CLIMATE CHANGE IMPACTS ON THE HYDROLOGY OF THE UPPER MISSISSIPPI RIVER BASIN AS DETERMINED BY AN ENSEMBLE OF GCMS

1
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
• gstakle_at_iastate.edu

2
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?

3
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
4
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

5
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
6
(No Transcript)
7
UMR Streamflow Measured at Grafton (Gage) and
Simulated with Observed Precipitation (Obs) and
Precipitation Generated by GCMs
8
UMR Streamflow Measured at Grafton (Gage) and
Simulated with Observed Precipitation (Obs) and
Precipitation Generated by GCMs
9
UMR Streamflow Measured at Grafton (Gage) and
Simulated with Observed Precipitation (Obs) and
Precipitation Generated by GCMs
10
UMR Streamflow Measured at Grafton (Gage) and
Simulated with Observed Precipitation (Obs) and
Precipitation Generated by GCMs
Model Mean
11
(No Transcript)
12
(No Transcript)
13
(No Transcript)
14
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
  4.1x10-5
• Conclusion high resolution improves simulation
  of UMRB streamflow

15
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

16
Hydrological Components Simulated by SWAT
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)
17
Hydrologic Components Simulated by SWAT Driven by
GCMs and GCM/RCM for 20C
Jha, M., Z. Pan, E. S. Takle, R. Gu, 2004. J.
Geophys. Res. 109, D09105, doi10.1029/2003JD00368
6
18
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
  lower-resolution 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

19
See Extended Abstract for summary of hydrologic
component biases (20C) and changes for 21st C as
simulated by SWAT
20
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

21
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

22
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.

23
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

24
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