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CLIMATE CHANGE IMPACTS ON THE
HYDROLOGY OF THE UPPER MISSISSIPPI
RIVER BASIN AS DETERMINED BY AN
ENSEMBLE OF GCMS
Eugene S. Takle1, Manoj Jha,1
Christopher J. Anderson2, and Philip
W. Gassman1
1Iowa
State University, Ames, IA
2NOAA Earth System Research Laboratory
Global Systems Division Forecast Research
Branch, NOAA/ESRL/GSD/FRB, Boulder, CO
[email protected]
Research Question
Previous research has shown
An acceleration of the hydrological cycle
Increased occurrence of extreme precipitation events
in the US Midwest
The Mississippi River is vital to the health and
economy of the Midwest.
How will streamflow and hydrologic
components in the Upper Mississippi
River Basin change in the future?
Sub-Basins of the
Upper Mississippi
River Basin
119 sub-basins
474 hydrological
response units
Outflow measured
at Grafton, IL
Approximately one
observing station
per sub-basin
Soil Water Assessment Tool
(SWAT)
Long-term, continuous watershed
simulation model (Arnold et al,1998)
Daily time steps
Assesses impacts of climate and
management on yields of water, sediment,
and agricultural chemicals
Physically based, including hydrology, soil
temperature, plant growth, nutrients,
pesticides and land management
Simulation of 20th C
Streamflow
Period 1961-2000 (Streamflow
observations are available)
Use 9 GCMs from the IPCC AR4 Data
Archive
We acknowledge the international modeling groups for providing their data for
analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI)
for collecting and archiving the model data, the JSC/CLIVAR Working Group on
Coupled Modeling (WGCM) and their Coupled Model Intercomparison Project
(CMIP) and Climate Simulation Panel for organizing the model data analysis
activity, and the IPCC WG1 TSU for technical support. The IPCC Data Archive at
Lawrence Livermore National Laboratory is supported by the Office of Science, US
Department of Energy
Table 1. Global models used in the SWAT-UMRB simulat ions.
Institution
Model Name
Lon x Lat
Resolution
W/m2
Cl. Sens
NOAA Geophysical Fluid Dynamics Laboratory
(USA)
GFDL-CM 2.0
2.5 o x 2.0 o
2.9
NOAA Geophysical Fluid Dynamics Laboratory
(USA)
GFDL-CM 2.1
2.5 o x 2.0 o
2.0
Center for Climate System Research (Japan)
MIROC3.2(medres)
2.8 o x 2.8 o
1.3
Center for Climate System Research (Japan)
MIROC3.2(hires)
1.125 o x 1.125o
1.4
Meteorological Research Institute (Japan)
MRI
2.8 o x 2.8 o
0.86
NASA Goddard Institute for Space Studies (USA)
GISS-AOM
4o x 3o
2.6
NASA Goddard Institute for Space Studies (USA)
GISS-ER
5o x 4o
2.7
Institut P ierre Simon Laplace (France)
IPSL-CM4.0
3.75 o x 2.5 o
1.25
Canadian Centre for Climate Modeling & Analysis
Canada)
CGCM3.1(T47)
3.8 o x 3.8 o
n/a
UMR Streamflow Measured at Grafton (Gage)
and Simulated with Observed Precipitation
(Obs) and Precipitation Generated by GCMs
800
700
500
400
300
200
100
RI
M
_m
ed
er
s
IR
M
M
IR
O
C
O
C
_h
ire
s
IP
SL
IS
S_
E_
R
G
IS
S
G
2.
1
G
FD
L
2.
0
FD
G
M
C
G
C
L
3.
0
BS
O
ag
e
0
G
Streamflow (mm)
600
UMR Streamflow Measured at Grafton (Gage)
and Simulated with Observed Precipitation
(Obs) and Precipitation Generated by GCMs
800
700
500
400
300
200
100
RI
M
_m
ed
er
s
IR
M
M
IR
O
C
O
C
_h
ire
s
IP
SL
IS
S_
E_
R
G
IS
S
G
2.
1
G
FD
L
2.
0
FD
G
M
C
G
C
L
3.
0
BS
O
ag
e
0
G
Streamflow (mm)
600
UMR Streamflow Measured at Grafton (Gage)
and Simulated with Observed Precipitation
(Obs) and Precipitation Generated by GCMs
800
700
500
400
300
200
100
RI
M
_m
ed
er
s
IR
M
M
IR
O
C
O
C
_h
ire
s
IP
SL
IS
S_
E_
R
G
IS
S
G
2.
1
G
FD
L
2.
0
FD
G
M
C
G
C
L
3.
0
BS
O
ag
e
0
G
Streamflow (mm)
600
UMR Streamflow Measured at Grafton (Gage)
and Simulated with Observed Precipitation
(Obs) and Precipitation Generated by GCMs
800
700
Model Mean
500
400
300
200
100
RI
M
_m
ed
er
s
IR
M
M
IR
O
C
O
C
_h
ire
s
IP
SL
IS
S_
E_
R
G
IS
S
G
2.
1
G
FD
L
2.
0
FD
G
M
C
G
C
L
3.
0
BS
O
ag
e
0
G
Streamflow (mm)
600
Table 2. P-values of T-test of individual GCM/SWAT streamflow and pooled
GCM/SWAT streamflow (labeled as GCM POOL) compared to OBS/SWAT.
GCMs
GFDL-CM 2.0
GFDL-CM 2.1
MIROC3.2(medres)
MIROC3.2(hires)
MRI
GISS-AOM
GISS-ER
IPSL-CM4.0
CGCM3.1(T47)
GCM POOL
P-value
4.8303E-17
3.3774E-5
4.1050E-5
0.8312
0.3963E-8
0.0098
0.0124
0.0050
0.0229
0.5979
Table 2. P-values of T-test of individual GCM/SWAT streamflow and pooled
GCM/SWAT streamflow (labeled as GCM POOL) compared to OBS/SWAT.
GCMs
GFDL-CM 2.0
GFDL-CM 2.1
MIROC3.2(medres)
MIROC3.2(hires)
MRI
GISS-AOM
GISS-ER
IPSL-CM4.0
CGCM3.1(T47)
GCM POOL
P-value
4.8303E-17
3.3774E-5
4.1050E-5
0.8312
0.3963E-8
0.0098
0.0124
0.0050
0.0229
0.5979
Table 2. P-values of T-test of individual GCM/SWAT streamflow and pooled
GCM/SWAT streamflow (labeled as GCM POOL) compared to OBS/SWAT.
GCMs
GFDL-CM 2.0
GFDL-CM 2.1
MIROC3.2(medres)
MIROC3.2(hires)
MRI
GISS-AOM
GISS-ER
IPSL-CM4.0
CGCM3.1(T47)
GCM POOL
P-value
4.8303E-17
3.3774E-5
4.1050E-5
0.8312
0.3963E-8
0.0098
0.0124
0.0050
0.0229
0.5979
Results of Statistical Analysis
all GCMs are serially uncorrelated at all lags and
form unimodal distributions
the data may be modeled as independent samples
from an identical normal distribution rather than as
time series
all pair-wise comparisons except
MIROC3.2(hires)/SWAT could be rejected at the
2% or higher level
The T-test for the MIROC3.2(hires) had a p-value of
0.8312, p-value for MIROC3.2(medres) was 4.1x105
Conclusion: high resolution improves simulation of
UMRB streamflow
Ensemble of GCMs
Individual time series of GCM/SWAT annual
streamflow are uncorrelated to one another
We may hypothesize that there is a population
from which all GCM/SWAT results represent
independent samples
Test of the hypothesis of zero difference between
mean annual streamflow of the pooled GCM/SWAT
and OBS/SWAT results gives a p-value of 0.5979
Conclusion: use of GCM output to form an
ensemble of streamflow results may provide a valid
approach for assessing annual streamflow in the
UMRB
Hydrological Components Simulated by SWAT
Table 3. Hydrological components simulated by SWAT.
Hydrological
components
OBS
(19681997)
HadCM2/
Measured
RegCM2
Data
~1990
GFDLCM 2.0
GFDLCM 2.1
MIROC
MIROC
3.2
3.2 (hires)
(medres)
MRI
GISSAOM
GISS-ER
IPSL-CM
4.0
CGCM
3.0
Precipitation
846
846
900
1032
910
736
821
707
746
746
793
859
Snowfall
118
-
244
213
196
110
104
134
125
95
202
140
Snowme lt
116
-
241
211
193
107
100
130
120
94
200
138
Surface runoff
100
-
148
215
140
55
75
58
63
51
147
80
Baseflow
181
-
213
330
223
145
213
109
170
182
196
161
Potential ET
967
-
788
759
854
1054
984
1011
744
729
692
970
Evapotranspiration (ET)
557
-
533
484
540
531
527
532
505
506
445
611
Total water yield
275
253
350
531
353
194
279
162
227
227
336
232
Notes: 1. Measured st reamflow data is at Grafton, IL (USGS gage # 05587450).
2. All values are average annual values (in mm) averaged over 1963-2000 (unless
otherwise specified); Years 1961 and 1962
are simulated as initialization
period.
3. HadCM2/RegCM2 SWAT simulat ions are average over 10-year period.
Takle, E. S., M. Jha, and C. J. Anderson, 2005: Hydrological cycle in the
Upper Mississippi River Basin: 20th century simulations by multiple GCMs.
Geophys. Res. Lett., 32, L18407 10.1029/2005GL023630 (28 September)
Hydrologic Components Simulated by SWAT Driven by
Table 4. Result s forGCMs
the ensemble
mean
of SWAT drivenfor
by GCMs
and
GCM/RCM
20Cand observed
meteorological condit ions for the 20C.
Hydrological
components
OBS/
SWAT
Measured
data
Precipitation
846
Snowfall
GCM/SWAT
MIROC 3.2 (hires)
HadCM2/RegCM2
/SWAT
Amount % Diff .
Mean
% diff .
Amount
% diff .
846
817
-03
821
-03
900
+ 06
118
-
147
+25
104
-12
244
+206
Snowme lt
116
-
144
+24
100
-13
241
+208
Surface runoff
100
-
98
-02
75
-25
148
+ 48
Baseflow
181
-
192
+06
213
+18
213
+ 18
Potential ET
967
-
866
-10
984
+02
788
- 15
ET
557
-
520
-07
527
-05
533
- 04
Total water yield
275
253
282
+11
279
+10
350
+ 38
Note: Percent differences are calculated from measured data when available and
otherwise from results of SWAT driven by observed meteorology. The datasets used
different averaging periods as follows: OBS/SWAT: 1968-1997; GCM/SWAT: 19632000; MIROC3.2 (hires)/SWAT: 1963-2000; and HadCM2/RegCM2/SWAT: 1990-1999.
Jha, M., Z. Pan, E. S. Takle, R. Gu, 2004:. J. Geophys. Res. 109, D09105,
doi:10.1029/2003JD003686
Results for 20C Simulations
Use of a GCM drawn at random to drive SWAT could
lead to sizable errors in streamflow and hydrological
cycle components
Use of the meteorological conditions from an ensemble
of GCM/SWAT simulations, by contrast, performs quite
well
The lone high-resolution GCM does as well as the
ensemble mean despite large errors in its lowerresolution sister model
Global model results downscaled by a regional model
(models chosen on the basis of availability) used to drive
SWAT are inferior to those resulting from the GCM
model mean and the high-resolution GCM
Table 2. Model biases and climate change for each hydrological cycle component.
Hydrologic
Hydrologic
Component/
Change
Component/
Change
Model
Bias(%)
(%)
Model
Bias(%)
(%)
Precipitation
GFDL 2.0
GISS AOM
GISS ER
IPSL
MIROC-hi
MIROC-med
MRI
Mean
Snowfall
22
-12
-12
-6
-3
-13
-16
-6
1
17
25
0
-4
-12
16
6
Snowmelt
GFDL 2.0
GISS AOM
GISS ER
IPSL
MIROC-hi
MIROC-med
MRI
Mean
83
5
-19
73
-12
-6
13
20
-32
-20
5
-43
-79
-65
-17
-36
-32
-22
3
-43
-80
-65
-18
-37
GFDL 2.0
GISS AOM
GISS ER
IPSL
MIROC-hi
MIROC-med
MRI
Mean
155
-24
-39
73
-9
-30
-21
15
-30
-2
32
-31
-38
-63
-7
-20
-54
-42
-49
-34
-24
-29
-34
-38
45
5
5
46
37
32
14
27
154
16
27
33
29
0
-8
33
43
-17
-18
-40
Potential ET
176
50
76
22
63
27
11
61
4
43
45
-5
-12
-32
38
12
ET
GFDL 2.0
GISS AOM
GISS ER
IPSL
MIROC-hi
MIROC-med
81
6
-19
71
-12
-7
13
19
Runoff
Baseflow
GFDL 2.0
GISS AOM
GISS ER
IPSL
MIROC-hi
MIROC-med
MRI
Mean
GFDL 2.0
GISS AOM
GISS ER
IPSL
MIROC-hi
MIROC-med
MRI
Mean
GFDL 2.0
GISS AOM
GISS ER
IPSL
MIROC-hi
MIROC-med
MRI
Mean
Total Water
-37
-26
-30
-25
-18
-20
16
7
12
12
6
3
GFDL 2.0
GISS AOM
GISS ER
IPSL
MIROC-hi
MIROC-med
See Extended
Abstract for summary
of hydrologic
component biases
(20C) and changes
for 21st C as
simulated by SWAT
Biases in Hydrologic Components
GCMs underestimate annual precipitation by a modest
amount, but overestimate streamflow
Most models produce too much snow
Models are inconsistent regarding the amount of runoff
Baseflow is uniformly high
PET and ET are uniformly low by 25 - 38%
Total water yield is overestimated by all but one model
Deficiency in ET forces a model to partition more soil water
input to baseflow, which explains uniformly excessive
baseflow and hence streamflow because baseflow is the
dominant contributor to total water yield
Streamflow is over-predicted in this basin by global models
because of failure to resolve daily maximum temperatures
in summer due to coarse resolution
21st Century Climate Simulations
Results from 7 models were available at the time
of analysis
GFDL-CM 2.0
MIROC 3.2 (medres)
MIROC 3.2 (hires)
MRI
GISS-AOM
BIS_ER
IPSL-CM 4.0
Period 2082-2099
Simulated Climate Change
Although there is inconsistency among models, the mean precipitation
created by the ensemble suggests an increase of 6% due to climate
change.
Changes in ET and PET are positive for all models, with more
uniformity in ET. These changes likely result from temperature
increases in the warm season.
Substantial decreases in snowfall suggest that warming is strong in
winter.
Runoff decreases substantially for most models, possibly due to
enhanced drying of soils (due to enhance ET) between rains, which
then can hold more precipitation when the next event occurs.
Total water yield shows wide variance among the models, with the
ensemble mean showing almost no change from the contemporary
climate.
Conclusions
Use of a single low-resolution GCM for assessing
impact of climate change on hydrology of the
UMRB carries the possibility of high bias
High-resolution GCM might have substantially
reduced biases (except for ET)
Ensemble of GCMs reproduces observed 20C
streamflow of UMRB quite well
GCM/RCM has biases comparable to GCM
Simulated climate change includes 6% increase in
precipitation, increase in ET, decrease in snowfall,
decrease in runoff and essentially no change in
streamflow
Acknowledgement
We acknowledge the international modeling groups for
providing their data for analysis, the Program for Climate
Model Diagnosis and Intercomparison (PCMDI) for
collecting and archiving the model data, the JSC/CLIVAR
Working Group on Coupled Modeling (WGCM) and their
Coupled Model Intercomparison Project (CMIP) and
Climate Simulation Panel for organizing the model data
analysis activity, and the IPCC WG1 TSU for technical
support. The IPCC Data Archive at Lawrence Livermore
National Laboratory is supported by the Office of Science,
US Department of Energy