A 576‐Year Weber River Streamflow Reconstruction from Tree Rings for Water Resource Risk Assessment in the Wasatch Front,Utah

We present a 576‐year tree‐ring‐based reconstruction of streamflow for northern Utah's Weber River that exhibits considerable interannual and decadal‐scale variability. While the 20th Century instrumental period includes several extreme individual dry years, it was the century with the fewest such years of the entire reconstruction. Extended droughts were more severe in duration, magnitude, and intensity prior to the instrumental record, including the most protracted drought of the record, which spanned 16 years from 1703 to 1718. Extreme wet years and periods are also a regular feature of the reconstruction. A strong early 17th Century pluvial exceeds the early 20th Century pluvial in magnitude, duration, and intensity, and dwarfs the 1980s wet period that caused significant flooding along the Wasatch Front. The long‐term hydroclimatology of northern Utah is marked by considerable uncertainty; hence, our reconstruction provides water managers with a more complete record of water resource variability for assessment of the risk of droughts and floods for one of the largest and most rapidly growing population centers in the Intermountain West.     

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

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.     

Spatial and covariate-varying relationships among dominant tree species in Utah

Environmental and Ecological Statistics - The presence and establishment of a tree species at a particular spatial location is influenced by multiple physiological and environmental filters such as...