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Wind River Range Snowpack Reconstruction Using Dendochronology and Sea Surface Temperatures

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Wind River Range Snowpack Reconstruction Using Dendochronology and Sea Surface Temperatures University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Masters Theses Graduate School 12-2010 Wind River Range Snowpack Reconstruction Using Dendochronology and Sea Surface Temperatures SallyRose Anderson University of Tennessee - Knoxville, [email protected] Follow this and additional works at: https://trace.tennessee.edu/utk_gradthes Part of the Environmental Engineering Commons Recommended Citation Anderson, SallyRose, "Wind River Range Snowpack Reconstruction Using Dendochronology and Sea Surface Temperatures. " Master's Thesis, University of Tennessee, 2010. https://trace.tennessee.edu/utk_gradthes/771 This Thesis is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Masters Theses by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a thesis written by SallyRose Anderson entitled "Wind River Range Snowpack Reconstruction Using Dendochronology and Sea Surface Temperatures." I have examined the final electronic copy of this thesis for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Master of Science, with a major in Environmental Engineering. Glenn Tootle, Major Professor We have read this thesis and recommend its acceptance: John Schwartz, Mary Sue Younger Accepted for the Council: Carolyn R. Hodges Vice Provost and Dean of the Graduate School (Original signatures are on file with official studentecor r ds.) To the Graduate Council: I am submitting herewith a thesis written by SallyRose Anderson entitled “Wind River Range Snowpack Reconstruction Using Dendochronology and Sea Surface Temperatures.” I have examined the final electronic copy of this thesis for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Master of Science, with a major in Environmental Engineering. Glenn Tootle, Major Professor We have read this thesis and recommend its acceptance: John Schwartz Mary Sue Younger Accepted for the Council: Carolyn R. Hodges Vice Provost and Dean of the Graduate School Wind River Range Snowpack Reconstruction Using Dendochronology and Sea Surface Temperatures A thesis presented for the Masters of Science Degree The University of Tennessee, Knoxville SallyRose Anderson December 2010 ABSTRACT Multiple reconstructions of April 1 st 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 1 st 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. ii Table of Contents 1.0 INTRODUCTION ............................................................................................................... 1 1.1 Introduction and Literature Review ................................................................................. 1 2.0 REGION OF STUDY .......................................................................................................... 4 3.0 DATA .................................................................................................................................. 7 3.1 SNOTEL Stations ............................................................................................................. 7 3.2 Tree-Ring Chronologies ................................................................................................... 8 3.3 Southern Oscillation Index (SOI) ................................................................................... 11 3.4 Sea Surface Temperatures (SSTs) .................................................................................. 11 4.0 Reconstruction Methodology ............................................................................................. 13 4.1 Concentration of Data .................................................................................................... 13 4.1.1 Principal Component Analysis for SNOTEL Station Data..................................... 13 4.1.2 Correlation for Tree-Ring Chronologies ................................................................. 13 4.1.3 Range Selection for SOI and SST data ................................................................... 14 4.2 Singular Value Decomposition (SVD) ........................................................................... 14 4.2.1 Application of SVD ................................................................................................ 17 4.3 Stepwise Regression ....................................................................................................... 19 4.4 Regression Analysis ....................................................................................................... 21 4.4.1 Autocorrelation ....................................................................................................... 21 5.0 Fit Statistics for Calibration Period.................................................................................... 22 5.1 Standard Error of the Regression (S) ............................................................................. 22 5.2 Durbin-Watson Statistic ................................................................................................. 23 5.3 R2 .................................................................................................................................... 23 5.4 Adjusted R 2 .................................................................................................................... 24 5.5 Predicted R 2 .................................................................................................................... 24 6.0 Results ................................................................................................................................ 26 7.0 Conclusion and Future Work ............................................................................................. 31 8.0 Acknowledgements ............................................................................................................ 33 Literature Cited ............................................................................................................................. 34 iii Appendices .................................................................................................................................... 41 Appendix 1: Data Source Summary ............................................................................................ 42 Appendix 2: Principal Components Analysis Report .................................................................. 43 Appendix 3: MiniTab Output for Model Calibration .................................................................. 44 General Models: Stepwise Regression, Regression, Autocorrelation ...................................... 44 Specific SST Models: Stepwise Regression, Regression, Autocorrelation .............................. 53 Appendix 4: Model Calibration Fits ............................................................................................ 62 General Models ......................................................................................................................... 62 Specific SST Models................................................................................................................. 63 Appendix 5: Regression Equations for Reconstructions ............................................................. 64 Appendix 6: Chronology Data Used in Reconstructions ............................................................. 65 BLE ........................................................................................................................................... 65 NPU..........................................................................................................................................
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