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Global net increase in surface water connectivity in river–floodplain systems

Nature Geoscience volume 19, pages 556–564 (2026) Cite this article

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Abstract

Surface water connectivity in river–floodplain systems—the flow exchange between river channels and their surrounding floodplains—is a regulator of global water cycles, biogeochemical fluxes, geomorphology and ecosystem health. However, global assessments of its spatial and temporal patterns remain limited. Here we use nearly four decades (1984–2019) of satellite observations to analyse changes in connectivity across 1.6 million km, representing 73% of the total global river length. We reveal a net global increase (+3%) in connectivity, with continuous gains observed across 17% of the river length—about 1.5 times the length experiencing continuous losses. These gains are most pronounced in eastern Asia and high-latitude regions, while arid and semi-arid regions exhibit widespread declines. Climatic drivers, including shifts in precipitation and evapotranspiration, predominantly shape these changes, with additional modulation by human activities such as dam construction. Importantly, we identify a strong positive coupling between surface water connectivity and riverine sediment transport in regions experiencing pronounced connectivity changes, underscoring its role in shaping sediment fluxes and associated biogeochemical processes. These findings provide a global record of connectivity in river–floodplain systems and its evolution, offering essential insights to guide sustainable management under escalating climatic and anthropogenic pressures.

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Fig. 1: Global surface water connectivity of river–floodplain systems over four decades.
The alternative text for this image may have been generated using AI.
Fig. 2: Surface water connectivity changes across different periods.
The alternative text for this image may have been generated using AI.
Fig. 3: Influence of climate change on surface water connectivity.
The alternative text for this image may have been generated using AI.
Fig. 4: Coupled changes between surface water connectivity and SSC in river channels.
The alternative text for this image may have been generated using AI.

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Data availability

The GSWO dataset20 and the Global River Widths from Landsat (GRWL) dataset36 are available in the data archive of Google Earth Engine (https://developers.google.com/earth-engine/datasets). The floodplain datasets (GFPLAIN250m34 and SHIFT35) were obtained from https://figshare.com/articles/dataset/GFPLAIN250m/6665165/1 and https://doi.org/10.5281/zenodo.11835133 (ref. 85), respectively. The precipitation products, such as the Climatic Research Unit gridded time series (CRU TS)73, the Multi-Source Weighted-Ensemble Precipitation (MSWEP)74 and the ERA5L reanalysis dataset75, were downloaded from https://crudata.uea.ac.uk/cru/data/hrg/, http://www.gloh2o.org/mswep/ and https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means, respectively. The actual evapotranspiration products, such as Global Land Evaporation Amsterdam Model (GLEAM)76, the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) dataset77 and the ERA5L reanalysis dataset75, were downloaded from https://www.gleam.eu/, https://gmao.gsfc.nasa.gov/gmao-products/merra-2/data-access_merra-2/ and https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means. The suspended sediment concentrations dataset53 is available at https://figshare.com/s/dde3bffd8e12227e2b26. The HydroBASINS dataset82 is available at https://www.hydrosheds.org. The satellite-based surface water connectivity data at pixel level and reach level that support the findings of this study are available via Zenodo at https://doi.org/10.5281/zenodo.18501739 (ref. 86). Source data are provided with this paper.

Code availability

The MATLAB code developed for satellite-based surface water connectivity is also available at https://doi.org/10.5281/zenodo.18501739 (ref.86).

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Acknowledgements

We thank the European Commission’s Joint Research Centre for providing the Global Surface Water dataset and the Google Earth Engine for providing data processing resources. L.F. was supported by the National Natural Science Foundation of China (grant number 42425604), the National Key Research and Development Program of China (grant number 2022YFC3201802) and the Shenzhen Science and Technology Program (grant no. KCXFZ20240903093659003). E.P. acknowledges Singapore Ministry of Education (MOE) AcRFs Tier2 (MOE-T2EP50222-0007) and Tier 3 Climate Transformation Programme (MOE-MOET32022-0006). R.I.W. was supported by a UKRI Natural Environment Research Council (NERC) Independent Research Fellowship [NE/T011246/1].

Author information

Authors and Affiliations

  1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

    Qiuqi Luo, Lian Feng & Jianya Gong

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

    Qiuqi Luo, Lian Feng & Hongwei Fang

  3. National Institute of Education, Nanyang Technological University, Singapore, Singapore

    Edward Park

  4. Earth Observatory of Singapore and Asian School of the Environment, Nanyang Technological University, Singapore, Singapore

    Edward Park

  5. Center for Climate Change and Environmental Health, Nanyang Technological University, Singapore, Singapore

    Edward Park

  6. Department of Geography, Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK

    Des E. Walling

  7. The State Key Laboratory of Hydro Science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China

    Lei Huang & Hongwei Fang

  8. Department of Earth and Planetary Sciences, The University of Hong Kong, Hong Kong SAR, China

    Mengqiu Wang

  9. School of Ocean Sciences, Bangor University, Menai Bridge, UK

    R. Iestyn Woolway

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Contributions

Q.L. was responsible for the methodology, data processing, analyses and writing of the paper. L.F. was responsible for the conceptualization, methodology, funding acquisition, supervision and writing of the paper. E.P., D.E.W., L.H., M.W., R.I.W., H.F. and J.G. 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 Global floodplain extent and different statistical scales.

(a) The distribution of global floodplains, determined by integrating extents from the GFPLAIN250m34 and SHIFT35 datasets. (b, c, d) Representation of different statistical scales: basin-level (b), river reach-level (c), and pixel-level (d). Basemaps in a and b from Natural Earth (https://www.naturalearthdata.com) with global basin (level-3) data from the HydroBASINS dataset82.

Extended Data Fig. 2 Illustration of our method for the surface water connectivity index calculation.

The surface water connectivity of a given water pixel (\({SWC}\)) was calculated by dividing the number of water pixels connected to the given pixel (\({N}_{c}^{{window}}\)) by the total number of water pixels (\({N}^{{window}}\)) within a specified window (6 km in our study). Connected water patches were identified using the ‘bwlabel’ function applied to the binary image, with all pixels within a patch considered connected.

Extended Data Fig. 3 Sensitivity analysis of distance thresholds for surface water connectivity.

The left column (a, d, g) presents water occurrence maps derived from the GSWOdataset20, showcasing diverse hydrological contexts: (a) a region with abundant rivers and floodplain lakes, (d) extensive floodplains interspersed with paddy fields and aquaculture ponds, characterized by complex and fragmented water distribution, and (g) an area dominated by a single main river with a relatively simple water distribution. The middle column (b, e, h) and right column (c, f, i) display the change rate of surface water connectivity during flood and normal inundation conditions, respectively. Dark blue (orange) points represent changes between P1 (1984–1999) and P2 (2000–2009), while light blue (orange) points denote changes between P2 (2000–2009) and P3 (2010–2019). The red dotted vertical lines mark the threshold distance of 6 km, which was selected for calculating surface water connectivity based on sensitivity analysis.

Source data

Extended Data Fig. 4 Spatial comparison of surface water connectivity change rates: Trigg et al. vs. this study.

The upper maps (a, b) illustrate the spatial patterns of surface water connectivity change rates derived using the method proposed by Trigg et al.21, while the lower maps (c, d) show results calculated using our surface water connectivity index. The left-hand maps (a, c) represent the change rates between the periods 1984–1999 and 2000–2009, while the right-hand maps (b, d) depict the change rates between 2000–2009 and 2010–2019. Dark gray regions indicate areas with insufficient satellite coverage during earlier periods, which were excluded from the analysis. Notably, basin connectivity in this figure was calculated by averaging all water pixels across river–floodplain systems to maintain a consistent study area and facilitate comparison between the two studies, which differed from other figures in our study. Basemaps from Natural Earth (https://www.naturalearthdata.com) with global basin (level-4) data from the HydroBASINS dataset82.

Source data

Extended Data Fig. 5 Examples showing water occurrence and surface water connectivity over the past four decades.

The upper panels (a, c, e, g, i, k, m, o, q, s, u, w) show water occurrence maps derived from the GSWOdataset20, and the lower panels (b, d, f, h, j, l, n, p, r, t, v, x) show surface water connectivity under normal inundation conditions for each pixel and river reach during the period 1984–2019. To quantify connectivity between the inundated surfaces within the river–floodplain and the river’s centerline, we aggregated pixel-level estimates to river reach-level by averaging the connectivity values at the central points of each river reach. These river reach-level estimates represent the primary results of our study.

Extended Data Fig. 6 Relationship between water-to-basin area ratio and connectivity for global level-3 river basins (sample size = 197).

The water-to-basin area ratio is calculated by dividing the surface water area within river–floodplain systems by the total area of the basin. Data points represent the length-weighted mean connectivity of river reaches within each river basin under normal (GSWO ≥ 50%) conditions. Dots with black outlines correspond to the 26 largest river basins. Error bars indicate the variability in water-to-basin area ratio and connectivity under dry (GSWO ≥ 75%) and wet (GSWO ≥ 25%) conditions, providing insights into how water availability and connectivity vary across different climatic conditions for each individual basin.

Source data

Extended Data Fig. 7 Examples showing water occurrence and surface water connectivity over three time periods.

a–f, Example regions in the upper Mekong and upper Yangtze (a, c), Colorado (b), Tigris–Euphrates (d), Yellow (e), and GBM (f) basins. The upper three images in each panel show water occurrence produced from the JCR GSW dataset, while the lower three images show surface water connectivity under normal inundation conditions for each pixel and river reach over three time periods (1984–1999, 2000–2009, and 2010–2019). The locations of constructed dams in the 2000s (2000–2009) and 2010s (2010–2019) are shown, with data sourced from Sun et al.53.

Extended Data Fig. 8 Global patterns of surface water connectivity and surface water area in river–floodplain systems across different inundation conditions over the past four decades.

a-e, The reach-level surface water connectivity during flood, wet, normal, dry, and extremely dry conditions are calculated based on the binary images from five thresholds of the GSWO dataset (that is, GSWO ≥ 1%, 25%, 50%, 75%, 95%). f-j, The basin-level surface water area within the river–floodplains under different inundation conditions. Regions shaded in dark gray denote areas with insufficient satellite coverage during earlier observation periods, while light gray regions indicate areas where no rivers were detected by 30-m resolution Landsat imagery. Basemaps from Natural Earth (https://www.naturalearthdata.com) with global basin (level-3) data from the HydroBASINS dataset82.

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Extended Data Fig. 9 Surface water connectivity changes across different periods and inundation conditions.

The left, middle and right panels show the change rate of surface water connectivity calculated from 1980–1990s to 2000s, from 2000s to 2010s, and from 1980–1990s to 2010s, respectively. The five inundation conditions (that is, flood, wet, normal, dry, and extremely dry) are represented within each panel by five thresholds of GSWO (that is, GSWO ≥ 1%, 25%, 50%, 75%, 95%). Within each panel, pie charts display the fractions of river length with different change directions. Regions shaded in dark gray denote areas with insufficient satellite coverage during earlier observation periods, while light gray regions indicate areas where no rivers were detected by 30-m resolution Landsat imagery. Basemaps from Natural Earth (naturalearthdata.com).

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Extended Data Table 1 Numbers and length-weighted mean surface water connectivity of the world’s free-flowing (FFRs) and non-free-flowing rivers (NFFRs)
Full size table

Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–13 and Notes 1–4.

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Luo, Q., Feng, L., Park, E. et al. Global net increase in surface water connectivity in river–floodplain systems. Nat. Geosci. 19, 556–564 (2026). https://doi.org/10.1038/s41561-026-01953-y

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