El Niño and Floods in the US West: What can we expect?
Ocean conditions in the tropical Pacific seem to be signaling the emergence of an El Niño event
for 2002. Even though there is uncertainty as to what will happen in the next 2 to 3 seasons,
climate forecasting centers are starting to advise planners in ENSO sensitive regions to review
their contingency plans. Here, we focus on the potential for floods in the US West. The attribute
of interest is the annual maximum flood in the region. The seasonality, spatial structure and
teleconnections of this event to ENSO and the Pacific Decadal Oscillation (PDO) are analyzed in
this short note to aid the intuition of flood managers and insurers.
The timing (Figure 1) of the annual maximum flood and its causative factors vary across the
region. Nevertheless, correlations of the annual maximum flood with January-April averages of
the NINO3 and PDO indices (described later) are statistically significant over large contiguous
areas in the region, irrespective of the dominant season of occurrence, and the operative climate
mechanism. The correlations shown with NINO3 in Figure 2 suggest an enhanced probability of
winter floods in an El Niño year in California and Oregon, spring floods in S. Idaho, N.E. Utah
and Colorado, and summer floods in N. Mexico and S. Colorado. The likelihood of winter/spring
flooding in Washington, N. Idaho, Montana and Wyoming appears reduced. The modulation of
ENSO impacts on precipitation and streamflow in the region by the state of the extratropical
Pacific has been noted by Cayan et al (1999), and Livezey et al (1997) among others.
Consequently, it is instructive to examine the structure of ENSO teleconnections to regional
floods once the influence of the PDO is removed. The partial correlation of the NINO3 with
floods given PDO is shown in Figure 3. The PDO has been changing from strongly negative to
1
weakly negative over the last few months, suggesting that the 2002 Jan-April may record a
neutral or weakly negative PDO. Under this assumption, we note that the correlation of floods in
California to NINO3 is no longer statistically significant. Winter flood potential in Oregon,
Southern Washington, S. Utah and Arizona is enhanced, while that in N. Washington is
decreased. Spring flood potential in N. Utah is decreased while that in N. E. Utah and N.
Colorado continues to be enhanced. Spring/Summer flooding potential in N. Mexico and S.
Colorado is still enhanced, while stations in Central Colorado show a negative correlation.
The Jan-Apr average of the climate indices was used for the diagnostic analysis presented here
since it seems to give the strongest signal for annual maximum floods in the region.
Unfortunately, the April data is not yet available, and hence we have not pursued a formal
forecast using more sophisticated methods.
Data and Methods
One hundred thirty seven gauging stations that had over 70 years of data, drainage area greater
than 100 mi2, whose annual maximum flows were apparently not affected by regulation or
diversion, were selected from the USGS data base (http://water.usgs.gov/nwis/peak) The
drainage areas range from 100 to 69,100 mi2, with, 49 stations in the 1,000 to 10,000 mi2 range
and the remaining 81 stations are in the 100 to 1,000 mi2 range. One mi2 is equal to 2.56 km2.
Most of the stations belong to independent drainage basins, with the exception of the
Yellowstone (MT) and Gila River (AZ) basins, where stations are nested.
2
The January-April NINO3 index defined as the Sea Surface Temperature averaged over the 5ºS5º N and 150º W-90º W quadrant, in the eastern equatorial Pacific was obtained from
http://ingrid.ldgo.columbia.edu/SOURCES/.KAPLAN/.Indices/.NINO3/.
The January-April PDO index defined by Mantua et al (1997) as the leading principal component
of North Pacific monthly sea surface temperature variability (poleward of 20º N) was obtained
from http://www.jisao.washington.edu/pdo/.
The relevant flood seasons in the region were identified using a k-means cluster analysis
(Hartigan and Wong, 1979). At each site, we identified the month in which each annual
maximum flood occurred. The k-means algorithm was applied to these monthly counts to
partition the 137 sites into 4 clusters with similar attributes of the frequency of the month of
occurrence of the annual flood. The pooled histograms for the frequency of annual maximum
flood occurrence by calendar month are presented in Figure 1. Many of the “counts” in months
with low frequencies correspond to a failure of the typical flood mechanism (e.g., organized
frontal storms in Dec-Feb for Central California) in a given year. The resulting annual maximum
flood is usually small.
The correlations and partial correlations were computed using raw data with no transformation of
variables. A rank correlation between two series is computed by correlating the ranks of each
observation in the series, rather than the raw values. All correlations are computed over the years
in common at all stations. This leads to a selection of 70 years over the 1915 to 2000 period. The
1939-1986 period is the longest, continuous block (47 years) of common record. The partial
correlation is defined as:
3
r  x, y | z  
r x, y   r x, z * r  y, z 
1  r x, z  1  r  y, z  
2
2
Discussion
While flood non-stationarity at interannual to century scales were recognized for some time
(Hirschboeck, 1988; Redmond and Koch, 1991; Ropelewski and Halpert, 1986; Schonher and
Nicholson, 1989; Webb and Betancourt, 1992), advances in climate change and variability
research have led to a resurgence of interest in the topic (Cayan et al., 1999; Livezey et al., 1997;
Masutani and Leetma, 1999). Here, we considered two quasi-periodic modes of climate as a tool
for regionalization of floods and seasonal prediction of flood potential. The work extends the
analyses of Jain and Lall (2000, 2001) in the context of annual maximum floods at a site, and of
Jain (2001) who presents an extensive, multivariate analysis connecting annual maximum floods
that occur in winter/spring in part of the Western United States to prognostic climate variables.
The relationship between annual maximum floods and the two climate indices seems to extend to
the entire region, even though the mechanisms for flooding are regionally and seasonally
variable. Coherent subregions with similar response exist. In much of the region, the spatial
organization is provided by snow accumulation processes and their causative factors. In others,
frontal mechanisms and the frequency of cyclonic activity in response to identifiable boundary
conditions are implicated.
Jain and Lall (2000, 2001) suggest that the relationship between annual maximum floods and
climate indices may be nonlinear, particularly where multiple factors are considered. Often,
major changes in rainfall or floods occur only for extreme NINO3 or PDO values with the
4
intermediate values leading to no effect. Thus, a linear correlation will be highly leveraged by
these few points that also contribute to the skew and kurtosis of the flood distribution. Robust
correlation analysis (e.g., using ranks) will treat these cases as outliers and reduce their
contribution. However, these are the very effects we seek to identify. Using rank and raw
correlations, a limited diagnosis is possible. If the rank and raw correlations are of similar
strength and same sign, the response is linear (Figure 4, Western Washington). If the raw
correlation is of the same sign but much larger (Fig 2 Colorado and Northern California), then
the signal is carried by the extremes. If the rank correlation is stronger (Fig 4, Central Montana),
or of opposite sign, then the extremes act in a direction opposite to the intermediate values.
Dettinger et al. (2000) had published a forecast for the winter-spring 2001 streamflow
probabilities. Their forecast was based on the expectation of a neutral ENSO phase and a
negative PDO, as manifest in late 2000/early 2001. Insights into the corresponding situation for
annual maximum floods can be drawn from Figure 4, where we report the partial correlations of
annual maximum floods with PDO given NINO3. Note that since the PDO was negative in
2000/1 a negative partial correlation indicates a higher flood. Also, the seasons of annual
maximum floods vary, while the Dettinger et al work looked only at winter/spring total
streamflow. Nevertheless, the indications from Figure 4 are mostly consistent with their forecast
over corresponding regions, suggesting an increased probability of high flows in the Washington
region and decreased probabilities in the Oregon and southern Colorado- northern New Mexico
regions. The primary differences in the two analyses would have been in the northern California
and Wyoming and Montana regions.
5
Past analyses (Cayan et al, 1999, Cayan et al, 1998, Livezey et al., 1997, Mantua, 1999) of
extreme precipitation have identified some of these features but not over the entire region, and
over the entire year, and there has been some concern that ENSO correlations are epochally
variable (Mccabe and Dettinger, 1999). The partial correlation maps are helpful for
understanding why there may be differences in local response from one El Nino event to another.
The typical ENSO periodicity is 3-7 years, while that of the PDO is 16-22 years. The effect of
an ENSO event could then be different if PDO is in its negative or positive phase, and
contributes independent information at the site.
Traditional procedures for regional flood frequency analysis pool all regional flood data in some
form without considering the actual period of record at each site. Once we recognize the climatic
low frequency periodicity, it becomes clear that it may not be wise to directly mix flood data
from one site with a 1955-1975 period of record with that of another site with a 1975 to 1995
period of record if the underlying climate regimes are quite different. However, knowing the
dependence on these climate regimes may significantly improve the regional flood frequency
estimates and provide information on their temporal variation.
It may be possible to develop regional, seasonal forecasts of flood frequency in the Western
United States. The consideration of climatic factors as well as drainage network structure and the
induced spatial dependence of floods is necessary for effective forecasting. Depending on the
purpose, probabilistic forecasts of flow, or n-day volume, for a particular season may be
demanded. A robust identification of the operative climate-flood mechanisms for each subregion
is critical for suitable predictor selection. The pioneering work of Hirschboek (1988), Masutani
6
and Leetma (1999), Mo and Higgins(1998), among others provides a foundation for such
analyses.
Acknowledgements
The work presented here was partially supported by the National Science Foundation through
grants EAR 9973125. We gratefully acknowledge the review comments and suggestions of K.
Hirschboek that led to a significant improvement in this paper.
Authors
Gonzalo Pizarro and Upmanu Lall. For additional information contact G. Pizarro, Dept. of Earth
& Environmental Engineering, 918 Mudd, Columbia University, 500 W 120th St, MC 4711, New
York, NY 10027; Email: gep26@columbia.edu.
References
Cayan, D. R.; Dettinger, M. D.; Diaz, H. F.; Graham, N. E., Decadal Variability of Precipitation
over Western North America, Journal of Climate 11(12): 3148-3156, 1998
Cayan, D. R.; Redmond, K. T.; Riddle, L. G., ENSO and Hydrologic Extremes in the Western
United States, Journal of Climate, 12(9): 2881-2893, 1999.
Dettinger, M. D.; Cayan, D. R., McCabe, G. J.; Redmond, K.T., Winter-Spring 2001United
States Streamflow Probabilities based on Anticipated Neutral ENSO Conditions and Recent
NPO Status, Experimental Long Lead Forecasting Bulletin, 9(3), 2000.
Hartigan, J. A. and Wong, M. A., A k-means Clustering Algorithm, Applied Statistics 28, 1979.
7
Hirschboeck, K.K., Flood Hydroclimatology, in Baker, V.R., Kochel, R.C. and Patton, P.C.,
eds., Flood Geomorphology, John Wiley & Sons, 27-49, 1988.
Hirschboeck, K.K, Climate and floods, in Paulson, R.W., Chase, E.B., Roberts, R.S., and
Moody, D.W., Compilers, National Water Summary 1988-89--Hydrologic Events and Floods
and Droughts, U.S. Geological Survey Water-Supply Paper 2375, 67-88, 1991.
Jain, S., Multiscale Low-Frequency Hydroclimatic Variability: Implications for Changes in
Seasonality and Extremes, PhD Dissertation, Utah State University, 2001.
Jain, S. and Lall, U., Surface Water And Climate - Magnitude and Timing of Annual Maximum
Floods: Trends and Large-Scale Climatic Associations for the Blacksmith Fork River, Utah,
Water Resources Research, 36(12), 3641-3652, 2000
Jain, S., and U. Lall, Floods in a Changing Climate: Does the Past Represent the Future?, Water
Resources Research, 37(12), 3193-3206, 2001
Livezey, R. E.; Masutani, M.; Leetmaa, A.; Rui, H. ; Ji, M.; Kumar, A., Teleconnective
Response of the Pacific-North American Region Atmosphere to Large Central Equatorial Pacific
SST Anomalies, Journal of Climate 10(8), 1787-1820, 1997
Mantua, N.J. and S.R. Hare, Y. Zhang, J.M. Wallace, and R.C. Francis, A Pacific Interdecadal
Climate Oscillation With Impacts on Salmon Production, Bulletin of the American
Meteorological Society, 78, 1069-1079, 1997.
Mantua, N.J., The Pacific Decadal Oscillation and Climate Forecasting for North America,
Climate Risk Solutions Newsletter, 1(1), 10-13, 1999.
Masutani, M.; Leetmaa, A., Dynamical Mechanisms of the 1995 California Floods, Journal of
Climate, 12(11), 3220-3236, 1999.
8
McCabe, G.J., and Dettinger, M.D., Decadal Variations in the Strength of ENSO
Teleconnections with Precipitation in the Western United States, International Journal of
Climatology, 19(13), 1399-1410, 1999.
Mo, K. C.; Higgins, R. W., Tropical Influences on California Precipitation, Journal of Climate,
11(3), 412-430, 1998.
Redmond, K.; Koch, R., ENSO vs. surface climate variability in the western United States:
Water Resources Research, 27, 2381-2399, 1991.
Ropelewski, C.F.; Halpert, M.S., North American precipitation and temperature patterns
associated with the El Niño/Southern Oscillation (ENSO): Monthly Weather Review, 114, 23522362, 1986.
Schonher, T.; Nicholson, S.E., The relationship between California rainfall and ENSO events.
Journal of Climate, 2, 1258-1269,1989.
Webb, R.H., Betancourt, J.L., Climatic variability and flood frequency of the Santa Cruz River,
Pima County, Arizona: U.S. Geological Survey Water-Supply Paper, 2379, 1992.
9
60%
50%
40%
30%
20%
10%
0%
spring dominance (esp. May)
Cluster 1
1
2
3
4
5
6
7
8
9
10 11 12
Month
25%
20%
15%
10%
5%
0%
Cluster 2 winter dominance
1
2
3
4
5
6
7
8
9
10 11 12
Month
112 1
1 1
222241
1
3
1
2 22 1
1
1
22 2
11
3
1
33 3 3 3
2 22
3
22
21 11
3
3
3
2 22 22
13 3
3
3
2
1
2
2
2 12
22
1
2
2 22
13 11 1 11
2 212
4 1 33
2
4
3
2 3
1
4
1
1
13 11
2
4
4
4
1 4
4
4
2
144
4
2 4
2
22
444 4444
4
4 44
80%
40%
60% Cluster 3
30%
40%
20%
spring dominance
mid-late summer dominance
Cluster 4
20%
(esp June)
10%
0%
0%
1
2
3
4
5
6
7
8
9
10 11 12
1
Month
2
3
4
5
6
7
8
9
10 11 12
Month
Figure 1 Regional clustering of stations according to relative frequency of occurrence of annual
maximum flows in different months of the year. Based on the mode of the frequency
distribution, cluster1 corresponds to April-May-June; 2 to November through April, but
predominantly Dec-Feb; 3 to May-June-July, and 4 to July-August-September. Causal
mechanisms for the seasonal occurrence of the floods in these regions are discussed by
Hirschboeck (1991).
10
Correlations of Yearly Peak Flow and NINO3
Correlations of Ranks of Yearly Peak Flow and NINO3
(a)
++
+ -- ++ +
+
+
+
+ +
-
+
+
+
+
- -
++
(b)
-
-+
++
++ -+
+
- + ++ +
++
+
+
-
+
+
+
+
++
++ -
-
-
-
-
+
+
+ +
-
+
-
++
++
-+ +
++
+
--
+ +
+
+
+
Correlation > 0.37
Correlation >0.23
- ++
- ++
-+ + +
+
+
-
+ +
+
+-+-- -- - -
Correlation > 0.37
Correlation >0.23
- +
+
+ +
++
+
- +-+ ++- -+
- -
- -+
Figure 2 Correlation (raw data (a), and (b) ranked data) of Annual Maximum Flow Series with Jan- Apr composite of NINO3.Positive
correlations are indicated by white circles, and negative correlations by black circles. The two correlation levels selected correspond to
the 95% and 99% significance levels for rejecting a null hypothesis of correlation equal to zero.
11
Significant Correlations of Peak Flow and Nino3|PDO Correlations of Ranks of Yearly Peak Flow and NINO3|PDO
-
--- -
(a)
+ +
+
+
+
-
+
-+
+
+
(b)
+
+
-
+
-
-
+
+
+ +
+
++
-
+
+
+
-+
-- +
Correlation > 0.37
Correlation >0.23
+
+
-
++
+
+-
+
-
+
++
-
+
+
-+
+
-
+
+
+
+
-
+ +
-+
+- -
--
+
+
-
-
-
--++
--- +
-
+
- ++
-
+
+ +
++
++
-
+ +-
+
Correlation > 0.37
Correlation >0.23
+ +
+
+
-- +
+ + + +
-
+- - +
-
-
+ +-+
--
+ +
+- +
-
Figure 3 Partial Correlations (raw data (a), and (b) ranked data) of Annual Peak Flow Series with Jan- Apr composite of NINO3 given
PDO. Labels as in Figure 2.
12
Correlations of Peak Flow and PDO|Nino3
+
+
(a)
-
+
-
+
+
-+
+
+
+-
-
--
- - - -
-
+
-
+
+
+
++ - -
- +
-
+
+ +
+ ++
-
++
+
+
+
- -
++
--
+
+
-
+
++ +
+
+ +
-
-
--
+ -+
-
- +
+
++-- -+
-
+ - -
+
-
-
+
+ ++
-
+
-
--
Correlation > 0.37
Correlation >0.23
-
-
-
+
-
-
+
-
+
+-
++
(b)
-
-
- --
Correlations of Ranks of Yearly Peak Flow and PDO|NINO3
Correlation > 0.37
Correlation >0.23
++
+
++ - +- +
- +-
-
-
+ +
- +++
Figure 4 Partial Correlations (raw data (a), and (b) ranked data) of Yearly Peak Flow Series with Jan- Apr composite of PDO given
NINO3. Labels as in Figure 2.
13