| Summary: | Multiple reconstructions of April 1st snow water equivalent (SWE) are generated for the Wind River Range (WRR), located in west-central Wyoming, to determine the most accurate predictors. Predictors included climate signal data (Southern Oscillation Index), traditional predictors (tree-ring chronologies), and non-spatially biased Pacific Ocean sea surface temperatures (SSTs). Incorporation of Pacific Ocean SSTs as a whole provides a more comprehensive representation of oceanic-atmospheric variability. Rotated principal component analysis (PCA) was used to regionalize April 1st snowpack data (1961 – 1999) from snow telemetry stations (SNOTEL stations). Tree-ring chronologies that were stable across the period of overlapping records (1961 – 1999) and that were positively correlated with regional snowpack at 99% confidence levels or higher were retained. Singular value decomposition (SVD) was performed on Pacific Ocean SSTs and regional snowpack data to identify coupled regions of climate (SSTs) and hydrology (SWE). Stepwise regressions were performed across the calibration period to identify the best predictor combinations. When data from the instrumental based SST regions identified by SVD were included in the pool of predictors, an increase in reconstruction skill was observed. Further regressions were performed using tree based and coral based SST data. Reconstruction equations were obtained from these regressions and regional April 1st snowpack was reconstructed for the WRR for all three types of SST data. A higher degree of snowpack variance is explained by reconstructions utilizing tree based, coral based, and instrumental based data for the Pacific Ocean SST region identified by SVD than is possible utilizing only tree-ring and SOI data, indicating that non-spatially biased SSTs are excellent predictors for snowpack reconstruction in the WRR.
|
|---|
