4.1.3 The proposed method (Methods 1-3)
Predicted runoff using the Method 1 are closer to the observed values,
with a regression line slope of 0.90 and intercept of -0.30 (Table 3).
The regression line of the Method 1 is closer to the 1:1 line, and the
coefficient of determination R2 increases to
0.79 from 0.21 (the SCS-CN
method).
Moreover, the Method 1 performed satisfactorily as most values lie
closer to the line of perfect prediction, even for the large runoff
events, exhibiting a good match with the observed runoff (Fig. 3a). In
the calibration case, the performance of the Method 1 was better than
the above three methods because it yielded a higher NSE value of
75.84% and a lower RMSE value of 3.26 mm (Table 3). In the
validation case, the value of NSE increases to 77.45% for the
Method 1 from -182.60%
for
the original SCS-CN method, whereas the value of RMSE decreases
to 3.72 mm from 13.18 mm. Thus we can conclude that the Method 1
performed best of the four methods for both calibration and validation
datasets.
However, the points of Method 1 lie around the 1:1 line still scatter,
which might be inherent to the standard SCS-CN method due to the
adoption of the 0.2 value for the initial abstraction ratio λ. The
assumption of λ = 0.2 has frequently been questioned for its pragmatic
applications (Pronce and Hawkins, 1996; Baltas et al., 2007). Therefore,
in order to improve the SCS-CN method, we introduced Method 2 with
optimized λ based on the Method 1 to test the effect of initial
abstraction on the proposed method. Moreover, we also optimized
parameters in Eq.(6) (a1 anda2 ) (Method 3) based on Method 2 to test the
slope factor on the proposed method as compared with the fixed values
obtained from Huang et al.(2006).
A comparison of measured and estimated runoff depths by Methods 2 and 3,
presented in Fig. 3b and Fig. 3c, shows that the runoff estimation is
much improved over Method 1, with observation points close to the 1 : 1
line especially for runoff depths between 10 and 20 mm. The statistics
in Table 3 confirm the predictive capacity of Methods 2 and 3 with
higher model efficiency of 80.50% and 80.95%, respectively. However,
when compared between Methods 3 and 2, it can be found that there is
slightly improvement in Method 3 which using optimized slope parameter
(a1 =213.99 and a2 =25.38)
than Methods 2 with NSE values of 80.73% vs. 80.44% in
calibration and 81.21% vs. 80.58% in validation. The results indicated
that the proposed method with optimized λ (Methods 2-3) could further
improved the SCS-CN method for runoff prediction in this study area.