Download Hydrological effects of the increased CO2 and climate change in the

Climatic Change
DOI 10.1007/s10584-011-0087-8
Hydrological effects of the increased CO2 and climate
change in the Upper Mississippi River Basin
using a modified SWAT
Yiping Wu · Shuguang Liu · Omar I. Abdul-Aziz
Received: 17 August 2010 / Accepted: 11 April 2011
© U.S. Government 2011
Abstract Increased atmospheric CO2 concentration and climate change may significantly impact the hydrological and meteorological processes of a watershed
system. Quantifying and understanding hydrological responses to elevated ambient
CO2 and climate change is, therefore, critical for formulating adaptive strategies for
an appropriate management of water resources. In this study, the Soil and Water
Assessment Tool (SWAT) model was applied to assess the effects of increased CO2
concentration and climate change in the Upper Mississippi River Basin (UMRB).
The standard SWAT model was modified to represent more mechanistic vegetation
type specific responses of stomatal conductance reduction and leaf area increase to
elevated CO2 based on physiological studies. For estimating the historical impacts
of increased CO2 in the recent past decades, the incremental (i.e., dynamic) rises of
CO2 concentration at a monthly time-scale were also introduced into the model. Our
study results indicated that about 1–4% of the streamflow in the UMRB during 1986
through 2008 could be attributed to the elevated CO2 concentration. In addition to
evaluating a range of future climate sensitivity scenarios, the climate projections by
Y. Wu · O. I. Abdul-Aziz
ASRC Research and Technology Solutions at U.S. Geological Survey (USGS),
Earth Resources Observation and Science (EROS) Center,
Sioux Falls, SD 57198, USA
Y. Wu
e-mail: [email protected]
O. I. Abdul-Aziz
e-mail: [email protected]
S. Liu (B)
U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center,
Sioux Falls, SD 57198, USA
e-mail: [email protected]
S. Liu
Geographic Information Science Center of Excellence, South Dakota State University,
Brookings, SD 57007, USA
Climatic Change
four General Circulation Models (GCMs) under different greenhouse gas emission
scenarios were used to predict the hydrological effects in the late twenty-first century
(2071–2100). Our simulations demonstrated that the water yield would increase in
spring and substantially decrease in summer, while soil moisture would rise in spring
and decline in summer. Such an uneven distribution of water with higher variability
compared to the baseline level (1961–1990) may cause an increased risk of both
flooding and drought events in the basin.
1 Introduction
Global atmospheric concentrations of carbon dioxide (CO2 ), methane (CH4 ), and
nitrous oxide (N2 O) have increased markedly as a result of human activities since
1750 and now far exceed pre-industrial values determined from ice cores spanning
many thousands of years (IPCC 2007a). Continued greenhouse gas emissions at
or above current rates would cause further warming and induce many changes in
the global climate system during the twenty-first century that would very likely be
larger than those observed during the twentieth century (IPCC 2007a). Based on the
range of emission scenarios presented to the Intergovernmental Panel on Climate
Change (IPCC), CO2 concentrations are expected to increase from the baseline
concentration of 330 ppm (parts per million) to 549 ppm, 856 ppm, and 970 ppm for
the B1, A2, and A1FI greenhouse gas emission scenarios, respectively, by the end of
the twenty-first century (IPCC 2007b).
Global warming is projected to alter potential evapotranspiration (PET) with
the most immediate effect being an increase in the ability of air to absorb water
with rising temperatures (Jha et al. 2006). Elevated atmospheric CO2 concentration
not only raises mean air temperatures but also changes the temporal and spatial
distribution of precipitation, causing an increased risk of both heavy rainfall events
and droughts (Eckhardt and Ulbrich 2003). According to Schaake (1990), higher
temperatures initiated by the anthropogenic release of CO2 will lead to increased ET
rates, reduced runoff, and more frequent drought events (Rind et al. 1990; Schaake
1990). However, Labat et al. (2004) provided the first experimental data-based evidence demonstrating the link between global warming and the intensification of the
global hydrological cycle using a statistical wavelet-based method for annual global
runoffs over the last century. They demonstrated that a temperature increase of 1◦ C
may lead to a global runoff increase of 4% due to increased oceanic evaporation.
However, this was questioned by Legates et al. (2005) who argued that the evidence
for global runoff increase due to climate warming was not sufficient and no climatic
signal can be observed in the streamflows. Although the regional distribution is
uncertain, precipitation is generally expected to increase worldwide, especially in
higher and lower latitudes (Houghton et al. 2001; Bates et al. 2008).
On the other hand, a rising atmospheric CO2 will directly affect plant growth
which is inherently tied to the hydrologic cycle (Ficklin et al. 2009) because of stomatal conductance reduction and leaf area increase as ambient CO2 rises (Field et al.
1995; Saxe et al. 1998; Wand et al. 1999; Medlyn et al. 2001; Morison 1987). Therefore,
increased CO2 and climate change are expected to alter regional hydrological
conditions significantly and result in a variety of impacts on water resource systems.
Climatic Change
According to Sellers et al. (1996), an increase of CO2 concentration may contribute
to climatic warming by inducing a physiological response of the global vegetation—
a reduced stomatal conductance, which suppresses transpiration (Morison 1987;
Field et al. 1995; Saxe et al. 1998; Wand et al. 1999). Further, on a global scale,
Gedney et al. (2006) gave tentative evidence that CO2 forcing led to increases in
runoff due to the effects of elevated CO2 concentrations on plant physiology in the
twentieth century. Other climate change studies on hydrology also reported likely increases in the water yield with elevated CO2 (Jha et al. 2006; Chaplot 2007; Fontaine
et al. 2001). However, other research showed that leaf area may increase with
rising CO2 concentration (Wand et al. 1999; Saxe et al. 1998; Pritchard et al. 1999),
potentially offsetting the reduction of stomatal conductance to some extent (Betts
et al. 1997; Kergoat et al. 2002). Under a doubling of CO2 concentration, global mean
runoff has been predicted to increase by approximately 5% as a result of reduced
evapotranspiration (ET) due to this CO2 enrichment alone (Leipprand and Gerten
2006; Betts et al. 2007). Ficklin et al. (2009) analyzed the streamflow and irrigation
water use in California via the Soil and Water Assessment Tool (SWAT) (Arnold
et al. 1998) and their studies showed that the A1FI emission scenario (970 ppm of
CO2 and increase of 6.4◦ C) would raise the water yield by 36.5% in the northern
San Joaquin Valley watershed. However, this increase might be overestimated
because the standard SWAT did not consider the stomatal conductance reduction
and leaf area increase of specific vegetation types.
Many studies have reported the hydrological effects of climate variability and
change on the Mississippi River Basin. Using continuous wavelet analysis, Rossi
et al. (2009) identified the principal modes of variability in hydrologic time-series
for the Mississippi River from 1950 through 1975 and compared them with climate
fluctuations and anthropogenic changes in the watershed. Jha et al. (2004, 2006) gave
a comprehensive analysis of the climate change effects on snowfall, ET, and runoff
using the standard SWAT in combination with General Circulation Models (GCMs)
for the Upper Mississippi River Basin. A doubling of baseline CO2 to 660 ppm was
reported to increase the average annual streamflow by 36%, and variations of −20%
and 20% in precipitation with the current rainfall patterns changed the total water
yield by −49% and 58%, respectively.
The standard SWAT model has been used for studying the potential effects
of increased CO2 and climate change at the watershed scale (Arnold et al. 1998;
Gassman et al. 2007). The main objective of our study was to expand on the previous
studies by modifying the standard SWAT model to represent a more mechanistic
description of vegetation type specific responses of stomatal conductance reduction
and leaf area increase to elevated CO2 based on plant physiological studies. The
dynamic increase in CO2 concentration at monthly scale was also taken into account
in the modified SWAT for evaluating how incrementally increasing CO2 affected the
watershed hydrology of the Upper Mississippi River Basin (UMRB) in the recent
past decades. We applied the modified SWAT to investigate the climate-sensitivity
of hydrological responses to dynamic, rising levels of atmospheric CO2 concentration
and precipitation and temperature variations in the UMRB. In addition, the future
climate projections by GCMs under different greenhouse gas emissions scenarios (B1
and A2) were used as inputs to the modified SWAT to project the watershed-scale
changes in hydrological components (e.g., water yield, ET, and soil water) in the late
twenty-first century (2071–2100).
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2 Materials and methods
2.1 Model description
The SWAT model was developed by the USDA Agricultural Research Service
(Arnold et al. 1998) for exploring the effects of climate and land management
practices on water, sediment and agricultural chemical yields. This physically based
watershed scale model simulates the hydrological cycle, cycles of plant growth, the
transportation of sediment, and agricultural chemical yields on a daily time step
(Arnold et al. 1998). The hydrological part of the model is based on the water
balance equation in the soil profile with processes, including precipitation, surface
runoff, infiltration, evapotranspiration, lateral flow, percolation and groundwater
flow (Arnold et al. 1998; Neitsch et al. 2005b). Surface runoff volume is predicted
from daily rainfall by using the SCS (Soil Conservation Service) curve number equation (Arnold et al. 1998; Neitsch et al. 2005b; Bouraoui et al. 2004). Three methods
for estimating potential evapotranspiration (PET) have been incorporated into the
SWAT: the Penman-Monteith method (Monteith 1965; Allen 1986; Allen et al.
1989), the Priestley-Taylor method (Priestly and Taylor 1972), and the Hargreaves
method (Hargreaves and Samani 1985). For the present study, the Penman-Monteith
method was selected for the final modeling assessment. The daily value of the leaf
area index is used to partition the PET into potential soil evaporation and potential
plant transpiration (Bouraoui et al. 2004). For simulating the crop growth that
influences the hydrological cycle intimately, the EPIC model (Sharpley and Williams
1990; Williams 1995) was incorporated into SWAT. Thus, the impacts of vapor
pressure deficit and radiation-use efficiency (RUE) on leaf conductance and plant
growth (Stockle et al. 1992a,b) have been accounted for in this model (Neitsch et al.
2005b). Besides, the stresses of temperature, water and nitrogen were considered
to predict the actual plant growth. Therefore, incorporation of the plant growth
model, EPIC (including crop responses to climate change), in SWAT strengthened
its capability to evaluate the climate change effects on water cycle. The SWAT
simulates the organic and mineral nitrogen and phosphorus fractions by separating
each nutrient into component pools, which can increase or decrease depending on
the transformation and/or the additions/losses occurring within each pool (Green
and van Griensven 2008). Neitsch et al. (2005b) described the details of the nutrient
process equations.
2.2 Model modification on the implications of CO2
CO2 enrichment of the atmosphere has two potential competing implications for
evapotranspiration from vegetation (Bates et al. 2008). Higher CO2 concentrations
can reduce transpiration because of the less stomata opening of leaves in order to
absorb the necessary amount of CO2 for photosynthesis (Gedney et al. 2006; Field
et al. 1995; Bates et al. 2008). Conversely, higher CO2 concentrations can increase
plant growth, resulting in increased leaf area, and thus increased transpiration (Wand
et al. 1999; Saxe et al. 1998; Pritchard et al. 1999).
By compiling plant physiological data from studies on the influence of increased
CO2 concentration, Eckhardt and Ulbrich (2003) incorporated variable stomatal
conductance and leaf area index (LAI) into a conceptual eco-hydrological model
Climatic Change
SWAT-G; this is a revised version of SWAT99.2, which predicts climate change
impacts in a low mountain range catchment. Table 1 indicated how a doubled
atmospheric CO2 concentration changes stomatal conductance and maximum leaf
area index based on physiological studies (Eckhardt and Ulbrich 2003; Medlyn et al.
2001; Wand et al. 1999; Pritchard et al. 1999).
In the standard SWAT (Neitsch et al. 2005b), a doubling of CO2 concentration
would lead to a general decrease of 40% reduction in stomatal conductance (Morison
1987) irrespective of the land cover type. This reduction of conductance is assumed
to be linear over the entire range of CO2 concentrations between 330 ppm and
660 ppm (Morison and Gifford 1983). Standard SWAT then adopted the following
modification to the leaf conductance term for simulating CO2 effects on evapotranspiration (Easterling et al. 1992):
CO2
gl,CO2 = gl × 1.4 − 0.4 ×
330
(1)
where gl,CO2 is the conductance modified to reflect CO2 effects (m/s); gl is the conductance without the effect of CO2 (m/s); CO2 is the atmospheric CO2 concentration
(ppm); 330 represents the baseline CO2 concentration of 330 ppm. This equation
may lead to overestimation of the evapotranspiration reduction in a watershed that
is not solely covered by cropland (Eckhardt and Ulbrich 2003; Ficklin et al. 2009). In
our study, therefore, we provided an alternative equation to overcome this drawback
based on the physiological studies stated previously:
CO2
gl,CO2 = gl × (1 + p) − p ×
330
(2)
where p is the percentage decrease in leaf conductance specific to vegetation types
(see Table 1); other terms are previously defined with Eq. 1. If the land cover type is
crop, Eq. 2 with substitution of p by 40% is equivalent to Eq. 1.
The standard SWAT (SWAT2005) does not also account for the leaf area increase
caused by the rising levels of ambient CO2 (Ficklin et al. 2009). This may lead to further overestimation of ET reduction and water yield increase. We have modified the
Table 1 Assumed ideal responses in stomatal conductance and maximum leaf area index to a
doubled atmospheric CO2 concentration
Land cover Subtype
Stomatal conductance
(%) Reference
Leaf area index
(%) Reference
Crop land
Forest
−40
−16
−24
−8
−26
−24
−29
−21
+37
+7
+7
+7
+20
+15
+25
+15
Pasture
Range
a Mean
Generic
Mixeda
Deciduous
Coniferous
Generica
C3
C4
–
(Morison 1987)
–
(Medlyn et al. 2001)
(Medlyn et al. 2001)
–
(Wand et al. 1999)
(Wand et al. 1999)
(Eckhardt and Ulbrich 2003)
(Pritchard et al. 1999)
–
(Eckhardt and Ulbrich 2003)
(Eckhardt and Ulbrich 2003)
–
(Wand et al. 1999)
(Wand et al. 1999)
(Pritchard et al. 1999)
value of subtypes was adopted for mixed forest and generic pasture; positive and negative
signs refer to increases and decreases, respectively
Climatic Change
maximum leaf area index (LAI) based on physiological studies and the assumption
of linear increase in leaf area over the range of CO2 concentrations as
L AImx,CO2
CO2
= L AImx × (1 − q) + q ×
330
(3)
where L AImx,CO2 is the maximum LAI modified to reflect CO2 effects; LAI mx is the
maximum LAI without the effect of CO2 ; q is the percentage of the maximum LAI
increase and its value is specific vegetation types (see Table 1), and other notations
are as previously defined with Eq. 2.
The concentration of CO2 in the atmosphere has risen from around 280 ppm
in 1800 (Farquhar et al. 2001); this occurred slowly at first and then progressively
faster to a current value of 385 ppm in 2008, which echoes the increasing pace
of global agricultural and industrial developments. Monthly CO2 concentrations
(NOAA/ESRL 2010) have been measured directly with high precision since 1957;
these measurements agree with ice-core measurements, and show a continuation of
the increasing trend (Houghton et al. 2001). In this study, to detect and understand
the impacts of rising levels of CO2 concentration in history, the standard SWAT
model was modified to take into account the incremental (dynamic) increase in CO2
and associated vegetation-specific responses (both stomatal conductance reduction
and leaf area increase) at a monthly scale from the late 1980s to the present.
3 Regional application to the Upper Mississippi River Basin
3.1 Study area
The Upper Mississippi River Basin (UMRB) (Fig. 1) is a headwater basin of the
Mississippi River, which is one of the longest rivers in the world (Rossi et al. 2009).
The UMRB extends from the source of the Mississippi River at Lake Itasca in Minnesota and to an area just north of Cairo, Illinois (Gassman et al. 2006). This study
focused on the watershed area above Grafton (Illinois), which is about 443,700 km2
and draining approximately 90% of the entire UMRB. The primary land uses/covers
are divided into six groups: agricultural, forest, pasture/range, urban, wetland, and
water. The annual average precipitation in the basin is approximately 824 mm/yr
and the annual average discharge is close to 3,200 m3 /s at Grafton during the
30-year (1961–1990) period.
3.2 Model inputs
The SWAT model requires inputs on weather, topography, soils, land cover and
land management (Arnold et al. 2000). A GIS interface, ArcSWAT, was used to
automate the development of model input parameters. In this study, the 10 m
National Elevation Dataset (NED) was resampled to create 90-m resolution digital
elevation model (DEM) data for delineating subbasins. This discretisation resulted
in the definition of 187 subbasins for the UMRB. National Land Cover Data (2001)
(30-m resolution) and soil STATSGO data were used to parameterize the SWAT
Climatic Change
Fig. 1 The Upper Mississippi River Basin (above Grafton)
model. The multiple Hydrological Response Unit (HRU) model option was used
for representing the dominant land uses and soil types as separate HRUs within a
subbasin. As a result, the study area was discretised into 972 HRUs.
Climate data required by the model are daily precipitation, maximum/minimum
air temperature, solar radiation, wind speed, and relative humidity. In this study, the
historical daily precipitation and air temperatures were obtained from the National
Weather Service, and daily values of solar radiation, wind speed, and relative
humidity were generated internally using the WXGEN weather generator (Sharpley
and Williams 1990) in SWAT. The monthly climatic statistical information for driving
the weather generator was developed for the entire United States based on long-term
weather records (Neitsch et al. 2005a; Winchell et al. 2009). These statistics were left
constant in the model, and this may cause a model bias that could potentially affect
our estimations of the effect of increased CO2 for the historical period.
GCMs are an important tool in the assessment of climate change, because they
can produce regional or local representations of meteorological variables under
global changes. However, climate sensitivity studies of temperature and precipitation
variations can also provide important information regarding different hydrologic
systems’ responses and vulnerabilities to climate change, especially in light of the
substantial uncertainty of GCM climate projections (Wolock and McCabe 1999).
Therefore, both climate change sensitivity scenarios and future climate projections
by GCMs for the late twenty-first century (2071–2100) were analyzed at a watershed
scale in the study. The four GCMs associated with this study were GFDL-CM2.1
(GFDL), CSIRO-MK3.0 (CSIRO), CCCma-CGCM3 (CGCM), and UKMOHADCM3 (HADCM). They were developed by the Center of Geophysical Fluid
Dynamics Laboratory in the United States, Australia’s Commonwealth Scientific
and Industrial Research Organization, the Canadian Center for Climate Modeling
and Analysis, and the Center of United Kingdom Met Office, respectively.
Climatic Change
Precipitation and air temperature simulations for the twentieth century, as well
as projections for the future under IPCC B1 (lower) and A2 (higher) greenhouse
emissions scenarios (SRES 2000), of each GCM were taken from a model grid
located in the middle of the UMRB. A simple “delta” method was applied to correct
the future projections for individual model biases. Mean monthly biases were estimated from the difference between the observations and simulations, both averaged
over the baseline period (1961–1990). Future temperature and precipitation were
then corrected by adding the respective monthly biases to the corresponding model
projections. This method was reasonable since the focus here was on the relative
changes in monthly and annual mean responses of hydrologic components in the
basin based on averages of the two 30-year periods of 2071–2100 (future) and 1961–
1990 (baseline).
The management operations were based on default assumptions provided by
ArcSWAT and consisted of planting, harvesting, and auto-application of fertilizer for
the agricultural lands (Arnold et al. 2000). A heat unit schedule algorithm was used
to find probable planting dates and the end of the growing season for perennials,
and an automated irrigation algorithm replenished soil water to field capacity when
crop stress reached a specified level. The irrigation area in the model setup involved
some agriculture land located in South Dakota only (see Fig. 1) based on the
statistical data from Farm and Ranch Irrigation Survey (FRIS 1998) summarized by
the Economic Research Service (U.S. Department of Agriculture). The potential
irrigation practices in other parts of the UMRB, which were not identified with the
FRIS data, were omitted in our model simulations. Similarly, crops were fertilized
according to an automated routine that attempts to apply nitrogen and phosphorus
to meet crop requirements (Arnold et al. 2000).
4 Model evaluations
Hydrological models usually contain parameters that cannot be determined by using
field measurements directly (Beven 2001). These parameters need to be estimated
through calibration, so that observed and predicted output values agree (Zhang et al.
2009). In this study, the original SWAT model was calibrated using the observed
streamflow at Grafton with a 10-year (1991–2000) record of the daily streamflow,
and then validated using data collected for the subsequent 8 years (2001–2008). A 5year warm-up period was used to minimize the impacts of uncertain initial conditions
(e.g., soil water storage) in the model simulation. The model was also validated with
data for the 30-year timeframe of 1961–1990, which constituted the baseline period
for assessing climate change impacts in the basin.
In this study, through investigation of literature related to SWAT calibration
(Santhi et al. 2001; Muleta and Nicklow 2005; Arabi et al. 2008; Jha et al. 2006),
as well as testing of sensitive parameters reported therein, seven parameters were
selected for model calibration in this watershed (Table 2). The unit of baseflow
alpha factor is days, and other parameters listed are dimensionless. The parameter
ranges were obtained from SWAT documentation by Neitsch et al. (2005b). In
SWAT2005, an auto-calibration procedure (van Griensven et al. 2006; Green and van
Griensven 2008) is available, which incorporates the Shuffled Complex EvolutionUniversity of Arizona (SCE-UA) algorithm (Duan et al. 1992). This procedure was
used to optimize the parameters across the basin until an acceptable fit between
Climatic Change
Table 2 Calibrated parameters for the Upper Mississippi River Basin
Parameter
Description
Range
Calibrated
value/change
CN2
ESCO
EPCO
SURLAG
CH_N
RCHG_DP
ALPHA_BF
SCS curve number for moisture condition II
Soil evaporation compensation factor
Plant uptake compensation factor
Surface runoff lag coefficient
Manning’s n for main channel
Deep aquifer percolation fraction
Baseflow alpha factor (days)
−8 − +8%
0.001–1
0.001–1
0–10
0.001–0.2
0–1
0.001–1
−3%a
0.68
0.22
2.5
0.10
0.07
0.06
a CN2
decreased 3% relative to the default values
the observation and simulation was obtained at the watershed outlet. Then the
original SWAT model with the calibrated parameters was applied to period I (2001–
2008) and period II (1961–1990) for model validations. The criteria to assess model
performance compared to observations were given in the Appendix.
5 Results and discussion
The graphical comparisons of monthly simulated streamflow against observed
streamflow during the calibration and validation periods were shown in Fig. 2. The
monthly streamflow simulations matched well with the observations, including most
of the peak and low flows during calibration as well as two validation periods. In
addition to the visual comparisons, four numeric criteria (see Appendix) including
percentage bias (PB), Nash-Sutcliffe efficiency (NSE), R2 and Root Mean Square
Error—observation Standard deviation Ratio (RSR) were presented in Table 3 for
evaluating the model performance. As shown, the model efficiencies for daily and
monthly simulation results were 0.72 and 0.77 for calibrations, and 0.74 and 0.79 for
daily and monthly validations. Based on the performance ratings of Moriasi et al.
(2007) assuming typical uncertainty in observations, the streamflow simulations in
this study may be evaluated as “very good” (PB ≤ 10%, 0.75 < NSE and RSR ≤
0.5). Even for the 30-year baseline period (i.e., validation period II), the model
efficiencies for streamflow simulations reached 0.75 and 0.85 at monthly and annual
scales (Table 3), respectively. Therefore, the model performance should be good for
conducting climate change sensitivity assessment in this study.
5.1 Historical impacts of increased CO2
Model considering the mechanism of CO2 effects could be helpful to detect the
influences of increased CO2 on streamflow at the watershed scale during the past
decades. Two scenarios were proposed to represent the constant baseline CO2
concentration level (i.e., without considering effects of increased CO2 ) using standard SWAT (Scenario I) and time varying (monthly, dynamic) CO2 concentration
(Scenario II) using the modified SWAT. Since all other conditions remained the
same between these two scenarios, other anthropogenic effects (e.g., changes in land
use and irrigation) were not directly considered under this methodology. Although
this can be seen as a weakness of this study, our main objective was to investigate
Climatic Change
Monthly streamflow (m3/s)
16000
Simulated
(a)
Observed
12000
8000
4000
0
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Monthly streamflow (m3/s)
16000
Simulated
(b)
Observed
12000
8000
4000
0
2001
2002
2003
2004
2005
2006
2007
2008
Monthly streamflow (m3/s)
12000
Simulated
Observed
(c)
9000
6000
3000
1989
1987
1985
1983
1981
1979
1977
1975
1973
1971
1969
1967
1965
1963
1961
0
Fig. 2 Monthly time series comparison of observed versus simulated streamflow by SWAT at
Grafton, Illinois, during 10-year (1991–2000) calibration (a), 8-year (2001–2008) validation I (b), and
30-year (1961–1990) validation II (c) periods
the effects of dynamic CO2 increases on streamflow. Careful interpretation of our
results would guide future studies that can incorporate other anthropogenic changes
if reliable data are available.
From the difference of the annual streamflow under these two scenarios, the contribution percentage (CP) to the streamflow by the elevated CO2 can be estimated as,
CP =
Qv − Qc
× 100%
Qv
(4)
where Qc is the streamflow depth (mm) simulated by the original SWAT under
the constant baseline CO2 concentration of 330 ppm, Qv is the streamflow depth
Climatic Change
Table 3 Evaluation of model performance in streamflow simulation at Grafton
Period
Time scale
Mean (m3 /s)
Observed Simulated
PB (%)
NSE
R2
RSR
Calibration (1991–2000)
Daily
Monthly
Yearly
Daily
Monthly
Yearly
Daily
Monthly
Yearly
3,887
3,704
−4.7
3,367
3,452
3,209
3,196
0.72
0.77
0.94
0.74
0.79
0.94
0.69
0.74
0.85
0.74
0.79
0.97
0.75
0.80
0.95
0.70
0.75
0.85
0.53
0.48
0.23
0.51
0.45
0.23
0.56
0.51
0.38
Validation I (2001–2008)
Validation II (1961–1990)
2.52
−0.4
Positive and negative signs refer to overestimations and underestimations, respectively
(mm) simulated by the modified SWAT that accounts for the variable, measured
CO2 concentration, and Qv − Qc represents the increase in streamflow depth (mm)
due to the elevated CO2 concentration.
Further, to compare the estimated impacts of increased CO2 concentration with
and without considering variable stomatal conductance reduction and leaf area
increase, two versions of the modified SWAT were used for Scenario II. They
were denoted as SWAT1 (time varying CO2 concentrations and fixed stomatal
conductance reduction regardless of the vegetation type) and SWAT2 (time varying
CO2 concentrations, and variable stomatal conductance reduction and leaf area
increase specific to vegetation type). In essence, SWAT1 is an intermediate version
of our model modifications and it was only used for comparison with SWAT2, so the
“modified SWAT” in this paper refers to SWAT2 unless denoted otherwise.
As mentioned in Section 4, the calibration and validation was conducted using the
original (or standard) SWAT model during 1991 through 2008. The modified SWAT
(SWAT2), which has no extra parameters, was also applied without re-calibration
during the above 18-year (1991–2008) period for comparison. The percentage bias
(PB) of streamflow simulation including effects of the incremental increase in CO2
(SWAT2) was 0.06%. This was lower than that (−1.9%) without including effects
of dynamic CO2 (original SWAT), although the monthly NSE (0.78) and R2 (0.79)
were similar in both cases. These results indicated that a new calibration for the
modified SWAT model was not required. The modified SWAT inherently gives a
better representation of this increasing CO2 effects on streamflow than that in the
original SWAT by incorporating the CO2 effects dynamically in more process-based
details (see details in Section 2.2).
Figure 3 showed the increased streamflow (Qv − Qc ) and contribution percentage
(CP) by SWAT1 and SWAT2, respectively, during 1986 through 2008 when the
atmospheric CO2 increase ranged from about 10 ppm to 60 ppm. From the figure,
the streamflow increased by about 2–12 mm using SWAT1 and about 1–8 mm using
SWAT2 with rising CO2 concentrations during this period. Thus, about 2–6% with
a mean of 3% (by SWAT1) and about 1–4% with a mean of 2% (by SWAT2) of
streamflows could be attributed to the increase in CO2 concentration over the past
23 years. Figure 3 also showed that the effects of increased CO2 concentration on
water yield using SWAT1, which did not account for the leaf area increase, may be
overestimated by about 50% in contrast to SWAT2. Overall, the effects of stomatal
Climatic Change
7
12
Increased Streamflow by SWAT2
6
10
Contribution percentage by SWAT1
5
Contribution percentage by SWAT2
2008
2006
0
2004
0
2002
1
2000
2
1998
2
1996
4
1994
3
1992
6
1990
4
1988
8
Contribution percentage (%)
Increased Streamflow by SWAT1
1986
Simulated streamflow increase (mm)
14
Fig. 3 Simulation of streamflow increase due to the elevated atmospheric CO2 and contribution
percentage since 1986 using both SWAT1 and SWAT2
closure exceeded those of increasing leaf area and resulted in reduced evapotranspiration and increased water yield, as reported by some dynamic global vegetation
models (Bates et al. 2008; Betts et al. 2007; Leipprand and Gerten 2006).
Based on a 23-year CO2 concentrations dataset (NOAA/ESRL 2010) and streamflow data simulated by SWAT2, Fig. 4 showed a strong linear relationship between
the cumulative increase in CO2 concentration and the corresponding increase in
streamflow per 100 mm precipitation. The regression demonstrates that every 10 ppm
increase in CO2 concentration, which takes about 5 years at the current emission
rate (NOAA/ESRL 2010), may increase the streamflow per 100 mm precipitation by
around 0.15 mm in the UMRB. Specifically, a doubling of baseline CO2 concentration (330 ppm × 2 = 660 ppm) may lead to an annual increase of about 42 mm in
streamflow under baseline climate conditions (824 mm precipitation per year) in this
basin.
1.0
Increase in streamflow
(mm / 100 mm Precipitation)
Fig. 4 Relationship between
the increase in streamflow per
100 mm precipitation and the
increase in CO2 concentration
y = 0.0154x - 0.0286
R² = 0.9310
0.8
0.6
0.4
0.2
0.0
10
20
30
40
50
Cumulative increase in CO2 concentration (ppm)
60
Climatic Change
5.2 Climate sensitivity scenarios
Although an assessment of the sensitivity to climate change does not necessarily
provide a projection of the likely consequences of climate change, such studies
provide valuable insights into the sensitivity of the hydrological systems to changes
in climate (Arnell and Liv 2001). To perform climate change sensitivity, a baseline
scenario which is assumed to reflect the baseline conditions, is generally used. In this
study, a 30-year period (1961–1990) was used to define baseline conditions with an
atmospheric CO2 concentration of 330 ppm.
For detecting the hydrological responses to different climate components, sensitivity scenarios were set to change one of the three climate variables (CO2 , precipitation, and air temperature) while holding the others constant (see Table 4). A total of
8 sensitivity scenarios were run during the simulation period of the baseline scenario.
5.2.1 Doubled CO2 concentration
As shown in Table 4, Scenario 1 focused on a doubling of CO2 concentration
(660 ppm) without any changes in precipitation and temperature. This scenario was
simulated by both the original and modified SWATs. The modification considered
the variable stomatal conductance reduction and leaf area increase specific to vegetation type. Figure 5 showed the simulated monthly average water yield, ET, and soil
water content under baseline condition and doubled CO2 concentration (Scenario 1).
In general, reduced ET as well as increased water yield, and soil water were
predicted by both models. However, the effects of elevated CO2 concentration were
likely overestimated by the original SWAT, which assigns greatly reduced values of
stomatal conductance for non-crop types while ignoring leaf area increase due to
CO2 enrichment. For example, annual water yield was predicted to rise 23% in the
modified model, and 35% in the original model, while ET was estimated to reduce
by 11% and 16% using the modified and original models, respectively.
Simulations using the modified SWAT showed that the doubled CO2 concentration could result in more reduction of ET in summer (June to August) than the rest
of the year and the soil moisture may rise by 5% in April to 12% in August. The
annual average relative increases in soil moisture was predicted to be 7% under the
doubled CO2 concentration (Table 5).
In addition, we quantified the sensitivity of water yield to a series of different, constant scenarios CO2 concentrations between the baseline (330 ppm) and a doubled
Table 4 Climate sensitivity
scenarios
a Scenario
1 was conducted
using both original and
modified SWATs, while other
seven scenarios were run using
the original SWAT only;
positive and negative signs
refer to increases and
decreases, respectively
Scenario
CO2 concentration
(ppm)
Precipitation
change (%)
Temperature
increase (◦ C)
Reference
1
2
3
4
5
6
7
8
330
660a
330
330
330
330
330
330
330
0
0
+10
+20
−10
−20
0
0
0
0
0
0
0
0
0
1
2
4
Climatic Change
CO2=330
CO2=660 by original SWAT
CO2=660 by modified SWAT
Water yield (mm/mon)
40
(a)
30
20
10
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
120
ET (mm/mon)
(b)
90
60
30
0
Jan
Soil water content (mm)
250
(c)
220
190
160
130
Jan
Fig. 5 Comparison of simulated water yield (a), ET (b), and soil water content (c) under baseline
and doubled CO2 concentration by original and modified SWATs
baseline level (660 ppm) using the modified SWAT model. Figure 6 showed the
30-year average water yield and its change relative to the baseline situation (1961–
1990) under this series of CO2 concentrations in the UMRB. A positive power
relationship was apparent between the relative change of water yield and increasing
CO2 concentration, with 1.6% and 23% increases in water yield for the 10% and
100% increases in CO2 concentration, respectively.
5.2.2 Precipitation change
Scenarios 2 though 5 (Table 4) represented the precipitation changes of +10%,
+20%, −10% and −20% while holding the reference level of CO2 concentration
Climatic Change
Table 5 Predicted relative changes (percent of baseline levels) in annual average hydrological
components for the Upper Mississippi River Basin for the climate sensitivity simulations
Hydrological
components
Reference
(mm/year)
Water yield
Evapotranspiration
Soil water content
245
560
190b
Climate sensitivity simulations
CO2 Precipitation
660a +10% +20% −10%
Percent change
23
−11
7
28
2
3
57
4
6
−26
−3
−5
−20%
Air temperature
+1◦ C +2◦ C +4◦ C
−50
−6
−12
−6
3
−1
−14
6
−3
−33
15
−8
a Results (percent change) for Scenario 1 (doubled CO ) was from the simulations by the modified
2
SWAT, while results for the other seven scenarios were by the original SWAT
b Unit of soil water content is mm; positive and negative signs refer to increases and decreases,
respectively
(330 ppm) and air temperature unchanged. The simulated water yield, ET, and soil
water content under these four scenarios as compared to the baseline scenario were
shown in Fig. 7. Nearly linear changes in monthly water yields were predicted for the
decreases or increases in precipitation, and the annual average water yield changes
were 28%, 57%, −26% and −50% corresponding to the four scenarios (Scenarios
2 to 5, respectively) (Table 5). Similarly, the soil water content showed nearly linear
changes to precipitation variations (Fig. 7 and Table 5). However, simulated monthly
ET in winter (December to February) (less than 1% of change) was nearly insensitive
to the precipitation changes, while the maximum increase of 5 mm (in July) and
maximum decrease of 6 mm (in August) occurred for precipitation changes of +20%
and −20%, respectively (Fig. 7). The annual average ET in the UMRB changed by
2%, 4%, −3% and −6% for precipitation change scenarios of 2 to 5, respectively.
Thus, the hydrological responses of the UMRB were generally positively related with
the precipitation changes.
25
310
Water yield (mm)
290
20
Relative change
Power (Relative change)
270
15
250
10
230
5
0
210
0
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Relative increase in CO2 concentration
Fig. 6 Sensitivity of the effects of increased CO2 concentrations on water yield in the UMRB
Relative change (%)
y = 0.603x1.530
R² = 0.998
Water yield
Climatic Change
Baseline
+10%
+20%
-10%
-20%
Water yield (mm/mon)
48
(a)
36
24
12
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
120
ET (mm/mon)
(b)
90
60
30
0
Jan
Soil water content (mm)
240
(c)
210
180
150
120
Jan
Fig. 7 Comparison of simulated water yield (a), ET (b), and soil water content (c) under different
precipitation change scenarios
5.2.3 Temperature increase
Three air temperature sensitivity scenarios (Scenarios 6 through 8; Table 4) were
used to represent monthly average increases of 1◦ C, 2◦ C, and 4◦ C while keeping
other climate variables (CO2 concentration and precipitation) unchanged. As shown
in Fig. 8, temperature rise caused substantial changes of ET in May and June, with
average increases in these 2 months of about 5 mm, 10 mm, and 23 mm under the
temperature rises of 1◦ C, 2◦ C, and 4◦ C, respectively. However, ET decreased in
July and August due to much lower soil water content than the baseline scenario.
Further, the maximum ET and the lowest soil water content occurred, respectively,
in June and July, 1 month earlier than those under the baseline scenario. The annual
average soil water content decreased by 1%, 3%, and 8% corresponding to the
Climatic Change
+1o C
Baseline
+2oC
+4oC
Water yield (mm/mon)
32
(a)
26
20
14
8
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
124
ET (mm/mon)
(b)
93
62
31
0
Jan
Soil water content (mm)
220
(c)
195
170
145
120
Jan
Fig. 8 Comparison of simulated water yield (a), ET (b), and soil water content (c) under different
temperature increase scenarios
temperature increases of 1◦ C, 2◦ C, and 4◦ C (Table 5). Thus, the annual average
water yield decreased more substantially in spring through early summer (March
to July) than in winter (December to February) (Fig. 8). This may be caused by the
increased snowmelt runoff (due to temperature increase) in winter, which offset the
increased evapotranspiration. Therefore, the hydrological system in the UMRB is
quite sensitive to temperature increase based on the simulation of water yield, ET,
and soil water content under these three hypothetical scenarios.
Overall, annual average water yield and soil water content declines with a decrease
in precipitation or an increase in temperature. And this phenomenon is much clearer
in late spring through early summer (April to July) and may result in larger risks of
both heavy rainfall and droughts during these months.
Climatic Change
5.3 Climate projections by GCMs
In assessing the hydrological effects of future climate change, projections of precipitation, and temperature by four GCMs (GFDL, CSIRO, CGCM, and HADCM)
under B1 (lower) and A2 (higher) greenhouse gas emissions scenarios during 2071–
2100 were used in this study (IPCC 2006). For atmospheric CO2 concentration in
this 30-year period, the mean values of 518 ppm for the B1 scenario and 650 ppm
for the A2 scenario based on decadal CO2 concentration from NOAA/ESRL (2010)
were adopted in SWAT. Simulated water yield, ET, and soil water content in the
basin using the projections by the four GCMs were shown in Fig. 9 for the A2
scenario as examples, while the monthly and annual percent changes relative to the
Baseline
GFDL
CSIRO
CGCM
HADCM
44
Water yield (mm)
(a)
33
22
11
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
120
(b)
ET (mm)
90
60
30
0
Jan
Soil water content (mm)
240
(c)
210
180
150
120
Jan
Fig. 9 Comparison of simulated water yield (a), ET (b), and soil water content (c) under the A2
scenario by four GCMs (GFDL, CSIRO, CGCM, and HADCM)
Climatic Change
Table 6 Predicted relative changes (percent of baseline levels) in monthly and annual average
hydrological components for the Upper Mississippi River Basin under the A2 scenario by four GCMs
GCMs
HV
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Ave
GFDL
WY
ET
SW
WY
ET
SW
WY
ET
SW
WY
ET
SW
39
66
1
72
104
7
85
57
7
49
88
3
22
43
2
93
87
8
103
55
8
102
96
5
22
24
2
−3
25
4
11
24
5
−12
41
1
17
14
0
17
4
2
15
13
1
−4
16
−1
35
21
0
47
15
3
29
19
1
31
31
−1
−14
25
−10
25
19
−2
20
27
−5
−22
26
−13
−62
−29
−18
8
−12
−1
−20
−20
−6
−64
−35
−17
−71
−23
−10
10
−15
13
−20
−16
8
−57
−20
−4
−57
15
−10
37
24
9
−9
21
4
−36
24
−5
−31
13
−7
14
17
4
30
24
3
10
23
−3
−10
15
−4
15
12
3
33
16
3
−4
22
−2
−8
35
−1
30
51
5
49
20
5
7
57
0
−9
6
−4
26
10
4
20
9
3
−6
10
−3
CSIRO
CGCM
HADCM
Positive and negative signs refer to increases and decreases, respectively, and the units are %
SER scenario, HV hydrological variables, WY water yield, ET evapotranspiration, SW soil water
content, Ave average
baseline levels for both the B1 and A2 scenarios were presented in Tables 6 and 7,
respectively.
5.3.1 Water yield
The predicted water yield greatly varied among four GCMs within a single month.
Under the A2 scenario, larger prediction ranges were apparent in summer (July
through September) (Fig. 9) with relative changes ranging from −71% to 37% using
the four GCMs data. As previously, positive and negative signs refer to the increases
and decreases, respectively. The highest water yield (42 mm) by CSIRO in May was
47% higher and the lowest water yield (5 mm) projected by GFDL in August was
71% lower than their respective baseline levels (Table 6). Figure 9 also showed that
the water yield would increase dramatically in winter and spring, but it decreased
Table 7 Predicted relative changes (percent of baseline levels) in monthly and annual average
hydrological components for the Upper Mississippi River Basin under the B1 scenario by four GCMs
GCMs
HV
Jan
Feb Mar Apr May Jun
WY −18 30
ET
24 30
SW
−2 −1
CSIRO
WY
32 38
ET
70 64
SW
3
4
CGCM
WY
53 38
ET
27 22
SW
4
5
HADCM WY
39 57
ET
56 65
SW
2
3
GFDL
Same note as for Table 6
1
16
0
−18
16
3
4
14
3
−23
33
0
−12
45
6
13
−1
1
−7
11
2
7
0
1
−5
14
6
8
0
0
−36 −11
8
16
−4 −3
32
22
−2
16
14
−2
17
16
−2
−24
25
−10
Jul
Aug Sep
−10
−5
−8
−1
−5
−3
−1
−7
−4
−53
−17
−17
−31
−17
−2
−6
−19
5
−14
−22
5
−39
−19
−4
Oct
Nov Dec Ave
−40 −29 −20
16
11
15
−4 −5 −4
19
6
3
15
11
8
5
2
1
−7
25
9
9
12
9
3
2
1
−19 −9 −6
21
18
24
−3 −4 −3
−24 −3
18
7
−3 −2
7
5
35
6
2
2
37
10
16
4
2
2
15 −16
66
10
−1 −3
Climatic Change
substantially in summer. Few exceptions to this were noted with a little decrease in
March when using projections from CSIRO and HADCM, as well as slight increases
during July through September using projections from CSIRO. Annual average
water yield was estimated to increase 26% and 20% using projections from CSIRO
and CGCM, respectively, and decrease 9% and 6% using projections from GFDL
and HADCM, respectively (Table 6). However, the increased peak water yield and
decreased low water yield showed higher variability than the baseline scenario and
this may lead to more severe flooding and drought events in the future.
In contrast to the A2 scenario, the variability of water yield under the B1 scenario
is milder, but it was still higher than baseline level. The annual average water yields
using the four GCMs projections under the B1 scenario were predicted to change
in the same direction (increase or decrease) as under the A2 scenario, but the
magnitudes were smaller (Tables 6 and 7). However, water yield decreased further
(16%) under B1 than that (6%) under A2 using projections from HADCM (Tables 6
and 7). This may be explained by the combined effects of annual average increase of
4◦ C in temperature and 1.4% in precipitation under the B1 scenario, which reduced
the water yield in larger quantities than under the A2 scenario that projected annual
average increase of 6◦ C in temperature and 5.2% in precipitation.
5.3.2 ET and soil water
Due to the increased precipitation and the increased temperature, as predicted by
the four GCMs, simulated ET under the A2 scenario was generally higher than the
baseline level except in July and August (Fig. 9). The largest rise of 27% in ET
occurred in June (Table 6) using projections from CGCM, which is 1 month earlier
than that of the baseline scenario. Consequently, soil water decreased substantially in
June compared to the baseline level, and this resulted in a decrease of ET by 12–35%
in July due to the lower water availability within the soil profile (Table 6), although
the temperature in July was higher than in June. Again, the highest temperature in
July reduced the soil water content further to the lowest level, which was also reached
1 month earlier than in the baseline scenario. Therefore, the changes in soil water
content and ET were caused by the combined effects of increased temperature and
precipitation variation within a year. The maximum changes of soil water content
were predicted during June through August, with relative changes ranging from
−18% (in July with projections by GFDL) to 13% (in August by CSIRO) (Table 6).
More deficiency in soil water may cause severe droughts during the summer (June to
August) growing seasons of crops, although the annual average soil moisture varied
within ±4% using the four GCMs projections in this study (Table 6).
Compared to the A2 scenario, the annual average simulated ET under the B1
scenario decreased by 40% and 56% using projections from CSIRO and CGCM,
respectively. However, no apparent difference was found between the two scenarios
when using projections from both GFDL and HADCM (Tables 6 and 7). From
Table 7, the magnitude of decrease in ET (5–17%) in July under the B1 scenario
was clearly less than that (12–35%) under the A2 scenario. In terms of soil water
content, the magnitudes of increase or decrease in annual average amount under the
B1 scenario were a little less than those under the A2 scenario, except for nearly no
change when using projections from HADCM (Tables 6 and 7).
In brief, the higher variability of water yield means more uneven temporal distribution of water, which may cause flooding disasters or reduce the water availability,
Climatic Change
although the total water yield in the UMRB could increase by the end of this
century according to the projections from CSIRO and CGCM. Moreover, concurrent
dramatic decreases of soil moisture and water yield in July and August due to
decreased rainfall and increased ET may cause severe drought and significantly
influence the growth of crops in the UMRB.
We used the external climate inputs from GCMs to drive the watershed model,
SWAT. Although this kind of method was widely used in numerous climate change
studies (Jha et al. 2006; Ficklin et al. 2009; Young et al. 2009; Xu et al. 2009; Vicuna
et al. 2007; Fontaine et al. 2001; Chaplot 2007), there are still some limitations due
to the lack of interaction between climate and vegetation (especially the feedback of
vegetation to climate). This is, in particular, a subject of further studies.
Baseline
A2 with baseline CO2
A2 with projected CO2
Water yield (mm)
44
(a)
33
22
11
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
140
(b)
ET (mm)
105
70
35
0
Jan
Soil water content (mm)
240
(c)
210
180
150
120
Jan
Fig. 10 Comparison of simulated water yield (a), ET (b), and soil water content (c) under the A2
scenario by CGCM with baseline CO2 (330 ppm) and projected CO2 (650 ppm)
Climatic Change
5.4 Impact of increased CO2 in climate projections
Using the A2 scenario climate projection by CGCM as an example, we compared
the simulations with baseline CO2 concentration (330 ppm) and projected CO2
concentration (650 ppm) with same set of climate variables (e.g. precipitation and
temperature). Figure 10 showed the simulated monthly average water yield, ET, and
soil water content under the two CO2 concentration scenarios. As shown, the water
yield would drastically decrease by about 36% in summer and decrease by about 2%
annually under the baseline CO2 concentration (330 ppm), while an annual increase
of about 22% (Table 6) was predicted under the projected CO2 concentration. By
comparing the streamflow simulations using the two scenarios, the elevated CO2
(650–330 = 320 ppm) would result in a significant contribution percentage (CP)
of 22% to streamflow by the end of this century, although only about 1–4% was
estimated in the recent decades (see Section 5.1). The annual average ET and soil
water would increase by 19% and decrease by 4%, respectively, under the baseline
CO2 concentration, compared to the 9% increase for ET and 3% decrease for soil
water (Table 6) under the projected CO2 concentration. Further, it was noted that
soil water was projected to decrease from August to December using A2 under the
baseline CO2 concentration, while it increased during the same period under the
projected CO2 concentration (Fig. 10c).
The impacts of projected high atmospheric CO2 concentration (close to a doubling
of baseline CO2 ) on water resources by the end of the twenty-first century can be
substantial. Without considering the effects of CO2 , the drought predicted in summer
would be overestimated and the risk of flooding in May and June would be underestimated. Future watershed management decisions are likely to be misled if some of
the predicted, distinct trends of water yield (March to June) and soil water (August
to December) with increasing CO2 concentrations are not taken into account.
6 Conclusion
A regional elevated CO2 and climate change impact study for the Upper Mississippi River Basin was conducted using a modified SWAT model that incorporated
plant responses (e.g., the variable stomatal conductance reduction and leaf area
increase) associated with rising levels of CO2 concentration. Furthermore, the model
was modified to consider the incremental, monthly increases in atmospheric CO2
concentration.
Results showed that 1–4% of streamflow in the UMRB was likely attributed
to elevated CO2 concentrations from 1986 through 2008. The 23-year simulation
demonstrated an increase of 0.15 mm in streamflow per 100 mm precipitation for
every 10 ppm increase in CO2 concentration, which takes about 5 years to accumulate
at the current emission rate. Thus, streamflow could increase about 42 mm per year
if the atmospheric CO2 concentration (660 ppm) doubled under baseline conditions
in this basin. Additionally, through sensitivity scenarios, the water yield increased
23% using the modified SWAT instead of 35% via the original model, which greatly
reduced stomatal conductance and ignored the increase in leaf area under a doubling
of CO2 concentration in future.
Climate sensitivity studies showed highly sensitive responses of the UMRB hydrology to change scenarios representing some hypothetical climate changes. Nearly
Climatic Change
linear response of water yield of −50% to 57% was predicted when change in
precipitation ranged from −20% to +20% relative to the 1961–1990 baseline level.
Water yield and soil water content decreased drastically for a 1◦ C increase in temperature, but increases in evapotranspiration were essentially proportional to the temperature rises.
Future hydrological responses were also modeled using climate projections during the late twenty-first century (2071–2100) by four GCMs (GFDL, CSIRO,
CGCM, and HADCM) and mean atmospheric CO2 concentration for the period
(NOAA/ESRL 2010). Under the IPCC A2 (higher emissions) scenario, the 30-year
mean of the changes in annual average water yield, ET, and soil water relative to the
baseline level ranged from −9% to 26%, 6% to10%, and −4% to 4%, respectively.
In contrast to that in the baseline scenario, this much higher variability of water yield
and soil water may pose increased threats to water availability, since the uneven
temporal distribution of water yield and larger fluctuations of soil moisture can result
in more severe flooding in spring and droughts in summer by the end of this century
in this basin.
Overall, the results of this study suggest that the rising levels of CO2 concentration
and climate change (e.g., precipitation and temperature) may have significant effects
on the hydrological components in the UMRB. The qualitative and quantitative
hydrological responses to the rising CO2 concentrations and potential climate change
would be helpful in formulating the decisions for the proper management of the
watershed facing the challenges of the twenty-first century.
Acknowledgements This study was funded by the NASA Land Cover and Land Use Change
Program (Impact of Rapid Land-Use Change in the Northern Great Plains: Integrated Modeling of
Land-Use Patterns, Biophysical Responses, Sustainability, and Economic and Environmental Consequences) NNH07ZDA001N. Work of Y. Wu was performed under USGS contract 08HQCN0007.
Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. We gratefully acknowledge insightful comments and suggestions from
the three anonymous reviewers.
Appendix
In order to assess model performance compared to observations, the following
popular criteria were used in this study:
(I) The percentage bias (PB) measures the average difference between measurements and model simulations. The optimal value of PB is 0.0, with
low-magnitude values indicating accurate model simulation, while positive or negative values indicate over-prediction or under-prediction bias,
respectively,
PB =
n 1 Yi,sim − Yi,ob s
× 100
n i=1
Yi,ob s
(5)
(II) The Nash-Sutcliffe Efficiency (NSE) (Nash and Sutcliffe 1970) measures
the goodness of fit and approaches unity if the simulation is satisfactorily
representing the observation. The NSE describes the explained variance for
the observed values over time that is accounted for by the model (Green and
Climatic Change
van Griensven 2008). If the efficiency becomes negative, model predictions
are worse than a prediction performed using the average of all observations.
n NSE = 1 −
Yi,sim − Yi,ob s
i=1
n Yi,ob s − Ȳob s
2
(6)
2
i=1
(III) The R2 evaluates how accurately the model tracks the variation of the observed values. It can reveal the strength and direction of a linear relationship
between the simulation and observation. The difference between the NSE
and the R2 is that the NSE can interpret model performance in replicating
individually observed values, while the R2 does not (Green and van Griensven
2008).
R2 =
n Yi,ob s − Ȳob s
i=1
n Yi,ob s − Ȳob s
Yi,sim − Ȳsim
n 2 i=1
2
Yi,sim − Ȳsim
2
(7)
i=1
(IV) The Root Mean Square Error (RMSE)—observation Standard deviation
Ratio (RSR) recommended by Singh et al. (2003), which standardizes RMSE
using the observation standard deviation. It is calculated as the ratio of the
RMSE and standard deviation of measured data as shown in the equation
below. RSR varies from the optimal value of 0 to a large positive value. The
lower the RSR, the better the model simulation performance (Moriasi et al.
2007).
RSR =
n Yi,ob s − Yi,sim
i=1
RMSE
= ST DEVob s
n Yi,ob s − Ȳob s
2
2
(8)
i=1
where n is number of observation/simulation data for comparison, Yi,ob s and
Yi,sim are observed and simulated data, respectively, on each time step i (e.g.,
day or month), Ȳob s and Ȳsim are mean values for observed and simulated data
during the examination period.
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