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Environmental and Ecological Statistics

, Volume 24, Issue 1, pp 131–150 | Cite as

Statistical treatment for the wet bias in tree-ring chronologies: a case study from the Interior West, USA

  • Yan SunEmail author
  • Matthew F. Bekker
  • R. Justin DeRose
  • Roger Kjelgren
  • S.-Y. Simon Wang
Article

Abstract

Dendroclimatic research has long assumed a linear relationship between tree-ring increment and climate variables. However, ring width frequently underestimates extremely wet years, a phenomenon we refer to as ‘wet bias’. In this paper, we present statistical evidence for wet bias that is obscured by the assumption of linearity. To improve tree-ring-climate modeling, we take into account wet bias by introducing two modified linear regression models: a linear spline regression (LSR) and a likelihood-based wet bias adjusted linear regression (WBALR), in comparison with a quadratic regression (QR) model. Using gridded precipitation data and tree-ring indices of multiple species from various sites in Utah, both LSR and WBALR show a significant improvement over the linear regression model and out-perform QR in terms of in-sample \({R}^{2}\) and out-of-sample MSE. This further shows that the wet bias emerges from nonlinearity of tree-ring chronologies in reconstructing precipitation. The pattern and extent of wet bias varies by species, by site, and by precipitation regime, making it difficult to generalize the mechanisms behind its cause. However, it is likely that dis-coupling between precipitation amounts (e.g., percent received as rain/snow or percent infiltrating the soil) and its availability to trees (e.g., root zone dynamics), is the primary mechanism driving wet bias.

Keywords

Dendrochronology Dendroclimatology Likelihood-based modeling Saturation 

Notes

Acknowledgements

The authors would like to thank the editor and two anonymous referees for their valuable comments and suggestions, which have led to a much better presentation of the paper. The Wasatch Dendroclimatology Research Group (WADR) played a crucial role in funding and guiding this project. This research was funded by a U.S. Bureau of Reclamation WaterSmart Grant (No. R13AC80039) and a U.S. Department of Energy Grant (DE-SC0016605). We would like to thank Jennefer Parker on the Logan Ranger District and Billy Preston on the Spanish Fork Ranger District, Uinta-Wasatch-Cache National Forest, Karl Fuelling on the Minidoka Ranger District, Sawtooth National Forest, Del Barnhurst on the Fillmore Ranger District, Fishlake National Forest, and Charley Gilmore for sampling permission. This paper was prepared in part by an employee of the US Forest Service as part of official duties and is therefore in the public domain.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsUtah State UniversityLoganUSA
  2. 2.Department of GeographyBrigham Young UniversityProvoUSA
  3. 3.Forest Inventory and AnalysisRocky Mountain Research StationOgdenUSA
  4. 4.Department of Plants, Soils, and ClimateUtah State UniversityLoganUSA

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