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Human alteration of global surface water storage variability

Nature volume 591, pages 78–81 (2021)Cite this article

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An Addendum to this article was published on 12 June 2023

Abstract

Knowing the extent of human influence on the global hydrological cycle is essential for the sustainability of freshwater resources on Earth1,2. However, a lack of water level observations for the world’s ponds, lakes and reservoirs has limited the quantification of human-managed (reservoir) changes in surface water storage compared to its natural variability3. The global storage variability in surface water bodies and the extent to which it is altered by humans therefore remain unknown. Here we show that 61% per cent of the Earth’s seasonal surface water storage variability occurs in human-managed reservoirs. Using measurements from NASA’s ICESat-2 satellite laser altimeter, which was launched in late 2018, we assemble an extensive global water level dataset that quantifies water level variability for 227,386 water bodies from October 2018 to July 2020. We find that seasonal variability in human-managed reservoirs averages 0.86 metres, whereas natural water bodies vary by only 0.22 metres. Natural variability in surface water storage is greatest in tropical basins, whereas human-managed variability is greatest in the Middle East, southern Africa and the western USA. Strong regional patterns are also found, with human influence driving 67 per cent of surface water storage variability south of 45 degrees north and nearly 100 per cent in certain arid and semi-arid regions. As economic development, population growth and climate change continue to pressure global water resources4, our approach provides a useful baseline from which ICESat-2 and future satellite missions will be able to track human modifications to the global hydrologic cycle.

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Fig. 1: Median seasonal variability in water level by hydrologic basin from October 2018 to July 2020.
Fig. 2: Seasonal variability of water levels within the 40 largest hydrologic basins over October 2018 to July 2020.
Fig. 3: Proportion of seasonal surface water storage variability associated with reservoirs by hydrologic basin from October 2018 to July 2020.
Fig. 4: Proportion of seasonal surface water storage variability associated with human-managed reservoirs for the 40 largest hydrologic basins from October 2018 to July 2020.

Data availability

ICESat-2 data are available from the National Snow and Ice Data Center (NSIDC) at https://nsidc.org/data/atl08. The Global Surface Water Occurrence dataset can be downloaded from https://global-surface-water.appspot.com/. The Global Reservoirs and Dams database (GRanD) can be downloaded from Global Dam Watch at http://globaldamwatch.org/grand/ and the GlObal GeOreferenced Database of Dams (GOODD) can be downloaded from Global Dam Watch at http://globaldamwatch.org/goodd/. USGS gauge data can be downloaded from https://waterdata.usgs.gov/nwis/rt, California Department of Water Resources data can be downloaded from https://cdec.water.ca.gov/, and G-REALM data can be accessed at https://ipad.fas.usda.gov/cropexplorer/global_reservoir/. The water-body masks derived from GSWO, water levels derived from ICESat-2 and all validation analyses are available at https://doi.org/10.5281/zenodo.4489056.

Code availability

The scripts used to create the water-body masks, derive water levels from ICESat-2 and produce the figures are publicly available at https://github.com/sarahwcooley/icesat2-water-levels.

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Acknowledgements

This research was funded by the NASA Studies with ICESat-2 programme (grant #80NSSC20K0963) managed by T. Markus, and the NASA Surface Water and Ocean Topography mission (grant #80NSSC20K1144S) managed by N. Vinogradova-Shiffer. S.W.C. gratefully acknowledges support from an NSF Graduate Research Fellowship and the Stanford Science Fellows programme. J.C.R. is grateful for support from a Voss Postdoctoral Fellowship through the Institute at Brown for Environment and Society. We thank M. Mulligan at King’s College London for making the GOODD dataset publicly available at Global Dam Watch.

Author information

Authors and Affiliations

  1. Department of Earth System Science, Stanford University, Stanford, CA, USA

    Sarah W. Cooley

  2. Department of Geography, University of Oregon, Eugene, OR, USA

    Sarah W. Cooley & Jonathan C. Ryan

  3. Institute at Brown for Environment and Society, Brown University, Providence, RI, USA

    Jonathan C. Ryan & Laurence C. Smith

  4. Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA

    Laurence C. Smith

Authors
  1. Sarah W. Cooley
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Contributions

S.W.C. and J.C.R. conceived the project. S.W.C. developed the methodology, carried out the data analysis, interpreted results and wrote the manuscript. J.C.R. and L.C.S. interpreted results and co-wrote the manuscript.

Corresponding author

Correspondence to Sarah W. Cooley.

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The authors declare no competing interests.

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Peer review information Nature thanks Lori Magruder, Kuo-Hsin Tseng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Number of ICESat-2 observations received by each water body over the 22-month (October 2018 to July 2020) study period.

a, Histogram of the number of observations. b, Number of observations by lake area (in km2). Note that observations are aggregated to monthly time steps and therefore the actual number of ICESat-2 observations is slightly higher. In b, the x axis is plotted on a log scale. In total, 43% of lakes receive two observations, 24% receive three observations, and 32% receive four or more observations. There is a strong positive correlation with lake area, as shown in b. For example, although the average across all water bodies is 3.3 observations over the 22-month period, lakes larger than 10 km2 average 7.3 observations.

Extended Data Fig. 2 Monthly time series of water levels as observed by ICESat-2 (orange) and G-REALM (blue).

a–e, The x axis spans the ICESat-2 record (October 2018 to July 2020) and the y axis shows the normalized elevation (elevation − mean elevation over this period), which is required owing to differences in the vertical datum between the two datasets. From top to bottom the lakes are: a, La Grande Rivière Reservoir in Canada (53.788, −74.822): area = 4,054 km2, ICESat-2 range = 5.33 m, G-REALM range = 5.21 m. b, Lago Argentino in Argentina (−50.257, −72.711): area = 2,540 km2, ICESat-2 range = 2.63 m, G-REALM range = 2.51 m. c, Lake Tana in Ethiopia (12.001, 37.336): area = 3,580 km2, ICESat-2 range = 1.99 m, G-REALM range = 1.99 m. d, Lake Chad in central-west Africa (13.094, 14.480): area = 1,358 km2, ICESat-2 range = 1.52 m, G-REALM range = 1.59 m. e, Lake Bankim in Cameroon (6.160, 11.382): area = 137 km2, ICESat-2 range = 7.57 m, G-REALM range = 7.74 m.

Extended Data Fig. 3 Comparison between ATL03 (global geolocated photon data) and ATL08 (land and vegetation height product).

The y axis shows the elevation in m, and the x axis (along-track distance) refers to the distance (in m) from the start of the ICESat-2 track. The lakes shown are: a, Antelope Lake, California, USA (40.179, −120.595). b, Crater Lake, Oregon, USA (42.939, −122.109). c, Fern Ridge Lake, Oregon, USA (44.096, −123.301). d, Trinity Lake, California, USA (40.961, −122.681).

Extended Data Fig. 4 Evaluation of ICESat-2 height retrievals against in situ gauge measurements.

Each point represents a temporal change in height between two dates from both ICESat-2 and gauge measurements. a, b, Plots of ICESat-2 height difference against the corresponding gauge height difference; b shows a magnified version (ranging from −1 to 1 m) of a. c, Histogram of the difference between ICESat-2 and USGS gauge measurements. Overall, we find very good agreement between ICESat-2 and gauge measurements, with an MAE of 0.14 m and a mean bias of −0.019 m.

Extended Data Fig. 5 Evaluation of ICESat-2 range in water level against in situ gauge measurements.

Here, each point represents a single water body. a, The total range in water level (that is, maximum minus minimum) observed by gauges and ICESat-2 over the 22-month ICESat-2 record, October 2018 to July 2020. On average, we find that ICESat-2 observes 49% of the variability observed by gauges (as indicated by the dashed line). b, The mean annual range in water level over 2010–2020 (by water year) versus our observed ICESat-2 range. Here we find that, on average, ICESat-2 observes 58% (dashed line) of the 10-year mean variability.

Extended Data Fig. 6 Evaluation of ICESat-2 observed storage change versus California Department of Water Resources storage change estimates.

a, For pairs of dates with both ICESat-2 observations and CDWR storage estimates for the same lake, we calculate the difference in storage observed by ICESat-2 by multiplying the height change by our lake area estimate from the water-body mask and compare this to the CDWR storage change between these two dates. b, A histogram of the difference between our ICESat-2-derived and CDWR storage estimates. Overall, we find that ICESat-2 slightly underestimates storage variability relative to CDWR storage observations thanks to our use of a conservative water mask, though, importantly, this underestimation is consistent across all water bodies.

Extended Data Table 1 Water level variability in lakes found within the 40 largest hydrologic basins, containing at least 10 water bodies, as defined by the GRDC Major River Basins dataset
Full size table
Extended Data Table 2 Height retrieval error sensitivity analysis
Full size table
Extended Data Table 3 Mean absolute error and mean bias in ICESat-2 height retrievals by lake area
Full size table
Extended Data Table 4 Mean per cent of the true range (defined as range observed by USGS/G-REALM) observed by ICESat-2, grouped by lake area
Full size table

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Cooley, S.W., Ryan, J.C. & Smith, L.C. Human alteration of global surface water storage variability. Nature 591, 78–81 (2021). https://doi.org/10.1038/s41586-021-03262-3

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