Supplementary Online Material
Fig. S1 Map of the Sycamore Creek watershed, a 505 km2 catchment northeast of
Phoenix, Arizona. Data from three climate stations were used in the SWAT model.
The daily USGS gauge discharge data (USGS gauge #09510200) were used to
calibrate the hydrologic process of model, and the field-observed nitrate data were
used to calibrate model nitrate flux. The watershed areas of the field station and the
USGS gauge site are 272 km2 and 426km2, respectively. Three weather stations used
in the SWAT model are outside the watershed because there is no available climatic
data within the Sycamore Creek watershed.
Fig. S2 Sensitivity range of daily streamflow based on parameter distribution as
generated with the Markov Chain Monte Carlo (MCMC) application during the
10-year calibration period (1977-1986); sd is the standard deviation of the model
response. The light gray shade by Min-Max represents the manimum and maximum
model response at each time step, whereas the dark gray shade by Mean±sd refers to
the mean model response plus/minus one standard deviation. Narrower shade area
represents lower uncertainty in the calibrated SWAT model.
Fig. S3 Monthly mean minimum (A) and maximum (B) observed and downscaled air
temperature, and deviations from the base period (1961–1990) predicted for the 2020s
(2011–2040), 2050s (2041–2070), and 2080s (2071–2100).
Fig. S4 Observed and downscaled average monthly precipitation for the base period
(1961–1990), and projections of future precipitation for the 2020s (2011–2040),
2050s (2041–2070), and 2080s (2071–2100).
Table S1. Distribution of changes in averaged annual temperatures (oC) and
precipitation (% change) in 2041-2070 in the Southwest region for the high (A2) and
low (B1) emissions scenarios based on 15 CMIP3 models, and for the statistically
downscaled data from CGCM2 for the SWAT model. The reference period is
1971-2000. Comparing the averaged change from 15 CMIP3 models, we conclude
that the statistically downscaled future climatic data used in this study are reasonable.
The downscaled temperature falls within the range of projected temperature of 15
CMIP3 models (Garfin et al. 2013). The decrease in the statistically downscaled
precipitation for the three selected climatic stations, however, is larger than the
averaged precipitation change over the entire Southwest region. Projected
precipitation in the Southwest is characterized by high spatial variability: CMIP3
models projected increased precipitation in the northern part of the region but
decreased prediciptation in the southern portions. For the region as a whole, the
decrease in averaged precipitation in the Southwest is therefore smaller than the
statistically downscaled data for our research area (in the southern part of the
Southwest region).
Variables
Temperature
Precipitation
15 CMIP3 models
A2
B1
o
1.1-3.3 C 0.4-2.2 oC
-17–7%
-10–3%
Statistically downscaled data
Station A Station B Station C
1.49 oC
1.52 oC
1.53 oC
-28%
-20%
-23%
*Data source: Garfin G, Jardine A, Merideth R, Black M, LeRoy S, eds. (2013).
Assessment of Climate Change in the Southwest United States: A Report Prepared for
the National Climate Assessment. A report by the Southwest Climate Alliance.
Washington, DC: Island Press.
Table S2. Variables selected for temperature and precipitation downscaling. The
information for each variable is in the documents of the second version of Canadian
Centre for Climate Modelling and Analysis Coupled Global Climate Model
(CGCM2).
Variables Description
Max and min temperature
500 hPa airflow strength
p5_f
500 hPa zonal velocity
p5_u
500 hPa geopotential height
p500
Mean temperature at 2m
temp
Precipitation
500 hPa meridional velocity
p5_v
500 hPa vorticity
p5_z
500 hPa geopotential height
p500
500 hPa divergence
p5zh
Table S3. The data used in the SWAT model and their sources.
Data
Digital elevation
model (DEM)
Soil map
Land cover
Climate data
Discharge data
Water quality data
Source
USGS National Elevation Dataset (NED)
(http://ned.usgs.gov/).
U.S. General Soil Map (STATSGO2) from the Natural
Resources Conservation Service (NRCS)
(http://soils.usda.gov/survey/geography/statsgo/)
2001 National Land Cover Data (NLCD 2001) from U.S. EPA
(http://www.epa.gov/mrlc/nlcd-2001.html)
Daily data of three stations from National Climatic Data
Center (http://www.ncdc.noaa.gov/oa/ncdc.html)
Daily data of station (USGS # 09510200) from U.S.
Geological Survey (http://waterdata.usgs.gov/az/nwis/rt)
From field observation
Table S4. List of parameters that are sensitive to stream discharge and nitrate flux and
their final calibrated values. Further description of each parameter may be found in the
SWAT user manual (Neitsch, Arnold et al. 2005).
Sensitive
Description
imet
Calibrated
parameters
value
Parameters sensitive to both discharge and nitrate flux
1
0.0859
ALPHA_BF Baseflow alpha factor (days)
Biological mixing effeciency
1
0.6948
BIOMIX
Maximum potential leaf area index
1
0.3010
BLAI
Maximum canopy storage (mm H2O)
1
8.3800
CANMX
Effective hydraulic conductivity in main
1
9.4799
CH_K2
channel alluvium (mm/hr)
Manning’s n value for main channel
1
0.1622
CH_N2
Initial SCS runoff curve number for
3
0.7469
CN2
moisture condition II
Plant uptake compensation factor
1
1.0000
EPCO
Soil
evaporation
compensation
factor
1
0.7584
ESCO
2
6.8865
GW_DELAY Groundwater delay time (days)
Average slope steepness (m/m)
3
-24.3570
SLOPE
Average slope length (m)
3
-4.5246
SLSUBBSN
Moist soil albedo
3
-6.9736
SOL_ALB
Available water capacity for the soil layer
3
25.0000
SOL_AWC
(mm H2O/mm soil)
Saturated hydraulic conductivity (mm/hr)
3
-19.0760
SOL_K
Depth from soil surface to bottom of layer
3
22.6520
SOL_Z
(mm)
Surface runoff lag coefficient
1
0.5604
SURLAG
Parameter sensitive to discharge only
2
-0.0133
GW_REVAP Groundwater “revap” coefficient
Threshold depth of water in the shallow
2
-574.3900
GWQMN
aquifer required for return flow to occur
(mm H2O)
Threshold depth of water in the shallow
2
-36.7860
REVAPMN
aquifer for “revap” or percolation to the
deep aquifer to occur (mm H2O)
Parameters sensitive to nitrate flux only
Nitrogen percolation coefficient
1
0.3600
NPERCO
Phosphorus soil partitioning coefficient
1
140.1200
PHOSKD
Phosphorus percolation coefficient
1
16.0820
PPERCO
1
0.7357
RCHRG_DP Deep aquifer percolation fraction
1
0.4256
USLE_P
imet: 1- replacement of initial parameter by value, 2 - adding value to initial
parameter, 3 - multiplying initial parameter by value (in percentage)