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On the occasion of the 75th anniversary of the Laboratory of Tree-Ring Research, it is appropriate to reflect on the origin of the LTRR and the oft overlooked early period of its history. The period from the “Bridging the gap” event in 1929 to the semi-retirement of A.E. Douglass in 1958 was a crucial time in the development of the LTRR. Although this paper focuses on the history of the LTRR between those events, at points the history of the LTRR is, essentially, the history of the field, making a holistic understanding all the more important. The information presented here is rooted in a series of transcribed historical lectures delivered in 1992 and 1993 by Director/Professor Emeritus Bryant Bannister, and several historical reports composed by him between 1963 and 1998.
This paper reports on investigation of the dendroclimatic potential of three Polylepis species, P. pepei, P. subsericans and P. rugulosa in Peru and Bolivia in the tropical Andes, where they form the world's highest treeline forests up to 5,000 m a.s.l. In Bolivia, P. pepei trees were sampled close to La Paz City. In Peru, P. pepei and P. subsericans were sampled in the Vilcanota Mountains close to Urubamba City, and P. rugulosa in the Arequipa region on the slope of Coropuna Volcano. Chronologies span the 20th Century and all three species show intermediate values of mean sensitivity, common variance and signal-to-noise ratio. In general, correlation and response-function analyses revealed significant positive relationships with temperature during the rainy season for all three species in Peru and Bolivia. Relationships with precipitation were more difficult to interpret as positive relationships were observed between radial growth and precipitation at the beginning of the rainy season in all three species in Peru, whereas for P. pepei in Bolivia, the relationships with precipitation appeared to be controlled by local conditions including slope and substrate (moraine or scree slope).
The Green River is the largest tributary of the Colorado River. Given that snowpack is the primary driver of streamflow, information on the long-term regional snowpack (regionalized April 1 Snow Water Equivalent (SWE)) variability would provide useful information for water managers and planners. Previous research efforts were unable to develop skillful SWE reconstructions using tree-ring chronologies in the Upper Green River Basin (UGRB) of Wyoming because of limited tree-ring chronologies in the area. The current research uses Principal Components Analysis to regionalize April 1 snowpack data in the UGRB. Recent research efforts developed six new tree-ring chronologies in and adjacent to the UGRB. These new chronologies, along with 38 existing chronologies, were correlated with the regionalized SWE data. Chronologies positively correlated at a 95% confidence level or higher were retained. Stepwise linear regressions were performed and a reconstruction of UGRB regional April 1 SWE was achieved (R2 = 0.21). Climate signals (Pacific Decadal Oscillation (PDO) and Southern Oscillation Index (SOI)) were introduced to the predictor variables and an additional regression was performed. Inclusion of the SOI resulted in a statistically skillful reconstruction (R2 = 0.58). Temporal drought periods for SWE and for streamflow were examined for the UGRB and a direct relationship was observed.
We present a method for obtaining a true flat surface on cross-sections of large logs that exceed the width of many belt sanders, to aid in digital scanning and computer-aided ring-width measurement. The method uses a vertical mill that is available in most university machine shops, gradually removing thin layers of wood to achieve a surface that is planar within ca. 0.3-mm precision. We have tested the method on several sizes, shapes, and decay states of log samples and found that it performs well across these variations. Samples can then be directly sanded with medium- to fine-grit sandpaper to achieve a finished surface that lies flat on a scanner plate and shows rings and cell structure with high clarity.
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