Phenology of Phytoplankton Blooms in a Trophic Lake Observed from

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Phenology of Phytoplankton Blooms in a Trophic Lake Observed from Long-Term MODIS Data Kun Shi, Yunlin Zhang, Yibo Zhang, Na Li, Boqiang Qin, Guangwei Zhu, and Yongqiang Zhou Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06887 • Publication Date (Web): 18 Feb 2019 Downloaded from http://pubs.acs.org on February 18, 2019

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Environmental Science & Technology

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Phenology of Phytoplankton Blooms in a Trophic Lake Observed from Long-Term

2

MODIS Data

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Kun Shi 1, 2*, 3, Yunlin Zhang1, 2, Yibo Zhang1, 2, Na Li1, 2, Boqiang Qin1, 2, Guangwei Zhu1, 2,

4

Yongqiang Zhou1, 2

5 6

1

7

Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,

8

Nanjing 210008, China

Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and

9 10

2

3

University of Chinese Academy of Sciences, Beijing 100049, China

CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China

11 12 13 14 15

* Corresponding author. Email: [email protected]

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Phone: (+86) -25-86882174, Fax: (+86) 25-57714759

17

University of Chinese Academy of Sciences, Beijing 100049, China

18

1

ACS Paragon Plus Environment


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ABSTRACT

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Phytoplankton phenology critically affects elements biogeochemical cycles, ecosystem

21

structure, and productivity. However, our understanding about the phenological process and

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driving mechanism is still very limited due to the shortage of long-term observation data. We

23

used all available daily MODIS-Aqua data from 2003 to 2017 to determine bloom start dates

24

(BSDs) in a typical trophic lake (Lake Taihu) and investigate how phytoplankton BSDs respond

25

to climate change and environmental factors. The results indicate that BSDs have advanced

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29.9 days for the entire Lake Taihu from 2003 to 2017. Spatially, an earlier phytoplankton

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bloom was recorded in the northern bays and the littoral regions than in the center open water.

28

Air temperature, wind speed, and N/P ratio (N: total nitrogen; P: total phosphorus) were three

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important factors affecting phytoplankton phenology. Multiple linear correlation showed that

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air temperature, wind speed, and N/P ratio in spring could explain 59.9% variability of BSDs

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for Lake Taihu. This study provides a quantitative assessment of phytoplankton phenological

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shifts and elucidates the interrelationship between phenology parameters and environmental

33

factors, thus improving our understanding on the potential impact of climate change and

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eutrophication on lake ecosystems. The starting earlier and lasting longer of phytoplankton are

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consistent with the expected effects of climate warming on aquatic ecosystem in recent decades,

36

which will bring new challenges for algal bloom management in eutrophic Lake Taihu.

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Key words: Phytoplankton phenology, Remote sensing, Climate warming, MODIS, Lake

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Taihu

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2


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TOC

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3



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INTRODUCTION

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Global change has been proven to influence the temporal-spatial variation and magnitude

44

of several climatic conditions, such as air temperature, rainfall, and wind. Meanwhile, it exerts

45

a series of extensive and profound effects on the global ecosystem 1 - 3. Phenological processes

46

and changes are the critical response mechanisms of ecosystem to climate change. Therefore,

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extensive studies have been conducted on the phenology in terrestrial and aquatic systems 4 - 6.

48

However, changes in the temperature, precipitation, and wind speed regimes are neither

49

spatially nor temporally uniform, implying spatially heterogeneous responses of ecosystems to

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climatic change 3. However, a comprehensive and profound understanding of the phenological

51

process at individual, species, population, community and ecosystem levels is still very limited,

52

and more data, evidences and studies are needed to support ecosystem management.

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Traditionally, a time series for monitoring the phenology of terrestrial and aquatic

54

ecosystems is built by regularly recording the timing and collect in situ samples in certain sites

55

5 - 8.

56

over intervals of a week to two weeks during the stratified period and monthly during the

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unstratified period since 1962, which was used to address the impacts of climate change on the

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phenology of lake processes 6. Remotely sensed data provide strong evidence and promote

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significant progresses for monitoring phenological processes of terrestrial and aquatic systems

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from local, regional to global scales since the mid-1980s because of their synoptic coverage,

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repeated temporal sampling and low cost that satellite observations afford 9 - 11. Several studies

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have demonstrated different remote sensing water color products deriving from SeaWiFS,

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MODIS, and MERIS images can be used to examine the timing, magnitude, and duration of

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phytoplankton blooms 12 - 14.

For example, the limnological data have been obtained at a central site of Lake Washington

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Aquatic ecosystems and especially phytoplankton are highly sensitive to environmental

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and climate changes 15 - 17. Recently, several studies have demonstrated that eutrophication and

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climate warming are anticipated to prompt harmful algal blooms in various aquatic ecosystems

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over the world 18, 19. Accurately quantifying phytoplankton dynamics (phenology) is essential

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for improving our understanding in the food web, fishery resources and carbon cycle with

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different trophic levels in aquatic ecosystems 5. Previous studies indicated that the timing of

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phytoplankton blooms advanced much faster than that of plants on land under when facing 4


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climate warming 20, 21. For example, the annual phytoplankton bloom maximum has advanced

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by up to 50 days from 1997 to 2009 as a consequence of changes in seasonal ice cover which

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may have significant consequences for the Arctic food web structure and carbon cycling 9.

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As one of the most biologically productive regions in the world, Lake Taihu is

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characterized by frequent algal blooms 22 - 24. Climate change is likely to prompt the occurrence

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of harmful algal blooms and ecological disasters 25 - 27. For example, a combination of excessive

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nutrient loading and extreme climatic conditions induced a severe cyanobacterial bloom of

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Lake Taihu in 2007, resulting that approximately 2, 000, 000 people did not have drinking water

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for more than seven days 24. Characterization of the phytoplankton phenology in Lake Taihu

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has important implications for the understanding the roles of climate change and eutrophication

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in controlling the phytoplankton phenology and instigating ecological disasters.

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Here we combined a long-term series of remotely sensed chlorophyll a (Chla) with climate,

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hydrology and water chemistry data in Lake Taihu to address how BSDs have shifted from

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2003 to 2017 and determine which climatic, hydrological or water chemistry factors mainly

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influence the initial time of phytoplankton blooms. The results will have important implications

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in the monitoring, early warning and management of algal bloom to mitigate the disaster effects

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in eutrophic Lake Taihu.

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MATERIALS AND METHODS

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Study Area

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As a large and shallow water body located in the flood plain in Yangtze River Delta, the

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water quality of Lake Taihu has been seriously affected by eutrophication and phytoplankton

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blooms

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industry, agriculture, tourism activities. Taihu Lake is a unique and complex lake ecosystem

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that provides an indispensable habitat for a variety of species, as well as diverse natural and

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economic resources to surrounding cities 23. Over the last four decades, however, Lake Taihu

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has a changed from a dominant position in macrophyte to a phytoplankton state, and is affected

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by anthropogenic pressure 28. Excessive nitrogen and phosphorus nutrients promote the growth

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of phytoplankton, leading to the proliferation of surface cyanobacterial blooms 26.

100 101

23, 24.

It receives a large amount of pollutants from various sources, mainly including

MODIS-Derived Daily Chlorophyll a Concentration (Chla) Data MODIS-Aqua data have a maximum spatial resolution of 250 meters (bands 1 and 2) and 5



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a very short revisit interval (1 image/day), which have been available free of charge since 2002.

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We downloaded more than 5,000 MODIS-Aqua L-0 images acquired from January 2003 to

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December

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http://oceancolor.gsfc.nasa.gov/). MODIS-Aqua images often suffer the impacts from clouds,

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cloud shadows, or thick aerosols, meaning that not all the acquired images could be useful for

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this study. After visual examination of these images, we selected 1,401 high-quality images to

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process to Level-1 (calibrated spectral radiance) data with the SeaDAS software package

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(version 7.4). The atmospherically Rayleigh-corrected MODIS-Aqua data (Rrc) were then

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derived following the methods detailed by previous study 26.

111

2017

from

NASA's

Goddard

Space

Flight

Center

website

(GSFC,

Using 250 matched data pairs of in situ Chla – MODIS Rrc, Shi et al. (2017) developed

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and validated a statistically emperical model for deriving Chla for Lake Taihu

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1545.3*[(EXP(Rrc(645)) − EXP(Rrc(859)))/(EXP(Rrc(645)) + EXP(Rrc(859)))]+69.346. For the

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validation dataset, the relative errors of the model were from 0.4% to 64.5% with a mean-

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absolute-percent-error of 27.1% (root-mean-square error = 15.01 μg/L), suggesting that the

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model performed well in Lake Taihu. Here, we applied this model to the selected MODIS-Aqua

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images (atmospherically Rayleigh-corrected MODIS-Aqua products) to derive daily Chla data

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for Lake Taihu from January 2003 to December 2017 26.

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Derivation of Phenological Metrics

26:

Chla=-

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Phenological metrics are defined to represent the critical characteristics of the

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phytoplankton annual cycle. They were selected to focus on the phytoplankton biomass and on

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the time of the main growing period. The extraction of BSDs generally has two steps:

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smoothing daily Chla time series and defining BSDs. The process of data smoothing is critical

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for accurately deriving BSDs from MODIS-derived Chla data because the smoothing can

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remove noise and abnormal data. Smoothing of the MODIS-derived daily Chla data was

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performed using the TIMESAT software, which was developed originally for retrieving and

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mapping phenology features by use of terrestrial vegetation indices’ maps. This software can

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also be applied to Chla time series of lakes which exhibit the regularly cycling characteristics

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as terrestrial vegetation indices do 12, 29. The TIMESAT software integrated three methods for

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smoothing: Double Logistic, asymmetric Gaussian and Savitzky – Golay filtering 29. Palmer et

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al. (2015) 12 suggested that asymmetric Gaussian filtering may be better for inland waters and 6


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thus we selected this method for processing our MODIS-derived Chla data in this study.

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The BSDs are usually defined in two ways. One way is to use the date at which the input

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mapped value (Chla) rises above a pre-defined percentage of the detected peak; the other uses

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the date at which the mapped value rises above a pre-defined Chla value 12. The two approaches

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to determining BSDs, agree upon each other in determining the large-scale pattern of

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phytoplankton phenology 12. Previous studies for assessing BSDs in pelagic oceanic and inland

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define the BSDs as the dates when Chla rising above background median concentrations + 5%

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12, 30, 31.

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Meiliang Bay, Zhushan Bay, Open area and entire Lake Taihu for the full 2003 - 2017 time

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series + 5% were found to range between 20.03 and 33.43 μg/L, between 22.05 and 37.11 μg/L,

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between 13.42 and 23.96 μg/L, and between 14.53 and 25.12 μg/L, respectively. It should be

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noted that the median values varied significantly across different regions of Lake Taihu. This

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indicates that the TIMESAT software was not appropriate for deriving BSDs in Lake Taihu as

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the values defining bloom start timing cannot be assigned on a per-pixel basis. We calculated

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yearly median + 5% values for a pixel position from 2003 to 2017 for Lake Taihu and assigned

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these median + 5% values as threshold of BSDs. Subsequently, we could derive BSDs values

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and map the spatial distribution of BSDs.

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Chemical Data

Here, we adapted the similar approach to defining BSDs. Median Chla values of

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In situ pH, dissolved oxygen, Secchi disc depth (SDD) etc. were measured during the

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sampling cruise and other chemical parameters including total nitrogen (TN), total phosphorus

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(TP), Chla were measured by collecting samples for analysis in Taihu Laboratory for Lake

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Ecosystem Research (TLLER).These monthly or seasonal in situ data were collected from 2003

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to 2017 via many ship surveys at pre-defined sampling stations distributed in Meiliang Bay,

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Zhushan Bay and the open water. We used a standard 30-cm diameter Secchi disk to measure

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SDD. TN and TP concentrations were determined using alkaline potassium persulfate digestion,

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followed by absorbance analysis, using a Shimadzu UV–2550PC spectrophotometer.

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Meteorological Data

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To investigate the ecological response of phytoplankton phenology to meteorological

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conditions, daily meteorological data including air temperature, wind speed; sunshine duration

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and precipitation were downloaded from the China Meteorological Data Sharing Service 7



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System (http://cdc.nmic.cn). These meteorological data were measured at Dongshan station

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(31.06o N, 120.43o E). The water temperature at 0.5 m depth in the open water along TLLER

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was measured three times/day at 8:00, 14:00 and 20:00 since 1992. In order to exhibit the long-

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term trend and maintain the consistency of observation of air and water temperature, the data

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from 2003 to 2017 were used in this study.

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Statistical Approach

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We used the Statistical Program for Social Sciences (SPSS) 20.0 software to investigate

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the relations between the variables. Multiple linear regression was performed to assess the joint

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effects of key meteorological, chemical factors on BSDs. As the temporal trends of

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environmental factors and BSDs were hypothesized to be non-linear and the generalized

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additive model (GAM) provided a flexible and effective technique for modeling a nonlinear

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time-series, the trend for air temperature, water temperature, sunshine duration, precipitation,

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wind speed, SDD, TN, TP, N/P ratio (N: total nitrogen; P: total phosphorus), pH, and BSDs

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was fitted using the GAM procedures (“mgcv” function) in R software (Version 3.0.0.).

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RESULTS

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Temporal and Spatial Patterns of BSDs

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The Chla concentration derived from the daily MODIS images between 2003 and 2017

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demonstrated apparent seasonal cycles, with the yearly peaks observed in July–August (Fig. 1).

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In addition, a marked Chla concentration peak is recorded in 2017 but not for Meiliang Bay.

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Although this peak was not present for Meiliang Bay, the average Chla concentration of this

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year was higher than that of other years. The spatial distribution of phytoplankton BSDs (day

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of the year) from 2003 to 2017 in Lake Taihu was shown in Fig. 2. There are significant

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temporal and spatial differences in phytoplankton BSDs (Fig. 1). Overall, the northern bays

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such as Zhushan Bay and Meiliang Bay and the coastal regions showed earlier phytoplankton

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bloom than the center open water (Figs. 2 and 3). The mean BSDs from 2003 to 2017 are 106.6,

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111.5 and 121.7 in Meiliang Bay, Zhushan Bay, and Open area, respectively. Generally, the

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mean start day of the dominant phytoplankton blooms varied with latitude. Obviously, latitude

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is not the controlling factor of the fine spatial differences of phytoplankton BSDs in Lake Taihu.

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Higher nutrients and water temperature in northern bays and coastal regions may be the

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potential controlling factors. 8


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Figure 1 Long time series of Chla derived from MODIS-Aqua image data in Lake Taihu from

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2003 to 2017 and the corresponding BSDs

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Figure 2 Maps of phytoplankton BSDs based on MODIS 250 m spatial resolution daily Chla

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data in Lake Taihu from 2003 to 2017

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Long Term Trend of BSDs

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Fig. 3 shows the long term trend of BSDs and GAM fitting line for Meiliang Bay, Zhushan

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Bay, Open area and entire Lake Taihu. Both linear and GAM fitting show that BSDs have

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significantly advanced for all the lake regions (p ≤ 0.05) especially for the past 15 years. BSDs

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have advanced 48.8, 40.0, 29.4, and 32.0 days in Meiliang Bay, Zhushan Bay, Open area and

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entire Lake Taihu based on the linear fitting. Therefore, BSDs advance more in northern lake

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bays with high nutrients than in Open area.

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Fig. 3 Long term trend of BSDs in Lake Taihu from 2003 to 2017 Affecting Factors

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Our efforts detect the relationships between abiotic factors and phytoplankton bloom

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timing and size to elucidate the potential affecting factors. The impacts of climatic and water

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chemical factors on phytoplankton phenology were examined using the linear fitting approach.

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BSDs were found to correlate with air temperature, water temperature, wind speed, sunshine

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duration, precipitation, Secchi disc depth, total nitrogen, total phosphorus, N/P ratio and pH, 11



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but exhibited different spatial and seasonal patterns. Overall, high and significantly positive

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correlations are found between BSDs and spring mean wind speed, and between BSDs and

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spring mean N/P ratio, but negative correlations are expectedly found between BSDs and spring

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mean air temperature (also water temperature), and between BSDs and spring mean pH (Fig.

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4). Therefore, these climatic variables in spring are the key predictors of BSDs in Lake Taihu.

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Fig. 4 Linear correlations between BSDs and key meteorological, chemical factors. (a): spring

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mean air temperature; (b): spring mean wind speed; (c): spring mean N/P ratio; (d): spring

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mean pH. The “Entire”, “Meiliang”, “Zhushan”, and “Open” in the legend stand for “entire

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Lake Taihu”, “Meiliang Bay”, “Zhushan Bay”, “Open area”, respectively.

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DISCUSSION

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Advantages of MODIS Data Derived Phytoplankton Phenology

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Although 15-year remote sensing data are not as long those by many traditional site

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specific observations of phytoplankton phenology which generally exceed 40 years

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However, there are at least two marked advantages for MODIS data derived phytoplankton

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phenology. Our MODIS remote sensing data enable to observe day-by-day fine evolution of

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phytoplankton phenology monthly or seasonal in situ observation. At present, sampling at a

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few pre-defined stations (e.g. 32 stations carried out by TLLER) at monthly or, seasonal 12


6 - 8.

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frequency is generally insufficient for phytoplankton phenology analysis in Lake Taihu because

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the temporal and spatial resolution of these data is too low to derive BSDs. The increase of

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observation frequency can reduce the uncertainty of derived phytoplankton phenology

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parameters. In addition, reliable phytoplankton phenology assessments are restricted by the

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spatial coverage of in situ observation which may miss patchy and transient phytoplankton

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blooms. By use of remote sensing data, we found that phytoplankton phenology markedly

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advanced in Meiliang Bay and Zhushan Bay as compared to the Open area, which was not

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observed in previous phytoplankton phenology studies of Lake Taihu

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phytoplankton phenology features should be very promising method of phenology study and

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be an important continued direction of water remote sensing.

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Roles of Climate Change and Eutrophication in Controlling Phytoplankton Phenology

22, 25, 27.

This

250

A study using Chla concentration time series during 1998–2015 over a global grid showed

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the timing and size of phytoplankton blooms have changed on both regional and global scales

252

32.

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small to large lakes 6, 7, 12, 33. The BSDs' advance of 2.1 days per year resulting from linear fitting

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to the 15-year daily remote sensing observations for the entire Lake Taihu further confirmed

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this fact. Many previous studies attributed BSDs advances to climate warming because limited

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nutrient data make it difficult to assess the impact of nutrient concentration on BSDs. In fact,

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in addition to climate warming inducing an increased water temperature and thermal

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stratification, BSDs also depend upon several variables, such as light and nutrient availability,

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wind-driven vertical mixing, grazing and predating , which influence population growth and

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loss rates of phytoplankton 7, 8, 33, 34. Indeed, our statistical analysis showed that spring mean air

261

temperature, spring mean wind speed and spring mean N/P ratio were three key meteorological

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and chemical factors which are responsible for the significant advance of BSDs. This is in

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agreement with the observations by many previous studies on significantly increased air

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temperature but wind decreased speed 28, 35. Meanwhile, N/P ratio significantly decreased from

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2003 to 2017 and more and more reach to 16:1. The value is the most favorable for

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cyanobacteria blooms 36. The pH value of Lake Taihu was found to have high linear correlation

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to BSDs but pH should not be the cause of phytoplankton blooms because many studies have

268

shown that phytoplankton blooms resulted in high pH

Meanwhile, BSDs have been shown to advance in lake ecosystems from shallow to deep and

37, 38.

Therefore, we conclude that the

13



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BSDs' advance is not only determined by climate warming but also nutrient availability. The

270

same observation was also reported in other studies. For example, a 58-year site specific

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observation study for four lakes of English Lake District catchment indicated that the effect of

272

climate warming was not consistent on the three phytoplankton taxa with some showing

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advanced BSDs but others delayed BSDs. On the contrast, soluble reactive phosphorus

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concentration, had a more consistent effect upon the phenology of all taxa in nearly all lakes 7.

275

It is worth mentioning that we have considered various possible drivers here, but the influence

276

of parasitism and predation cannot be addressed owing to the lack of more long term data

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records.. Therefore, future research and additional statistical matrices will help separate climate

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vs. non-climate drivers for phytoplankton phenology.

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Implications for Algal Bloom Management

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The objectives of algal bloom management are to protect drinking water and fisheries,

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minimize economic and ecosystem losses, and protect public health. Therefore, the most critical

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step of algal bloom management is to develop an effective monitoring method and a robust

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forecast model. Normally, remote sensing, in situ monitoring, and algal bloom forecast

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ensuring drinking water supply were performed from May to October every year

285

study results clearly demonstrated that the BSDs of Lake Taihu have significantly advanced

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and been driven by climate and non-climate factors. For example, a massive algal bloom event

287

began two months earlier than previously documented for Microcystis blooms in Taihu in 2007

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due to an unusually warm spring, which caused a serious drinking water crisis 24. Therefore,

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advanced BSDs means more material, manpower and finical resources are needed to manage

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this lake especially in warm spring. In addition, advanced BSDs resulting from climate

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warming could offset the already time consuming and costly management strategies that are

292

designed to control algal blooms and achieve lake restoration goal. Therefore, more strictly

293

nutrient control and reduction strategies are needed to mitigate the pollution by sewage, waste

294

discharges, fertilizer use, agricultural non-point and industrial point sources. In short, climate

295

warming poses enormous challenges for lake managers, requiring that traditional lake

296

restoration techniques and nutrient control have to be practiced and enforced strictly.

297 14


35, 39.

for Our

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ACKNOWLEDGMENTS

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This study was jointly funded by the National Natural Science Foundation of China (grants

300

41771472 and 41621002), the Key Research Program of Frontier Sciences of the Chinese

301

Academy of Sciences (QYZDB-SSW-DQC016), Jiangsu 333 Talents Program (BRA2018092),

302

the Key Program of the Chinese Academy of Sciences (ZDRW-ZS-2017-3-4), “Strategic

303

Priority Research Program” of the Chinese Academy of Sciences (XDA19070301), and Youth

304

Innovation Promotion Association (CAS) (2017365). We are grateful to all staff of TLLER for

305

assisting with sample collection, experiment measurement, and data analysis of long-term water

306

quality observations.

307 308

AUTHORS INFORMATION

309

Corresponding Author

310

Phone: (+86) -25-86882174; Email: [email protected]

311

Notes

312

The authors declare no competing financial interest.

313 314

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