Appendix 1. Comparison of land use change scenarios for California rangelands, based
narratives of Intergovernmental Panel on Climate Change Special Report on Emission
Scenarios, and corresponding climate projections from downscaled global climate models
(GCMs)
Scenario
A2
A1B
B1
Global Drivers
 High population growth
 Medium GDP growth
 Slow technology
diffusion
 Fossil fuel intensive
 Conservation lower
priority
 Regional development
 Moderate population
growth
 Very high GDP growth
 Rapid technology
diffusion
 Energy balanced
between several sources
 Mixed-use based
conservation
 Globalization
 Moderate population
growth
 High GDP growth
 Rapid technology
diffusion
 Rapid diffusion of green
energy resources
 Conservation high
priority
 Globalization
GCMs
Local Translation
PCM – warm, wet
GFDL – warmer, dry
 Development—low density
 Agriculture—intensive, less
innovation
 Conservation—low priority; no
active conservation planning
CSIRO – warm, wet
MIROC – warmer, dry
 Development—low density
 Agriculture—intensive, focus
on high value perennial crops
 Conservation—mixed use
emphasis; 500,000 acres
protected by 2100 near urban
centers
PCM – warm, wet
GFDL – warmer dry
 Development—high density
 Agriculture—moderate growth
 Conservation—biodiversity
high priority; 1 million acres
protected by 2100 in high
biodiversity areas
Appendix 2. Variables selected to generate suitability surfaces for development, cropland, hay/pasture, and grassland land use-land cover
(LULC) classes for use in the FORE-SCE LULC change model for the two EPA Level III ecoregions modeled: Central Valley and Chapparal
and Oak Woodland. Positive or negative associations between variables and LULC classes are shown as + or - .
Central Valley
Cropland
Compound Topographic
Indexa
Slopeb
Chaparral and Oak Woodland
Development
Hay/
Pasture
-
Grassland
Cropland
-
National Elevation
-
Datasetb
-
Development
Urban radiusc
-
-
-
Soil organic
Crop
+
+
carbond
+
-
capabilityd
-
Average water contentd
+
Population densityc
Housing densityc
Distance to cityb
Distance to
waterb
Distance to
streamsb
Distance to roadsb
Distance to
railb
-
-
+
-
Pasture
-
Y coordinateb
X coordinateb
Hay/
-
Grassland
-
-
-
-
Hydric soilsd
Wilting
-
+
Soil porositye
+
Soil thicknesse
+
Field
-
pointe
-
-
+
+
+
-
+
-
-
-
-
+
capacitye
Precipitatione
Potential
evapotranspiratione
Summer maximume
Winter
+
Climatic water
deficite
Sources
a
(Moore et al. 1991)
USGS
c
US Census
d
USDA SSURGO
e
(Flint and Flint 2012; Flint et al. 2013)
b
-
minimume
+
-
+
-
-
-
+
+
+
+
+
Appendix 3.
Table 1. Results of analysis of covariance to compare regression lines of recharge, runoff and
streamflow to precipitation for two cases: modeled future urbanization and no future
urbanization in six case study watersheds. For each analysis, n=38.
Recharge
Runoff
Streamflow
Alameda Creek
F(3,34)
152.6
134.1
154.2
Prob > F
0.00
0.00
0.00
R2
0.93
0.92
0.93
RMSE
19.0
44.9
37.3
219.8
62.4
74.3
Prob > F
0.00
0.00
0.00
R2
0.95
0.85
0.87
RMSE
23.2
45.1
38.5
126.1
31.1
32.8
Prob > F
0.00
0.00
0.00
R2
0.92
0.73
0.74
RMSE
24.0
27.8
23.9
244.0
31.5
35.7
Prob > F
0.00
0.00
0.00
R2
0.96
0.74
0.76
RMSE
24.6
44.0
37.2
113.3
194.5
170.5
Prob > F
0.00
0.00
0.00
R2
0.91
0.94
0.94
RMSE
30.9
61.4
57.7
104.1
290.5
264.5
Prob > F
0.00
0.00
0.00
R2
0.90
0.96
0.96
9.6
25.9
23.7
Cosumnes
F(3,34)
Estrella
F(3,34)
Lower Butte
F(3,34)
Upper Stony
F(3,34)
Upper Tule
F(3,34)
RMSE
Table 2. Statistical tests for differences in slopes and Y-intercepts of regression lines for
recharge, runoff and streamflow vs. precipitation generated with future urbanization and no
future urbanization for three case study watershed where future development is prevalent under
land use-land cover change scenarios. If slopes are significantly different, then comparisons of
Y-intercepts are not performed.
Recharge
Alameda Creek
Cosumnes
Lower Butte
Slope
(F1,34 = 20.41
P = 0.000)
Intercept
Slope
N/A
(F1,34 = 6.48
P = 0.016)
Intercept
N/A
Slope
Intercept
(F1,34 = 1.01
(F1,35 = 7.84
P = 0.32)
P = 0.008)
Slope
(F1,34 = 8.65
P = 0.006)
Intercept
N/A
Slope
(F1,34 = 3.13
P = 0.086)
Intercept
(F1,35 = 102.51
P = 0.000)
Slope
Intercept
(F1,34 = 0.42
(F1,35 = 26.67
P = 0.52)
P = 0.000)
Slope
(F(1,34) = 10.63
P = 0.003)
Intercept
Slope
N/A
(F(1,34) = 4.74
P = 0.037)
Intercept
N/A
Slope
(F(1,34) = 0.54
(F(1,35) = 31.28
Runoff
Alameda Creek
Cosumnes
Lower Butte
Streamflow
Alameda Creek
Cosumnes
Lower Butte
Intercept
P = 0.467)
P = 0.000)
Table 3. Calculation of precipitation values and recharge and runoff values and their standard errors at the intersection of regression
lines for recharge and runoff vs. precipitation. Regression line intersections were calculated for six case study watersheds assuming no
future urbanization, and again for three watersheds assuming future urbanization. Precipitation values at interceptions represent the
threshold at which dominant watershed hydrology processes shift between recharge and runoff. In some cases regression lines do not
5
intercept at positive recharge and runoff values.
Alameda Creek
Urban/
No Urban
No Urban
Alameda Creek
Urban
Cosumnes
No Urban
Cosumnes
Urban
Estrella
Watershed
517.7
Recharge (std. error)
(106 x m3)
146.5
206.3
2.4
Precipitation (mm)
(3.6)
Runoff (std. error)
(106 x m3)
146.1
(5.3)
(13.9)
2.9
(37.1)
(35.2)
763.1
263.2
N/A
1300.0
765.8
N/A
No Urban
221.2
N/A
N/A
Lower Butte
No Urban
258.8
N/A
N/A
Lower Butte
Urban
435.7
87.2
(10.6)
88.2
(21.3)
Upper Stony
No Urban
733.4
377.2
(7.9)
382.8
(15.6)
Upper Tule
No Urban
458.9
43.8
(4.5)
43.6
(11.7)
(93.0)
References
Flint L, Flint A (2012) Downscaling future climate scenarios to fine scales for hydrologic and
ecological modeling and analysis. Ecological Processes 1(1):2
Flint L, Flint A, Thorne J, Boynton R (2013) Fine-scale hydrologic modeling for regional
landscape applications: the California Basin Characterization Model development and
performance. Ecological Processes 2(1):25
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: A review of hydrological,
geomorphological, and biological applications. Hydrological Processes 5(1):3-30