Geoscience Reference
In-Depth Information
The watersheds with different levels of urbanization in the study area are good for
examining how the impact of urbanization on water quality varies across the urban-
ization gradient. Furthermore, the great spatial variability in urbanization indicators
suggests that the significant spatial non-stationarity might exist in the relationships
between urbanization and water quality variables over the study area, so GWR is
a more appropriate method than traditional regression techniques, which is already
confirmed by model evaluation (Tu and Xia 2008 ).
9.5.2 Relationships Between Developed Land and Water Quality
Because of space considerations, not all the maps of parameter estimates and
local R 2 values from the GWR models can be presented here. The ranges of local
parameter estimates and R 2 s are summarized in Table 9.1 . Parameter estimates for
an independent variable actually reflect the correlations between the independent
Table 9.1 Ranges of local parameter estimates and R 2 from GWR models
Local R 2
Local parameter estimates
Water quality
indicator
% developed
land
population
density
% developed
land
population
density
SC
Min
0.200
-0.462
0.293
0.210
Max
0.374
2.022
0.644
0.973
NH 3 -N
Min
0.596
1.188
0.272
0.414
Max
1.024
1.684
0.624
0.710
NO 2 -N
Min
15.18
10.90
0.349
0.309
Max
25.75
88.96
0.655
0.928
KN
Min
0.040
0.040
0.344
0.099
Max
0.100
1.372
0.999
0.997
NO 3 -N +
NO 2 -N
Min
0.001
0.240
0.317
0.194
Max
0.032
0.980
0.630
0.791
P
Min
0.006
0.410
0.130
0.110
Max
0.059
3.280
0.380
0.918
PO 4 -P
Min
2.25
17.68
0.140
0.077
Max
0.51
51.13
0.501
0.890
Ca
Min
0.017
0.280
0.511
0.243
Max
0.122
1.690
0.996
0.935
Mg
Min
0.014
0.100
0.364
0.312
Max
0.021
0.710
0.664
0.931
Na
Min
0.011
0.100
0.172
0.438
Max
0.065
1.270
0.992
0.996
K
Min
0.007
0.131
0.243
0.370
Max
0.024
0.234
0.999
0.650
Cl
Min
0.060
0.940
0.203
0.246
Max
0.270
4.400
0.972
0.841
SO 4
Min
0.006
0.040
0.345
0.345
Max
0.045
0.790
0.991
0.991
DS
Min
0.0001
0.001
0.302
0.147
Max
0.0023
0.032
0.998
0.945
 
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