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Maximizing US nitrate removal through wetland protection and restoration


Growing populations and agricultural intensification have led to raised riverine nitrogen (N) loads, widespread oxygen depletion in coastal zones (coastal hypoxia)1 and increases in the incidence of algal blooms.Although recent work has suggested that individual wetlands have the potential to improve water quality2,3,4,5,6,7,8,9, little is known about the current magnitude of wetland N removal at the landscape scale. Here we use National Wetland Inventory data and 5-kilometre grid-scale estimates of N inputs and outputs to demonstrate that current N removal by US wetlands (about 860 ± 160 kilotonnes of nitrogen per year) is limited by a spatial disconnect between high-density wetland areas and N hotspots. Our model simulations suggest that a spatially targeted increase in US wetland area by 10 per cent (5.1 million hectares) would double wetland N removal. This increase would provide an estimated 54 per cent decrease in N loading in nitrate-affected watersheds such as the Mississippi River Basin. The costs of this increase in area would be approximately 3.3 billion US dollars annually across the USA—nearly twice the cost of wetland restoration on non-agricultural, undeveloped land—but would provide approximately 40 times more N removal. These results suggest that water quality improvements, as well as other types of ecosystem services such as flood control and fish and wildlife habitat, should be considered when creating policy regarding wetland restoration and protection.

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Fig. 1: Our approach to estimating wetland N removal across the USA.
Fig. 2: Wetland densities, N surplus magnitudes and wetland N removal across the contiguous US.
Fig. 3: Spatial relationships between N source areas and existing wetlands.
Fig. 4: Estimated N mass removal and costs for the three wetland restoration scenarios.
Fig. 5: Wetland restoration simulation results for a 10% increase in wetland area.

Data availability

Nitrogen mass balance data were obtained from the TREND-nitrogen dataset, available through the PANGAEA Data Publisher ( The National Wetlands Inventory dataset was retrieved from US-FWS ( The Watershed Boundary Data set used for HUC-8 boundaries was retrieved from the USGS website USGS water quality data were retrieved from Oelsner et al.54Source data are provided with this paper.

Code availability

The MATLAB software used for the present analysis is available from Mathworks (; R (version 3.5.2) used for geospatial analysis is available from the R Core Team ( Codes for the estimation of current wetland N removal, wetland restoration scenarios and cost analysis are available at


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The present work was financially supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) grant (reference number 386129293) for F.Y.C. and N.B.B., an NSERC graduate scholarship for F.Y.C., an NSERC Discovery Grant and an Ontario Early Researcher Award for N.B.B. and D.K.B., and by startup funds from the University of Illinois at Chicago for K.J.V.M. Additional funding support was received through the Global Water Futures project Lake Futures for N.B.B.

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Authors and Affiliations



N.B.B., K.J.V.M. and F.Y.C. conceived the study. N.B.B., F.Y.C. and K.J.V.M. developed the wetland N removal models. F.Y.C. ran simulations for current wetland N removal, and K.J.V.M. carried out the wetland restoration simulations and cost analysis. D.K.B., K.J.V.M. and N.B.B. provided N input data for the model simulations. K.J.V.M. wrote the paper with direct contributions from N.B.B., F.Y.C. and D.K.B.

Corresponding author

Correspondence to N. B. Basu.

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The authors declare no competing interests.

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Peer review information Nature thanks Jacques Finlay, Patrick Inglett, John Tyndall and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data figures and tables

Extended Data Fig. 1 Nitrogen surplus distributions across US hydrologic regions.

a, Histograms of N surplus by hydrologic region. The counts in the histograms refer to individual HUC-8 watersheds within the hydrologic regions. b, Map of hydrologic regions defined by the USGS. Boundaries of the Mississippi River Basin are drawn in yellow.

Extended Data Fig. 2 Modelled N removal by hydrologic region.

The counts in the histograms refer to individual HUC-8 watersheds within the corresponding hydrologic regions. See Extended Data Fig. 1b for region locations.

Extended Data Fig. 3 Analysis of empirical data used by Cheng &Basu3 to develop the kτ relationship used in our study.

a, N removed at the individual wetland scale. Data were obtained from a global meta-analysis of 178 wetlands. b, N removal efficiency, ρ, calculated as the ratio between N removal and N inputs to the wetland. c, N removal-rate constant, k, estimated as a function of ρ and empirically based estimates of wetland residence times, τ, assuming that N removal within the wetland follows first-order kinetics. d, A strong inverse relationship was found between k and wetland residence time τ. This relationship between size and N removal-rate constants allows us, in this work, to more accurately upscale to the continental US scale than has previously been achieved.

Extended Data Fig. 4 Costs of wetland restoration.

a, b, Estimated costs for restoration of a 1-ha wetland in 48 states across the contiguous US on cropland (a) and pastureland (b). Whereas construction and maintenance costs are considered to be constant across states, land rental costs vary by state and by land use. Costs are annualized over a 50-year management horizon.

Extended Data Table 1 N surplus and wetland N removal magnitudes for a subset of nitrate-affected watersheds
Extended Data Table 2 Ranges of parameters used in the Monte Carlo simulations of wetland N removal

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Cheng, F.Y., Van Meter, K.J., Byrnes, D.K. et al. Maximizing US nitrate removal through wetland protection and restoration. Nature 588, 625–630 (2020).

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