Transcript Detectability of Streamflow Timing Trends

Maurer, E.P.1, I.T. Stewart1, C. Bonfils2, P.D. Duffy3 and D. Cayan4
1
Santa Clara University, 2U.C. Merced, 3Lawrence Livermore National Lab, 4Scripps Institution of Oceanography, UCSD and WRD, USGS
(
Abstract
We examine the seasonal timing of flows on four major rivers in California, and how these are
affected by climate variability and change. We measure seasonal timing of soil runoff and river flows
by the “center timing” (CT), defined as the day when half the annual flow has passed a given
measurement point. We use a physically-based surface hydrologic model driven by meteorological
input from a global climate model to quantify the year-to-year variability in CT resulting from natural
internal climate variability (the internal oscillations of the climate system). We find that estimated 50year trends in CT due to natural internal climate variability often exceed the trends in CT observed
over the last 50 years. Thus, although observed trends in CT may be statistically significant, they are
not necessarily a result of external influences on climate such as increased greenhouse gases. To
estimate when CT changes might be expected to exceed levels possible from natural climate
variability, we calculate the sensitivity of CT to increases in temperature ranging from 1 to 5 degrees.
We find that at elevations between 2000 – 2800 m are most sensitive to temperature increases in
this range, and can experience changes in CT exceeding 45 days. As temperatures rise, so do the
elevations that are most sensitive to further increases in temperature. Based on these sensitivities,
we estimate that changes in CT will exceed those possible from natural climate variability by the midor late 21st century, depending on rates of future greenhouse gas emissions.
1
Parallel Climate Model (PCM): Before using Control Run (constant
1870 atmosphere): How does it simulate 20th Century in California?
VIC modeling results – shift 1961-1990
temperatures by fixed values, calculate CT
•VIC Model is driven with GCM-simulated (biascorrected, downscaled) P, T and reproduces Q for
historic period
VIC Model Features:
•Developed over 15 years
•Energy and water budget closure at each time step
•Multiple vegetation classes in each cell
•Sub-grid elevation band definition (for snow)
•Subgrid infiltration/runoff variability
3
binned results
Streamflow Timing at Key Locations – PCM control run results
CT shift for individual VIC grid cells under
specified temperature shifts relative to 19611990. Stewart et al. (2005) points (against basin
average elevation) in the SSJ basin are added
as diamonds, and for these diamonds red
indicates significance at the 90% level.
Sacramento-San Joaquin Basin: Key points
(inflows to major reservoirs):
Feather R at
Oroville
American R at
Folsom Dam
Tuolumne at
New Don
Pedro Res
Kings R. at
Pine Flat Dam
Drainage Area, km2
9350
4850
3970
4000
Mean Basin Elevation, m
1553
1335
1755
2196
Max Basin Elevation, m
2655
3009
3802
4086
Site Name
Preliminary PCM Control Run Analysis
Where will streamflow timing change with different T?
4
2 Obligatory VIC Graphic
1950-1999 streamflow timing trends at these 4 points (based on VIC modeling):
1950-1976 period for one grid cell
Site Name
2 m Surface Air Temperature
Timing Shift, days
(- indicates earlier)
PCM “20c3m" “run1" IPCC AR4 experiment
Feather R at
Oroville
American R at
Folsom Dam
Tuolumne at
New Don
Pedro Res
Kings R. at
Pine Flat Dam
+1
-9
+4
+2
No significant trends. Because these sites include rain dominated area their timing is
less sensitive to historic inter-annual temperature variability than other areas.
OBS is gridded monthly observations
Sept-Jan:
good interannual variability
small biases
•Impacts through +2°C focused North of Lake Tahoe
•Maximum impact in 2000-2800 m range
•For up to 2°C rise peak impact is in 2000-2400m range
•Above that, peak impact shifts to 2400-2800m range
What is the variability in 50-year streamflow timing trends in California?
629 years of control
PCM simulated CT
dates for Feather R.
Feb-Aug:
PCM underestimates interannual variability
low bias in temperature simulation
5
Incremental change in CT for an incremental
change in T. Each bar charts the increase in CT
beyond that already experienced with the next
lowest temperature shift. Whiskers and bar
representing 10, 25, 75 and 90 percentile
elevations within each basin.
When will these Ts and CTs happen?
Projected Changes in Temperature Relative to 1961-1990 (from Hayhoe et al. and Cayan et al.)
Biases are different at different points, with PCM overestimating interannual
variability at other locations. Overall for all California variability is close to observed.
This spatially variable GCM bias means raw output is not useful for hydrology: bias
correction and downscaling is needed
P (scale) and T (shift) factor time series
At GCM scale, CDFs of Precipitation and Temperature for each
month are developed for Observations and GCM for climatological developed
to 1/8° grid cell centers
period. Quantiles for GCM are mapped onto CDF for Observations Factors interpolated
(about 150 km2 per grid cell)
Applied to entire 629-year control run
Mean and variance of observed data are reproduced for
climatological period
Temperature trends into future in GCM output are preserved
Relative changes in mean and variance in future period GCM
output are preserved, mapped onto observed variance
•Q10 is the value not exceeded in 10% of the
trend segments.
•Q10 varies from 17-19 days for these sites.
Downscaling GCM Output
Fig: A. Wood
•Cumulative distribution functions for CT trend
(days/50 years) for PCM control run.
125%
118%
116%
120%
116%
112%
117%
109%
107%
108%
105%
102%
•Based on this control run a 50-year trend in
CT would need to shift 17-19 days earlier to
achieve statistical confidence level of 90%
But haven’t past studies shown that streamflow timing is changing?
Yes. Past study by Stewart et al. found a CT shift of 17.7 and 20.5 days earlier over the
1948 to 2002 period for two of their three sites that obtained 90% confidence within the
Sacramento-San Joaquin basin.
Those were smaller basins in snow-dominated areas, not inflows to managed water
system.
Can we identify hypsometric characteristics of basins that will be
most vulnerable to streamflow timing shifts under warming
temperatures?
T under Higher
Emissions (A1fi), °C
T under Mid-High Emissions (A2), °C
End of 21st Century
Early 21st Century
3.8-5.8
0.5-1.5
Mid 21st Century End of 21st Century
1.3-2.3
T under Low Emissions (B1), °C
Early 21st Century
2.6-4.5 (3.7)
0.5-1.4
Mid 21st Century End of 21st Century
0.8-2.2
1.5-2.7 (2.3)
Ensemble mean of 11 GCMs
Projected Changes in Timing Relative to 1961-1990 (from Maurer, 2006)
Basin
CT under Mid-High Emissions (A2), days
Early 21st Century
Mid 21st Century End of 21st Century
CT under Low Emissions (B1), days
Early 21st Century
Mid 21st Century End of 21st Century
Feather R.
-14
-18
-23
-10
-11
-17
American R.
-19
-23
-31
-17
-20
-26
Tuolumne R.
-9
-20
-33
-10
-14
-23
Kings R.
-9
-21
-36
-8
-16
-24
• CTs for these 2 lower elevation basins will statistically significant levels my early-to-mid 21st century
under lower emissions, or mid-to-late 21st century under higher emissions.
• CTs for higher elevation basins will be delayed, but could eventually exhibit greater changes than
lower elevation basins under higher emissions.
• A lower emissions future avoids much of the impact on timing for areas above 2400 m
Cayan, D., E. Maurer, M. Dettinger, M. Tyree, K. Hayhoe, C. Bonfils, P. Duffy, and B. Santer, 2006, Climate scenarios for California, California Climate Change Center publication no. CEC-500-2005-203-SF
Hayhoe, K., Cayan, D., Field, C., Frumhoff, P., Maurer, E., Miller, N., Moser, S., Schneider, S., Cahill, K., Cleland, E., Dale, L., Drapek, R., Hanemann, R.M., Kalkstein, L., Lenihan, J., Lunch, C., Neilson, R., Sheridan, S., and Verville, J.: 2004,
‘Emissions pathways, climate change, and impacts on California’, Proceedings of the National Academy of Sciences (PNAS) 101 (34), 12422–12427.
Maurer, E.P., 2006, Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California under two emissions scenarios, Climatic Change (in press)
Stewart, I. T., D. R. Cayan, and M. D. Dettinger, 2004, Changes in snowmelt runoff timing in western North America under a 'business as usual' climate change scenario, Climatic Change, 62, 217-232