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Droughts impede water balance recovery from fires in the Western United States

Nature Ecology & Evolution volume 8, pages 229–238 (2024)Cite this article

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Abstract

A steady rise in fires in the Western United States, coincident with intensifying droughts, imparts substantial modifications to the underlying vegetation, hydrology and overall ecosystem. Drought can compound the ecosystem disturbance caused by fire, although how these compound effects on hydrologic and ecosystem recovery vary among ecosystems is poorly understood. Here we use remote sensing-derived high-resolution evapotranspiration (ET) estimates from before and after 1,514 fires to show that ecoregions dominated by grasslands and shrublands are more susceptible to drought, which amplifies fire-induced ET decline and, subsequently, shifts water flux partitioning. In contrast, severely burned forests recover from fire slowly or incompletely, but are less sensitive to dry extremes. We conclude that moisture limitation caused by droughts influences the dynamics of water balance recovery in post-fire years. This finding explains why moderate to extreme droughts aggravate impacts on the water balance in non-forested vegetation, while moisture accessed by deeper roots in forests helps meet evaporative demands unless severe burns disrupt internal tree structure and deplete fuel load availability. Our results highlight the dominant control of drought on altering the resilience of vegetation to fires, with critical implications for terrestrial ecosystem stability in the face of anthropogenic climate change in the West.

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Fig. 1: Domain overview and fire characteristics.
Fig. 2: Impact of fires from 2014 to 2020 on major WUS ecoregions.
Fig. 3: Water balance recovery trends with burn severity and landcover type.
Fig. 4: Impact of droughts on water balance recovery.

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Data availability

The data that support the findings of this study are all publicly available from their respective sources. Processed dataset on all the selected fire events in this study with respective fire characteristics, centroid locations and variables necessary to calculate water balance shift during pre- and post-fire years are available at https://doi.org/10.5281/zenodo.10041786.

Code availability

The Jupyter notebooks to analyse the fire impacts as described in this study are available in the GitHub repository at https://github.com/sahmad3/eis-notebooks/tree/main/FIRE_HYDRO.

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Acknowledgements

This research was supported by funding from NASA Headquarters for the Earth Information System (EIS) project (https://www.earthdata.nasa.gov/eis). Computing was supported by NASA Goddard Science Managed Cloud Environment (SMCE), which is a service of the Computational & Information Sciences and Technology Office (CISTO) at the NASA Goddard Space Flight Center. This research was partially supported by funding from NIFA McIntire Stennis grant MISZ-721160. OpenET data used in this study were produced on Google Earth Engine and we gratefully acknowledge Google, Inc. for the computing support and resources used to produce and process these data.

The research was also partially supported by funding from National Aeronautics and Space Administration (NASA) Applied Science Program (grant number NNX12AD05A, F.M.).

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Authors and Affiliations

  1. Hydrological Sciences Lab, NASA Goddard Space Flight Center (GSFC), Greenbelt, MD, USA

    Shahryar K. Ahmad, Thomas R. Holmes, Sujay V. Kumar, Timothy M. Lahmers, Pang-Wei Liu, Augusto Getirana, Elijah Orland, Rajat Bindlish & Kim A. Locke

  2. Science Applications International Corporation, McLean, VA, USA

    Shahryar K. Ahmad, Augusto Getirana & Kim A. Locke

  3. Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD, USA

    Timothy M. Lahmers

  4. Science Systems and Applications, Inc., Lanham, MD, USA

    Pang-Wei Liu

  5. Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA

    Wanshu Nie

  6. GESTAR II, University of Maryland, Baltimore County, Baltimore, MD, USA

    Elijah Orland

  7. Biospheric Sciences Branch, NASA Ames Research Center (ARC), Moffett Field, Santa Clara, CA, USA

    Alberto Guzman & Forrest S. Melton

  8. California State University, Monterey Bay, Seaside, CA, USA

    Alberto Guzman & Forrest S. Melton

  9. NASA Marshall Space Flight Center (MSFC), Huntsville, AL, USA

    Christopher R. Hain

  10. Department of Forestry, Mississippi State University, Starkville, MS, USA

    Yun Yang

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  1. Shahryar K. Ahmad
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Contributions

S.K.A., T.R.H. and S.V.K. conceived the research and experiments. S.K.A. collected the data, wrote the paper and prepared visualizations. T.R.H. and S.V.K. contributed to paper revision. T.M.L., P.-W.L., W.N., A. Getirana, E.O., R.B., A. Guzman, C.R.H., F.S.M., K.A.L. and Y.Y. carried out reviewing and editing.

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Correspondence to Shahryar K. Ahmad.

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Extended data

Extended Data Fig. 1 Distribution of forest and non-forest vegetation types from different landcover products.

(a) Fraction of vegetation classes relative to total area remapped forested and non-forested types of shrublands (SHB), grasslands (GRS), and others (OTH) using individual landcover products, where multiple thresholds are applied on LANDFIRE product to derive the classes. The three numbers denote percent thresholds on tree, shrub, and herbaceous canopy covers respectively. Landcover products on y-axis refer to: LANDFIRE existing vegetation cover (LF), MODIS MCD12Q1.061 International Geosphere-Biosphere Programme (IGBP) classification, Copernicus Global Land Cover (CGLS-LC100), European Space Agency′s Climate Change Initiative (CCI), ESA WorldCover (ESA), and the National Land Cover Database (NLCD). (b) Distribution of classes from landcover products over pixels where tree cover from LANDFIRE EVC is >40%. (c) Distribution of classes from landcover products over pixels where shrub and herbaceous cover from LANDFIRE EVC is >20%.

Extended Data Fig. 2 Water balance shift as a function of burn severity, landcover type, and drought severity from different landcover products.

(a) Spread in water balance shift obtained using six landcover products – GLC, NLCD, ESA WorldCover, MODIS IGBP, ESA CCI, and LANDFIRE (with least stringent threshold of 10% for tree, shrub, and herbaceous cover) characterized with burn severity, (b) water balance shift characterized by landcover type. (c) Scatter plot between normalized water-balance shift index (NWSI) and annual SPEI-90 for fire events during 2016 to 2018, characterized by burn severity, based on LANDFIRE combinations of various thresholds over tree (T), shrub (S), and herbaceous (G) percent covers. Darker colors refer to more stringent constraint on the vegetation covers.

Extended Data Fig. 3

Water balance recovery trends with burn severity and landcover type for different ET products. Water balance shift caused by fires during 2016–18 in three post-fire water years (Y1, Y2, and Y3) obtained using remaining four (DisALEXI is shown in main text) ET sources of (a) eeMETRIC, (b) geeSEBAL, (c) PT-JPL, and (d) MODIS, and characterized by burn severity and landcover type. Average values are shown with green and orange markers, filled area extends over the entire data range.

Extended Data Fig. 4 Impact of droughts on water balance recovery for different ET products.

Scatter plots between NWSI and SPEI-90 for 2016 to 2018 fires over forest and non-forest landcovers, characterized by burn severity, where NWSI is obtained using four different ET sources of (a) eeMETRIC, (b) geeSEBAL, (c) PT-JPL, and (d) MODIS. Green and yellow solid lines represent the linear best fit between NWSI and SPEI for forests and non-forests, respectively, and shaded regions show 95% confidence intervals around the best fit lines.

Extended Data Fig. 5 Change in NWSI in post-fire year Y3 compared to Y1 for high and moderate severity fires of 2016–18.

Two scenarios of BLS (baseline) and DRS (drought) are compared using four sources of ET observations over (a) forest and (b) non-forest landcovers. Positive change denotes increase in water balance shift while negative values denote recovery.

Extended Data Fig. 6 Control polygon definition.

Steps to identify control polygon using MTBS burn polygons, burn severity, MODIS landcover, SRTM digital elevation model, and MODIS water mask product.

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2.

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Supplementary Data 1

Spreadsheet describing fire events in the Western United States during 2014–2020, with respective fire characteristics, centroid locations and variables necessary to calculate water balance shift during pre- and post-fire years (source data for all the figures).

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Ahmad, S.K., Holmes, T.R., Kumar, S.V. et al. Droughts impede water balance recovery from fires in the Western United States. Nat Ecol Evol 8, 229–238 (2024). https://doi.org/10.1038/s41559-023-02266-8

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