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Global mapping reveals increase in lacustrine algal blooms over the past decade

Nature Geoscience volume 15, pages 130–134 (2022)Cite this article

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Abstract

Algal blooms constitute an emerging threat to global inland water quality, yet their spatial and temporal distribution at the global scale remains largely unknown. Here we establish a global bloom database, using 2.91 million Landsat satellite images from 1982 to 2019 to characterize algal blooms in 248,243 freshwater lakes, representing 57.1% of the global lake area. We show that 21,878 lakes (8.8%) spread across six continents have experienced algal blooms. The median bloom occurrence of affected lakes was 4.6%, but this frequency is increasing; we found increased bloom risks in the 2010s, globally (except for Oceania). The most pronounced increases were found in Asia and Africa, mostly in developing countries that remain reliant on agricultural fertilizer. As algal blooms continue to expand in scale and magnitude, this baseline census will be vital towards future risk assessments and mitigation efforts.

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Fig. 1: Global patterns of lacustrine algal blooms between 1982 and 2019.
Fig. 2: Scatterplot of MBE and median BO for 65 countries in six continents.
Fig. 3: BO maps for selected lakes where frequent algal blooms were detected.
Fig. 4: Decadal changes in BO.

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Data availability

Source data are provided with this paper. The Landsat images and the GSWO dataset are available in the data archive of Google Earth Engine (https://developers.google.com/earth-engine/datasets), the HydroLAKES dataset was obtained at https://www.hydrosheds.org/pages/hydrolakes and the endorheic basin polygons were downloaded from https://doi.org/10.1594/PANGAEA.895895?format=html#download. Global land surface temperature data were obtained from https://disc.gsfc.nasa.gov/datasets/FLDAS_NOAH01_C_GL_M_001/summary and annual temperature anomalies can be found at https://www.ncdc.noaa.gov/cag/global/time-series/globe/land/ytd.

Code availability

The code for the automatic algal bloom detection algorithm can be assessed at https://code.earthengine.google.com/325dfdde02b381419bb81a71bee769d7 and the code for other analysis of this study is available from the corresponding author upon reasonable request.

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Acknowledgements

We thank the US Geological Survey for providing Landsat data and Google Earth Engine for providing image-processing resources. L.F. and C.Z. acknowledge the National Natural Science Foundation of China (Nos. 41890852 and 41971304), and J.L. is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA20060402) and the High-level Special Funding of the Southern University of Science and Technology (Grant Nos. G02296302 and G02296402). L.F. also acknowledges the Shenzhen Science and Technology Innovation Committee (No. JCYJ20190809155205559), the Stable Support Plan Program of the Shenzhen Natural Science Fund (No. 20200925155151006) and the Shenzhen Science and Technology Program (No. KCXFZ20201221173007020).

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Authors and Affiliations

  1. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Xuejiao Hou, Lian Feng, Yanhui Dai, Luke Gibson, Ying Wang, Junguo Liu, Yi Zheng & Chunmiao Zheng

  2. College of Marine Science, University of South Florida, St. Petersburg, FL, USA

    Chuanmin Hu

  3. Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden

    Jing Tang

  4. Terrestrial Ecology Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark

    Jing Tang

  5. School for the Environment, University of Massachusetts Boston, Boston, MA, USA

    Zhongping Lee

  6. Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China

    Xiaobin Cai

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Contributions

X.H.: methodology, data processing and analyses, and writing; L.F.: conceptualization, methodology, funding acquisition, supervision and writing. Y.D., Y.W. and X.C. performed the data processing and analysis. C.H., L.G., J.T., Z.L., J.L., Y.Z. and C.Z. participated in interpreting the results and refining the manuscript.

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Correspondence to Lian Feng.

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Extended data

Extended Data Fig. 1 Development of the CIE-based bloom detection algorithm.

(a) CIE color space; (b) Density plots of the selected bloom-containing pixels in the CIE xy chromaticity coordinates and their distributions in the CIE color space; (c) Examples to show the performances of the CIE algorithm in classifying algal blooms (green features in the right panels) in Hongze Lake, China and Lake Okeechobee, USA. More examples are shown in Supplementary Fig. 2.

Source data

Extended Data Fig. 2 Maximum bloom extent (MBE, in km2) and bloom occurrence (BO, in %) grouped by different lake sizes and depths.

In the box plots, the bottom and top of the boxes are the first and third quartiles, respectively, the bar in the middle shows the median, and the whiskers show the minimum and maximum values.

Source data

Extended Data Fig. 3 Global maps of multidecadal maximum bloom extent (MBE, in km2) changes presented as 1˚×1˚ grid cells in different periods.

(a) 1980-1990s to 2000s; (b) 2000s to 2010s; (c) 1980-1990s vs 2010s.

Source data

Extended Data Fig. 4 Statistics of multidecadal algal bloom changes for six continents and the entire globe.

(a) Summarized maximum bloom extent (MBE, in km2); (b) Box plots of bloom occurrence (BO, in %). In the box plots, the bottom and top of the boxes are the first and third quartiles, respectively, the bar in the middle shows the median, and the whiskers show the minimum and maximum values.

Source data

Extended Data Fig. 5 Long-term changes in fertilizer consumption on six continents.

(a) Phosphate; (b) Nitrogen. The results are recompiled country-level data from the Food and Agricultural Organization (FAO).

Source data

Extended Data Fig. 6 Box plots of residence time for bloom affected lakes in different continents.

In the box plots, the bottom and top of the boxes are the first and third quartiles, respectively, the bar in the middle shows the median, and the whiskers show the minimum and maximum values.

Source data

Extended Data Fig. 7 Long-term changes in global land temperature.

The data are presented as the anomalies with respect to long-term average (1901-2000), and the slopes of trends in different periods are annotated.

Source data

Extended Data Fig. 8 Spatial distribution and temporal coverage of cloud-free Landsat 4, 5, 7 and 8 observations for different periods.

Number of cloud-free observations for different periods: (a) 1980-1990s, (b) 2000s, (c) 2010s and (d) 1982-2019. The three panels to the right (e, f and g) show the fraction of cloud-free Landsat images to the total number of Landsat images during different periods; (h) Mask used to determine the examined lakes in this study (that is, black areas), and the light gray show areas we have less confidence in interpreting the data from those lakes (see Methods).

Extended Data Fig. 9 Practical workflow for algal bloom detection.

Working steps for lake algal bloom detection using a novel CIE-based algorithm, several masks were performed to exclude potential disturbances from poor image quality and other environmental impacts.

Extended Data Table 1 Accuracy assessments of the CIE-based algorithm
Full size table

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2, Figs. 1–3 and refs. 1–45.

Supplementary Table 1

Supplementary Table 1.

Supplementary Table 2

Supplementary Table 2.

Supplementary Table 3

Supplementary Table 3.

Source data

Source Data Fig. 1

Source files for global MBE and BE, which are aggregated into 1° × 1° grid cells. The Excel file provides the continental statistics.

Source Data Fig. 2

MBE (in km2) and median BO (in %) for 62 countries in six continents.

Source Data Fig. 3

BO (in %) images for selected lakes where frequent algal blooms were detected.

Source Data Fig. 4

Global maps of decadal changes in BO (in %), which are aggregated into 1° × 1° grid cells.

Source Data Extended Data Fig. 1

Source data for Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Source data for Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Source data for Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Source data for Extended Data Fig. 4.

Source Data Extended Data Fig. 5

Source data for Extended Data Fig. 5.

Source Data Extended Data Fig. 6

Source data for Extended Data Fig. 6.

Source Data Extended Data Fig. 7.

Source data for Extended Data Figs. 1–7.

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Hou, X., Feng, L., Dai, Y. et al. Global mapping reveals increase in lacustrine algal blooms over the past decade. Nat. Geosci. 15, 130–134 (2022). https://doi.org/10.1038/s41561-021-00887-x

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