1. Spatiotemporal SWE Variability of the Columbia River Basin
Abigail Lute1 and John T. Abatzoglou12
1 Water Resources Program, University of Idaho, Moscow, ID, USA
2 Department of Geography, University of Idaho, Moscow, ID, USA
Introduction
Mountain snowpack across the Columbia River Basin (CRB) in a given year is often characterized as above normal, near normal, or below normal relative to climatology. While broad scale modes of climate variability such as the El Niño Southern Oscillation (ENSO) do indeed contribute to regional scale snowpack anomalies, these coarse descriptions forgo the detail necessary for many applications including water supply forecasting and ecological requirements. Studies of smaller scale variability of snowpack in this region have focused on April 1 snowpack (e.g. Mote, [2006], McCabe and Dettinger, [2002], and Cayan, [1996]). Questions remain as to how snowpack variability in this region evolves over the spring season and what the primary drivers of the spatial and temporal patterns of snowpack variability are.
The present study evaluates March 1, April 1, and May 1 snow water equivalent (SWE) data at SNOTEL sites and snow courses in the region to provide a sub-basin scale characterization of modes of SWE variability and their evolution over the spring season. These results are explained in the context of geographic and climatic variables.
Temporal Analysis
Figure 2. Time series of standardized temporal scores of the 1st, 2nd, and 3rd rotated PCs of April 1 SWE.
Figure 3. Correlations of temporal scores of April 1 PCs with ENSO, PNA, the number of PE events, mean regional temperatures and cumulative precipitation for January to February, January to March, and January to April. Asterisks indicate significant correlations at alpha=0.05.
Figure 4. Temporal scores of April PC2 (blue line) and standardized anomaly of number of PE events each year (yellow bars). Correlation between the time series is shown in upper right.
Data & Methods
SNOTEL (115) and Snow Course (128) data from the U.S. portion of the CRB for water years
Selected March 1, April 1, and May 1 SWE at stations with at least 75% complete records
Infilled monthly SWE data was normalized and the yearly regional average was removed
A rotated principal component analysis (RPCA) was conducted using normalized varimax rotation
Parallel analysis indicated that the first three components should be retained
RPCA results were correlated with the following additional datasets:
Correlations with RPCA loadings (spatial):
Station elevation, latitude, and longitude
Corresponding monthly mean temperature and cumulative precipitation at stations (PRISM)
Correlations with RPCA scores (temporal):
Oct-Mar means of the Multivariate ENSO Index (MEI, Wolter and Timlin, [1993])
Oct-Feb, -Mar, -Apr respectively, means of the Pacific North American pattern (PNA)
Number of landfalling Pineapple Express (PE) events between 32-55N for water years (Dettinger et al., [2011] and personal communication)
Regional mean temperature and cumulative precipitation (from the National Climatic Data Center)
Composite maps
Mean Nov-Mar 500 mb height anomalies for Northern Hemisphere (ECMWF ERA-Interim)
Mean Nov-Mar 250 mb zonal and meridional wind anomalies for Northern Hemisphere (ECMWF ERA-Interim) a) b) c) d)
RPCA Results
Figure 1. Spatial loadings of 1st, 2nd, and 3rd rotated principal components of March 1, April 1, and May 1 SWE. The percent of variance explained by each PC is indicated in the upper right of each plot.
Figure 5. Composite standardized anomalies of 500 mb heights for years with April 1 standardized temporal scores above 1 (plots a and b) and below -1 (plots c and d). Stippling in plots a and c indicates heights significantly different from climatology using a bootstrap resampling technique. Vectors in plots b and d represent composite standardized anomalies of 250 mb wind fields. Only significant vectors are plotted (alpha=0.05). Subplots represent PCs 1, 2, and 3.
Spatial Analysis
Findings
Spatial loading patterns are relatively robust from month to month throughout the spring season. Further analysis is needed to determine whether the drivers behind these patterns are the same from month to month.
Spatial loading patterns are often characterized by either a North-South (PC 1) or East-West (PC 2) spread.
April PC 1 is tied to the PNA and latitudinal variations in the jet stream just off shore, which allows storm tracks to cross the CRB further north during years with positive scores and vise versa.
April PC 2 is positively correlated with interannual precipitation variability, complementing the positive correlation between April PC 2 and the number of Pineapple Express events each winter. Geopotential height composites reveal a potential pathway for these events as well as broader scale latitudinal variations in the jet stream off shore.
April PC 3 is associated with anomalous geopotential heights over the Gulf of Alaska, which creates cyclonic flow during years with positive PC 3 scores, bringing warm moist air into the region. This is corroborated by the positive correlations of PC 3 with interannual temperature and precipitation variability.
While many studies of Western U.S. snowpack variability have identified ENSO as a major driver (e.g. McCabe and Dettinger, [2002]), our analysis finds that at smaller scales, intra-regional variability is informed by other factors, such as the PNA and Pineapple Express events.
More nuanced understanding of subregional snowpack variability and its relations to broader climate can improve water supply forecasting, particularly at local scales.
This analysis can also inform ecological flow requirement planning, forest fire risk evaluation, and wolverine habitat vulnerability assessment.
Figure 6. Correlations of spatial loadings of 1st, 2nd, and 3rd rotated principal components of April 1 SWE with site elevation, latitude, longitude, and mean April average temperature and cumulative precipitation from PRISM. Asterisks indicate significant correlations at alpha=0.05.
References
Cayan, D.R., 1996: Interannual climate variability and snowpack in the western United States. J. of Climate, 9(5),
Dettinger, M.D., Ralph, F.M., Das, T., Neiman, P.J., and Cayan, D., 2011: Atmospheric rivers, floods, and the water resources of California. Water, 3 (Special Issue on Managing Water Resources and Development in a Changing Climate), , doi: /w
McCabe, G.J., and Dettinger, M.D., 2002: Primary modes and predictability of year-to-year snowpack variations in the western United States from teleconnections with Pacific Ocean climate. Journal of Hydrometeorology, 3,
Mote, P.W., 2006: Climate-Driven Variability and Trends in Mountain Snowpack in Western North America*. J. of Climate, 19,
Wolter, K., and Timlin, M.S., 1993: Monitoring ENSO in COADS with a seasonally adjusted principal component index. Proceedings of the 17th Climate Diagnostics Workshop, Norman, OK, NOAA/N MC/CAC, NSSL, Oklahoma Climate Survey, CIMMS and the School of Meteorology, University of Oklahoma, 52–57.
Acknowledgements
Funding provided by NOAA Regional Integrated Science Assessment program grant number: NA10OAR