ELECTRONIC SUPPLEMENTARY MATERIAL
Title: Reconstructed cool- and warm-season precipitation over the tribal lands of northeastern
Arizona
Journal: Climatic Change
Authors: Holly L. Faulsticha,b, Connie A. Woodhousea,b, and Daniel Griffina,b
Affiliations: aLaboratory of Tree-Ring Research, 105 West Stadium, University of Arizona,
Tucson, AZ, 85721, USA
b
School of Geography and Development, 409 Harvill Building, University of Arizona, Tucson,
AZ, 85721, USA
Corresponding Author’s Email: hfaulstich@gmail.com
Online Resource 1. Data and methods used to develop seasonal reconstructions of precipitation
1.1.
Tree-ring data
The Four Corners region is home to a number of previously developed tree-ring
chronologies. For this study, we focused on 15 archive collections; nine of Douglas-fir
(Pseudotsuga menziesii) and six of ponderosa pine (Pinus ponderosa; Fig. 1) that are housed in
the Laboratory of Tree-Ring Research archive at the University of Arizona. To update these
collections to the twenty-first century, increment cores were collected from each original
sampling site after the 2008 growing season (Table S1). The updated collections (two cores from
at least 20 trees per site) were mounted, sanded and cross-dated visually under a microscope
using standard dendrochronological techniques (e.g. Stokes and Smiley 1968). EW and LW
widths of the archived and newly collected tree-ring specimens were measured to an accuracy of
0.001 mm using the anatomical criterion described by Griffin et al. (2011). Each ring-width
series was statistically checked for cross-dating and measurement errors using the quality-control
program COFECHA (Holmes 1983).
Numerical chronologies were computed using the operational protocol described by
Griffin et al. (2011), which is appropriate for open-canopy, semiarid forests with minimal
disturbance and competition effects, such as the ones sampled in this study. EW- and LW-width
measurements were detrended using a cubic smoothing spline with a frequency response of 0.5
at a wavelength equal to 100 years (Cook and Peters 1981). This spline was selected to preserve
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the majority of variance at wavelengths of 30 years or less while removing the effects of age and
bole geometry on tree growth (Fritts and Swetnam 1989). Ring-width indices were computed as
the ratio of the measured width value to the 100-year spline curve fit value of the corresponding
year. Significant low-order autocorrelation, thought to be of biological origin (Fritts 1976) was
removed by fitting an autoregressive model to the individual EW- and LW-width time series,
producing a set of ‘residual’ index time series (Cook 1985; Box and Jenkins 1976). To isolate the
width variability unique to LW and enhance the corresponding monsoon precipitation signal, the
dependence of LW width on EW width was removed by regressing the LW index on the
antecedent EW index (Meko and Baisan 2001). The residuals from the regression, referred to as
adjusted latewood (LWadj) indices, were statistically uncorrelated to indices of EW width. Sitelevel EW- and LWadj-index chronologies were computed using a robust Tukey biweight mean
function (Cook et al. 1990).
1.2.
Climate data
Total monthly precipitation data from the Parameter-elevation Regressions on
Independent Slopes Model (PRISM) dataset (Daly et al. 2008) were used in this study, both to
assess the spatial characteristics of cool- and warm-season precipitation across the tribal lands
and as a target for calibrating the reconstructions. Using the program SEASCORR (Meko et al.
2011), the relationships between tree growth and monthly precipitation were explored with
correlation analysis. Seasons were defined, based on regional climatology and the results from
SEASCORR analysis, as October-April (cool season) and July-August (warm season). The
PRISM dataset’s ability to adequately capture regional spatial variability was first confirmed
through a comparison with a subset of high quality climate data available from Cooperative
Network climate stations (http://www.wrcc.dri.edu) and the U.S. Historical Climatology
Network (http://cdiac.ornl.gov/epubs/ndp/ushcn/ushcn.html).
Seasonal precipitation totals from PRISM gridpoints corresponding to locations of 20
high-quality climate stations were submitted to (Varimax) rotated principle components analysis
(RPCA; not shown). The RPCA groupings were highly consistent with the patterns found by
Dean (1988) on the southern Colorado Plateau. These groupings were used to inform the extent
of the climate region targeted for both reconstructions. Based on these results, monthly
precipitation values from the PRISM dataset (1895-2010) were extracted from a polygon
encompassing a majority of the Hopi and Navajo reservations (Fig. 1).
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1.3.
Climate reconstruction
Complimentary reconstructions of cool- and warm-seasonal precipitation were developed
for the study area using methods standard to dendroclimatology (e.g., Fritts 1976; Fritts et al.
1990). First, the pool of potential predictors was restricted to only those chronologies
significantly correlated (p<0.01) with the seasonal rainfall totals over the full period (1896-2008)
and split periods (1896-1952 and 1953-2008). We further constrained the pool of candidate
predictor chronologies by setting a minimum length requirement of 500 years (extending through
2008 or later) for the EW chronologies and 400 years for the LW chronologies, since fewer long
records were available. The final predictor pools for the reconstructions included five Douglasfir EW chronologies (cool season), and four ponderosa pine LW chronologies (warm season;
Table S2). The species-specific response to seasonal precipitation suggests that in this region of
the Southwest, ponderosa pine LW contains a more robust monsoon signal than Douglas-fir LW.
Although beyond the scope of this paper, the authors are intrigued by this pattern and plan to
investigate further in a subsequent study. The expressed population signal (EPS; Wigley et al.
1984) was used to assess the signal strength over time as a function of sample size in each
chronology, and a threshold EPS of 0.85 determined the critical year when sample depth was
adequate for capturing the common variance (Table S2). Finally, potential multicollinearity
among predictor variables was assessed using the variance inflation factor (VIF; Haan 2002).
The mean VIF values for cool-season (1.54) and warm-season predictors (1.36) were well below
the critical threshold (larger than five; Haan 2002), suggesting that mulicollinearity is not an
issue among the predictors.
Stepwise multiple linear regression was used to generate reconstruction models by
calibrating the tree-ring chronologies with precipitation data over their common period (18962008). Regression residuals met the assumptions of the multiple linear regression model (Ostrom
1990), exhibiting an approximately normal distribution and displaying no significant trend,
autocorrelation or correlation with the predictors. The models were validated using ‘leave-oneout’ cross-validation (Michaelson 1987; Meko and Baisan 2001) and model accuracy was
summarized by the root-mean-square error (RMSE) statistic and by the reduction of error (RE)
statistic, which measures the model skill in estimating values of the predictand in the crossvalidation process (Wilkes 1995; Fritts 1976). The sign test was also employed, which indicates
how often the signs of the observed and reconstructed values agree or disagree (Fritts 1976;
Table 1).
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For the cool-season, only two EW predictor variables stepped into the model (Fig. 1;
Table 1), explaining more than half the variance in instrumental precipitation (adjusted R2 = 0.53,
Fig. S1a). The RE statistic was positive and close to the calibration coefficient of determination
(R2) value. The RMSE and calibration standard error of the estimate (SE) values were relatively
similar and the sign test was significant at p<0.01 (Table 1).
Four LW predictor variables were included in the warm-season reconstruction model
(Fig. 1; Table 1), explaining nearly half of the variance in the observed record (adjusted R2=0.42,
Fig. S1b). The validation statistics indicated that the model was well-verified and the sign test
was significant at p<0.01. To simplify the analysis of multiyear and decadal-scale drought
variability, the reconstructions were converted to z-scores (standardized to have a mean of zero
and unit variance over the full reconstruction period; Fig. S2).
Online Resource 2. Comparison of reconstructed droughts with regional tree-ring records
The reconstructed periods of below-average precipitation in this study are analogous with
the findings of other regional reconstructions of drought conditions. The most severe and
sustained episode of interseasonal drought in this record (1726-1744) is documented in numerous
tree-ring studies. In a streamflow reconstruction of the Salt River, the authors estimate that
between 1728 and 1739 the average annual discharge was only 43% of the long-term mean and
not a single year exceeded the mean (Smith and Stockton 1981). Moisture-sensitive tree-ring
chronologies from northeastern Utah (Gray et al 2004), northern and central New Mexico (Rose
et al 1981; Parks et al. 2006) and eastern Colorado (Woodhouse & Brown 2001) also find strong
evidence for drought during this interval. Furthermore, through the use of historical documents,
anecdotal evidence and paleoclimate proxies, Brenneman (2004) identified the period 1728 to
1742 as one in which abnormally hot, dry conditions prevailed in northwestern Mexico and
contributed to significant social unrest.
Several other major droughts are corroborated by regional reconstructions, including the
exceptionally dry interval from 1817 to 1829, which is indicated by extremely low discharge in
the upper Gila and Colorado River flows (Meko and Graybill 1995; Meko et al. 1995). The
Colorado Plateau rainfall reconstruction shows this interval as being the driest in over 1400 years
(Salzer and Kipfmueller 2005). In addition, the turn-of-the-century drought (1893-1906) is
estimated to be one of the worst decadal-scale intervals of drought in northwestern New Mexico
(D’Arrigo and Jacoby 1991), and “one of the most severe and sustained [cool-season] droughts
4
of the past two millennia” (Stahle et al 2009). Based on tree-ring reconstructions of streamflow,
it was one of the lowest flow periods on the Colorado River at Lee’s Ferry as well as in upper
Colorado River tributaries (Woodhouse et al. 2006; Gray et al. 2011).
A July precipitation reconstruction for western New Mexico (Stahle et al. 2009) is the
only summer rainfall reconstruction in close proximity to our study site. The two records share a
low fraction of the variance over their common period (r=0.32, R2=0.10) due to considerable
skew in the July rainfall record. However, certain notable periods of synchronicity (e.g. the
Puebloan drought spans the same 14 years in both reconstructions), provide independent
verification of the warm-season droughts reconstructed in this study.
Tables and Figures
Table S1 Four Corners region tree-ring chronologies and site information
Code
Site Name
Speciesa
Inner yr
Outer yr
Lat N
Long W
Elevation (m)
DCU
Ditch Canyon
PSME
1612
2008
37.00
107.80
2036
EAM
Echo Amphitheater
PSME
1296
2008
36.36
106.53
2029
MVM
Mesa Verde
PSME
481
2008
37.17
108.52
2042
PCM
Pueblito Canyon
PSME
1644
2009
36.70
107.32
2005
SPM
Satan Pass
PSME
1315
2008
35.61
108.13
2306
TCM
Tsegi Canyon
PSME
864
2008
36.68
110.54
2068
TUB
El Malpais
PSME
-134
2004
34.83
108.18
2375
WCM
White Canyon
PSME
1349
2009
37.62
110.00
1895
WMF
Walnut Canyon
PSME
1648
2010
35.17
111.51
1995
FTV
Fort Valley
PIPO
1818
2008
35.27
111.75
2260
RPU
Rio Pueblo
PIPO
1544
2008
36.16
105.59
2455
SFK
South Fork
PIPO
1569
2008
37.66
106.66
2591
TSM
Turkey Springs
PIPO
1597
2008
35.40
108.53
2374
VPU
Platt Bradbury
PIPO
1664
2008
37.47
106.30
2835
2010
35.17
111.51
1995
WMP Walnut Canyon
PIPO
1531
a
PSME = Pseudotsuga menziesii, PIPO = Pinus ponderosa
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Table S2 Summary statistics for the pool of predictor chronologies
Site
Typea
Species
Period
Lengthb
Maxc
Criticald
First year
EPS > 0.85
EAM
EW
PSME
1296-2008
273.7
34
3
1367
MVM
EW
PSME
481-2008
302.3
48
2
481
SPM
EW
PSME
1315-2008
285.7
56
2
1382
TCM
EW
PSME
864-2008
272.1
65
3
901
WCM
EW
PSME
1349-2009
322.5
45
3
1384
RPU
LWadj
PIPO
1544-2008
209.9
37
14
1776
SFK
LWadj
PIPO
1569-2008
244.1
40
16
1680
TSM
LWadj
PIPO
1597-2008
203.2
42
10
1659
WMP
LWadj
PIPO
1531-2010
263.5
46
8
1608
a Chronology type: earlywood width (EW) or adjusted latewood width (LWadj)
b Mean length of series
c Maximum number of trees in any year
d Number of trees needed for the critical expressed population signal (EPS) threshold of 0.85
Table S3 Average seasonal drought characteristics for the instrumental period, the preinstrumental period and the full reconstruction
Period
Modern (1900-2008)
Pre-modern (1597-1899)
Full (1597-2008)
Season
Ave.
Magnitude
(z-score)
Ave.
Duration
(yrs)
Ave.
Intensity
(z-score)
Number
of
Events
Cool
-2.30
2.67
-0.94
9
Warm
-2.10
3.17
-0.65
12
Cool
-2.58
3.03
-0.89
35
Warm
-2.06
2.91
-0.72
43
Cool
-2.52
2.95
-0.90
44
Warm
-2.07
2.96
-0.71
55
6
Fig. S1 Standardized precipitation values (z-scores) for the cool-season, Oct-Apr (a), and warmseason, Jul-Aug (b). Observed values (dashed line) are plotted against reconstructed values (solid
line) for the calibration period, 1896-2008
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Fig. S2 Precipitation reconstructions for the cool season, Oct-Apr (a), and the warm season, JulAug (b), 1597-2008. Annual z-score values (dark blue and red lines) have been smoothed with
10-year cubic smoothing splines to emphasize decadal variability (black lines). Grey error bars
are based on root-mean-squared error of validation (±2×RMSEv)
8
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