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Reversal of the levee effect towards sustainable floodplain management

Nature Sustainability volume 6, pages 1578–1586 (2023)Cite this article

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Abstract

Levees constrain roaring floodwater but are blamed for reducing people’s perception of flood risks and promoting floodplain human settlements unprepared for high-consequence flood events. Yet the interplay between levee construction and floodplain development remains poorly quantified, obscuring an objective assessment of human–water relations. Here, to quantitatively assess how floodplain urban expansion is linked to levee construction, we develop a multiscale composite analysis framework leveraging a national levee database and decades of annual land-cover maps. We find that in the contiguous United States, levee construction is associated with a 62% acceleration in floodplain urban expansion, outpacing that of the county (29%), highlighting a clear change in risk perception after levees are built. Regions historically lacking strong momentum for population growth while experiencing frequent floods tend to rely more strongly on levees and we suggest these areas to develop a more diversified portfolio to cope with floods. Temporally, the positive levee effect is found to have weakened and then reversed since the late 1970s, reflecting the role of legislative regulations to suppress floodplain urban expansion. Our quantitative framework sheds light on how structural and non-structural measures jointly influence floodplain urban growth patterns. It also provides a viable framework to objectively assess the floodplain management strategies currently in place, which may provide useful guidance for managing flood risks towards sustainable development goals.

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Fig. 1: Composite analysis of the LE, lumping over the contiguous United States.
Fig. 2: Spatial pattern of the LE.
Fig. 3: Temporal dynamics of the LE.
Fig. 4: Conceptual diagram illustrating the role of levees in flood risk management.

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Data availability

All data used in this study were obtained from openly accessible data sources. The NLD27 shapefiles were downloaded from https://levees.sec.usace.army.mil/#/, accessed November 2021. Historical maps of the USGS LULCC datasets (1938–2005)28,29 were obtained from https://www.sciencebase.gov/catalog/item/59d3c73de4b05fe04cc3d1d1 and https://www.sciencebase.gov/catalog/item/5b96c2f9e4b0702d0e826f6d. The shapefiles of the Watershed Boundary Dataset (WBD)53 were downloaded from https://apps.nationalmap.gov/downloader/#/. The dam data were obtained from the US National Inventory of Dams (USNID) dataset39 (https://nid.sec.usace.army.mil). Data on the fatalities and economic losses due to flooding in each country are from the EM-DAT database1 at https://public.emdat.be/data. Major cities43: https://hub.arcgis.com/datasets/esri::usa-major-cities/about. Our organized data are available from GitHub at https://github.com/peironglinlin/leveeRS/blob/main/data/COMID_systemID_intersection.csv and https://github.com/peironglinlin/leveeRS/blob/main/processed_data/Processed_data_for_Fig1c_and_Fig1d.csv. Source data are provided with this paper.

Code availability

Codes for data processing and analyses are openly available via GitHub at https://github.com/peironglinlin/leveeRS.

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Acknowledgements

This study was supported by the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals, Grant No. CBAS2022ORP05. We acknowledge the funding support from the Fundamental Research Funds for the Central Universities, Peking University on ‘Numerical modelling and remote sensing of global river discharge’ (no. 7100604136). M.D. acknowledges the travel funding supported by the Department of Geography and Geospatial Sciences, Graduate School, and the College of Arts and Sciences at Kansas State University. We thank P. F. Kline from USACE for providing detailed information about NLD, and X. He for helpful discussions.

Author information

Author notes

  1. These authors contributed equally: Meng Ding, Peirong Lin.

Authors and Affiliations

  1. Institute of Remote Sensing and Geographic Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China

    Meng Ding, Peirong Lin, Kaihao Zheng & Yu Liu

  2. Department of Geography and Geospatial Sciences, Kansas State University, Manhattan, KS, USA

    Meng Ding & Jida Wang

  3. International Research Center of Big Data for Sustainable Development Goals, Beijing, China

    Peirong Lin

  4. School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, USA

    Shang Gao

  5. Department of Geography and Geographic Information Science, University of Illinois Urbana–Champaign, Urbana, IL, USA

    Jida Wang

  6. Department of Civil and Environmental Engineering, Southern University of Science and Technology, Shenzhen, China

    Zhenzhong Zeng

  7. Global Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

    Xudong Zhou & Dai Yamazaki

  8. Gwinnett County Department of Water Resources, Lawrenceville, GA, USA

    Yige Gao

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Contributions

P.L. conceived the study. P.L. and M.D. designed the methodology. M.D. and P.L. performed the analysis and conducted the validation. P.L. and M.D. drafted the original paper with inputs from S.G., J.W., Z.Z., D.Y., X.Z., Y.G. and Y.L. M.D., P.L. and K.Z. produced the figures and tables. M.D., P.L. and K.Z. revised the paper with inputs from all co-authors. All authors contributed to the interpretation of results, writing and revision of the paper.

Corresponding author

Correspondence to Peirong Lin.

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Supplementary Figs. 1–10 and Tables 1–3.

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Source Data Fig. 1

Processed data for drawing Fig.1c,d.

Source Data Fig. 2

Processed data for drawing Fig. 2a,b.

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Processed data for drawing Fig. 3.

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Ding, M., Lin, P., Gao, S. et al. Reversal of the levee effect towards sustainable floodplain management. Nat Sustain 6, 1578–1586 (2023). https://doi.org/10.1038/s41893-023-01202-9

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