the Por phyra mariculture region, results of this study could be used to evaluate the impact of further expansion of Porphyra mariculture region, and the risk map could be used as reference for the early intercept and salvage of
floating macroalgae. 5. Conclusions In this study, an U. prolifera growth model was integrated with a hydrodynamic model through particle tracking. The biophysical model achieved a satisfactory performance in reproducing the biomass spatiotemporal variation during the green tides in 2012. The contribu tions of the macroalgae released at the different locations and times were analyzed. The results imply that the northern and central zones are more important in controlling the intensity of green tides and that the central zone is also the key area in controlling the amount of biomass landed on beaches. Considering the limitation of temperature and 10
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review & editing. Tao Bai: Visualization. Yitao Wang: Writing - review & editing. Zexun Wei: Supervision, Resources. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements We deeply appreciate the outstanding efforts of scientists who pub lished the experimental data and those who took part in the cruises in 2012. This work was supported by the National Key Research and Development Program of China [grant numbers 2018YFD0900705, 2018YFD0900906]; the National Natural Science Foundation of China [grant numbers 41606038, 41606040]; the Natural Science Foundation of Jiangsu Province [grant number BK20180940]; the Integration and Application of Global Ocean Dynamic Environmental Forecasting Sys tem [grant number 2016YFC1401409]; the Central Public-interest Sci entific Institution Basal Research, CAFS & Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture and Rural Affairs of the People’s Republic of China [grant number 2019HYXKQ01]; and the Central Public-interest Scientific Institution Basal Research Fund, YSFRI, CAFS [grant number 20603022018003]. References Bao, M., Guan, W., Yang, Y., Cao, Z., Chen, Q., 2015. Drifting trajectories of green algae in the western Yellow Sea during the spring and summer of 2012. Estuar. Coast. Shelf Sci. 163, 9–16. https://doi.org/10.1016/j.ecss.2015.02.009. Brooks, M.T., Coles, V.J., Hood, R.R., Gower, J.F.R., 2018. Factors controlling the seasonal distribution of pelagic Sargassum. Mar. Ecol. Prog. Ser. 599, 1–18. https:// doi.org/10.3354/meps12646. China Fishery Statistical Yearbook (in Chinese), 1982-2019. Bureau of Fisheries of the Ministry of Agriculture. Beijing, China. Cui, J., Shi, J., Zhang, J., Wang, L., Fan, S., Xu, Z., Huo, Y., Zhou, Q., Lu, Y., He, P., 2018. Rapid expansion of Ulva blooms in the Yellow Sea, China through sexual reproduction and vegetative growth. Mar. Pollut. Bull. 130, 223–228. https://doi. org/10.1016/j.marpolbul.2018.03.036. Editorial Board for Marine Atlas, 1993. Marine Atlas of Bohai Sea, Yellow Sea, East China Sea: Hydrology (in Chinese). China Ocean Press. Egbert, G.D., Erofeeva, S.Y., 2002. Efficient inverse modeling of barotropic ocean tides. J. Atmos. Ocean. Tech. 19 (2), 183–204. https://doi.org/10.1175/1520-0426(2002) 019<0183:EIMOBO>2.0.CO;2. Fan, S., Fu, M., Wang, Z., Zhang, X., Song, W., Li, Y., Liu, G., Shi, X., Wang, X., Zhu, M., 2015. Temporal variation of green macroalgal assemblage on Porphyra aquaculture rafts in the Subei Shoal, China. Estuar. Coast. Shelf Sci. 163, 23–28. https://doi.org/ 10.1016/j.ecss.2015.03.016. Hu, C., Li, D., Chen, C., Ge, J., Muller-Karger, F.E., Liu, J., Yu, F., He, M., 2010. On the recurrent Ulva prolifera blooms in the Yellow Sea and East China Sea. J. Geophys. Res. 115, C05017. https://doi.org/10.1029/2009JC005561. Hu, L., Zeng, K., Hu, C., He, M., 2019. On the remote estimation of Ulva prolifera areal coverage and biomass. Remote Sens. Environ. 223, 194–207. https://doi.org/ 10.1016/j.rse.2019.01.014. Keesing, J.K., Liu, D., Fearns, P., Garcia, R., 2011. Inter- and intra-annual patterns of Ulva prolifera green tides in the Yellow Sea during 2007–2009, their origin and relationship to the expansion of coastal seaweed aquaculture in China. Mar. Pollut. Bull. 62, 1169–1182. https://doi.org/10.1016/j.marpolbul.2011.03.040. Keesing, J.K., Liu, D., Shi, Y., Wang, Y., 2016. Abiotic factors influencing biomass accumulation of green tide causing Ulva spp. on Pyropia culture rafts in the Yellow Sea, China. Mar. Pollut. Bull. 105, 88–97. https://doi.org/10.1016/j. marpolbul.2016.02.051. Lee, J.H., Pang, I., Moon, I., Ryu, J., 2011. On physical factors that controlled the massive green tide occurrence along the southern coast of the Shandong peninsula in 2008: a numerical study using a particle-tracking experiment. J. Geophys. Res. 116, C12036. https://doi.org/10.1029/2011JC007512. Li, Y., Song, W., Xiao, J., Wang, Z., Fu, M., Zhu, M., Li, R., Zhang, X., Wang, X., 2014. Tempo-spatial distribution and species diversity of green algae micro-propagules in the Yellow Sea during the large-scale green tide development. Harmful Algae 39, 40–47. https://doi.org/10.1016/j.hal.2014.05.013. Liu, D., Zhou, M., 2018. Green tides of the Yellow Sea: Massive free-floating blooms of Ulva prolifera. 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. Springer, Cham, pp. 317–326. https://doi.org/ 10.1007/978-3-319-70069-4_16.
Fig. 10. Distribution of the time with highest risk. The time was defined as the date when the particle with the highest GR released in each release point. The main Porphyra mariculture region is shown with brown dots. The three zones marked as N, C, and S represent the northern, central, and southern parts, respectively, of the Porphyra mariculture region. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Table 1 Advised schedule for the facility recycling in each zone. April
May
N-zone
1st
23th
18th
30th
C-zone
1st
15th
16th
30th
S-zone
1st
30th
Note: The periods with the averaged biomass multiple lower than 35 were shaded.
nutrients, an earlier or postponed facility recycling might effectively reduce the magnitude of green tides in 2012. Nevertheless, the most effective way to mitigate green tides is to dispose attached green mac roalgae on land, and an advice for Porphyra facility recycling was pro vided. Considering the variability in the sea surface wind and temperature between years, the biomass interannual variation should be investigated in the future. CRediT authorship contribution statement Ke Sun: Writing - original draft, Conceptualization, Validation. Junchuan Sun: Methodology, Software. Qing Liu: Writing - review & editing. Zhan Lian: Writing - review & editing. Jeffrey S. Ren: Writing 11
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Marine Pollution Bulletin 161 (2020) 111805 the green tide in Yellow Sea. Limnol. Oceanogr. 60 (4), 1105–1117. https://doi.org/ 10.1002/lno.10083. Wang, C., Su, R., Guo, L., Yang, B., Zhang, Y., Zhang, L., Xu, H., Shi, W., Wei, L., 2019a. Nutrient absorption by Ulva prolifera and the growth mechanism leading to greentides. Estuar. Coast. Shelf Sci. 227, 106329. https://doi.org/10.1016/j. ecss.2019.106329. Wang, W., Zhang, G., Sun, X., Zhang, F., Zhang, X., 2019b. Temporal variability in zooplankton community in the western Yellow Sea and its possible links to green tides. PeerJ 7, e6641. https://doi.org/10.7717/peerj.6641. Wei, Q., Wang, B., Yao, Q., Fu, M., Sun, J., Xu, B., Yu, Z., 2018. Hydro-biogeochemical processes and their implications for Ulva prolifera blooms and expansion in the world’s largest green tide occurrence region (Yellow Sea, China). Sci. Total Environ. 645, 257–266. https://doi.org/10.1016/j.scitotenv.2018.07.067. Xiao, Y., Zhang, J., Cui, T., Gong, J., Liu, R., Chen, X., 2019. Remote sensing estimation of the biomass of floating Ulva prolifera and analysis of the main factors driving the interannual variability of the biomass in the Yellow Sea. Mar. Pollut. Bull. 140, 330–340. https://doi.org/10.1016/j.marpolbul.2019.01.037. Xing, Q., An, D., Zheng, X., Wei, Z., Wang, X., Li, L., Tian, L., Chen, J., 2019. Monitoring seaweed aquaculture in the Yellow Sea with multiple sensors for managing the disaster of macroalgal blooms. Remote Sens. Environ. 231, 111279 https://doi.org/ 10.1016/j.rse.2019.111279. Xu, Q., Zhang, H., Cheng, Y., Zhang, S., Zhang, W., 2016. Monitoring and tracking the green tide in the yellow sea with satellite imagery and trajectory model. IEEE JSTARS 9 (11), 5172–5181. https://doi.org/10.1109/JSTARS.2016.2580000. Yang, D., Yin, B., Liu, Z., Feng, X., 2011. Numerical study of the ocean circulation on the East China Sea shelf and a Kuroshio bottom branch northeast of Taiwan in summer. J. Geophys. Res. 116, C05015. https://doi.org/10.1029/2010JC006777. Ye, N., Zhang, X., Mao, Y., Liang, C., Xu, D., Zou, J., Zhuang, Z., Wang, Q., 2011. ‘Green tides’ are overwhelming the coastline of our blue planet: taking the world’s largest example. Ecol. Res. 26, 477–485. https://doi.org/10.1007/s11284-011-0821-8. Zhang, Q., Liu, Q., Yu, R., Kang, Z., Guo, W., Ding, Y., Chen, J., Wang, Y., Li, Y., Yan, T., Zhou, M., 2015. Application of a fluorescence in situ hybridization (FISH) method to study green tides in the Yellow Sea. Estuar. Coast. Shelf Sci. 163, 112–119. https:// doi.org/10.1016/j.ecss.2014.09.005. Zhang, J., Shi, J., Gao, S., Huo, Y., Cui, J., Shen, H., Liu, G., He, P., 2019a. Annual patterns of macroalgal blooms in the Yellow Sea during 2007-2017. PLoS One 14 (1), e0210460. https://doi.org/10.1371/journal.pone.0210460. Zhang, Y., He, P., Li, H., Li, G., Liu, J., Jiao, F., Zhang, J., Huo, Y., Shi, X., Su, R., Ye, N., Liu, D., Yu, R., Wang, Z., Zhou, M., Jiao, N., 2019b. Ulva prolifera green-tide outbreaks and their environmental impact in the Yellow Sea, China. Natl. Sci. Rev. 6, 825–838. https://doi.org/10.1093/nsr/nwz026. Zhou, M.J., Liu, D.Y., Anderson, D.M., Valiela, I., 2015. Introduction to the special issue on green tides in the yellow sea. Estuar. Coast. Shelf Sci. 163, 3–8. https://doi.org/ 10.1016/j.ecss.2015.06.023.
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