Edwin P. Maurer(1) and Philip B. Duffy(2)
Poster: U53A-0705
(1)Department
of Civil Engineering, Santa Clara University, Santa Clara, CA 95053
(2)Atmospheric Science Division, Lawrence Livermore National Laboratory, Livermore CA 94551
2
Abstract
Understanding the uncertainty in the projected impacts of climate change on California’s Sierra Nevada hydrology will
clarify where hydrologic impacts can be expected with higher confidence, and will help address scientific questions
related to possible improvements in climate modeling. In this study, we focus on California, a region that is vulnerable
to hydrologic impacts of climate change. We statistically bias correct and downscale the monthly temperature and
precipitation projections from 10 global climate models (GCMs) from the Coupled Model Intercomparison Project.
These GCM simulations include both a control period (with unchanging CO2 and other atmospheric forcing) and a
perturbed period with a 1 percent per year increase in CO2 concentration. We force a distributed hydrologic model
with bias-corrected and statistically downscaled GCM data, and generate streamflow at strategic points in the
Sacramento-San Joaquin River basin. Among our findings are that inter-model variability does not prevent significant
detection of decreases in summer low flows, increases in winter flows or the shifting of flow to earlier in the year.
Uncertainty due to sampling of a 20-year period in an extended GCM simulation accounts for the majority of intermodel variability for summer and fall months, while varying GCM responses to future (perturbed) temperature and
precipitation forcing add to the variability in the winter. Inter-model variation in projected precipitation accounts for
most of the uncertainty in winter and spring flow increases in both the North and South regions, with a greater
influence in the North. The influence of inter-model precipitation variability on late summer streamflow decreases in
later years, as higher temperatures dominate the hydrologic response, and melting snowpack has less influence.
1
Selection and Use of GCMs In This Study
Output from all 10 GCMs participating in most recent phase of Coupled Model
Intercomparison Project. Model simulations included:
•Specified control (constant CO2)
•Perturbed (1%/year CO2 increase) simulations
Abbrev.
Model, Year
Sponsor
Abbrev.
Model, Year
Sponsor
CCCMA
CCCMA, 2001
Canadian Centre for
Climate Modelling and
Analysis
MD
ECHO-G, 1999
Model & Data Group
(Germany)
Commonwealth Scientific
& Industrial Research
Organization
MPI
GFDL_R30_c,
1996
Geophysical Fluid
Dynamics Laboratory
MRI
HadCM2, 1995
UK Meteorological Office
NCAR
CSIRO
CSIRO_Mk2,
1997
GFDL
HadCM2
HadCM3
HadCM3, 1997
Cannot use GCM
output directly:
Precipitation
UK Meteorological Office
PCM
ECHAM4_OPYC3,
1996
Max Planck Institut fur
Meteorologie
MRI_CGCM2.3,
2002
Meteorological Research
Institute (Japan)
CCSM2.0, 2002
National Center for
Atmospheric Research
PCM, 1999
3
Implementation of Hydrologic Model
Simulation Set 1 – Streamflow Simulations with 10 GCMs
Bias corrected precipitation and temperature are spatially downscaled to a
1/8° resolution by interpolation of scale and shift factors of each month to
the 1961-1990 month’s base period average. Downscaling over the study
domain is illustrated below.
Bias-corrected HadCM3
Precipitation, mm/d
125%
118%
116%
120%
116%
112%
117%
109%
107%
108%
105%
• 3 northern gauges lumped together – inflows to major reservoirs in Northern Sierra.
• 4 southern gauges lumped – inflows from major reservoirs in higher elevation, southern Sierra
Nevada.
• Together they account for most of the Sacramento-San Joaquin streamflow originating from the
Sierra Nevada mountains.
Bias-corrected, downscaled
HadCM3 Precipitation
Northern Gauges
•Control period: minor variability due to differences
in flow sequencing and spatial correlation in GCMs.
•Inter-model variation appears within first few
decades, reflecting differences in GCM
parameterization, resolution, CO2 sensitivity.
•Between 30 and 60 years, uncertainty does not
appear to increase, except perhaps in early Spring
in South.
Southern Gauges
•VIC Model is driven with GCM-simulated (biascorrected, downscaled) P, T
•Reproduces Q for historic period
•Produces runoff, streamflow, snow, soil
moisture,…
Simulation Set 1: Streamflow statistics for the composite hydrograph of the northern three gauges. Mean and standard deviation
(SD) are in ft3/s, tprob is the probability (according to a 2-tailed t-test for differences in mean from two distributions with unequal
variances) of claiming the mean is different from the control period mean when they are actually the same. 1-tprob is the
confidence level that the mean of the perturbed is different from the mean of the control. CV is the coefficient of variation. Statistics
are calculated across different climate models and thus measure the degree of consistency between results of different models.
VIC Model Features:
•Developed over 10 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
Northern Gauges Streamflow
Control 1-40
Future climate for California – Simulation Set 1
Precipitation and Temperature Projections – 70
years at 1%/year CO2 increase
Precipitation
Temperature
P displays no
apparent
trend
Temperature
Example of Bias in GCMs
T shows
increasing
trend in all
seasons and
for all GCMs
40-year control period GCM
simulations
Control
41-60
Perturbed 21-40
Southern Gauges Streamflow
Perturbed 51-70
Control 1-40
SD
Mean
SD
CV
1-tprob
%
Mean
SD
CV
1-tprob
%
Month
Mean
Control
41-60
Perturbed 21-40
Perturbed 51-70
SD
Mean
SD
CV
1-tprob
%
Mean
SD
CV
1-tprob
%
Month
Mean
1
25299
4876
27431
6959
0.25
63.9
30188
10444
0.35
75.1
1
6074
1313
6876
1901
0.28
78.4
7981
2995
0.38
87.7
2
30383
3262
35570
9126
0.26
89.1
39464
10048
0.25
93.7
2
7925
1088
9815
3380
0.34
88.8
11314
3309
0.29
95.0
3
30889
3797
33406
4650
0.14
87.4
37358
6815
0.18
96.5
3
8516
1296
9513
1445
0.15
93.9
12724
2789
0.22
99.8
4
26955
3084
28045
3935
0.14
58.9
29717
4220
0.14
83.6
4
10524
1155
12394
1628
0.13
99.4
14761
2726
0.18
99.8
5
21502
2146
20189
2562
0.13
84.4
19542
3166
0.16
83.8
5
17004
2017
17542
3086
0.18
40.1
18567
3772
0.2
66.3
σ TP − σ T
Fraction =
σ TP
Perturbed 51-70
Mea
n
SD
CV
1tprob
%
6
15400
1608
13158
1549
0.12
99.8
12059
1849
0.15
99.8
6
13743
2087
12190
2357
0.19
92.8
10595
2998
0.28
97.2
Statistic
Mean
Mean
SD
CV
7
8692
610
7780
620
0.08
99.8
7501
798
0.11
99.5
7
5877
861
4797
938
0.2
99.3
4301
1196
0.28
99.0
8
5960
249
5668
269
0.05
99.1
5547
358
0.06
98.3
8
2651
211
2383
225
0.09
99.4
2242
249
0.11
99.6
9
5024
155
4972
267
0.05
44.2
4891
181
0.04
77.5
9
2108
105
1990
167
0.08
94.6
1891
108
0.06
99.3
Day of year
to runoff
centroid:
North
74
67
7
0.11
98.4
63
7
0.11
99.9
10
5517
598
5126
594
0.12
93.1
5062
389
0.08
92.7
10
2113
185
1966
153
0.08
98.5
1869
158
0.08
99.3
11
10114
3173
10557
1128
0.11
73.0
9752
2053
0.21
36.3
11
3040
724
3243
354
0.11
88.7
2934
469
0.16
41.8
12
17935
3869
22941
8618
0.38
89.9
27334
8076
0.3
96.7
12
4746
923
6182
2247
0.36
92.4
7295
2098
0.29
97.2
Day of year
to runoff
centroid:
South
119
110
9
0.08
99.4
101
7
0.07
100
North, %
Table shows the percent of inter-model variability in monthly streamflow for the composite
North and South hydrographs attributable to inter-model variability in precipitation. The
remainder is attributable to inter-model temperature variability.
Future climate for California – Simulation Set 2
TP indicates both T and P vary between
all GCMs (Set 1); T indicates only T
varies between GCMs (Set 2)
Perturbed 21-40
1-tprob
%
• Inter-model variation in projected precipitation accounts for 72-90% of total inter-model
variation for Oct-Feb flow changes.
The fraction of streamflow variability attributed to precipitation
is calculated as:
Control Period
Simulation Set 2 – PCM Precipitation for all GCMs
One grid cell: Latitude 39N
Longitude 123W
Second set of simulations used same P, T forcing as Set 1, but
with PCM simulated P for all GCMs. This helped isolate the
contribution of inter-model P variability, generally considered
more variable between models. PCM was selected since its
showed the greatest correspondence each season between
climatological P and also was least sensitive to CO2 changes.
Statistical comparison of the day of year
to the centroid of the annual (water year)
runoff hydrograph.
•Inter-model variability due to sampling a 20-year time slice (unsynchronized low frequency variability in GCMs ) accounts for much
almost all summer and fall intermodel variability. Differing GCM responses to CO2 future forcing plays larger role in winter/spring
•Greater uncertainty of changes during seasonal transitions (November and May), especially late in perturbed period (shown by lower
significance).
•Increase in March-April flows more significant in South than North
•Shift in timing of annual hydrograph (occurrence of center of mass of Oct-Sep flow volume) 11 days earlier in North, 18 days earlier in
South – very robust across models.
Regional P, T
for California
•GCMs have biases on order of anticipated changes
•GCM spatial scale is incompatible with hydrologic processes
To correct for the bias in the GCMs, the technique of Wood et al. (2004; 2002) was applied.
This uses a quantile mapping technique that constrains the GCM to reproduce all statistical
moments of the observed precipitation and temperature for a climatological (control) period,
while allowing both the mean, variance, and other moments to evolve in the future as simulated
by each GCM.
Perturbed Years 21-40 Perturbed Years 51-70
Control Period
102%
Department of Energy (USA)
Biases in both median and
variability
Results
• Inter-model precipitation variability more dominant than temperature variability for streamflow
uncertainty except during May-July in the North and June-August in the South.
• Precipitation variability in September is less important in later period, showing lessened effect
on late-summer low flow.
4
South, %
Perturbed
21-40
Perturbed
51-70
Perturbed
21-40
Perturbed
51-70
1
90
91
90
90
2
78
76
77
74
3
81
84
70
69
4
77
67
40
72
5
43
52
69
63
6
24
48
35
60
7
48
54
49
54
8
57
61
52
38
9
74
57
66
39
10
91
84
72
72
11
83
97
75
86
12
90
84
88
82
Month
Parting Thoughts
•Intermodel variability between GCMs does not prevent significant detection of decreases in summer streamflow, even by years 21-40.
•Both increases in winter streamflow and decreases in summer low flows exceed intermodel variability by years 51-70, as is the retreat
of the midpoint of the annual hydrograph.
•As temperatures continue to rise, lagging effects of snow and soil moisture are less able to persist through summer (due to more winter
precipitation falling as rain and higher evapotransipiration), and winter precipitation variability becomes less important for late summer
low flow changes.