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Widespread global increase in intense lake phytoplankton blooms since the 1980s

Matters Arising to this article was published on 17 February 2021


Freshwater blooms of phytoplankton affect public health and ecosystem services globally1,2. Harmful effects of such blooms occur when the intensity of a bloom is too high, or when toxin-producing phytoplankton species are present. Freshwater blooms result in economic losses of more than US$4 billion annually in the United States alone, primarily from harm to aquatic food production, recreation and tourism, and drinking-water supplies3. Studies that document bloom conditions in lakes have either focused only on individual or regional subsets of lakes4,5,6, or have been limited by a lack of long-term observations7,8,9. Here we use three decades of high-resolution Landsat 5 satellite imagery to investigate long-term trends in intense summertime near-surface phytoplankton blooms for 71 large lakes globally. We find that peak summertime bloom intensity has increased in most (68 per cent) of the lakes studied, revealing a global exacerbation of bloom conditions. Lakes that have experienced a significant (P < 0.1) decrease in bloom intensity are rare (8 per cent). The reason behind the increase in phytoplankton bloom intensity remains unclear, however, as temporal trends do not track consistently with temperature, precipitation, fertilizer-use trends or other previously hypothesized drivers. We do find, however, that lakes with a decrease in bloom intensity warmed less compared to other lakes, suggesting that lake warming may already be counteracting management efforts to ameliorate eutrophication10,11. Our findings support calls for water quality management efforts to better account for the interactions between climate change and local hydrological conditions12,13.

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Fig. 1: Global distribution of lake bloom intensity trends shows that the peak summertime bloom intensity has increased since the 1980s.
Fig. 2: Lake bloom histories follow one of four prototypical pathways.
Fig. 3: Lakes that experienced improvements in bloom conditions tend to have experienced little to no warming.
Fig. 4: Lake bloom histories show no consistent correspondence with temperature, precipitation and fertilizer use.

Data availability

The Landsat 5 Thematic Mapper imagery used in this study is available from the US Geological Survey ( and through Google Earth Engine ( The bloom intensity trend estimates, historical pathway categories and environmental driver variables generated for each lake and analysed in this study are provided in Supplementary Table 1. The temperature, precipitation, fertilizer use and lake geomorphological data supporting the findings of this study are publicly available23,79,80 (see ‘Environmental driver, watershed, and geomorphological characteristic data sets’ in the Supplementary Information).

Code availability

Google Earth Engine’s web interface allows the bloom detection algorithm21 to be applied on any Landsat 5 Thematic Mapper images. Access will be provided upon request.


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We thank T. Ballard and D. Del Giudice for discussions and input, Y. Fang and E. Sinha for help with obtaining and processing environmental driver datasets, as well as N. Gorelick and T. A. Erickson for guidance with Google Earth Engine. This research was supported by the National Science Foundation (NSF) under grant 1313897. Additional support was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) under a Postgraduate Scholarship-Doctoral award (PGSD3-438855-2013), by a 2015 Google Earth Engine Research Award, by NASA ROSES grant NNX16AI16G and by USGS Landsat Science Team Award 140G0118C0011. CRU-NCEP precipitation was provided by the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP; Funding for the MsTMIP activity was provided through NASA grant NNX10AG01A. Data management support for preparing, documenting and distributing model driver data was performed by the Modeling and Synthesis Thematic Data Center at Oak Ridge National Laboratory (ORNL;, with funding from NASA grant NNH10AN681.

Author information




J.C.H. and A.M.M. designed the research and analysed the results. J.C.H. and A.M.M. wrote the manuscript with input from N.P. J.C.H. performed the majority of the computations with input from A.M.M. N.P. performed the MODTRAN simulations, analysed the MODTRAN results and wrote the corresponding sections of the Methods.

Corresponding authors

Correspondence to Jeff C. Ho or Anna M. Michalak.

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The authors declare no competing interests.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Peer review information Nature thanks Xi Chen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Lakes with evidence of cyanobacteria and with well documented evidence of major ecological changes show trends of a similar magnitude to the trends in other lakes.

Lake names in colour indicate that there is evidence of cyanobacteria in that lake; bold lake names indicate that there is evidence of major ecological changes. The y axis shows temporal trends in peak bloom intensity before normalization for all 71 study lakes, categorized by historical pathway. These temporal trends are the Thiel–Sen’s slope values calculated using the maximum summertime lake-wide bloom intensity time series for each lake. Trends for lakes of the ‘improvement then deterioration’ pathway are separated into trends for 1984–1997 and 1998–2012 to show trend values in each sub-period separately.

Extended Data Fig. 2 Low correlations between trends in bloom intensity and environmental factors.

ac, Scatter plots of the trend in bloom intensity compared with the trends in temperature (a), total precipitation (b) and fertilizer application (c) for study lakes with at least 14 years of data (n = 49). Each circle represents one lake. Red lines indicate the linear fit of the white circles.

Extended Data Fig. 3 No relationship is observed between bloom intensity and environmental factors collected from all lakes.

ac, Scatter plots of bloom intensity z-score compared with temperature (a; n = 784), precipitation (b; n = 936) and fertilizer (c; n = 980) z-scores. Each circle represents one year for one lake. The z-score of each lake variable is calculated using the mean and s.d. of its own time series. Red lines indicate the linear fit of the white circles. d, Box plots of bloom intensity z-score (n = 980 total). Each box plot shows the distribution of z-scores for all lakes with available data each year. Each box extends from the first to the third quartile values, with a line at the median. The whiskers extend to 1.5× the interquartile range from the edges of the box. The plus symbols show outlier values past the end of the whiskers.

Extended Data Fig. 4 Availability of bloom intensity data during the study period.

Number of lakes with a bloom intensity observation after correction for clouds and number of composites (see Methods) divided by the total number of study lakes (n = 71) for each year.

Extended Data Fig. 5 Distributions of lake variables by historical pathway.

ac, Distributions of environmental drivers. di, Distributions of geomorphological factors. The data in a are equivalent to those in Fig. 3.

Extended Data Fig. 6 Global distribution of trends in lake temperature.

For the lakes with at least 14 years of bloom data (n = 49), the maps show the temporal trend in lake surface water temperature (°C per decade). The base map was generated using Generic Mapping Tools33.

Extended Data Fig. 7 Spectral reflectance curves used in simulations to test algorithm sensitivity.

Spectral reflectance curves (ρw[·]) associated with three phytoplankton bloom concentrations and one non-bloom water condition measured in Lake Erie are shown44. Blooms I, II and III correspond to near-surface chlorophyll-a concentrations of 100.1 mg m−3, 143.7 mg m−3 and 106.3 mg m−3, respectively, and total suspended solid concentrations of 30.1 g m−3, 20.0 g m−3 and 22.7 g m−3, respectively. The non-bloom curve corresponds to chlorophyll-a and total suspended solid concentrations of 5.8 mg m−3 and 1.8 g m−3, respectively. The normalized spectral response of the near-infrared channel of L5 TM is also shown. The spectra used in the sensitivity analyses demonstrate the robustness of the bloom intensity measure used in this study (Eq. (2)).

Extended Data Fig. 8 Observed bloom intensity shows minimal sensitivity to changes in aerosol optical thickness (AOT) or to changes in the solar zenith angle that would result from a change in Landsat 5 orbit.

a, b, The sensitivity of derived bloom intensity varies on the order of 0.001 for waters dominated by chlorophyll (Chl) (a) and coloured dissolved organic matter (CDOM) (b) for changes in solar zenith angle that would be expected owing to a change in satellite orbit. The simulated variation due to solar zenith angle is even smaller for coarse aerosol types (that is, smaller values of AOT). The environments in a and b correspond to bloom III and bloom I, respectively, in Extended Data Fig. 7.

Extended Data Fig. 9 Historical bloom intensity patterns for four additional lakes with well documented temporal trends.

Graphs as in Fig. 4 for four additional lakes that had well documented temporal trends. Panels show five-year moving averages of normalized bloom intensity, summer lake temperatures, and total precipitation and fertilizer application rate over each lake’s watershed. Thicker temperature, precipitation and fertilizer lines indicate that the Pearson correlation coefficient with bloom intensity is significant (P < 0.1). Dashed lines indicate anti-correlations.

Supplementary information

Supplementary Methods

Additional descriptions of methods supporting the findings of the main text, including analyses of algorithm sensitivity and of the robustness of global bloom intensity trends, evaluations of changes in bloom timing and of Landsat data availability, descriptions of auxiliary datasets, analysis of correlations with temperature, precipitation, and fertilizer use, and assessments of lake geomorphological characteristics.

Supplementary Table 1

List of study lakes, together with overall surface bloom intensity trend, historical pathway, and key characteristics Characteristics include countries in which each lake is located, geomorphological characteristics, and number of years (n) of available bloom intensity data. For the subset of 49 lakes with n ≥ 14, additional information on trend in environmental drivers and watershed characteristics is provided. Empty cells represent missing data.

Supplementary Table 2

List of geographic region comparisons within lakes with documented spatial gradients in bloom intensity Each row specifies the two regions being compared within each lake, the expected average bloom intensity of each region (low, medium, or high), the references used to identify the expected bloom intensity gradient, and an assessment of the strength of the evidence for the expected gradient (strong, medium, or weak). The difference in intensity between regions is evaluated by comparing the mean intensity across all pixels within each region of the lake over the full study period. Study lakes with documented spatial gradients are as follows: ErieS1–2; VictoriaS3–8; ClearS9–10; SimcoeS11–13; Saint-ClairS14–15; OkeechobeeS16–22; BalatonS23–26; ChaoS27–30; TurkanaS31–32; WinnebagoS33; HongzeS34–35; ChapalaS36–37; ManaguaS38; Walker-LakeS39; Songkhla-LakeS40–41; NasserS42; BostenS43; TanaS44; KaribaS45–46; GaoyouS47; PoopoS48; and Salton-SeaS49–50. See Supplementary Methods for the list of references in Supplementary Table 2.

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Ho, J.C., Michalak, A.M. & Pahlevan, N. Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature 574, 667–670 (2019).

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