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Geophysical Research Letter
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Supporting Information for
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Is climate change implicated in the 2013-2014 California drought? A hydrologic
perspective
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Yixin Maoa, Bart Nijssena, and Dennis P. Lettenmaierb
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a Department of Civil and Environmental Engineering, University of Washington, Seattle, WA
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b Department of Geography, University of California, Los Angeles, CA
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Contents of this file
Text S1 to S4
Figures S1 to S12
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Text S1. Comparison of simulated SWE with SNODAS
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We obtained Apr 1 SWE from Snow Data Assimilation System (SNODAS), which
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assimilates satellite-derived, airborne, and ground-based observations of snow covered area and
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snow water equivalent (SWE) into an operational snow accumulation and ablation model
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[NOHRSC, 2004]. SNODAS SWE data are available from Sep 2003 to 2014 at 30 arc second
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spatial resolution. We compared VIC-simulated, average Apr 1 SWE over 2004-2014 at our 1/16
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degree spatial resolution over our entire study domain with SNODAS SWE aggregated to 1/16
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degree (Figure S1). We binned both VIC and SNODAS SWE values based on the elevation of
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each 1/16 degree grid cell. The VIC simulation is generally comparable with SNODAS data,
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especially at mid-to-low elevation.
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Text S2. Analysis for the period 1970-2014
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Because the climate change signal is strongest post-1970, we repeated our trend and
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frequency analyses for the period 1970-2014, using the same methods as for 1920-2014. We find
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that most variables other than spring runoff show a stronger trend post-1970 (see black lines in
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Figure S5). Winter P decreased by 14.0 km3/100 years over 1970-2014 (compared with 4.5
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km3/100 years over 1920-2014), winter T increased by 1.4 oC/100 years (compared with 0.43
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o
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However, spring runoff decreased by 5.4 km3/100 years (compared with 6.2 km3/100 years). The
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trends are generally less significant for 1970-2014 (larger p-values), primarily because the
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shorter record reduces the degrees of freedom in the test (and secondarily due to larger apparent
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natural variability post-1970).
C/100 years), Apr 1 SWE decreased by 12.2 km3/100 years (compared with 5.0 km3/100 years).
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Similar to our analysis of the entire record, we removed the warming trend in the 45
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years to the beginning (1970, cold) and the end (2014, warm) of the period. After we did so and
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simulated SWE and runoff by running the VIC model, the trend magnitudes of both Apr 1 SWE
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and spring runoff were much smaller (-7.6 and -6.5 km3/100 years in cold and warm scenarios
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for SWE, respectively; and -2.5 and -2.7 km3/100 years, respectively, in the two scenarios for
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spring runoff), suggesting that the warming is one of the main contributors to the decreasing
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trends in hydrological variables (see red and blue lines in Figure S5). Frequency analysis again
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shows the severity of the 2014 drought, and removal of warming only slightly affects the
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extreme events (see Figure S6). This analysis confirms that our general conclusions for 1920-
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2014 are not dependent on the period of record we chose, and also confirms that our results are
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generally consistent with those of other studies that have focused on the more recent past.
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Text S3. Spatial variations of trends in Apr 1 SWE
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The Apr 1 SWE trends that we calculated for the second half of the twentieth century (see
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Figure S8a) generally have a consistent spatial pattern with those shown in Figure 1b in Mote et
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al. [2005] for the period 1950-1997, such as stronger decreasing trends in the northern part of the
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domain. There are inconsistencies in the southern part of the domain, where our trends show
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weak increasing trends while there is almost no trend at high elevation in this area in Mote et al.
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[2005].
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Comparing Figure S8a and S8b, the dependence of spatial patterns of SWE trends on the
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period of analysis is apparent. Over the longer 1920-2014 period, the entire southern part of the
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study area experienced decreasing trends, while there are weak increasing trends at high
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elevation in the southern part of the domain for 1950-1997. Also, there are weak increasing
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trends at high elevation in the northern part of the study domain over 1920-2014, while
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decreasing trends dominate this area for 1950-1997.
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Text S4. Spatial variations of trends in runoff timing
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Spatial patterns of trends in runoff timing for the second half of the twentieth century
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simulated by the VIC model (see Figure S9a and S10a) are generally consistent with those
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shown in Figures 2b and 4d in Stewart et al. [2005], even though our results are VIC simulations
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and Stewart et al. [2005] based their analysis on stream gauge observations. Figure S9a shows
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stronger decreasing trends in Apr-Jul fractional runoff in the central part of the domain and
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weaker trends in the southern and northern parts, which is consistent with Figure 4d in Stewart et
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al. [2005]. Figure S10a shows the date of center of mass of annual runoff [CT; Stewart et al.,
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2005] from our results, showing strong earlier CT shifts in the central part of the domain and
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weaker earlier shifts in the south, which is comparable with Figure 2b in Stewart et al. [2005].
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Figure S10a shows strong later shifts on the eastern side of the domain, while the stream gauge
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observations are not available in Stewart et al. [2005].
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Comparing the trends in runoff timing over different periods (Figures S9 and S10), the
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dependence of trends on the period chosen is again notable. Trends in Apr-Jul fractional runoff
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are generally much weaker for 1921-2013 than for 1948-2002, with opposite trend directions in
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the northernmost and southeastern parts of the domain (Figures S9). The directions of trends in
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runoff CT in some of the area in the central and northwestern parts of the domain are opposite
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over the two periods, and the trends in the southern part of the domain are stronger for 1921-
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2013 than for 1948-2002 (Figure S10).
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References
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Mote P. W., A. F. Hamlet, M. P. Clark, and D. P. Lettenmaier (2005), Declining Mountain
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Snowpack in western North America, Bull. Am. Meteorol. Soc., 86(1), 39-49,
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doi:10.1175/BAMS-86-1-39.
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National Operational Hydrologic Remote Sensing Center (NOHRSC) (2004), Snow Data
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Assimilation System (SNODAS) Data Products at NSIDC, [Apr 1, 2004 - Apr 1, 2014].
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Boulder, Colorado USA: National Snow and Ice Data Center. (Available at
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http://dx.doi.org/10.7265/N5TB14TC).
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Stewart I. T., D. R. Cayan, and M. D. Dettinger (2005), Changes toward earlier streamflow
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timing across western North America, J. Clim., 18(8), 1136-1155, doi:
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10.1175/JCLI3321.1.
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Figure S1. Average Apr 1 SWE from VIC simulation (red) and SNODAS (green), where
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SNODAS output has been aggregated from its 30 arc second native resolution to the VIC 1/16
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degree grid. Both SWE data are averaged over the SNODAS 2004-2014 period of record.
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Results from both data sets are binned by elevation. Median values in each 200-m elevation bin
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are shown as dots, the range of values is shown as dashed lines, and the span from the 10th to
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90th percentiles is shown as solid lines for bins with at least 10 values. The two data sets are
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offset vertically by 40 m for clarity (bins are the same for both).
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Figure S2. Time series of a) simulated spring fractional runoff and b) simulated SWE/P ratio for
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1920-2014. Black solid lines are the base scenario, with Sen slope trends (black dashed lines).
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Blue and red lines are the cold and warm detrended-T scenarios, respectively.
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b)
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Figure S3. ECDFs of a) simulated spring fractional runoff and b) simulated SWE/P ratio for
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1920-2014. Black symbols and lines are the base scenario, and blue and red lines are the cold
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and warm detrended-T scenarios, respectively. The five years with the lowest values are labeled.
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Figure S4. Correlation analysis for a) Apr 1 SWE and winter P and b) Apr 1 SWE and winter T.
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b)
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Figure S5. Same as in Figure 2 in the main text, but for 1970-2014.
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c)
Figure S6. Same as in Figure 3 in the main text, but for 1970-2014.
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Figure S7. Simulated aggregate Apr 1 SWE at higher (blue), middle (green) and lower (magenta)
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elevations, with Sen slope trends (dashed lines), for a) 1950-1999; b) 1920-2014. The three
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elevation regions have roughly the same area.
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Figure S8. Trend maps for simulated Apr 1 SWE for a) 1950-1997; b) 1920-2014. Trends are
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shown as the changes in Apr 1 SWE relative to the starting value for the Sen slope fit, simulated
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by the VIC model.
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Figure S9. Trend maps for Apr-Jul simulated fractional flow for a) 1948-2002; b) 1921-2014.
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Figure S10. Trend maps for runoff CT for a) 1948-2002; b) 1921-2014.
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Figure S11. Same as in Figure 3 in the main text, but as averages for 2-year events.
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Figure S12. Same as in Figure 3 in the main text, but as averages for 3-year events.