4.3. Sensitivity analyses
The above results indicate the Method 2 predicts runoff with greater
accuracy than the other SCS-CN methods. A sensitivity analyses can
identify the primary importance parameters that affecting the
performance of the model. In this study, sensitivity analyses were
conducted by observing the effect of variation of the calibrated
parameter on the runoff prediction in term of NSE with the
datasets of the plots in XDG watershed.
Figure 5 illustrates the sensitivity of the predictions to the various
parameters of Method 2. The sensitive variables are parameters for whichNSE changes dramatically when the parameter is increased or
decreased from the calibrated value. Parameter b1is apparently rather sensitive to variation, with the NSEdecreasing from 80.58% to 47.83% when the b1value varies in a narrow range from 130% to 80% of the calibrated
value, whereas the initial ratio λ appears to be the least sensitive. In
general, the parameters of soil moisture (b1 andb2 ) and storm duration equations (c ) are
more sensitive than those parameters of slope equation
(a1 and a2 ) and the
initial abstraction ratio λ .