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Predicting risks of tornado and severe thunderstorm damage to southeastern U.S. forests

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Abstract

Context

The southeastern U.S. experiences tornadoes and severe thunderstorms that can economic and ecological damages to forest stands resulting in loss of timber, reduction in short-term carbon sequestration, and increased susceptibility to forest pests and pathogens.

Objectives

This project sought to determine landscape-scale patterns of recurring wind damages and their relationships to topographic attributes, overall climatic patterns and soil characteristics in southeastern forests.

Methods

We assembled post-damage assessment data collected since 2012 by the National Oceanic and Atmospheric Administration (NOAA). We utilized a regularized Generalized Additive Model (GAM) framework to identify and select influencing topographic, soil and climate variables and to discriminate between damage levels (broken branches, uprooting, or trunk breakage). Further, we applied a multinomial GAM utilizing the identified variables to generate predictions and interpolated the results to create predictive maps for tree damage.

Results

Terrain characteristics of slope and valley depth, soil characteristics including erodibility factor and bedrock depth, and climatic variables including temperatures and precipitation levels contributed to damage severity for pine trees. In contrast, valley depth and soil pH, along with climactic variables of isothermality and temperature contributed to damage severity for hardwood trees. Areas in the mid-south from Mississippi to Alabama, and portions of central Arkansas and Oklahoma showed increased probabilities of more severe levels of tree damage.

Conclusions

Our project identified important soil and climatic predictors of tree damage levels, and areas in the southeastern U.S. that are at greater risk of severe wind damage, with management implications under continuing climate change.

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Data availability

The datasets compiled and analyzed during the current study are available from sources indicated within the manuscript or from the corresponding author on reasonable request.

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Acknowledgements

Funding for this project was provided by the USDA, Southern Research Station, and the University of Georgia, D.B. Warnell School of Forestry and Natural Resources and the Plantation Management Research Cooperative. Special thanks to Holly Munro (National Council for Air and Stream Improvement) for valuable input on the project.

Funding

Funding for this project was provided by the USDA Forest Service, Southern Research Station, and the University of Georgia, D.B. Warnell School of Forestry and Natural Resources and the Plantation Management Research Cooperative.

Author information

Authors and Affiliations

  1. D.B. Warnell School of Natural Resources, University of Georgia, 180 E Green Street, Athens, GA, 30602, USA

    Christine C. Fortuin, Cristian R. Montes & Kamal J. K. Gandhi

  2. USDA Forest Service, Southern Research Station, 320 E Green Street, Athens, GA, 30602, USA

    James T. Vogt

Authors
  1. Christine C. Fortuin
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  2. Cristian R. Montes
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  3. James T. Vogt
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  4. Kamal J. K. Gandhi
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by CCF and CRM. The first draft of the manuscript was written by CCF and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Christine C. Fortuin.

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The authors declare that they have no competing financial interests that could have appeared to influence the work reported in this paper.

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Supplementary Information

Below is the link to the electronic supplementary material.

10980_2022_1451_MOESM1_ESM.docx

Supplementary file1 S1: List of Topographic (A), Climate (B), and Soil (C) variables utilized in the GAMSEL variable selection (DOCX 28 kb)

10980_2022_1451_MOESM2_ESM.png

Supplementary file2 S2: Pine damage uncertainty: Errors are calculated from the standard errors on logit values, converted to probability. Above maps represent the difference between the interpolated upper or lower bounds of the probability and the predicted probability (i.e., if predicted probability is 0.05 (5%) and lower bound of the prediction is 0.045 (4.5%) then the difference is 0.045-0.05 = -0.005). Fit 1 represents the probability of broken branches vs. the higher damage classes (uprooting or trunk breakage). Fit 2 represents the probability of uprooting vs. trunk breakage. a: fit 1 upper error, b: fit 1 lower error, C: fit 2 upper error, D: fit 2 lower error. (PNG 2708 kb)

10980_2022_1451_MOESM3_ESM.png

Supplementary file3 S3: Hardwood damage uncertainty: Errors are calculated from the standard errors on logit values, converted to probability. Above maps represent the difference between the interpolated upper or lower bounds of the probability and the predicted probability [i.e., if predicted probability is 0.05 (5%) and lower bound of the prediction is 0.045 (4.5%) then the difference is 0.045-0.05 = -0.005]. Fit 1 represents the probability of broken branches vs. the higher damage classes (uprooting or trunk breakage). Fit 2 represents the probability of uprooting vs. trunk breakage. a: fit 1 upper error, b: fit 1 lower error, C: fit 2 upper error, D: fit 2 lower error (PNG 3050 kb)

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Fortuin, C.C., Montes, C.R., Vogt, J.T. et al. Predicting risks of tornado and severe thunderstorm damage to southeastern U.S. forests. Landsc Ecol 37, 1905–1919 (2022). https://doi.org/10.1007/s10980-022-01451-7

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